Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of layer sliding on the interfacial electronic properties of intercalated silicene/indium selenide van der Waals heterostructure

Methods capable of tuning the properties of van der Waals (vdW) layered materials in a controlled and reversible manner are highly desirable. Interfacial electronic properties of two-dimensional vdW heterostructure consisting of silicene and indium selenide (InSe) have been calculated using density functional theory-based computational code. Furthermore, in order to vary the aforementioned properties, silicene is slid over a InSe layer in the presence of Li intercalation. On intercalation of the heterostructure, the buckling parameter associated with the corrugation of silicene decreases from 0.44 Å to 0.36 Å, whereas the InSe structure remains unaffected. Potential energy scans reveal a significant increase in the sliding energy barrier for the case of intercalated heterostructure as compared with the unintercalated heterostructure. The sliding of the silicene encounters the maximum energy barrier of 0.14 eV. Anisotropic analysis shows the noteworthy differences between calculated in-plane and out-of-plane part of dielectric function. A variation of the planar average charge density difference, dipole charge transfer and dipole moment have been discussed to elucidate the usability spectrum of the heterostructure. The employed approach based on intercalation and layer sliding can be effectively utilized for obtaining next-generation multifunctional devices.
ArabGend: Gender Analysis and Inference on Arabic Twitter

Gender analysis of Twitter can reveal important socio-cultural differences between male and female users. There has been a significant effort to analyze and automatically infer gender in the past for most widely spoken languages’ content, however, to our knowledge very limited work has been done for Arabic. In this paper, we perform an extensive analysis of differences between male and female users on the Arabic Twitter-sphere. We study differences in user engagement, topics of interest, and the gender gap in professions. Along with gender analysis, we also propose a method to infer gender by utilizing usernames, profile pictures, tweets, and networks of friends. In order to do so, we manually annotated gender and locations for ~166K Twitter accounts associated with ~92K user location, which we plan to make publicly available. Our proposed gender inference method achieve an F1 score of 82.1% (47.3% higher than majority baseline). We also developed a demo and made it publicly available.
Control of the Schrödinger equation by slow deformations of the domain

The aim of this work is to study the controllability of the Schr\"odinger equation \begin{equation}\label{eq_abstract} i\partial_t u(t)=-\Delta u(t)~~~~~\text{ on }\Omega(t) \tag{$\ast$} \end{equation} with Dirichlet boundary conditions, where $\Omega(t)\subset\mathbb{R}^N$ is a time-varying domain. We prove the global approximate controllability of \eqref{eq_abstract} in $L^2(\Omega)$, via an adiabatic deformation $\Omega(t)\subset\mathbb{R}$ ($t\in[0,T]$) such that $\Omega(0)=\Omega(T)=\Omega$. This control is strongly based on the Hamiltonian structure of \eqref{eq_abstract} provided by [18], which enables the use of adiabatic motions. We also discuss several explicit interesting controls that we perform in the specific framework of rectangular domains.
The Exploration of Local VolumE Satellites (ELVES) Survey: A Nearly Volume-limited Sample of Nearby Dwarf Satellite Systems

We present the final sample of the Exploration of Local VolumE Satellites (ELVES) survey, a survey of the dwarf satellites of a nearly volume-limited sample of Milky Way (MW)−like hosts in the Local Volume. Hosts are selected simply via a cut in luminosity ( MKs<−22.1 mag) and distance (D < 12 Mpc). We cataloged the satellites of 25 of the 31 such hosts, with another five taken from the literature. All hosts are surveyed out to at least 150 projected kpc ( ∼ R vir/2), with the majority surveyed to 300 kpc ( ∼ R vir). Satellites are detected using a consistent semiautomated algorithm specialized for low surface brightness dwarfs. As shown through extensive tests with injected galaxies, the catalogs are complete to M V ∼ −9 mag and μ 0,V ∼ 26.5 mag arcsec−2. Candidates are confirmed to be real satellites through distance measurements including redshift, tip of the red giant branch, and surface brightness fluctuations. Across all 30 surveyed hosts, there are 338 confirmed satellites with M V < −9 mag, with a further 106 candidates awaiting distance measurement. For the vast majority of these, we provide consistent multiband Sérsic photometry. We show that satellite abundance correlates with host mass, with the MW being quite typical among comparable systems, and that satellite quenched fraction rises steeply with decreasing satellite mass, mirroring the quenched fraction for the MW and M31. The ELVES survey represents a massive increase in the statistics of surveyed systems with known completeness, and the provided catalogs are a unique data set to explore various aspects of small-scale structure and dwarf galaxy evolution.
Study on the Position/force Control of an Electro-hydraulic Actuator Based on AMEsim/Simulink

