Reviewerly/exHarmony huggingface dataset:
Quantum teleportation of qudits by means of generalized quasi-Bell states of light

Quantum superpositions of coherent states are produced both in microwave and optical domains, and are considered realizations of the famous "Schr\"odinger cat" state. The recent progress shows an increase in the number of components and the number of modes involved. Our work creates a theoretical framework for treatment of multicomponent two-mode Schr\"odinger cat states. We consider a class of single-mode states, which are superpositions of $N$ coherent states lying on a circle in the phase space. In this class we consider an orthonormal basis created by rotationally-invariant circular states (RICS). A two-mode extension of this basis is created by splitting a single-mode RICS on a balanced beam-splitter. We show that these states are generalizations of Bell states of two qubits to the case of $N$-level systems encoded into superpositions of coherent states on the circle, and we propose for them the name of generalized quasi-Bell states. We show that using a state of this class as a shared resource, one can teleport a superposition of coherent states on the circle (a qudit). Differently from some other existing protocols of quantum teleportation, the proposed protocol provides the unit fidelity for all input states of the qudit. We calculate the probability of success for this type of teleportation and show that it approaches unity for the average number of photons in one component above $N^2$. Thus, the teleportation protocol can be made unit-fidelity and deterministic at finite resources.
Better Character Language Modeling through Morphology

We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint.Analyzing the CLMs shows that inflected words benefit more from explicitly modeling morphology than uninflected words, and that morphological supervision improves performance even as the amount of language modeling data grows.We then transfer morphological supervision across languages to improve language modeling performance in the low-resource setting.
No Need to Ask

This article will describe how permissionless metadata blockchains could be created to overcome two significant limitations in current cataloging practices: centralization and a lack of traceability. The process would start by creating public and private keys, which could be managed using digital wallet software. After creating a genesis block, nodes would submit either a new record or modifications to a single record for validation. Validation would rely on a Federated Byzantine Agreement consensus algorithm because it offers the most flexibility for institutions to select authoritative peers. Only the top tier nodes would be required to store a copy of the entire blockchain thereby allowing other institutions to decide whether they prefer to use the abridged version or the full version.
A Sampling Rate Selecting Algorithm for the Arbitrary Waveform Generator

The arbitrary waveform generator is now commonly used to generate waveforms in many fields. The Nyquist sampling theorem only provides an open interval for the sampling rate selection, but how to determine a specific sampling rate within this open interval has not been discussed. In practice, the sample rate conversion is adopted to convert the arbitrarily selected sample rate to a safe one which keeps the mirror frequency out of a fixed passband of an anti-aliasing filter in an arbitrary waveform generator whose sample rate is variable. To select a safe sample rate with low computational cost and memory requirements for conversion, an algorithm, with fractional sample rate conversion in mind, is proposed in this paper to guarantee the safety of the target sample rate and the bound of resource cost. The experimental results show that not only the computational cost and memory requirements of sampling rate conversion but also the frequency stability and harmonics of the generated signal in the proposed algorithm overcome the method that arbitrary converts the sampling rate to a fixed maximum one.
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In this work, we consider a data-mining approach to enhance the GPS signal. We build a large-scale high precision GPS receiver grid system to collect real-time GPS signals for training. The Gaussian Process (GP) regression is chosen to model the vertical Total Electron Content (vTEC) distribution of the ionosphere of the Earth. Our experiments show that the noise in the real-time GPS signals often exceeds the breakdown point of the conventional robust regression methods resulting in sub-optimal system performance. We propose a three-step approach to address this challenge. In the first step we perform a set of signal validity tests to separate the signals into clean and dirty groups. In the second step, we train an initial model on the clean signals and then reweigting the dirty signals based on the residual error. A final model is retrained on both the clean signals and the reweighted dirty signals. In the theoretical analysis, we prove that the proposed three-step approach is able to tolerate much higher noise level than the vanilla robust regression methods if two reweighting rules are followed. We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods. Our method achieves centimeter positioning accuracy in the benchmark region with probability $78.4%$ , outperforming the second best baseline method by a margin of $8.3%$. The benchmark takes 6 hours on 20,000 CPU cores or 14 years on a single CPU.
Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts

In this paper, we propose a method for whyquestion answering (why-QA) that uses an adversarial learning framework.Existing why-QA methods retrieve answer passages that usually consist of several sentences.These multi-sentence passages contain not only the reason sought by a why-question and its connection to the why-question, but also redundant and/or unrelated parts.We use our proposed Adversarial networks for Generating compact-answer Representation (AGR) to generate from a passage a vector representation of the non-redundant reason sought by a why-question and exploit the representation for judging whether the passage actually answers the why-question.Through a series of experiments using Japanese why-QA datasets, we show that these representations improve the performance of our why-QA neural model as well as that of a BERT-based why-QA model.We show that they also improve a state-of-the-art distantly supervised open-domain QA (DS-QA) method on publicly available English datasets, even though the target task is not a why-QA.
Genesis of IT Economy: from Cryptocurrency to Digital Asset

Background:Globalisation of the world economy and computarisation of financial systems form a new branch -ІТ economy.There appear some alternative currencies generated by computarisation of financial systems.A remarkable example of this process is
Evaluating Deep Learning Methods for Word Segmentation of Scripta Continua Texts in Old French and Latin

Tokenization of modern and old Western European languages seems to be fairly simple, as it stands on the presence mostly of markers such as spaces and punctuation. However, when dealing with old sources like manuscripts written in scripta continua, antiquity epigraphy or Middle Age manuscripts, (1) such markers are mostly absent, (2) spelling variation and rich morphology make dictionary based approaches difficult. Applying convolutional encoding to characters followed by linear categorization to word-boundary or in-word-sequence is shown to be effective at tokenizing such inputs. Additionally, the software is released with a simple interface for tokenizing a corpus or generating a training set.
Rima: An RDMA-Accelerated Model-Parallelized Solution to Large-Scale Matrix Factorization

Matrix factorization (MF) is a fundamental technique in machine learning and data mining, which gains wide application in many fields. When the matrix becomes large, MF cannot be processed on a single machine. Considering this, many distributed SGD algorithms (e.g. DSGD) have been developed to solve large-scale MF on multiple machines in a model-parallel way. Existing distributed algorithms are primarily implemented under Map/Reduce or PS (parameter server)-based architectures, which incur significant communication overheads. Besides, existing solutions cannot well embrace the benefit of RDMA/RoCE transport and suffer from scalability problems. Targeting at these drawbacks, we propose Rima, which uses ring-based model parallelism to solve large-scale MF with higher communication efficiency. Compared with PS-based SGD algorithms, Rima also consumes less queue pairs (QPs) and can thus better leverage the power of RDMA/RoCE to accelerate the training speed. Our experiment shows that, compared with PS-based DSGD when solving 1M × 1M MF, Rima achieves comparable convergence performance after equal number of iterations, but reduces the training time by 68.7% and 85.4% via TCP and RDMA respectively.
A Fast Factorisation of Semi-Primes Using Sum of Squares

For several centuries, prime factorisation of large numbers has drawn much attention due its practical applications and the associated challenges. In computing applications, encryption algorithms such as the Rivest–Shamir–Adleman (RSA) cryptosystems are widely used for information security, where the keys (public and private) of the encryption code are represented using large prime factors. Since prime factorisation of large numbers is extremely hard, RSA cryptosystems take advantage of this property to ensure information security. A semi-prime being, a product of two prime numbers, has wide applications in RSA algorithms and pseudo number generators. In this paper, we consider a semi-prime number whose construction consists of primes, N = p 1 p 2 , being Pythagorean and having a representation on the Cartesian plane such that, p = x 2 + y 2 . We prove that the product of two such primes can be represented as the sum of four squares, and further, that the sums of two squares can be derived. For such a semi-prime, if the original construction is unknown and the sum of four squares is known, by Euler’s factorisation the original construction p 1 p 2 can be found. By considering the parity of each of the squares, we propose a new method of factorisation of semi-primes. Our factorisation method provides a faster alternative to Euler’s method by exploiting the relationship between the four squares. The correctness of the new factorisation method is established with mathematical proofs and its practical value is demonstrated by generating RSA-768 efficiently.
Projection super-resolution based on convolutional neural network for computed tomography

