Reviewerly/exHarmony huggingface dataset:
On the Sample Complexity of HGR Maximal Correlation Functions for Large Datasets

The Hirschfeld-Gebelein-Rényi (HGR) maximal correlation and the corresponding functions have been shown useful in many machine learning scenarios. In this paper, we study the sample complexity of estimating the HGR maximal correlation functions by the alternating conditional expectation (ACE) algorithm using training samples from large datasets. Specifically, we develop a mathematical framework to characterize the learning errors between the maximal correlation functions computed from the true distribution, and the functions estimated from the ACE algorithm. For both supervised and semi-supervised learning scenarios, we establish the analytical expressions for the error exponents of the learning errors. Furthermore, we demonstrate that for large datasets, the upper bounds for the sample complexity of learning the HGR maximal correlation functions by the ACE algorithm can be expressed using the established error exponents. Moreover, with our theoretical results, we investigate the sampling strategy for different types of samples in semi-supervised learning with a total sampling budget constraint, and an optimal sampling strategy is developed to maximize the error exponent of the learning error. Finally, the numerical simulations are presented to support our theoretical results.
Modality Analysis on the Official Website of Indonesian Tourism

Tourism is believed as the primary motivation to improve the economy of a country. In order to attract some tourists to visit tourism destinations in Indonesia, The Ministry of Tourism establishes an official website to share the information regarding the tourism destinations, activities, and attractions. This study is conducted to observe how the official website of Indonesian tourism utilizes modality to present Indonesian tourism discourse. This paper analysed ten destination highlights of the website covering the areas of Medan, Raja Ampat, Bali, Banyuwangi, Bintan Island, Jakarta, Yogyakarta, Bandung, Makassar, and Lombok through Discourse Analysis approach utilizing Halliday’s Systemic Functional Linguistics. This paper found modality is used in the forms of probability, usuality, inclination, and obligation. Those different types of modality are employed to describe three domains of tourism discourse: itineraries and access, place to play, and authenticity. It is concluded that the government uses the website to ensure the readers’ being well informed of Indonesian tourism destinations and safety during their visit to Indonesia.
Large-Scale Migration Patterns of Wastewater-Induced Earthquakes in the Central U.S.

&amp;lt;p&amp;gt;It is understood that the recent acceleration of seismic event occurrences in Kansas and Oklahoma, U.S., can be connected to the large-volume disposal of wastewater. These highly saline fluids are co-produced with oil and gas and are re-injected under gravity into the highly porous Arbuckle aquifer. Since 2015, injection rates have been decreasing. However, the seismic hazard in that region remains elevated. Furthermore, it has been noticed that some events in Kansas occur far from disposal wells.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To analyse spatio-temporal patterns between the fluid injection and earthquake locations, we applied a time-dependent 2D cross-correlation technique. This reveals a vectorial migration pattern of the seismic events. Whereas early events occur towards the east-sourtheast, later events are located preferably in northeastern direction of large volume injectors. With time, event locations migrate further in that direction. We explain this observation as well as measured Arbuckle pore pressures by a directional pore-fluid pressure diffusion and poroelastic stress propagation. This also follows from our principal two-dimension poroelastic finite element model which is of predictive power and identifies controlling parameters of the observations. These are mainly the permeability of the target injection formation and the seismogenic basement as well as the anisotropic permeability and the critical fault strength distribution. Our results lead to the conclusion that remote locations are destabilised also when injection rates are declining.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Thus, volume reductions may only provide a direct effect to lower earthquake rates locally. However, a state-wide decrease of the seismicity may require longer times such that the seismic hazard due to wastewater disposal induced seismicity may remain for decades.&amp;amp;#160;&amp;lt;/p&amp;gt;
Kesiapan Guru Sekolah Avicenna Jagakarsa dalam Melaksanakan E-Learning di Masa Pandemi Covid-19

&lt;em&gt;The purpose of this research is to find out e-learning readiness during the COVID-19 pandemic at Avicenna Jagakarsa High School and to find out what factors are still lacking and need to be improved. The E-Learning Readiness model used in this research is the Aydin &amp;amp; Tasci e-learning readiness (ELR) model with its rating scale grouped into four factors. These factors are human, self-development, technology, and innovation. E-learning readiness can not only be used in the preparation but also be used by organizations or institutions that have implemented e-learning. This research method used a questionnaire with a descriptive quantitative approach, namely by describing or elaborating the existing phenomena, both natural phenomena and human engineering based on the Aydin &amp;amp; Tasci e-learning readiness (ELR) model. The results showed that the Avicenna Jagakarsa teacher had an e-learning readiness index, an overall ELR score of ¯X = 4.31. This indicated that the Avicenna Jagakarsa School was ready to implement e-learning but still needed improvement.&lt;/em&gt;
Ultrafast optical control over spin and momentum in solids

