Reviewerly/exHarmony huggingface dataset:
Exploration of temporal bone anatomy using mixed reality (HoloLens): development of a mixed reality anatomy teaching resource prototype

Mixed reality (MR), a technology which supplements the real world with virtual objects, is increasingly becoming available as a teaching tool in medical education. The Microsoft HoloLens device allows operators to experience MR using a head-mounted device without interfering with their physical reality, stimulating a realistic learning experience using virtual objects. This project aimed to develop a MR anatomy teaching application with HoloLens for exploring the anatomy of the temporal bone. The educational application was developed from a multidisciplinary collaboration between undergraduate and postgraduate students across several academic disciplines with Medtronic, a medical technology company. 3D anatomical models were built using ZBrush and Blender, while the HoloLens1 application was developed using Windows 10, Visual Studio 2017, Unity and Mixed Reality Toolkit (MRTK). Modules developed within the application included a basic HoloLens tutorial, a virtual temporal bone with surgical anatomy landmarks and free drilling of the temporal bone. The basic tutorial allows the operator to adapt to the MR environment prior to exploring the anatomical landmarks of the 3D temporal bone. The free drilling of the temporal bone using vertex displacement and texture stretching replicates a real-time bone drilling experience and allows the operator to explore the anatomical relationship between different otological structures.
There and back again: A circuit extraction tale

Translations between the quantum circuit model and the measurement-based one-way model are useful for verification and optimisation of quantum computations. They make crucial use of a property known as gflow. While gflow is defined for one-way computations allowing measurements in three different planes of the Bloch sphere, most research so far has focused on computations containing only measurements in the XY-plane. Here, we give the first circuit-extraction algorithm to work for one-way computations containing measurements in all three planes and having gflow. The algorithm is efficient and the resulting circuits do not contain ancillae. One-way computations are represented using the ZX-calculus, hence the algorithm also represents the most general known procedure for extracting circuits from ZX-diagrams. In developing this algorithm, we generalise several concepts and results previously known for computations containing only XY-plane measurements. We bring together several known rewrite rules for measurement patterns and formalise them in a unified notation using the ZX-calculus. These rules are used to simplify measurement patterns by reducing the number of qubits while preserving both the semantics and the existence of gflow. The results can be applied to circuit optimisation by translating circuits to patterns and back again.
Portfolio optimization in the era of digital financialization using cryptocurrencies

The Fourth industrial revolution has seen many innovative technologies that are now challenging traditional economies. The innovative and technological financial instruments are inspiring individuals and expert investors to investigate the broader investment spectrum, and consequently diversify their portfolios. Going beyond the conventional portfolios and developing state-of-the-art strategies that comply with the ever-changing financial and technological advancements are the keys to long term sustainability. Therefore, to cater to the needs of all segments of the society, the investment strategies during the fourth industrial revolution demand exposure to technological and digital financial innovations. This study investigates the impact of diversification with the addition of five cryptocurrencies from November 2015 to November 2019 on four traditional asset portfolios. The results show that the diversification increased the returns in most of the cases, and reduced the portfolio volatility in all portfolios, and also provided higher returns as compared to the traditional portfolios for the same level of risk. This study also revealed that the results might improve when short sales are allowed. Moreover, we can conclude that the addition of multiple cryptocurrencies in a portfolio provides enhanced results for diversification, and Ethereum provides a better diversification opportunity as compared to Bitcoin.
Neural reverse engineering of stripped binaries using augmented control flow graphs

We address the problem of reverse engineering of stripped executables, which contain no debug information. This is a challenging problem because of the low amount of syntactic information available in stripped executables, and the diverse assembly code patterns arising from compiler optimizations. We present a novel approach for predicting procedure names in stripped executables. Our approach combines static analysis with neural models. The main idea is to use static analysis to obtain augmented representations of call sites; encode the structure of these call sites using the control-flow graph (CFG) and finally, generate a target name while attending to these call sites. We use our representation to drive graph-based, LSTM-based and Transformer-based architectures. Our evaluation shows that our models produce predictions that are difficult and time consuming for humans, while improving on existing methods by 28% and by 100% over state-of-the-art neural textual models that do not use any static analysis. Code and data for this evaluation are available at https://github.com/tech-srl/Nero .
Communication optimization strategies for distributed deep neural network training: A survey

Recent trends in high-performance computing and deep learning have led to the proliferation of studies on large-scale deep neural network training. However, the frequent communication requirements among computation nodes drastically slow the overall training speeds, which causes bottlenecks in distributed training, particularly in clusters with limited network bandwidths. To mitigate the drawbacks of distributed communications, researchers have proposed various optimization strategies. In this paper, we provide a comprehensive survey of communication strategies from both an algorithm viewpoint and a computer network perspective. Algorithm optimizations focus on reducing the communication volumes used in distributed training, while network optimizations focus on accelerating the communications between distributed devices. At the algorithm level, we describe how to reduce the number of communication rounds and transmitted bits per round. In addition, we elucidate how to overlap computation and communication. At the network level, we discuss the effects caused by network infrastructures, including logical communication schemes and network protocols. Finally, we extrapolate the potential future challenges and new research directions to accelerate communications for distributed deep neural network training.
Hybrid quantum-classical algorithms for approximate graph coloring

