Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Disentangled Avatars with Hybrid 3D Representations

Tremendous efforts have been made to learn animatable and photorealistic human avatars. Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e.g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata. For example, meshes are generally not suitable for modeling clothing and hair. Motivated by this, we present Disentangled Avatars~(DELTA), which models humans with hybrid explicit-implicit 3D representations. DELTA takes a monocular RGB video as input, and produces a human avatar with separate body and clothing/hair layers. Specifically, we demonstrate two important applications for DELTA. For the first one, we consider the disentanglement of the human body and clothing and in the second, we disentangle the face and hair. To do so, DELTA represents the body or face with an explicit mesh-based parametric 3D model and the clothing or hair with an implicit neural radiance field. To make this possible, we design an end-to-end differentiable renderer that integrates meshes into volumetric rendering, enabling DELTA to learn directly from monocular videos without any 3D supervision. Finally, we show that how these two applications can be easily combined to model full-body avatars, such that the hair, face, body and clothing can be fully disentangled yet jointly rendered. Such a disentanglement enables hair and clothing transfer to arbitrary body shapes. We empirically validate the effectiveness of DELTA's disentanglement by demonstrating its promising performance on disentangled reconstruction, virtual clothing try-on and hairstyle transfer. To facilitate future research, we also release an open-sourced pipeline for the study of hybrid human avatar modeling.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

Abstract The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.
CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts

Social media platforms play an essential role in crisis communication, but analyzing crisis-related social media texts is challenging due to their informal nature. Transformer-based pre-trained models like BERT and RoBERTa have shown success in various NLP tasks, but they are not tailored for crisis-related texts. Furthermore, general-purpose sentence encoders are used to generate sentence embeddings, regardless of the textual complexities in crisis-related texts. Advances in applications like text classification, semantic search, and clustering contribute to effective processing of crisis-related texts, which is essential for emergency responders to gain a comprehensive view of a crisis event, whether historical or real-time. To address these gaps in crisis informatics literature, this study introduces CrisisTransformers, an ensemble of pre-trained language models and sentence encoders trained on an extensive corpus of over 15 billion word tokens from tweets associated with more than 30 crisis events, including disease outbreaks, natural disasters, conflicts, and other critical incidents. We evaluate existing models and CrisisTransformers on 18 crisis-specific public datasets. Our pre-trained models outperform strong baselines across all datasets in classification tasks, and our best-performing sentence encoder improves the state-of-the-art by 17.43% in sentence encoding tasks. Additionally, we investigate the impact of model initialization on convergence and evaluate the significance of domain-specific models in generating semantically meaningful sentence embeddings. All models are publicly released (https://huggingface.co/crisistransformers), with the anticipation that they will serve as a robust baseline for tasks involving the analysis of crisis-related social media texts.
Reservoir Computing Model For Multi-Electrode Electrophysiological Data Analysis

In this paper we present a computational model which decodes the spatio-temporal data from electrophysiological measurements of neuronal networks and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. The model is based on reservoir computing network (RCN) approach, where experimental data is used as training and validation data. Consequently, the model can be used to study the functionality of different neuronal cultures and simulate the network response to external stimuli.
Latte: Lightweight Aliasing Tracking for Java

Many existing systems track aliasing and uniqueness, each with their own trade-off between expressiveness and developer effort. We propose Latte, a new approach that aims to minimize both the amount of annotations and the complexity of invariants necessary for reasoning about aliasing in an object-oriented language with mutation. Our approach only requires annotations for parameters and fields, while annotations for local variables are inferred. Furthermore, it relaxes uniqueness to allow aliasing among local variables, as long as this aliasing can be precisely determined. This enables support for destructive reads without changes to the language or its run-time semantics. Despite this simplicity, we show how this design can still be used for tracking uniqueness and aliasing in a local sequential setting, with practical applications, such as modeling a stack.
Towards Reliable Domain Generalization: A New Dataset and Evaluations

