Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for enhanced star formation rates in $z 0.35$ cluster galaxies undergoing ram pressure stripping

Ram-pressure stripping (RPS) is the mechanism most often invoked to explain the observed differences between cluster and field galaxies. In the local Universe, its effect on the star-forming properties of the galaxies has been largely elucidated and the general consensus is that this process first compresses the gas available in galaxy disks, boosting the star formation for a limited amount of time, and then removes the remaining gas, leading to quenching. Much less is known about the effect and preponderance of RPS at higher redshifts, due to the lack of statistical samples. Exploiting VLT/MUSE observations of galaxies at $0.2<z<0.55$ and a published catalog of ram-pressure-stripped galaxies, we compare the global star formation rate–mass (SFR-- relation of 29 cluster galaxies undergoing RPS to that of 26 undisturbed field and cluster galaxies that constitute our control sample. Stripping galaxies occupy the upper envelope of the SFR– relation of the control sample, showing a systematic enhancement of SFR at any given mass. The boost is $>3 when considering the SFR occurring in both the tail and the disk of the galaxies. The enhancement is also seen on local scales: Considering spatially resolved data, ram-pressure stripped galaxies have large values overall, especially for RPS seems to leave the same imprint on the SFR-- and relations both in the local Universe and at $z
XAGen: 3D Expressive Human Avatars Generation

Recent advances in 3D-aware GAN models have enabled the generation of realistic and controllable human body images. However, existing methods focus on the control of major body joints, neglecting the manipulation of expressive attributes, such as facial expressions, jaw poses, hand poses, and so on. In this work, we present XAGen, the first 3D generative model for human avatars capable of expressive control over body, face, and hands. To enhance the fidelity of small-scale regions like face and hands, we devise a multi-scale and multi-part 3D representation that models fine details. Based on this representation, we propose a multi-part rendering technique that disentangles the synthesis of body, face, and hands to ease model training and enhance geometric quality. Furthermore, we design multi-part discriminators that evaluate the quality of the generated avatars with respect to their appearance and fine-grained control capabilities. Experiments show that XAGen surpasses state-of-the-art methods in terms of realism, diversity, and expressive control abilities. Code and data will be made available at https://showlab.github. io/xagen.
Two-stage Synthetic Supervising and Multi-view Consistency Self-supervising based Animal 3D Reconstruction by Single Image

Pixel-aligned Implicit Function (PIFu) effectively captures subtle variations in body shape within a low-dimensional space through extensive training with human 3D scans, its application to live animals presents formidable challenges due to the difficulty of obtaining animal cooperation for 3D scanning. To address this challenge, we propose the combination of two-stage supervised and self-supervised training to address the challenge of obtaining animal cooperation for 3D scanning. In the first stage, we leverage synthetic animal models for supervised learning. This allows the model to learn from a diverse set of virtual animal instances. In the second stage, we use 2D multi-view consistency as a self-supervised training method. This further enhances the model's ability to reconstruct accurate and realistic 3D shape and texture from largely available single-view images of real animals. The results of our study demonstrate that our approach outperforms state-of-the-art methods in both quantitative and qualitative aspects of bird 3D digitization. The source code is available at https://github.com/kuangzijian/drifu-for-animals.
Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem

Same-Day Delivery (SDD) services aim to maximize the fulfillment of online orders while minimizing delivery delays but are beset by operational uncertainties such as those in order volumes and courier planning. Our work aims to enhance the operational efficiency of SDD by focusing on the ultra-fast Order Dispatching Problem (ODP), which involves matching and dispatching orders to couriers within a centralized warehouse setting, and completing the delivery within a strict timeline (e.g., within minutes). We introduce important extensions to ultra-fast ODP such as order batching and explicit courier assignments to provide a more realistic representation of dispatching operations and improve delivery efficiency. As a solution method, we primarily focus on NeurADP, a methodology that combines Approximate Dynamic Programming (ADP) and Deep Reinforcement Learning (DRL), and our work constitutes the first application of NeurADP outside of the ride-pool matching problem. NeurADP is particularly suitable for ultra-fast ODP as it addresses complex one-to-many matching and routing intricacies through a neural network-based VFA that captures high-dimensional problem dynamics without requiring manual feature engineering as in generic ADP methods. We test our proposed approach using four distinct realistic datasets tailored for ODP and compare the performance of NeurADP against myopic and DRL baselines by also making use of non-trivial bounds to assess the quality of the policies. Our numerical results indicate that the inclusion of order batching and courier queues enhances the efficiency of delivery operations and that NeurADP significantly outperforms other methods. Detailed sensitivity analysis with important parameters confirms the robustness of NeurADP under different scenarios, including variations in courier numbers, spatial setup, vehicle capacity, and permitted delay time.
Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion

This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecting biometric system accuracy and po-tential improvements. Our analysis delves into physiological and behavioral traits, exploring their pros and cons. We discuss factors influencing biometric system effectiveness and highlight areas for enhancement. Our study differs from previous surveys by extensively examining biometric traits, exploring various application domains, and analyzing measures to mitigate cyberattacks. This paper aims to inform researchers and practitioners about the biometric authentication landscape and guide future advancements.
Non-Gaussian Correlations in the Steady State of Driven-Dissipative Clouds of Two-Level Atoms

We report experimental measurements of the second-order coherence function $g^{(2)}(\tau)$ of the light emitted by a laser-driven dense ensemble of $^{87}$Rb atoms. We observe a clear departure from the Siegert relation valid for Gaussian chaotic light. Measuring intensity and first-order coherence, we conclude that the violation is not due to the emergence of a coherent field. This indicates that the light obeys non-Gaussian statistics, stemming from non-Gaussian correlations in the atomic medium. More specifically, the steady-state of this driven-dissipative many-body system sustains high-order correlations in the absence of first-order coherence. These findings call for new theoretical and experimental explorations to uncover their origin and they open new perspectives for the realization of non-Gaussian states of light.
Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression

We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters $\theta$ of a continuous attribute function $f: \mathbb{R}^3 \mapsto \mathbb{R}$ are quantized to $\hat{\theta}$ and encoded, so that discrete samples $f_{\hat{\theta}}(\mathbf{x}_i)$ can be recovered at known 3D points $\mathbf{x}_i \in \mathbb{R}^3$ at the decoder. Specifically, we consider a nested sequences of function subspaces $\mathcal{F}^{(p)}_{l_0} \subseteq \cdots \subseteq \mathcal{F}^{(p)}_L$, where $\mathcal{F}_l^{(p)}$ is a family of functions spanned by B-spline basis functions of order $p$, $f_l^*$ is the projection of $f$ on $\mathcal{F}_l^{(p)}$ and encoded as low-pass coefficients $F_l^*$, and $g_l^*$ is the residual function in orthogonal subspace $\mathcal{G}_l^{(p)}$ (where $\mathcal{G}_l^{(p)} \oplus \mathcal{F}_l^{(p)} = \mathcal{F}_{l+1}^{(p)}$) and encoded as high-pass coefficients $G_l^*$. In this paper, to improve coding performance over [1], we study predicting $f_{l+1}^*$ at level $l+1$ given $f_l^*$ at level $l$ and encoding of $G_l^*$ for the $p=1$ case (RAHT($1$)). For the prediction, we formalize RAHT(1) linear prediction in MPEG-PCC in a theoretical framework, and propose a new nonlinear predictor using a polynomial of bilateral filter. We derive equations to efficiently compute the critically sampled high-pass coefficients $G_l^*$ amenable to encoding. We optimize parameters in our resulting feed-forward network on a large training set of point clouds by minimizing a rate-distortion Lagrangian. Experimental results show that our improved framework outperformed the MPEG G-PCC predictor by $11$ to $12\%$ in bit rate reduction.
Hinge-Wasserstein: Mitigating Overconfidence in Regression by Classification

Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein -- a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.
A framework for robotic excavation and dry stone construction using on-site materials

Automated building processes that enable efficient in situ resource utilization can facilitate construction in remote locations while simultaneously offering a carbon-reducing alternative to commonplace building practices. Toward these ends, we present a robotic construction pipeline that is capable of planning and building freeform stone walls and landscapes from highly heterogeneous local materials using a robotic excavator equipped with a shovel and gripper. Our system learns from real and simulated data to facilitate the online detection and segmentation of stone instances in spatial maps, enabling robotic grasping and textured 3D scanning of individual stones and rubble elements. Given a limited inventory of these digitized stones, our geometric planning algorithm uses a combination of constrained registration and signed-distance-field classification to determine how these should be positioned toward the formation of stable and explicitly shaped structures. We present a holistic approach for the robotic manipulation of complex objects toward dry stone construction and use the same hardware and mapping to facilitate autonomous terrain-shaping on a single construction site. Our process is demonstrated with the construction of a freestanding stone wall (10 meters by 1.7 meters by 4 meters) and a permanent retaining wall (65.5 meters by 1.8 meters by 6 meters) that is integrated with robotically contoured terraces (665 square meters). The work illustrates the potential of autonomous heavy construction vehicles to build adaptively with highly irregular, abundant, and sustainable materials that require little to no transportation and preprocessing. Description An autonomous excavator builds stone walls and forms landscapes from natural and recycled materials.
Preclinical, randomized phase 1, and compassionate use evaluation of REGN4461, a leptin receptor agonist antibody for leptin deficiency