The coupling of laser light to matter can exert sub-cycle coherent control over material properties, with optically induced currents and magnetism shown to be controllable on ultrafast femtosecond time scales. Here, by employing laser light consisting of both linear and circular pulses, we show that charge of specified spin and crystal momentum can be created with precision throughout the first Brillouin zone. Our hybrid pulses induce in a controlled way both adiabatic intraband motion as well as vertical interband excitation between valence and conduction bands, and require only a gapped spin split valley structure for their implementation. This scenario is commonly found in the 2d semi-conductors, and we demonstrate our approach with monolayer WSe$_2$. We thus establish a route from laser light to local control over excitations in reciprocal space, opening the way to the preparation of momenta specified excited states at ultrafast time scales.
Complete weight enumerators of a class of linear codes with four or five weights

In this paper, based on the theory of defining sets, a class of four-weight or five-weight linear codes over Fp is constructed. The complete weight enumerators of the linear codes are determined by means of Weil sums. In some case, there is an almost optimal code with respect to Griesmer bound, which is also an optimal one according to the online code table. This is an extension of the results raised by Zhang et al.(2020).
Explanation from Specification.

Explainable components in XAI algorithms often come from a familiar set of models, such as linear models or decision trees. We formulate an approach where the type of explanation produced is guided by a specification. Specifications are elicited from the user, possibly using interaction with the user and contributions from other areas. Areas where a specification could be obtained include forensic, medical, and scientific applications. Providing a menu of possible types of specifications in an area is an exploratory knowledge representation and reasoning task for the algorithm designer, aiming at understanding the possibilities and limitations of efficiently computable modes of explanations. Two examples are discussed: explanations for Bayesian networks using the theory of argumentation, and explanations for graph neural networks. The latter case illustrates the possibility of having a representation formalism available to the user for specifying the type of explanation requested, for example, a chemical query language for classifying molecules. The approach is motivated by a theory of explanation in the philosophy of science, and it is related to current questions in the philosophy of science on the role of machine learning.
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments

We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments. Our goal is to produce a high-quality rendering of the scene that preserves the color and mood from the ambient illumination of the noisy no-flash image, while recovering surface texture and detail revealed by the flash. Our network outputs a gain map and a field of kernels, the latter obtained by linearly mixing elements of a per-image low-rank kernel basis. We first apply the kernel field to the no-flash image, and then multiply the result with the gain map to create the final output. We show our network effectively learns to produce high-quality images by combining a smoothed out estimate of the scene's ambient appearance from the no-flash image, with high-frequency albedo details extracted from the flash input. Our experiments show significant improvements over alternative captures without a flash, and baseline denoisers that use flash no-flash pairs. In particular, our method produces images that are both noise-free and contain accurate ambient colors without the sharp shadows or strong specular highlights visible in the flash image.
The Design of a Information System of Student Attandance Recording with RESTful Web Service Architecture in SMK Batik 2 Surakarta

&lt;p&gt;The student attendance is one of the important aspects of the education process to assess the level of discipline of students and be an indicator of whether students need specific guidance related to the attendance. This research aims to design a web-based information system with RESTful web service architecture to help teachers activities for checking student attendance and recapitulation of data recording with more easily. This research uses the methods of research and development (R&amp;amp;D) and implemented at SMK 2 Batik Surakarta. System development method that used in the information system is Waterfall Model. The steps of Waterfall Model consist of analysis, design, code, and test. The end result of this research is the feasibility of the system based on the results of testing on the aspects of functionality yielded a value of 1 (good) while on the aspects of usability obtained percentage amounting to 81.47% (very high), so it can be inferred that This system is worth to use.&lt;/p&gt;
Variational preparation of finite-temperature states on a quantum computer

