Reviewerly/exHarmony huggingface dataset:
A Multi-Target Track-Before-Detect Particle Filter Using Superpositional Data in Non-Gaussian Noise

We propose a particle filter (PF) for tracking time-varying states (e.g., position, velocity) of multiple targets jointly from superpositional data, which depend on the sum of all target signals. Many conventional methods perform thresholding for detection prior to tracking, which severely limits tracking performance at a low signal-to-noise ratio. In contrast, the proposed PF can operate directly on unthresholded sensor signals. Though there also exist methods applicable to unthresholded sensor signals called track-before-detect (TBD), the proposed PF has significant advantages over them. First, it is general without any restrictions on the form of a function that maps each target's states to its signal (e.g., disjoint, binary) or on the statistics of observation noise (e.g., Gaussian). Second, it can track an unknown, time-varying number of targets without knowing their initial states owing to Septier et al.'s state modeling with a birth/death process. The proposed PF includes Salmond et al.'s TBD PF for at most one target as a particular instance up to some implementation details. We present a simulation example in the context of radio-frequency tomography, where the proposed PF significantly outperformed Nannuru et al.'s state-of-the-art method based on random finite sets in terms of the optimal subpattern assignment (OSPA) metric.
A continued fraction based approach for the Two-photon Quantum Rabi Model

Abstract We study the Two Photon Quantum Rabi Model by way of its spectral functions and survival probabilities. This approach allows numerical precision with large truncation numbers, and thus exploration of the spectral collapse. We provide independent checks and calibration of the numerical results by studying an exactly solvable case and comparing the essential qualitative structure of the spectral functions. We stress that the large time limit of the survival probability provides us with an indicator of spectral collapse, and propose a technique for the detection of this signal in the current and upcoming quantum simulations of the model.
SUR-FeatNet: Predicting the satisfied user ratio curve for image compression with deep feature learning

The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to apply maximum likelihood estimation and the Anderson-Darling test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a siamese convolutional neural network, transfer learning, and deep feature learning, using pairs consisting of a reference image and a compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at a median of the first JND distribution were 0.58, 0.69, and 0.58 dB. Further experiments on the JND-Pano dataset showed that the method transfers well to high resolution panoramic images viewed on head-mounted displays.
Machine Learning for MU-MIMO Receive Processing in OFDM Systems

Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers. However, it is still unclear if such methods are truly competitive with respect to conventional methods in realistic scenarios and under practical constraints. In addition to enabling accurate signal reconstruction on realistic channel models, MU-MIMO receive algorithms must allow for easy adaptation to a varying number of users without the need for retraining. In contrast to existing work, we propose an ML-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture. It preserves the interpretability and scalability of the LMMSE receiver, while improving its accuracy in two ways. First, convolutional neural networks (CNNs) are used to compute an approximation of the second-order statistics of the channel estimation error which are required for accurate equalization. Second, a CNN-based demapper jointly processes a large number of orthogonal frequency-division multiplexing (OFDM) symbols and subcarriers, which allows it to compute better log likelihood ratios (LLRs) by compensating for channel aging. The resulting architecture can be used in the up- and downlink and is trained in an end-to-end manner, removing the need for hard-to-get perfect channel state information (CSI) during the training phase. Simulation results demonstrate consistent performance improvements over the baseline which are especially pronounced in high mobility scenarios.
Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence

Powerful incentives are driving the adoption of FAIR practices among a broad cross-section of stakeholders. This adoption process must factor in numerous considerations regarding the use of both domain-specific and infrastructural resources. These considerations must be made for each of the FAIR Guiding Principles and include supra-domain objectives such as the maximum reuse of existing resources (i.e., minimised reinvention of the wheel) or maximum interoperation with existing FAIR data and services. Despite the complexity of this task, it is likely that the majority of the decisions will be repeated across communities and that communities can expedite their own FAIR adoption process by judiciously reusing the implementation choices already made by others. To leverage these redundancies and accelerate convergence onto widespread reuse of FAIR implementations, we have developed the concept of FAIR Implementation Profile (FIP) that captures the comprehensive set of implementation choices made at the discretion of individual communities of practice. The collection of community-specific FIPs compose an online resource called the FIP Convergence Matrix which can be used to track the evolving landscape of FAIR implementations and inform optimisation around reuse and interoperation. Ready-made and well-tested FIPs created by trusted communities will find widespread reuse among other communities and could vastly accelerate decision making on well-informed implementations of the FAIR Principles within and particularly between domains.
Answering Legal Questions by Learning Neural Attentive Text Representation

