Reviewerly/exHarmony huggingface dataset:
PM-MMUT: Boosted Phone-mask Data Augmentation using Multi-modeing Unit  Training for Robust Uyghur E2E Speech Recognition

Consonant and vowel reduction are often encountered in Uyghur speech, which might cause performance degradation in Uyghur automatic speech recognition (ASR). Our recently proposed learning strategy based on masking, Phone Masking Training (PMT), alleviates the impact of such phenomenon in Uyghur ASR. Although PMT achieves remarkably improvements, there still exists room for further gains due to the granularity mismatch between masking unit of PMT (phoneme) and modeling unit (word-piece). To boost the performance of PMT, we propose multi-modeling unit training (MMUT) architecture fusion with PMT (PM-MMUT). The idea of MMUT framework is to split the Encoder into two parts including acoustic feature sequences to phoneme-level representation (AF-to-PLR) and phoneme-level representation to word-piece-level representation (PLR-to-WPLR). It allows AF-to-PLR to be optimized by an intermediate phoneme-based CTC loss to learn the rich phoneme-level context information brought by PMT. Experi-mental results on Uyghur ASR show that the proposed approaches improve significantly, outperforming the pure PMT (reduction WER from 24.0 to 23.7 on Read-Test and from 38.4 to 36.8 on Oral-Test respectively). We also conduct experiments on the 960-hour Librispeech benchmark using ESPnet1, which achieves about 10% relative WER reduction on all the test sets without LM fusion comparing with the latest official ESPnet1 pre-trained model.
Ground-state separability and criticality in interacting many-particle systems

We analyze exact ground state (GS) separability in general $N$-particle systems with two-site couplings. General necessary and sufficient conditions for full separability in the form of one- and two-site eigenvalue equations are first derived. The formalism is then applied to a class of $\mathrm{SU}(n)$-type interacting systems where each constituent has access to $n$-local levels, and where the total number parity of each level is preserved. Explicit factorization conditions for parity-breaking GSs are obtained, which generalize those for $XYZ$ spin systems and correspond to a fundamental GS multilevel parity transition where the lowest ${2}^{n\ensuremath{-}1}$ energy levels cross. We also identify a multicritical factorization point with exceptional high degeneracy proportional to ${N}^{n\ensuremath{-}1}$, arising when the total occupation number of each level is preserved, in which any uniform product state is an exact GS. Critical entanglement properties (such as full range pairwise entanglement) are shown to emerge in the immediate vicinity of factorization. Illustrative examples are provided.
High-fidelity indirect readout of trapped-ion hyperfine qubits

We propose and demonstrate a protocol for high-fidelity indirect readout of trapped ion hyperfine qubits, where the state of a $^9\text{Be}^+$ qubit ion is mapped to a $^{25}\text{Mg}^+$ readout ion using laser-driven Raman transitions. By partitioning the $^9\text{Be}^+$ ground state hyperfine manifold into two subspaces representing the two qubit states and choosing appropriate laser parameters, the protocol can be made robust to spontaneous photon scattering errors on the Raman transitions, enabling repetition for increased readout fidelity. We demonstrate combined readout and back-action errors for the two subspaces of $1.2^{+1.1}_{-0.6} \times 10^{-4}$ and $0^{+1.9}_{-0} \times 10^{-5}$ with 68% confidence while avoiding decoherence of spectator qubits due to stray resonant light that is inherent to direct fluorescence detection.
Realizing Repeated Quantum Error Correction in a Distance-Three Surface  Code

Quantum computers hold the promise of solving computational problems which are intractable using conventional methods. For fault-tolerant operation quantum computers must correct errors occurring due to unavoidable decoherence and limited control accuracy. Here, we demonstrate quantum error correction using the surface code, which is known for its exceptionally high tolerance to errors. Using 17 physical qubits in a superconducting circuit we encode quantum information in a distance-three logical qubit building up on recent distance-two error detection experiments. In an error correction cycle taking only $1.1\,\mu$s, we demonstrate the preservation of four cardinal states of the logical qubit. Repeatedly executing the cycle, we measure and decode both bit- and phase-flip error syndromes using a minimum-weight perfect-matching algorithm in an error-model-free approach and apply corrections in postprocessing. We find a low error probability of $3\,\%$ per cycle when rejecting experimental runs in which leakage is detected. The measured characteristics of our device agree well with a numerical model. Our demonstration of repeated, fast and high-performance quantum error correction cycles, together with recent advances in ion traps, support our understanding that fault-tolerant quantum computation will be practically realizable.
The Coming Battle of Digital Currencies

