Miracle-dz/UniFAR-FTUs huggingface dataset:
Skyrmion Lattice Phases in Thin Film Multilayer

Phases of matter are ubiquitous with everyday examples including solids and liquids. In reduced dimensions, particular phases, such as the 2D hexatic phase and corresponding phase transitions occur. A particularly exciting example of 2D ordered systems are skyrmion lattices, where in contrast to previously studied 2D colloid systems, the skyrmion size and density can be tuned by temperature and magnetic fields. This allows for the system to be driven from a liquid phase to the onset of a hexatic phase as deduced from the analysis of the hexagonal order. Using coarse‐grained molecular dynamics simulations of soft disks, the skyrmion interaction potentials are determined, and it is found that the simulations are able to reproduce the phase behavior. This shows that not only the static behavior of skyrmions is qualitatively well described in terms of a simple 2D model system but skyrmion lattices are versatile and tunable 2D model systems that allow for studying phases and phase transitions in reduced dimensions.
Frequency-Response Analysis and Design Rules for Capacitive Feedback Transimpedance Amplifier

Precision instrumentation systems, such as optical receivers and other current-output measurement systems, often contain a transimpedance amplifier (TIA). The commonly used resistive feedback TIA (RF-TIA) has a drawback in its practical implementation; the system bandwidth is limited by the feedback resistor and its parasitic capacitance in high-speed applications. To overcome this drawback, a capacitive feedback TIA (CF-TIA) can be used. The gain and bandwidth performance of the CF-TIA are theoretically equivalent to those of the RF-TIA. Although CF-TIA has been introduced in previous studies for reducing thermal noise and addressing the difficulty in integrating a high resistance with the CMOS technology, past analyses have not been in sufficient depth. The ultimate goal of this study is providing designers in this field with helpful design rules and analytical tools. In particular, a transfer function model, the critical design parameters, and a stability analysis are presented. Furthermore, a new CF-TIA configuration involving a simplified integrator dc-feedback loop is introduced, which can be deployed as a single stage rather than the conventional two-stage topology. While the applications considered in this study were high-speed sensor measurements and devices with a high input capacitance, such as a laser position detector, the analytical results obtained herein are also applicable to a variety of other applications such as emerging biosensors and optical communication system.
Time Adaptive Reinforcement Learning

Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tasks and unable to adapt to different contexts. Here we consider the case of adapting RL agents to different time restrictions, such as finishing a task with a given time limit that might change from one task execution to the next. We define such problems as Time Adaptive Markov Decision Processes and introduce two model-free, value-based algorithms: the Independent Gamma-Ensemble and the n-Step Ensemble. In difference to classical approaches, they allow a zero-shot adaptation between different time restrictions. The proposed approaches represent general mechanisms to handle time adaptive tasks making them compatible with many existing RL methods, algorithms, and scenarios.
Hidden independence in unstructured probabilistic models

We describe a novel way to represent the probability distribution of a random binary string as a mixture having a maximally weighted component associated with independent (though not necessarily identically distributed) Bernoulli characters. We refer to this as the latent independent weight of the probabilistic source producing the string, and derive a combinatorial algorithm to compute it. The decomposition we propose may serve as an alternative to the Boolean paradigm of hypothesis testing, or to assess the fraction of uncorrupted samples originating from a source with independent marginals. In this sense, the latent independent weight quantifies the maximal amount of independence contained within a probabilistic source, which, properly speaking, may not have independent marginals.
Electrically injected GeSn lasers on Si operating up to 100  K

The significant progress of GeSn material development has enabled a feasible solution to the long-desired monolithically integrated lasers on the Si platform. While there are many reports focused on optically pumped lasers, GeSn lasers through electrical injection have not been experimentally achieved yet. In this work, we report the first demonstration of electrically injected GeSn lasers on Si. A GeSn/SiGeSn heterostructure diode grown on a Si substrate was fabricated into ridge waveguide laser devices and tested under pulsed conditions. Special considerations were given for the structure design to ensure effective carrier confinement and optical confinement that lead to lasing. Lasing was observed at temperatures from 10 to 100 K with emission peaks at around 2300 nm. The minimum threshold of 598 A/cm2 was recorded at 10 K and the threshold increased to 842 A/cm2 at 77 K. The spectral linewidth of a single peak was measured as small as 0.13 nm (0.06 meV). The maximum characteristic temperature was extracted as 99 K over the temperature range of 10-77 K.
From the butterfly effect to intrinsic randomness: the spontaneous growth of singular shear flows

