Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergent collective behavior of active Brownian particles by visual perception

Systems comprised of self-steering active Brownian particles are studied via simulations for a minimal cognitive flocking model. The dynamics of the active Brownian particles is extended by an orientational response with limited maneuverability to an instantaneous visual input of the positions of neighbors within a vision cone and a cut-off radius. The system exhibits large-scale self-organized structures, which depend on selected parameter values, and, in particular, the presence of excluded-volume interactions. The emergent structures in two dimensions, such as worms, worm-aggregate coexistence, and hexagonally close-packed structures, are analysed and phase diagrams are constructed. The analysis of the particle's mean-square displacement shows ABP-like dynamics for dilute systems and the worm phase. In the limit of densely packed structures, the active diffusion coefficient is significantly smaller and depends on the number of particles in the cluster. Our analysis of the cluster-growth dynamics shows distinct differences to processes in systems of short-range attractive colloids in equilibrium. Specifically, the characteristic time for the growth and decay of clusters of a particular size is longer than that of isotropically attractive colloids, which we attribute to the non-reciprocal nature of the directed visual perception. Our simulations reveal a strong interplay between ABP-characteristic interactions, such as volume exclusion and rotational diffusion, and cognitive-based interactions and navigation.
Scaling of the Saﬀman helicity integral in decaying magnetically-dominated turbulence

The Saﬀman helicity integral of Hosking and Schekochihin (2021, PRX 11, 041005) has emerged as an important quantity that may govern the decay properties of magnetically dominated turbulence. Using a range of diﬀerent computational methods, we conﬁrm that this quantity is indeed gauge-invariant and nearly perfectly conserved in the limit of large Lundquist numbers. For direct numerical simulations with ordinary viscosity and magnetic diﬀusivity operators, we ﬁnd that the solution develops in a nearly self-similar fashion. In a diagram quantifying the instantaneous decay coeﬃcients of magnetic energy and integral scale, we ﬁnd that the solution evolves along a line that is indeed suggestive of the governing role of the Saﬀman helicity integral. The solution settles near a line in this diagram that is expected for a self-similar evolution of the magnetic energy spectrum. However, the solution may settle in a slightly diﬀerent position when the magnetic diﬀusivity decreases with time, which would be compatible with the decay being governed by the reconnection time scale rather than the Alfvén time.
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation

Inspired by the concept of preconditioning, we propose a novel method to increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters. We demonstrate that recasting the optimization problem to a non-linear least-squares formulation provides a principled way to actively enforce a $\textit{well-conditioned}$ parameter space for meta-learning models based on the concepts of the condition number and local curvature. Our comprehensive evaluations show that the proposed method significantly outperforms its unconstrained counterpart especially during initial adaptation steps, while achieving comparable or better overall results on several few-shot classification tasks -- creating the possibility of dynamically choosing the number of adaptation steps at inference time.
Atmospheric Overturning Circulation on Dry, Tidally Locked Rocky Planets Is Mainly Driven by Radiative Cooling

In this study, we examine the driving mechanism for the atmospheric overturning circulation on dry, tidally locked rocky planets without the condensation of water vapor or other species. We find that the main driving process is the radiative cooling of CO2 (or other noncondensable greenhouse gases) rather than CO2 greenhouse warming or stellar radiation. Stellar radiation is the ultimate mechanism but not the direct mechanism. Due to the combination of the uneven distribution in the stellar radiation and effective horizontal energy transports in the free troposphere, there is strong temperature inversion in the area away from the substellar region. This inversion makes CO2 have a radiative cooling effect rather than a radiative warming effect for the atmosphere, the same as that in the stratosphere of Earth’s atmosphere. This cooling effect produces negative buoyancy and drives large-scale downwelling, supporting the formation of a global-scale overturning circulation. If CO2 is excluded from the atmosphere, the overturning circulation becomes very weak, regardless of the level of stellar radiation. This mechanism is completely different from that for the atmospheric overturning circulation on Earth or on moist, tidally locked rocky planets, where latent heat release and/or baroclinic instability are the dominated mechanisms. Our study improves the understanding of the atmospheric circulation on tidally locked exoplanets and also on other dry planets, such as Venus and Mars in the solar system.
Learning Hands-On Electronics from Home: A Simulator for Fritzing