The preparation of thermal equilibrium states is important for the simulation of condensed-matter and cosmology systems using a quantum computer. We present a method to prepare such mixed states with unitary operators, and demonstrate this technique experimentally using a gate-based quantum processor. Our method targets the generation of thermofield double states using a hybrid quantum-classical variational approach motivated by quantum-approximate optimization algorithms, without prior calculation of optimal variational parameters by numerical simulation. The fidelity of generated states to the thermal-equilibrium state smoothly varies from 99 to 75% between infinite and near-zero simulated temperature, in quantitative agreement with numerical simulations of the noisy quantum processor with error parameters drawn from experiment.
Reactive Temporal Logic Planning for Multiple Robots in Unknown Occupancy Grid Maps

This paper proposes a new reactive temporal logic planning algorithm for multiple robots that operate in environments with unknown geometry modeled using occupancy grid maps. The robots are equipped with individual sensors that allow them to continuously learn a grid map of the unknown environment using existing Simultaneous Localization and Mapping (SLAM) methods. The goal of the robots is to accomplish complex collaborative tasks, captured by global Linear Temporal Logic (LTL) formulas. The majority of existing LTL planning approaches rely on discrete abstractions of the robot dynamics operating in known environments and, as a result, they cannot be applied to the more realistic scenarios where the environment is initially unknown. In this paper, we address this novel challenge by proposing the first reactive, abstraction-free, and distributed LTL planning algorithm that can be applied for complex mission planning of multiple robots operating in unknown environments. The proposed algorithm is reactive, i.e., planning is adapting to the updated environmental map and abstraction-free as it does not rely on designing abstractions of the robot dynamics. Also, our algorithm is distributed in the sense that the global LTL task is decomposed into single-agent reachability problems constructed online based on the continuously learned map. The proposed algorithm is complete under mild assumptions on the structure of the environment and the sensor models. We provide extensive numerical simulations and hardware experiments that illustrate the theoretical analysis and show that the proposed algorithm can address complex planning tasks for large-scale multi-robot systems in unknown environments.
SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression

Due to recent breakthroughs in state-of-the-art DNA sequencing technology, genomics data sets have become ubiquitous. The emergence of large-scale data sets provides great opportunities for better understanding of genomics, especially gene regulation. Although each cell in the human body contains the same set of DNA information, gene expression controls the functions of these cells by either turning genes on or off, known as gene expression levels. There are two important factors that control the expression level of each gene: (1) Gene regulation such as histone modifications can directly regulate gene expression. (2) Neighboring genes that are functionally related to or interact with each other that can also affect gene expression level. Previous efforts have tried to address the former using Attention-based model. However, addressing the second problem requires the incorporation of all potentially related gene information into the model. Though modern machine learning and deep learning models have been able to capture gene expression signals when applied to moderately sized data, they have struggled to recover the underlying signals of the data due to the nature of the data's higher dimensionality. To remedy this issue, we present SimpleChrome, a deep learning model that learns the latent histone modification representations of genes. The features learned from the model allow us to better understand the combinatorial effects of cross-gene interactions and direct gene regulation on the target gene expression. The results of this paper show outstanding improvements on the predictive capabilities of downstream models and greatly relaxes the need for a large data set to learn a robust, generalized neural network. These results have immediate downstream effects in epigenomics research and drug development.
Use and translation of abbreviations and acronyms in scientific texts

Most new concepts both in the Russian and English languages are expressed using phrases or compound words, because such complex words make it possible to represent a particular concept with completeness and accuracy. But multicomponent terms—complex words and phrases—are cumbersome; therefore, there is a need to abbreviate them in one way or another. In some cases this leads to the use of short versions of the term in the form of only one main component, while in others, various types of abbreviations are used, which can save time. However, their imprecise or incorrect translation can change or confuse the intended meaning. The paper discusses the differences in using abbreviations and acronyms in British and American scientific texts, as well as difficulties of their translation and optimal strategies of interlanguage adaptation. The investigation is performed using various research techniques, including a comparative method, a continuous sampling method, semantic structure analysis, and contextual analysis. It is shown that the existing modern classifications of abbreviations greatly differ in linguistic scientific literature and lexical units are abbreviated using various methods. It is found that there exist various traditions of their usage in scientific and technical texts. It is demonstrated that various standards for introducing, spelling, and punctuating abbreviations and acronyms in British and American scientific journals pose additional difficulties in the work of a translator in the field of science and technology, provokes translation errors and requires the use of normalization and explication as the main strategies for their translation. The paper may be of interest for those who translate scientific texts for British and American readership.
Automatic 3D Object Detection from RGB-D Data Using PU-GAN