Deficiency in the adipose-derived hormone leptin or leptin receptor signaling causes class 3 obesity in individuals with genetic loss-of-function mutations in leptin or its receptor LEPR and metabolic and liver disease in individuals with hypoleptinemia secondary to lipoatrophy such as in individuals with generalized lipodystrophy. Therapies that restore leptin-LEPR signaling may resolve these metabolic sequelae. We developed a fully human monoclonal antibody (mAb), REGN4461 (mibavademab), that activates the human LEPR in the absence or presence of leptin. In obese leptin knockout mice, REGN4461 normalized body weight, food intake, blood glucose, and insulin sensitivity. In a mouse model of generalized lipodystrophy, REGN4461 alleviated hyperphagia, hyperglycemia, insulin resistance, dyslipidemia, and hepatic steatosis. In a phase 1, randomized, double-blind, placebo-controlled two-part study, REGN4461 was well tolerated with an acceptable safety profile. Treatment of individuals with overweight or obesity with REGN4461 decreased body weight over 12 weeks in those with low circulating leptin concentrations (<8 ng/ml) but had no effect on body weight in individuals with higher baseline leptin. Furthermore, compassionate-use treatment of a single patient with atypical partial lipodystrophy and a history of undetectable leptin concentrations associated with neutralizing antibodies to metreleptin was associated with noteable improvements in circulating triglycerides and hepatic steatosis. Collectively, these translational data unveil an agonist LEPR mAb that may provide clinical benefit in disorders associated with relatively low leptin concentrations. Description LEPR agonist antibody REGN4461 (mibavademab) improves metabolic consequences of leptin deficiency. Editor’s summary Genetic deficiency of leptin signaling is often treated with the leptin analog metreleptin; however, some individuals show immunogenicity to the drug. Altarejos et al. show that monoclonal antibody (mAb)–based leptin receptor agonism is an alternative strategy for treating leptin deficiency. mAb administration lowered food intake and improved body weight and metabolic parameters in mouse models of leptin deficiency or generalized lipodystrophy. The mAb was well tolerated and improved weight in low-leptin, high-BMI individuals in phase I testing. Treatment of one patient with atypical partial lipodystrophy and a known immune response to metreleptin additionally improved hepatosteatosis and circulating triglycerides. —Catherine Charneski
Contemporary HIV-1 consensus Env with redesigned hypervariable loops promote antibody binding

An effective HIV-1 vaccine must elicit broadly neutralizing antibodies (bnAbs) against the highly diverse Envelope glycoproteins (Env) present globally. Since Env with the longest hypervariable (HV) loops were more resistant to the cognate bnAbs than Env with shorter HV loops, we redesigned hypervariable loops for updated HIV-1 Env consensus sequences of subtypes B and C and circulating recombinant form AE (CRF01_AE). We reduced the length of V1HV, V2H, and V5HV while maintaining the integrity of the Env structure and glycan shield, and we modified V4HV to account for its diverse structural context. Redesiged HV loops consisted mainly of glycine and serine to limit strain-specific targeting. Redesigned consensus Env of subtype B or CRF01_AE demonstrated increased magnitude of binding responses to pooled plasma samples and representative bnAbs. Together with other antigen optimization techniques, consensus Env with redesigned hypervariable loops can improve future HIV-1 vaccine antigens to elicit bnAbs.
Geochronological and structural investigations of the Northern Pelagonian crystalline zone. Constraints from K/Ar and zircon and apatite fission track dating

The northern par! ofthe Pelagonian Zone experienced a pnlyphase deformation and melamorphism. Four pnupsof K/Ar biotilc and while mica ages clocumenl (I) Hercynian emplacement of plulomc rocks. (II) Eohellenic nappe stacking associated with penetrative deformation / recrystallisation, (III) a paleocene event in the Mesocoic cover rocks in Greece and (IV) a westward movement fWest Pelagonian nappes during Eocene to Oligocene time. First zircon/apatite fission track ages clustering around 70 Ma and 45 to 30 Ma.
Selfish grower behavior can group-optimally eradicate plant diseases caused by coinfection