The butterfly effect is today commonly identified with the sensitive dependence of deterministic chaotic systems upon initial conditions. However, this is only one facet of the notion of unpredictability pioneered by Lorenz, who actually predicted that multiscale fluid flows could spontaneously lose their deterministic nature and become intrinsically random. This effect, which is radically different from chaos, have remained out of reach for detailed physical observations. Here, we substantiate this scenario by showing that it is inherent to the elementary Kelvin--Helmholtz hydrodynamical instability of an initially singular shear layer. We moreover provide evidence that the resulting macroscopic flow displays universal statistical properties that are triggered by, but independent of specific physical properties at micro-scales. This spontaneous stochasticity is interpreted as an Eulerian counterpart to Richardson's relative dispersion of Lagrangian particles, giving substance to the intrinsic nature of randomness in turbulence.
Personality Assessment from Text for Machine Commonsense Reasoning

This article presents PerSense, a framework to estimate human personality traits based on expressed texts and to use them for commonsense reasoning analysis. The personality assessment approaches include an aggregated Probability Density Functions (PDF), and Machine Learning (ML) models. Our goal is to demonstrate the feasibility of using machine learning algorithms on personality trait data to predict humans' responses to open-ended commonsense questions. We assess the performance of the PerSense algorithms for personality assessment by conducting an experiment focused on Neuroticism, an important personality trait crucial in mental health analysis and suicide prevention by collecting data from a diverse population with different Neuroticism scores. Our analysis shows that the algorithms achieve comparable results to the ground truth data. Specifically, the PDF approach achieves 97% accuracy when the confidence factor, the logarithmic ratio of the first to the second guess probability, is greater than 3. Additionally, ML approach obtains its highest accuracy, 82.2%, with a multilayer Perceptron classifier. To assess the feasibility of commonsense reasoning analysis, we train ML algorithms to predict responses to commonsense questions. Our analysis of data collected with 300 participants demonstrate that PerSense predicts answers to commonsense questions with 82.3% accuracy using a Random Forest classifier.
A Deep Learning Approach to Object Affordance Segmentation

Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting the object parts that enable specific types of human-object interaction, the so-called "object affordances". However, most works treat it as a static semantic segmentation problem, focusing solely on object appearance and relying on strong supervision and object detection. In this paper, we propose a novel approach that exploits the spatio-temporal nature of human-object interaction for affordance segmentation. In particular, we design an autoencoder that is trained using ground-truth labels of only the last frame of the sequence, and is able to infer pixel-wise affordance labels in both videos and static images. Our model surpasses the need for object labels and bounding boxes by using a soft-attention mechanism that enables the implicit localization of the interaction hotspot. For evaluation purposes, we introduce the SOR3D-AFF corpus, which consists of human-object interaction sequences and supports 9 types of affordances in terms of pixel-wise annotation, covering typical manipulations of tool-like objects. We show that our model achieves competitive results compared to strongly supervised methods on SOR3D-AFF, while being able to predict affordances for similar unseen objects in two affordance image-only datasets.
Discounted cost linear quadratic Gaussian control for descriptor systems

We consider the linear quadratic Gaussian control problem with a discounted cost functional for descriptor systems on the infinite time horizon. Based on recent results from the deterministic framework, we characterise the feasibility of this problem using a linear matrix inequality. In particular, conditions for existence and uniqueness of optimal controls are derived, which are weaker compared to the standard approaches in the literature. We further show that also for the stochastic problem, the optimal control is given in terms of the stabilising solution of the Lur'e equation, which generalises the algebraic Riccati equation. We conclude our paper by examining an application of our theory to fluid dynamics problems.
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network

Airborne light detection and ranging (LiDAR) plays an increasingly significant role in urban planning, topographic mapping, environmental monitoring, power line detection and other fields thanks to its capability to quickly acquire large-scale and high-precision ground information. To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures. And some recent works proposed graph convolution neural network based on the inherent topology of point clouds. However, the above point cloud deep learning models only pay attention to exploring local geometric structures, yet ignore global contextual relationships among all points. In this paper, we present a graph attention convolution neural network (GACNN) that can be directly applied to the classification of unstructured 3D point clouds obtained by airborne LiDAR. Specifically, we first introduce a graph attention convolution module that incorporates global contextual information and local structural features. Based on the proposed graph attention convolution module, we further design an end-to-end encoder-decoder network, named GACNN, to capture multiscale features of the point clouds and therefore enable more accurate airborne point cloud classification. Experiments on the ISPRS 3D labeling dataset show that the proposed model achieves a new state-of-the-art performance in terms of average F1 score (71.5\%) and a satisfying overall accuracy (83.2\%). Additionally, experiments further conducted on the 2019 Data Fusion Contest Dataset by comparing with other prevalent point cloud deep learning models demonstrate the favorable generalization capability of the proposed model.
Protecting Classifiers From Attacks. A Bayesian Approach

