Reviewerly/exHarmony huggingface dataset:
A New Multiple Hypothesis Tracker Using Validation Gate with Motion Direction Constraint

In multi-target tracking scenarios with dense and heterogeneous clutter, there is a substantial increase in the false measurements that originated from the clutter within the validation gate, and consequently, the number of measurement-to-track association hypothesis grows rapidly in traditional multiple hypothesis tracker (MHT), leading to a sharp decrease in data association accuracy and tracking performance. A new multiple hypothesis tracker using validation gate with motion direction constraint (MHT-MDC) is proposed to solve these problems. In the MHT-MDC, a motion direction constraint (MDC) gate is designed by considering the prior target maneuvering information, which effectively reduces the volume of validation gate and, thus, diminishes the number of false measurements in the gate when the innovation covariance is large. Subsequently, the clutter density in the MDC gate is adaptively estimated by the conditional mean estimator of clutter density (CMECD), based on which the score functions in the MDC gate can be calculated. The MHT-MDC is compared with the MHT algorithm in simulations, and the experimental results demonstrate its superior tracking performance for weakly maneuvering targets in high clutter density scenarios.
Sphere estimation network: three-dimensional nuclei detection of fluorescence microscopy images

Purpose: Fluorescence microscopy visualizes three-dimensional subcellular structures in tissue with two-photon microscopy achieving deeper penetration into tissue. Nuclei detection, which is essential for analyzing tissue for clinical and research purposes, remains a challenging problem due to the spatial variability of nuclei. Recent advancements in deep learning techniques have enabled the analysis of fluorescence microscopy data to localize and segment nuclei. However, these localization or segmentation techniques would require additional steps to extract characteristics of nuclei. We develop a 3D convolutional neural network, called Sphere Estimation Network (SphEsNet), to extract characteristics of nuclei without any postprocessing steps. Approach: To simultaneously estimate the center locations of nuclei and their sizes, SphEsNet is composed of two branches to localize nuclei center coordinates and to estimate their radii. Synthetic microscopy volumes automatically generated using a spatially constrained cycle-consistent adversarial network are used for training the network because manually generating 3D real ground truth volumes would be extremely tedious. Results: Three SphEsNet models based on the size of nuclei were trained and tested on five real fluorescence microscopy data sets from rat kidney and mouse intestine. Our method can successfully detect nuclei in multiple locations with various sizes. In addition, our method was compared with other techniques and outperformed them based on object-level precision, recall, and F1 score. Our model achieved 89.90% for F1 score. Conclusions: SphEsNet can simultaneously localize nuclei and estimate their size without additional steps. SphEsNet can be potentially used to extract more information from nuclei in fluorescence microscopy images.
Automated Nucleus Segmentation of Leukemia Blast Cells : Color Spaces Study

Leukemia detection using computer vision algorithms is a significant step in computer-assisted diagnosis for a pathologist. In order to extract the blood cells, many color space models are used for image enhancement and as the preprocessing steps. The present work compares the effect of the green, saturation, Cb and M component of RGB, HSV, YCbCr and CMY color spaces for segmentation of nucleus of blast cells in a leukemia patient's blood smear. The segmentation result of each color space for every ten images is divided into three categories i.e. only WBC segmentation, WBC with peripheral cells and all blood cell segmentation. The study demonstrates that the performance of segmentation is negatively correlated with contrast and illuminance of the input image. HSV and CMY models obtained 85% segmentation accuracy. The present study would help researchers to narrow down their selection when choosing a color space model for segmenting the nucleus of leukemia blast cells.
An Empirical Comparison of Generative Adversarial Network (GAN) Measures

Generative adversarial network (GAN) is the most potent unsupervised learning generative model in deep learning. Though numerous impressive results have been published in computer vision tasks, it is still complex to compare and validate the performance of GAN algorithms. In this work, the review of quantitative evaluation measures of GAN is done with the performance of Frechet inception distance (FID) and the inception score (IS). Evaluations of several recently proposed GAN approaches are based on these two metrics. These evaluations demonstrate an evident variation in their performance based on key factors like training model and hyperparameters such as dimensions of the latent space, learning rate, and gradient penalty. This work discovers the proper dimension of latent space and compares FID and IS that are implemented for evaluation of generated data distribution. FID and IS are the best metrics for evaluating generated data distribution. This work gives an emphasis on appropriate dimension of the latent space and compares these two metrics concerning the improved GAN models. The experimental analysis shows that FID gives better performance compared to IS. NS-GAN and LS-GAN perform more precise. The generator generates better results for 10-dimensional latent spaces, which are not really distinct from the consequence of the normal 100-dimensions. It is recommended to use LS-GAN foe better performance and understanding of algorithms.
Community resilience in a rural food system: documenting pathways to nutrition solutions