A Fuzzy position and force hybrid controller was proposed to meet the particular requirement of an electro-hydraulic actuator of the underwater-simulated device.A model combined with controller and electric-hydraulic was established in AMEsim and MATLAB/Simulink.Strategy and realization of the hybrid controller for position and force control was dissertated.The effectiveness and robust of the proposed control were demonstrated by simulation hybrid of AMEsim/Simulink.
Memory Planning for Deep Neural Networks

We study memory allocation patterns in DNNs during inference, in the context of large-scale systems. We observe that such memory allocation patterns, in the context of multi-threading, are subject to high latencies, due to \texttt{mutex} contention in the system memory allocator. Latencies incurred due to such \texttt{mutex} contention produce undesirable bottlenecks in user-facing services. Thus, we propose a"memorization"based technique, \texttt{MemoMalloc}, for optimizing overall latency, with only moderate increases in peak memory usage. Specifically, our technique consists of a runtime component, which captures all allocations and uniquely associates them with their high-level source operation, and a static analysis component, which constructs an efficient allocation"plan". We present an implementation of \texttt{MemoMalloc} in the PyTorch deep learning framework and evaluate memory consumption and execution performance on a wide range of DNN architectures. We find that \texttt{MemoMalloc} outperforms state-of-the-art general purpose memory allocators, with respect to DNN inference latency, by as much as 40\%.
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.
On Testability of the Front-Door Model via Verma Constraints

The front-door criterion can be used to identify and compute causal effects despite the existence of unmeasured confounders between a treatment and outcome. However, the key assumptions -- (i) the existence of a variable (or set of variables) that fully mediates the effect of the treatment on the outcome, and (ii) which simultaneously does not suffer from similar issues of confounding as the treatment-outcome pair -- are often deemed implausible. This paper explores the testability of these assumptions. We show that under mild conditions involving an auxiliary variable, the assumptions encoded in the front-door model (and simple extensions of it) may be tested via generalized equality constraints a.k. a Verma constraints. We propose two goodness-of-fit tests based on this observation, and evaluate the efficacy of our proposal on real and synthetic data. We also provide theoretical and empirical comparisons to instrumental variable approaches to handling unmeasured confounding.
Earthquake Control: An Emerging Application for Robust Control. Theory and Experimental Tests

This article addresses the possibility of using robust control theory for preventing earthquakes through fluid injections in the Earth’s crust. The designed robust controllers drive aseismically a fault system to a new equilibrium point of lower energy by tracking a slow reference signal. The control design is based on a reduced-order nonlinear model able to reproduce earthquake-like instabilities. Uncertainties related to the frictional and mechanical properties of the underlying physical process and external perturbations are considered. Two types of controllers are derived. The first one is based on the sliding-mode theory and leads to local finite-time convergence of the tracking error and rejection of Lipschitz w.r. t. time perturbations. The second controller is based on linear quadratic regulator (LQR) control and presents global exponential stability of the tracking error and rejection of Lipschitz w.r. t. state perturbations. Both the controllers generate a continuous control signal, attenuating the chattering effect in the case of the sliding-mode algorithms. The developed controllers are tested extensively and compared on the basis of numerical simulations and experiments in the laboratory. The present work opens new perspectives for the application of the robust nonlinear control theory to complex geosystems, earthquakes, and sustainable energy production.
Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers

As technology advances, the need for safe, efficient, and collaborative human-robot-teams has become increasingly important. One of the most fundamental collaborative tasks in any setting is the object handover. Human-to-robot handovers can take either of two approaches: (1) direct hand-to-hand or (2) indirect hand-to-placement-to-pick-up. The latter approach ensures minimal contact between the human and robot but can also result in increased idle time due to having to wait for the object to first be placed down on a surface. To minimize such idle time, the robot must preemptively predict the human intent of where the object will be placed. Furthermore, for the robot to preemptively act in any sort of productive manner, predictions and motion planning must occur in real-time. We introduce a novel prediction-planning pipeline that allows the robot to preemptively move towards the human agent's intended placement location using gaze and gestures as model inputs. In this paper, we investigate the performance and drawbacks of our early intent predictor-planner as well as the practical benefits of using such a pipeline through a human-robot case study.
Explainability for identification of vulnerable groups in machine learning models

If a prediction model identifies vulnerable individuals or groups, the use of that model may become an ethical issue. But can we know that this is what a model does? Machine learning fairness as a field is focused on the just treatment of individuals and groups under information processing with machine learning methods. While considerable attention has been given to mitigating discrimination of protected groups, vulnerable groups have not received the same attention. Unlike protected groups, which can be regarded as always vulnerable, a vulnerable group may be vulnerable in one context but not in another. This raises new challenges on how and when to protect vulnerable individuals and groups under machine learning. Methods from explainable artificial intelligence (XAI), in contrast, do consider more contextual issues and are concerned with answering the question"why was this decision made? ". Neither existing fairness nor existing explainability methods allow us to ascertain if a prediction model identifies vulnerability. We discuss this problem and propose approaches for analysing prediction models in this respect.
PhyCOM: A Multi-Layer Parametric Network for Joint Linear Impairments Compensation and Symbol Detection

In this paper, we focus on the joint impairments compensation and symbol detection problem in communication systems. First, we introduce a new multi-layer channel model that represents the underlying physics of multiple impairments in communication systems. This model is composed of widely linear parametric layers that describe the input-output relationship of the front-end impairments and channel effects. Using this particular model, we show that the joint compensation and zero-forcing detection problem can be solved by a particular feedforward network called PhyCOM. Because of the small number of network parameters, a PhyCOM network can be trained efficiently using sophisticated optimization algorithms and a limited number of pilot symbols. Numerical examples are provided to demonstrate the effectiveness of PhyCOM networks with communication systems corrupted by transmitter and receiver IQ imbalances, carrier frequency offset, finite impulse response channels, and transmitter and receiver phase noise distortions. Compared to conventional digital signal processing approaches, simulation results show that the proposed technique is much more flexible and offers better statistical performance both in terms of MSE and SER with a moderate increase of the computation complexity.
Descriptor for slip-induced crack blunting in refractory ceramics

Understanding the competition between brittleness and plasticity in refractory ceramics is of importance for aiding design of hard materials with enhanced fracture resistance. Inspired by experimental observations of crack shielding due to dislocation activity in TiN ceramics [Int J Plast 27 (2011) 739], we carry out comprehensive atomistic investigations to identify mechanisms responsible for brittleness and slip-induced plasticity in Ti-N systems. First, we validate a semi-empirical interatomic potential against density-functional theory results of Griffith and Rice stress intensities for cleavage (KIc) and dislocation emission (KIe) as well as ab initio molecular dynamics mechanical-testing simulations of pristine and defective TiN lattices at temperatures between 300 and 1200 K. The calculated KIc and KIe values indicate intrinsic brittleness, as KIc<<KIe. However, KI-controlled molecular statics simulations - which reliably forecast macroscale mechanical properties through nanoscale modelling - reveal that slip-plasticity can be promoted by a reduced sharpness of the crack and/or the presence of anion vacancies. Classical molecular dynamics simulations of notched Ti-N supercell models subject to tension provide a qualitative understanding of the competition between brittleness and plasticity at finite temperatures. Although crack growth occurs in most cases, a sufficiently rapid accumulation of shear stress at the notch tip may postpone or prevent fracture via nucleation and emission of dislocations. Furthermore, we show that the probability to observe slip-induced plasticity leading to crack-blunting in flawed Ti-N lattices correlates with the ideal tensile/shear strength ratio (Iplast) of pristine Ti-N crystals. We propose that the Iplast descriptor should be considered for ranking the ability of ceramics to blunt cracks via dislocation-mediated plasticity at finite temperatures.
An Analytical Approach to Compute the Exact Preimage of Feed-Forward Neural Networks