Classification problems in security settings are usually modeled as confrontations in which an adversary tries to fool a classifier manipulating the covariates of instances to obtain a benefit. Most approaches to such problems have focused on game-theoretic ideas with strong underlying common knowledge assumptions, which are not realistic in the security realm. We provide an alternative Bayesian framework that accounts for the lack of precise knowledge about the attacker's behavior using adversarial risk analysis. A key ingredient required by our framework is the ability to sample from the distribution of originating instances given the possibly attacked observed one. We propose a sampling procedure based on approximate Bayesian computation, in which we simulate the attacker's problem taking into account our uncertainty about his elements. For large scale problems, we propose an alternative, scalable approach that could be used when dealing with differentiable classifiers. Within it, we move the computational load to the training phase, simulating attacks from an adversary, adapting the framework to obtain a classifier robustified against attacks.
S3Library: Automatically Eliminating C/C++ Buffer Overflow using Compatible Safer Libraries

Annex K of C11, bounds-checking interfaces, recently introduced a set of alternative functions to mitigate buffer overflows, primarily those caused by string/memory functions. However, poor compatibility limits their adoption. Failure oblivious computing can eliminate the possibility that an attacker can exploit memory errors to corrupt the address space and significantly increase the availability of systems. In this paper, we present S3Library (Saturation-Memory-Access Safer String Library), which is compatible with the standard C library in terms of function signature. Our technique automatically replaces unsafe deprecated memory/string functions with safer versions that perform bounds checking and eliminate buffer overflows via boundless memory. S3Library employs MinFat, a very compact pointer representation following the Less is More principle, to encode metadata into unused upper bits within pointers. In addition, S3Library utilizes Saturation Memory Access to eliminate illegal memory accesses into boundless padding area. Even if an out-of-bounds access is made, the fault program will not be interrupted. We implement our scheme within the LLVM framework on X86-64 and evaluate our approach on correctness, security, runtime performance and availability.
Computing for the DUNE Long-Baseline Neutrino Oscillation Experiment

This paper is based on a talk given at Computing in High Energy Physics in Adelaide, South Australia, Australia in November 2019. It is partially intended to explain the context of DUNE Computing for computing specialists. The Deep Underground Neutrino Experiment (DUNE) collaboration consists of over 180 institutions from 33 countries. The experiment is in preparation now, with commissioning of the first 10kT fiducial volume Liquid Argon TPC expected over the period 2025-2028 and a long data taking run with 4 modules expected from 2029 and beyond. An active prototyping program is already in place with a short test-beam run with a 700T, 15,360 channel prototype of single-phase readout at the Neutrino Platform at CERN in late 2018 and tests of a similar sized dual-phase detector scheduled for mid-2019. The 2018 test-beam run was a valuable live test of our computing model. The detector produced raw data at rates of up to 2GB/s. These data were stored at full rate on tape at CERN and Fermilab and replicated at sites in the UK and Czech Republic. In total 1.2 PB of raw data from beam and cosmic triggers were produced and reconstructed during the six week testbeam run. Baseline predictions for the full DUNE detector data, starting in the late 2020’s are 30-60 PB of raw data per year. In contrast to traditional HEP computational problems, DUNE’s Liquid Argon TPC data consist of simple but very large (many GB) 2D data objects which share many characteristics with astrophysical images. This presents opportunities to use advances in machine learning and pattern recognition as a frontier user of High Performance Computing facilities capable of massively parallel processing.
On the Approximate Nearest Neighbor Queries among Curves under the Fréchet Distance

Approximate nearest neighbor search (\textsc{ANNS}) is a long-studied problem in computational geometry that has received considerable attentions by researchers in the community. In this paper, we revisit the problem in the presence of curves under the Frechet distance. Given a set ${\cal P}$ of $n$ curves of size at most $m$ each in $\mathbb{R}^d$ and a real $\delta>0$, we aim to preprocess ${\cal P}$ into a data structure so that for any given query curve $Q$ of size $k$, report all curves in ${\cal P}$ whose Frechet distances to $Q$ are at most $\delta$. In case that $k$ is known in the preprocessing stage we propose a fully deterministic data structure whose space is $O(n({32d^{1/2}/\varepsilon^3})^{d(k+1)} )$ and can answer the \textsc{$(1+\varepsilon)\delta$-ANNS} queries in $O(kd)$ query time. Considering $k$ as part of the query slightly changes the space to $O( n({64d^{1/2}/\varepsilon^3})^{md} )$ with $O(kd)$ query time within $5(1+\varepsilon)$ approximation factor. We also show that our data structure could give an alternative treatment of the approximate subtrajectory range counting (\textsc{ASRC}) problem studied by de Berg et al.~\cite{bcg-ffq-13}.
StereoSet: Measuring stereotypical bias in pretrained language models

