Reviewerly/exHarmony huggingface dataset:
Large scale PIV-measurement to evaluate the flow isolation device (FID) efficiency in the stocker room of wafer masks

Silicon chip demand increased in recent years while the linewidth of the integrated circuits (ICs) has decreased. Improvement of the wafer process yield has become a key factor in chip manufacturing. The yield of the wafer process is closely related to the quality of the mask in the lithography process. The environmental conditions of the mask stocker room need to be carefully monitored and controlled to maintain the mask quality. The moist air invasion from the stocker room when the mask box exchange door is open can significantly increase the internal ambient humidity. This humidity leads to mask defects. Therefore, the moist air invasion from the stocker room should be minimized. In this study, an innovative flow isolation device (FID) is designed and manufactured. This device is used to block the flow of air through the mask exchange door. Smoke flow visualization and particle image velocimetry (PIV) were performed to evaluate the FID performance. The experimental results show that when the pressure of the stocker room is 0.3 Pa and the face velocity of FID is 2.0 m/s, the isolation efficiency is around 83.03%. The newly designed FID can significantly reduce the volume of CDA used during manufacturing. This can significantly reduce waste and energy cost.
Adaptive Mapping Relationship between Quantum Networks and Classical Networks

We study the adaptive mapping relationship between quantum and classical networks in terms of transmission, switching and networking. Two metrics are introduced to visualize the correlations, which are interaction loss rate and synergistic degree.
RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection

We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degeneration. We introduce a novel modulated rotation loss to alleviate the problem and propose a rotation sensitivity detection network (RSDet) which is consists of an eight-param single-stage rotated object detector and the modulated rotation loss. Our proposed RSDet has several advantages: 1) it reformulates the rotated object detection problem as predicting the corners of objects while most previous methods employ a five-para-based regression method with different measurement units. 2) modulated rotation loss achieves consistent improvement on both five-param and eight-param rotated object detection methods by solving the discontinuity of loss. To further improve the accuracy of our method on objects smaller than 10 pixels, we introduce a novel RSDet++ which is consists of a point-based anchor-free rotated object detector and a modulated rotation loss. Extensive experiments demonstrate the effectiveness of both RSDet and RSDet++, which achieve competitive results on rotated object detection in the challenging benchmarks DOTA1.0, DOTA1.5, and DOTA2.0. We hope the proposed method can provide a new perspective for designing algorithms to solve rotated object detection and pay more attention to tiny objects. The codes and models are available at: https://github.com/yangxue0827/RotationDetection.
Image Transliteration from Vernacular Languages

India is a multi-cultured and multi-language country, which has 23 official languages, and Hindi is known as the mother language as most of the people speak Hindi here. This paper implements a model which can extract text from images and then transliterates the text written from Indic language (Hindi) to English language. It presents distinctive issues of various text styles and patterns, comparability in structure. Therefore, it is essential to discover viable techniques for text extraction. It can be useful for a tourist to easily read the signboards or names of shops in North India as the focus of this work is to work on Hindi language. A great deal of enhancements has been made in the field deep learning and natural learning processing. This paper implements text extraction and transliteration in an efficient manner by using approaches which provide accurate results. Google Vision API and natural language processing have been used for text extraction and performing transliteration, respectively, in this paper.
Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to extract the aspect-related information. In prior work, attention mechanisms and dependency graph networks are commonly adopted to capture the relations between the context and given aspect. And the weighted sum of context hidden states is used as the final representation fed to the classifier. However, the information related to the given aspect may be already discarded and adverse information may be retained in the context modeling processes of existing models. This problem cannot be solved by subsequent modules and there are two reasons: first, their operations are conducted on the encoder-generated context hidden states, whose value cannot change after the encoder; second, existing encoders only consider the context while not the given aspect. To address this problem, we argue the given aspect should be considered as a new clue out of context in the context modeling process. As for solutions, we design several aspect-aware context encoders based on different backbones: an aspect-aware LSTM and three aspect-aware BERTs. They are dedicated to generate aspect-aware hidden states which are tailored for ABSA task. In these aspect-aware context encoders, the semantics of the given aspect is used to regulate the information flow. Consequently, the aspect-related information can be retained and aspect-irrelevant information can be excluded in the generated hidden states. We conduct extensive experiments on several benchmark datasets with empirical analysis, demonstrating the efficacies and advantages of our proposed aspect-aware context encoders.
Soft skills of bachelors in the institutes of culture within the context of competence-based approach: The conceptual and theoretical analysis

