Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a 3D physical model of diffusive polymer chains

Recent studies in polymer physics have created macro-scale analogs to solute microscopic polymer chains like DNA by inducing diffusive motion on a chain of beads. These bead chains have persistence lengths of O(10) links and undergo diffusive motion under random fluctuations like vibration. We present a bead chain model within a new stochastic forcing system: an air fluidizing bed of granular media. A chain of spherical 6 mm resin beads crimped onto silk thread are buffeted randomly by the multiphase flow of grains and low density rising air “bubbles”. We “thermalize” bead chains of various lengths at different fluidizing airflow rates, while X-ray imaging captures a projection of the chains’ dynamics within the media. With modern 3D printing techniques, we can better represent complex polymers by geometrically varying bead connections and their relative strength, e.g., mimicking the variable stiffness between adjacent nucleotide pairs of DNA. We also develop Discrete Element Method (DEM) simulations to study the 3D motion of the bead chain, where the bead chain is represented by simulated spherical particles connected by linear and angular spring-like bonds. In experiment, we find that the velocity distributions of the beads follow exponential distributions rather than the Gaussian distributions expected from polymers in solution. Through use of the DEM simulation, we find that this difference can likely be attributed to the distributions of the forces imparted onto the chain from the fluidized bed environment. We anticipate expanding this study in the future to explore a wide range of chain composition and confinement geometry, which will provide insights into the physics of large biopolymers.
Deep learning-based Turkish spelling error detection with a multi-class false positive reduction model

: Spell checking and correction is an important step in the text normalization process. These tasks are more challenging in agglutinative languages such as Turkish since many words can be derived from the root word by combining many suﬀixes. In this study, we propose a two-step deep learning-based model for misspelled word detection in the Turkish language. A false positive reduction model is integrated into the system to reduce the false positive predictions originating from the use of foreign words and abbreviations that are commonly used in Internet sharing platforms. For this purpose, we create a multi-class dataset by developing a mobile application for labeling. We compare the effect of using different types of tokenizers including character-based, syllable-based, and byte-pair encoding (BPE) approaches together with Long Short-Term Memory (LSTM) and Bi-directional LSTM (Bi-LSTM) networks. The findings show that the proposed Bi-LSTM-based model with the BPE tokenizer is superior to the benchmarking methods. The results also indicate that the false positive reduction step significantly increased the precision of the base detection model in exchange for a comparably less drop in its recall.
Quadratic programming based partitioning for Block Cimmino with correct value representation

: The block Cimmino method is successfully used for the parallel solution of large linear systems of equations due to its amenability to parallel processing. Since the convergence rate of block Cimmino depends on the orthogonality between the row blocks, advanced partitioning methods are used for faster convergence. In this work, we propose a new partitioning method that is superior to the state-of-the-art partitioning method, GRIP, in several ways. Firstly, our proposed method exploits the Mongoose partitioning library which can outperform the state-of-the-art methods by combining the advantages of classical combinatoric methods and continuous quadratic programming formulations. Secondly, the proposed method works on the numerical values in a floating-point format directly without converting them to integer format as in GRIP. This brings an additional advantage of obtaining higher quality partitionings via better representation of numerical values. Furthermore, the preprocessing time is also improved since there is no overhead in converting numerical values to integer format. Finally, we extend the Mongoose library, which originally partitions graphs into only two parts, by using the recursive bisection paradigm to partition graphs into more than two parts. Extensive experiments conducted on both shared and distributed memory architectures demonstrate the effectiveness of the proposed method for solving different types of real-world problems.
Enterprises Build Sustainable Innovation Loop: Take ByteDance Launching Lark as an Example

This paper uses the method of formal model construction to model the path of enterprise sustainable innovation and explains it by case analysis. Some suggestions are put forward then. In the changing market environment, enterprises can adapt to and strive for more living space only through continuous innovation. The construction of a sustainable innovation loop makes the previous research on sustainable innovation become relatively concrete from the abstract. Enterprises should construct and complete the loop of innovation path according to their own characteristics. In the process of launching Lark, ByteDance takes scientiﬁc and technological innovation as the basis, and business model innovation as the key link, and ﬁnally completes the cycle of innovation path, and the innovation is still ongoing, achieving the purpose of sustainable innovation, reﬂecting the enterprise innovation strategy and corporate culture.
Integration of neural network and distance relay to improve the fault localization on transmission lines

Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still not perfect with nonzero errors of the fault locations. This paper will present a new approach using the neural networks in addition to a distance relays to correct the fault location estimation of the relay. The solution will be based only on the voltage and current signals measured at the beginning of the lines. The training samples' signals of the transient states on the lines are generated using ATP/EMTP, and then regenerated into the relay tester Omicron CMC-356 to test with the real Siemens 7SA522 relay to improve its fault location results. The numerical results will show that the solution had helped to reduce the average fault location error from 0.92% to 0.42% for 4 types of shortage faults on the lines. [ FROM AUTHOR] Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
The Impact of Social Media Sentiment on Stock Market Based on User Classification

The relationship between social media sentiment and the stock market has been receiving much attention. Based on the perspective of social media user classification, using Weibo and Shanghai Composite Index data, quantile regression and instrumental variable quantile regression (IVQR) model is built to explore the impact of sentiment and sentiment fluctuation on authenticated and non- authenticated users on stock market returns respectively. The research results show that Weibo sentiment and sentiment fluctuation have a positive impact on stock market returns, but the effects of the two types of users are different. The sentiment of authenticated users has a stronger and longer impact on stock market returns, while only moderate sentiment and sentiment fluctuation of non-authenticated users have a positive impact on stock market returns. The research provides evidence for the relationship between social media sentiment and stock market, and has some practical significance for both social media platforms and users.
An analytical solution of fractional order PI controller design for stable/unstable/integrating processes with time delay

: This paper aims to put forward an analytical solution for tuning parameters of a fractional order PI (FOPI) controller for stable, unstable, and integrating processes with time delay. Following this purpose, the analytical weighted geometrical center (AWGC) method has been extended to the design of fractional order PI controllers. To apply AWGC, the stability equations of the closed-loop system are written in terms of process and fractional order PI controller parameters. With the proposed method, the centroid can be calculated analytically, and the controller parameters can be easily calculated without the need of repetitive drawings of the stability boundary regions. Additionally, analytical equations are derived to calculate fractional integral order, λ , using the integral of squared error (ISE) objective function. The proposed analytical equations are simple and time-saving which might attract controller engineers for applying them on the industrial level. To show the eﬀiciency of the suggested method compared to the given methods in the literature, several simulation examples are considered. Comparisons between the reported methods are figured out in terms of unit step responses for nominal and perturbed cases. Also, rise time, settling time, maximum sensitivity (Ms), integral of squared error (ISE), and total variation (TV) values are considered to compare the performance and robustness issues. Also, a real-time application of an inverted pendulum setup is deemed to prove the feasibility of the suggested method.
STATISTICAL CHARACTERISTICS ON TROPICAL CYCLONES LANDFALL IN CHINA

With the data from the Tropical Cyclone Yearbooks between 1970 and 2001, statistical analyseswere performed to study the climatic features of landfall TCs (noted as TCs hereafter) in China with particularattention focused on landfall frequency, locations, sustaining, decaying, transition, intensification and dissipationetc. The results indicate that the sustaining periods of TC over land are quite different for different landfall spots,and increased from Guangxi to Zhejiang. The most obvious decreasing of TC intensity occurs mainly within 12hours after landfall. The stronger a TC is,the more it decays. The areas over which TCs are dissipated can be inHeilongjiang, the northernmost, and Yunnan, the westernmost. Besides, Guangxi is an area with highdissipating rate and subject to TC dissipation as compared with the other coastal regions.
A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer

Strategy-optimization is a fundamental element of dynamic and complex team sports such as soccer, American football, and basketball. As the amount of data that is collected from matches in these sports has increased, so has the demand for data-driven decisionmaking support. If alternative strategies need to be balanced, a data-driven approach can uncover insights that are not available from qualitative analysis. This could tremendously aid teams in their match preparations. In this work, we propose a novel Markov modelbased framework for soccer that allows reasoning about the specific strategies teams use in order to gain insights into the efficiency of each strategy. The framework consists of two components: (1) a learning component, which entails modeling a team’s offensive behavior by learning a Markov decision process (MDP) from event data that is collected from the team’s matches, and (2) a reasoning component, which involves a novel application of probabilistic model checking to reason about the efficacy of the learned strategies of each team. In this paper, we provide an overview of this framework and illustrate it on several use cases using real-world event data from three leagues. Our results show that the framework can be used to reason about the shot decision-making of teams and to optimise the defensive strategies used when playing against a particular team. The general ideas presented in this framework can easily be extended to other sports.
Research and Progress of Digital Content Industry

