Miracle-dz/UniFAR-FTUs huggingface dataset:
Make Bipedal Robots Learn How to Imitate

Bipedal robots do not perform well as humans since they do not learn to walk like we do. In this paper we propose a method to train a bipedal robot to perform some basic movements with the help of imitation learning (IL) in which an instructor will perform the movement and the robot will try to mimic the instructor movement. To the best of our knowledge, this is the first time we train the robot to perform movements with a single video of the instructor and as the training is done based on joint angles the robot will keep its joint angles always in physical limits which in return help in faster training. The joints of the robot are identified by OpenPose architecture and then joint angle data is extracted with the help of angle between three points resulting in a noisy solution. We smooth the data using Savitzky-Golay filter and preserve the Simulatore data anatomy. An ingeniously written Deep Q Network (DQN) is trained with experience replay to make the robot learn to perform the movements as similar as the instructor. The implementation of the paper is made publicly available.
SYNTHESIS AND CHARACTERIZATION OF NOVEL 1,3-OXAZEPINE DERIVATIVES FROM AMINOPYRAZINE

In this work new 1,3-oxazepine subsidiaries have been readied, by two stages: The initial step incorporate amino gathering of the 2aminopyrazine was dense with various fragrant aldehydes within the sight of supreme ethanol to give novel Schiff bases subordinates [A1 – A5] separately. The second step, the subsequent imines subordinates [A1-A5] were responded with maleic anhydride and phathalic anhydride in dry toluene to give new 1,3-oxazepine-4,7-dione ring subsidiaries [B1a-B5a] and [B1b-B5b] individually scheme[I]. Every one of these mixes were described by liquefying focuses and FT.IR spectroscopy, several of them were portrayed by 1H-NMR spectroscopy and C.H. N. S. examination.
Improving Vulnerability Prediction of JavaScript Functions Using Process Metrics

Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be a huge breakthrough. The first step towards this is to automatically recognize the vulnerable parts in our code. Researchers put a lot of effort into creating machine learning models that could determine if a given piece of code, or to be more precise, a selected function, contains any vulnerabilities or not. We aim at improving the existing models, building on previous results in predicting vulnerabilities at the level of functions in JavaScript code using the well-known static source code metrics. In this work, we propose to include several so-called process metrics (e.g., code churn, number of developers modifying a file, or the age of the changed source code) into the set of features, and examine how they affect the performance of the function-level JavaScript vulnerability prediction models. We can confirm that process metrics significantly improve the prediction power of such models. On average, we observed a 8.4% improvement in terms of F-measure (from 0.764 to 0.848), 3.5% improvement in terms of precision (from 0.953 to 0.988) and a 6.3% improvement in terms of recall (from 0.697 to 0.760).
Multi-scale super-resolution generation of low-resolution scanned pathological images

Background. Digital pathology has aroused widespread interest in modern pathology. The key of digitalization is to scan the whole slide image (WSI) at high magnification. The lager the magnification is, the richer details WSI will provide, but the scanning time is longer and the file size of obtained is larger. For example, the file size of each WSI of 40 times magnification (40X) exceeds 1 Gigabyte, which leads to huge storage capacity, very slow scanning and network exchange, seriously increasing time and storage costs for digital pathology.
Gravity waves and primordial black holes in scalar warm little inflation

In warm inflation, dissipation due to the interactions of the inflaton field to other light degrees of freedom leads naturally to the enhancement of the primordial spectrum during the last 10-20 efolds of inflation. We study this effect in a variant of the Warm Little Inflaton model, where the inflaton couples to light scalars, with a quartic chaotic potential. These large fluctuations on re-entry will form light, evaporating Primordial Black Holes, with masses lighter than 106 g. But at the same time they will act as a source for the tensors at second order. The enhancement is maximal near the end of inflation, which result in a spectral density of Gravitational Waves (GW) peaked at frequencies f ∼ O(105-106) Hz today, and with an amplitude ΩGW ∼ 10-9. Although the frequency range is outside the reach of present and planned GW detectors, it might be reached in future high-frequency gravitational waves detectors, designed to search for cosmological stochastic GW backgrounds above MHz frequencies.
Cohort Shapley value for algorithmic fairness