We develop a dynamic general equilibrium model of global competition among national fiat currencies, cryptocurrencies, and Central Bank Digital Currencies (CBDCs) in which the strength of a country and of its currency are mutually reinforcing. The endogenous rise of cryptocurrencies hurts stronger currencies, but can benefit weaker currencies by reducing competition from stronger fiat currencies. Countries strategically implement CBDCs in response to competition from emerging cryptocurrencies and other currencies. Our model suggests the following pecking order: Countries with strong but non-dominant currencies (e.g., China) are most incentivized to launch CBDC due to both technological first-mover advantage and potential reduction in dollarization. The strongest currencies (e.g., the US) benefit from developing CBDC early on to nip cryptocurrency growth in the bud and later on to counteract competitors' CBDCs. The weakest nations forego implementing CBDCs and adopt cryptocurrencies instead. Strong fiat competition and the emergence of cryptocurrencies spur financial innovation and digital currency development. Our findings help rationalize recent developments in currency and payment digitization and provide insights into the global battle of currencies and the future of money.
Research on the Construction Path of "Double First-class" in China's Western Universities Based on Excellent Performance

In recent years, higher education has stepped into a new era with the goal of building global first-class universities and first-class disciplines, and the pursuit of excellence and the improvement of quality have become the development theme of higher education. In this paper, according to the Criteria for Performance Excellence and the evaluation index system for the implementation of the "double first-class" strategy, by analyzing the hierarchy of the influence factors of the construction of "double first-class" in China's western universities with ISM, 22 influence factors can be divided into five levels, which contact and influence each other. The results show that there is a stepwise hierarchical relationship among the evaluation indicators, and such four indexes as the construction of discipline and scientific research, international exchange and cooperation, the mode of talent cultivation and the public service have a direct impact on the construction of "double first-class" in China's western universities. What's more, factors such as organization and lead, social responsibility, understanding of students, relevant people and market, and working system play a supporting role. Thus, a reasonable construction path and measures for the construction of "double first-class" in China's western universities is proposed in this study.
Launch of Celeros Flow Technology

Celeros Flow Technology has launched as a standalone company following SPX Flow Inc's divestment of its Power &amp; Energy division to an affiliate of funds managed by private equity company Apollo Global Management Inc (see Pump Industry Analyst, March 2020).
TOWARDS GLOBAL OPTIMIZATION OF NEURAL NETWORK: A COMPARATIVE ANALYSIS USING GENETIC AND WHALE OPTIMIZATION ALGORITHMS

The intelligence and adaptability features of the neural network has made it a technique that is widely used to solve problems in diverse areas such as; detection, monitoring, prediction, diagnostics, data mining, classification, recognition, robotics, biomedicine, etc. However, determination of the optimal number of hidden layers of neural network and other parameters are still a difficult task. Usually, these parameters are decided by trial-and-error which increases the computational complexity and it is human dependent in obtaining the optimal model and parameters alike for any particular task. Optimization has received enormous attention in recent years, primarily because of the rapid progress in computer technology, including the development and availability of user-friendly software, high-speed and parallel processors, and artificial neural networks. This research work is to propose a neuro-evolutionary model using the computational intelligence techniques by combining ANN, GA and WOA for binary classification problems. The proposed optimized ANN-GA and WOA models is to circumvent the problem that is characterized in the trade-off between smoothness and accuracies in selecting the models and optimal parameters of neural network.
Developing and Testing a Software for Search and Rescue in Rubble Piles Based on Microseismic Signals

Summary Passive microseismics is used in Search and Rescue operations to localize people under piles of rubble. Currently, an operator can evaluate the approximate position of the survivor using an array of geophones by an iterative move-and-listen procedure which is time-consuming. To shorten this procedure a completely new way of using a microseismic equipment is proposed. The idea is to deploy the geophones on the piles and elaborate the digitized noise produced by the survivor with a software able to localize his/her position. The objective of this work is to develop and test this software which is able to produce an automatic and fast localization in the 3D space taking advantage not only of signal intensity but also of the information given by time delays between different geophones. The results are encouraging: the percentage of microseismic events that were localized with the desired accuracy is between 50% and 75% depending on debris properties, geophone configuration and array size. Further improvements are expected by refining the automatic picking procedure which represents the major challenge. By introducing this approach within the search and rescue operations, the total time required from sensor deployment to 3D location can be reduced to less than 10 minutes.
Detection of Data Leaks through Large Scale Distributed Query Processing using Machine Learning