To describe and determine the barriers and facilitators to food-related health behaviors of residents in a rural Mississippi Delta community. Descriptive study of qualitative interviews. A non-random sample of 34 low-income, food-insecure adults residing in a rural Mississippi Delta community were interviewed using fuzzy cognitive mapping, a mixed methods approach. Participants strongly emphasized the time restraints they faced in both procuring and preparing foods, due to substantial travel time required to procure groceries. Participants also identified key facilitators to healthy eating behaviors, including seasonal produce stands, foraging, fishing, home provisioning, and access to the local food pantry. These barriers and facilitators are highly interconnected with other influential factors including poverty, lack of health care, unemployment, and faith-based support systems. While the connection between low food access and poor eating habits is well researched, this novel mixed-method approach details two important elements missing from the literature: (1) other factors often overlooked that mitigate or exacerbate this relationship and (2) the dynamic nature of the relationships between these factors, poor eating habits, and health. Additionally, this research examines these relationships in an underrepresented rural minority population where resources needed to mitigate poor health are often much more limited. Findings from this study are critical to health and food policy in Mississippi and more generally, rural communities.
Improving answer selection with global features

Abstract Given a question and its answer candidates (named QA corpus), answer selection is the task of identifying the most relevant answers to the question. Answer selection is widely used in question answering, web search, and so on. Current deep neural network models primarily utilize local features extracted from input question‐answer pairs (QA pairs). However, the global features contained in QA corpora are under‐utilized, and we argue that these global features substantially contribute to the answer selection task. To verify this point of view, we propose a novel model that combines local and global features for answer selection. In our model, two different global feature extractors are employed to extract statistical global features and deep global features from a QA corpus, respectively. Furthermore, we investigate the integration of these global features with local features in various experimental settings: statistical global features, deep global features, and a combination of statistical and deep global features. Our experimental results show that the global features are effective for answer selection. Our model obtains new state‐of‐the‐art results on two public answer selection datasets and performs especially well on YahooCQA, where it achieves 9.2 and 6% higher precision@1 (P@1) and mean reciprocal rank (MRR) scores than previously published models.
The Role of Blockchain Technology on Supply Chain Finance

The blockchain technology developed in recent years is gradually applied in the supply chain. The application of this technology to the accounts receivable business model of supply chain finance can effectively solve the existing problems. Therefore, it is of great practical significance to actively explore the application of blockchain technology in the accounts receivable business model of supply chain finance. This article first briefly describes the development process, current situation and problems faced by the traditional accounts receivable business model of supply chain finance, and then analyzes the advantages of applying blockchain technology in supply chain finance to facilitate the establishment of supply chain financial credit system. Then this paper combines with the case of S platform of A Bank to analysis the problems of the accounts receivable business model of supply chain finance based on the blockchain technology, and finally provides suggestions for the application of blockchain in the accounts receivable business model of supply chain finance in the future.
Quantum Blahut-Arimoto Algorithms

We generalize alternating optimization algorithms of Blahut-Arimoto type to the quantum setting. In particular, we give iterative algorithms to compute the mutual information of quantum channels, the thermodynamic capacity of quantum channels, the coherent information of less noisy quantum channels, and the Holevo quantity of classical-quantum channels. Our convergence analysis is based on quantum entropy inequalities and leads to a priori additive ε-approximations after O (ε <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> log N) iterations, where N denotes the input dimension of the channel. We complement our analysis with an a posteriori stopping criterion which allows us to terminate the algorithm after significantly fewer iterations compared to the a priori criterion in numerical examples. Finally, we discuss heuristics to accelerate the convergence.
Speculative query execution in RDBMS based on analysis of query stream multigraphs

