A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals

Abstract:
Summary

Background
 Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system—which we introduce as full-stack federated learning—to train and evaluate machine learning models across four UK hospital groups without centralising patient data.
Methods
 We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion.
Findings
 Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560–0·589) at OUH and 0·622 (0·608–0·637) at PUH using the locally trained models to 0·872 (0·862–0·882) at OUH and 0·876 (0·865–0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893–0·942), with 89·7% sensitivity (83·6–93·6) and 76·6% specificity (73·9–79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01).
Interpretation
 We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site.
Funding
 The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.


A Dual Stream Fusion Network for Underwater Image Enhancement of Multi-scale Turbidity Restoration and Multi-path Color Correction

Abstract: Underwater images frequently experience color distortion and blurred details due to the absorption and scattering of light, which can hinder underwater visual tasks. To address these challenges, we propose a dual-stream fusion network for enhancing underwater images. Our multi-scale turbidity restoration module (MTRM) adopts a two-stage dehazing process from coarse to fine, while employing the SOS boosting strategy and frequency-based dense connections to further improve the performance of the U-Net. The multi-path color correction module (MCCM) utilizes the multi-path residual block as the basic unit to construct RGB enhancement paths. It selectively establishes inter-color channels through attention-based cross connections, which efficiently harness the distinctive features from various color channels. Additionally, non-local spatial and channel attention provide essential correlation information for the final fusion stage. Qualitative and quantitative evaluations conducted on various underwater datasets have demonstrated the excellent performance of our method.


MTIE-Net: Multi-technology fusion of low-light image enhancement network

Abstract: Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications.


Personalized Impression Generation for PET Reports Using Large Language Models

Abstract: Large language models (LLMs) have shown promise in accelerating radiology reporting by summarizing clinical findings into impressions. However, automatic impression generation for whole-body PET reports presents unique challenges and has received little attention. Our study aimed to evaluate whether LLMs can create clinically useful impressions for PET reporting. To this end, we fine-tuned twelve open-source language models on a corpus of 37,370 retrospective PET reports collected from our institution. All models were trained using the teacher-forcing algorithm, with the report findings and patient information as input and the original clinical impressions as reference. An extra input token encoded the reading physician's identity, allowing models to learn physician-specific reporting styles. To compare the performances of different models, we computed various automatic evaluation metrics and benchmarked them against physician preferences, ultimately selecting PEGASUS as the top LLM. To evaluate its clinical utility, three nuclear medicine physicians assessed the PEGASUS-generated impressions and original clinical impressions across 6 quality dimensions (3-point scales) and an overall utility score (5-point scale). Each physician reviewed 12 of their own reports and 12 reports from other physicians. When physicians assessed LLM impressions generated in their own style, 89% were considered clinically acceptable, with a mean utility score of 4.08/5. On average, physicians rated these personalized impressions as comparable in overall utility to the impressions dictated by other physicians (4.03, P = 0.41). In summary, our study demonstrated that personalized impressions generated by PEGASUS were clinically useful in most cases, highlighting its potential to expedite PET reporting by automatically drafting impressions.


YOLOv7-TS: A Traffic Sign Detection Model Based on Sub-Pixel Convolution and Feature Fusion

Abstract: In recent years, significant progress has been witnessed in the field of deep learning-based object detection. As a subtask in the field of object detection, traffic sign detection has great potential for development. However, the existing object detection methods for traffic sign detection in real-world scenes are plagued by issues such as the omission of small objects and low detection accuracies. To address these issues, a traffic sign detection model named YOLOv7-Traffic Sign (YOLOv7-TS) is proposed based on sub-pixel convolution and feature fusion. Firstly, the up-sampling capability of the sub-pixel convolution integrating channel dimension is harnessed and a Feature Map Extraction Module (FMEM) is devised to mitigate the channel information loss. Furthermore, a Multi-feature Interactive Fusion Network (MIFNet) is constructed to facilitate enhanced information interaction among all feature layers, improving the feature fusion effectiveness and strengthening the perception ability of small objects. Moreover, a Deep Feature Enhancement Module (DFEM) is established to accelerate the pooling process while enriching the highest-layer feature. YOLOv7-TS is evaluated on two traffic sign datasets, namely CCTSDB2021 and TT100K. Compared with YOLOv7, YOLOv7-TS, with a smaller number of parameters, achieves a significant enhancement of 3.63% and 2.68% in the mean Average Precision (mAP) for each respective dataset, proving the effectiveness of the proposed model.


A color image encryption scheme based on cellular neural networks and linear feedback shift registers

Abstract: Abstract In this paper, a cellular neural network (CNN) chaotic system is constructed and the multiple stability of the system and its rich chaotic properties are confirmed by studying the effect of parameters on the system, coexisting attractors, and offset boosting behavior. As linear feedback shift registers (LFSR) can be applied to cryptography, this paper applies LFSR to generate encrypted key matrices to enhance the randomness of encryption algorithms. Based on CNN and LFSR, a new color image encryption algorithm is designed by combining DNA coding and bit-plane decomposition with high bit-plane Zigzag dislocation changes. Experimental results and security tests show that the algorithm is highly secure and resistant to a variety of common attacks, such as differential attacks, cropping attacks, and noise attacks.


Qudit-based high-dimensional controlled-not gate

Abstract: High-dimensional quantum systems expand quantum channel capacity and information storage space. By implementing high-dimensional quantum logic gates, the speed of quantum computing can be practically enhanced. We propose a deterministic 4 × 4-dimensional controlled-not (CNOT) gate for a hybrid system without ancillary qudits required, where the spatial and polarization states of a single photon serve as a control qudit of four dimensions, whereas two electron-spin states in nitrogen-vacancy (NV) centers act as a four-dimensional target qudit. As the control qudits are easily operated employing simple optical elements and the target qudits are available for storage, the CNOT gate works in a deterministic way, and it can be flexibly extended to n × n-dimensional (n > 4) quantum gates for other hybrid systems or different photonic degrees of freedoms. The efficiency and fidelity of the CNOT gate are analyzed aligning with current technological capabilities, finding that they have satisfactory performances.


Artificial Intelligence for Breast Ultrasound: <i>AJR</i> Expert Panel Narrative Review

Abstract: Breast ultrasound is used in a wide variety of clinical scenarios, including both diagnostic and screening applications. Limitations of ultrasound, however, include its low specificity and, for automated breast ultrasound screening, the time necessary to review whole-breast ultrasound images. As of this writing, four AI tools that are approved or cleared by the FDA address these limitations. Current tools, which are intended to provide decision support for lesion classification and/or detection, have been shown to increase specificity among non-specialists and to decrease interpretation times. Potential future applications include triage of patients with palpable masses in low-resource settings, preoperative prediction of axillary lymph node metastasis, and preoperative prediction of neoadjuvant chemotherapy response. Challenges in the development and clinical deployment of AI for ultrasound include: the limited availability of curated training datasets compared to mammography; the high variability in ultrasound image acquisition due to equipment- and operator-related factors (which may limit algorithm generalizability); and the lack of post-implementation evaluation studies. Furthermore, current AI tools for lesion classification were developed based on 2D data, but diagnostic accuracy could potentially be improved if multimodal ultrasound data were used, such as color Doppler, elastography, cine clips, and 3D imaging.