Neural networks are a convenient way to automatically fit functions that are too complex to be described by hand. The downside of this approach is that it leads to build a black-box without understanding what happened inside. Finding the preimage would help to better understand how and why such neural networks had given such outputs. Because most of the neural networks are noninjective function, it is often impossible to compute it entirely only by a numerical way. The point of this study is to give a method to compute the exact preimage of any Feed-Forward Neural Network with linear or piecewise linear activation functions for hidden layers. In contrast to other methods, this one is not returning a unique solution for a unique output but returns analytically the entire and exact preimage.
Exploring and Adapting Chinese GPT to Pinyin Input Method

While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored.In this work, we make the first exploration to leverage Chinese GPT for pinyin input method.We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin.However, the performance drops dramatically when the input includes abbreviated pinyin.A reason is that an abbreviated pinyin can be mapped to many perfect pinyin, which links to even larger number of Chinese characters.We mitigate this issue with two strategies,including enriching the context with pinyin and optimizing the training process to help distinguish homophones. To further facilitate the evaluation of pinyin input method, we create a dataset consisting of 270K instances from fifteen domains.Results show that our approach improves the performance on abbreviated pinyin across all domains.Model analysis demonstrates that both strategiescontribute to the performance boost.
Piezoelectric Strain FET (PeFET)-Based Nonvolatile Memories

We propose nonvolatile memory (NVM) designs based on piezoelectric strain FET (PeFET) utilizing piezoelectric/ferroelectric material (PE/FE such as PZT) coupled with 2-D transition metal dichalcogenide (2-D-TMD such as MoS<inline-formula> <tex-math notation="LaTeX">$_{{2}}{)}$ </tex-math></inline-formula>-based transistor. The proposed NVMs store bit information in the form of polarization (<inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>) of FE/PE, use electric-field driven <inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>-switching for write, and employ piezoelectricity-induced dynamic bandgap modulation of 2-D-TMD channel for bit sensing. We analyze PeFET with COMSOL-based 3-D modeling, showing that circuit-driven optimization of PeFET geometry is essential to achieve effective strain transduction and adequate bandgap modulation for NVM read. We achieve distinguishability of up to <inline-formula> <tex-math notation="LaTeX">$11\times $ </tex-math></inline-formula> in binary states of PeFETs. We propose various flavors of PeFET NVMs, namely: 1) high-density (HD) NVM featuring a compact access-transistor-less bit-cell; 2) 1T-1PeFET NVM with segmented architecture, targeted for optimized write energy and latency; and 3) cross-coupled (CC) NVM offering a tradeoff between area and latency. PeFET NVMs offer up to <inline-formula> <tex-math notation="LaTeX">$7\times $ </tex-math></inline-formula> smaller cell area, 66% lower write energy, 87% lower read energy, and 44% faster read compared to 2-D-FET static random access memory (SRAM). This comes at the cost of high write latency in PeFET NVMs, which can be minimized by virtue of optimized PE geometry.
A Unified 3D Framework for Organs-at-Risk Localization and Segmentation for Radiation Therapy Planning

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of 0.9260 ± 0.18% on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs.
Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores

Automated essay scoring plays an important role in judging students’ language abilities in education. Traditional approaches use handcrafted features to score and are time consuming and complicated. Recently, neural network approaches have improved performance without any feature engineering. Unlike other natural language processing tasks, only a small number of datasets are publicly available for automated essay scoring, and the size of the dataset is not sufficiently large. Considering that the performance of a neural network is closely related to the size of the dataset, the lack of data limits the performance improvement of the automated essay scoring model. In this study, we proposed a method to increase the number of essay-score pairs using back translation and score adjustment and applied it to the Automated Student Assessment Prize dataset for augmentation. We evaluated the effectiveness of the augmented data using models from prior work. In addition, performance was evaluated in a model using long short-term memory, which is widely used for automated essay scoring. The performance was improved by using augmented data.