Reviewerly/exHarmony huggingface dataset:
Overlapping two standing waves in a microcavity for a multi-atom photon interface

We develop a light-matter interface enabling strong and uniform coupling between a chain of cold atoms and photons of an optical cavity. This interface is a fiber Fabry-Perot cavity, doubly resonant for both the wavelength of the atomic transition and for a geometrically commensurate red-detuned intracavity trapping lattice. Fulfilling the condition of a strong and uniform atom-photon coupling requires optimization of the spatial overlap between the two standing waves in the cavity. In a strong-coupling cavity, where the mode waists and Rayleigh range are small, we derive the expression of the optimal trapping wavelength, taking into account the Gouy phase. The main parameter controlling the overlap of the standing waves is the relative phase shift at the reflection on the cavity mirrors between the two wavelengths, for which we derive the optimal value. We have built a microcavity optimized according to these results, employing custom-made mirrors with engineered reflection phase for both wavelengths. We present a method to measure with high precision the relative phase shift at reflection, which allows us to determine the spatial overlap of the two modes in this cavity.
Regularized Graph Convolutional Networks for Short Text Classification

Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce.The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning.Simpler methods that rely on bag-of-words representations tend to perform on par with complex deep learning methods.To tackle the limitations of textual features in short text, we propose a Graph-regularized Graph Convolution Network (GR-GCN), which augments graph convolution networks by incorporating label dependencies in the output space.Our model achieves state-of-the-art results on both proprietary and external datasets, outperforming several baseline methods by up to 6% .Furthermore, we show that compared to baseline methods, GR-GCN is more robust to noise in textual features.
Research of YOLO Architecture Models in Book Detection

Deep neural networks are widely used in different fields of human activity, including spheres which are connected with large amount of operations such as data obtaining and processing information from the outside world.This article deals with the creation of the deep convolutional neural network based on the YOLO architecture for book detection in real time.The architecture chosen as the basis of the neural network possesses a number of advantages which make it highly competitive with other models, so it can be considered as the most suitable option for the creation of deep neural network for object detection.Creation of the original dataset and the deep neural network training are described.Several variants of neural networks based on the YOLO architecture are discussed and the results of their comparison are shown.The results obtained during the training of a deep neural network allow us to use it as a basis for further development of the application.
A Differentially Private Text Perturbation Method Using Regularized Mahalanobis Metric

Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data.A popular approach for privacypreserving text analysis is noise injection, in which text data is first mapped into a continuous embedding space, perturbed by sampling a spherical noise from an appropriate distribution, and then projected back to the discrete vocabulary space.While this allows the perturbation to admit the required metric differential privacy, often the utility of downstream tasks modeled on this perturbed data is low because the spherical noise does not account for the variability in the density around different words in the embedding space.In particular, words in a sparse region are likely unchanged even when the noise scale is large.In this paper, we propose a text perturbation mechanism based on a carefully designed regularized variant of the Mahalanobis metric to overcome this problem.For any given noise scale, this metric adds an elliptical noise to account for the covariance structure in the embedding space.This heterogeneity in the noise scale along different directions helps ensure that the words in the sparse region have sufficient likelihood of replacement without sacrificing the overall utility.We provide a text-perturbation algorithm based on this metric and formally prove its privacy guarantees.Additionally, we empirically show that our mechanism improves the privacy statistics to achieve the same level of utility as compared to the state-of-the-art Laplace mechanism.
Margolus–Levitin speed limit across quantum to classical regimes based on trace distance*

The classical version of Mandelstam-Tamm speed limit based on the Wigner function in phase space is reported by B. Shanahan et al. [Phys. Rev. Lett. 120, 070401 (2018)]. In this paper, the Margolus-Levitin speed limit across the quantum-to-classical transition is given in phase space based on the trace distance. The Margolus-Levitin speed limit is set by the Schatten L1 norm of the generator of time dependent evolution for both the quantum and classical domains. As an example, the time-dependent harmonic oscillator is considered to illustrate the result.
POOR READING COMPREHENSION ISSUE IN EFL CLASSROOM AMONG INDONESIAN SECONDARY SCHOOL STUDENTS: Scrutinizing the causes, impacts and possible solutions