Conventional and Unconventional Dicke Models: Multistabilities and Nonequilibrium Dynamics

Abstract: The Dicke model describes the collective behavior of a subwavelength--size ensemble of two-level atoms (i.e., spin-$1/2$) interacting identically with a single quantized radiation field of a cavity. Across a critical coupling strength it exhibits a zero-temperature phase transition from the normal state to the superradiant phase where the field is populated and the collective spin acquires a nonzero $x$ component, which can be imagined as ferromagnetic ordering of the atomic spins along $x$. Here we introduce a variant of this model where two subwavelength--size ensembles of spins interact with a single quantized radiation field with different strengths. Subsequently, we restrict ourselves to a special case where the coupling strengths are opposite (which is unitarily equivalent to equal-coupling strengths). Because of the conservation of the total spin in each ensemble individually, the system supports two distinct superradiant states with $x$-ferromagnetic and $x$-ferrimagnetic spin ordering, coexisting with each other in a large parameter regime. The stability and dynamics of the system in the thermodynamic limit are examined using a semiclassical approach, which predicts nonstationary behaviors due to the multistabilities. At the end, we also perform small-scale full quantum-mechanical calculations, with results consistent with the semiclassical ones.


Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

Abstract: The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven. Tracking moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns. This allows them and the onboard camera to be oriented toward the tracked target. In simulations, the AR Drone 2.0 model of the Parrot quadcopter is used, which will make it possible to easily verify the method in real flights in future research.


Remote Charging and Degradation Suppression for the Quantum Battery

Abstract: The quantum battery (QB) makes use of quantum effects to store and supply energy, which may outperform its classical counterpart. However, there are two challenges in this field. One is that the environment-induced decoherence causes the energy loss and aging of the QB, the other is that the decreasing of the charger-QB coupling strength with increasing their distance makes the charging of the QB become inefficient. Here, we propose a QB scheme to realize a remote charging via coupling the QB and the charger to a rectangular hollow metal waveguide. It is found that an ideal charging is realized as long as two bound states are formed in the energy spectrum of the total system consisting of the QB, the charger, and the electromagnetic environment in the waveguide. Using the constructive role of the decoherence, our QB is immune to the aging. Additionally, without resorting to the direct charger-QB interaction, our scheme works in a way of long-range and wireless-like charging. Effectively overcoming the two challenges, our result supplies an insightful guideline to the practical realization of the QB by reservoir engineering.


ARPruning: An automatic channel pruning based on attention map ranking

Abstract: Structured pruning is a representative model compression technology for convolutional neural networks (CNNs), aiming to prune some less important filters or channels of CNNs. Most recent structured pruning methods have established some criteria to measure the importance of filters, which are mainly based on the magnitude of weights or other parameters in CNNs. However, these judgment criteria lack explainability, and it is insufficient to simply rely on the numerical values of the network parameters to assess the relationship between the channel and the model performance. Moreover, directly utilizing these pruning criteria for global pruning may lead to suboptimal solutions, therefore, it is necessary to complement search algorithms to determine the pruning ratio for each layer. To address these issues, we propose ARPruning (Attention-map-based Ranking Pruning), which reconstructs a new pruning criterion as the importance of the intra-layer channels and further develops a new local neighborhood search algorithm for determining the optimal inter-layer pruning ratio. To measure the relationship between the channel to be pruned and the model performance, we construct an intra-layer channel importance criterion by considering the attention map for each layer. Then, we propose an automatic pruning strategy searching method that can search for the optimal solution effectively and efficiently. By integrating the well-designed pruning criteria and search strategy, our ARPruning can not only maintain a high compression rate but also achieve outstanding accuracy. In our work, it is also experimentally concluded that compared with state-of-the-art pruning methods, our ARPruning method is capable of achieving better compression results. The code can be obtained at https://github.com/dozingLee/ARPruning.


Multiscale Cross-Connected Dehazing Network With Scene Depth Fusion

Abstract: In this article, we propose a multiscale cross-connected dehazing network with scene depth fusion. We focus on the correlation between a hazy image and the corresponding depth image. The model encodes and decodes the hazy image and the depth image separately and includes cross connections at the decoding end to directly generate a clean image in an end-to-end manner. Specifically, we first construct an input pyramid to obtain the receptive fields of the depth image and the hazy image at multiple levels. Then, we add the features of the corresponding dimensions in the input pyramid to the encoder. Finally, the two paths of the decoder are cross-connected. In addition, the proposed model uses wavelet pooling and residual channel attention modules (RCAMs) as components. A series of ablation experiments shows that the wavelet pooling and RCAMs effectively improve the performance of the model. We conducted extensive experiments on multiple dehazing datasets, and the results show that the model is superior to other advanced methods in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual effects. The source code and supplementary are available at https://github.com/CCECfgd/MSCDN-master.


Efficient quantum simulation of open quantum system dynamics on noisy quantum computers

Abstract: Quantum simulation represents the most promising quantum application to demonstrate quantum advantage on near-term noisy intermediate-scale quantum (NISQ) computers, yet available quantum simulation algorithms are prone to errors and thus difficult to be realized. Herein, we propose a novel scheme to utilize intrinsic gate errors of NISQ devices to enable controllable simulation of open quantum system dynamics without ancillary qubits or explicit bath engineering, thus turning unwanted quantum noises into useful quantum resources. Specifically, we simulate energy transfer process in a photosynthetic dimer system on IBM-Q cloud. By employing designed decoherence-inducing gates, we show that quantum dissipative dynamics can be simulated efficiently across coherent-to-incoherent regimes with results comparable to those of the numerically-exact classical method. Moreover, we demonstrate a calibration routine that enables consistent and predictive simulations of open-quantum system dynamics in the intermediate coupling regime. This work provides a new direction for quantum advantage in the NISQ era.


Boosting the Convergence of Reinforcement Learning-Based Auto-Pruning Using Historical Data

Abstract: Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural networks (DNNs) for applications in power-constrained scenarios, such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process, and pruning and evaluating each network comes at high computational expense. These problems have greatly restricted the real-world application of RL-based auto-pruning. Thus, we propose an efficient auto-pruning framework that solves this problem by taking advantage of the historical data from the previous auto-pruning process. In our framework, we first boost the convergence of the RL-pruner by transfer learning. Then, an augmented transfer learning scheme is proposed to further speed up the training process by improving the transferability. Finally, an assistant learning process is proposed to improve the sample efficiency of the RL agent. The experiments show that our framework can accelerate the auto-pruning process by 1.5x 2.5x for ResNet20, and 1.81x 2.375x for other neural networks, such as ResNet56, ResNet18, and MobileNet v1.


Experiences of Teaching Software Testing in an Undergraduate Class Using Different Approaches for the Group Projects

Abstract: Abstract The increased ubiquity of software has resulted in the need for more software engineers to service all aspects of the software development life cycle, including maintaining software quality. In 2018, It is estimated that the poor quality of software in the US cost approximately $2.26 trillion. Software testing continues to be one of the main approaches used to validate software as software systems become more complex. Although there is increased testing of software in industry throughout the development life cycle, academic institutions continue to lag behind in offering the number of software testing courses to adequately prepare students for jobs in industry. In addition, educational institutions continue to grapple with how best to integrate software testing in their curricula. This paper presents the experiences of teaching an undergraduate software testing course over four non-consecutive years, focusing mainly on the class project. The semester-long project consists of several deliverables and is a major component of the testing course. Over the four years, different approaches were used to implement the project activities. The approaches used for the group projects include (a) testing different capstone projects that are sponsored by local companies, (b) all student teams testing a single project that is being developed during the semester, and (c) testing a single project that has been previously developed. A detailed description of the testing course is presented, and the dependencies with other classes in the curriculum are explained. Specific details of the different approaches for the class projects are presented, such as, the structure of the deliverables and how they were assessed. Project data for each semester is also presented and includes class enrollments, number of project teams, types of project, and level of complexity. Data on the tools used to develop and test the projects are also described. Testing each type of project presented a variety of advantages and challenges for students and the instructor. Some of the challenges include access to appropriate software testing tools, not meeting the coverage requirements when testing the project, assessing the large variety of group projects sponsored by local companies, and technical support for the testing tools. Based on the experience of teaching the course with different types of group projects, we present lessons learned and recommendations for future editions of the course. Some of our recommendations are consistent with other experience reports described in the literature.


