Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Pruning of Large Language Model with Adaptive Estimation Fusion

Large language models (LLMs) have become crucial for many generative downstream tasks, leading to an inevitable trend and significant challenge to deploy them efficiently on resource-constrained devices. Structured pruning is a widely used method to address this challenge. However, when dealing with the complex structure of the multiple decoder layers, general methods often employ common estimation approaches for pruning. These approaches lead to a decline in accuracy for specific downstream tasks. In this paper, we introduce a simple yet efficient method that adaptively models the importance of each substructure. Meanwhile, it can adaptively fuse coarse-grained and finegrained estimations based on the results from complex and multilayer structures. All aspects of our design seamlessly integrate into the endto-end pruning framework. Our experimental results, compared with state-of-the-art methods on mainstream datasets, demonstrate average accuracy improvements of 1.1%, 1.02%, 2.0%, and 1.2% for LLaMa-7B,Vicuna-7B, Baichuan-7B, and Bloom-7b1, respectively.
3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models

In this work, we show that synthetic data created by generative models is complementary to computer graphics (CG) rendered data for achieving remarkable generalization performance on diverse real-world scenes for 3D human pose and shape estimation (HPS). Specifically, we propose an effective approach based on recent diffusion models, termed HumanWild, which can effortlessly generate human images and corresponding 3D mesh annotations. We first collect a large-scale human-centric dataset with comprehensive annotations, e.g., text captions and surface normal images. Then, we train a customized ControlNet model upon this dataset to generate diverse human images and initial ground-truth labels. At the core of this step is that we can easily obtain numerous surface normal images from a 3D human parametric model, e.g., SMPL-X, by rendering the 3D mesh onto the image plane. As there exists inevitable noise in the initial labels, we then apply an off-the-shelf foundation segmentation model, i.e., SAM, to filter negative data samples. Our data generation pipeline is flexible and customizable to facilitate different real-world tasks, e.g., ego-centric scenes and perspective-distortion scenes. The generated dataset comprises 0.79M images with corresponding 3D annotations, covering versatile viewpoints, scenes, and human identities. We train various HPS regressors on top of the generated data and evaluate them on a wide range of benchmarks (3DPW, RICH, EgoBody, AGORA, SSP-3D) to verify the effectiveness of the generated data. By exclusively employing generative models, we generate large-scale in-the-wild human images and high-quality annotations, eliminating the need for real-world data collection.
Susceptibility of Communities against Low-Credibility Content in Social News Websites

Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites sites is the formation of echo chambers, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities within a social news website that are prone to uncredible or highly biased news. We employ a user embedding pipeline that detects user communities based on their stances towards posts and news sources. We then project each community onto a credibility-bias space and analyze the distributional characteristics of each projected community to identify those that have a high risk of adopting beliefs with low credibility or high bias. This approach also enables the prediction of individual users' susceptibility to low credibility content, based on their community affiliation. Our experiments show that latent space clusters effectively indicate the credibility and bias levels of their users, with significant differences observed across clusters -- a $34\%$ difference in the users' susceptibility to low-credibility content and a $8.3\%$ difference in the users' susceptibility to high political bias.
Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks

This paper introduces an innovative deep joint source-channel coding (DeepJSCC) approach to image transmission over a cooperative relay channel. The relay either amplifies and forwards a scaled version of its received signal, referred to as DeepJSCC-AF, or leverages neural networks to extract relevant features about the source signal before forwarding it to the destination, which we call DeepJSCC-PF (Process-and-Forward). In the full-duplex scheme, inspired by the block Markov coding (BMC) concept, we introduce a novel block transmission strategy built upon novel vision transformer architecture. In the proposed scheme, the source transmits information in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal to be conveyed to the destination. To enhance practicality, we introduce an adaptive transmission model, which allows a single trained DeepJSCC model to adapt seamlessly to various channel qualities, making it a versatile solution. Simulation results demonstrate the superior performance of our proposed DeepJSCC compared to the state-of-the-art BPG image compression algorithm, even when operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, for both half-duplex and full-duplex relay scenarios.
DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages

Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is language variation in the form of non-standard dialects or language varieties (hereafter, varieties). Most NLP benchmarks are limited to standard language varieties. To fill this gap, we propose DIALECTBENCH, the first-ever large-scale benchmark for NLP on varieties, which aggregates an extensive set of task-varied variety datasets (10 text-level tasks covering 281 varieties). This allows for a comprehensive evaluation of NLP system performance on different language varieties. We provide substantial evidence of performance disparities between standard and non-standard language varieties, and we also identify language clusters with large performance divergence across tasks. We believe DIALECTBENCH provides a comprehensive view of the current state of NLP for language varieties and one step towards advancing it further. Code/data: https://github.com/ffaisal93/DialectBench
The emerging prominence of computer electrocardiography in large population-based surveys.