There are ubiquitous distribution shifts in the real world. However, deep neural networks (DNNs) are easily biased towards the training set, which causes severe performance degradation when they receive out-of-distribution data. Many methods are studied to train models that generalize under various distribution shifts in the literature of domain generalization (DG). However, the recent DomainBed and WILDS benchmarks challenged the effectiveness of these methods. Aiming at the problems in the existing research, we propose a new domain generalization task for handwritten Chinese character recognition (HCCR) to enrich the application scenarios of DG method research. We evaluate eighteen DG methods on the proposed PaHCC (Printed and Handwritten Chinese Characters) dataset and show that the performance of existing methods on this dataset is still unsatisfactory. Besides, under a designed dynamic DG setting, we reveal more properties of DG methods and argue that only the leave-one-domain-out protocol is unreliable. We advocate that researchers in the DG community refer to dynamic performance of methods for more comprehensive and reliable evaluation. Our dataset and evaluations bring new perspectives to the community for more substantial progress. We will make our dataset public with the article published to facilitate the study of domain generalization.
Trust-Region Neural Moving Horizon Estimation for Robots

Accurate disturbance estimation is essential for safe robot operations. The recently proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural network to model the MHE's weightings, has shown promise in further pushing the accuracy and efficiency boundary. Currently, NeuroMHE is trained through gradient descent, with its gradient computed recursively using a Kalman filter. This paper proposes a trust-region policy optimization method for training NeuroMHE. We achieve this by providing the second-order derivatives of MHE, referred to as the MHE Hessian. Remarkably, we show that much of computation already used to obtain the gradient, especially the Kalman filter, can be efficiently reused to compute the MHE Hessian. This offers linear computational complexity relative to the MHE horizon. As a case study, we evaluate the proposed trust region NeuroMHE on real quadrotor flight data for disturbance estimation. Our approach demonstrates highly efficient training in under 5 min using only 100 data points. It outperforms a state-of-the-art neural estimator by up to 68.1% in force estimation accuracy, utilizing only 1.4% of its network parameters. Furthermore, our method showcases enhanced robustness to network initialization compared to the gradient descent counterpart.
Rotation sensing using tractor atom interferometry

We investigate the possible realization of an ultracold-atom rotation sensor that is based on recently proposed tractor atom interferometry (TAI). An experimental design that includes the generation of a Laguerre–Gaussian-beam-based “pinwheel” optical lattice and multi-loop interferometric cycles is discussed. Numerical simulations of the proposed system demonstrate TAI rotation sensitivity comparable to that of contemporary matter-wave interferometers. We analyze a regime of TAI rotation sensors in which nonadiabatic effects may hinder the system's performance. We apply quantum optimal control to devise a methodology suitable to address this nonadiabaticity. Our studies are of interest for current efforts to realize compact and robust matter-wave rotation sensors, as well as for fundamental physics applications of TAI.
Real-Time Semantic Segmentation: A brief survey and comparative study in remote sensing

Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a tradeoff between effectiveness and efficiency. It has many applications, including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. With the success of efficient deep learning methods [i.e., efficient deep neural networks (DNNs)] for real-time semantic segmentation in computer vision, researchers have adopted these efficient DNNs in remote sensing image analysis. This article begins with a summary of the fundamental compression methods for designing efficient DNNs and provides a brief but comprehensive survey, outlining the recent developments in real-time semantic segmentation of remote sensing imagery. We examine several seminal efficient deep learning methods, placing them in a taxonomy based on the network architecture design approach. Furthermore, we evaluate the quality and efficiency of some existing efficient DNNs on a publicly available remote sensing semantic segmentation benchmark dataset, OpenEarthMap. The experimental results of an extensive comparative study demonstrate that most of the existing efficient DNNs have good segmentation quality, but they suffer low inference speed (i.e., a high latency rate), which may limit their capability of deployment in real-time applications of remote sensing image segmentation. We provide some insights into the current trend and future research directions for real-time semantic segmentation of remote sensing imagery.
Confounder selection via iterative graph expansion

Confounder selection, namely choosing a set of covariates to control for confounding between a treatment and an outcome, is arguably the most important step in the design of observational studies. Previous methods, such as Pearl's celebrated back-door criterion, typically require pre-specifying a causal graph, which can often be difficult in practice. We propose an interactive procedure for confounder selection that does not require pre-specifying the graph or the set of observed variables. This procedure iteratively expands the causal graph by finding what we call"primary adjustment sets"for a pair of possibly confounded variables. This can be viewed as inverting a sequence of latent projections of the underlying causal graph. Structural information in the form of primary adjustment sets is elicited from the user, bit by bit, until either a set of covariates are found to control for confounding or it can be determined that no such set exists. Other information, such as the causal relations between confounders, is not required by the procedure. We show that if the user correctly specifies the primary adjustment sets in every step, our procedure is both sound and complete.
Joint Measurability in Lindbladian Open Quantum Systems

