Reviewerly/exHarmony huggingface dataset:
Probabilistic fault-tolerant universal quantum computation and sampling problems in continuous variables

Continuous-variable (CV) devices are a promising platform for demonstrating large-scale quantum information protocols. In this framework we define a general quantum computational model based on a CV hardware. It consists of vacuum input states, a finite set of gates---including non-Gaussian elements---and homodyne detection. We show that this model incorporates encodings sufficient for probabilistic fault-tolerant universal quantum computing. Furthermore, we show that this model can be adapted to yield sampling problems that cannot be simulated efficiently with a classical computer, unless the polynomial hierarchy collapses. This allows us to provide a simple paradigm for experiments to probe quantum advantage relying on Gaussian states, homodyne detection, and some form of non-Gaussian evolution. We finally address the recently introduced model of instantaneous quantum computing in CV, and prove that the hardness statement is robust with respect to some experimentally relevant simplifications in the definition of that model.
Blockchain Finance: Questions Regulators Ask

This chapter provides a discussion on some issues in blockchain finance that regulators are concerned about – an area which bitcoin promoters have remained silent about. Blockchain technology in finance has several benefits for financial intermediation in the financial system; notwithstanding, several issues persist which if addressed can make the adoption of blockchain technology in finance easier and accepted by regulators. The blockchain issues discussed in this chapter are relevant for recent debates in blockchain finance.
A Hierarchical Model for Human Action Recognition From Body-Parts

As increasing attention is paid to human action recognition from skeleton data, this paper focuses on such tasks by proposing a hierarchical model to discover the structure information of body-parts involved in actions for better analysis of human actions in the skeleton data. Considering human actions as simultaneous motions of body-parts of the human skeleton, we propose a hierarchical model to simultaneously apply discriminative body-parts selection at a same scale and group coupling of bundles of body-parts at different scales, while we decompose the human skeleton into a hierarchy of body-parts of varying scales. To represent such hierarchy of body-parts, we accordingly build a hierarchical rotation and relative velocity (HRRV) descriptor. The hierarchical representations encoded by Fisher vectors of the HRRV descriptors are properly formulated into the hierarchical model via the proposed mixed norm, to apply the sparse selection of body-parts and regularize the structure of such hierarchy of body-parts. The extensive evaluations on three challenging datasets demonstrate the effectiveness of our proposed approach, which achieves superior performance compared to the state-of-the-art algorithms on datasets with various sizes, showing it is more widely applicable than existing approaches.
End-to-end neural opinion extraction with a transition-based model

Abstract Fine-grained opinion extraction has received increasing interests in the natural language processing community. It usually involves several subtasks. Recently, joint methods and neural models have been investigated by several studies, achieving promising performance by using graph-based models such as conditional random field. In this work, we propose a novel end-to-end neural model alternatively for joint opinion extraction, by using a transition-based framework. First, we exploit multi-layer bi-directional long short term memory (LSTM) networks to encode the input sentences, and then decode incrementally based on partial output results dominated by a transition system. We use global normalization and beam search for training and decoding. Experiments on a standard benchmark show that the proposed end-to-end model can achieve competitive results compared with the state-of-the-art neural models of opinion extraction.
Robust optimization through neuroevolution

We propose a method for evolving neural network controllers robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The method specifies how the fitness of candidate solutions can be evaluated, how the environmental conditions should vary during the course of the evolutionary process, which algorithm can be used, and how the best solution can be identified. The obtained results show how the method proposed is effective and computational tractable. It allows to improve performance on an extended version of the double-pole balancing problem, outperform the best available human-designed controllers on a car racing problem, and generate effective solutions for a swarm robotic problem. The comparison of different algorithms indicates that the CMA-ES and xNES methods, that operate by optimizing a distribution of parameters, represent the best options for the evolution of robust neural network controllers.
New classes of complete permutation polynomials

In this paper, we propose several classes of complete permutation polynomials over a finite field based on certain polynomials over its subfields or subsets. In addition, a class of complete permutation trinomials with Niho exponents is studied, and the number of these complete permutation trinomials is also determined.
Extreme spin squeezing in the steady state of a generalized Dicke model

