Miracle-dz/UniFAR-FTUs huggingface dataset:
A general procedure for selecting a class of fully symmetric space‐time covariance functions

Sometimes, space‐time covariance models can be derived as solutions of partial differential equations, for phenomena which can be described by physical laws. When the complexity of natural processes does not allow such a description, it would be useful to have a guide for selecting an appropriate class of space‐time covariance models for a given data set, among the classes constructed in the last years. Thus, the main aim of this paper is to provide a general procedure for space‐time modeling, where the first step to be made is not the choice of a particular covariance model but the choice of the class of covariance functions suitable for the variable under study. Hence, starting from a space‐time data set, an adequate class of models will be properly chosen by considering the main characteristics, such as full symmetry, separability, behavior at the origin, anisotropy aspects, as well as type of non separability and asymptotic behavior. In the literature, these aspects have never been considered in a unified way; nevertheless, they can be relevant for selecting a suitable class of covariance models. The proposed procedure has been applied to both a simulated data set and an environmental case study. Copyright © 2016 John Wiley & Sons, Ltd.
Comparison of Alveolar Bone Thickness, Sagittal Root Positions, and Arch Forms in Class I, II, and III Malocclusions: A Cephalometric Study

Objective: This retrospective study aimed to evaluate the sagittal root position, arch shapes, and alveolar bone thickness in classes I, II, and III of skeletal patterns. Material and methods: Alveolar bone thickness, sagittal root placements, and arch morphologies in classes I, II, and III malocclusions were measured using 30 study models and 30 lateral cephalograms, both with a mean age of 16.5 years. Based on their sagittal relations, 30 participants were classified into three groups each (classes I, II, and III). The ANOVA test was used to calculate the results. Results: Between the three classes, there were statistically significant differences in the AP jaw relationship and root placements. Sagittal root location and dental arch shape showed no statistically significant correlation. Conclusion: There was no correlation between the dental arch form and sagittal root position, and classes II and III were found to have higher mandibular incisor inclination. On all levels, oval arch forms were prevalent.
Near ML Performance for Linear Block Codes Using an Iterative Vector SISO Decoder

In this paper, we present an iterative soft-decision decoding algorithm applicable to binary linear block codes. The algorithm uses a modified Vector SISO algorithm that utilizes soft input information to form candidate codewords. A threshold test is used to determine whether the codeword is likely to be the ML codeword. If the codeword is not accepted, the decoder biases the input vector ? away? from the candidate codeword and the decoding repeats this process up to a maximum number of decodings. Occasionally, this process does not find an acceptable codeword. When this happens the algorithm perturbs the input vector by modification of the sign of the input values and repeats the decoding process. The average complexity of the algorithm at reasonable Eb=N0 is low and the performance is near ML. A detailed description of the algorithm and simulations results are presented for low rate linear block codes. We present results for (80,40,16), (104,52,20) and (168,84,24) quadratic residue codes and (256,191,18) and (128,29,44) extended BCH codes.
A Siamese Network based Writer Independent Offline Signature Verification

Signatures are frequently used for both personal authorization and verification. The signatures on numerous papers, including legal agreements and bank checks, must be verified. Offline signature verification is a type of authentication that examines the physical action of signing while measuring the characteristics of an user's handwriting. In the present paper, an offline signature verification method was developed utilising Convolutional Siamese Network of Deep Learning [4]. Convolutional Siamese networks, which are dual networks that shares parameters, can be developed to acquire a feature map. Thus, Convolutional Siamese network is employed to verify the person's signature to a input signature that is saved in the database. Research has been conducted with a dataset of signatures that includes 750 signatures from 30 different individuals. An Accuracy of 91.6% was attained by the proposed solution.
Formation of foreign language communicative competence of future lawyers

