Reviewerly/exHarmony huggingface dataset:
AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit’s activation via adjoint operators

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 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

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

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

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

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 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 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

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.
Neural Generalization of Multiple Kernel Learning

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

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 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

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

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

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.