Text representation plays a vital role in retrieval-based question answering, especially in the legal domain where documents are usually long and complicated.The better the question and the legal documents are represented, the more accurate they are matched.In this paper, we focus on the task of answering legal questions at the article level.Given a legal question, the goal is to retrieve all the correct and valid legal articles, that can be used as the basic to answer the question.We present a retrieval-based model for the task by learning neural attentive text representation.Our text representation method first leverages convolutional neural networks to extract important information in a question and legal articles.Attention mechanisms are then used to represent the question and articles and select appropriate information to align them in a matching process.Experimental results on an annotated corpus consisting of 5,922 Vietnamese legal questions show that our model outperforms state-of-the-art retrieval-based methods for question answering by large margins in terms of both recall and NDCG.
Dynamics-based data science in biology

With the increasingly accumulated bio-data, dynamics-based data-science has been progressing as an efficient way to reveal mechanisms of dynamical biological processes. We review three applications on detecting the tipping-points of diseases, quantifying cell's potency, and predicting time-series, to show the importance of dynamics-based data-science.
A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset

Under Industry 4.0, manufacturing quality prediction has been gaining increased interest from researchers and manufacturers. From the analysis of previous studies on quality predictions using machine learning, it became clear that the high dimensionality and imbalance of data are major and common problems affecting the learning performance. This work uses a hybrid method to address this issue, applying a Synthetic Minority Oversampling Technique & TomekLinks balancing approach to create balanced data and using Random Forest as the feature selecting measurement to reduce the dimensionality of data. In addition, a Fine Gaussian Support Vector Machine (Fine Gaussian SVM) based on the representative set of features selected by the hybrid method utilized is employed in this work to predict product quality. The results of experimentation demonstrate that the hybrid method proposed in this work performs well for manufacturing quality prediction and offers a simple, quick and powerful way to address the problem of feature selection encountered by the imbalanced classification.
MMConv: An Environment for Multimodal Conversational Search across Multiple Domains

Although conversational search has become a hot topic in both dialogue research and IR community, the real breakthrough has been limited by the scale and quality of datasets available. To address this fundamental obstacle, we introduce the Multimodal Multi-domain Conversational dataset (MMConv), a fully annotated collection of human-to-human role-playing dialogues spanning over multiple domains and tasks. The contribution is two-fold. First, beyond the task-oriented multimodal dialogues among user and agent pairs, dialogues are fully annotated with dialogue belief states and dialogue acts. More importantly, we create a relatively comprehensive environment for conducting multimodal conversational search with real user settings, structured venue database, annotated image repository as well as crowd-sourced knowledge database. A detailed description of the data collection procedure along with a summary of data structure and analysis is provided. Second, a set of benchmark results for dialogue state tracking, conversational recommendation, response generation as well as a unified model for multiple tasks are reported. We adopt the state-of-the-art methods for these tasks respectively to demonstrate the usability of the data, discuss limitations of current methods and set baselines for future studies.
Enhanced Weak-Value Amplification via Photon Recycling

In a quantum-noise limited system, weak-value amplification using postselection normally does not produce more sensitive measurements than standard methods for ideal detectors: the increased weak value is compensated by the reduced power due to the small postselection probability. Here, we experimentally demonstrate recycled weak-value measurements using a pulsed light source and optical switch to enable nearly deterministic weak-value amplification of a mirror tilt. Using photon counting detectors, we demonstrate a signal improvement by a factor of $4.4\ifmmode\pm\else\textpm\fi{}0.2$ and a signal-to-noise ratio improvement of $2.10\ifmmode\pm\else\textpm\fi{}0.06$, compared to a single-pass weak-value experiment, and also compared to a conventional direct measurement of the tilt. The signal-to-noise ratio improvement could reach around six for the parameters of this experiment, assuming lower loss elements.
High speed imaging of spectral-temporal correlations in Hong-Ou-Mandel interference