3D object detection from RGB-D data in outdoor scenes is crucial in various industrial applications such as autonomous driving, robotics, etc. However, the points obtained from range sensor scans are usually sparse and non-uniform, which seriously limit the detection performance, especially for far-away objects. By learning a rich variety of point distributions from the latent space, we believe that 3D upsampling techniques may fill up the missing knowledge due to the sparsity of the 3D points. Hence, a 3D object detection method using 3D upsampling techniques has been presented in this paper. The main contributions of the paper are two-fold. First, based on the Frustum PointNets pipeline, a 3D object detection method using PU-GAN has been implemented. A state-of-the-art 3D upsampling method, PU-GAN, is used to complement the sparsity of point cloud. Second, some effective strategies have been proposed to improve the detection performance using upsampled dense points. Extensive experimental results on KITTI benchmark show that the impacts of PU-GAN upsampled points on object detection are closely related to the object distances from the camera. They show their superiority when they are applied on objects located at around 30 meters away. By carefully designing the criteria to employ the upsampled points, the developed method can outperform the baseline Frustum PointNets by a large margin for all the categories, including car, pedestrian and cyclist objects.
MATHEMATICAL MODELS OF GEOFILTRRATION AND GEOMIGRATION IN POROUS MEDIA WITH FRACTAL STRUCTURE

In this study 2-D linear models are coming from generalised, Boussinesq eqution describing geofiltration in soils with fractal structures are presented. In this study are presented too mathematical models geomigration of contaminations with groundwater in classical way and in soils with fractal structures.
The computer-aided system of e-publication contests

The authors discuss Internet-based contests aimed to reveal high-grade (approved by experts) scientific publications and graduates’ qualifying papers. Such contests are to facilitate science and education. They are held on the federal level or regionally, within an individual discipline or university. Contest performance and objectivity depend significantly on expert board to assess the entries. The modern Inter-net technologies enable to submit digital versions of printed publication and to involve experts of geographically remote regions. This approach is introduced by Direct-Media Publishers that has been the organizer and co-founder of a number of contests of students’, post-graduates’ and scientists’ papers. In 2019, a specialized computer-aided system was designed based on the expert acquisition system operating efficiently at the RAS Library for Natural Sciences. In the expert acquisition system, publications’ information value is assessed by their abstracts with a simple point system. In the contest system, experts shall be submitted to full texts to be assessed in the more complicated way. The system has to be tuned individually to each contest. The system is described in full detail. It supports acquiring and maintaining databases of experts and entries, expertise functionality (study of entries and assessing process), results analysis with marks awarded. The experts database comprises brief information on experts while the entries database comprises publication metadata which enables to identify and classify them within the classification adopted for a contest. The system was tested within 2019 “Science Foundation” and «BeFirst» contests.
Practical use of Windows data collector process and testing analysis

The paper demonstrates the Windows data collectordevelopment process with the built back-end from the require-ments gathering stage till the implementation and testing phase.Each phase throughout the development life cycle of the systemis defined in details. The whole system idea and the objectivesbehind developing this kind of framework is described in earlierpapers that creates the importance of introducing the backgroundbefore reading this paper. The detailed information about thedata collector features that are provided and their appropriatetesting types, documentation are demonstrated thoroughly. Be-sides, the process of development includes both design and overallsystem architecture description.
Web-based Secure Degree Certificate Legalization System Using Advanced Encryption Standard Algorithm

Degree certificate legalization system with encryption security feature using Advanced Encryption Standard (AES) in Semarang University has a purpose to support academic to do online document legalization through a system. The main problem which occurs in academic administration is a long document legalization process that causes an ineffective and inefficient legalization process.To solve the problem, a system that can encrypt a document for better security is required. This system is built with the Advanced Encryption Standard algorithm with a 128-bit sized key to encrypt confidential information inside the document. During the encryption process, this algorithm operates using 4x4 bit array blocks and passing many encryption processes for at least 10 (ten) times. The application is analyzed with object-oriented analysis and modeled with Unified Modeling Language.  The result of this research is a system which can secure document with AES algorithm with a 256-bit sized key. The security element in this algorithm will make easier to identify the owner of the document. The secured document is easily accessible through PHP-based web or available QR code. When decrypting the document, the application will activate the camera function and decrypt the information document.