Cohort Shapley value is a model-free method of variable importance grounded in game theory that does not use any unobserved and potentially impossible feature combinations. We use it to evaluate algorithmic fairness, using the well known COMPAS recidivism data as our example. This approach allows one to identify for each individual in a data set the extent to which they were adversely or beneficially affected by their value of a protected attribute such as their race. The method can do this even if race was not one of the original predictors and even if it does not have access to a proprietary algorithm that has made the predictions. The grounding in game theory lets us define aggregate variable importance for a data set consistently with its per subject definitions. We can investigate variable importance for multiple quantities of interest in the fairness literature including false positive predictions.
Room to Grow: Understanding Personal Characteristics Behind Self Improvement Using Social Media

Many people aim for change, but not everyone succeeds. While there are a number of social psychology theories that propose motivation-related characteristics of those who persist with change, few computational studies have explored the motivational stage of personal change. In this paper, we investigate a new dataset consisting of the writings of people who manifest intention to change, some of whom persist while others do not. Using a variety of linguistic analysis techniques, we first examine the writing patterns that distinguish the two groups of people. Persistent people tend to reference more topics related to long-term self-improvement and use a more complicated writing style. Drawing on these consistent differences, we build a classifier that can reliably identify the people more likely to persist, based on their language. Our experiments provide new insights into the motivation-related behavior of people who persist with their intention to change.
Follow the Money: Analyzing @slpng_giants_pt's Strategy to Combat Misinformation

In 2020, the activist movement @sleeping_giants_pt (SGB) made a splash in Brazil. Similar to its international counterparts, the movement carried "campaigns" against media outlets spreading misinformation. In those, SGB targeted companies whose ads were shown in these outlets, publicly asking them to remove the ads. In this work, we present a careful characterization of SGB’s activism model, analyzing the three campaigns carried by the movement up to September 2020. We study how successful its complaints were and what factors are associated with their success, how attention towards the targeted media outlets progressed, and how online interactions with the companies were impacted after they were targeted. Leveraging an annotated corpus of SGB’s tweets as well as other data from Twitter and Google Search, we show that SGB’s “campaigns” were largely successful: over 83% of the requests (n=192) were acceptably answered, and, for those that received an answer, we find user pressure to be negatively correlated with the time companies take to respond (r=-0.67; p<0.001). Finally, we find that, although changes in the interactions with companies were transient, the impact in targeted media outlets endured: all three outlets experienced a significant decrease in engagement on Twitter and search volume on Google following the start of SGB’s campaigns. Overall, our work suggests that internet-based activism can leverage the transient attention it captures towards concrete goals to have a long-lasting impact.
BDANet: Multiscale Convolutional Neural Network With Cross-Directional Attention for Building Damage Assessment From Satellite Images

Fast and effective responses are required when a natural disaster (e.g., earthquake and hurricane) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of predisaster and postdisaster satellite images, building damage assessment aims at predicting the extent of damage to buildings. With the powerful ability of feature representation, deep neural networks have been successfully applied to building damage assessment. Most existing works simply concatenate predisaster and postdisaster images as input of a deep neural network without considering their correlations. In this article, we propose a novel two-stage convolutional neural network for building damage assessment, called BDANet. In the first stage, a U-Net is used to extract the locations of buildings. Then, the network weights from the first stage are shared in the second stage for building damage assessment. In the second stage, a two-branch multiscale U-Net is employed as the backbone, where predisaster and postdisaster images are fed into the network separately. A cross-directional attention module is proposed to explore the correlations between predisaster and postdisaster images. Moreover, CutMix data augmentation is exploited to tackle the challenge of difficult classes. The proposed method achieves state-of-the-art performance on a large-scale dataset—xBD. The code is available at https://github.com/ShaneShen/BDANet-Building-Damage-Assessment.
Quantum computation of nonlinear maps

Quantum algorithms for computing classical nonlinear maps are widely known for toy problems but might not suit potential applications to realistic physics simulations. Here, we propose how to compute a general differentiable invertible nonlinear map on a quantum computer using only linear unitary operations. The price of this universality is that the original map is represented adequately only on a finite number of iterations. More iterations produce spurious echos, which are unavoidable in any finite unitary emulation of generic non-conservative dynamics. Our work is intended as the first survey of these issues and possible ways to overcome them in the future. We propose how to monitor spurious echos via auxiliary measurements, and we illustrate our results with numerical simulations.
How Does Regression Test Selection Affect Program Repair? An Extensive Study on 2 Million Patches

