Reviewerly/exHarmony huggingface dataset:
Quantum Speedup Based on Classical Decision Trees

Lin and Lin have recently shown how starting with a classical query algorithm (decision tree) for a function, we may find upper bounds on its quantum query complexity. More precisely, they have shown that given a decision tree for a function $f:\{0,1\}^n\to[m]$ whose input can be accessed via queries to its bits, and a guessing algorithm that predicts answers to the queries, there is a quantum query algorithm for $f$ which makes at most $O(\sqrt{GT})$ quantum queries where $T$ is the depth of the decision tree and $G$ is the maximum number of mistakes of the guessing algorithm. In this paper we give a simple proof of and generalize this result for functions $f:[\ell]^n \to [m]$ with non-binary input as well as output alphabets. Our main tool for this generalization is non-binary span program which has recently been developed for non-binary functions, and the dual adversary bound. As applications of our main result we present several quantum query upper bounds, some of which are new. In particular, we show that topological sorting of vertices of a directed graph $\mathcal{G}$ can be done with $O(n^{3/2})$ quantum queries in the adjacency matrix model. Also, we show that the quantum query complexity of the maximum bipartite matching is upper bounded by $O(n^{3/4}\sqrt m + n)$ in the adjacency list model.
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification

Neural Document-level Multi-aspect Sentiment Classification (DMSC) usually requires a lot of manual aspect-level sentiment annotations, which is time-consuming and laborious.As document-level sentiment labeled data are widely available from online service, it is valuable to perform DMSC with such free document-level annotations.To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision.Specifically, we connect aspect-level and document-level sentiment by formulating this problem as multiple instance learning, providing a way to learn aspect-level classifier from the back propagation of document-level supervision.Two diversified regularizations are further introduced in order to avoid the overfitting on document-level signals during training.Diversified textual regularization encourages the classifier to select aspect-relevant snippets, and diversified sentimental regularization prevents the aspect-level sentiments from being overly consistent with document-level sentiment.Experimental results on TripAdvisor and BeerAdvocate datasets show that D-MILN remarkably outperforms recent weaklysupervised baselines, and is also comparable to the supervised method.
Automatic Translating Between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora

The Chinese language has evolved a lot during the long-term development. Therefore, native speakers now have trouble in reading sentences written in ancient Chinese. In this paper, we propose to build an end-to-end neural model to automatically translate between ancient and contemporary Chinese. However, the existing ancient-contemporary Chinese parallel corpora are not aligned at the sentence level and sentence-aligned corpora are limited, which makes it difficult to train the model. To build the sentence level parallel training data for the model, we propose an unsupervised algorithm that constructs sentence-aligned ancient-contemporary pairs by using the fact that the aligned sentence pair shares many of the tokens. Based on the aligned corpus, we propose an end-to-end neural model with copying mechanism and local attention to translate between ancient and contemporary Chinese. Experiments show that the proposed unsupervised algorithm achieves 99.4% F1 score for sentence alignment, and the translation model achieves 26.95 BLEU from ancient to contemporary, and 36.34 BLEU from contemporary to ancient.
Blockchain-Based Dynamic Spectrum Sharing for 5G and Beyond Wireless Communications

The blockchain technology written in smart contract has the advantages of the enabling intelligent settlement, value transfer and resource sharing, which provides a new secure and trusted platform for the dynamic spectrum sharing system. In this paper, we first summarize the application of blockchain technology in dynamic spectrum sharing considering the requirements of spectrum sharing. Then we discuss the key problems that need to be solved when applying blockchain to large-scale spectrum sharing system. After that, we analyze the key challenges in depth, and provide corresponding key technologies and possible solutions to the above problems.
Runners in Action