With the growth in the distributed data processing and data being the fuel for each of the processes, the query processes of the data are expected to be significantly lower.Hence, the distribution of the data is highly expected and during the distributing of the data, the chances for data leakage increases to a significant extend.The data leakage problems are not generally caused by intentional errors, rather this is caused by the higher visibility of the data over multiple clusters.Henceforth, the detection process is also very critical.Many of the parallel research attempts have demonstrated various methods for the detection and as well as the prevention methods.The works in the direction of the detection of the data leaks are highly dependent either on the historical information of the leaks or depends on the contextual importance of the data.In both the cases, the outcomes of the detection process accuracy cannot be ensured.In the other hand, the preventive measures can also turn into a reactive process for detection by reversing the principles proposed in these research outcomes, but the computational complexities are significantly higher.Thus, this work proposes a novel strategy for detection of the data leakages after the data distribution during the query processing events.This work proposes an initial Occurrence Based Rule Set Extraction method using Adaptive Threshold for generating the rulesets, further for reducing the time complexity and reducing the loss of dataset attribute information, this work introduces yet another algorithm for Dynamic Inference-based Rule Set Reduction.After the inferences are generated, finally this work deploys the Attribute Subset Equivalence-based Leak Detection mechanism for final detection of the clusters with data leaks.This work demonstrates nearly 89% accuracy for the detection process.
Boris Asaf'yev's Unpublished Marginalia in Modest Musorgsky's Manuscript Full Score of Boris Godunov

The subject matter of this article is related to the initial stage of the history of studying Modest Musorgsky’s orchestral style. The first scholars who got access to the composer’s manuscript full score were Boris Asaf’yev and Pavel Lamm. They edited the full score of the opera Boris Godunov for the première, which took place in Leningrad, at the State Academic Opera and Ballet Theatre, on February 16, 1928. It was the opera’s first performance in the author’s original orchestration after a half-century interval. Two short but fundamentally important articles by Asaf’yev, as well as his marginalia in the manuscript full score, opened a new stage in the scholarly and artistic perception of M. P. Musorgsky’s orchestral dramaturgy and orchestral style.
Content-Based Image Retrieval Using Deep Learning

In last few decades, digital images are growing with a rapid pace on and off the Internet, and given the volume of the images, the need of better storage, processing, and retrieval of images has raised. One of the retrieval techniques that is focus of this work is content-based image retrieval (CBIR) in which similar images are searched from a pool of images without manually annotating them; rather, in CBIR, other features of images that discriminate them from other images are used. By finding the better discriminative features of a collection of images, an efficient and generalized CBIR system can be built. The success and the efficiency of such a system depend on the choice of the features of images used to identify them. Generally, the images are stored at a very low level in pixels, but to get better results or in other words better features, we need to store these images at a very high level in order to reduce the semantic gap. There has been very extensive research on CBIR using the traditional methods of image processing. With the advancement of deep learning systems, deep learning can be used for large range of problems. In this work, we investigated the use of deep learning, more precisely auto-encoders, for the feature extraction and representation of images in CBIR, and we reached to the retrieval efficiency of ≈ 80%.
Learning and Control of Active Perception Policy in Autonomous Robots

We consider how to control behaviors of the sensor’s perceptual policy during the training in our earlier work that simultaneously learns the robot’s movement policy and the sensor’s perceptual policy. In our algorithm, we simultaneously train two agents which control the robot and its sensor on the robot to achieve a task. Since the exploration space changes greatly depending on the action of the sensor, we aim to achieve efficient exploration behavior by introducing a new parameter to control the sensor action in a training process. We conducted experiments on navigation tasks in a 2D and a 3D environment. The experimental results show that our algorithm can achieve higher success rates than conventional reinforcement learning algorithms and we found that the introduced parameters worked effectively in controlling the behavior of the sensor.
Technology, regulation and theory