APR techniques can be extremely time consuming since (1) a large number of patches can be generated for a given bug, and (2) each patch needs to be executed on the original tests to ensure its correctness. In the literature, various techniques (e.g., based on learning, mining, and constraint solving) have been proposed/studied to reduce the number of patches. However, there is limited study on the impact of test selection for each patch (e.g., only the tests affected by the patch need to be executed as the other tests would keep the same outcomes and can be skipped), and few APR systems actually apply test selection. Therefore, this paper conducts the first extensive study to investigate the impact of Regression Test Selection (RTS) on APR. More specifically, we implemented widely-used RTS techniques at different levels for 12 state-of-the-art APR systems with over 2M patches. Our study reveals various practical guidelines for future APR, including: (1) the number of patches widely used for measuring APR efficiency can incur skewed conclusions, and the use of inconsistent RTS configurations can further skew the conclusion; (2) all studied RTS techniques can substantially improve APR efficiency and should be considered in future APR work; (3) method- and statement-level RTS outperform class-level RTS substantially, and should be preferred; (4) RTS techniques can substantially outperform state-of-the-art test prioritization techniques for APR, and combining them can further improve APR efficiency; and (5) traditional regression test prioritization widely studied in regression testing performs even better than APR-specific test prioritization when combined with most RTS techniques. Furthermore, we also present the detailed impact of different patch categories and patch validation strategies on our findings.
Global well-posedness and decay estimate of smooth solutions for 3-D compressible Navier-Stokes-Allen-Cahn system

In this paper, we study a diffuse interface model for the compressible immiscible two-phase viscous flow in 3-D. This model is a nonlinear coupling of the compressible barotropic Navier-Stokes equations and the modified Allen-Cahn equation. For the initial problem in R, we prove that there exists a global unique smooth solution under the assumptions of small initial perturbations. Moreover, we establish the time decay rates for higher-order spatial derivatives of density, velocity and mass concentration difference of the mixture fluids. For the physical explanation of this result, which means that, if a small perturbation is given to the initial condition, the two-phase immiscible flow still maintains phase separation and the solution decays algebraically with time. MSC 2020: 35B40, 35B65, 35L65, 76N05, 76N10, 76T10.
Multi-Modal Visual Place Recognition in Dynamics-Invariant Perception Space

Visual place recognition is one of the essential and challenging problems in the fields of robotics. In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments. We achieve this by first designing a novel deep learning architecture to generate the static semantic segmentation and recover the static image directly from the corresponding dynamic image. We then innovatively leverage the spatial-pyramid-matching model to encode the static semantic segmentation into feature vectors. In parallel, the static image is encoded using the popular Bag-of-words model. On the basis of the above multi-modal features, we finally measure the similarity between the query image and target landmark by the joint similarity of their semantic and visual codes. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach for place recognition in dynamic environments.
A numerical study of long-term multiwavelength blazar variability

Decade-long monitoring of blazars at optical and infrared (OIR) wavelengths with the Small and Moderate Aperture Research Telescope System (SMARTS) in Chile and in γ-rays with the Fermi Large Area Telescope (LAT) has enabled the systematic study of their multi-wavelength long-term variability. In this work we investigate, from a theoretical perspective, the long-term variability properties of blazar emission by introducing an observationally motivated time-dependence to four main parameters of the one-zone leptonic model: injection luminosity of relativistic electrons, strength of magnetic field, Doppler factor, and external photon field luminosity. For the first time, we use both the probability density function and the power spectral density of the 10 year-long Fermi-LAT light curves to create variation patterns for the model parameters. Using as test beds two bright blazars from the SMARTS sample (PKS 2155-304 and 3C 273), we compute 10 year-long OIR, X-ray, and γ-ray model light curves for different varying parameters. We compare the findings of our theoretical investigation with multi-wavelength observations using various measures of variability. While no single-varying parameter simulation can explain all multiwavelength variability properties, changes in the electron luminosity and external radiation field in PKS 2155-304 and 3C 273, respectively, can account for most of them. Our results motivate future time-dependent studies with coupling between two or more physical parameters to describe the multi-wavelength long-term blazar variability.
Case Study: Context Interactions&Physics Faculty's Professional Development

This paper investigates the interactions between context and professional development of physics instructors in a case study of two physics faculty. A phenomenological-case study approach was used to analyze two physics faculty at diﬀerent institutions over a year and a half using three semi-structured interviews each. The data enabled the identiﬁcation of relevant context elements; and the impact of these elements on physics faculty was explored by adapting a framework that examines instructors’ professional development. The analysis shows that both case study subjects used their physics expertise and growing understanding of their context to develop their physics teaching. However, this growth was enacted diﬀerently given the nature of their context, highlighting instructors’ strengths in navigating their local context to improve their physics teaching. The results show the subtleties of how context has a salient, complex, and evolving role in moderating faculty’s professional development. By taking a faculty-centric approach, this paper broadens the community’s awareness of the ways physics instructors develop their physics teaching. This work contributes to a relatively new lens by which the physics community views, discusses, and supports the professional development of physics faculty.
STRIDE: Scene Text Recognition In-Device

