Miracle-dz/UniFAR-FTUs huggingface dataset:
GALA: Generating Animatable Layered Assets from a Single Scan

We present GALA, a framework that takes as input a single-layer clothed 3D human mesh and decomposes it into complete multi-layered 3D assets. The outputs can then be combined with other assets to create novel clothed human avatars with any pose. Existing reconstruction approaches often treat clothed humans as a single-layer of geometry and overlook the inherent compositionality of humans with hairstyles, clothing, and accessories, thereby limiting the utility of the meshes for downstream applications. Decomposing a single-layer mesh into separate layers is a challenging task because it requires the synthesis of plausible geometry and texture for the severely occluded regions. Moreover, even with successful decomposition, meshes are not normalized in terms of poses and body shapes, failing coherent composition with novel identities and poses. To address these challenges, we propose to leverage the general knowledge of a pretrained 2D diffusion model as geometry and appearance prior for humans and other assets. We first separate the input mesh using the 3D surface segmentation extracted from multi-view 2D segmentations. Then we synthesize the missing geometry of different layers in both posed and canonical spaces using a novel pose-guided Score Distillation Sampling (SDS) loss. Once we complete inpainting high-fidelity 3D geometry, we also apply the same SDS loss to its texture to obtain the complete appearance including the initially occluded regions. Through a series of decomposition steps, we obtain multiple layers of 3D assets in a shared canonical space normalized in terms of poses and human shapes, hence supporting effortless composition to novel identities and reanimation with novel poses. Our experiments demonstrate the effectiveness of our approach for decomposition, canonicalization, and composition tasks compared to existing solutions.
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness

Recent empirical and theoretical studies have established the generalization capabilities of large machine learning models that are trained to (approximately or exactly) fit noisy data. In this work, we prove a surprising result that even if the ground truth itself is robust to adversarial examples, and the benignly overfitted model is benign in terms of the ``standard'' out-of-sample risk objective, this benign overfitting process can be harmful when out-of-sample data are subject to adversarial manipulation. More specifically, our main results contain two parts: (i) the min-norm estimator in overparameterized linear model always leads to adversarial vulnerability in the ``benign overfitting'' setting; (ii) we verify an asymptotic trade-off result between the standard risk and the ``adversarial'' risk of every ridge regression estimator, implying that under suitable conditions these two items cannot both be small at the same time by any single choice of the ridge regularization parameter. Furthermore, under the lazy training regime, we demonstrate parallel results on two-layer neural tangent kernel (NTK) model, which align with empirical observations in deep neural networks. Our finding provides theoretical insights into the puzzling phenomenon observed in practice, where the true target function (e.g., human) is robust against adverasrial attack, while beginly overfitted neural networks lead to models that are not robust.
ACS: Concurrent Kernel Execution on Irregular, Input-Dependent Computational Graphs

GPUs are widely used to accelerate many important classes of workloads today. However, we observe that several important emerging classes of workloads, including simulation engines for deep reinforcement learning and dynamic neural networks, are unable to fully utilize the massive parallelism that GPUs offer. These applications tend to have kernels that are small in size, i.e., have few thread blocks that do not saturate compute resources. Executing independent kernels concurrently is a promising approach to improve parallelism and utilization. However, this inter-kernel concurrency is difficult to leverage in such workloads with existing approaches: First, the inter-kernel dependencies and computational graph are input-dependent and vary each time the application is executed. Second, the computational graphs tend to be irregular, requiring fine-grain scheduling and synchronization; thus incurring significant synchronization overheads if kernel execution is parallelized. In this work, we propose ACS, a framework that enables lightweight detection of inter-kernel dependencies and low overhead kernel scheduling at runtime. The key idea behind ACS is to perform inter-kernel dependency checks for a small window of kernels at runtime, similar to out-of order instruction scheduling. This enables concurrent execution of kernels in applications whose computational graphs are input dependent and require fine-grained scheduling. We propose ACS-SW, a software-only open-source implementation of ACS and ACS-HW, a hardware-software cooperative implementation. ACS-HW further reduces synchronization overheads by reducing communication between the CPU and GPU. We evaluate ACS for deep RL simulation and dynamic DNNs on both real hardware and a GPU simulator. We demonstrate speedups of up to 2.19x (1.56x on average) by improving GPU utilization with concurrent kernel execution.
The shape of the front of multidimensional branching Brownian motion