Runners of algebraic effects, also known as comodels, provide a mathematical model of resource management. We show that they also give rise to a programming concept that models top-level external resources, as well as allows programmers to modularly define their own intermediate "virtual machines". We capture the core ideas of programming with runners in an equational calculus $\lambda_{\mathsf{coop}}$, which we equip with a sound and coherent denotational semantics that guarantees the linear use of resources and execution of finalisation code. We accompany $\lambda_{\mathsf{coop}}$ with examples of runners in action, provide a prototype language implementation in OCaml, as well as a Haskell library based on $\lambda_{\mathsf{coop}}$.
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion prediction. The proposed system has two levels of attention to hierarchically build a meaningful representation. We evaluate our system on the benchmark dataset of SemEval 2016 Task 6 and also compare it with the state-of-the-art systems on Stance Sentiment Emotion Corpus. Experimental results show that the proposed system improves the performance of sentiment analysis by 3.2 F-score points on SemEval 2016 Task 6 dataset. Our network also boosts the performance of emotion analysis by 5 F-score points on Stance Sentiment Emotion Corpus.
Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.
Noncommutative phase-space effects in thermal diffusion of Gaussian states

Noncommutative phase-space and its effects have been studied in different settings in physics, in order to unveil a better understanding of phase-space structures. Here, we use the thermal diffusion approach to study how noncommutative effects can influence the time evolution of a one-mode Gaussian state when in contact with a thermal environment obeying the Markov approximation. Employing the cooling process and considering the system of interest as a one-mode Gaussian state, we show that the fidelity comparing the Gaussian state of the system in different times and the asymptotic thermal state is useful to sign noncommuative effects. Besides, by using the monotonicity behavior of the fidelity, we discuss some aspects of non-Markovianity during the dynamics.
Efficient verification of bosonic quantum channels via benchmarking

We aim to devise feasible, efficient verification schemes for bosonic channels. To this end, we construct an average-fidelity witness that yields a tight lower bound for average fidelity plus a general framework for verifying optimal quantum channels. For both multi-mode unitary Gaussian channels and single-mode amplification channels, we present experimentally feasible average-fidelity witnesses and reliable verification schemes, for which sample complexity scales polynomially with respect to all channel specification parameters. Our verification scheme provides an approach to benchmark the performance of bosonic channels on a set of Gaussian-distributed coherent states by employing only two-mode squeezed vacuum states and local homodyne detections. Our results demonstrate how to perform feasible tests of quantum components designed for continuous-variable quantum information processing.
A Family of Neighborhood Contingency Logics

This article proposes the axiomatizations of contingency logics of various natural classes of neighborhood frames. In particular, by defining a suitable canonical neighborhood function, we give sound and complete axiomatizations of monotone contingency logic and regular contingency logic, thereby answering two open questions raised by Bakhtiari, van Ditmarsch, and Hansen. The canonical function is inspired by a function proposed by Kuhn in 1995. We show that Kuhn’s function is actually equal to a related function originally given by Humberstone.
COMPARISON OF IMAGE ENHANCEMENT TECHNIQUES FOR RAPID PROCESSING OF POST FLOOD IMAGES

Abstract. Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equalization, Local Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Gamma Correction, and Linear Contrast Stretch) algorithms for enhancing post-flood satellite images. Flood images with different levels of clouds and shadows were enhanced and output generated were evaluated in terms of processing time and quality as measured by Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), a no-reference image quality metric. Findings from this study will provide valuable information to image analysts for selecting a suitable algorithm for rapidly processing post-flood satellite images.
Descriptions of issues and comments for predicting issue success in software projects

Software development tasks must be performed successfully to achieve software quality and customer satisfaction. Knowing whether software tasks are likely to fail is essential to ensure the success of software projects. Issue Tracking Systems store information of software tasks (issues) and comments, which can be useful to predict issue success; however; almost no research on this topic exists. This work studies the usefulness of textual descriptions of issues and comments for predicting whether issues will be resolved successfully or not. Issues and comments of 588 software projects were extracted from four popular Issue Tracking Systems. Seven machine learning classifiers were trained on 30k issues and more than 120k comments, and more than 6000 experiments were performed to predict the success of three types of issues: bugs, improvements and new features. The results provided evidence that descriptions of issues and comments are useful for predicting issue success with more than 85% of accuracy and precision, and that the predictions of issue success vary over time. Words related to software development were particularly relevant for predicting issue success. Other communication aspects and their relationship to the success of software projects must be researched in detail using data from software tools.
Directional Pruning of Deep Neural Networks

