Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified virtual build methodology for computer‐aided ergonomics and safety

In the past, computer‐based ergonomic analysis has been time‐consuming and has required many assumptions when the virtual human motion paths have been programmed. Hence, it appears that many in the human factors and ergonomics profession have not developed much expertise in the Digital Human Modeling area. The Virtual Build method first demonstrated by the Ford Motor Company appears to improve the accuracy and speed of the ergonomic analyses used in design of assembly processes. It allows capture of real‐motion and virtual process interaction rather than assuming expected posture and task motion. A variation of the Virtual Build methodology, presented in this article, affords the benefit that both product and process design can be analyzed prior to production and limitations have been overcome. Hardware limitations in the Virtual Build environment previously prevented transfer of the data collection and analysis methods to the real manufacturing environment and limited the ability to validate the predictions from the virtual industrial environment. Preliminary results of the case illustrating the modified Virtual Build research methodology presented in this article appear to be promising in support of computer‐aided ergonomics and safety for design of new or existing workstations and consumer products. A mixed‐mode virtual‐real environment that provides force feedback can be used to supplement the experimental setup and improve fidelity of the interactions in a work design assessment. A summary of the comfort assessment for a product design example that was tested with constrained human motion is outlined in the results section and presented in the context of a summary of key issues in Virtual Interactive Design and Virtual Build Assessments. © 2007 Wiley Periodicals, Inc. Hum Factors Man 17: 413–422, 2007.
A template for the arxiv style

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has greatly focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter for comparing the moral behaviors of Japanese users with English users. The five basic moral foundations i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence are compared for English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup and Purity. As far as emotions related to morality are concerned, the English tweets expressed more positive emotions for all moral dimensions. Considering the role of moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority and Purity for English and Ingroup for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English and Japanese speaking users. ar X iv :2 10 8. 10 64 3v 1 [ cs . C L ] 2 4 A ug 2 02 1 Morality-based Assertion and Homophily on Social Media
A template for the arxiv style

Heterogeneity exists in most camera images. This heterogeneity manifests itself across the image space as varied Moire ringing, motion-blur, color-bleaching or lens based projection distortions. Moreover, combinations of these image artifacts can be present in small or large pixel neighborhoods, within an acquired image. Current camera image processing pipelines, including deep trained versions, tend to rectify the issue applying a single filter that is homogeneously applied to the entire image. This is also particularly true when an encoder-decoder type deep architecture is trained for the task. In this paper, we present a structured deep learning model that solves the heterogeneous image artifact filtering problem. We call our deep trained model the Patch Subspace Variational Autoencoder (PS-VAE) for Camera ISP. PS-VAE does not necessarily assume uniform image distortion levels nor similar artifact types within the image. Rather, our model attempts to learn to cluster different patches extracted from images into artifact type and distortion levels, within multiple latent subspaces (e.g. Moire ringing artifacts are often a higher dimensional latent distortion than a Gaussian motion blur artifact). Each image’s patches are encoded into soft-clusters in their appropriate latent sub-space, using a prior mixture model. The decoders of the PS-VAE are also trained in an unsupervised manner for each of the image patches in each soft-cluster. Our experimental results demonstrates the flexibility and performance that one can achieve through improved heterogeneous filtering. We compare our results to a conventional one-encoder-one-decoder architecture.
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather than their properties. This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR (Findable, Accessible, Interoperable, Reusable) dataset with 91.8% F1-score and extended the dataset with data published since its release. The produced data is formatted and normalized, enabling its direct utilization as input in subsequent data analysis. This feature empowers materials scientists to develop models by selecting high-quality review articles within their domain. Additionally, we designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs). Our results demonstrate comparable performance to traditional machine learning methods without feature selection, highlighting the potential of LLMs to acquire scientific knowledge and design new materials akin to materials scientists.
ON THE $O(1/K)$ CONVERGENCE RATE OF THE ALTERNATING DIRECTION METHOD OF MULTIPLIERS IN A COMPLEX DOMAIN

We focus on the convergence rate of the alternating direction method of multipliers (ADMM) in a complex domain. First, the complex form of variational inequality (VI) is established by using the Wirtinger calculus technique. Second, the $O(1/K)$ convergence rate of the ADMM in a complex domain is provided. Third, the ADMM in a complex domain is applied to the least absolute shrinkage and selectionator operator (LASSO). Finally, numerical simulations are provided to show that ADMM in a complex domain has the $O(1/K)$ convergence rate and that it has certain advantages compared with the ADMM in a real domain.
Controlling Spread of Rumor Using Neighbor Centrality