Making digital content industry research and practice of progress that both at home and abroad as the main line,defined the meaning of the digital content industry,analyzed and summarized the basic characteristics of the development situation of digital content industry in various countries and regions,put forward to promote the strategy of China's digital content industry development.
Research and realization of recognition for shoe soles based on outline feature

This paper proposed a method of recognition for shoe soles based on outline feature,which found that the two outline points at the longest distance according to the feature that the length of shoe sole was longer than any other parts in a shoe sole,and then calculated the angle shaped by the two points and horizontal axes.It proofreaded the shoe soles in terms of angle calculated,extracted 9 features of shoe sole automatically,and measured the similarities of shoe soles by Euclidean distance.The experimental results show the method used to recognize shoe soles is effective.
A FIXED POINT THEOREM FOR GENERALIZED CYCLIC CONTRACTIVE MAPPINGS IN B-METRIC SPACES

The purpose of this paper is to introduce the notion of a generalized cyclic contractive mapping in $b$-metric~spaces by adding four terms $\cfrac{d(T^2x,x)+d(T^2x,Ty)}{2s}$, $d(T^2x,Tx)$, $d(T^2x,y)$, $d(T^2x,Ty)$ and state a fixed point theorem for this kind of mappings. Also, some corollaries are derived from this theorem. In addition, some examples are given to illustrate the obtained results.
CS 224N: MinBERT and Downstream Tasks

Generalizing Natural Language Processing (NLP) models to multiple tasks can provide advantages including robustness, improved real-world applicability, and greater data efficiency. This is because multi-task models are capable of utilizing diverse input data types during their training, and can develop a better understanding of patterns in language [1]. The BERT language embedding model achieves high accuracy in downstream language tasks, although separate fine-tuning is necessary for each individual task. [2]. In this paper, we conduct experiments to create a multi-task model that leverages BERT embeddings for various downstream tasks, demonstrating the benefits of multitask learning and achieving good performance. We focus on sentiment classification, paraphrase detection, and semantic textual similarity tasks through exploring different model architectures, loss functions, optimizers, and hyperparameters [Sec 3]. We present our results for the three downstream tasks [Tab 4].
Enhancing the organic solvent resistance of transaminase from Aspergillus terreus by regional random mutation

Biocatalysis in high-concentration organic solvents has been applied to produce various industrial products with many advantages. However, using enzymes in organic solvents often suffers from inactivation or decreased catalytic activity and stability. So, improving the tolerance of enzymes in organic solvents is essential. Herein, the method of regional random mutation combined with combinatorial mutation was used to improve the resistance of transaminase from Aspergillus terreus (AtATA) in organic solvents, and the best mutant T23I/T200K/P260S (M3) was acquired. In different concentrations of dimethyl sulfoxide (DMSO), the catalytic efficiency toward 1-acetylnaphthalene and the stability were higher than the wild-type (WT) of AtATA. M3 also showed enhanced stability against six organic solvents with different oil-water partition coefficients (log P values). The results of decreased Root Mean Square Fluctuation (RMSF) values via 20-ns molecular dynamics simulations under different concentration DMSO revealed that mutant M3 had lower flexibility, acquiring a more stable protein structure and contributing to its organic solvents stability than WT. Intraand intermolecular interaction analysis indicated that the increased hydrogen bonds and hydrophobic interactions within monomers or at the interface of two monomers also strengthened the stability of the overall structure against organic solvents. Furthermore, M3 was applied to convert 1-acetylnaphthalene for synthesizing (R)-(+)-1(1-naphthyl)-ethylamine ((R)-NEA), which was and an intermediate of Cinacalcet Hydrochloride. Moreover, 3 ̃10 mM 1-acetylnaphthalene can be converted to (R)-NEA with 94.2 ̃38.9% yield and a strict R-stereoselectivity within 10 h under 25% DMSO, which was higher than WT and expected to be a potential biocatalyst for industrial application. Enhancing the organic solvent resistance of transaminase fromAspergillus terreus by regional random mutation Chun-Ning Wang, Shuai Qiu*, Fang-Fang Fan, Chang-Jiang Lyu, Sheng Hu, Wei-Rui Zhao, Jia-Qi Mei, Le-He Mei*, Jun Huang* Key Laboratory of Chemical and Biological Processing Technology for Farm Products of Zhejiang Province, Zhejiang Provincial Collaborative Innovation Center of Agricultural Biological Resources Biochemical Manufacturing, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China School of Biological and Chemical Engineering, Ningbo Tech University, Ningbo, 315100, China Hangzhou Huadong Medicine Group Co. Ltd, Hangzhou 310011, China College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China Jinhua Advanced Research Institute, Jinhua, 321019, China Corresponding author: Prof. Jun Huang, Dr. Shuai Qiu, Prof. Le-He Mei Email: huangjun@zust.edu. cn(Jun Huang),qiushuai@zust.edu. cn (Shuai Qiu), meilh@zju.edu. cn (Le-He Mei)
Active Learning for Object Detection on Satellite Images