In this work we demonstrate spectral-temporal correlation measurements of the Hong-Ou-Mandel (HOM) interference effect with the use of a spectrometer based on a photon-counting camera. This setup allows us to take, within seconds, spectral temporal correlation measurements on entangled photon sources with sub-nanometer spectral resolution and nanosecond timing resolution. Through post processing, we can observe the HOM behaviour for any number of spectral filters of any shape and width at any wavelength over the observable spectral range. Our setup also offers great versatility in that it is capable of operating at a wide spectral range from the visible to the near infrared and does not require a pulsed pump laser for timing purposes. This work offers the ability to gain large amounts of spectral and temporal information from a HOM interferometer quickly and efficiently and will be a very useful tool for many quantum technology applications and fundamental quantum optics research.
Health Information System's Responses to COVID-19 Pandemic in China: A National Cross-sectional Study

After the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, Chinese hospitals and health information technology (HIT) vendors collaborated to provide comprehensive information technology support for pandemic prevention and control. This study aims to describe the responses from the health information systems (HIS) to the COVID-19 pandemic and provide empirical evidence in the application of emerging health technologies in China.This observational descriptive study utilized a nationally representative, cross-sectional survey of hospitals in China (N = 1,014) from 30 provincial administrative regions across the country. Participants include hospital managers, hospital information workers, and health care providers.Among all the responses, the most popular interventions and applications include expert question-and-answer sessions and science popularization (61.74%) in online medical consultation, online appointment registration (58.97%) in online medical service, and remote consultation (75.15%) in telehealth service. A total of 63.71% of the participating hospitals expanded their fever clinics during the pandemic, 15.38% hospitals used new or upgraded mobile ward rounds systems, and 44.68% hospitals applied online self-service systems. Challenges and barriers include protecting network information security (57.00%) since some hospitals experienced cybersecurity incidents. 71.79% participants hope to shorten wait time and optimize the treatment process. Health care workers experienced increased amount of work during the pandemic, while hospital information departments did not experience significant changes in their workload.In the process of fighting against the COVID-19, hospitals have widely used traditional and emerging novel HITs. These technologies have strengthened the capacity of prevention and control of the pandemic and provided comprehensive information technology support while also improving accessibility and efficiency of health care delivery.
A Comprehensive Review on Scatter Search: Techniques, Applications, and Challenges

Recent years have witnessed the use of metaheuristic algorithms to solve the optimization problems that usually require extensive computations and time. Among others, scatter search is the widely used evolutionary metaheuristic algorithm. It uses the information of global optima, which is stored in a different and unique set of solutions. In this paper, an updated review of scatter search (SS) is given. SS has been successfully applied in a variety of research problems, namely, data mining, bioinformatics, and engineering design. This paper presents the modified and hybrid versions of SS with their applications. The control strategies are discussed to show their impact on the performance of SS. various issues and future directions related to SS are also discussed. It inspires innovative researchers to use this algorithm for their research domains.
GaussianPath:A Bayesian Multi-Hop Reasoning Framework for Knowledge Graph Reasoning

Recently, multi-hop reasoning over incomplete Knowledge Graphs (KGs) has attracted wide attention due to its desirable interpretability for downstream tasks, such as question answer and knowledge graph completion. Multi-Hop reasoning is a typical sequential decision problem, which can be formulated as a Markov decision process (MDP). Subsequently, some reinforcement learning (RL) based approaches are proposed and proven effective to train an agent for reasoning paths sequentially until reaching the target answer. However, these approaches assume that an entity/relation representation follows a one-point distribution. In fact, different entities and relations may contain different certainties. On the other hand, since REINFORCE used for updating the policy in these approaches is a biased policy gradients method, the agent is prone to be stuck in high reward paths rather than broad reasoning paths, which leads to premature and suboptimal exploitation. In this paper, we consider a Bayesian reinforcement learning paradigm to harness uncertainty into multi-hop reasoning. By incorporating uncertainty into the representation layer, the agent trained by RL has uncertainty in a region of the state space then it should be more efficient in exploring unknown or less known part of the KG. In our approach, we build a Bayesian Q-learning architecture as a state-action value function for estimating the expected long-term reward. As initialized by Gaussian prior or pre-trained prior distribution, the representation layer drives uncertainty that allows regularizing the training. We conducted extensive experiments on multiple KGs. Experimental results show a superior performance than other baselines, especially significant improvements on the automated extracted KG.
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario

Robotic systems are more present in our society everyday. In human–robot environments, it is crucial that end-users may correctly understand their robotic team-partners, in order to collaboratively complete a task. To increase action understanding, users demand more explainability about the decisions by the robot in particular situations. Recently, explainable robotic systems have emerged as an alternative focused not only on completing a task satisfactorily, but also on justifying, in a human-like manner, the reasons that lead to making a decision. In reinforcement learning scenarios, a great effort has been focused on providing explanations using data-driven approaches, particularly from the visual input modality in deep learning-based systems. In this work, we focus rather on the decision-making process of reinforcement learning agents performing a task in a robotic scenario. Experimental results are obtained using 3 different set-ups, namely, a deterministic navigation task, a stochastic navigation task, and a continuous visual-based sorting object task. As a way to explain the goal-driven robot’s actions, we use the probability of success computed by three different proposed approaches: memory-based, learning-based, and introspection-based. The difference between these approaches is the amount of memory required to compute or estimate the probability of success as well as the kind of reinforcement learning representation where they could be used. In this regard, we use the memory-based approach as a baseline since it is obtained directly from the agent’s observations. When comparing the learning-based and the introspection-based approaches to this baseline, both are found to be suitable alternatives to compute the probability of success, obtaining high levels of similarity when compared using both the Pearson’s correlation and the mean squared error.
Decentralized On-Chain Data Access via Smart Contracts in Ethereum Blockchain

Smart contracts in Ethereum blockchain network are self-executable scripts used for managing tokenized assets and access rights between different entities on the blockchain network. They are significant addition to blockchain technology for achieving data transparency and reliability in maintaining the digital relationship between two or more entities. However, the smart contract is in its nascent stage and suffers from various limitations, including immutability and code secrecy. In this paper, we address the inability of smart contracts to access the on-chain data. The smart contract currently fetches the on-chain data by taking external oracle assistance, which can be compromised to influence the customers. To address the issue, we propose a decentralized mechanism for accessing the blockchain data directly from the smart contracts by indexing single or multiple parameters of a transaction in each block using the Merkle-Patricia Trie (MPT). The idea is to increase the data transparency of blockchain applications. The paper provides a sequential search methodology that can retrieve transactions from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> blocks, satisfying specific conditions, in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${O}$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> ). The complexity is further reduced to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${O}$ </tex-math></inline-formula> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N/k</i> ) by partitioning the blockchain data into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> disjoint subsets to achieve parallelism in the search procedure. We experimentally verify the effectiveness of the proposed methodology with a case study on the Ethereum blockchain data.
Variational quantum algorithm based on the minimum potential energy for solving the Poisson equation

Computer-aided engineering techniques are indispensable in modern engineering developments. In particular, partial differential equations are commonly used to simulate the dynamics of physical phenomena, but very large systems are often intractable within a reasonable computation time, even when using supercomputers. To overcome the inherent limit of classical computing, we present a variational quantum algorithm for solving the Poisson equation that can be implemented in noisy intermediate-scale quantum devices. The proposed method defines the total potential energy of the Poisson equation as a Hamiltonian, which is decomposed into a linear combination of Pauli operators and simple observables. The expectation value of the Hamiltonian is then minimized with respect to a parameterized quantum state. Because the number of decomposed terms is independent of the size of the problem, this method requires relatively few quantum measurements. Numerical experiments demonstrate the faster computing speed of this method compared with classical computing methods and a previous variational quantum approach. We believe that our approach brings quantum computer-aided techniques closer to future applications in engineering developments. Code is available at https://github.com/ToyotaCRDL/VQAPoisson.