Evaluating a Software Project Management Course Collaboration Framework at a Second Institution

Abstract: Abstract Previous work has described and evaluated a collaboration between a software project management (SPM) course and an introductory software development course. That collaboration has successfully supported the experiential learning of SPM skills as well as skills to effectively interact with a project manager. This paper further evaluates that collaboration framework by reporting its application at a second institution for two semesters. Overall, the learning outcomes and student experiences achieved at the second institution were similarly positive to those achieved at the first institution. The major components of the framework were fully implemented at the second institution with specific aspects customized to meet the institution's educational outcomes and setting. In that customization, three factors were identified for consideration when adapting the collaboration framework: the existence of a course structure to support such collaboration, the ratio between the students in the two courses, and the ability to schedule regular interactions between these students.


Flexible-bandwidth needlets

Abstract: We investigate here a generalized construction of spherical wavelets/needlets which admits extra-flexibility in the harmonic space, i.e., it allows the corresponding support in multipole (frequency) space to vary in more general forms than in the standard constructions. We study the analytic properties of this system and we investigate its behaviour when applied to isotropic random fields: more precisely, we establish asymptotic localization and uncorrelation properties (in the high-frequency sense) under broader assumptions than typically considered in the literature.


Fairness-Driven Private Collaborative Machine Learning

Abstract: The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated methods for private collaborative machine learning, the fairness of such collaborative algorithms was overlooked. In this work we suggest a feasible privacy-preserving pre-process mechanism for enhancing fairness of collaborative machine learning algorithms. Our experimentation with the proposed method shows that it is able to enhance fairness considerably with only a minor compromise in accuracy.


Quantum algorithms to compute the neighbour list of N-body simulations

Abstract: Abstract One of the strategies to reduce the complexity of N -body simulations is the computation of the neighbour list. However, this list needs to be updated from time to time, with a high computational cost. This paper focuses on the use of quantum computing to accelerate such a computation. Our proposal is based on a well-known oracular quantum algorithm (Grover). We introduce an efficient quantum circuit to build the oracle that marks pairs of closed bodies, and we provide three novel algorithms to calculate the neighbour list under several hypotheses which take into account a-priori information of the system. We also describe a decision methodology for the actual use of the proposed quantum algorithms. The performance of the algorithms is tested with a statistical simulation of the oracle, where a fixed number of pairs of bodies are set as neighbours. A statistical analysis of the number of oracle queries is carried out. The results obtained with our simulations indicate that when the density of bodies is low, our algorithms clearly outperform the best classical algorithm in terms of oracle queries.


AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit’s activation via adjoint operators

Abstract: Adjoint operators have been found to be effective in the exploration of CNN’s inner workings (Wan and Choe, 2022). However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an extended normed space that includes bias in all CNN layers as part of the extended space and propose an adjoint-operator-based algorithm that maps high-level weights back to the extended input space for reconstructing an effective hypersurface. Such hypersurface can be computed for an arbitrary unit in the CNN, and we prove that this reconstructed hypersurface, when multiplied by the original input (through an inner product), will precisely replicate the output value of each unit. We show experimental results based on the CIFAR-10 and CIFAR-100 data sets where the proposed approach achieves near 0 activation value reconstruction error.


Model Change Active Learning in Graph-Based Semi-supervised Learning

Abstract: Abstract Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels. “Model Change” active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s). We pair this idea with graph-based semi-supervised learning (SSL) methods, that use the spectrum of the graph Laplacian matrix, which can be truncated to avoid prohibitively large computational and storage costs. We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution. We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.


Deep Unsupervised Active Learning on Learnable Graphs

Abstract: Recently, deep learning has been successfully applied to unsupervised active learning. However, the current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving huge room to design more effective representation learning mechanisms for unsupervised active learning. In this brief, we propose a novel deep unsupervised active learning model via learnable graphs, named ALLGs. ALLG benefits from learning optimal graph structures to acquire better sample representation and select representative samples. To make the learned graph structure more stable and effective, we take into account k -nearest neighbor graph as a priori and learn a relation propagation graph structure. We also incorporate shortcut connections among different layers, which can alleviate the well-known over-smoothing problem to some extent. To the best of our knowledge, this is the first attempt to leverage graph structure learning for unsupervised active learning. Extensive experiments performed on six datasets demonstrate the efficacy of our method.


Bio inspired Boolean artificial bee colony based feature selection algorithm for sentiment classification

Abstract: Over the past few years, sentiment analysis has become most predominant aspect to be extracted in the modern digital era. Due to digitization all over the world, the number of existing digital documents increasing exponentially day by day. Analyzing such documents to extract the exact meaning itself becomes a tedious task. And analyzing those documents in sentimental point of view has increased the modality of difficultness to another level. In this research work, an effective bio-inspired filter-based feature selection algorithm is proposed to effectively handle the documents from sentiment point of view. A popular bio inspired algorithm called Artificial Bee Colony algorithm is modified using Boolean operators to effectively handle feature selection problem in classifying the documents from sentimental aspect. A total of 9 different documents instances are taken for evaluating the performance of proposed model. A total of 5 different filter-based measures are used to quantify the working model of the algorithm. The outputs are analyzed to state-of-the-art algorithms, and the comparative results demonstrate a substantial improvement over the existing models.


Compressing Features for Learning With Noisy Labels

Abstract: Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the distilled information might not be relevant. In fact, recent research shows that networks can easily overfit all labels including those that are corrupted, and hence can hardly generalize to clean datasets. In this article, we focus on the problem of learning with noisy labels and introduce compression inductive bias to network architectures to alleviate this overfitting problem. More precisely, we revisit one classical regularization named Dropout and its variant Nested Dropout. Dropout can serve as a compression constraint for its feature dropping mechanism, while Nested Dropout further learns ordered feature representations with respect to feature importance. Moreover, the trained models with compression regularization are further combined with co-teaching for performance boost. Theoretically, we conduct bias variance decomposition of the objective function under compression regularization. We analyze it for both single model and co-teaching. This decomposition provides three insights: 1) it shows that overfitting is indeed an issue in learning with noisy labels; 2) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; and 3) it gives explanations on the performance boost brought by incorporating compression regularization into co-teaching. Experiments show that our simple approach can have comparable or even better performance than the state-of-the-art methods on benchmarks with real-world label noise including Clothing1M and ANIMAL-10N. Our implementation is available at https://yingyichen-cyy.github.io/CompressFeatNoisyLabels/.


Product Recognition for Unmanned Vending Machines

Abstract: Recently, the emerging concept of "unmanned retail" has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this article, we propose a method for large-scale categories product recognition based on intelligent UVMs. It can be divided into two parts: 1) first, we explore the similarities and differences between products through manifold learning, and then we build a hierarchical multigranularity label to constrain the learning of representation; and 2) second, we propose a hierarchical label object detection network, which mainly includes coarse-to-fine refine module (C2FRM) and multiple granularity hierarchical loss (MGHL), which are used to assist in capturing multigranularity features. The highlights of our method are mine potential similarity between large-scale category products and optimization through hierarchical multigranularity labels. Besides, we collected a large-scale product recognition dataset GOODS-85 based on the actual UVMs scenario. Experimental results and analysis demonstrate the effectiveness of the proposed product recognition methods.