This paper focuses on the development of foreign language communicative competence of students studying law. The research used different methods to study how students studying law can improve their ability to communicate in a foreign language. There was looked at existing research and did a survey to understand more about how students can improve their language skills. The results of the study reveal the main directions of scientific developments on issues related to the formation of foreign language communicative competence of students of law specialties. The priority of development, assessment of components, and the procedure of this process from a practical point of view have been clarified. The paper emphasizes the importance of foreign language communicative competence in the field of law and highlights the challenges that students face in acquiring these skills.
EFL Undergraduate Students’ Competence, Relatedness, and Autonomy in Online Learning: A Self-Determination Perspective

Due to the pandemic of Covid-19, numerous educational institutions have attempted to shift their pedagogical instruction from face-to-face to online teaching and learning. A plethora of studies have been published on how online learning should be provided. However, there is a paucity of research on the psychological needs that online learning provides for language learners. To bridge that gap, this descriptive study aims to investigate the university students' perspectives on psychological needs, particularly regarding their competence, relatedness, and autonomy concerning self-determination theory. The quantitative research design study attempts to investigate students' autonomy, competence, and relatedness in online tertiary learning. Sixty people were asked to fill out a survey-based questionnaire that included questions about autonomy (5 items), competence (5 items), and relatedness (8 items). The study's findings reveal that online grammar sessions meet undergraduate students' basic physiological learning needs despite the lack of physical human interaction. Among those three aspects, students' sense of relatedness had the highest mean. It meant the undergraduate students had no feeling of isolation while attending online learning. This present study suggests that teachers should consider students' interests to improve autonomy, competence, and relatedness.
Bounded Rationality in Economics: A Game Theorist's View

In this paper, we will argue that it is essential to incorporate bounded rationality into game theory. Game theory has been applied to economics such as industrial organization on the basis of the naive interpretation of game theory, which requires players to be ideally rational in an extremely unrealistic way. We will stress the importance of establishing the perceptive interpretation of game theory by taking boundedly rational players' inductive reasoning processes into account. We will explain my recent work, Matsushima(1997), which shows that the subjective games perceived by players in the long run are entirely different from the true objective game, and are trivial games in the sense that there exists a strictly dominant and subjectively Pareto-efficient strategy profile.
Examining the Relationship between Antisocial Behavior and the Academic Performance of Teenagers: The Role of Schools and Causes of the Antisocial Behavior

This research paper aimed to examine the academic performance of teenagers, the role of schools, and causes of the antisocial behavior. Our relationship between antisocial behavior and the sample consisted of 127 participants from different schools in the USA aged 13–15. A cross-sectional research design was used. We used primary data to conduct our research. The results suggested that there is a negative relation between antisocial behavior and the academic performance of teenagers. However, there was a significant difference between male and female students’ academic performance. Male children were involved more in antisocial behavior compared to female students. Therefore, we would like to recommend further research works that need to be done on this topic; teachers should closely monitor the behavior of their teenage students as these are the most vulnerable years in a kid’s life. Parents, teachers, and counselors must work together to implement modification strategies to help minimize teenagers’ behavioral problems. This will ultimately result in improving their behavior and also academic performance.
Research on Data Mining Classification Rule Based on Rough Set Theory

In order to improve the precision and interpretation ability of data mining, the authors study two important issues of attribute reduction and property value reduction in rough set theory. And based on the study, an algorithm of data mining is put forward, and this algorithm can find the conditional attributes related to decision attributes in data set. Through an example, it is illustrated how to discover classification rule in database, and the result of the example shows that the algorithm can greatly improve the resolution of knowledge.
Unified Net Willans Line Model for Estimating the Energy Consumption of Battery Electric Vehicles

Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power using a linear relationship. A base model and three normalized models are developed by fitting the UDDS and HWFET energy consumption test data published by the EPA for all BEVs in the U.S. market. Out of the models analyzed, normalizing by weight performs best with the lowest RMSE values at 0.384 kW, 0.747 kW, and 0.988 kW for predicting the UDDS, HWY, and US06 data points, respectively, and 0.653 kW using the combined data of all three data sets. Consideration of accessory loads at 0.5 kW improves the model normalized by weight and performance by a reduction of over 20% in RMSE for predictions with all data sets combined. Removing outliers in addition to the consideration of accessory loads improves the model normalized by weight and performance by a reduction of over 36% in RMSE for predictions with all data sets combined. Overall, results suggest that a unified net Willans line is largely achievable with accessible energy consumption data on U.S. regulatory cycles.
A Hybrid Interpolation Method for Geometric Nonlinear Spatial Beam Elements with Explicit Nodal Force

Based on geometrically exact beam theory, a hybrid interpolation is proposed for geometric nonlinear spatial Euler-Bernoulli beam elements. First, the Hermitian interpolation of the beam centerline was used for calculating nodal curvatures for two ends. Then, internal curvatures of the beam were interpolated with a second interpolation. At this point, C1 continuity was satisfied and nodal strain measures could be consistently derived from nodal displacement and rotation parameters. The explicit expression of nodal force without integration, as a function of global parameters, was founded by using the hybrid interpolation. Furthermore, the proposed beam element can be degenerated into linear beam element under the condition of small deformation. Objectivity of strain measures and patch tests are also discussed. Finally, four numerical examples are discussed to prove the validity and effectivity of the proposed beam element.
Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordance

The choice of a grasp plays a critical role in the success of downstream manipulation tasks. Consider a task of placing an object in a cluttered scene; the majority of possible grasps may not be suitable for the desired placement. In this paper, we study the synergy between the picking and placing of an object in a cluttered scene to develop an algorithm for task-aware grasp estimation. We present an object-centric action space that encodes the relationship between the geometry of the placement scene and the object to be placed in order to provide placement affordance maps directly from perspective views of the placement scene. This action space enables the computation of a one-to-one mapping between the placement and picking actions allowing the robot to generate a diverse set of pick-and-place proposals and to optimize for a grasp under other task constraints such as robot kinematics and collision avoidance. With experiments both in simulation and on a real robot we demonstrate that with our method, the robot is able to successfully complete the task of placement-aware grasping with over 89 % accuracy in such a way that generalizes to novel objects and scenes.
Bottomonium transport in 
p
-Pb and Pb-Pb collisions at energies available at the CERN Large Hadron Collider

We employ the Boltzmann transport model to study the sequential suppression pattern of $\Upsilon(1S,2S,3S)$ states in both small (p-Pb) and the large (Pb-Pb) collision systems at $\sqrt{s_{NN}}=5.02$ TeV. The cold nuclear matter effects happen before the formation of bottomonium, which is the same for different bottomonium states $\Upsilon(1S,2S,3S)$. The sequential suppression pattern of bottomonium states is treated as a signal of the hot medium effects, where bottomonium states suffer different magnitudes of the color screening and parton inelastic scatterings due to their different geometry sizes and binding energies. Bottomonium excited states are more easily dissociated with smaller binding energy via the final-state interactions. Including both cold and hot medium effects, transport model consistently explains the experimental data about bottomonium in both small and large collision systems.
GANHead: Towards Generative Animatable Neural Head Avatars

To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME [22] parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting.
Poissonian Pair Correlation for $\alpha n^{\theta }$ mod 1

We show that sequences of the form $\alpha n^{\theta } \pmod {1}$ with $\alpha> 0$ and $0 < \theta < \tfrac {43}{117} = \tfrac {1}{3} + 0.0341 \ldots $ have Poissonian pair correlation. This improves upon the previous result by Lutsko, Sourmelidis, and Technau, where this was established for $\alpha> 0$ and $0 < \theta < \tfrac {14}{41} = \tfrac {1}{3} + 0.0081 \ldots $. We reduce the problem of establishing Poissonian pair correlation to a counting problem using a form of amplification and the Bombieri–Iwaniec double large sieve. The counting problem is then resolved non-optimally by appealing to the bounds of Robert–Sargos and (Fouvry–Iwaniec–)Cao–Zhai. The exponent $\theta = \tfrac {2}{5}$ is the limit of our approach.
Objective Distinctions Between Genuine Plane Symmetries and Pseudosymmetries in Crystal Patterns of Graphic Artwork

