Reviewerly/exHarmony huggingface dataset:
Discovering and deciphering relationships across disparate data modalities

Understanding the relationships between different properties of data, such as whether a genome or connectome has information about disease status, is increasingly important. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes and often are not interpretable. Our approach, ‘Multiscale Graph Correlation’ (MGC), is a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors, kernel methods, and multiscale analysis. Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships, with dimensionality ranging from 1 to 1000. Moreover, MGC uniquely characterizes the latent geometry underlying the relationship, while maintaining computational efficiency. In real data, including brain imaging and cancer genetics, MGC detects the presence of a dependency and provides guidance for the next experiments to conduct.
Fundamental Law of Memory Recall

Human memory appears to be fragile and unpredictable. Free recall of random lists of words is a standard paradigm used to probe episodic memory. We proposed an associative search process that can be reduced to a deterministic walk on random graphs defined by the structure of memory representations. The corresponding graph model can be solved analytically, resulting in a novel parameter-free prediction for the average number of memory items recalled ($R$) out of $M$ items in memory: $R=\sqrt{3\ensuremath{\pi}M/2}$. This prediction was verified with a specially designed experimental protocol combining large-scale crowd-sourced free recall and recognition experiments with randomly assembled lists of words or common facts. Our results show that human memory can be described by universal laws derived from first principles.
Calculation of Molecular Vibrational Spectra on a Quantum Annealer

Until recently molecular energy calculations using quantum computing hardware have been limited to gate-based quantum computers. In this paper, a new methodology is presented to calculate the vibrational spectrum of a molecule on a quantum annealer. The key idea of the method is a mapping of the ground state variational problem onto an Ising or quadratic unconstrained binary optimization (QUBO) problem by expressing the expansion coefficients using spins or qubits. The algorithm is general and represents a new revolutionary approach for solving the real symmetric eigenvalue problem on a quantum annealer. The method is applied to two chemically important molecules: O2 (oxygen) and O3 (ozone). The lowest two vibrational states of these molecules are computed using both a hardware quantum annealer and a software based classical annealer. Extension of the algorithm to higher dimensions is explicitly demonstrated for an N-dimensional harmonic oscillator (N ≤ 5). The algorithm scales exponentially with dimensionality if a direct product basis is used but will exhibit polynomial scaling for a nondirect product basis.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>l</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> -norm coherence of assistance

We introduce and study the ${l}_{1}$-norm coherence of assistance both theoretically and operationally. We first provide an upper bound for the ${l}_{1}$-norm coherence of assistance and show a necessary and sufficient condition for the saturation of the upper bound. For two- and three-dimensional quantum states, the analytical expression of the ${l}_{1}$-norm coherence of assistance is given. Operationally, the mixed quantum coherence can always be increased with the help of another party's local measurement and one way classical communication since the ${l}_{1}$-norm coherence of assistance, as well as the relative-entropy coherence of assistance, are shown to be strictly larger than the original coherence. The relation between the ${l}_{1}$-norm coherence of assistance and entanglement is revealed. Finally, a comparison between the ${l}_{1}$-norm coherence of assistance and the relative-entropy coherence of assistance is made.
Intrinsic Gaussian Processes on Complex Constrained Domains

Summary We propose a class of intrinsic Gaussian processes (GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irregularly shaped spaces arising as subsets or submanifolds of R, R2, R3 and beyond. For example, intrinsic GPs can accommodate spatial domains arising as complex subsets of Euclidean space. Intrinsic GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces. The key novelty of the approach proposed is to utilize the relationship between heat kernels and the transition density of Brownian motion on manifolds for constructing and approximating valid and computationally feasible covariance kernels. This enables intrinsic GPs to be practically applied in great generality, whereas existing approaches for smoothing on constrained domains are limited to simple special cases. The broad utilities of the intrinsic GP approach are illustrated through simulation studies and data examples.
DSRN: A Deep Scale Relationship Network for Scene Text Detection