The recent pandemic has forced us to teach online, which is especially difficult for hand-on courses in robotics, like basic electronics. In this paper, I present a simulator which tries to replicate the same experience students will encounter during the exercises in the laboratory. The simulator has been developed in Fritzing, uses realistic multimeters for measuring and checks for common mistakes. Results show not only that the simulator was extremely useful during the pandemic, but also that it can supplement laboratory exercises when teaching in-classroom.
HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence

As social robots and other intelligent machines enter the home, artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. To accomplish such efficacious interaction, the next-generation AEI need comprehensive human emotion models for training. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tools. In practice, the strongest models need robust coverage, which means defining the smallest core set of emotions from which all others can be derived. To achieve the desired coverage, we turn to word embeddings from natural language processing. Using unsupervised clustering techniques, our experiments show that with as few as 15 discrete emotion categories, we can provide maximum coverage across six major languages--Arabic, Chinese, English, French, Spanish, and Russian. In support of our findings, we also examine annotations from two large-scale emotion recognition datasets to assess the validity of existing emotion models compared to human perception at scale. Because robust, comprehensive emotion models are foundational for developing real-world affective computing applications, this work has broad implications in social robotics, human-machine interaction, mental healthcare, and computational psychology.
VisageSynTalk: Unseen Speaker Video-to-Speech Synthesis via Speech-Visage Feature Selection

The goal of this work is to reconstruct speech from a silent talking face video. Recent studies have shown impressive performance on synthesizing speech from silent talking face videos. However, they have not explicitly considered on varying identity characteristics of different speakers, which place a challenge in the video-to-speech synthesis, and this becomes more critical in unseen-speaker settings. Our approach is to separate the speech content and the visage-style from a given silent talking face video. By guiding the model to independently focus on modeling the two representations, we can obtain the speech of high intelligibility from the model even when the input video of an unseen subject is given. To this end, we introduce speech-visage selection that separates the speech content and the speaker identity from the visual features of the input video. The disentangled representations are jointly incorporated to synthesize speech through visage-style based synthesizer which generates speech by coating the visage-styles while maintaining the speech content. Thus, the proposed framework brings the advantage of synthesizing the speech containing the right content even with the silent talking face video of an unseen subject. We validate the effectiveness of the proposed framework on the GRID, TCD-TIMIT volunteer, and LRW datasets.
Beamforming in Integrated Sensing and Communication Systems with Reconfigurable Intelligent Surfaces

We consider transmit beamforming and reflection pattern design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems to jointly precode communication symbols and radar waveforms. We treat two settings of multiple users and targets. In the first, we use a single RIS to enhance the communication performance of the ISAC system and design beams with good cross-correlation properties to match a desired beam pattern while guaranteeing a desired signal-to-interference plus noise ratio (SINR) for each user. In the second setting, we use two dedicated RISs to aid the ISAC system, wherein the beams are designed to maximize the worst-case target illumination power while guaranteeing a desired SINR for each user. We propose solvers based on alternating optimization as the design problems in both cases are non-convex optimization problems. Through a number of numerical simulations, we demonstrate the advantages of RIS-assisted ISAC systems. In particular, we show that the proposed single-RIS assisted ISAC system improves the minimum user SINR while suffering from a moderate loss in radar target illumination power. On the other hand, the dual-RIS assisted ISAC system improves both minimum user SINR as well as worst-case target illumination power at the targets, especially when the users and targets are not directly visible.
Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations

Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of temperatures and sample sizes is still a challenge. Herein we present a systematic investigation of the thermal transport properties of a-Si by employing large-scale molecular dynamics (MD) simulations with an accurate and efficient machine-learned neuroevolution potential (NEP) trained against abundant reference data calculated at the quantum-mechanical density-functional-theory level. The high efficiency of NEP allows us to study the effects of finite size and quenching rate in the formation of a-Si in great detail. We find that it requires a simulation cell up to $64,000$ atoms (a cubic cell with a linear size of 11 nm) and a quenching rate down to $10^{11}$ K s$^{-1}$ for fully convergent thermal conductivity. Structural properties, including short- and medium-range order as characterized by the pair correlation function, angular distribution function, coordination number, ring statistics and structure factor are studied to demonstrate the accuracy of NEP and to further evaluate the role of quenching rate. Using both the heterogeneous and the homogeneous nonequilibrium MD methods and the related spectral decomposition techniques, we calculate the temperature- and thickness-dependent thermal conductivity values of a-Si and show that they agree well with available experimental results from 10 K to room temperature. Our results also highlight the importance of quantum effects in the calculated thermal conductivity and support the quantum correction method based on the spectral thermal conductivity.
Online Contextual Decision-Making with a Smart Predict-then-Optimize Method