We study the shape of the outer envelope of a branching Brownian motion (BBM) in $\mathbb{R}^d$, $d\geq 2$. We focus on the extremal particles: those whose norm is within $O(1)$ of the maximal norm amongst the particles alive at time $t$. Our main result is a scaling limit, with exponent $3/2$, for the outer-envelope of the BBM around each extremal particle (the"front"); the scaling limit is a continuous random surface given explicitly in terms of a Bessel(3) process. Towards this end, we introduce a point process that captures the full landscape around each extremal particle and show convergence in distribution to an explicit point process. This complements the global description of the extremal process given in Berestycki et. al. (Ann. Probab., to appear), where the local behavior at directions transversal to the radial component of the extremal particles is not addressed.
The twin peaks of learning neural networks

Recent works demonstrated the existence of a double-descent phenomenon for the generalization error of neural networks, where highly overparameterized models escape overfitting and achieve good test performance, at odds with the standard bias-variance trade-off described by statistical learning theory. In the present work, we explore a link between this phenomenon and the increase of complexity and sensitivity of the function represented by neural networks. In particular, we study the Boolean mean dimension (BMD), a metric developed in the context of Boolean function analysis. Focusing on a simple teacher-student setting for the random feature model, we derive a theoretical analysis based on the replica method that yields an interpretable expression for the BMD, in the high dimensional regime where the number of data points, the number of features, and the input size grow to infinity. We find that, as the degree of overparameterization of the network is increased, the BMD reaches an evident peak at the interpolation threshold, in correspondence with the generalization error peak, and then slowly approaches a low asymptotic value. The same phenomenology is then traced in numerical experiments with different model classes and training setups. Moreover, we find empirically that adversarially initialized models tend to show higher BMD values, and that models that are more robust to adversarial attacks exhibit a lower BMD.
A comparison of two conceptual models for the simulation of hydro-climatic variability over 50 years in a large Sudano-Sahelian catchment

Environmental and climatic changes have occurred in western African regions over many decades. These changes, which are variable in space and time, can have lasting effects on water resources. Hydrological modelling can help to assess the impact of these changes by representing the processes governing the relationship between climatic data and river flow regimes. This paper compares two reservoir-based hydrological models (HydroStrahler and GR4J) operating on a daily time step. The models are tested over an approx. 50-year period in a large, poorly gauged Sudano-Sahelian catchment that has undergone significant hydro-climatic variability. A calibration/validation exercise is performed using lumped and semi-distributed approaches. The simulations are compared via a multicriteria analysis based on a variety of goodness-of-fit indices. Both models simulate the rainfall-runoff relationship over the catchment area with a fair degree of realism. Given the calibration strategy, the flood dynamics are better reproduced using the HydroStrahler model, while the GR4J model gives a more accurate estimate of cumulated discharge. The semi-distributed approach allows for a better representation of the hydrological processes within the watershed, which does not necessarily lead to improved outlet simulations compared to the lumped approach. A sensitivity analysis of the parameters also shows that equifinality problems are reduced when the calibration is considered within a multiobjective framework. Furthermore, the robustness of the simulations indicates that the models can be applied to various climatic conditions. The performance indexes prove satisfactory in validation periods containing either wet or dry spells. As a result, these models can be used to forecast future water availability using mid-term climatic scenarios in the basin.
Hubungan antara ukuran-ukuran tubuh dengan bobot badan kambing Peranakan Etawah jantan di Kabupaten Klaten

The objective of the research was to determine the relationship between body weight and body measurement of Peranakan Etawah (PE) goat male at different age in Klaten Regency. The research was conducted in District Karangnongko and Kemalang. The research used 98 male goats aged 0-60 months and the data were analyzed using correlation and regression. The study found a very positive relationship between the chest girth, the body length, shoulders height, chest depth and body weight on goats aged 0-6 months until 12-24 months of age. Another findings were goats with 24-36 months of age had a very positive relationship between chest girth and the body length with body weight, while shoulders height and chest depth had a positive correlation with body weight. A very positive relationship was also found on the chest girth with body weight of goats with age of 36-60 months, while other body measurements had a very low positive relationship. The study concluded that chest girth had a close correlation with body weight of the PE goat. Keywords: Body measurements, body weight, Peranakan Etawah
Mini-batch Submodular Maximization