We present a scheme to generate steady-state atomic spin squeezing in a cavity QED system using cavity-mediated Raman transitions to engineer effective atom-photon interactions, which include both linear and nonlinear (dispersive) atom-cavity couplings, on a potentially equal footing. We focus on a regime where the dispersive coupling is very large and find that the steady state of the system can in fact be a strongly spin-squeezed Dicke state, $|N/2,0\rangle$, of the atomic ensemble. These states offer Heisenberg-limited metrological properties and feature genuine multipartite entanglement among the entire atomic ensemble.
A novel product recommendation model consolidating price, trust and online reviews

Purpose The purpose of this paper is to propose a model for product recommendation to improve the accuracy of recommendation based on the current search engines used in e-commerce platforms like Tmall.com. Design/methodology/approach First, the proposed model comprehensively considers price, trust and online reviews, which all represent critical factors in consumers’ purchasing decisions. Second, the model introduces the quantization methods for these criteria incorporating fuzzy theory. Third, the model uses a distance measure between two single valued neutrosophic sets based on the prioritized average operator to consolidate the influences of positive, neutral and negative comments. Finally, the model uses multi-criteria decision-making methods to integrate the influences of price, trust and online reviews on purchasing decisions to generate recommendations. Findings To demonstrate the feasibility and efficiency of the proposed model, a case study is conducted based on Tmall.com. The results of case study indicate that the recommendations of our model perform better than those of current search engines of Tmall.com. The proposed model can significantly improve the accuracy of product recommendations based on search engines. Originality/value The product recommendation method can meet the critical challenge from the search engines on e-commerce platforms. In addition, the proposed method could be used in practice to develop a new application for e-commerce platforms.
Can a universal quantum cloner be used to design an experimentally feasible near-deterministic CNOT gate?

We propose a non-deterministic CNOT gate based on a quantum cloner, a quantum switch based on all optical routing of single photon by single photon, a quantum dot spin in a double-sided optical microcavity with two photonic qubits, delay lines and other linear optical photonic devices. Our CNOT provides a fidelity of 78% with directly useful outputs for a quantum computing circuit and requires no ancillary qubits or electron spin measurements.
Exact gate decompositions for photonic quantum computing

We propose a method for decomposing continuous-variable operations into a universal gate set, without the use of any approximations. We fully characterize a set of transformations admitting exact decompositions and describe a process for obtaining them systematically. Gates admitting these decompositions can be synthesized exactly, using circuits that are several orders of magnitude smaller than those achievable with previous methods. Our method relies on strategically using unitary conjugation and a lemma to the Baker-Campbell-Hausdorff formula to derive new exact decompositions from previously known ones, leading to exact decompositions for a large class of gates. We demonstrate the wide applicability of these exact gate decompositions by identifying several quantum algorithms and simulations of bosonic systems that can be implemented with higher precision and shorter circuit depths using our techniques.
Faster Multiplication in $$\mathbb {Z}_{2^m}[x]$$ on Cortex-M4 to Speed up NIST PQC Candidates

In this paper we optimize multiplication of polynomials in $$\mathbb {Z}_{2^m}[x]$$ on the ARM Cortex-M4 microprocessor. We use these optimized multiplication routines to speed up the NIST post-quantum candidates RLizard, NTRU-HRSS, NTRUEncrypt, Saber, and Kindi. For most of those schemes the only previous implementation that executes on the Cortex-M4 is the reference implementation submitted to NIST; for some of those schemes our optimized software is more than factor of 20 faster. One of the schemes, namely Saber, has been optimized on the Cortex-M4 in a CHES 2018 paper; the multiplication routine for Saber we present here outperforms the multiplication from that paper by 42%, yielding speedups of $$22\%$$ for key generation, $$20\%$$ for encapsulation and $$22\%$$ for decapsulation. Out of the five schemes optimized in this paper, the best performance for encapsulation and decapsulation is achieved by NTRU-HRSS. Specifically, encapsulation takes just over $$400\,000$$ cycles, which is more than twice as fast as for any other NIST candidate that has previously been optimized on the ARM Cortex-M4.
Non-Malleable Codes Against Bounded Polynomial Time Tampering