Reading comprehension is considered a challenging part of learning English for many Indonesian secondary school students. This study aims to explore the causes, impacts, and possible solutions for poor reading comprehension issues. The study scrutinizes the issue by reviewing previous studies, in which a number of articles and books were critically analyzed. The results indicate that poor reading comprehension occurs due to three remarkable factors, which are students’ lack of motivation, low prior knowledge, and poor English vocabulary. Moreover, this issue also leads to three main adverse impacts, such as reducing students’ learning achievement, hindering students’ problem-solving skills, and inhibiting students’ future studies and careers. Therefore, in responding to the problem above, this study proposes two learning approaches, which are the Cooperative Integrated and Reading Composition (CIRC) Technique and Metacognitive Strategy.
A novel hybrid scheme using genetic algorithms and deep learning for the reconstruction of portuguese tile panels

This paper presents a novel scheme, based on a unique combination of genetic algorithms (GAs) and deep learning (DL), for the automatic reconstruction of Portuguese tile panels, a challenging real-world variant of the jigsaw puzzle problem (JPP) with important national heritage implications. Specifically, we introduce an enhanced GA-based puzzle solver, whose integration with a novel DL-based compatibility measure (DLCM) yields state-of-the-art performance, regarding the above application. Current compatibility measures consider typically (the chromatic information of) edge pixels (between adjacent tiles), and help achieve high accuracy for the synthetic JPP variant. However, such measures exhibit rather poor performance when applied to the Portuguese tile panels, which are susceptible to various real-world effects, e.g., monochromatic panels, non-squared tiles, edge degradation, etc. To overcome such difficulties, we have developed a novel DLCM to extract high-level texture/color statistics from the entire tile information.
A-HRNet: Attention Based High Resolution Network for Human pose estimation

Recently, human pose estimation has received much attention in the research community due to its broad range of application scenarios. Most architectures for human pose estimation use multiple resolution networks, such as Hourglass, CPN, HRNet, etc. High Resolution Network (HRNet) is the latest SOTA architecture improved from Hourglass. In this paper, we propose a novel attention block that leverages a special Channel-Attention branch. We use this attention block as the building block and adopt the architecture of HRNet to build our Attention Based HRNet (A-HRNet). Experiments show that our model can consistently outperform HRNet on different datasets. Moreover, our model achieves the state-of-the-art performance on the COCO keypoint detection val2017 dataset (77.7 AP) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Melmint: Trustless Stable Cryptocurrency

Decentralized cryptocurrencies have gathered increasing interest in the past few years, raising hopes of a new era of non-sovereign electronic money.Unfortunately, cryptocurrencies perform poorly as actual money due to their unacceptably volatile purchasing power."Stablecoins" aiming to reduce this volatility, on the other hand, tend to peg to an external currency like the US dollar, gravely weakening the decentralization that makes cryptocurrencies so attractive.Melmint is a mechanism for issuing a trustlessly stable cryptocurrency, the mel, designed for the prototype Themelio blockchain but easily portable to others.Mels are defined without any reference to external pegs such as the US dollar, eliminating the need for oracles and other trusted third parties.This solves a major open problem in the field.We use Elasticoin, an existing proposal to reduce cryptocurrency volatility, as a building block for a trustless monetary policy that gives the mel robustly stable purchasing power.We evaluate Melmint through both theoretical economic arguments and stochastic market simulation, an approach not seen in the existing literature.In all our tests, Melmint is shown to be exceptionally robust in both mundane and extreme economic conditions.
Uncertainty Aware Semi-Supervised Learning on Graph Data

Thanks to graph neural networks (GNNs), semi-supervised node classification has shown the state-of-the-art performance in graph data. However, GNNs have not considered different types of uncertainties associated with class probabilities to minimize risk of increasing misclassification under uncertainty in real life. In this work, we propose a multi-source uncertainty framework using a GNN that reflects various types of predictive uncertainties in both deep learning and belief/evidence theory domains for node classification predictions. By collecting evidence from the given labels of training nodes, the Graph-based Kernel Dirichlet distribution Estimation (GKDE) method is designed for accurately predicting node-level Dirichlet distributions and detecting out-of-distribution (OOD) nodes. We validated the outperformance of our proposed model compared to the state-of-the-art counterparts in terms of misclassification detection and OOD detection based on six real network datasets. We found that dissonance-based detection yielded the best results on misclassification detection while vacuity-based detection was the best for OOD detection. To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.
History-Based Specification and Verification of Java Collections in KeY