We present the first mini-batch algorithm for maximizing a non-negative monotone decomposable submodular function, $F=\sum_{i=1}^N f^i$, under a set of constraints. We improve over the sparsifier based approach both in theory and in practice. We experimentally observe that our algorithm generates solutions that are far superior to those generated by the sparsifier based approach.
DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations

Recent studies have introduced a new class of generative models for synthesizing implicit neural representations (INRs) that capture arbitrary continuous signals in various domains. These models opened the door for domain-agnostic generative models, but they often fail to achieve high-quality generation. We observed that the existing methods generate the weights of neural networks to parameterize INRs and evaluate the network with fixed positional embeddings (PEs). Arguably, this architecture limits the expressive power of generative models and results in low-quality INR generation. To address this limitation, we propose Domain-agnostic Latent Diffusion Model for INRs (DDMI) that generates adaptive positional embeddings instead of neural networks' weights. Specifically, we develop a Discrete-to-continuous space Variational AutoEncoder (D2C-VAE), which seamlessly connects discrete data and the continuous signal functions in the shared latent space. Additionally, we introduce a novel conditioning mechanism for evaluating INRs with the hierarchically decomposed PEs to further enhance expressive power. Extensive experiments across four modalities, e.g., 2D images, 3D shapes, Neural Radiance Fields, and videos, with seven benchmark datasets, demonstrate the versatility of DDMI and its superior performance compared to the existing INR generative models.
EEND-M2F: Masked-attention mask transformers for speaker diarization

In this paper, we make the explicit connection between image segmentation methods and end-to-end diarization methods. From these insights, we propose a novel, fully end-to-end diarization model, EEND-M2F, based on the Mask2Former architecture. Speaker representations are computed in parallel using a stack of transformer decoders, in which irrelevant frames are explicitly masked from the cross attention using predictions from previous layers. EEND-M2F is lightweight, efficient, and truly end-to-end, as it does not require any additional diarization, speaker verification, or segmentation models to run, nor does it require running any clustering algorithms. Our model achieves state-of-the-art performance on several public datasets, such as AMI, AliMeeting and RAMC. Most notably our DER of 16.07% on DIHARD-III is the first major improvement upon the challenge winning system.
Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management

We present a multi-agent Deep Reinforcement Learning (DRL) framework for managing large transportation infrastructure systems over their life-cycle. Life-cycle management of such engineering systems is a computationally intensive task, requiring appropriate sequential inspection and maintenance decisions able to reduce long-term risks and costs, while dealing with different uncertainties and constraints that lie in high-dimensional spaces. To date, static age- or condition-based maintenance methods and risk-based or periodic inspection plans have mostly addressed this class of optimization problems. However, optimality, scalability, and uncertainty limitations are often manifested under such approaches. The optimization problem in this work is cast in the framework of constrained Partially Observable Markov Decision Processes (POMDPs), which provides a comprehensive mathematical basis for stochastic sequential decision settings with observation uncertainties, risk considerations, and limited resources. To address significantly large state and action spaces, a Deep Decentralized Multi-agent Actor-Critic (DDMAC) DRL method with Centralized Training and Decentralized Execution (CTDE), termed as DDMAC-CTDE is developed. The performance strengths of the DDMAC-CTDE method are demonstrated in a generally representative and realistic example application of an existing transportation network in Virginia, USA. The network includes several bridge and pavement components with nonstationary degradation, agency-imposed constraints, and traffic delay and risk considerations. Compared to traditional management policies for transportation networks, the proposed DDMAC-CTDE method vastly outperforms its counterparts. Overall, the proposed algorithmic framework provides near optimal solutions for transportation infrastructure management under real-world constraints and complexities.
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment. To address this challenge, we introduce the outlier-aware weight quantization (OWQ) method, which aims to minimize LLM's footprint through low-precision representation. OWQ prioritizes a small subset of structured weights sensitive to quantization, storing them in high-precision, while applying highly tuned quantization to the remaining dense weights. This sensitivity-aware mixed-precision scheme reduces the quantization error notably, and extensive experiments demonstrate that 3.1-bit models using OWQ perform comparably to 4-bit models optimized by OPTQ. Furthermore, OWQ incorporates a parameter-efficient fine-tuning for task-specific adaptation, called weak column tuning (WCT), enabling accurate task-specific LLM adaptation with minimal memory overhead in the optimized format. OWQ represents a notable advancement in the flexibility, efficiency, and practicality of LLM optimization literature. The source code is available at https://github.com/xvyaward/owq.
Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas

The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.
LSD1 inhibition suppresses ASCL1 and de-represses YAP1 to drive potent activity against neuroendocrine prostate cancer

Lysine-specific demethylase 1 (LSD1 or KDM1A) has emerged as a critical mediator of tumor progression in metastatic castration-resistant prostate cancer (mCRPC). Among mCRPC subtypes, neuroendocrine prostate cancer (NEPC) is an exceptionally aggressive variant driven by lineage plasticity, an adaptive resistance mechanism to androgen receptor axis-targeted therapies. Our study shows that LSD1 expression is elevated in NEPC and associated with unfavorable clinical outcomes. Using genetic approaches, we validated the on-target effects of LSD1 inhibition across various models. We investigated the therapeutic potential of bomedemstat, an orally bioavailable, irreversible LSD1 inhibitor with low nanomolar potency. Our findings demonstrate potent antitumor activity against CRPC models, including tumor regressions in NEPC patient-derived xenografts. Mechanistically, our study uncovers that LSD1 inhibition suppresses the neuronal transcriptional program by downregulating ASCL1 through disrupting LSD1:INSM1 interactions and de-repressing YAP1 silencing. Our data support the clinical development of LSD1 inhibitors for treating CRPC – especially the aggressive NE phenotype. Statement of Significance Neuroendocrine prostate cancer presents a clinical challenge due to the lack of effective treatments. Our research demonstrates that bomedemstat, a potent and selective LSD1 inhibitor, effectively combats neuroendocrine prostate cancer by downregulating the ASCL1- dependent NE transcriptional program and re-expressing YAP1.
Calcitriol impairs the secretion of IL-4 and IL-13 in Th2 cells via modulating VDR-Gata3-Gfi1 axis

Calcitriol, the bioactive form of vitamin D, exerts its biological functions by binding to its cognate receptor VDR. The indicators of the severity of allergies and asthma have been linked to low vitamin D levels. However, the role of calcitriol in regulating IL-4 and IL-13; two cytokines pivotal to allergic inflammation, remained unclear. Our study observed a decrease in IL-4 and IL-13 secretion in murine and human Th2 cells, treated with calcitriol. In murine Th2 cells, Gata3 expression was attenuated by calcitriol. However, the expression of the transcriptional repressor Gfi1, that prevents Th1 fate of activated CD4+ T cells, too was attenuated in the presence of calcitriol. Ectopic expression of either Gfi1 or VDR impaired the secretion of IL-13 in Th2 cells. Gfi1 significantly impaired Il13 promoter activation; which calcitriol failed to restore. Ecr, a conserved region between the two genes, which enhanced the transactivation of Il4 and Il13 promoters; is essential for calcitriol-mediated suppression of both the genes. In murine Th2 cells, VDR interacted with Gata3 but not Gfi1. Calcitriol augmented the recruitment of VDR to the Il13 promoter and Ecr regions. Gata3 recruitment was significantly impaired at Il13 and Ecr locus in the presence of calcitriol but increased at Il4 promoter. However, in the presence of calcitriol, the overall recruitment of Gfi1 remained unchanged at Il4 but increased at Il13 locus in Th2 cells. Together, our study clearly elucidates that calcitriol modulates VDR, Gata3, and Gfi1 to suppress IL-4 and IL-13 production in Th2 cells.
Priors for natural image statistics inform confidence in perceptual decisions

Decision confidence plays a critical role in humans’ ability to make adaptive decisions in a noisy perceptual world. Despite its importance, there is currently little consensus about the computations underlying confidence judgements in perceptual decisions. In order to better understand these mechanisms, in this study we sought to address the extent to which confidence is informed by a naturalistic prior probability distribution. Contrary to previous research, we did not require participants to internalise the parameters of an arbitrary prior distribution. Instead we used a novel psychophysical paradigm which allowed us to capitalise on probability distributions of low-level image features in natural scenes, which are well-known to influence perception. Participants reported the subjective upright of naturalistic image target patches, and then reported their confidence in their orientation responses. We used computational modelling to relate the statistics of the low-level features in the targets to the distribution of these features across many natural images. As expected, we found that participants used an internalised prior of the regularities of low-level natural image statistics to inform their perceptual judgements. Critically, we also show that the same low-level image statistics predict participants’ confidence judgements. Overall, our study highlights the importance of using naturalistic task designs that capitalise on existing, long-term priors to further our understanding of the computational basis of confidence.