The year 1987 marks the centennial of electrocardiography. As Beamish reminds us, it was the first and foremost technical aid to the cardiologist. During the history of cardiovascular epidemiology, which dates back roughly forty years, electrocardiography has played a central role as a relatively specific marker for coronary and hypertensive heart disease. The lack of sensitivity of electrocardiography and the advent of new technologies such as ultrasonography for assessment of such heart disease in cardiology and cardiovascular epidemiology has raised questions as to whether electrocardiography might be replaced in the next century. Recent developments in computer electrocardiography have rendered suggestions of the decreased importance of electrocardiography premature. Use of computer analysis of electrocardiograms in epidemiologic studies, which dates back less than three decades, has renewed interest in and expanded the role of electrocardiography in cardiology in general and cardiovascular epidemiology in particular. The computer analysis of electrocardiograms has allowed the processing and analysis of large numbers of electrocardiograms in a rapid, efficient, systematic and consistent manner. This has led to new, more sensitive criteria for detection of left ventricular hypertrophy as a discrete variable. In addition, the numerous waveform amplitude and duration measurements by computer have allowed the development of mathematical models to express the likelihood of coronary heart disease (myocardial infarction) and degree of left ventricular hypertrophy (estimates of left ventricular mass) on continuous scales. These developments have contributed substantially to the potential of electrocardiography (and particularly computer electrocardiography) in future studies and clinical applications. (ABSTRACT TRUNCATED AT 250 WORDS)
Broadband Cavity-Enhanced Kerr Comb Spectroscopy on Chip

The broad and equidistant spectrum of frequency combs has had a profound impact on spectroscopic studies. Particularly, experiments involving the coupling of frequency combs to cavities have already enabled unprecedented broadband and sensitive spectroscopy on a single-molecule level. The emergence of integrated, compact and broadband Kerr-microcombs holds promise to bring many metrological and spectroscopic studies outside of the lab. However, performing cavity-enhanced direct frequency comb spectroscopy on-chip has remained a challenge. Here, we couple a microcomb source with a microcavity to extend the advantages of cavity-enhanced spectroscopy to photonically integrated circuits. By harnessing the coherent nature of the Kerr-comb and high-Q microcavity enhancement, we obtain a detailed dispersion landscape of the guided-wave mode and comprehensive frequency-dependent cavity lineshapes. Our microcomb-cavity coupling can facilitate photonically integrated cavity-enhanced biochemical spectroscopy by evanescently coupling analytes to the cavity's guided mode, a mode of operation we analyze and provide guidelines for. Demonstrated detailed dispersion measurements, overperforming state-of-the-art table-top tunable lasers in available bandwidth, show potential for integrated nonlinear optics applications, as precise dispersion management is crucial for such processes. Our chip-scale comb-cavity coupled platform suggests an integrated, broadband, cost-effective and accurate tool for the nonlinear optics studies as well as for ultra-compact bio- and chemical- sensing platform.
A PRACTICAL LOCALIZATION SYSTEM FOR ORCHARD VEHICLES