Today, the educational paradigm in the library and information sphere engages the concepts of “competency” and “competence”. The authors analyze the so-called soft skills that are seen as universal ones. The accent is made on the contents of the soft skills of the bachelors studying within the specialization 51. 03. 06 “Library and information activities”. According to the Higher Education Federal State Education Standards, the bachelors in the sphere of library and information activities must master the soft skills like the ability to apply system approach for efficient problem solving, to integrate with the society, to acknowledge intercultural diversity, and to follow self-development path. The authors argue that the educational processes do not meet the standard requirements yet as the curricula structure have been changing and auditorium teaching load has been decreasing. The soft skills are increasingly decisive for employers, as the employers are interested in efficient professionals and employeers able to build social communications.
Identifying patients at risk of unplanned re-hospitalisation using statewide electronic health records

Preventing unplanned hospitalisations, including readmissions and re-presentations to the emergency department, is an important strategy for addressing the growing demand for hospital care. Significant successes have been reported from interventions put in place by hospitals to reduce their incidence. However, there is limited use of data-driven algorithms in hospital services to identify patients for enrolment into these intervention programs. Here we present the results of a study aiming to develop algorithms deployable at scale as part of a state government's initiative to address rehospitalizations and which fills several gaps identified in the state-of-the-art literature. To the best of our knowledge, our study involves the largest-ever sample size for developing risk models. Logistic regression, random forests and gradient boosted techniques were explored as model candidates and validated retrospectively on five years of data from 27 hospitals in Queensland, Australia. The models used a range of predictor variables sourced from state-wide Emergency Department(ED), inpatient, hospital-dispensed medications and hospital-requested pathology databases. The investigation leads to several findings: (i) the advantage of looking at a longer patient data history, (ii) ED and inpatient datasets alone can provide useful information for predicting hospitalisation risk and the addition of medications and pathology test results leads to trivial performance improvements, (iii) predicting readmissions to the hospital was slightly easier than predicting re-presentations to ED after an inpatient stay, which was slightly easier again than predicting re-presentations to ED after an EDstay, (iv) a gradient boosted approach (XGBoost) was systematically the most powerful modelling approach across various tests.
Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?

Yes. Interval statistics have been used to conclude that major earthquakes are random events in time and cannot be anticipated or predicted. Machine learning is a powerful new technique that enhances our ability to understand the information content of earthquake catalogs. We show that catalogs contain significant information on current hazard and future predictability for large earthquakes.
Locally Differentially Private Frequent Pattern Mining for High-Dimensional Data in Mobile Smart Services

Collecting users’ historical data such as movie watching and music listening, and mining frequent items from them, can improve the utility of smart services, but there is also a risk of compromising user privacy. Local differential privacy is a strict definition of privacy and has been widely used in various privacy-preserving data collection scenarios. However, the accuracy of existing locally differentially private frequent items mining methods decreases significantly with the increase in the dimensions of data to be collected. In this paper, we propose a new locally differentially private frequent item mining method for high-dimensional data, which decreases the dimension used for data perturbation by grouping the contents and improving the interference matrix generation method, so as to improve the data reconstruction accuracy. The experimental results show that our proposed method can significantly improve the accuracy of frequent item mining and provide a better trade-off between privacy and accuracy compared with existing methods.
DeltaBound Attack: Efficient decision-based attack in low queries regime

Deep neural networks and other machine learning systems, despite being extremely powerful and able to make predictions with high accuracy, are vulnerable to adversarial attacks. We proposed the DeltaBound attack: a novel, powerful attack in the hard-label setting with $\ell_2$ norm bounded perturbations. In this scenario, the attacker has only access to the top-1 predicted label of the model and can be therefore applied to real-world settings such as remote API. This is a complex problem since the attacker has very little information about the model. Consequently, most of the other techniques present in the literature require a massive amount of queries for attacking a single example. Oppositely, this work mainly focuses on the evaluation of attack's power in the low queries regime $\leq 1000$ queries) with $\ell_2$ norm in the hard-label settings. We find that the DeltaBound attack performs as well and sometimes better than current state-of-the-art attacks while remaining competitive across different kinds of models. Moreover, we evaluate our method against not only deep neural networks, but also non-deep learning models, such as Gradient Boosting Decision Trees and Multinomial Naive Bayes.
Uncertainty-Driven Active Vision for Implicit Scene Reconstruction