We study an online contextual decision-making problem with resource constraints. At each time period, the decision-maker ﬁrst predicts a reward vector and resource consumption matrix based on a given context vector and then solves a downstream optimization problem to make a decision. The ﬁnal goal of the decision-maker is to maximize the summation of the reward and the utility from resource consumption, while satisfying the resource constraints. We propose an algorithm that mixes a prediction step based on the “Smart Predict-then-Optimize (SPO)” method with a dual update step based on mirror descent. We prove regret bounds and demonstrate that the overall convergence rate of our method depends on the O ( T − 1 / 2 ) convergence of online mirror descent as well as risk bounds of the surrogate loss function used to learn the prediction model. Our algorithm and regret bounds apply to a general convex feasible region for the resource constraints, including both hard and soft resource constraint cases, and they apply to a wide class of prediction models in contrast to the traditional settings of linear contextual models or ﬁnite policy spaces. We also conduct numerical experiments to empirically demonstrate the strength of our proposed SPO-type methods, as compared to traditional prediction-error-only methods, on multi-dimensional knapsack and longest path instances.
Lazy Queries Can Reduce Variance in Zeroth-Order Optimization

A major challenge of applying zeroth-order (ZO) methods is the high query complexity, especially when queries are costly. We propose a novel gradient estimation technique for ZO methods based on adaptive lazy queries that we term as LAZO. Unlike the classic one-point or two-point gradient estimation methods, LAZO develops two alternative ways to check the usefulness of old queries from previous iterations, and then adaptively reuses them to construct the low-variance gradient estimates. We rigorously establish that through judiciously reusing the old queries, LAZO can reduce the variance of stochastic gradient estimates so that it not only saves queries per iteration but also achieves the regret bound for the symmetric two-point method. We evaluate the numerical performance of LAZO, and demonstrate the low-variance property and the performance gain of LAZO in both regret and query complexity relative to several existing ZO methods. The idea of LAZO is general and can be applied to other variants of ZO methods.
A Unified Sequence Interface for Vision Tasks

While language tasks are naturally expressed in a single, unified, modeling framework, i.e., generating sequences of tokens, this has not been the case in computer vision. As a result, there is a proliferation of distinct architectures and loss functions for different vision tasks. In this work we show that a diverse set of"core"computer vision tasks can also be unified if formulated in terms of a shared pixel-to-sequence interface. We focus on four tasks, namely, object detection, instance segmentation, keypoint detection, and image captioning, all with diverse types of outputs, e.g., bounding boxes or dense masks. Despite that, by formulating the output of each task as a sequence of discrete tokens with a unified interface, we show that one can train a neural network with a single model architecture and loss function on all these tasks, with no task-specific customization. To solve a specific task, we use a short prompt as task description, and the sequence output adapts to the prompt so it can produce task-specific output. We show that such a model can achieve competitive performance compared to well-established task-specific models.
Polarization dependent photoemission as a probe of the magnetic ground state in the van der Waals ferromagnet VI3

Van der Waals ferromagnets are thrilling materials from both a fundamental and technological point of view. VI3 is an interesting example, with a complex magnetism that differentiates it from the first reported Cr based layered ferromagnets. Here, we show in an indirect way through angle resolved photoemission spectroscopy experiments, the importance of spin–orbit coupling setting the electronic properties of this material. Our light polarized photoemission measurements point to a ground state with a half-filled [Formula: see text] doublet, where a gap opening is triggered by spin–orbit coupling enhanced by electronic correlations.
Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates

Robust and sparse estimation of linear regression coefficients is investigated. The situation addressed by the present paper is that covariates and noises are sampled from heavy-tailed distributions, and the covariates and noises are contaminated by malicious outliers. Our estimator can be computed efficiently. Further, the error bound of the estimator is nearly optimal.
Sequential Optimization Numbers and Conjecture about Edge-Symmetry and Weight-Symmetry Shortest Weight-Constrained Path

