Miracle-dz/UniFAR-FTUs huggingface dataset:
LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control

Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori. The problem is challenging in decentralized settings with robots that have limited communication and sensing capabilities. We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem, wherein a convolution neural network (CNN) processes localized perception; a graph neural network (GNN) facilitates robot communications; finally, a shallow multi-layer perceptron (MLP) computes robot actions. The GNN enables collaboration in the robot swarm by computing what information to communicate with nearby robots and how to incorporate received information. Evaluations show that the LPAC models -- trained using imitation learning -- outperform standard decentralized and centralized coverage control algorithms. The learned policy generalizes to environments different from the training dataset, transfers to larger environments with more robots, and is robust to noisy position estimates. The results indicate the suitability of LPAC architectures for decentralized navigation in robot swarms to achieve collaborative behavior.
Information scrambling and butterfly velocity in quantum spin glass chains

We make lattice generalization of two well-known zero dimensional models of quantum spin glass, Sachdev-Ye (SY) and spherical quantum $p$-spin glass model, to one dimension for studying crossovers in non-local scrambling dynamics due to glass transition, complex dynamics, and quantum and thermal fluctuations in paramagnetic (PM) and spin glass (SG) phases. In the SY chain of quantum dots, each described by infinite-range random Heisenberg model of $N$ spin-$S$ $SU(M)$ spins, we obtain the quantum Lyapunov exponent $\lambda_\mathrm{L}$ and butterfly velocity $v_B$ as a function of temperature $T$ and the quantum parameter $S$ across the PM-SG phase boundary using a bosonic spinon representation in the large-$N,M$ limit. In particular, we extract asymptotic $T$ and $S$ dependence, e.g., power laws, for $\lambda_\mathrm{L}$ and $v_B$ in different regions deep inside the phases and near the replica symmetry breaking SG transition. We find the chaos to be non-maximal almost over the entire phase diagram. Very similar results for chaos indicators are found for the $p$-spin glass chain as a function of temperature and a suitable quantum parameter $\Gamma$, with some important qualitative differences. In particular, $\lambda_\mathrm{L}$ and $v_B$ exhibit a maximum, coinciding with onset of complex glassy relaxation, above the glass transition as a function of $T$ and $\Gamma$ in the PM phase of the $p$-spin glass model. In contrast, the maximum is only observed as a function of $S$, but not with temperature, in the PM phase of SY model. The maximum originates from enhanced chaos due to maximal complexity in the glassy landscape. Thus, the results in the SY model indicate very different evolution of glassy complexity with quantum and thermal fluctuations.
Reconstruction of shredded random matrices

A matrix is given in ``shredded'' form if we are presented with the multiset of rows and the multiset of columns, but not told which row is which or which column is which. The matrix is reconstructible if it is uniquely determined by this information. Let $M$ be a random binary $n\times n$ matrix, where each entry independently is $1$ with probability $p=p(n)\le\frac12$. Atamanchuk, Devroye and Vicenzo introduced the problem and showed that $M$ is reconstructible with high probability for $p\ge (2+\varepsilon)\frac{1}{n}\log n$. Here we find that the sharp threshold for reconstructibility is at $p\sim\frac{1}{2n}\log n$.
Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination

Although affective expressions of individuals have been extensively studied using social media, research has primarily focused on the Western context. There are substantial differences among cultures that contribute to their affective expressions. This paper examines the differences between Twitter (X) in the United States and Sina Weibo posts in China on two primary dimensions of affect - valence and arousal. We study the difference in the functional relationship between arousal and valence (so-called V-shaped) among individuals in the US and China and explore the associated content differences. Furthermore, we correlate word usage and topics in both platforms to interpret their differences. We observe that for Twitter users, the variation in emotional intensity is less distinct between negative and positive emotions compared to Weibo users, and there is a sharper escalation in arousal corresponding with heightened emotions. From language features, we discover that affective expressions are associated with personal life and feelings on Twitter, while on Weibo such discussions are about socio-political topics in the society. These results suggest a West-East difference in the V-shaped relationship between valence and arousal of affective expressions on social media influenced by content differences. Our findings have implications for applications and theories related to cultural differences in affective expressions.
Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics

In the past several years, road anomaly segmentation is actively explored in the academia and drawing growing attention in the industry. The rationale behind is straightforward: if the autonomous car can brake before hitting an anomalous object, safety is promoted. However, this rationale naturally calls for a temporally informed setting while existing methods and benchmarks are designed in an unrealistic frame-wise manner. To bridge this gap, we contribute the first video anomaly segmentation dataset for autonomous driving. Since placing various anomalous objects on busy roads and annotating them in every frame are dangerous and expensive, we resort to synthetic data. To improve the relevance of this synthetic dataset to real-world applications, we train a generative adversarial network conditioned on rendering G-buffers for photorealism enhancement. Our dataset consists of 120,000 high-resolution frames at a 60 FPS framerate, as recorded in 7 different towns. As an initial benchmarking, we provide baselines using latest supervised and unsupervised road anomaly segmentation methods. Apart from conventional ones, we focus on two new metrics: temporal consistency and latencyaware streaming accuracy. We believe the latter is valuable as it measures whether an anomaly segmentation algorithm can truly prevent a car from crashing in a temporally informed setting.
Numerically Computing Finite Temperature Loop Integrals using pySecDec

Finite-temperature quantum field theory provides the foundation for many important phenomena in the Standard Model and extensions, including phase transitions, baryogenesis, and gravitational waves. Methods are developed to enable application of pySecDec (a Python-language-based package designed for numerical calculation of dimensionally-regulated loop integrals) to numerically evaluate finite-temperature loop integrals in the imaginary time (Matsubara) formalism. These methods consist of two main elements: an inverse Wick rotation that converts a finite-temperature loop integral into a form applicable to pySecDec, and asymptotic techniques to regulate and accelerate convergence of the Matsubara frequency summations. Numerical pySecDec evaluation of finite-temperature, two-point and three-point, one-loop topologies for scalar fields is used to illustrate and validate these new methodologies. Advantages of these finite-temperature pySecDec numerical methods are illustrated by the inclusion of multiple mass and external momentum scales.
How predictable is language model benchmark performance?

We investigate large language model performance across five orders of magnitude of compute scaling in eleven recent model architectures. We show that average benchmark performance, aggregating over many individual tasks and evaluations as in the commonly-used BIG-Bench dataset, is decently predictable as a function of training compute scale. Specifically, when extrapolating BIG-Bench Hard performance across one order of magnitude in compute, we observe average absolute errors of 6 percentage points (pp). By contrast, extrapolation for individual BIG-Bench tasks across an order of magnitude in compute yields higher average errors of 18pp. Nonetheless, individual task performance remains significantly more predictable than chance. Overall, our work suggests compute scaling provides a promising basis to forecast AI capabilities in diverse benchmarks, though predicting performance in specific tasks poses challenges.
Hyperbolic Machine Learning Moment Closures for the BGK Equations

We introduce a hyperbolic closure for the Grad moment expansion of the Bhatnagar-Gross-Krook's (BGK) kinetic model using a neural network (NN) trained on BGK's moment data. This closure is motivated by the exact closure for the free streaming limit that we derived in our paper on closures in transport \cite{Huang2022-RTE1}. The exact closure relates the gradient of the highest moment to the gradient of four lower moments. As with our past work, the model presented here learns the gradient of the highest moment in terms of the coefficients of gradients for all lower ones. By necessity, this means that the resulting hyperbolic system is not conservative in the highest moment. For stability, the output layers of the NN are designed to enforce hyperbolicity and Galilean invariance. This ensures the model can be run outside of the training window of the NN. Unlike our previous work on radiation transport that dealt with linear models, the BGK model's nonlinearity demanded advanced training tools. These comprised an optimal learning rate discovery, one cycle training, batch normalization in each neural layer, and the use of the \texttt{AdamW} optimizer. To address the non-conservative structure of the hyperbolic model, we adopt the FORCE numerical method to achieve robust solutions. This results in a comprehensive computing model combining learned closures with methods for solving hyperbolic models. The proposed model can capture accurate moment solutions across a broad spectrum of Knudsen numbers. Our paper details the multi-scale model construction and is run on a range of test problems.
Score Distillation Sampling with Learned Manifold Corrective

Score Distillation Sampling (SDS) is a recent but already widely popular method that relies on an image diffusion model to control optimization problems using text prompts. In this paper, we conduct an in-depth analysis of the SDS loss function, identify an inherent problem with its formulation, and propose a surprisingly easy but effective fix. Specifically, we decompose the loss into different factors and isolate the component responsible for noisy gradients. In the original formulation, high text guidance is used to account for the noise, leading to unwanted side effects. Instead, we train a shallow network mimicking the timestep-dependent denoising deficiency of the image diffusion model in order to effectively factor it out. We demonstrate the versatility and the effectiveness of our novel loss formulation through several qualitative and quantitative experiments, including optimization-based image synthesis and editing, zero-shot image translation network training, and text-to-3D synthesis.
Prompting Large Language Models for Recommender Systems: A Comprehensive Framework and Empirical Analysis