Multi-view implicit scene reconstruction methods have become increasingly popular due to their ability to represent complex scene details. Recent efforts have been devoted to improving the representation of input information and to reducing the number of views required to obtain high quality reconstructions. Yet, perhaps surprisingly, the study of which views to select to maximally improve scene understanding remains largely unexplored. We propose an uncertainty-driven active vision approach for implicit scene reconstruction, which leverages occupancy uncertainty accumulated across the scene using volume rendering to select the next view to acquire. To this end, we develop an occupancy-based reconstruction method which accurately represents scenes using either 2D or 3D supervision. We evaluate our proposed approach on the ABC dataset and the in the wild CO3D dataset, and show that: (1) we are able to obtain high quality state-of-the-art occupancy reconstructions; (2) our perspective conditioned uncertainty definition is effective to drive improvements in next best view selection and outperforms strong baseline approaches; and (3) we can further improve shape understanding by performing a gradient-based search on the view selection candidates. Overall, our results highlight the importance of view selection for implicit scene reconstruction, making it a promising avenue to explore further.
CAT(0) geometry, robots, and society

How do we move a robot efficiently from one position to another? To answer this question, we need to understand its configuration space, a 'map' where we can find every possible position of the robot. Unfortunately, these maps are very large, they live in high dimensions, and they are very difficult to visualize. Fortunately, for some discrete robots they are CAT(0) cubical complexes, a family of spaces with favorable properties. In this case, using ideas from combinatorics and geometric group theory, we can construct a 'remote control' to navigate these complicated maps, and move the robots optimally. Along the way, we face larger ethical questions that we cannot ignore.
DHCR_SmartNet: A smart Devanagari Handwritten Character Recognition using Level-wised CNN Architecture

Handwritten Script Recognition is a vital application of Machine Learning domain. Applications like automatic number plate detection, pin code detection and managing historical documents increasing more attention towards handwritten script recognition. English is the most widely spoken language, hence there has been a lot of research into identifying a script using a machine. Devanagari is popular script used by a huge number of people in the Indian Subcontinent. In this paper, level-wised efficient transfer learning approach presented on VGG16 model of Convolutional Neural Network (CNN) for identification of Devanagari isolated handwritten characters. In this work a new dataset of Devanagari characters is presented and made accessible publicly. Newly created dataset comprises 5800 samples for 12 vowels, 36 consonants and 10 digits. Initially simple CNN is implemented and trained on this new small dataset. In next stage transfer learning approach is implemented on VGG16 model and in last stage fine-tuned efficient VGG16 model is implemented. The training and testing accuracy of fine-tuned model are obtained as 98.16% and 96.47% respectively.
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Łojasiewicz Inequality

We study the complexity of finding the global solution to stochastic nonconvex optimization when the objective function satisfies global Kurdyka-Lojasiewicz (KL) inequality and the queries from stochastic gradient oracles satisfy mild expected smoothness assumption. We first introduce a general framework to analyze Stochastic Gradient Descent (SGD) and its associated nonlinear dynamics under the setting. As a byproduct of our analysis, we obtain a sample complexity of $\mathcal{O}(\epsilon^{-(4-\alpha)/\alpha})$ for SGD when the objective satisfies the so called $\alpha$-PL condition, where $\alpha$ is the degree of gradient domination. Furthermore, we show that a modified SGD with variance reduction and restarting (PAGER) achieves an improved sample complexity of $\mathcal{O}(\epsilon^{-2/\alpha})$ when the objective satisfies the average smoothness assumption. This leads to the first optimal algorithm for the important case of $\alpha=1$ which appears in applications such as policy optimization in reinforcement learning.
Aplicación móvil para el análisis de grafos bajo un enfoque de Ingeniería de Software