The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)- based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projectiondomain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of low- and high-resolution projection. The network label is high-resolution projection and the input is its corresponding interpolation data after down sampling. FDK algorithm is utilized for three-dimensional image reconstruction and one slice of reconstruction image is taken as an example to evaluate the performance of the proposed method. Qualitative and quantitative results show that the proposed method is potential to improve the resolution of projection and enables the reconstructed image with higher quality.
Joint Learning of NNeXtVLAD, CNN and Context Gating for Micro-Video Venue Classification

Currently, micro-videos have grown explosively on various online social platforms.Accordingly, how to encode them to yield effective representation attracts our attention.NeXtVLAD is such an effective network that aggregates frame-level features into a compact supervector.However, the discriminant capability of such a supervector is still limited due to the lack of non-linear transformation and L2 normalization at the head and tail of original NeXtVLAD network, respectively.In order to address such problems, we propose an improved neural network architecture, normalized NeXtVLAD (NNeXtVLAD), which is extended with ReLU function and L2 normalization.In the light of such a new network, we build up an end-to-end framework which jointly learns NNeXtVLAD, CNN layer, and context gating for microvideo venue classification.Specifically, we first apply NNeXtVLAD layers as three-stream architecture to aggregate visual, acoustic, and textual features.We then pack and embed the aggregated features into CNN layer for enhancing the sparse concept-level representation.Finally, context gating is used to capture the interdependency among different network activations.Extensive experimental results on a real-world micro-video dataset exhibit that our proposed model significantly outperforms the state-of-the-art baselines in terms of both Micro-F1 and Macro-F1 scores.
Removal of Methyl Blue Dye from Aqueous Solution by Adsorption unto Ground Nut Waste

Esther Unyime Etim* Author Affiliations Department of Chemistry, Nigeria Received: February 07, 2019 | Published: March 01, 2019 Corresponding author: Esther Unyime Etim, Department of Chemistry, Nigeria DOI: 10.26717/BJSTR.2019.15.002701
A Holistic Framework for Forecasting Transformative AI

In this paper we describe a holistic AI forecasting framework which draws on a broad body of literature from disciplines such as forecasting, technological forecasting, futures studies and scenario planning. A review of this literature leads us to propose a new class of scenario planning techniques that we call scenario mapping techniques. These techniques include scenario network mapping, cognitive maps and fuzzy cognitive maps, as well as a new method we propose that we refer to as judgmental distillation mapping. This proposed technique is based on scenario mapping and judgmental forecasting techniques, and is intended to integrate a wide variety of forecasts into a technological map with probabilistic timelines. Judgmental distillation mapping is the centerpiece of the holistic forecasting framework in which it is used to inform a strategic planning process as well as for informing future iterations of the forecasting process. Together, the framework and new technique form a holistic rethinking of how we forecast AI. We also include a discussion of the strengths and weaknesses of the framework, its implications for practice and its implications on research priorities for AI forecasting researchers.
Benchmarking large scale variants of CMA-ES and L-BFGS-B on the bbob-largescale testbed

In this paper we benchmark five variants of CMA-ES for optimization in large dimension on the novel large scale testbed of COCO under default or modified parameter settings. In particular, we compare the performance of the separable CMA-ES, of VD-CMA-ES and VkD-CMA-ES, of two implementations of the Limited Memory CMA-ES and of the Rank m Evolution Strategy, RmES. For VkD-CMA-ES we perform experiments with different complexity models of the search distribution and for RmES we study the impact of the number of evolution paths employed by the algorithm. The quasi-Newton L-BFGS-B algorithm is also benchmarked and we investigate the effect of choosing the maximum number of variable metric corrections for the Hessian approximation. As baseline comparison, we provide results of CMA-ES up to dimension 320.
Fast and robust ellipse detector based on edge following method

This study presents a fast and robust ellipse detector based on edge following method. The detector first extracts segments using an edge predictor based on curvature analysis. Then, line segments are generated based on length condition other than least-squares approximation. After that, potential ellipses are detected based on edge curvature and convexity. In addition, a re-find contours detection method is introduced to improve the accuracy by searching edge points in the missing part of the ellipse. The performance of the detector has been tested on different datasets containing both synthetic and real images with three other algorithms based on the edge following method. Experimental results indicate that the proposed method always has the fastest execution time. Besides, it advances the state of the art in accuracy in most cases. Generally speaking, it is a fast, robust and effective ellipse detector for real-time applications.
YNU NLP at SemEval-2019 Task 5: Attention and Capsule Ensemble for Identifying Hate Speech