We study joint measurability of quantum observables in open systems governed by a master equation of Lindblad form. We briefly review the historical perspective of open systems and conceptual aspects of quantum measurements, focusing subsequently on describing emergent classicality under quantum decoherence. While decoherence in quantum states has been studied extensively in the past, the measurement side is much less understood — here we present and extend some recent results on this topic.
Padding-Free Convolution Based on Preservation of Differential Characteristics of Kernels

Convolution is a fundamental operation in image processing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, however, can introduce undesirable boundary effects. We present a non-padding-based method for size-keeping convolution based on the preservation of differential characteristics of kernels. The main idea is to make convolution over an incomplete sliding window “collapse“ to a linear differential operator evaluated locally at its central pixel, which no longer requires information from the neighbouring missing pixels. While the underlying theory is rigorous, our final formula turns out to be simple: the convolution over an incomplete window is achieved by convolving its nearest complete window with a transformed kernel. This formula is computationally lightweight, involving neither interpolation or extrapolation nor restrictions on image and kernel sizes. Our method favours data with smooth boundaries, such as high-resolution images and fields from physics. Our experiments include: i) filtering analytical and non-analytical fields from computational physics and, ii) training convolutional neural networks (CNNs) for the tasks of image classification, semantic segmentation and super-resolution reconstruction. In all these experiments, our method has exhibited visible superiority over the compared ones.
Can large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end?

A speech spoofing countermeasure (CM) that discriminates between unseen spoofed and bona fide data requires diverse training data. While many datasets use spoofed data generated by speech synthesis systems, it was recently found that data vocoded by neural vocoders were also effective as the spoofed training data. Since many neural vocoders are fast in building and generation, this study used multiple neural vocoders and created more than 9,000 hours of vocoded data on the basis of the VoxCeleb2 corpus. This study investigates how this large-scale vocoded data can improve spoofing countermeasures that use data-hungry self-supervised learning (SSL) models. Experiments demonstrated that the overall CM performance on multiple test sets improved when using features extracted by an SSL model continually trained on the vocoded data. Further improvement was observed when using a new SSL distilled from the two SSLs before and after the continual training. The CM with the distilled SSL outperformed the previous best model on challenging unseen test sets, including the ASVspoof 2019 logical access, WaveFake, and In-the-Wild.
Local conservation laws of continuous Galerkin method for the incompressible Navier-Stokes equations in EMAC form

We consider {\it local} balances of momentum and angular momentum for the incompressible Navier-Stokes equations. First, we formulate new weak forms of the physical balances (conservation laws) of these quantities, and prove they are equivalent to the usual conservation law formulations. We then show that continuous Galerkin discretizations of the Navier-Stokes equations using the EMAC form of the nonlinearity preserve discrete analogues of the weak form conservation laws, both in the Eulerian formulation and the Lagrangian formulation (which are not equivalent after discretizations). Numerical tests illustrate the new theory.
Maxwell's Current in Mitochondria and Nerve

Maxwell defined a true or total current in a way not widely used today. He said". true electric current . is not the same thing as the current of conduction but that the time-variation of the electric displacement must be taken into account in estimating the total movement of electricity". We show that true or total current is a universal property of electrodynamics independent of properties of matter. We use mathematics without a dielectric constant. The resulting Maxwell Current Law is a generalization of the Kirchhoff Law of Current used in circuit analysis, that also includes displacement current. The generalization is not a long-time low frequency approximation in contrast to traditional presentation of Kirchhoff's Law. The Maxwell Current Law does not require currents to be in circuits. It has been applied to three dimensional systems like the signaling system of nerve and muscle fibers. The Maxwell Current Law clarifies the flow of electrons, protons, and ions in mitochondria that generate ATP, the molecule that stores chemical energy throughout life. The currents are globally coupled because mitochondria are short. Focusing on Maxwell current reinterprets the classical chemiosmotic hypothesis of ATP production. The conduction current of protons in mitochondria is driven by the protonmotive force including its component electrical potential, just as in the classical chemiosmotic hypothesis. The electrical potential is now the electrical potential as defined in physical sciences by Maxwell partial differential equations. The conduction current is now just a part of the true current analyzed by Maxwell. Details of accumulation of charges do not have to be considered in analysis of true current because true current does not accumulate. It is true total current that provides the energy that generates the ATP, not just the protonmotive force.
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise

The negative impact of label noise is well studied in classical supervised learning yet remains an open research question in meta-learning. Meta-learners aim to adapt to unseen learning tasks by learning a good initial model in meta-training and consecutively fine-tuning it according to new tasks during meta-testing. In this paper, we present the first extensive analysis of the impact of varying levels of label noise on the performance of state-of-the-art meta-learners, specifically gradient-based $N$-way $K$-shot learners. We show that the accuracy of Reptile, iMAML, and foMAML drops by up to 42% on the Omniglot and CifarFS datasets when meta-training is affected by label noise. To strengthen the resilience against label noise, we propose two sampling techniques, namely manifold (Man) and batch manifold (BatMan), which transform the noisy supervised learners into semi-supervised ones to increase the utility of noisy labels. We first construct manifold samples of $N$-way $2$-contrastive-shot tasks through augmentation, learning the embedding via a contrastive loss in meta-training, and then perform classification through zeroing on the embedding in meta-testing. We show that our approach can effectively mitigate the impact of meta-training label noise. Even with 60% wrong labels \batman and \man can limit the meta-testing accuracy drop to ${2.5}$, ${9.4}$, ${1.1}$ percent points, respectively, with existing meta-learners across the Omniglot, CifarFS, and MiniImagenet datasets.
The 50 Mpc Galaxy Catalog (50 MGC): Consistent and Homogeneous Masses, Distances, Colors, and Morphologies

We assemble a catalog of 15424 nearby galaxies within 50 Mpc with consistent and homogenized mass, distance, and morphological type measurements. Our catalog combines galaxies from HyperLeda, the NASA-Sloan Atlas, and the Catalog of Local Volume Galaxies. Distances for the galaxies combine best-estimates for flow-corrected redshift-based distances with redshift-independent distances. We also compile magnitude and color information for 11740 galaxies. We use the galaxy colors to estimate masses by creating self-consistent color—mass-to-light ratio relations in four bands; we also provide color transformations of all colors into Sloan g–i by using galaxies with overlapping color information. We compile morphology information for 13744 galaxies, and use the galaxy color information to separate early- and late-type galaxies. This catalog is widely applicable for studies of nearby galaxies and for placing these studies in the context of more distant galaxies. We present one application here: a preliminary analysis of the nuclear X-ray activity of galaxies. Out of 1506 galaxies within the sample that have available Chandra X-ray observations, we find that 291 have detected nuclear sources. Of the 291 existing Chandra detections, 249 have log(L X) > 38.3 and available stellar mass estimates. We find that the X-ray active fractions in early-type galaxies are higher than in late-type galaxies, especially for galaxy stellar masses between 109 and 1010.5 M ⊙. We show that these differences may be due at least in part to the increased astrometric uncertainties in late-type galaxies relative to early types.
Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models

We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a parametric pseudo-variance that can contain parametric restrictions with respect to the conditional expectation. The specification of the pseudo-variance and the parametric restrictions follow naturally in observation-driven models with bounds in the support of the observable process, such as count processes and double-bounded time series. We derive the asymptotic properties of the estimators and a validity test for the parameter restrictions. We show that the results remain valid irrespective of the correct specification of the pseudo-variance. The key advantage of the restricted estimators is that they can achieve higher efficiency compared to alternative quasi-likelihood methods that are available in the literature. Furthermore, the testing approach can be used to build specification tests for parametric time series models. We illustrate the practical use of the methodology in a simulation study and two empirical applications featuring integer-valued autoregressive processes, where assumptions on the dispersion of the thinning operator are formally tested, and autoregressions for double-bounded data with application to a realized correlation time series.