In the light of the fact that the stochastic gradient descent (SGD) often finds a flat minimum valley in the training loss, we propose a novel directional pruning method which searches for a sparse minimizer in or close to that flat region. The proposed pruning method does not require retraining or the expert knowledge on the sparsity level. To overcome the computational formidability of estimating the flat directions, we propose to use a carefully tuned $\ell_1$ proximal gradient algorithm which can provably achieve the directional pruning with a small learning rate after sufficient training. The empirical results demonstrate the promising results of our solution in highly sparse regime (92% sparsity) among many existing pruning methods on the ResNet50 with the ImageNet, while using only a slightly higher wall time and memory footprint than the SGD. Using the VGG16 and the wide ResNet 28x10 on the CIFAR-10 and CIFAR-100, we demonstrate that our solution reaches the same minima valley as the SGD, and the minima found by our solution and the SGD do not deviate in directions that impact the training loss. The code that reproduces the results of this paper is available at this https URL.
Formulation of the Social Workers’ Problem in Quadratic Unconstrained Binary Optimization Form and Solve It on a Quantum Computer

The problem of social workers visiting their patients at home is a class of combinatorial optimization problems and belongs to the class of problems known as NP-Hard.These problems require heuristic techniques to provide an efficient solution in the best of cases.In this article, in addition to providing a detailed resolution of the social workers' problem using the Quadratic Unconstrained Binary Optimization Problems (QUBO) formulation, an approach to mapping the inequality constraints in the QUBO form is given.Finally, we map it in the Hamiltonian of the Ising model to solve it with the Quantum Exact Solver and Variational Quantum Eigensolvers (VQE).The quantum feasibility of the algorithm will be tested on IBMQ computers.
Probing the Tavis-Cummings Level Splitting with Intermediate-Scale Superconducting Circuits

We demonstrate the local control of up to eight two-level systems interacting strongly with a microwave cavity. Following calibration, the frequency of each individual two-level system (qubit) is tunable without influencing the others. Bringing the qubits one by one on resonance with the cavity, we observe the collective coupling strength of the qubit ensemble. The splitting scales up with the square root of the number of the qubits, which is the hallmark of the Tavis-Cummings model. The local control circuitry causes a bypass shunting the resonator, and a Fano interference in the microwave readout, whose contribution can be calibrated away to recover the pure cavity spectrum. The simulator's attainable size of dressed states with up to five qubits is limited by reduced signal visibility, and---if uncalibrated---by off-resonance shifts of subcomponents. Our work demonstrates control and readout of a quantum coherent mesoscopic multiqubit system of intermediate scale under conditions of noise.
The Bang Calculus and the Two Girard's Translations

We study the two Girard's translations of intuitionistic implication into linear logic by exploiting the bang calculus, a paradigmatic functional language with an explicit box-operator that allows both call-by-name and call-by-value λ -calculi to be encoded in.We investigate how the bang calculus subsumes both call-by-name and call-by-value λ -calculi from a syntactic and a semantic viewpoint.
Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition

Suppose we want to classify a query item Q with a classification model that consists of a large set of predefined classes L and suppose we have a knowledge that indicates to us that the target class of Q belongs to a small subset from L. Naturally, this filtering will improve the accuracy of any classifier, even random guessing.Based on this principle, this paper proposes a new classification approach using convolutional neural networks (CNN) and computational geometry (CG) algorithms.The approach is applied and tested on the recognition of isolated handwritten Arabic characters (IHAC).The main idea of the proposed approach is to direct CNN using a filtering layer, which reduces the set of possible classes for a query item.The rules of the relative neighborhood graph (RNG) and Gabriel's graph (GG) are combined for this purpose.The choice of RNG-GG was based on its great capacity to correctly reduce the list of possible classes.This capacity is measured by a new indicator that we call "the appearance rate".In recent years and due to strong data growth, CNNs have performed classification tasks very well.On the contrary, CG algorithms yield limited results in huge datasets and suffer from high computational time, but they generally reach high appearance rates and do not require any training phase.Consequently, the proposed approach uses an optimal architecture to exploit the advantages of the two techniques and overcome the computational time issue.Experiments carried out on the IFHCDB database have shown that the suggested approach outperforms a normal CNN and yield satisfactory results.
Joint sentiment/topic modeling on text data using a boosted restricted Boltzmann Machine