Social networks are collaboration of individual entities where propagation of information, disease or ideas could take place because of the interaction between the entities. In such type of networks, it is often observed that propagation starts from a few unknown nodes and spreads through the whole network. However, propagation of a malicious information or a disease is often not desired and tried to be restricted to as few nodes as possible, using some external interventions. Inoculation is one such strategy, where few nodes are barred from further communication in order to restrict the spread. Such inoculations are costly, as they could require actions such as vaccination or disruption of normal operation of a node. Hence, we have minimization objective on both the parameters the final number of affected nodes and the number of inoculations. For such purpose, centrality measures, that rank the nodes according to some importance, are often used in identifying the nodes to be inoculated. Such measures mostly require the topology of the whole network in order to compute the centrality of the nodes. We propose a new centrality measure that requires information only on the neighboring nodes and can work in a distributed fashion depending upon the local information, but can also be used in centralized global inoculations. We empirically show that the centrality outperforms the existing ones in minimizing the spread in both the strategies.
Medical Deep Learning - A systematic Meta-Review

Deep learning had a remarkable impact in different scientific disciplines during the last years. This was demonstrated in numerous tasks, where deep learning algorithms were able to outperform the state-of-art methods, also in image processing and analysis. Moreover, deep learning delivers good results in tasks like autonomous driving, which could not have been performed automatically before. There are even applications where deep learning outperformed humans, like object recognition or games. Another field in which this development is showing a huge potential is the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for an automatic and reliable processing and analysis of this information. Patient data is not only collected in clinical centres, like hospitals, but it relates also to data coming from general practitioners, healthcare smartphone apps or online websites, just to name a few. This trend resulted in new, massive research efforts during the last years. In Q2/2020, the search engine PubMed returns already over 11.000 results for the search term $'$deep learning$'$, and around 90% of these publications are from the last three years. Hence, a complete overview of the field of $'$medical deep learning$'$ is almost impossible to obtain and getting a full overview of medical sub-fields gets increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been presented within the last years. They focused, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as foundation, the aim of this contribution is to provide a very first high-level, systematic meta-review of medical deep learning surveys.
Robust Position Sensing with Wave Fingerprints in Dynamic Complex Environments

Irregular propagation environments with complex scattering effects challenge traditional ray-tracing-based localization. However, the environment's complexity enables solutions based on wave fingerprints (WFPs). Yet, since WFPs rely on the extreme sensitivity of the chaotic wave field to geometrical details, it is not clear how viable WFP techniques may be in a realistic dynamically evolving environment. Here, we reveal that environmental perturbations reduce both the diversity of the WFP dictionary and the effective signal-to-noise ratio (SNR), such that the amount of information that can be obtained per measurement is reduced. This unfavorable effect can, however, be fully compensated by taking more measurements. We show in simulations and experiments with a low-cost software-defined radio that WFP localization of non-cooperative objects is possible even when the scattering strength of the environmental perturbation significantly exceeds that of the object to be localized. Our results underline that diversity is only one important ingredient to achieve high sensing accuracy in compressed sensing, the other two being SNR and the choice of decoding method. We find that sacrificing diversity for SNR may be worthwhile and observe that artificial neural networks outperform traditional decoding methods in terms of the achieved sensing accuracy, especially at low SNR.
PX-NET: Simple and Efﬁcient Pixel-Wise Training of Photometric Stereo Networks - Supplementary Material

This document provides the supplementary material for the main publication. Section 1 provides additional information about the 50 objects dataset used to train CNN-PS [1]. Section 2 provides an in depth explanation of the pixelwise data generation with all the relevant hyperparam-eters. Section 3 contains additional visualisations of the qualitative results on the DiLiGenT dataset.
THE IONIZED FIELD CALCULATION UNDER DIFFERENT HAZE WEATHER LEVELS BASED ON IMPROVED UPSTREAM MESHLESS METHOD

The haze-prone areas are usually places with limited transmission line corridors and large power loads. The performance of transmission lines is under threat of haze. The haze particulates around the transmission lines would be charged and affect the electric field near high voltage direct current (HVDC) transmission lines. According to the influence mechanism of haze on ionized field, the electric performance of HVDC transmission lines under haze weather is discussed. In the paper, an improved meshless local Petrov-Galerkin method (MLPG) is proposed to investigate the distribution of ionized electric field, and example of an actual transmission line is studied to verify the validation of the proposed method at first. It is proved that the proposed method agrees well with the measurements. Then the ionized field distribution under different haze weather levels, as well as influenced factor, is discussed. Results indicate that the ionized electric field and ion current density on the ground would increase under haze weather, but with similar trends to good weather condition. Meanwhile, the haze weather levels have greater influence on the ionized electric field than ion current density, where the increase of corona and space charge are the main reasons.
VOLDOR: Visual Odometry from Log-logistic Dense Optical ﬂow Residuals