Optical Character Recognition (OCR) systems have been widely used in various applications for extracting semantic information from images. To give the user more control over their privacy, an on-device solution is needed. The current state of the art models are too heavy and complex to be deployed on-device. We develop an efficient lightweight scene text recognition (STR) system, which has only 0.88M parameters and performs real-time text recognition. Attention modules tend to boost the accuracy of STR networks but are generally slow and not optimized for device inference. So, we propose the use of convolution attention modules to the text recognition networks, which aims to provide channel and spatial attention information to the LSTM module by adding very minimal computational cost. It boosts our word accuracy on ICDAR 13 dataset by almost 2%. We also introduce a novel orientation classifier module, to support the simultaneous recognition of both horizontal and vertical text. The proposed model surpasses on-device metrics of inference time and memory footprint and achieves comparable accuracy when compared to the leading commercial and other open-source OCR engines. We deploy the system on-device with an inference speed of 2.44 ms per word on the Exynos 990 chipset device and achieve an accuracy of 88.4% on ICDAR-13 dataset.
N-ary Huffman Encoding Using High-Degree Trees - A Performance Comparison

In this paper we implement an n-ary Huffman Encoding and Decoding application using different degrees of tree structures. Our goal is to compare the performance of the algorithm in terms of compression ratio, decompression speed and weighted path length when using higher degree trees, compared to the 2-ary Huffman Code. The Huffman tree degrees that we compare are 2-ary, 3-ary, 4-ary, 5-ary, 6-ary, 7-ary, 8-ary and 16-mal. We also present the impact that branch prediction has on the performance of the n-ary Huffman Decoding.
Calibrating sufficiently

When probabilistic classifiers are trained and calibrated, the so-called grouping loss component of the calibration loss can easily be overlooked. Grouping loss refers to the gap between observable information and information actually exploited in the calibration exercise. We investigate the relation between grouping loss and the concept of sufficiency, identifying comonotonicity as a useful criterion for sufficiency. We revisit the probing reduction approach proposed by John Langford and Bianca Zadrozny in 2005 and find that it produces an estimator of probabilistic classifiers that reduces grouping loss. Finally, we discuss Brier curves as tools to support training and ‘sufficient’ calibration of probabilistic classifiers.
Probabilistic robust linear quadratic regulators with Gaussian processes

Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. While learning-based control has the potential to yield superior performance in demanding applications, robustness to uncertainty remains an important challenge. Since Bayesian methods quantify uncertainty of the learning results, it is natural to incorporate these uncertainties into a robust design. In contrast to most state-of-the-art approaches that consider worst-case estimates, we leverage the learning method’s posterior distribution in the controller synthesis. The result is a more informed and, thus, more efﬁcient tradeoff between performance and robustness. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin. The formulation is based on a recently proposed algorithm for linear quadratic control synthesis, which we extend by giving probabilistic robustness guarantees in the form of credibility bounds for the system’s stability. Comparisons to existing methods based on worst-case and certainty-equivalence designs reveal superior performance and robustness properties of the proposed method.
Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks

Unpaired image-to-image translation using Generative Adversarial Networks (GAN) is successful in converting images among multiple domains. Moreover, recent studies have shown a way to diversify the outputs of the generator. However, since there are no restrictions on how the generator diversifies the results, it is likely to translate some unexpected features. In this paper, we propose Style-Restricted GAN (SRGAN) to demonstrate the importance of controlling the encoded features used in style diversifying process. More specifically, instead of KL divergence loss, we adopt three new losses to restrict the distribution of the encoded features: batch KL divergence loss, correlation loss, and histogram imitation loss. Further, the encoder is pre-trained with classification tasks before being used in translation process. The study reports quantitative as well as qualitative results with Precision, Recall, Density, and Coverage. The proposed three losses lead to the enhancement of the level of diversity compared to the conventional KL loss. In particular, SRGAN is found to be successful in translating with higher diversity and without changing the class-unrelated features in the CelebA face dataset. To conclude, the importance of the encoded features being well-regulated was proven with two experiments. Our implementation is available at https://github.com/shinshoji01/Style-Restricted_GAN.