Technological advances contribute to educational improvement, supporting teachers and students in the teaching-learning process inside and outside the classroom to overcome certain difficulties of accessibility, place, and time to carry out learning activities. Particularly, in discrete mathematics, there is a need for students to validate the results of their class exercises without the direct intervention of the teacher. Reason why this article aims to describe the process of creating a mobile application implemented in Android that allows solving graph theory exercises through 7 optimization algorithms, its development was based on the Object-oriented Model under an incremental life cycle with the support of the Unified Language Modeling for the construction of the artifacts that describe its functionality. The artifacts presented in this article are the result of the refinement obtained through the increments made. The resulting application represents an improvement to the applications identified in the related works and there are wide expectations of its implementation and use for educational purposes.
A Multi-user Oriented Live Free-viewpoint Video Streaming System Based On View Interpolation

As an important application form of immersive multimedia services, free-viewpoint video(FVV) enables users with great immersive experience by strong interaction. However, the computational complexity of virtual view synthesis algorithms poses a significant challenge to the real-time performance of an FVV system. Furthermore, the individuality of user interaction makes it difficult to serve multiple users simultaneously for a system with conventional architecture. In this paper, we novelly introduce a CNN-based view interpolation algorithm to synthesis dense virtual views in real time. Based on this, we also build an end-to-end live free-viewpoint system with a multi-user oriented streaming strategy. Our system can utilize a single edge server to serve multiple users at the same time without having to bring a large view synthesis load on the client side. We analyze the whole system and show that our approaches give the user a pleasant immersive experience, in terms of both visual quality and latency.
Combinatorics of minimal absent words for a sliding window

A string $w$ is called a minimal absent word (MAW) for another string $T$ if $w$ does not occur in $T$ but the proper substrings of $w$ occur in $T$. For example, let $\Sigma = \{\mathtt{a, b, c}\}$ be the alphabet. Then, the set of MAWs for string $w = \mathtt{abaab}$ is $\{\mathtt{aaa, aaba, bab, bb, c}\}$. In this paper, we study combinatorial properties of MAWs in the sliding window model, namely, how the set of MAWs changes when a sliding window of fixed length $d$ is shifted over the input string $T$ of length $n$, where $1 \leq d < n$. We present \emph{tight} upper and lower bounds on the maximum number of changes in the set of MAWs for a sliding window over $T$, both in the cases of general alphabets and binary alphabets. Our bounds improve on the previously known best bounds [Crochemore et al., 2020].
Frequency-robust Mølmer-Sørensen gates via balanced contributions of multiple motional modes

In this work, we design and implement frequency-robust Molmer-Sorensen gates on a linear chain of trapped ions, using Gaussian amplitude modulation and a constant laser frequency. We select this frequency to balance the entanglement accumulation of all motional modes during the gate to produce a strong robustness to frequency error, even for long ion chains. We demonstrate this technique on a three-ion chain, achieving $<\,1\%$ reduction from peak fidelity over a $20\,$kHz range of frequency offset, and we analyze the performance of this gate design through numerical simulations on chains of two to 33 ions.
Research on data display based on mobile handheld instrument interface

The existing mobile handheld instrument focuses more on detection technology research, ignoring the interface interaction principles. In this context, improving the interface reading efficiency of such handheld instruments is essential to improve the user experience. Based on several common charts, this paper conducts interactive analysis through eye movement experiments to find a visual chart that is easy to read. Take the element analyzer as the research object to conduct case studies. Firstly, four types of commonly used chart structures are screened by investigating the presentation methods of existing mobile device charts. Then, through eye movement experiments, extract the eye movement indicators of 4 types of charts to verify the legibility. Finally, combined with the instrument operation background, the most suitable chart scheme is selected and refined. This paper analyzes the legibility of the existing mobile handheld instrument's interface through eye movement experiments, and finds a visual chart that is easy to read in usage scenarios, which can improve the data reading efficiency of operators in the work process and the operability of the equipment.
A Unique Representation of Cyclic Codes over GR(pn,r)

Let R be a Galois ring, GR(pn,r), of characteristic pn and of order pnr. In this article, we study cyclic codes of arbitrary length, N, over R. We use discrete Fourier transform (DFT) to determine a unique representation of cyclic codes of length, N, in terms of that of length, ps, where s=vp(N) and vp are the p-adic valuation. As a result, Hamming distance and dual codes are obtained. In addition, we compute the exact number of distinct cyclic codes over R when n=2.