Game-theoretic behavioral and epidemiological models suggest that it is impossible to eradicate a disease through voluntary control actions when individuals behave according to their own interests. The simple reason is that free-riding behavior, which is best for self-interest, leads to a control coverage on the group level that is insufficient to eradicate the disease. Here we show that, for diseases that are caused by coinfection, selfinterest can actually result in the socially optimal outcome of disease eradication. Our result challenges the conventional wisdom that selfish behavior undermines the group interest; it resolves a social dilemma in the absence of any cooperation, institutional arrangements, or social norms. Our model is motivated by coinfecting plant viruses, common among agricultural crops and natural plants, and the behavioral dynamics of growers to adopt protective action (biological or chemical control). The epidemiological scenario, in which the disease is eradicated by self-interest, is characterized by a positive feedback process in which coinfection enhances infectivity. Similar feedback structures exist for a range of typical epidemiological processes that facilitate disease persistence if prevalence is sufficiently large. The underlying mechanism may therefore be applicable to other diseases.
Double dissociation of visuomotor interaction mediated by visual feedback during continuous de novo motor learning

While the sensorimotor cortices are central neural substrates for motor control and learning, how the interaction between their subregions with visual cortices contributes to acquiring de novo visuomotor skills is poorly understood. We designed a continuous visuomotor task in fMRI where participants learned an arbitrary visuomotor mapping. To dissociate motor and somatosensory cortices functions, we manipulated visual feedback of a cursor such that they learned to control using fingers under two alternating conditions: online cursor feedback is available or unavailable except when a target is reached. We found double dissociation of fMRI activity in subregions of the sensorimotor and visual cortices and their interaction, which were mediated by the visual feedback. We also found a significant reduction in functional connectivity between somatosensory cortices and early visual cortices, which was highly correlated with performance improvement. These findings support the distinct interaction between subregions of sensorimotor cortices and visual cortices while highlighting the more dominant role of somatosensory cortices over motor cortices during de novo motor learning.
The Lifeact-EGFP Quail: A New Avian Model For Studying Actin Dynamics In Vivo

Here we report the generation of a transgenic Lifeact–EGFP quail line for the investigation of actin organization and dynamics during morphogenesis in vivo. This transgenic avian line allows for the high-resolution visualization of actin structures within the living embryo, from the subcellular filaments that guide cell shape to the supracellular assemblies that coordinate movements across tissues. The unique suitability of avian embryos to live imaging facilitates the investigation of previously intractable processes during embryogenesis. Using high-resolution live imaging approaches, we present the dynamic behaviours and morphologies of cellular protrusions in different tissue contexts. Furthermore, through the integration of live imaging with computational segmentation, we reveal the dynamics of cells undergoing apical constriction and the emergence of large-scale actin structures such as supracellular cables and rosettes within the neuroepithelium. These findings not only enhance our understanding of tissue morphogenesis but also demonstrate the utility of the Lifeact–EGFP transgenic quail as a new model system for live in vivo investigations of the actin cytoskeleton.
Mechanosensory and command contributions to the Drosophila grooming sequence

Flies groom in response to competing mechanosensory cues in an anterior to posterior order using specific legs. From behavior screens, we identified a pair of cholinergic command-like neurons, Mago-no-Te (MGT), whose optogenetic activation elicits thoracic grooming by hind legs. Thoracic grooming is typically composed of body sweeps and leg rubs in alternation, but clonal analysis coupled with amputation experiments revealed that MGT activation only commands the body sweeps: initiation of leg rubbing requires contact between leg and thorax. With new electron microscopy (EM) connectome data for the ventral nerve cord (VNC), we uncovered a circuit-based explanation for why stimulation of posterior thoracic mechanosensory bristles initiates cleaning by the hind legs. Our previous work showed that flies weigh mechanosensory inputs across the body to select which part to groom, but we did not know why the thorax was always cleaned last. Here, the connectome for the VNC enabled us to identify a pair of GABAergic inhibitory neurons, UMGT1, that receive diverse sensory inputs and synapse onto both MGT and components of its downstream pre-motor circuits. Optogenetic activation of UMGT1 suppresses thoracic cleaning, representing a mechanism by which mechanosensory stimuli on other body parts could take precedence in the grooming hierarchy. We also mapped the pre-motor circuit downstream of MGT, including inhibitory feedback connections that may enable rhythmicity and coordination of limb movement during thoracic grooming.