Nowadays, scene text detection has become increasingly important and popular. However, the large variance of text scale remains the main challenge and limits the detection performance in most previous methods. To address this problem, we propose an end-to-end architecture called Deep Scale Relationship Network (DSRN) to map multi-scale convolution features onto a scale invariant space to obtain uniform activation of multi-size text instances. Firstly, we develop a Scale-transfer module to transfer the multi-scale feature maps to a unified dimension. Due to the heterogeneity of features, simply concatenating feature maps with multi-scale information would limit the detection performance. Thus we propose a Scale Relationship module to aggregate the multi-scale information through bi-directional convolution operations. Finally, to further reduce the miss-detected instances, a novel Recall Loss is proposed to force the network to concern more about miss-detected text instances by up-weighting poor-classified examples. Compared with previous approaches, DSRN efficiently handles the large-variance scale problem without complex hand-crafted hyperparameter settings (e.g. scale of default boxes) and complicated post processing. On standard datasets including ICDAR2015 and MSRA-TD500, the proposed algorithm achieves the state-of-art performance with impressive speed (8.8 FPS on ICDAR2015 and 13.3 FPS on MSRA-TD500).
Binarized Collaborative Filtering with Distilling Graph Convolutional Network

The efficiency of top-K item recommendation based on implicit feedback are vital to recommender systems in real world, but it is very challenging due to the lack of negative samples and the large number of candidate items. To address the challenges, we firstly introduce an improved Graph Convolutional Network~(GCN) model with high-order feature interaction considered. Then we distill the ranking information derived from GCN into binarized collaborative filtering, which makes use of binary representation to improve the efficiency of online recommendation. However, binary codes are not only hard to be optimized but also likely to incur the loss of information during the training processing. Therefore, we propose a novel framework to convert the binary constrained optimization problem into an equivalent continuous optimization problem with a stochastic penalty. The binarized collaborative filtering model is then easily optimized by many popular solvers like SGD and Adam. The proposed algorithm is finally evaluated on three real-world datasets and shown the superiority to the competing baselines.
GPU-Accelerated GLRLM Algorithm for Feature Extraction of MRI

Abstract The gray level run length matrix (GLRLM) whose entries are statistics recording distribution and relationship of images pixels is a widely used method for extracting statistical features for medical images, e.g., magnetic resonance (MR) images. Recently these features are usually employed in some artificial neural networks to identify and distinguish texture patterns. But GLRLM construction and features extraction are tedious and computationally intensive while the images are too big with high resolution, or there are too many small or intermediate Regions of Interest (ROI) to process in a single image, which makes the preprocess a time consuming stage. Hence, it is of great importance to accelerate the procedure which is nowadays possible with the rapid development of massively parallel Graphics Processing Unit, i.e. the GPU computing technology. In this article, we propose a new paradigm based on mature parallel primitives for generating GLRLMs and extracting multiple features for many ROIs simultaneously in a single image. Experiments show that such a paradigm is easy to implement and offers an acceleration over 5 fold increase in speed than an optimized serial counterpart.
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding

The Multi-agent Path Finding (MAPF) problem consists in all agents having to move to their own destinations while avoiding collisions. In practical applications to the problem, such as for navigation in an automated warehouse, MAPF must be solved iteratively. We present here a novel approach to iterative MAPF, that we call Priority Inheritance with Backtracking (PIBT). PIBT gives a unique priority to each agent every timestep, so that all movements are prioritized. Priority inheritance, which aims at dealing effectively with priority inversion in path adjustment within a small time window, can be applied iteratively and a backtracking protocol prevents agents from being stuck. We prove that, regardless of their number, all agents are guaranteed to reach their destination within finite time, when the environment is a graph such that all pairs of adjacent nodes belong to a simple cycle of length 3 or more (e.g., biconnected). Our implementation of PIBT can be fully decentralized without global communication. Experimental results over various scenarios confirm that PIBT is adequate both for finding paths in large environments with many agents, as well as for conveying packages in an automated warehouse.
Triplet Enhanced AutoEncoder: Model-free Discriminative Network Embedding