This paper describes the system submitted to SemEval 2019 Task 5: Multilingual detection of hate speech against immigrants and women in Twitter (hatEval).Its main purpose is to conduct hate speech detection on Twitter, which mainly includes two specific different targets, immigrants and women.We participate in both subtask A and subtask B for English.In order to address this task, we develope an ensemble of an attention-LSTM model based on HAN and a BiGRU-capsule model.Both models use fastText pre-trained embeddings, and we use this model in both subtasks.In comparison to other participating teams, our system is ranked 16th in the Subtask A for English, and 12th in the Subtask B for English.
Influence of Network Delay in Virtual Reality Multiplayer Exergames: Who is actually delayed?

One of the fields where Virtual Reality (VR) is finding a potentially growing market is in the combination of exercising and gaming - also called exergaming. When it comes to competition in gaming, is important to investigate how different levels of delay influence overall quality of experience (QoE) in VR multiplayer exergames. The experimental setup consisted of a VR application coupled with a rowing ergometer, allowing races between the user and an artificially created opponent that is following the player with a similar speed and keeping the race tight. To investigate the influence of the delay, on both user's and opponent's side three levels of network delay were introduced (30ms, 100ms, and 500ms) and mixed throughout different conditions. After each session, participants rated perceived flow, sense of presence, and the degree to which they have noticed the delay in their or the opponent's system. Interestingly, results show different perception of delay and QoE depending on user's own delay. Participants perceived the opponent's player as being delayed even if only the player itself had network delay along with significantly lower rating of QoE only when their delay was high.
Blockchain and mortgage lending process: A study of people, process, and technology involved

Blockchain technology is on the rise and considered to be a revolutionary technology. It has been applied to many domains including personal health records, regulatory investigation, and global supply chain. Applications that may potentially benefit from blockchain technology are those, which involve multiple parties across the different organization, each performing a subset of many steps needed to complete a given transaction and involve different technologies. In addition, security and trust is a major concern. Given this, a good candidate for blockchain technology is mortgage lending. The purpose of this paper is to prepare a model that identifies different elements that are needed when applying blockchain technology in the mortgage origination process. This paper uses a popular framework used in customer relationship management (CRM) that combines three constructs: People, Process and Technology to develop the intended model.
An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models With IID Noise Variables

The discovery of causal relationships from the observational data is an important task. To identify the unique causal structure belonging to a Markov equivalence class, a number of algorithms, such as the linear non-Gaussian acyclic model (LiNGAM), have been proposed. However, two challenges remain to be met: 1) these algorithms fail to work on the data which follow linear structural equation model with Gaussian noise and 2) they misjudge the causal direction when the data contain additional measurement errors. In this paper, we propose an entropy-based two-phase iterative algorithm for arbitrary distribution data with additional measurement errors under some mild assumptions. In the first phase of the algorithm, based on the property that entropy can measure the amount of information behind the data with arbitrary distribution, we design a general approach for the identification of exogenous variable on both Gaussian and non-Gaussian data, and we give the corresponding theoretical derivation. In the second phase, to eliminate the effects of measurement errors, we revise the value of the exogenous variable by removing its measurement error and further use the revised value to remove its effect on the remaining variables. Experimental results on real-world causal structures are presented to demonstrate the effectiveness and stability of our method. We also apply the proposed algorithm on the mobile-base-station data with measurement errors, and the results further prove the effectiveness of our algorithm.
Pardeep at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Deep Learning

The rise of social media has made information exchange faster and easier among the people.However, in recent times, the use of offensive language has seen an upsurge in social media.The main challenge for a service provider is to correctly identify such offensive posts and take necessary action to monitor and control their spread.In this work, we try to address this problem by using sophisticated deep learning techniques like LSTM, Bidirectional LSTM and Bidirectional GRU.Our proposed approach solves 3 different Sub-tasks provided in the SemEval-2019 task 6 which incorporates identification of offensive tweets as well as their categorization.We obtain significantly better results in the leader-board for Sub-task B and decent results for Sub-task A and Subtask C validating the fact that the proposed models can be used for automating the offensive post-detection task in social media.