The paper presents an insight into a speculative execution model of queries in RDBMS based on the analysis of the stream of current queries appearing at the database input. A specific multigraph representation of input query stream is created and used to determine the speculative queries for execution. A group of worker threads execute the chosen speculative queries in parallel with the execution of the standard input stream of user queries. The obtained speculative results are then used to support faster query execution. First, the paper briefly reminds the assumed graph modelling and analysis methods. Then, additional rules are presented which enable combining results of multiple speculative queries in execution of a single user input query. The quality of executed and used speculations is then analysed based on the defined quality metrics and structural details of speculative queries. Conclusions from this analysis are used to modify the selection method of target queries for speculative execution. It aims at intensification of the use of multiple speculative query results and further reduction of the user query execution time. Experimental results are presented in a multi-threaded speculative experimental environment cooperating with a SQLite database. They show that with the improved algorithm we can obtain more varied speculative query results, and thus, more intensive use of multiple speculative query results by the stream of user queries sent to the database.
CamAspect: An Intelligent Automated Real-Time Surveillance System With Smartphone Indexing

In this letter, the authors propose a novel approach to develop an intelligent automated real-time surveillance system based solely on efficient tracking and facial recognition. With this architecture, we aim at compensating for the deficiencies of face-recognition-based surveillance methods that are computationally intractable or unreliable to be deployed for real-time automated surveillance. The presented work aims to construct a standalone video surveillance system, which is capable of detecting, identifying, and tracking persons in a video feed, snapshotting interesting physical feature changes, and transmitting this information along with the snapshots and identification parameter to the user. Specifically, we adopt a computationally cheap and accurate object tracker Deep SORT and combine it with a very accurate and computationally heavy facial recognition model FaceNet. The proposed method shows a 115% increase in runtime for the aforementioned combined architecture. The system also consists of a smartphone interface for efficient searching and indexing of identifications across timestamps.
Identification of hydrothermal alteration minerals for exploring porphyry copper deposit using ASTER data: a case study of Varzaghan area, NW Iran

Alteration is regarded as significant information for mineral exploration. In this study, ASTER image data are used for recognizing and extracting alteration zones in Varzaghan area, northwestern Iran is located in the northern part of the Cenozoic Urumieh–Dokhtar magmatic arc. This belt was formed by subduction of the Arabian plate beneath central Iran during the Alpine orogeny and hosts many porphyry copper deposits. The principal component analysis (PCA), Minimum Noise Fraction (MNF) and Band Ration TIR and SWIR ASTER bands were employed for hydrothermal alteration zone extractions such as argillic, phyllic and propylitic alterations. The alteration zone that identified from remote sensing were evaluated and compared in detail along with Sungun copper porphyry mine. The results show that the OH−alteration is a main indicator of argillic, phyllic, and propylitic alterations. These alterations are closely related to porphyry copper deposits. Results show that PCA and band ratio methods clearly manifest different altered zones of the region. However, band ratio more effectively shows the alterations. Also, ASTER images provide preliminary mineralogy information and geo-referenced alteration maps at low cost and with high accuracy for reconnaissance porphyry copper mineralization.
Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply reinforcement learning to quantum hypothesis testing, where one designs measurements that can distinguish between multiple quantum states {ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> }| <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">=</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> while minimizing the error probability. Although the Helstrom measurement is known to be optimal when there are m=2 states, the general problem of finding a minimal-error measurement is challenging. Additionally, in the case where the candidate states correspond to a quantum system with many qubit subsystems, implementing the optimal measurement on the entire system may be impractical. In this work, we develop locally-adaptive measurement strategies that are experimentally feasible in the sense that only one quantum subsystem is measured in each round. RLNN is used to find the optimal measurement protocol for arbitrary sets of tensor product quantum states. Numerical results for the network performance are shown. In special cases, the neural network testing-policy achieves the same probability of success as the optimal collective measurement.
A Least-Squares Temporal Difference based method for solving resource allocation problems