Deep autoencoder is widely used in dimensionality reduction because of the expressive power of the neural network. Therefore, it is naturally suitable for embedding tasks, which essentially compresses high-dimensional information into a low-dimensional latent space. In terms of network representation, methods based on autoencoder such as SDNE and DNGR have achieved comparable results with the state-of-arts. However, all of them do not leverage label information, which leads to the embeddings lack the characteristic of discrimination. In this paper, we present Triplet Enhanced AutoEncoder (TEA), a new deep network embedding approach from the perspective of metric learning. Equipped with the triplet-loss constraint, the proposed approach not only allows capturing the topological structure but also preserving the discriminative information. Moreover, unlike existing discriminative embedding techniques, TEA is independent of any specific classifier, we call it the model-free property. Extensive empirical results on three public datasets (i.e, Cora, Citeseer and BlogCatalog) show that TEA is stable and achieves state-of-the-art performance compared with both supervised and unsupervised network embedding approaches on various percentages of labeled data. The source code can be obtained from https://github.com/yybeta/TEA.
Out-of-Domain Detection for Low-Resource Text Classification Tasks

Out-of-domain (OOD) detection for lowresource text classification is a realistic but understudied task.The goal is to detect the OOD cases with limited in-domain (ID) training data, since we observe that training data is often insufficient in machine learning applications.In this work, we propose an OODresistant Prototypical Network to tackle this zero-shot OOD detection and few-shot ID classification task.Evaluation on real-world datasets show that the proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task, while maintaining a competitive performance on ID classification task.
An End-to-End Generative Architecture for Paraphrase Generation

Generating high-quality paraphrases is a fundamental yet challenging natural language processing task.Despite the effectiveness of previous work based on generative models, there remain problems with exposure bias in recurrent neural networks, and often a failure to generate realistic sentences.To overcome these challenges, we propose the first endto-end conditional generative architecture for generating paraphrases via adversarial training, which does not depend on extra linguistic information.Extensive experiments on four public datasets demonstrate the proposed method achieves state-of-the-art results, outperforming previous generative architectures on both automatic metrics (BLEU, METEOR, and TER) and human evaluations.
Auto-GNN: Neural Architecture Search of Graph Neural Networks

Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials are usually required to identify a suitable GNN architecture. It is because the performance of a GNN architecture is significantly affected by the choice of graph convolution components, such as aggregate function and hidden dimension. Neural architecture search (NAS) has shown its potential in discovering effective deep architectures for learning tasks in image and language modeling. However, existing NAS algorithms cannot be directly applied to the GNN search problem. First, the search space of GNN is different from the ones in existing NAS work. Second, the representation learning capacity of GNN architecture changes obviously with slight architecture modifications. It affects the search efficiency of traditional search methods. Third, widely used techniques in NAS such as parameter sharing might become unstable in GNN. To bridge the gap, we propose the automated graph neural networks (AGNN) framework, which aims to find an optimal GNN architecture within a predefined search space. A reinforcement learning based controller is designed to greedily validate architectures via small steps. AGNN has a novel parameter sharing strategy that enables homogeneous architectures to share parameters, based on a carefully-designed homogeneity definition. Experiments on real-world benchmark datasets demonstrate that the GNN architecture identified by AGNN achieves the best performance, comparing with existing handcrafted models and tradistional search methods.
From LCF to Isabelle/HOL

Abstract Interactive theorem provers have developed dramatically over the past four decades, from primitive beginnings to today’s powerful systems. Here, we focus on Isabelle/HOL and its distinctive strengths. They include automatic proof search, borrowing techniques from the world of first order theorem proving, but also the automatic search for counterexamples. They include a highly readable structured language of proofs and a unique interactive development environment for editing live proof documents. Everything rests on the foundation conceived by Robin Milner for Edinburgh LCF: a proof kernel, using abstract types to ensure soundness and eliminate the need to store proofs. Compared with the research prototypes of the 1970s, Isabelle is a practical and versatile tool. It is used by system designers, mathematicians and many others.
Towards a Theory of Explanations for Human–Robot Collaboration

Abstract This paper makes two contributions towards enabling a robot to provide explanatory descriptions of its decisions, the underlying knowledge and beliefs, and the experiences that informed these beliefs. First, we present a theory of explanations comprising (i) claims about representing, reasoning with, and learning domain knowledge to support the construction of explanations; (ii) three fundamental axes to characterize explanations; and (iii) a methodology for constructing these explanations. Second, we describe an architecture for robots that implements this theory and supports scalability to complex domains and explanations. We demonstrate the architecture’s capabilities in the context of a simulated robot (a) moving target objects to desired locations or people; or (b) following recipes to bake biscuits.
Dynamics of transient cat states in degenerate parametric oscillation with and without nonlinear Kerr interactions