This paper addresses the design, development and field-testing of a localization system for agricultural vehicles. The Autonomous Prime Movers (APMs) are electrical vehicles designed to operate in specialty crops, more specifically in orchards. In order to accomplish geo-referenced tasks inside the crops, the vehicles must know their pose with sub-metric accuracy. One of our requirements is that the localization system shouldn’t add to the vehicle’s hardware cost, so as to keep the acquisition cost to growers as low as possible. Therefore, we confine ourselves to solutions that use only the sensor suite already installed on the robot for navigation in our case, laser scanners and steering and wheel encoders. The developed localization methodology employs an Extended Kalman Filter. The APM pose is predicted using encoder odometry and updated with point and line features detected with the laser. Tests conducted at our experimental orchard-like environment in Pittsburgh and actual apple orchards in Pennsylvania and Washington states indicate that the localization system is able to estimate the vehicle pose with sub-metric accuracy. Keywords— Autonomous Agricultural Vehicles, GPS-Free Localization, Extended Kalman Filter. Resumo— Este artigo apresenta o projeto, implementação e testes de campo de um sistema de localização para véıculos agŕıcolas. Os Autonomous Prime Movers (APMs) são véıculos elétricos projetados para operações em pomares e viveiros. A fim de realizar tarefas geo-referenciadas dentro da plantação, os APMs devem conhecer sua posição com precisão sub-métrica. O sistema de localização não deve adicionar custos ao véıculo, de forma a facilitar sua aquisição por produtores agŕıcolas. Desse modo, o sistema desenvolvido utiliza apenas os sensores já dispońıveis no véıculo para navegação no caso, laser scanners e encoders, integrados utilizando um filtro de Kalman estendido (EKF). Testes realizados em pomares comerciais e experimentais demonstram que o sistema de localização é capaz de estimar a posição do véıculo de maneira eficiente, e indicam condições de operação onde o sistema apresenta precisão sub-métrica. Palavras-chave— Véıculos Agŕıcolas Autônomos, Localização sem GPS, Extended Kalman Filter.
Is Playing Exergames Really Exercising? A Meta-Analysis of Energy Expenditure in Active Video Games

This article reports a meta-analysis of energy expenditure (EE) of playing active video games (AVGs). In particular, heart rate (HR), oxygen consumption (VO2), and EE were assessed and three moderators for the effects of AVGs--types of AVG, player age, and player weight status--were analyzed. The results show that playing AVGs significantly increased HR, VO2, and EE from resting. The effect sizes of playing AVGs on HR, VO2, and EE were similar to traditional physical activities. AVG type and player age were significant moderators for the effects of AVGs. The finding suggests that AVGs are effective technologies that may facilitate light- to moderate-intensity physical activity promotion.
Exploiting Distributed Spatial Diversity in Wireless Networks

We develop energy-ef£cient transmission protocols for wireless networks that exploit spatial diversity available at distributed antennas to combat multipath fading through coordinated transmission and/or processing by several radios. We discuss the problem in a general network setting and focus on a multiple-access case with suf£cient symmetry to make the presentation concise. In particular, we examine several possibilities for the strategy employed by the assisting radios, or relays, including decoding and forwarding and amplifying and forwarding. To characterize performance, we develop outage regions and associated outage probabilities that indicate robustness of the transmissions to varying signal-to-noise ratios (SNRs). The outage regions are treated as set-functions of the observed SNRγ on the channel between the two source radios. The outage regions are thus (conditional) events de£ned in terms of the other SNR parameters of the channel, and can be readily interpreted in coded and uncoded settings. Our results suggest that the relays employ a threshold rule, namely, for SNRs satisfying γ > γ∗, for some valueγ∗, the relays decide to cooperate and pass along each other’s transmissions, preferably by decoding and re-encoding with a separate codebook for suf£ciently high γ; for SNRs satisfyingγ < γ∗, the relays can do better by simply retransmitting their own signals.
Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning

It is desirable for an agent to be able to solve a rich variety of problems that can be specified through language in the same environment. A popular approach towards obtaining such agents is to reuse skills learned in prior tasks to generalise compositionally to new ones. However, this is a challenging problem due to the curse of dimensionality induced by the combinatorially large number of ways high-level goals can be combined both logically and temporally in language. To address this problem, we propose a framework where an agent first learns a sufficient set of skill primitives to achieve all high-level goals in its environment. The agent can then flexibly compose them both logically and temporally to provably achieve temporal logic specifications in any regular language, such as regular fragments of linear temporal logic. This provides the agent with the ability to map from complex temporal logic task specifications to near-optimal behaviours zero-shot. We demonstrate this experimentally in a tabular setting, as well as in a high-dimensional video game and continuous control environment. Finally, we also demonstrate that the performance of skill machines can be improved with regular off-policy reinforcement learning algorithms when optimal behaviours are desired.
Vitamin A–Not for Your Eyes Only: Requirement for Heart Formation Begins Early in Embryogenesis

Vitamin A insufficiency has profound adverse effects on embryonic development. Major advances in understanding the role of vitamin A in vertebrate heart formation have been made since the discovery that the vitamin A active form, all-trans-retinoic acid, regulates many genes, including developmental genes. Among the experimental models used, the vitamin A-deficient avian embryo has been an important tool to study the function of vitamin A during early heart formation. A cluster of retinoic acid-regulated developmental genes have been identified that participate in building the heart. In the absence of retinoic acid the embryonic heart develops abnormally leading to embryolethality.
usfAD Based Effective Unknown Attack Detection Focused IDS Framework