Recently by the development of the Internet and the Web, different types of social media such as web blogs become an immense source of text data. Through the processing of these data, it is possible to discover practical information about different topics, individual’s opinions and a thorough understanding of the society. Therefore, applying models which can automatically extract the subjective information from documents would be efficient and helpful. Topic modeling methods and sentiment analysis are the raised topics in natural language processing and text mining fields. In this paper a new structure for joint sentiment-topic modeling based on a Restricted Boltzmann Machine (RBM) which is a type of neural networks is proposed. By modifying the structure of RBM as well as appending a layer which is analogous to sentiment of text data to it, we propose a generative structure for joint sentiment topic modeling based on neural networks. The proposed method is supervised and trained by the Contrastive Divergence algorithm. The new attached layer in the proposed model is a layer with the multinomial probability distribution which can be used in text data sentiment classification or any other supervised application. The proposed model is compared with existing models in the experiments such as evaluating as a generative model, sentiment classification, information retrieval and the corresponding results demonstrate the efficiency of the method.
Separability criteria based on Bloch representation of density matrices

We study separability criteria in multipartite quantum systems of arbitrary dimensions by using the Bloch representation of density matrices. We first derive the norms of the correlation tensors and obtain the necessary conditions for separability under partition of tripartite and four-partite quantum states. Moreover, based on the norms of the correlation tensors,we obtain the separability criteria by matrix method. Using detailed examples, our results are seen to be able to detect more entangled states than previous studies. Finally, necessary conditions of separability for multipartite systems are given under arbitrary partition.
Thermodynamic inference of data manifolds

The Gibbs-Boltzmann distribution offers a physically interpretable way to massively reduce the dimensionality of high dimensional probability distributions where the extensive variables are `features' and the intensive variables are `descriptors'. However, not all probability distributions can be modeled using the Gibbs-Boltzmann form. Here, we present TMI: TMI, {\bf T}hermodynamic {\bf M}anifold {\bf I}nference; a thermodynamic approach to approximate a collection of arbitrary distributions. TMI simultaneously learns from data intensive and extensive variables and achieves dimensionality reduction through a multiplicative, positive valued, and interpretable decomposition of the data. Importantly, the reduced dimensional space of intensive parameters is not homogeneous. The Gibbs-Boltzmann distribution defines an analytically tractable Riemannian metric on the space of intensive variables allowing us to calculate geodesics and volume elements. We discuss the applications of TMI with multiple real and artificial data sets. Possible extensions are discussed as well.
LApps

Following in the footsteps of pioneer Bitcoin, many altcoins as well as coloured coins have been being developed and merchandised adopting blockchain as the core enabling technology. However, since interoperability and scalability, due to high and capped (in particular cases) transaction latency are deep-rooted in the architecture of blockchain technology, they are by default inherited in any blockchain based applications. Lightning Network (LN) is one of the supporting technologies developed to eliminate this impediment of blockchain technology by facilitating instantaneous transfers of cryptos. Since the potentials of LN is still relatively unknown, this paper investigates the current states of development along with possible non-monetary usage of LN, especially in settlement coloured coins such as securities, as well as creation of new business models based on Lightning Applications (LApps) and microchannel payments as well as micro-trades. The legal challenges that may act as impediment to the adoption of LN is also discussed.
Optimizing Word Segmentation for Downstream Task

In traditional NLP, we tokenize a given sentence as a preprocessing, and thus the tokenization is unrelated to a target downstream task.To address this issue, we propose a novel method to explore a tokenization which is appropriate for the downstream task.Our proposed method, optimizing tokenization (Op-Tok), is trained to assign a high probability to such appropriate tokenization based on the downstream task loss.OpTok can be used for any downstream task which uses a vector representation of a sentence such as text classification.Experimental results demonstrate that OpTok improves the performance of sentiment analysis and textual entailment.In addition, we introduce OpTok into BERT, the state-ofthe-art contextualized embeddings and report a positive effect.