The regulation of technology is an important and topical area of law, relevant to almost all aspects of society. Technology Law: Australian and International Perspectives presents a thorough exploration of the new legal challenges created by evolving technologies, from the use of facial recognition technology in criminal investigations to the rise and regulation of cryptocurrencies. A well-written and fascinating introduction to technology law in Australia and internationally, Technology Law provides thorough coverage of the theoretical perspectives, legislation, cases and developing issues where technology and the law interact. The text covers data protection and privacy, healthcare technology, criminal justice technology, commercial transactions, cybercrime, social media and intellectual property, and canvasses the future of technology and technology law. Written by leading experts in the field, Technology Law is an excellent resource for law students and legal professionals with an interest in the area.
Discriminative Linear Classifiers

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
Lifetime Prediction of LiFePO4 Batteries Using Multilayer Classical-Quantum Hybrid Classifier

This article presents a multilayer hybrid classical-quantum classifier for predicting the lifetime of LiFePO4 batteries using early degradation data. The multilayer approach uses multiple variational quantum circuits in cascade, which allows more parameters to be used as weights in a single run hence increasing accuracy and provides faster cost function convergence for the optimizer. The proposed classifier predicts with an accuracy of 92.8% using data of the first four cycles. The effectiveness of the hybrid classifier is also presented by validating the performance using untrained data with an accuracy of 84%. We also demonstrate that the proposed classifier outperforms traditional machine learning algorithms in classification accuracy. In this paper, we show the application of quantum machine learning in solving a practical problem. This study will help researchers to apply quantum machine learning algorithms to more complex real-world applications, and reducing the gap between quantum and classical computing.
Segmentation

Segmentation identifies subgroups better served if treated separately, especially for risk-heterogeneous populations. Trade-offs occur between the resulting extra lift and the extra costs and complexities. It provides little where risk-homogeneity is enforced by strong filtering mechanisms. (1) <bold>Overview</bold>—i) drivers—operational, strategic, feedstock or interactional; ii) inhibitors—limits on the number of segments {insufficient data, costs of development, implementation, monitoring}; iii) mitigators—steps to reduce model count {interaction characteristics, alternative transformation and development methodologies}. (2) <bold>Analysis</bold>—i) learning types—supervised and unsupervised; ii) finding interactions—how to measure interactions for binary targets; iii) segment mining—comparing multiple options; iv) boundary analysis—assessing the impact for cases that switch segments. (3) <bold>Presentation</bold>—tabular and graphic means of presenting comparisons of different options, especially against having a single model. It includes performance within and across segments, drill-downs into segments and strategy curves showing differences in Accept and Bad rates.
Walking on the thin intersectional lines of disciplines

Elishai Ezra Tsur, a multidisciplinary researcher, talks about the challenges that conventional academic mindset brought to his professional life. He, DeWolf, and Supic introduce us with their viewpoint about "data science" and its role in their research. In their recent work published in this issue of Patterns, they tackle the inverse kinematics problem using brain-inspired neuronal architectures.
The evolving role of printed publications in the digital age and relevance for their continued development

Purpose The purpose of this article is to adhere to the construction of foreign print books. Of course, when the funds are limited or reduced, we can adjust the collection structure according to the utilization situation and reduce the subjects or languages that readers use poorly. Provide data support for library collection development plan. Design/methodology/approach This paper analyzes the development and utilization of printed foreign language books based on the case of the National Library of China (NLC).The quality of library collection is analyzed by list checking method, and the utilization of library collection is analyzed by comparative analysis method and the statistical analysis method is used for language and subject areas, the questionnaire analysis method is used for users in NLC. Findings Foreign printed books plays a certain role in supporting domestic social users. The construction advantages of these print books need to be adhered to and developed. They should not be interrupted or cut down sharply because of many libraries are developing towards digital library or smart library nowadays. Libraries need to balance printed and digital resources in their collection and optimize their printed collection composition in terms of subject areas and unique resources to keep up with evolving needs of their patrons. Research limitations/implications It is not compared with the utilization of e-books. Practical implications In the transition period from traditional library to digital library, quantitative analysis of the construction and utilization of printed book resources can provide research support for the formulation of collection development planning, better service to users and better use of funds. Social implications In the period of library transformation and development, it is of great practical significance to study the construction and utilization of printed books in NLC for resource transformation and service transformation. This paper gives the corresponding analysis and discussion on whether printed books should adhere to and whether they can be optimized. Originality/value In the period of Library Transformation and development, it is of great practical significance to study the construction and utilization of foreign language printed books in NLC. The printed book resources can provide research support for the formulation of collection development plan, better service to users and better use of funds.