Input: Estimated depth map θ from VOLDOR Output: Ground plane height h Ground normal vector n Crop a region of interest (ROI) at the image bottom. Compute a normal vector nj for each pixel j in the ROI, where ‖nj‖ = 1. Estimate a height hj for each pixel, where hj = nj · θK[xjyj1] Compute median height hmed. Meanshift on the 4-vector space of [nj , hj/hmed] with initial start mean [0,−1, 0, 1]. If result mean [n̂, ĥ/hmed] has near perpendicular normal vector that n̂ · [0,−1, 0] < Return the result height ĥ Else Ground plane registration failed
A Prediction of the Coronal Structure of the 21 August 2017 Great American Solar Eclipse

In what is being dubbed as the great American solar eclipse, on 21 August 2017, a total solar eclipse will sweep across the contiguous United States providing excellent opportunities for diagnostics of the Sun corona. The Sun’s coronal magnetic field is notoriously difficult to observe and well-constrained theoretical computer models are therefore necessary to understand how the corona is heated to a million degrees and how coronal magnetic field structures spawn solar storms that generate severe space weather. Here we present a technique for predicting and inferring the structure of the coronal field based on a data driven solar surface flux transport model which is forward run to 21 August 2017 to predict the Sun’s surface field distribution. The predicted solar surface field is subsequently utilized as input in a potential field source surface model to generate the corona. Our simulated prediction indicates the presence of two broad helmet streamers, one each in the eastern and western limb on the southern solar hemisphere and a third possible, narrow streamer on the West limb of the northern hemisphere. The (open) polar flux in the South pole appears to be stronger relative to the North, indicating an asymmetric sunspot cycle 25 with stronger activity in the southern hemisphere. Corresponding author: Dibyendu Nandy dnandi@iiserkol.ac. in
On Model Selection Consistency of Lasso for High-Dimensional Ising Models on Tree-like Graphs

We consider the problem of high-dimensional Ising model selection using neighborhoodbased least absolute shrinkage and selection operator (Lasso). It is rigorously proved that under some mild coherence conditions on the population covariance matrix of the Ising model, consistent model selection can be achieved with sample sizes n = Ω(d log p) for any tree-like graph in the paramagnetic phase, where p is the number of variables and d is the maximum node degree. The obtained sufficient conditions for consistent model selection with Lasso are the same in the scaling of the sample complexity as that of `1-regularized logistic regression.
Comment on "Detecting Novel Associations In Large Data Sets" by Reshef Et Al, Science Dec 16, 2011

The proposal of Reshef et al. (2011) is an interesting new approach for discovering non-linear dependencies among pairs of measurements in exploratory data mining. However, it has a potentially serious drawback. The authors laud the fact that MIC has no preference for some alternatives over others, but as the authors know, there is no free lunch in Statistics: tests which strive to have high power against all alternatives can have low power in many important situations. To investigate this, we ran simulations to compare the power of MIC to that of standard Pearson correlation and distance correlation (dcor). We simulated pairs of variables with different relationships (most of which were considered by the Reshef et. al.), but with varying levels of noise added. To determine proper cutoffs for testing the independence hypothesis, we simulated independent data with the appropriate marginals. As one can see from the Figure, MIC has lower power than dcor, in every case except the somewhat pathological high-frequency sine wave. MIC is sometimes less powerful than Pearson correlation as well, the linear case being particularly worrisome.
Clinical Study Clear Cell Sarcoma ( Malignant Melanoma ) of Soft Parts : A Clinicopathologic Study of 52 Cases

1 Melanoma Committee, Gustave Roussy Institute, 114 Rue Edouard Vaillant, 94805 Villejuif, Cedex, France 2 Sarcoma Committee, Gustave Roussy Institute, 114 Rue Edouard Vaillant, 94805 Villejuif, Cedex, France 3 Department Of Radiology, Gustave Roussy Institute, 114 Rue Edouard Vaillant, 94805 Villejuif, Cedex, France 4 Department Of Biostatistics And Epidemiology, Gustave Roussy Institute, 114 Rue Edouard Vaillant, 94805 Villejuif, Cedex, France 5 Sarcoma Group Of Bergonié Institute, 229 Cours De L’argonne, 33076 Bordeaux, Cedex, France
Nonlinear Analysis: Hybrid Systems