Observation of Josephson harmonics in tunnel junctions

Abstract: Abstract Approaches to developing large-scale superconducting quantum processors must cope with the numerous microscopic degrees of freedom that are ubiquitous in solid-state devices. State-of-the-art superconducting qubits employ aluminium oxide (AlO x ) tunnel Josephson junctions as the sources of nonlinearity necessary to perform quantum operations. Analyses of these junctions typically assume an idealized, purely sinusoidal current–phase relation. However, this relation is expected to hold only in the limit of vanishingly low-transparency channels in the AlO x barrier. Here we show that the standard current–phase relation fails to accurately describe the energy spectra of transmon artificial atoms across various samples and laboratories. Instead, a mesoscopic model of tunnelling through an inhomogeneous AlO x barrier predicts percent-level contributions from higher Josephson harmonics. By including these in the transmon Hamiltonian, we obtain orders of magnitude better agreement between the computed and measured energy spectra. The presence and impact of Josephson harmonics has important implications for developing AlO x -based quantum technologies including quantum computers and parametric amplifiers. As an example, we show that engineered Josephson harmonics can reduce the charge dispersion and associated errors in transmon qubits by an order of magnitude while preserving their anharmonicity.


Rapid exchange cooling with trapped ions

Abstract: Abstract The trapped-ion quantum charge-coupled device (QCCD) architecture is a leading candidate for advanced quantum information processing. In current QCCD implementations, imperfect ion transport and anomalous heating can excite ion motion during a calculation. To counteract this, intermediate cooling is necessary to maintain high-fidelity gate performance. Cooling the computational ions sympathetically with ions of another species, a commonly employed strategy, creates a significant runtime bottleneck. Here, we demonstrate a different approach we call exchange cooling. Unlike sympathetic cooling, exchange cooling does not require trapping two different atomic species. The protocol introduces a bank of “coolant" ions which are repeatedly laser cooled. A computational ion can then be cooled by transporting a coolant ion into its proximity. We test this concept experimentally with two 40 Ca + ions, executing the necessary transport in 107 μ s, an order of magnitude faster than typical sympathetic cooling durations. We remove over 96%, and as many as 102(5) quanta, of axial motional energy from the computational ion. We verify that re-cooling the coolant ion does not decohere the computational ion. This approach validates the feasibility of a single-species QCCD processor, capable of fast quantum simulation and computation.


A New Approach to Capacity Scaling Augmented with Unreliable Machine Learning Predictions

Abstract: Modern data centers suffer from immense power consumption. As a result, data center operators have heavily invested in capacity-scaling solutions, which dynamically deactivate servers if the demand is low and activate them again when the workload increases. We analyze a continuous-time model for capacity scaling, where the goal is to minimize the weighted sum of flow time, switching cost, and power consumption in an online fashion. We propose a novel algorithm, called adaptive balanced capacity scaling (ABCS), that has access to black-box machine learning predictions. ABCS aims to adapt to the predictions and is also robust against unpredictable surges in the workload. In particular, we prove that ABCS is [Formula: see text] competitive if the predictions are accurate, and yet, it has a uniformly bounded competitive ratio even if the predictions are completely inaccurate. Finally, we investigate the performance of this algorithm on a real-world data set and carry out extensive numerical experiments, which positively support the theoretical results. Funding: This work was partially supported by the Division of Computing and Communication Foundations [Grant 2113027]. The authors also acknowledge financial support for this project from the Algorithm and Randomness Center–Transdisciplinary Research Institute for Advancing Data Science Fellowship at Georgia Tech.


Abstract: Abstract Multiple Kernel Learning (MKL) is a conventional way to learn the kernel function in kernel-based methods. MKL algorithms enhance the performance of kernel methods. However, these methods have a lower complexity compared to deep models and are inferior to them regarding recognition accuracy. Deep learning models can learn complex functions by applying nonlinear transformations to data through several layers. In this paper, we show that a typical MKL algorithm can be interpreted as a one-layer neural network with linear activation functions. By this interpretation, we propose a Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the conventional MKL framework to a multi-layer neural network with nonlinear activation functions. Our experiments show that the proposed method, which has a higher complexity than traditional MKL methods, leads to higher recognition accuracy on several benchmarks.


An approach for designing fast public key encryption systems using white-box cryptography techniques

Abstract: I present an approach for designing fast public key encryption cryptosystems using random primitives, white-box cryptography techniques and obfuscated error permutation. An encryption speed of such systems allows to use them for "on-the-fly" public key encryption and makes them useful for real-time communications. A small error size allows to use this approach for designing digital signature schemes and systems of Digital Rights Management (DRM). An underlying hard problem is the problem of finding low weight codewords which is known to be NP-hard.


Diversifying Pathways in Cybersecurity through the Design of Holistic Competitions

Abstract: Abstract Cybersecurity competitions are touted as a good method for getting high school students interested in career paths in cybersecurity fields. From observations of high school cybersecurity competitions, we find that typical high school cybersecurity competitions focus narrowly on computer-technical competencies. A byproduct of these competitions is to create an intimidating atmosphere that rewards young adults who are already proficient in computer IT activities, but a discouraging environment to students who may have burgeoning interests in cybersecurity. Additionally, the skill set needed for cybersecurity professionals is much broader than purely computer-technical skills and include competencies like teamwork, communications skills and critical thinking, none of which are emphasized in typical high school cybersecurity competitions. In this paper, we present a high school cybersecurity competition event called the Digital Forensics Challenge (DFC). The DFC is designed to reward cybersecurity competition teams that have a wider range of competencies than typical cybersecurity competitions and be more accepting of students who may not be singularly focused on the technical aspects of cybersecurity. We describe the elements that were added to emphasize critical thinking, global thinking, teamwork and communications skills. We find that by adding non-technical competencies to the DFC, competitors find that their holistic set of skills are more valued while at the same time competitors also claim that the amount of technical content of the DFC is greater than other cybersecurity competitions.


Asymptotically optimal procedures for sequential joint detection and estimation

Abstract: We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we propose an asymptotically optimal procedure, i.e., a procedure that becomes optimal when the tolerated detection and estimation error levels tend to zero. The implementation of the resulting asymptotically optimal stopping rule is computationally cheap and, hence, applicable for high-dimensional data. We further propose a projected quasi-Newton method to optimally choose the coefficients that parameterize the instantaneous cost function such that the constraints are fulfilled with equality. The proposed theory is validated by numerical examples.


Educational Neural Network Development and Simulation Platform

Abstract: An educational software platform has been developed to introduce students to the design and operation of neural networks.The platform consists of a graphical user interface (GUI) written in C# where network parameters can be applied in the construction of the network.Currently, the platform can manage a fully-connected three-layer neural network that recognizes the handwritten digits in the MNIST database and can function as an educational tool to introduce neural network computing concepts.The GUI allows the user to specify the number of neurons in the various network layers, select from 3 different activation functions as well as a number of other network parameters.The weights, biases and layer output values can be modified from double representation to power-of-two integer representation with a specified number of hot-bits.The application capability to read and modify previously trained weights and biases and run the network in inference mode enables quick evaluation of the modifications effect on network accuracy.The functionality to visualize a block diagram of the network as well as the construction of convolutional neural networks (CNNs) to recognize images in the CIFAR-10 database is underway.The platform has been used as development tool to provide weight and bias files to be used as memory initialization files to implement the neural network on reconfigurable hardware (e.g.FPGAs); thus, evaluation of the impact of various weight and bias representations on hardware can be assessed.