Recently, large language models such as ChatGPT have showcased remarkable abilities in solving general tasks, demonstrating the potential for applications in recommender systems. To assess how effectively LLMs can be used in recommendation tasks, our study primarily focuses on employing LLMs as recommender systems through prompting engineering. We propose a general framework for utilizing LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. To conduct our analysis, we formalize the input of LLMs for recommendation into natural language prompts with two key aspects, and explain how our framework can be generalized to various recommendation scenarios. As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs. As for prompt engineering, we further analyze the impact of four important components of prompts, \ie task descriptions, user interest modeling, candidate items construction and prompting strategies. In each section, we first define and categorize concepts in line with the existing literature. Then, we propose inspiring research questions followed by experiments to systematically analyze the impact of different factors on two public datasets. Finally, we summarize promising directions to shed lights on future research.
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation

Prior work on English monolingual retrieval has shown that a cross-encoder trained using a large number of relevance judgments for query-document pairs can be used as a teacher to train more efficient, but similarly effective, dual-encoder student models. Applying a similar knowledge distillation approach to training an efficient dual-encoder model for Cross-Language Information Retrieval (CLIR), where queries and documents are in different languages, is challenging due to the lack of a sufficiently large training collection when the query and document languages differ. The state of the art for CLIR thus relies on translating queries, documents, or both from the large English MS MARCO training set, an approach called Translate-Train. This paper proposes an alternative, Translate-Distill, in which knowledge distillation from either a monolingual cross-encoder or a CLIR cross-encoder is used to train a dual-encoder CLIR student model. This richer design space enables the teacher model to perform inference in an optimized setting, while training the student model directly for CLIR. Trained models and artifacts are publicly available on Huggingface.
Thermomechanical Dissipative behaviour of CuZr metallic glasses

We performed molecular dynamics simulations of Zr$_{50}$Cu$_{50}$ metallic glass samples submitted to mechanical deformation at different strain rates. The simultaneous measurements of the stress-strain curve, and of the temperature evolution during the cyclic mechanical load, are used to determine the thermo-mechanical constitutive laws at the continuum scale. It is shown that plastic deformation acts as a heat source, but strong finite size effects affect the unfolding of shear bands and its related dissipation rate. Finally, a thermo-mechanical constitutive law is proposed to reproduce quantitavely self-heating processes at different scales.
AdvMT: Adversarial Motion Transformer for Long-term Human Motion Prediction

To achieve seamless collaboration between robots and humans in a shared environment, accurately predicting future human movements is essential. Human motion prediction has traditionally been approached as a sequence prediction problem, leveraging historical human motion data to estimate future poses. Beginning with vanilla recurrent networks, the research community has investigated a variety of methods for learning human motion dynamics, encompassing graph-based and generative approaches. Despite these efforts, achieving accurate long-term predictions continues to be a significant challenge. In this regard, we present the Adversarial Motion Transformer (AdvMT), a novel model that integrates a transformer-based motion encoder and a temporal continuity discriminator. This combination effectively captures spatial and temporal dependencies simultaneously within frames. With adversarial training, our method effectively reduces the unwanted artifacts in predictions, thereby ensuring the learning of more realistic and fluid human motions. The evaluation results indicate that AdvMT greatly enhances the accuracy of long-term predictions while also delivering robust short-term predictions
The ALMA-CRISTAL survey. Discovery of a 15 kpc-long gas plume in a $z=4.54$ Lyman-$\alpha$ blob