We show how to apply the recursive quantum approximate optimization algorithm (RQAOA) to MAX-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-CUT, the problem of finding an approximate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-vertex coloring of a graph. We compare this proposal to the best known classical and hybrid classical-quantum algorithms. First, we show that the standard (non-recursive) QAOA fails to solve this optimization problem for most regular bipartite graphs at any constant level <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math>: the approximation ratio achieved by QAOA is hardly better than assigning colors to vertices at random. Second, we construct an efficient classical simulation algorithm which simulates level-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math> QAOA and level-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math> RQAOA for arbitrary graphs. In particular, these hybrid algorithms give rise to efficient classical algorithms, and no benefit arising from the use of quantum mechanics is to be expected. Nevertheless, they provide a suitable testbed for assessing the potential benefit of hybrid algorithm: We use the simulation algorithm to perform large-scale simulation of level-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math> QAOA and RQAOA with up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>300</mml:mn></mml:math> qutrits applied to ensembles of randomly generated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>3</mml:mn></mml:math>-colorable constant-degree graphs. We find that level-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math> RQAOA is surprisingly competitive: for the ensembles considered, its approximation ratios are often higher than those achieved by the best known generic classical algorithm based on rounding an SDP relaxation. This suggests the intriguing possibility that higher-level RQAOA may be a potentially useful algorithm for NISQ devices.
Quantum Enhancements for Deep Reinforcement Learning in Large Spaces

Quantum computing and artificial intelligence are better integrated using an approach based on a new class of physics-inspired methods: New quantum algorithms can provide boosts to energy-based deep reinforcement learning.
Teaching in the Age of Covid-19—1 Year Later

This is an accepted manuscript of an article published by Springer in Postdigital Science and Education on 10/08/2021, available online: https://doi.org/10.1007/s42438-021-00243-7 The accepted version of the publication may differ from the final published version.
Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification

Lung and colon cancers are two of the most common causes of death and morbidity in humans. One of the most important aspects of appropriate treatment is the histopathological diagnosis of such cancers. As a result, the main goal of this study is to use a multi-input capsule network and digital histopathology images to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocarcinomas of the colon. Two convolutional layer blocks are used in the proposed multi-input capsule network. The CLB (Convolutional Layers Block) employs traditional convolutional layers, whereas the SCLB (Separable Convolutional Layers Block) employs separable convolutional layers. The CLB block takes unprocessed histopathology images as input, whereas the SCLB block takes uniquely pre-processed histopathological images. The pre-processing method uses color balancing, gamma correction, image sharpening, and multi-scale fusion as the major processes because histopathology slide images are typically red blue. All three channels (Red, Green, and Blue) are adequately compensated during the color balancing phase. The dual-input technique aids the model's ability to learn features more effectively. On the benchmark LC25000 dataset, the empirical analysis indicates a significant improvement in classification results. The proposed model provides cutting-edge performance in all classes, with 99.58% overall accuracy for lung and colon abnormalities based on histopathological images.
Jury Learning: Integrating Dissenting Voices into Machine Learning Models

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups’ labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier’s prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators’ models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent. A field evaluation finds that practitioners construct diverse juries that alter 14% of classification outcomes.
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow

Optical flow estimation aims to find the 2D motion field by identifying corresponding pixels between two images. Despite the tremendous progress of deep learning-based optical flow methods, it remains a challenge to accurately estimate large displacements with motion blur. This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images. The locality of convolutional features makes the computed correlations susceptible to various noises. On large displacements with motion blur, noisy correlations could cause severe errors in the estimated flow. To overcome this challenge, we propose a new architecture “CRoss-Attentional Flow Trans-former” (CRAFT), aiming to revitalize the correlation volume computation. In CRAFT, a Semantic Smoothing Trans-former layer transforms the features of one frame, making them more global and semantically stable. In addition, the dot-product correlations are replaced with trans-former Cross-Frame Attention. This layer filters out feature noises through the Query and Key projections, and computes more accurate correlations. On Sintel (Final) and KITTI (foreground) benchmarks, CRAFT has achieved new state-of-the-art performance. Moreover, to test the robust-ness of different models on large motions, we designed an image shifting attack that shifts input images to generate large artificial motions. Under this attack, CRAFT per-forms much more robustly than two representative meth-ods, RAFT and GMA. The code of CRAFT is is available at https://github.com/askerlee/craft.
Boafft: Distributed Deduplication for Big Data Storage in the Cloud