We construct efficient non-malleable codes (NMC) that are (computationally) secure against tampering by functions computable in any fixed polynomial time. Our construction is in the plain (no-CRS) model and requires the assumptions that (1) $$\mathbf {E}$$ is hard for $$\mathbf {NP}$$ circuits of some exponential $$2^{\beta n}$$ ( $$\beta >0$$ ) size (widely used in the derandomization literature), (2) sub-exponential trapdoor permutations exist, and (3) $$\mathbf {P}$$ -certificates with sub-exponential soundness exist. While it is impossible to construct NMC secure against arbitrary polynomial-time tampering (Dziembowski, Pietrzak, Wichs, ICS ’10), the existence of NMC secure against $$O(n^c)$$ -time tampering functions (for any fixed c), was shown (Cheraghchi and Guruswami, ITCS ’14) via a probabilistic construction. An explicit construction was given (Faust, Mukherjee, Venturi, Wichs, Eurocrypt ’14) assuming an untamperable CRS with length longer than the runtime of the tampering function. In this work, we show that under computational assumptions, we can bypass these limitations. Specifically, under the assumptions listed above, we obtain non-malleable codes in the plain model against $$O(n^c)$$ -time tampering functions (for any fixed c), with codeword length independent of the tampering time bound. Our new construction of NMC draws a connection with non-interactive non-malleable commitments. In fact, we show that in the NMC setting, it suffices to have a much weaker notion called quasi non-malleable commitments—these are non-interactive, non-malleable commitments in the plain model, in which the adversary runs in $$O(n^c)$$ -time, whereas the honest parties may run in longer (polynomial) time. We then construct a 4-tag quasi non-malleable commitment from any sub-exponential OWF and the assumption that $$\mathbf {E}$$ is hard for some exponential size $$\mathbf {NP}$$ -circuits, and use tag amplification techniques to support an exponential number of tags.
Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels

The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model.
Bilateral Angle 2DPCA for Face Recognition

Two-dimensional principal component analysis (2DPCA), as a state-of-the-art method for dimensionality reduction, has been widely used in face recognition. However, it is very sensitive to outliers since it minimizes the sum of squared F-norm, which is least-squares loss in nature. Recently, angle 2DPCA was presented to alleviate this problem by minimizing the sum of Fnorm, which is corresponding to L1 loss. But a vital unresolved problem of angle 2DPCA is that it needs many more coefficients for image representation because it works only in the row direction. In this letter, we first give a new angle 2DPCA called Sin-2DPCA by minimizing the relative error, which has a better explanation than the original one. Furthermore, in order to obtain better performance with fewer reduced coefficients, we project the input image to a lower dimension from right and left simultaneously, and then, the bilateral angle 2DPCA (BA2DPCA) is proposed. The experimental results on two benchmark face recognition datasets with outlier noises illustrate that the Sin-2DPCA has the similar performance with original angle 2DPCA, and BA2DPCA can obtain the highest performance in all compared algorithms with the minimal number of representation coefficients.
Spherical structure factor and classification of hyperuniformity on the sphere

Understanding how particles are arranged on the surface of a sphere is not only central to numerous physical, biological, soft matter, and materials systems but also finds applications in computational problems, approximation theory, and analysis of geophysical and meteorological measurements. Objects that lie on a sphere experience constraints that are not present in Euclidean (flat) space and that influence both how the particles can be arranged as well as their statistical properties. These constraints, coupled with the curved geometry, require a careful extension of quantities used for the analysis of particle distributions in Euclidean space to distributions confined to the surface of a sphere. Here, we introduce a framework designed to analyze and classify structural order and disorder in particle distributions constrained to the sphere. The classification is based on the concept of hyperuniformity, which was first introduced 15 years ago and since then studied extensively in Euclidean space, yet has only very recently been considered also for spherical surfaces. We employ a generalization of the structure factor on the sphere, related to the power spectrum of the corresponding multipole expansion of particle density distribution. The spherical structure factor is then shown to couple with cap number variance, a measure of density variations at different scales, allowing us to analytically derive different forms of the variance pertaining to different types of distributions. Based on these forms, we construct a classification of hyperuniformity for scale-free particle distributions on the sphere and show how it can be extended to include other distribution types as well. We demonstrate that hyperuniformity on the sphere can be defined either through a vanishing spherical structure factor at low multipole numbers or through a scaling of the cap number variance---in both cases extending the Euclidean definition, while at the same time pointing out crucial differences. Our work thus provides a comprehensive tool for detecting global, long-range order on spheres and for the analysis of spherical computational meshes, biological and synthetic spherical assemblies, and ordering phase transitions in spherically distributed particles.
Morphological Networks for Image De-raining