This paper deals with the problem of computing Lyapunov functions for asymptotic stability analysis of autonomous polynomial systems of differential equations. We propose a new semi-algebraic approach bymaking advantage of the local property of the Lyapunov function as well as its derivative. This is done by first constructing a semi-algebraic system and then solving this semi-algebraic system in an adaptive way. Experiment results show that our semi-algebraic approach is more efficient in practice, especially for low-order systems. © 2009 Elsevier Ltd. All rights reserved.
Black holes in higher dimensions

: Black holes in four dimensions have to be topologically spherical, dynamically stable and uniquely characterized by their conserved charges. In higher dimensions, black holes are less constrained. Black hole solutions have been found with non-spherical horizon topology, and the same conserved charges can describe diﬀerent types of black objects. This work presents a study of known exact black hole solutions in higher dimensions: the generalization of the Schwarzschild and Kerr metric (Schwarzschild-Tangherlini and Myers-Perry rotating in a single plane) and a brief introduction to black p-branes.
Theories-based Inclusive Education Practices

This review paper examines the theories of behaviourism, cognitivism, and constructivism that are considered to underpin the theory and practice of inclusive education. We argue that education practices related to the inclusion of students with special educational needs can be derived from each of the three theoretical perspectives. By adopting an eclectic approach to the use of theory-driven inclusive education, we suggest that inclusive education practices best serve the needs of all students, including those with special needs.
Que es un good code-switch? Testing the Functional Head Constraint within Noun Phrases

The Functional Head Constraint (Rubin and Toribio, 1994) states that no code-switch is allowed between a functional head and its complement. This was tested in an experiment with Spanish/English code-switched sentences. Subjects in the experiments were native speakers of Spanish (N=21), who were proficient English speakers, and had learned English during adulthood. The experiment used the Matching Task described in Freedman and Forster (1985). In this procedure, two sentences are presented sequentially on a computer screen, and are kept on the screen for 2 milliseconds. Subjects press a "yes" button or a "no" button to indicate whether the sentences on the screen are identical. In this experiment, three conditions were used: condition 1, where the determiner and the noun were Spanish; condition 2, where the determiner was Spanish and the noun was English; and condition 3, where both the determiner and the noun were English. Grammaticality of the code-switch was established by comparing matching times on all SAME items for the two conditions. Here, condition 2 violates the functional head constraint; thus, matching times for this condition should be significantly slower than for conditions 1 and 3. The results obtained are interpreted under the Minimalist Program of Chomsky (1992).
Hierarchical Neural Memory Network for Low Latency Event Processing

The section explains the details of W-MCA used for “upwrite” and “down-write” operations. We built W-MCA by extending the window based multi-head self-attention (WMSA) [17] with a minor modification. We first explain WMSA and then the modifications for our W-MCA. Window based multi-head self-attention [17]. Given an input X with a size (H ×W ×D), the W-MSA computes self-attention as follows:
Application of the parametric proper generalized decomposition to the frequency-dependent calculation of the impedance of an AC line with rectangular conductors

Abstract AC lines of industrial busbar systems are usually built using conductors with rectangular cross sections, where each phase can have several parallel conductors to carry high currents. The current density in a rectangular conductor, under sinusoidal conditions, is not uniform. It depends on the frequency, on the conductor shape, and on the distance between conductors, due to the skin effect and to proximity effects. Contrary to circular conductors, there are not closed analytical formulas for obtaining the frequency-dependent impedance of conductors with rectangular cross-section. It is necessary to resort to numerical simulations to obtain the resistance and the inductance of the phases, one for each desired frequency and also for each distance between the phases’ conductors. On the contrary, the use of the parametric proper generalized decomposition (PGD) allows to obtain the frequency-dependent impedance of an AC line for a wide range of frequencies and distances between the phases’ conductors by solving a single simulation in a 4D domain (spatial coordinates x and y, the frequency and the separation between conductors). In this way, a general “virtual chart” solution is obtained, which contains the solution for any frequency and for any separation of the conductors, and stores it in a compact separated representations form, which can be easily embedded on a more general software for the design of electrical installations. The approach presented in this work for rectangular conductors can be easily extended to conductors with an arbitrary shape.