As data progressively grows within data centers, the cloud storage systems continuously facechallenges in saving storage capacity and providing capabilities necessary to move big data within an acceptable time frame. In this paper, we present the Boafft, a cloud storage system with distributed deduplication. The Boafft achieves scalable throughput and capacity usingmultiple data servers to deduplicate data in parallel, with a minimal loss of deduplication ratio. Firstly, the Boafft uses an efficient data routing algorithm based on data similarity that reduces the network overhead by quickly identifying the storage location. Secondly, the Boafft maintains an in-memory similarity indexing in each data server that helps avoid a large number of random disk reads and writes, which in turn accelerates local data deduplication. Thirdly, the Boafft constructs hot fingerprint cache in each data server based on access frequency, so as to improve the data deduplication ratio. Our comparative analysis with EMC's stateful routing algorithm reveals that the Boafft can provide a comparatively high deduplication ratio with a low network bandwidth overhead. Moreover, the Boafft makes better usage of the storage space, with higher read/write bandwidth and good load balance.
Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection

Service selection is crucial to service composition in determining the composite Quality of Service (QoS). The proliferation of composable services on the Internet and the practical need for timely delivering optimized composite solutions motivate the adoption of population-based algorithms for QoS-aware service selection. However, existing population-based algorithms are generally complicated to use, and often used as a general approach to solving different optimization problems. We propose to develop specialized algorithms for QoS-aware service selection, based on the artificial bee colony algorithm (ABC). ABC is a new and simpler implementation of swarm intelligence, which has proven to be successful in solving many real-world problems, especially the numerical optimization problems. We develop an approximate approach for the neighborhood search of ABC, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. We present three algorithms based on the approach. All the three algorithms are designed to improve the performance and meanwhile preserve the simplicity of ABC. Each algorithm applies a different technique to leverage the unique characteristics of the service selection problem. Experimental results show higher accuracy and convergence speed of the proposed algorithms over the state of the art algorithms.
Elastic Load Balancing for Dynamic Virtual Machine Reconfiguration Based on Vertical and Horizontal Scaling

Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.
How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and without Importance Weights

Particle filters are a popular and flexible class of numerical algorithms to solve a large class of nonlinear filtering problems. However, standard particle filters with importance weights have been shown to require a sample size that increases exponentially with the dimension $D$ of the state space in order to achieve a certain performance, which precludes their use in very high-dimensional filtering problems. Here, we focus on the dynamic aspect of this “curse of dimensionality” (COD) in continuous-time filtering, which is caused by the degeneracy of importance weights over time. We show that the degeneracy occurs on a time scale that decreases with increasing $D$. In order to soften the effects of weight degeneracy, most particle filters use particle resampling and improved proposal functions for the particle motion. We explain why neither of the two can prevent the COD in general. In order to address this fundamental problem, we investigate an existing filtering algorithm based on optimal feedback control that sidesteps the use of importance weights. We use numerical experiments to show that this feedback particle filter (FPF) by [T. Yang, P. G. Mehta, and S. P. Meyn, IEEE Trans. Automat. Control, 58 (2013), pp. 2465--2480] does not exhibit a COD.
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity

We develop model-based methods for solving stochastic convex optimization problems, introducing the approximate-proximal point, or aProx, family, which includes stochastic subgradient, proximal point, and bundle methods. When the modeling approaches we propose are appropriately accurate, the methods enjoy stronger convergence and robustness guarantees than classical approaches, even though the model-based methods typically add little to no computational overhead over stochastic subgradient methods. For example, we show that improved models converge with probability 1 and enjoy optimal asymptotic normality results under weak assumptions; these methods are also adaptive to a natural class of what we term easy optimization problems, achieving linear convergence under appropriate strong growth conditions on the objective. Our substantial experimental investigation shows the advantages of more accurate modeling over standard subgradient methods across many smooth and non-smooth optimization problems.
General Square-Pattern Discretization Formulas via Second-Order Derivative Elimination for Zeroing Neural Network Illustrated by Future Optimization

Previous works provide a few effective discretization formulas for zeroing neural network (ZNN), of which the precision is a square pattern. However, those formulas are separately developed via many relatively blind attempts. In this paper, general square-pattern discretization (SPD) formulas are proposed for ZNN via the idea of the second-order derivative elimination. All existing SPD formulas in previous works are included in the framework of the general SPD formulas. The connections and differences of various general formulas are also discussed. Furthermore, the general SPD formulas are used to solve future optimization under linear equality constraints, and the corresponding general discrete ZNN models are proposed. General discrete ZNN models have at least one parameter to adjust, thereby determining their zero stability. Thus, the parameter domains are obtained by restricting zero stability. Finally, numerous comparative numerical experiments, including the motion control of a PUMA560 robot manipulator, are provided to substantiate theoretical results and their superiority to conventional Euler formula.
VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization

Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.