Value function approximation has a central role in Approximate Dynamic Programming (ADP) to overcome the so-called curse of dimensionality associated to real stochastic processes. In this regard, we propose a novel Least-Squares Temporal Difference (LSTD) based method: the “Multi-trajectory Greedy LSTD” (MG-LSTD). It is an exploration-enhanced recursive LSTD algorithm with the policy improvement embedded within the LSTD iterations. It makes use of multi-trajectories Monte Carlo simulations in order to enhance the system state space exploration. This method is applied for solving resource allocation problems modeled via a constrained Stochastic Dynamic Programming (SDP) based framework. In particular, such problems are formulated as a set of parallel Birth–Death Processes (BDPs). Some operational scenarios are defined and solved to show the effectiveness of the proposed approach. Finally, we provide some experimental evidence on the MG-LSTD algorithm convergence properties in function of its key-parameters.
Application of News Features in News Recommendation Methods: A Survey

In recent years, many traditional news websites developed corresponding recommendation systems to cater to readers' interests and news recommendation systems are widely applied in traditional PCs and mobile devices. News recommendation system has become a critical research hotspot in the field of recommendation system. As News contains more text information, it is more helpful to improve the recommendation effect to obtain the content related to news features (location, time, events) from the news. This survey summarizes news features-based recommendation methods including location-based news recommendation methods, time-based news recommendation methods, events-based news recommendation methods. It helps researchers to know the application of news features in news recommendation methods. Also, this suvery summarizes the challenges faced by the news recommendation system and the future research direction.
A Machine Vision Method for Correction of Eccentric Error Based on Adaptive Enhancement Algorithm

In the procedure of surface defects detection for large-aperture aspherical optical elements, it is of vital significance to adjust the optical axis of the element to be coaxial with the mechanical spin axis accurately. Therefore, a machine vision method for eccentric error correction is proposed in this article. Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an adaptive enhancement algorithm (AEA) is proposed to strengthen the crosshair image. AEA consists of the existed guided filter dark channel dehazing algorithm (GFA) and the proposed lightweight multiscale densely connected network (MDC-Net). The enhancement effect of GFA is excellent but time-consuming, and the enhancement effect of MDC-Net is slightly inferior but strongly real time. As AEA will be executed dozens of times during each correction procedure, its real-time performance is very important. Therefore, by setting the empirical threshold of definition evaluation function SMD2, GFA and MDC-Net are, respectively, applied to highly and slightly blurred crosshair images so as to ensure the enhancement effect while saving as much time as possible. AEA has certain robustness in time-consuming performance, which takes an average time of 0.2721 and 0.0963 s to execute GFA and MDC-Net separately on ten 200 pixels × 200 pixels region of interest (ROI) images with different degrees of blur, and also, the eccentricity error can be reduced to be within 10 μm by our method.
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction

We propose a new concept of rateless auto-encoders (RL-AEs) that enable a flexible latent dimensionality, which can be seamlessly adjusted for varying distortion. In the proposed RL-AEs, instead of a deterministic bottleneck architecture, we use an over-complete representation that is stochastically regularized with weighted dropouts. Our RL-AEs employ monotonically increasing dropout rates across the latent representation nodes such that the latent variables become sorted by importance like in principal component analysis (PCA). This is motivated by the rateless property of conventional PCA, where the least important principal components can be discarded to realize variable rate dimensionality reduction that gracefully degrades the distortion. Our proposed stochastic bottleneck framework enables seamless rate adaptation with high reconstruction performance, without requiring predetermined latent dimensionality at training. We experimentally demonstrate that the proposed RL-AEs can achieve variable dimensionality reduction while retaining nearly optimal distortion compared to conventional AEs.
Quantum Advantage via Qubit Belief Propagation

Quantum technologies are maturing by the day and their near-term applications are now of great interest. Deep-space optical communication involves transmission over the pure-state classical-quantum channel. For optimal detection, a joint measurement on all output qubits is required in general. Since this is hard to realize, current (sub-optimal) schemes perform symbol-by-symbol detection followed by classical post-processing. In this paper we focus on a recently proposed belief propagation algorithm by Renes that passes qubit messages on the factor graph of a classical error-correcting code. More importantly, it only involves single-qubit Pauli measurements during the process. For an example 5-bit code, we analyze the involved density matrices and calculate the error probabilities on this channel. Then we numerically compute the optimal joint detection limit using the Yuen-Kennedy-Lax conditions and demonstrate that the calculated error probabilities for this algorithm appear to achieve this limit. This represents a first step towards achieveing quantum communication advantage. We verify our analysis using Monte-Carlo simulations in practice.