In this feasibility study we discuss reasoning about the correctness of Java interfaces using histories, with a particular application to Java’s Collection interface. We introduce a new specification method (in the KeY theorem prover) using histories, that record method invocations including their parameters and return value, on an interface. We outline the challenges of proving client code correct with respect to arbitrary implementations, and describe a practical specification and verification effort of part of the Collection interface using KeY (including source and video material).
Cut-Free Calculi and Relational Semantics for Temporal STIT Logics

We present cut-free labelled sequent calculi for a central formalism in logics of agency: STIT logics with temporal operators. These include sequent systems for $$\mathsf {Ldm}$$ , $$\mathsf {Tstit}$$ and $$\mathsf {Xstit}$$ . All calculi presented possess essential structural properties such as contraction- and cut-admissibility. The labelled calculi $$\mathsf {G3Ldm}$$ and $$\mathsf {G3Tstit}$$ are shown sound and complete relative to irreflexive temporal frames. Additionally, we extend current results by showing that also $$\mathsf {Xstit}$$ can be characterized through relational frames, omitting the use of BT+AC frames.
Human Gait Cycle Analysis Using Kinect V2 Sensor

Examining a human movement can provide a wealth of information about a patient’s medical condition. The examination process can be used to diagnose abnormal changes (lesions), ability development and monitor the rehabilitation process of people with reduced mobility. There are several approaches to monitor people, among other things with sensors and various imaging and processing devices. In this case a Kinect V2 sensor and a self-developed LabView based application was used, to examine the movement of the lower limbs. The ideal gait pattern was recorded in the RoboGait training machine and the measured data was used to identify the phases of the human gait. During the evaluation, the position of the skeleton model, the associated body joints and angles can be calculated. The pre-recorded ideal and natural gait cycle can be compared. With the self-developed method the pre-recorded ideal and natural gait cycle can be compared and processed for further evaluation. The evaluated measurement data confirm that a reliable and mobile solution for gait analysis has been created.
Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations

In this paper, we demonstrate that by utilizing sparse word representations, it becomes possible to surpass the results of more complex task-specific models on the task of finegrained all-words word sense disambiguation.Our proposed algorithm relies on an overcomplete set of semantic basis vectors that allows us to obtain sparse contextualized word representations.We introduce such an information theory-inspired synset representation based on the co-occurrence of word senses and nonzero coordinates for word forms which allows us to achieve an aggregated F-score of 78.8 over a combination of five standard word sense disambiguating benchmark datasets.We also demonstrate the general applicability of our proposed framework by evaluating it towards part-of-speech tagging on four different treebanks.Our results indicate a significant improvement over the application of the dense word representations.
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of tasks or environments in which a policy will be trained. However, creating a useful distribution of environments is error prone, and takes a significant amount of developer time and effort. We propose Unsupervised Environment Design (UED) as an alternative paradigm, where developers provide environments with unknown parameters, and these parameters are used to automatically produce a distribution over valid, solvable environments. Existing approaches to automatically generating environments suffer from common failure modes: domain randomization cannot generate structure or adapt the difficulty of the environment to the agent's learning progress, and minimax adversarial training leads to worst-case environments that are often unsolvable. To generate structured, solvable environments for our protagonist agent, we introduce a second, antagonist agent that is allied with the environment-generating adversary. The adversary is motivated to generate environments which maximize regret, defined as the difference between the protagonist and antagonist agent's return. We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED). Our experiments demonstrate that PAIRED produces a natural curriculum of increasingly complex environments, and PAIRED agents achieve higher zero-shot transfer performance when tested in highly novel environments.
HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser

This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing.The task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG.Our solution consists of two subsystems: transition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG) and iterative inference parser for Flavor (2) frameworks (DRG, AMR).In the final evaluation, our system is ranked 3 rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.sequence iterative inference.In ACL, pages 1290-
Video Summarization Based on Mutual Information and Entropy Sliding Window Method

This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement boundaries or inexplicable frames, we propose a three-step method where the first step involves browsing a video, the second step applies the MIESW method to select candidate key frames, and the third step removes most redundant key frames. In detail, the first step is to convert the video into a sequence of frames and adjust the size of the frames. In the second step, a key frame extraction algorithm named MIESW is executed. The inter-frame mutual information value is used as a metric to adaptively adjust the size of the sliding window to group similar content of the video. Then, based on the entropy value of the frame and the average mutual information value of the frame group, the threshold method is applied to optimize the grouping, and the key frames are extracted. In the third step, speeded up robust features (SURF) analysis is performed to eliminate redundant frames in these candidate key frames. The calculation of Precision, Recall, and Fmeasure are optimized from the perspective of practicality and feasibility. Experiments demonstrate that key frames extracted using our method provide high-quality video summaries and basically cover the main content of the gesture video.
On the weak link between importance and prunability of attention heads