The Needs and Challenges of Workforce Development in Quantum Computing

Abstract: Abstract Quantum computing is one of the most interesting and demanding topics in the field of computer science and engineering. The potential of quantum computing in many areas such as computer security, big data, finance, health science, and many other fields are now clear because of its incredible capacity and high processing speed. In a traditional computing system, all data and information are represented with 0 and 1 bits. But quantum computers operate in a different way, using Quantum Bits, or qubits. These qubits are created and manipulated using quantum principles of superposition and entanglement and can perform certain types of complex and large calculations on vast amounts of data very rapidly. A few examples of potential applications of quantum computing include much more secure quantum encryption, tremendous speed up of database searches using Grover’s database search algorithm, protein folding, and modeling of electrons in materials and molecules. Many scientists including Christopher Monroe at the University of Maryland and IonQ believe, within the next five years, a quantum computer will be capable of calculations that could never be run on traditional computing machines. In order to foster and expedite quantum computing research and development, the United States House of Representatives recently passed “The National Quantum Initiative Act (H.R. 6227)” which includes an emphasis on workforce development. In the USA, a number of startups and big tech companies are now involved in building quantum computing machines using different mechanisms of quantum physics, including ions trapped in electrical fields, superconducting circuits, quantum cloud service, spins of a single electron, etc.. The computer industries are working hard to use their existing semiconductor resources and technologies combined with quantum principles to build the desired computing systems. In parallel, there is an urgent need for a new generation of engineers, scientists, and programmers who can use quantum computers to solve real-world problems. This paper discusses some opportunities and challenges in quantum computing and preparing college students to fulfill the workforce demand. It further discusses how complex quantum computing courses can be integrated into the curricula of computer science, engineering, and related programs to address future workforce development issues. A multifaceted strategy for boosting enrollment, retention and successful graduation in quantum computing is proposed in order to address workforce development issues because we need to succeed in the tough competition we are facing from other countries.


WeBWorK Online Homework in Material and Energy Balances

Abstract: Abstract WeBWorK is an open-source online homework system originally created for mathematical disciplines and now used in a wide variety of courses involving numerical problems. With the system instructors can assign homework sets of problems to students. These problems can take in a variety of student inputs with the most commonly used being mathematical formulas or numerical answers. Using variables, the system can generate individualized problems for each students. These problems are stored on the instructor’s course in WeBWork, but can also be shared with other instructors running the WeBWorK system through the Open Problem Library. WeBWorK was implemented in a second year Material and Energy Balances course with 190 students enrolled. In previous years homework sets were done using paper submissions. Analysis was done on the shift of these homework sets from paper to WeBWorK. This analysis includes instructor and teaching assistant time and effort put into creating and administering the two types of homework. Student usage of the system was monitored including number of attempts and problem completion. Students were also surveyed for their opinion of the system and for elements of the WeBWorK system that they valued of disliked. The results of these analyses are used to inform best practices implementing WeBWorK in a Chemical Engineering classroom. There are currently number of openly available resources for material and energy balance courses such as online videos and course packages from LearnChemE as well as multiple choice ConceptTests from the AIChE Concept Warehouse. Development of WeBWorK homework problems builds on these existing resources by providing homework problems with a greater variety of input possibilities.


GIFTS: Introduction to Technical Graphics and Hand Sketching Using a Tablet and Stylus

Abstract: Abstract The following abstract is for a GIFTS presentation: As part of The Ohio State University's new Digital Flagship initiative, all incoming first year students at The Ohio State University are provided with the Apple iPad Pro and Apple Pencil stylus. In an effort to utilize this technology in an introductory technical graphics course, students were given the option to use the iPad and Apple Pencil for hand sketching assignments vice the conventional pencil to paper method by importing orthographic and isometric grid paper into Notability, a note taking app provided to students as part of this initiative. The app allows students to draw with their stylus directly on the imported grid paper. Additionally, students were able to export their drawings to digital files that could be directly submitted to the class's Learning Management System (LMS). During this trial, students provided comments through informal discussion on their experience using the iPad. An exam covering hand sketching was administered which was required to be drawn in the conventional manner using pencil on paper. Overall, many students showed enthusiasm to using the iPads vice paper to pencil. The ability to create straight lines and circles, set different line types and colors, and easily erase lines if an error was made, resulted in much neater, professional looking drawings. This allowed students to focus less on their dexterity and ability to draw, and more on overall spatial visualization and the concepts of the orthographic and isometric projections. A major concern was that students who elected to use the iPad, would lose the opportunity to refine the motor skills necessary to sketch technical drawings by hand. However, it was observed that by providing a choice as to their sketching options, students felt empowered and were self-aware of the limitations of using the iPad. While no formal statistical analysis was performed, instructors observed no major difference in scores for those who utilized iPads and those who did not. There are many additional benefits to working on these assignments through a digital platform. The ability to seamlessly submit assignments to the LMS reduced student stress levels and submission times, and allowed for faster and better feedback to the student. One instructor observed his teaching assistant (TA) using their own tablet to draw corrections and make notes directly on a student submission while grading. The opportunity for greater collaboration both student-to-instructor and student-to-student should be explored further. Overall, this successful trial has led to multiple new and innovative ideas for incorporating technology into the classroom. As the data collected was from a small sample size during a short-time frame, there appears to a venue to study further. Further and longer studies are needed to determine if orthographic and isometric sketching with the iPad and Apple pencil transfer to hand-sketching requirements in the long-term. Increasing the data along with researching and utilizing iPad applications better suited for sketching are only two variables to consider. Using electronic tools built for collaboration is another option that should be explored.


Efficient in-situ image and video compression through probabilistic image representation

Abstract: Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. In this paper, we present an efficient in-situ method for multi-dimensional image and video compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. Furthermore, it adopts a multi-layer Bayesian hierarchical model to achieve in-situ compression along with self-tuning and regularization, with just one single parameter to be specified by the user to achieve the desired compression rate. The properties of our proposed method include high reconstruction quality at a wide range of compression rates while preserving key local details, applicability to a variety of different image/video types and of different dimensions, computational scalability, progressive transmission and ease of tuning. Extensive numerical experiments using a variety of datasets including 2D still images, real-life YouTube videos, and surveillance videos show that CARP compares favorably to—and often uniformly outperforms—a wide range of popular image/video compression approaches, including JPEG, JPEG2000, AVI, BPG, MPEG4, HEVC, AV1, and three neural network-based methods.


Prototypical Graph Contrastive Learning

Abstract: Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generally very expensive and time-consuming. To address this issue, graph contrastive learning constructs instance discrimination task which pulls together positive pairs (augmentation pairs of the same graph) and pushes away negative pairs (augmentation pairs of different graphs) for unsupervised representation learning. However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i.e., the negatives likely having the same semantic structure with the query, leading to performance degradation. To mitigate this sampling bias issue, in this paper, we propose a Prototypical Graph Contrastive Learning (PGCL) approach. Specifically, PGCL models the underlying semantic structure of the graph data via clustering semantically similar graphs into the same group, and simultaneously encourages the clustering consistency for different augmentations of the same graph. Then given a query, it performs negative sampling via drawing the graphs from those clusters that differ from the cluster of query, which ensures the semantic difference between query and its negative samples. Moreover, for a query, PGCL further reweights its negative samples based on the distance between their prototypes (cluster centroids) and the query prototype such that those negatives having moderate prototype distance enjoy relatively large weights. This reweighting strategy is proved to be more effective than uniform sampling. Experimental results on various graph benchmarks testify the advantages of our PGCL over state-of-the-art methods. Code is publicly available at https://github.com/ha-lins/PGCL.


The right complexity measure in locally private estimation: It is not the Fisher information

Abstract: We identify fundamental tradeoffs between statistical utility and privacy under local models of privacy in which data is kept private even from the statistician, providing instance-specific bounds for private estimation and learning problems by developing the local minimax risk. In contrast to approaches based on worst-case (minimax) error, which are conservative, this allows us to evaluate the difficulty of individual problem instances and delineate the possibilities for adaptation in private estimation and inference. Our main results show that the local modulus of continuity of the estimand with respect to the variation distance—as opposed to the Hellinger distance central to classical statistics—characterizes rates of convergence under locally private estimation for many notions of privacy, including differential privacy and its relaxations. As consequences of these results, we identify an alternative to the Fisher information for private estimation, giving a more nuanced understanding of the challenges of adaptivity and optimality.