Active learning has been successfully used as a method for reducing labeling cost in a classiﬁcation setting with a large amount of unlabeled data. This is done by starting the learning process with only a handful of labeled examples and then repeatedly ﬁnd the most informative unlabeled examples, ask for their label and update the classiﬁer with the new labeled examples. Active learning has been applied to image classiﬁcation tasks, but has shown to be more challenging for object detection problems. We present an active learning system for object detection. Our system is built on top of a linear SVM classiﬁer and relies on crowdsourcing for collecting labels. We used a dataset of satellite images to test our system. Our results show a signiﬁcant improvement of the learning curve when using active learning to select training examples compared to random selection
BERT and MNRLLie: Extending minBERT with Deep Metric Learning and Gradient Surgery

In this project, we implemented key aspects of the original BERT model, such as multi-head self-attention and a Transformer layer, to create a functional BERT model from scratch. We utilized this model to perform sentiment analysis on two different datasets: the Stanford Sentiment Treebank dataset and a dataset of movie reviews. In the latter half of the project, we fine-tuned and extended the BERT model to create sentence embeddings that can perform well across a wide range of downstream tasks. Overall, our project provides a comprehensive understanding of the BERT model and its capabilities in NLP tasks. Our implementation and fine-tuning of the BERT model provide insights into the effectiveness of the model in various downstream tasks.
Charity Begins at Home: Investigating How Culture Shapes In-Group Bias in Altruistic Behavior

The paper builds a structural model of altruistic transfers based on identity matches with another individual and estimates the weights that people place on the characteristic matches. We also take a comparative look at the role of culture in these transfers. The culture here is trying to contrast a traditional society versus a cosmopolitan one. I run an experiment in India to examine these differences. I find that in both the urban and rural samples individuals tend to give more to girls and donate largely on the basis of the need of the receiver. I also find that the rural group tends to give more to individuals from their own religion.
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison

Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which the media shape opinion is via the strategic inclusion or omission of partisan events that may support one side or the other. We develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. Our experiments first validate the existence of partisan event selection, and then show that article alignment and cross-document comparison detect partisan events and article ideology better than competitive baselines. Our results reveal the high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship. Our codebase and dataset are available at https://github.com/launchnlp/ATC.
CycleGAN based Speech Enhancement Using Time Frequency Masking

The process of separating one or more desired speech signals from a collection of mixed speech signals or noise is known as audio source separation. The proposed work aims to restore clear audio by reducing noise in mixed audio. These technologies, which are now among the most researched in the field of audio signal processing, are essential to the accomplishment of successful noise removal. Initial source separation is accomplished through time frequency filtering and conditional adversarial networks are used to enhance the quality of the separated sound. A pitch tracking method is used to determine the pitch tracks that are present in the mixed speech and binary masks are constructed that correspond to each pitch track and its harmonics. Using conditional adversarial networks built on CycleGAN and working on the spectrogram images, the noise is removed and speech source is separated. Cycle GAN is used to train the masked audio signal spectrograms and the clean ground truth spectrograms. By using the inverse Short-Time Fourier Transform, the reconstructed spectrogram is transformed back into speech signals. The model's effectiveness is evaluated objectively and the performance of the proposed model is comparable to that of the baseline models.