Given the success of Transformer-based models, two directions of study have emerged: interpreting role of individual attention heads and down-sizing the models for efficiency.Our work straddles these two streams: We analyse the importance of basing pruning strategies on the interpreted role of the attention heads.We evaluate this on Transformer and BERT models on multiple NLP tasks.Firstly, we find that a large fraction of the attention heads can be randomly pruned with limited effect on accuracy.Secondly, for Transformers, we find no advantage in pruning attention heads identified to be important based on existing studies that relate importance to the location of a head.On the BERT model too we find no preference for top or bottom layers, though the latter are reported to have higher importance.However, strategies that avoid pruning middle layers and consecutive layers perform better.Finally, during fine-tuning the compensation for pruned attention heads is roughly equally distributed across the un-pruned heads.Our results thus suggest that interpretation of attention heads does not strongly inform pruning.
Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings

Since the amount of information on the internet is growing rapidly, it is not easy for a user to find relevant information for his/her query. To tackle this issue, much attention has been paid to Automatic Document Summarization. The key point in any successful document summarizer is a good document representation. The traditional approaches based on word overlapping mostly fail to produce that kind of representation. Word embedding, distributed representation of words, has shown an excellent performance that allows words to match on semantic level. Naively concatenating word embeddings makes the common word dominant which in turn diminish the representation quality. In this paper, we employ word embeddings to improve the weighting schemes for calculating the input matrix of Latent Semantic Analysis method. Two embedding-based weighting schemes are proposed and then combined to calculate the values of this matrix. The new weighting schemes are modified versions of the augment weight and the entropy frequency. The new schemes combine the strength of the traditional weighting schemes and word embedding. The proposed approach is experimentally evaluated on three well-known English datasets, DUC 2002, DUC 2004 and Multilingual 2015 Single-document Summarization for English. The proposed model performs comprehensively better compared to the state-of-the-art methods, by at least 1% ROUGE points, leading to a conclusion that it provides a better document representation and a better document summary as a result.
Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning

In the last years, reinforcement learning received a lot of attention. One method to solve reinforcement learning tasks is Neuroevolution, where neural networks are optimized by evolutionary algorithms. A disadvantage of Neuroevolution is that it can require numerous function evaluations, while not fully utilizing the available information from each fitness evaluation. This is especially problematic when fitness evaluations become expensive. To reduce the cost of fitness evaluations, surrogate models can be employed to partially replace the fitness function. The difficulty of surrogate modeling for Neuroevolution is the complex search space and how to compare different networks. To that end, recent studies showed that a kernel based approach, particular with phenotypic distance measures, works well. These kernels compare different networks via their behavior (phenotype) rather than their topology or encoding (genotype). In this work, we discuss the use of surrogate model-based Neuroevolution (SMB-NE) using a phenotypic distance for reinforcement learning. In detail, we investigate a) the potential of SMB-NE with respect to evaluation efficiency and b) how to select adequate input sets for the phenotypic distance measure in a reinforcement learning problem. The results indicate that we are able to considerably increase the evaluation efficiency using dynamic input sets.
Challenging practical features of Bitcoin by the main altcoins

Abstract We study the fundamental differences that separate: Litecoin; Bitcoin Gold; Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw some analysis to how these features are appreciated by the market, to ultimately make an inference as to how future successful cryptocurrencies may be invented and behave. We use Google Trend data, as well as price, volume and market capitalization data sourced from coinmarketcap.com to support this analysis. We find that Litecoin’s shorter block times offer benefits in commerce, but drawbacks in the mining process through orphaned blocks. Zcash holds a niche use for anonymous transactions, benefitting areas of the world lacking in economic freedom. Bitcoin Cash suffers from centralization in the mining process, while the greater decentralization of Bitcoin Gold has generally left it to stagnate. Ether’s greater functionality offers the greatest threat to Bitcoin’s dominance in the market. A coin that incorporates several of these features can be technically better than Bitcoin, but the first-to-market advantage of Bitcoin should keep its dominant position in the market.