A cat state is formed as the steady-state solution for the signal mode of an ideal degenerate parametric oscillator, in the limit of negligible single-photon signal loss. In the presence of signal loss, this is no longer true over timescales much longer than the damping time. However, for sufficient parametric nonlinearity, a cat state can still exist as a transient state. In this paper we study the dynamics of the creation and decoherence of cat states in degenerate parametric oscillation, both with and without the Kerr nonlinearity found in recent superconducting-circuit experiments that generate cat states in microwave cavities. We determine the time of formation and the lifetime of a cat state of fixed amplitude in terms of three dimensionless parameters $\ensuremath{\lambda}$, $g$, and $\ensuremath{\chi}$. These relate to the driving strength, the parametric nonlinearity relative to signal damping, and the Kerr nonlinearity, respectively. We find that the Kerr nonlinearity has little effect on the threshold parametric nonlinearity ($g&gt;1$) required for the formation of cat states and does not significantly alter the decoherence time of the cat state, but can reduce the time of formation. The quality of the cat state increases with the value of $g$. To verify the existence of the cat state, we consider several signatures, including interference fringes and negativity. We emphasize the importance of taking into account more than one of these signatures. We simulate a superconducting-circuit experiment using published experimental parameters and find good agreement with experimental results, indicating that a nonclassical catlike state with a small Wigner negativity is generated in the experiment. Interference fringes, however, are absent, requiring higher $g$ values. Finally, we explore the feasibility of creating large cat states with a coherent amplitude of 20, corresponding to 400 photons, and study finite-temperature reservoir effects.
Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization

The data in this article provide details about MRI lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithms. Both K-means and GMM-EM algorithms can segment lesion area from the rest of brain MRI automatically. The performance metrics (accuracy, sensitivity, specificity, false positive rate, misclassification rate) were estimated for the algorithms and there was no significant difference between K-means and GMM-EM. In addition, lesion size does not affect the accuracy and sensitivity for either method.
A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers

Meeting tenant performance requirements through data replication while ensuring an economic profit is very challenging for cloud providers. For this purpose, we propose a data Replication Strategy that satisfies Performance tenant objective and provider profit in Cloud data centers (RSPC). Before the execution of each tenant query Q, data replication is considered only if: (i) the estimated Response Time of Q (RTQ) exceeds a critical RT threshold (per-query replication), or (ii) more often, if RTQ exceeds another (lower) RT threshold for a given number of times (replication per set of queries). Then, a new replica is really created only if a suitable replica placement is heuristically found so that the RT requirement is satisfied again while ensuring an economic profit for the provider. Both the provider's revenues and expenditures are also estimated while penalties and replication costs are taken into account. Furthermore, the replica factor is dynamically adjusted in order to reduce the resource consumption. Compared to four other strategies, RSPC best satisfies the RT requirement under high loads, complex queries and strict RT thresholds. Moreover, penalty and data transfer costs are significantly reduced, which impacts the provider profit.
Stacking Ensemble Technique for Classifying Breast Cancer

Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used.We used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble.Experimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data.We compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.
Countering Language Drift via Visual Grounding

Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans.We find that agents that were initially pretrained to produce natural language can also experience detrimental language drift: when a nonlinguistic reward is used in a goal-based task, e.g.some scalar success metric, the communication protocol may easily and radically diverge from natural language.We recast translation as a multi-agent communication game and examine auxiliary training constraints for their effectiveness in mitigating language drift.We show that a combination of syntactic (language model likelihood) and semantic (visual grounding) constraints gives the best communication performance, allowing pre-trained agents to retain English syntax while learning to accurately convey the intended meaning.
QuESTlink—Mathematica embiggened by a hardware-optimised quantum emulator<sup>*</sup>

We introduce QuESTlink, pronounced "quest link", an open-source Mathematica package which efficiently emulates quantum computers. By integrating with the Quantum Exact Simulation Toolkit (QuEST), QuESTlink offers a high-level, expressive and usable interface to a high-performance, hardware-accelerated emulator. Requiring no installation, QuESTlink streamlines the powerful analysis capabilities of Mathematica into the study of quantum systems, even utilising remote multicore and GPU hardware. We demonstrate the use of QuESTlink to concisely and efficiently simulate several quantum algorithms, and present some comparative benchmarking against core QuEST.