Understanding Pooling in Graph Neural Networks

Abstract: Many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs. In this article, we present an operational framework to unify this vast and diverse literature by describing pooling operators as the combination of three functions: selection, reduction, and connection (SRC). We then introduce a taxonomy of pooling operators, based on some of their key characteristics and implementation differences under the SRC framework. Finally, we propose three criteria to evaluate the performance of pooling operators and use them to investigate the behavior of different operators on a variety of tasks.


Multitargets Joint Training Lightweight Model for Object Detection of Substation

Abstract: The object detection of the substation is the key to ensuring the safety and reliable operation of the substation. The traditional image detection algorithms use the corresponding texture features of single-class objects and would not handle other different class objects easily. The object detection algorithm based on deep networks has generalization, and its sizeable complex backbone limits the application in the substation monitoring terminals with weak computing power. This article proposes a multitargets joint training lightweight model. The proposed model uses the feature maps of the complex model and the labels of objects in images as training multitargets. The feature maps have deeper feature information, and the feature maps of complex networks have higher information entropy than lightweight networks have. This article proposes the heat pixels method to improve the adequate object information because of the imbalance of the proportion between the foreground and the background. The heat pixels method is designed as a kind of reverse network calculation and reflects the object's position to the pixels of the feature maps. The temperature of the pixels indicates the probability of the existence of the objects in the locations. Three different lightweight networks use the complex model feature maps and the traditional tags as the training multitargets. The public dataset VOC and the substation equipment dataset are adopted in the experiments. The experimental results demonstrate that the proposed model can effectively improve object detection accuracy and reduce the time-consuming and calculation amount.


Ideal-Theoretic Explanation of Capacity-Achieving Decoding

Abstract: In this work, we present an abstract framework for some algebraic error-correcting codes with the aim of capturing codes that are list-decodable to capacity, along with their decoding algorithms. In the polynomial ideal framework, a code is specified by some ideals in a polynomial ring, messages are polynomials and the encoding of a message polynomial is the collection of residues of that polynomial modulo the ideals. We present an alternate way of viewing this class of codes in terms of linear operators, and show that this alternate view makes their algorithmic list-decodability amenable to analysis. Our framework leads to a new class of codes that we call affine Folded Reed-Solomon codes (which are themselves a special case of the broader class we explore). These codes are common generalizations of the well-studied Folded Reed-Solomon codes and Univariate Multiplicity codes as well as the less-studied Additive Folded Reed-Solomon codes, and lead to a large family of codes that were not previously known/studied. More significantly our framework also captures the algorithmic list-decodability of the constituent codes. Specifically, we present a unified view of the decoding algorithm for ideal-theoretic codes and show that the decodability reduces to the analysis of the distance of some related codes. We show that a good bound on this distance leads to a capacity-achieving performance of the underlying code, providing a unifying explanation of known capacity-achieving results. In the specific case of affine Folded Reed-Solomon codes, our framework shows that they are efficiently list-decodable up to capacity (for appropriate setting of the parameters), thereby unifying the previous results for Folded Reed-Solomon, Multiplicity and Additive Folded Reed-Solomon codes.


QUBO formulations for a system of linear equations

Abstract: With the advent of quantum computers, many quantum computing algorithms are being developed. Solving linear systems is one of the most fundamental problems in modern science and engineering. The Harrow Hassidim-Lloyd algorithm, a monumental quantum algorithm for solving linear systems on gate model quantum computers, was invented and several advanced variations have been developed. The algorithm was difficult to apply to general linear equations because it required various conditions for the matrix. In this paper, we introduce a new algorithm that can be applied to all linear systems. For a given general square matrix A∈Rn×n and a vector b→∈Rn, we will find quadratic unconstrained binary optimization (QUBO) models for a vector x→∈Rn that satisfies Ax→=b→. To formulate QUBO models for solving linear systems, we made use of a linear least-square problem with binary representation of the solution. We validated those QUBO models on the D-Wave system and discussed the results. For a simple system, we provide a Python code to calculate the matrix characterizing the relationship between the variables, and to print the test code that can be used directly in the D-Wave system.


Monte Carlo DropBlock for modeling uncertainty in object detection

Abstract: With the advancements made in deep learning, computer vision problems have seen a great improvement in performance. However, in many real-world applications such as autonomous driving vehicles, the risk associated with incorrect predictions of objects or segmentation of images is very high. Standard deep learning models for object detection and segmentation such as YOLO models are often overconfident in their predictions and do not take into account the uncertainty in predictions on out-of-distribution data. In this work, we propose an efficient and effective approach, Monte-Carlo DropBlock (MC-DropBlock), to model uncertainty in YOLO and convolutional vision Transformers for object detection. The proposed approach applies drop-block during training time and testing time on the convolutional layer of the deep learning models such as YOLO and convolutional transformer. We theoretically show that this leads to a Bayesian convolutional neural network capable of capturing the epistemic uncertainty in the model. Additionally, we capture the aleatoric uncertainty in the data using a Gaussian likelihood. We demonstrate the effectiveness of the proposed approach on modeling uncertainty in object detection and segmentation tasks using out-of-distribution experiments. Experimental results show that MC-DropBlock improves the generalization, calibration, and uncertainty modeling capabilities of YOLO models and convolutional Transformer models for object detection and segmentation.


Generating novel scene compositions from single images and videos

Abstract: Given a large dataset for training, generative adversarial networks (GANs) can achieve remarkable performance for the image synthesis task. However, training GANs in extremely low data regimes remains a challenge as overfitting often occurs, leading to memorization or training divergence. In this work, we introduce SIV-GAN, an unconditional generative model that can generate new scene compositions from a single training image or a single video clip. We propose a two-branch discriminator architecture with content and layout branches that are designed to judge internal content and scene layout realism separately from each other. This discriminator design enables the synthesis of visually plausible, novel compositions of a scene with varying content and layout while preserving the context of the original sample. Compared to previous single-image GANs, our model generates more diverse images of higher quality while not being restricted to a single image setting. We further introduce a new challenging task of learning from a few frames of a single video. In this training setup the training images are highly similar to each other, which makes it difficult for prior GAN models to achieve a synthesis of both high quality and diversity.


Work in Progress: STEM Students’ Experiences with Educational Technology Tools

Abstract: Dr. Anwar has over 12


Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution

Abstract: Knowledge distillation (KD) is an effective framework that aims to transfer meaningful information from a large teacher to a smaller student. Generally, KD often involves how to define and transfer knowledge. Previous KD methods often focus on mining various forms of knowledge, for example, feature maps and refined information. However, the knowledge is derived from the primary supervised task, and thus, is highly task-specific. Motivated by the recent success of self-supervised representation learning, we propose an auxiliary self-supervision augmented task to guide networks to learn more meaningful features. Therefore, we can derive soft self-supervision augmented distributions as richer dark knowledge from this task for KD. Unlike previous knowledge, this distribution encodes joint knowledge from supervised and self-supervised feature learning. Beyond knowledge exploration, we propose to append several auxiliary branches at various hidden layers, to fully take advantage of hierarchical feature maps. Each auxiliary branch is guided to learn self-supervision augmented tasks and distill this distribution from teacher to student. Overall, we call our KD method a hierarchical self-supervision augmented KD (HSSAKD). Experiments on standard image classification show that both offline and online HSSAKD achieves state-of-the-art performance in the field of KD. Further transfer experiments on object detection further verify that HSSAKD can guide the network to learn better features. The code is available at https://github.com/winycg/HSAKD.


