Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning 3D Human Shape and Pose From Dense Body Parts

Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods. The commonly occurred misalignment comes from the facts that the mapping from images to the model space is highly non-linear and the rotation-based pose representation of the body model is prone to result in the drift of joint positions. In this work, we investigate learning 3D human shape and pose from dense correspondences of body parts and propose a Decompose-and-aggregate Network (DaNet) to address these issues. DaNet adopts the dense correspondence maps, which densely build a bridge between 2D pixels and 3D vertexes, as intermediate representations to facilitate the learning of 2D-to-3D mapping. The prediction modules of DaNet are decomposed into one global stream and multiple local streams to enable global and fine-grained perceptions for the shape and pose predictions, respectively. Messages from local streams are further aggregated to enhance the robust prediction of the rotation-based poses, where a position-aided rotation feature refinement strategy is proposed to exploit spatial relationships between body joints. Moreover, a Part-based Dropout (PartDrop) strategy is introduced to drop out dense information from intermediate representations during training, encouraging the network to focus on more complementary body parts as well as neighboring position features. The efficacy of the proposed method is validated on both indoor and real-world datasets including Human3.6M, UP3D, COCO, and 3DPW, showing that our method could significantly improve the reconstruction performance in comparison with previous state-of-the-art methods. Our code is publicly available at https://hongwenzhang.github. io/dense2mesh.
'Alexa, Do You Know Anything?' The Impact of an Intelligent Assistant on Team Interactions and Creative Performance Under Time Scarcity

Human-AI collaboration is on the rise with the deployment of AI-enabled intelligent assistants (e.g. Amazon Echo, Cortana, Siri, etc.) across organizational contexts. It is claimed that intelligent assistants can help people achieve more in less time (Personal Digital Assistant - Cortana, n.d.). However, despite the increasing presence of intelligent assistants in collaborative settings, there is a void in the literature on how the deployment of this technology intersects with time scarcity to impact team behaviors and performance. To fill this gap in the literature, we collected behavioral data from 56 teams who participated in a between-subjects 2 (Intelligent Assistant: Available vs. Not Available) x 2 (Time: Scarce vs. Not Scarce/Control) lab experiment. The results show that teams with an intelligent assistant had significantly fewer interactions between its members compared to teams without an intelligent assistant. Teams who faced time scarcity also used the intelligent assistant more often to seek its assistance during task completion compared to those in the control condition. Lastly, teams with an intelligent assistant underperformed on a creative task compared to those without the device. We discuss implications of this technology from theoretical, empirical, and practical perspectives.
Improved Structural Discovery and Representation Learning of Multi-Agent Data

Central to all machine learning algorithms is data representation. For multi-agent systems, selecting a representation which adequately captures the interactions among agents is challenging due to the latent group structure which tends to vary depending on context. However, in multi-agent systems with strong group structure, we can simultaneously learn this structure and map a set of agents to a consistently ordered representation for further learning. In this paper, we present a dynamic alignment method which provides a robust ordering of structured multi-agent data enabling representation learning to occur in a fraction of the time of previous methods. We demonstrate the value of this approach using a large amount of soccer tracking data from a professional league.
Probing the interior physics of stars through asteroseismology

Years-long high-precision brightness measurements obtained with telescopes operating in space have become available for thousands of stars. Such data allowed us to measure the physics of stellar interiors via nonradial oscillations, opening a new avenue to study the stars in the Universe. Asteroseismology, the interpretation of the characteristics of oscillation modes in terms of the physical properties of the stellar interior, brought entirely new insights in how stars rotate and how they build up their chemistry throughout their evolution. We discuss how data-driven space asteroseismology delivered a drastic increase in the reliability of computer models mimicking the evolution of stars born with a variety of masses and metalicities. Such models are critical ingredients for modern physics as a whole, because they are used throughout various contemporary and multidisciplinary research fields in space science, including the search for life outside the solar system, archaeological studies of the Milky Way, and the study of single and binary supernova progenitors, among which future gravitational wave sources. We illustrate the specific role and potential of asteroseismology for those modern research fields. We end with current limitations of asteroseismology and highlight how they can be overcome with ongoing and future large infrastructures for survey astronomy combined with new theoretical research in the era of high-performance computing. This review presents results obtained thanks to major community efforts over the past decade. These breakthroughs were achieved in a collaborative and inclusive spirit so characteristic of the asteroseismology community. The aim was to write it so as to make this research field well accessible to graduate students and readers from other fields in physics, with incentives to enjoy and join future applications in this glorious domain of astrophysics.
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images. We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialog-based image retrieval. We believe that our dataset will encourage further work on developing more natural and real-world applicable conversational shopping assistants. 1
Exploiting morphological data from Pulsar Wind Nebulae via a spatiotemporal leptonic transport code