The rapid expansion of varied network systems, including the Internet of Things (IoT) and Industrial Internet of Things (IIoT), has led to an increasing range of cyber threats. Ensuring robust protection against these threats necessitates the implementation of an effective Intrusion Detection System (IDS). For more than a decade, researchers have delved into supervised machine learning techniques to develop IDS to classify normal and attack traffic. However, building effective IDS models using supervised learning requires a substantial number of benign and attack samples. To collect a sufficient number of attack samples from real-life scenarios is not possible since cyber attacks occur occasionally. Further, IDS trained and tested on known datasets fails in detecting zero-day or unknown attacks due to the swift evolution of attack patterns. To address this challenge, we put forth two strategies for semi-supervised learning based IDS where training samples of attacks are not required: 1) training a supervised machine learning model using randomly and uniformly dispersed synthetic attack samples; 2) building a One Class Classification (OCC) model that is trained exclusively on benign network traffic. We have implemented both approaches and compared their performances using 10 recent benchmark IDS datasets. Our findings demonstrate that the OCC model based on the state-of-art anomaly detection technique called usfAD significantly outperforms conventional supervised classification and other OCC based techniques when trained and tested considering real-life scenarios, particularly to detect previously unseen attacks.
Climate tolerance and interspecific variation in geographic range size

The fact that climate influences the continental–scale distributions of species is one of the central tenets of ecology and biogeography. Equally elemental is that species exhibit enormous variation in geographic range size, with most occupying comparatively small areas. The degree to which climate can account for this variation remains unclear. Here, I test three alternative climate–based hypotheses for variation in range size using a large sample of tree and shrub species native to North America north of Mexico. I show that the lowest value of January average daily minimum temperature encompassed by a species' geographic range (TMIN), representing the‘climate extremes hypothesis’, explains almost 80% of the variation in range size. Hypotheses based on seasonality and climate optima find substantially less support. The relationship between range size and TMIN does not change across the breadth of latitudes examined, and is general for conifers and hardwoods, and growth form (tree versus shrub). Differential freezing resistance gains support as the mechanism underlying interspecific variation in range size: using 35 species for which data were available, both TMIN and range size are shown to be strongly correlated with measures of freezing resistance.
Self-supervised co-salient object detection via feature correspondence at multiple scales

Our paper introduces a novel two-stage self-supervised approach for detecting co-occurring salient objects (CoSOD) in image groups without requiring segmentation annotations. Unlike existing unsupervised methods that rely solely on patch-level information (e.g. clustering patch descriptors) or on computation heavy off-the-shelf components for CoSOD, our lightweight model leverages feature correspondences at both patch and region levels, significantly improving prediction performance. In the first stage, we train a self-supervised network that detects co-salient regions by computing local patch-level feature correspondences across images. We obtain the segmentation predictions using confidence-based adaptive thresholding. In the next stage, we refine these intermediate segmentations by eliminating the detected regions (within each image) whose averaged feature representations are dissimilar to the foreground feature representation averaged across all the cross-attention maps (from the previous stage). Extensive experiments on three CoSOD benchmark datasets show that our self-supervised model outperforms the corresponding state-of-the-art models by a huge margin (e.g. on the CoCA dataset, our model has a 13.7% F-measure gain over the SOTA unsupervised CoSOD model). Notably, our self-supervised model also outperforms several recent fully supervised CoSOD models on the three test datasets (e.g., on the CoCA dataset, our model has a 4.6% F-measure gain over a recent supervised CoSOD model).
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection

Facial action unit (AU) detection is a fundamental block for objective facial expression analysis. Supervised learning approaches require a large amount of manual labeling which is costly. The limited labeled data are also not diverse in terms of gender which can affect model fairness. In this paper, we propose to use synthetically generated data and multi-source domain adaptation (MSDA) to address the problems of the scarcity of labeled data and the diversity of subjects. Specifically, we propose to generate a diverse dataset through synthetic facial expression re-targeting by transferring the expressions from real faces to synthetic avatars. Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset. Instead of aligning the overall distributions of different domains, we propose Paired Moment Matching (PM2) to align the features of the paired real and synthetic data with the same facial expression. To further improve gender fairness, PM2 matches the features of the real data with a female and a male synthetic image. Our results indicate that synthetic data and the proposed model improve both AU detection performance and fairness across genders, demonstrating its potential to solve AU detection in-the-wild.