This paper defines multidimensional sequential optimization numbers and prove that the unsigned Stirling numbers of first kind are 1dimensional sequential optimization numbers. This paper gives a recurrence formula and an upper bound of multidimensional sequential optimization numbers. We proof that the k-dimensional sequential optimization numbers, denoted by Ok(n,m), are almost in {Ok(n, α)}, where α ∈ [1, ek ∙ log(n − 1) + eπ 2 6 (2k − 1) +M1], n is the size of k-dimensional sequential optimization numbers and M1 is large positive integer. Many achievements of the Stirling numbers of first kind can be transformed into the properties of k-dimensional sequential optimization numbers by k-dimensional extension and we give some examples. Shortest weight-constrained path is NP-complete problem [1]. In the case of edge symmetry and weight symmetry, we use the definition of the optimization set to design 2-dimensional Bellman-Ford algorithm to solve it. According to the fact that P1(n,m > M) ≤ e−M1, where M = elog(n − 1) + e +M1, M1 is a positive integer and P1(n,m) is the probability of 1-dimensional sequential optimization numbers, this paper conjecture that the probability of solving edge-symmetry and weight-symmetry shortest weight-constrained path problem in polynomial time approaches 1 exponentially with the increase of constant term in algorithm complexity. The results of a large number of simulation experiments agree with this conjecture.
The SIGMORPHON 2022 Shared Task on Morpheme Segmentation

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.
Efficient Adaptive Ensembling for Image Classification

In recent times, with the exception of sporadic cases, the trend in computer vision is to achieve minor improvements compared to considerable increases in complexity. To reverse this trend, we propose a novel method to boost image classification performances without increasing complexity. To this end, we revisited ensembling, a powerful approach, often not used properly due to its more complex nature and the training time, so as to make it feasible through a specific design choice. First, we trained two EfficientNet‐b0 end‐to‐end models (known to be the architecture with the best overall accuracy/complexity trade‐off for image classification) on disjoint subsets of data (i.e., bagging). Then, we made an efficient adaptive ensemble by performing fine‐tuning of a trainable combination layer. In this way, we were able to outperform the state‐of‐the‐art by an average of 0.5% on the accuracy, with restrained complexity both in terms of the number of parameters (by 5–60 times), and the FLoating point Operations Per Second FLOPS by 10–100 times on several major benchmark datasets.
NatGen: generative pre-training by “naturalizing” source code

Pre-trained Generative Language models (e.g., PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and translation. These models have adopted varying pre-training objectives to learn statistics of code construction from very large-scale corpora in a self-supervised fashion; the success of pre-trained models largely hinges on these pre-training objectives. This paper proposes a new pre-training objective, “Naturalizing” of source code, exploiting code’s bimodal, dual-channel (formal & natural channels) nature. Unlike natural language, code’s bimodal, dual-channel nature allows us to generate semantically equivalent code at scale. We introduce six classes of semantic preserving transformations to introduce unnatural forms of code, and then force our model to produce more natural original programs written by developers. Learning to generate equivalent, but more natural code, at scale, over large corpora of open-source code, without explicit manual supervision, helps the model learn to both ingest & generate code. We fine-tune our model in three generative Software Engineering tasks: code generation, code translation, and code refinement with limited human-curated labeled data and achieve state-of-the-art performance rivaling CodeT5. We show that our pre-trained model is especially competitive at zero-shot and few-shot learning, and better at learning code properties (e.g., syntax, data flow)
Adaptive pointwise density estimation under local differential privacy

We consider the estimation of a density at a ﬁxed point under a local diﬀerential privacy constraint, where the observations are anonymised before being available for statistical inference. We propose both a privatised version of a projection density estimator as well as a kernel density estimator and derive their minimax rates under a privacy constraint. There is a twofold deterioration of the minimax rates due to the anonymisation, which we show to be unavoidable by providing lower bounds. In both estimation procedures a tuning parameter has to be chosen. We suggest a variant of the classical Goldenshluger-Lepski method for choosing the bandwidth and the cut-oﬀ dimension, respectively, and analyse its performance. It provides adaptive minimax-optimal (up to log-factors) estimators. We discuss in detail how the lower and upper bound depend on the privacy constraints, which in turn is reﬂected by a modiﬁcation of the adaptive method.