Massive star-forming galaxies in the high-redshift universe host large reservoirs of cold gas in their circumgalactic medium (CGM). Traditionally, these reservoirs have been linked to diffuse H I Lyman-$\alpha$ (Ly$\alpha)$ emission extending beyond $\approx 10$ kpc scales. In recent years, millimeter/submillimeter observations are starting to identify even colder gas in the CGM through molecular and/or atomic tracers such as the [C II] $158\,\mu$m transition. In this context, we study the well-known J1000+0234 system at $z=4.54$ that hosts a massive dusty star-forming galaxy (DSFG), a UV-bright companion, and a Ly$\alpha$ blob. We combine new ALMA [C II] line observations taken by the CRISTAL survey with data from previous programs targeting the J1000+0234 system, and achieve a deep view into a DSFG and its rich environment at a 0.2"resolution. We identify an elongated [C II]-emitting structure with a projected size of 15 kpc stemming from the bright DSFG at the center of the field, with no clear counterpart at any other wavelength. The plume is oriented $\approx 40^{\circ}$ away from the minor axis of the DSFG, and shows significant spatial variation of its spectral parameters. In particular, the [C II] emission shifts from 180 km/s to 400 km/s between the bottom and top of the plume, relative to the DSFG's systemic velocity. At the same time, the line width starts at 400-600 km/s but narrows down to 190 km/s at top end of the plume. We discuss four possible scenarios to interpret the [C II] plume: a conical outflow, a cold accretion stream, ram pressure stripping, and gravitational interactions. While we cannot strongly rule out any of these with the available data, we disfavor the ram pressure stripping scenario due to the requirement of special hydrodynamic conditions.
The Fate of the Interstellar Medium in Early-type Galaxies. III. The Mechanism of Interstellar Medium Removal and the Quenching of Star Formation

Understanding how galaxies quench their star formation is crucial for studies of galaxy evolution. Quenching is related to a decrease of cold gas. In the first paper we showed that the dust removal timescale in early-type galaxies (ETGs) is about 2.5 Gyr. Here we present carbon monoxide and 21 cm hydrogen line observations of these galaxies and measure the timescale of removal of the cold interstellar medium (ISM). We find that all the cold ISM components (dust and molecular and atomic gas) decline at similar rates. This allows us to rule out a wide range of potential ISM-removal mechanisms (including starburst-driven outflows, astration, or a decline in the number of asymptotic giant branch stars), and artificial effects like the stellar mass–age correlation, environmental influence, mergers, and selection bias, leaving ionization by evolved low-mass stars and ionization/outflows by Type Ia supernovae or active galactic nuclei as viable mechanisms. We also provide evidence for an internal origin of the detected ISMs. Moreover, we find that the quenching of star formation in these galaxies cannot be explained by a reduction in the gas amount alone, because the star formation rates (SFRs) decrease faster (on a timescale of about 1.8 Gyr) than the amount of cold gas. Furthermore, the star formation efficiency (SFE) of the ETGs ( SFE ≡ SFR / M H 2 ) is lower than that of star-forming galaxies, whereas their gas mass fractions ( f H 2 ≡ M H 2 / M * ) are normal. This may be explained by the stabilization of gas against fragmentation, for example due to morphological quenching, turbulence, or magnetic fields.
Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques

Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to lifetimes of around nanoseconds or longer. Lepton colliders allow LLP searches to be conducted in a clean environment, and such searches can reach their full physics potential when combined with machine learning (ML) techniques. In the case of LLPs searches from Higgs decay in $e^+e^-\to ZH$, we show that the LLP signal efficiency can be improved up to 99% with an LLP mass around 50 GeV and a lifetime of approximately $1$ nanosecond, using deep neural network based approaches. The signal sensitivity for the branching ratio of Higgs decaying into LLPs reaches $1.2 \times 10^{-6}$ with a statistics of $4 \times 10^{6}$ Higgs.
Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision

Event-based vision is an emerging research field involving processing data generated by Dynamic Vision Sensors (neuromorphic cameras). One of the latest proposals in this area are Graph Convolutional Networks (GCNs), which allow to process events in its original sparse form while maintaining high detection and classification performance. In this paper, we present the hardware implementation of a~graph generation process from an event camera data stream, taking into account both the advantages and limitations of FPGAs. We propose various ways to simplify the graph representation and use scaling and quantisation of values. We consider both undirected and directed graphs that enable the use of PointNet convolution. The results obtained show that by appropriately modifying the graph representation, it is possible to create a~hardware module for graph generation. Moreover, the proposed modifications have no significant impact on object detection performance, only 0.08% mAP less for the base model and the N-Caltech data set.Finally, we describe the proposed hardware architecture of the graph generation module.
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording

Relating speech to EEG holds considerable importance but is challenging. In this study, a deep convolutional network was employed to extract spatiotemporal features from EEG data. Self-supervised speech representation and contextual text embedding were used as speech features. Contrastive learning was used to relate EEG features to speech features. The experimental results demonstrate the benefits of using self-supervised speech representation and contextual text embedding. Through feature fusion and model ensemble, an accuracy of 60.29% was achieved, and the performance was ranked as No.2 in Task 1 of the Auditory EEG Challenge (ICASSP 2024). The code to implement our work is available on Github: https://github.com/bobwangPKU/EEG-Stimulus-Match-Mismatch.