Speech recognition datasets for low-resource Congolese languages

Abstract: Large pre-trained Automatic Speech Recognition (ASR) models have shown improved performance in low-resource languages due to the increased availability of benchmark corpora and the advantages of transfer learning. However, only a limited number of languages possess ample resources to fully leverage transfer learning. In such contexts, benchmark corpora become crucial for advancing methods. In this article, we introduce two new benchmark corpora designed for low-resource languages spoken in the Democratic Republic of the Congo: the Lingala Read Speech Corpus, with 4 h of labelled audio, and the Congolese Speech Radio Corpus, which offers 741 h of unlabelled audio spanning four significant low-resource languages of the region. During data collection, Lingala Read Speech recordings of thirty-two distinct adult speakers, each with a unique context under various settings with different accents, were recorded. Concurrently, Congolese Speech Radio raw data were taken from the archive of broadcast station, followed by a designed curation process. During data preparation, numerous strategies have been utilised for pre-processing the data. The datasets, which have been made freely accessible to all researchers, serve as a valuable resource for not only investigating and developing monolingual methods and approaches that employ linguistically distant languages but also multilingual approaches with linguistically similar languages. Using techniques such as supervised learning and self-supervised learning, they are able to develop inaugural benchmarking of speech recognition systems for Lingala and mark the first instance of a multilingual model tailored for four Congolese languages spoken by an aggregated population of 95 million. Moreover, two models were applied to this dataset. The first is supervised learning modelling and the second is for self-supervised pre-training.


Work in Progress: Using Machine Learning to Map Student Narratives of Understanding and Promoting Linguistic Justice

Abstract: Abstract This work-in-progress paper expands on a collaboration between engineering education researchers and machine learning researchers to automate the analysis of written responses to conceptually challenging questions in statics and dynamics courses (Authors, 2022). Using the Concept Warehouse (Koretsky et al., 2014), written justifications of ConcepTests (CTs) were gathered from statics and dynamics courses in a diverse set of two- and four-year institutions. Written justifications for CTs have been used to support active learning pedagogies which makes them important to investigate how students put together their problem-solving narratives of understanding. However, despite the large benefit that analysis of student written responses may provide to instructors and researchers, manual review of responses is cumbersome, limits analysis, and can be prone to human bias. In efforts to improve the analysis of student written responses, machine learning has been used in various educational contexts to analyze short and long texts (Burstein et al., 2020; Burstein et al., 2021). Natural Language Processing (NLP) uses transformer-based machine learning models (Brown et al., 2020; Raffel et al., 2019) which can be used through fine-tuning or in-context learning methods. NLP can be used to train algorithms that can automate the coding of written responses. Only a few studies for educational applications have leveraged transformer-based machine learning models further prompting an investigation into its use in STEM education. However, work in NLP has been criticized for heightening the possibility to perpetuate and even amplify harmful stereotypes and implicit biases (Chang et al., 2019; Mayfield et al., 2019). In this study, we detail the aim to use NLP for linguistic justice. Using methods like text summary, topic modeling, and text classification, we identify key aspects of student narratives of understanding in written responses to mechanics and statics CTs. Through this process, we seek to use machine learning to identify different ways students talk about a problem and their understanding at any point in their narrative formation process. Thus, we hope to help reduce human bias in the classroom and through technology by giving instructors and researchers a diverse set of narratives that include insight into their students’ histories, identities, and understanding. These can then be used towards connecting technological knowledge to students’ everyday lives. References Authors. (2022, June). [Title omitted for blind review]. Burstein, J., Horbach, A., Kochmar, K., Laarmann-Quante, R., Leacock, C., Madnani, Nitin., Pilan, I., Yannakoudakis, H. & Zesch,T.. (2021) Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, Association for Computational Linguistics Burstein,J., Kochmar, K., Leacock, C., Madnani, Nitin., Pilan, I., Yannakoudakis, H. & Zesch,T.. (2020). Proceedings of the 15th Workshop on Innovative Use of NLP for Building Educational Applications, Association for Computational Linguistics. Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. and Agarwal, S., (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. Chang, K.-W., Prabhakaran, V., and Ordonez, V. (2019). Bias and Fairness in Natural Language Processing [Paper presentation]. In Proceedings of the 2019 Conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP): Tutorial abstracts. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (China), Hong Kong, China. Association for Computational Linguistics. Koretsky, M. D., Falconer, J. L., Brooks, B. J., Gilbuena, D. M., Silverstein, D. L., Smith, C., & Miletic, M. (2014). The AIChE Concept Warehouse: A web-based tool to promote concept-based instruction. Advances in Engineering Education, 4(1), 7:1-27. Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Román, E., and Black, A. W. (2019, August). Equity beyond bias in language technologies for education [Paper presentation]. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications (Italy), Florence, Italy (pp. 444–460). Association for Computational Linguistics. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683.


Objective Dimensions on Fishermen's Quality of Life

Abstract: The purpose of this study was to find out fishermen’s quality of life construct validity used survey by involving 44 fishermen in Cimalaya, Karawang. Data analyzed by correlations test, cronbach’s alpha and factor analysis (CFA). The Study results revealed that there were four objective dimensions of fishermen’s quality of life that can be seen from the indicators: (1) income with value of 0.455; (2) Marital status with value of 0.545; (3) Health status with value of 0.691; and (4) Social relationship with value of 0.638. Those values were good since they were above 0.4 or had little tolerance to above 0.25. For this reason, the objective dimensions of quality of life can be measured in terms of harmony and ability which is the integration of quality of life.


EMF-Net: An edge-guided multi-feature fusion network for text manipulation detection

Abstract: Text images have a more uniform background than natural images, making it difficult to capture tampering traces. Thus, it poses significant challenges to the detection and localization of tampered text images. To effectively detect tampered text images, we propose the edge-guided multi-feature fusion network (EMF-Net) including four different modules, the difference semantic discriminator (DSD), the edge-guidance feature aggregation module (EFAM), the edge supervised module (ESM), and the multi-branch attention-induced fusion module (MAFM). The proposed network combines RGB images and learned noise-sensitive features to deeply excavate the hidden tampered region features to improve the detection accuracy due to the insufficient feature information. Meanwhile, the multi-branch attention-induced fusion and semantic discriminator methods are integrated to reduce false alarms by shielding the feature interference in non-tampered regions and diminish the external interference. Furthermore, we craft a dataset of 12,000 text images and their tempered versions with three tempering operations including copy-move, splicing and inpainting. Extensive experiments have shown that the proposed network can improve the generalization performance and achieve the higher detection accuracy compared to current other state-of-the-art methods.


Occurrence and characteristics of elderly With Covid-19 in southern Brazil

Abstract: This study aimed to describe the occurrence of COVID-19 in elderly patients treated at public health services in southern Brazil cities and their clinical and sociodemographic characteristics. This is a cross-sectional, observational, and retrospective study. From July 2020 to May 2021, medical records of elderly patients who underwent a test for COVID-19 at the molecular diagnostics laboratory of the University of Cruz Alta were selected. 310 elderly patients, with an average age of 60 to 99 years, were included in the study. Information was collected regarding the result of the RT-qPCR test, gender, symptoms, and comorbidities. We identified that 24.2% of the elderly evaluated in the study had a positive molecular diagnosis for COVID-19. The average age of the sample studied was 69 years (±8.89). Females had a higher proportion of positive cases (69.3%) compared to males (30.7%) (p=0.017). A higher proportion of individuals infected with SARS-Cov2 among those who reported cough, fever >37°C and pre-existing cardiovascular diseases (p = 0.028; 0.015; 0.017 respectively) was also identified. Learning the clinical and sociodemographic profile of the elderly with COVID-19 can contribute to strategies for care and prevention of the disease in this group. The study findings indicate that greater attention should be given to the elderly with heart diseases and those who have fevers and coughs.


Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency

Abstract: Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on 12 multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. The code is available at https://github.com/youweiliang/Multi-view_Graph_Learning.