Mathematical morphological methods have successfully been applied to filter out (emphasize or remove) different structures of an image. However, it is argued that these methods could be suitable for the task only if the type and order of the filter(s) as well as the shape and size of operator kernel are designed properly. Thus the existing filtering operators are problem (instance) specific and are designed by the domain experts. In this work we propose a morphological network that emulates classical morphological filtering consisting of a series of erosion and dilation operators with trainable structuring elements. We evaluate the proposed network for image de-raining task where the SSIM and mean absolute error (MAE) loss corresponding to predicted and ground-truth clean image is back-propagated through the network to train the structuring elements. We observe that a single morphological network can de-rain an image with any arbitrary shaped rain-droplets and achieves similar performance with the contemporary CNNs for this task with a fraction of trainable parameters (network size). The proposed morphological network (MorphoN) is not designed specifically for de-raining and can readily be applied to similar filtering/noise cleaning tasks. The source code can be found here https://github.com/ranjanZ/2D-Morphological-Network .
Mining Collaboration Patterns Between APIs for Mashup Creation in Web of Things

The Web of Things (WoT) extends the concept of ''Internet of Things (IoT)'' in that smart devices in the physical world can be interacted with or integrated via popular web technologies (e.g., HTML, HTTP, and Web API).With the WoT, smart devices can use Web APIs to make their data or functionalities accessible by software.With the popularization of Web 2.0 Mashup applications, creating Mashup applications for the IoT (or WoT) via combining different APIs, also has aroused increasing interests.This paper proposes an approach to mining collaboration patterns between APIs to aid mashup creation for the WoT.The goal of the approach is to disclose what kinds of Web APIs are frequently combined together in mashup creation and what kinds of API combination are popular.Based on a real-world mashup and Web API repository, PragrammableWeb.com,we exploit the text description and tags of Web APIs and employ an FP-growth-based association mining algorithm to discover popular collaboration patterns between APIs.To overcome the deficiency caused by tag sparsity, the approach also develops a method based on TF/IDF to expand the tags of Web APIs.The experimental results validated the performance of the proposed approach.
Toxicologic Pathology Forum*: Opinion on Integrating Innovative Digital Pathology Tools in the Regulatory Framework

Digital pathology is defined as the ability to examine digitized microscopic slides and to generate qualitative and quantitative data. The field of digital pathology is rapidly evolving and has the potential to revolutionize toxicologic pathology. Techniques such as automated 2-D image analysis, whole slide imaging, and telepathology are already considered "mature" technologies and have been used for decades in exploratory studies; however, many organizations are reluctant to use digital pathology in regulatory toxicology studies. Innovative technologies using digitized slides including high-content imaging modalities and artificial intelligence are still under development but are increasingly used in toxicologic pathology. While software validation requirements are already described, clear guidance for application of these rules to the digital pathology field are few and the acceptance of these technologies by regulatory authorities remains necessary for successful adoption of digital pathology into the mainstream of toxicologic pathology. This topic was discussed during a roundtable at the 2018 Annual Congress of the French Society of Toxicologic Pathology. This opinion article summarizes the discussion regarding the current questions and challenges on the integration of innovative digital pathology tools within a good laboratory practice framework and is meant to stimulate further discussion among the toxicologic pathology community. *This is an opinion article submitted to the Toxicologic Pathology Forum and does not constitute an official position of the Society of Toxicologic Pathology or the journal Toxicologic Pathology. The views expressed in this article are those of the authors and do not necessarily represent the policies, positions, or opinions of their respective agencies and organizations. The Toxicologic Forum is designed to stimulate broad discussion of topics relevant to regulatory issues in Toxicologic pathology. Readers of Toxicologic Pathology are encouraged to send their thoughts on these articles or ideas for new topics to toxicologicpathologyforum@toxpath.org .