A recently developed method for the objective identification of the plane symmetry group of a noisy crystal pattern is briefly described and subsequently applied to two pieces of graphic art. Pseudosymmetries do not distract from the beauty of graphic art but add to it. They are here distinguished from the genuine symmetries that combine to form the best-fitting plane symmetry group. The gray-value deviations of the individual pixel values of graphic artworks from their perfectly symmetric abstractions are considered to be chiefly due to the individual artists’ handiwork and employed creative procedures. As different graphic techniques/procedures were employed in the creation of the here classified crystal patterns, one may glean insights on how well a particular technique or procedure supports the realization of an intended crystallographic symmetry group in a graphic work of art.
On the dice loss gradient and the ways to mimic it

In the past few years, in the context of fully-supervised semantic segmentation, several losses -- such as cross-entropy and dice -- have emerged as de facto standards to supervise neural networks. The Dice loss is an interesting case, as it comes from the relaxation of the popular Dice coefficient; one of the main evaluation metric in medical imaging applications. In this paper, we first study theoretically the gradient of the dice loss, showing that concretely it is a weighted negative of the ground truth, with a very small dynamic range. This enables us, in the second part of this paper, to mimic the supervision of the dice loss, through a simple element-wise multiplication of the network output with a negative of the ground truth. This rather surprising result sheds light on the practical supervision performed by the dice loss during gradient descent. This can help the practitioner to understand and interpret results while guiding researchers when designing new losses.
Learning to Detect Touches on Cluttered Tables

We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables. We propose a real-time, on-device, learning-based touch detection algorithm that makes any tabletop interactive. The top-down configuration and learning-based algorithm makes our method robust to the presence of clutter, a main limitation of existing camera-projector tabletop systems. Our research prototype enables a set of experiences that combine hand interactions and objects present on the table. A video can be found at https://youtu.be/hElC_c25Fg8.
Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar's brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well-suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten-year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multi-output Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate times series with missing data and irregular sampling.
GraphBinMatch: Graph-based Similarity Learning for Cross-Language Binary and Source Code Matching

Matching binary to source code and vice versa has various applications in different fields, such as computer security, software engineering, and reverse engineering. Even though there exist methods that try to match source code with binary code to accelerate the reverse engineering process, most of them are designed to focus on one programming language. However, in real life, programs are developed using different programming languages depending on their requirements. Thus, cross-language binary-to-source code matching has recently gained more attention. Nonetheless, the existing approaches still struggle to have precise predictions due to the inherent difficulties when the problem of matching binary code and source code needs to be addressed across programming languages. In this paper, we address the problem of cross-language binary source code matching. We propose GraphBinMatch, an approach based on a graph neural network that learns the similarity between binary and source codes. We evaluate GraphBinMatch on several tasks, such as cross-language binary-to-source code matching and cross-language source-to-source matching. We also evaluate our approach performance on single-language binary-to-source code matching. Experimental results show that GraphBinMatch outperforms state-of-the-art significantly, with improvements as high as 15% over the F1 score.
Adjust factor with volatility model using MAXFLAT low-pass filter and construct portfolio in China A share market

In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this period. In this paper, we will show why we need adjust group of factors by our MAXFLAT low-pass volatility model. All of our experiments are under China's CSI 300 and CSI 500 universe which represent China's large cap stocks and mid-small cap stocks. Our result shows adjust factors by MAXFLAT volatility model have better performance in both large cap and small cap universe than original factors or other risk adjust factors in China A share. Also the portfolio constructed by MAXFLAT risk adjust factors have continuous excess return and lower beta compare with benchmark index.