A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or African Americans are athletic. Such beliefs (biases) are known to hurt target groups. Since pretrained language models are trained on large real-world data, they are known to capture stereotypical biases. It is important to quantify to what extent these biases are present in them. Although this is a rapidly growing area of research, existing literature lacks in two important aspects: 1) they mainly evaluate bias of pretrained language models on a small set of artificial sentences, even though these models are trained on natural data 2) current evaluations focus on measuring bias without considering the language modeling ability of a model, which could lead to misleading trust on a model even if it is a poor language model. We address both these problems. We present StereoSet, a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion. We contrast both stereotypical bias and language modeling ability of popular models like BERT, GPT-2, RoBERTa, and XLnet. We show that these models exhibit strong stereotypical biases. Our data and code are available at https://stereoset.mit. edu.
Intention Propagation for Multi-agent Reinforcement Learning

A hallmark of an AI agent is to mimic human beings to understand and interact with others. In this paper, we propose a collaborative multi-agent reinforcement learning algorithm to learn a \emph{joint} policy through the interactions over agents. To make a joint decision over the group, each agent makes an initial decision and tells its policy to its neighbors. Then each agent modifies its own policy properly based on received messages and spreads out its plan. As this intention propagation procedure goes on, we prove that it converges to a mean-field approximation of the joint policy with the framework of neural embedded probabilistic inference. We evaluate our algorithm on several large scale challenging tasks and demonstrate that it outperforms previous state-of-the-arts.
Color Image Segmentation using Adaptive Particle Swarm Optimization and Fuzzy C-means

Segmentation partitions an image into different regions containing pixels with similar attributes. A standard non-contextual variant of Fuzzy C-means clustering algorithm (FCM), considering its simplicity is generally used in image segmentation. Using FCM has its disadvantages like it is dependent on the initial guess of the number of clusters and highly sensitive to noise. Satisfactory visual segments cannot be obtained using FCM. Particle Swarm Optimization (PSO) belongs to the class of evolutionary algorithms and has good convergence speed and fewer parameters compared to Genetic Algorithms (GAs). An optimized version of PSO can be combined with FCM to act as a proper initializer for the algorithm thereby reducing its sensitivity to initial guess. A hybrid PSO algorithm named Adaptive Particle Swarm Optimization (APSO) which improves in the calculation of various hyper parameters like inertia weight, learning factors over standard PSO, using insights from swarm behaviour, leading to improvement in cluster quality can be used. This paper presents a new image segmentation algorithm called Adaptive Particle Swarm Optimization and Fuzzy C-means Clustering Algorithm (APSOF), which is based on Adaptive Particle Swarm Optimization (APSO) and Fuzzy C-means clustering. Experimental results show that APSOF algorithm has edge over FCM in correctly identifying the optimum cluster centers, there by leading to accurate classification of the image pixels. Hence, APSOF algorithm has superior performance in comparison with classic Particle Swarm Optimization (PSO) and Fuzzy C-means clustering algorithm (FCM) for image segmentation.
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner- Take-All Circuits

Networks of spiking neurons and Winner- Take-All spiking circuits (WTA -SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing of events of interest, e.g., clicks, as well as by categorical numerical values assigned to each event, e.g., like or dislike. Other use cases include object recognition from data collected by neuromorphic cameras, which produce, for each pixel, signed bits at the times of sufficiently large brightness variations. Existing schemes for training WTA -SNNs are limited to rate-encoding solutions, and are hence able to detect only spatial patterns. Developing more general training algorithms for arbitrary WTA -SNNs inherits the challenges of training (binary) Spiking Neural Networks (SNNs). These amount, most notably, to the non-differentiability of threshold functions, to the recurrent behavior of spiking neural models, and to the difficulty of implementing backpropagation in neuromorphic hardware. In this paper, we develop a variational online local training rule for WTA-SNNs, referred to as VOWEL, that leverages only local pre- and post-synaptic information for visible circuits, and an additional common reward signal for hidden circuits. The method is based on probabilistic generalized linear neural models, control variates, and variational regularization. Experimental results on real-world neuromorphic datasets with multi-valued events demonstrate the advantages of WTA-SNNs over conventional binary SNNs trained with state-of-the-art methods, especially in the presence of limited computing resources.
Tensor completion using enhanced multiple modes low-rank prior and total variation

In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An block successive upper-bound minimization algorithm is applied to solve the model. Subsequence convergence of our algorithm can be established, and our algorithm converges to the coordinate-wise minimizers in some mild conditions. Several experiments on three types of public data sets show that our algorithm can recover a variety of low-rank tensors from significantly fewer samples than the other testing tensor completion methods.