Tchebichef Transform Domain-Based Deep Learning Architecture for Image Super-Resolution

Abstract: Recent advances in the area of artificial intelligence and deep learning have motivated researchers to apply this knowledge to solve multipurpose applications in the area of computer vision and image processing. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the nonlinear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this article, we propose a deep learning-based image SR architecture in the Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the transform mentioned earlier is achieved using another layer known as the inverse TCL (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low- frequency representation of images that makes the task of SR simplified. Furthermore, a transfer learning-based approach is adopted to enhance the quality of images by considering Covid19 medical images as an additional experiment. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that may help in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain SR (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.


RCT: Resource Constrained Training for Edge AI

Abstract: Efficient neural network training is essential for in situ training of edge artificial intelligence (AI) and carbon footprint reduction in general. Train neural network on the edge is challenging because there is a large gap between limited resources on edge and the resource requirement of current training methods. Existing training methods are based on the assumption that the underlying computing infrastructure has sufficient memory and energy supplies. These methods involve two copies of the model parameters, which is usually beyond the capacity of on-chip memory in processors. The data movement between off-chip and on-chip memory incurs large amounts of energy. We propose resource constrained training (RCT) to realize resource-efficient training for edge devices and servers. RCT only keeps a quantized model throughout the training so that the memory requirement for model parameters in training is reduced. It adjusts per-layer bitwidth dynamically to save energy when a model can learn effectively with lower precision. We carry out experiments with representative models and tasks in image classification, natural language processing, and crowd counting applications. Experiments show that on average, 8-15-bit weight update is sufficient for achieving SOTA performance in these applications. RCT saves 63.5%-80% memory for model parameters and saves more energy for communications. Through experiments, we observe that the common practice on the first/last layer in model compression does not apply to efficient training. Also, interestingly, the more challenging a dataset is, the lower bitwidth is required for efficient training.


Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning

Abstract: Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.


BASS: Broad Network Based on Localized Stochastic Sensitivity

Abstract: The training of the standard broad learning system (BLS) concerns the optimization of its output weights via the minimization of both training mean square error (MSE) and a penalty term. However, it degrades the generalization capability and robustness of BLS when facing complex and noisy environments, especially when small perturbations or noise appear in input data. Therefore, this work proposes a broad network based on localized stochastic sensitivity (BASS) algorithm to tackle the issue of noise or input perturbations from a local perturbation perspective. The localized stochastic sensitivity (LSS) prompts an increase in the network's noise robustness by considering unseen samples located within a Q -neighborhood of training samples, which enhances the generalization capability of BASS with respect to noisy and perturbed data. Then, three incremental learning algorithms are derived to update BASS quickly when new samples arrive or the network is deemed to be expanded, without retraining the entire model. Due to the inherent superiorities of the LSS, extensive experimental results on 13 benchmark datasets show that BASS yields better accuracies on various regression and classification problems. For instance, BASS uses fewer parameters (12.6 million) to yield 1% higher Top-1 accuracy in comparison to AlexNet (60 million) on the large-scale ImageNet (ILSVRC2012) dataset.


Confusion-Based Metric Learning for Regularizing Zero-Shot Image Retrieval and Clustering

Abstract: Deep metric learning turns to be attractive in zero-shot image retrieval and clustering (ZSRC) task in which a good embedding/metric is requested such that the unseen classes can be distinguished well. Most existing works deem this "good" embedding just to be the discriminative one and race to devise the powerful metric objectives or the hard-sample mining strategies for learning discriminative deep metrics. However, in this article, we first emphasize that the generalization ability is also a core ingredient of this "good" metric and it largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the confusion-based metric learning (CML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing two interesting regularization terms, i.e., the energy confusion (EC) and diversity confusion (DC) terms. These terms daringly break away from the traditional deep metric learning idea of designing discriminative objectives and instead seek to "confuse" the learned model. These two confusion terms focus on local and global feature distribution confusions, respectively. We train these confusion terms together with the conventional deep metric objective in an adversarial manner. Although it seems weird to "confuse" the model learning, we show that our CML indeed serves as an efficient regularization framework for deep metric learning and it is applicable to various conventional metric methods. This article empirically and experimentally demonstrates the importance of learning an embedding/metric with good generalization, achieving the state-of-the-art performances on the popular CUB, CARS, Stanford Online Products, and In-Shop datasets for ZSRC tasks.


Flying in Dynamic Scenes With Multitarget Velocimetry and Perception-Enhanced Planning

Abstract: Micro drones have been widely applied in applications, such as aerial cinematography and environment exploration. Due to the presence of numerous demanding scenes, such as crowded obstacles and dynamic objects, remotely controlling a drone poses a significant challenge for humans. As a result, there is an urgent need for highly autonomous flight capabilities. This article presents a fully autonomous flight system in a complex dynamic environment, showing satisfactory performance in real-world tests, and outperforms the state-of-the-art works in both dynamic object perception, and flight safety and efficiency. A lightweight but effective multiobject velocimetry based on a cross-correlation algorithm and local points feature is proposed, with a robust image-based object classifier as the front end. Also, we plan the flight trajectory considering the camera's field of view and the uncertainty in the dynamic object's constant velocity model. At last, we further explore the benefits of vehicle's active yaw control for improving perception quality and flight safety.


Data, language and graph-based reasoning methods for identification of human malicious behaviors in nuclear security

Abstract: Identification of human malicious behaviors is a demanding problem in nuclear security due to its potential to cause huge personnel and economic casualties. With the visibly rapid development of deep learning technology especially computer vision, the automatic identification of human malicious behaviors become feasible, offering significant savings in human resources. However, there is few research focusing on the explainable reasoning process based on the calculation results of computer vision. In this paper, an innovative framework is proposed, which contains two modules called computer vision module and reasoning module. Among them, the computer vision module could extract and detect human actions by some computer vision technologies, while these actions would be comprehensively utilized in reasoning module to derive the reasoning results. Furthermore, within the reasoning module, three novel reasoning methods are developed which are data-based reasoning method, language-based reasoning method and graph-based reasoning method. For evaluating the practicality and effectiveness of these methods, experiments are conducted on four typical scenarios in nuclear security, which are normal status, fence climbing, wire cutting and weapon holding. The results show that the proposed language-based reasoning method turns out to be the best one, which obtain a higher precision value of 0.7917 and a perfect recall value of 1.0000.


Diagnostic Performance of Artificial Intelligence–Based Computer-Aided Detection Software for Automated Breast Ultrasound

Abstract: This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS).ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared.For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing.AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.


An Implicit-Explicit Prototypical Alignment Framework for Semi-Supervised Medical Image Segmentation

Abstract: Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level annotation in medical image segmentation tasks. Consistency learning, serving as a mainstream method in semi-supervised training, suffers from low efficiency and poor stability due to inaccurate supervision and insufficient feature representation. Prototypical learning is one potential and plausible way to handle this problem due to the nature of feature aggregation in prototype calculation. However, the previous works have not fully studied how to enhance the supervision quality and feature representation using prototypical learning under the semi-supervised condition. To address this issue, we propose an implicit-explicit alignment (IEPAlign) framework to foster semi-supervised consistency training. In specific, we develop an implicit prototype alignment method based on dynamic multiple prototypes on-the-fly. And then, we design a multiple prediction voting strategy for reliable unlabeled mask generation and prototype calculation to improve the supervision quality. Afterward, to boost the intra-class consistency and inter-class separability of pixel-wise features in semi-supervised segmentation, we construct a region-aware hierarchical prototype alignment, which transmits information from labeled to unlabeled and from certain regions to uncertain regions. We evaluate IEPAlign on three medical image segmentation tasks. The extensive experimental results demonstrate that the proposed method outperforms other popular semi-supervised segmentation methods and achieves comparable performance with fully-supervised training methods.


Applying artificial intelligence and computational modeling to nanomedicine

Abstract: Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle–biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.