The next era of ground-based Cherenkov telescope development will see a great increase in both quantity and quality of $\gamma$-ray morphological data. This initiated the development of a spatio-temporal leptonic transport code to model pulsar wind nebulae. We present results from this code that predicts the evolution of the leptonic particle spectrum and radiation at different radii in a spherically-symmetric source. We simultaneously fit the overall broadband spectral energy distribution, the surface brightness profile and the X-ray photon index vs.\ radius for PWN 3C~58, PWN G21.5$-$0.9 and PWN G0.9+0.1. Such concurrent fitting of disparate data sets is non-trivial and we thus investigate the utility of different goodness-of-fit statistics, specifically the traditional $\chi^2$ test statistic and a newly developed scaled-flux-normalised test statistic to obtain best-fit parameters. We find reasonable fits to the spatial and spectral data of all three sources, but note some remaining degeneracies that will have to be broken by future observations.
SelectFusion: A Generic Framework to Selectively Learn Multisensory Fusion

Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g. locations and orientations. Although deep learning approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion - a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models also suffer from a lack of interpretability. To this extent, we propose SelectFusion, an end-to-end selective sensor fusion module which can be applied to useful pairs of sensor modalities such as monocular images and inertial measurements, depth images and LIDAR point clouds. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities and estimate both trajectory at scale and global pose. In particular, we propose two fusion modules based on different attention strategies: deterministic soft fusion and stochastic hard fusion, and we offer a comprehensive study of the new strategies compared to trivial direct fusion. We evaluate all fusion strategies in both ideal conditions and on progressively degraded datasets that present occlusions, noisy and missing data and time misalignment between sensors, and we investigate the effectiveness of the different fusion strategies in attending the most reliable features, which in itself, provides insights into the operation of the various models.
Portfolio Optimization under Correlation Constraint

We consider the problem of portfolio optimization with a correlation constraint. The framework is the multi-period stochastic financial market setting with one tradable stock, stochastic income, and a non-tradable index. The correlation constraint is imposed on the portfolio and the non-tradable index at some benchmark time horizon. The goal is to maximize a portofolio’s expected exponential utility subject to the correlation constraint. Two types of optimal portfolio strategies are considered: the subgame perfect and the precommitment ones. We find analytical expressions for the constrained subgame perfect (CSGP) and the constrained precommitment (CPC) portfolio strategies. Both these portfolio strategies yield significantly lower risk when compared to the unconstrained setting, at the cost of a small utility loss. The performance of the CSGP and CPC portfolio strategies is similar.
Target Localization With Jammer Removal Using Frequency Diverse Array

A foremost task in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is to efficiently obtain the target signal in the presence of interferences. In this paper, we employ a novel “low-rank + low-rank + sparse” decomposition model to extract the low-rank desired signal and suppress the jamming signals from both barrage and burst jammers. In the literature, the barrage jamming signals, which are intentionally interfered by enemy jammer radar, are usually assumed Gaussian distributed. However, such assumption is oversimplified to hold in practice as the interferences often exhibit non-Gaussian properties. Those non-Gaussian jamming signals, known as impulsive noise or burst jamming, are involuntarily deviated from friendly radar or other working radio equipment including amplifier saturation and sensor failures, thunderstorms and man-made noise. The performance of the existing estimators, relied crucially on the Gaussian noise assumption, may degrade substantially since the probability density function (PDF) of burst jamming has heavier tails that exceed a few standard deviations than the Gaussian distribution. To capture a more general signal model with burst jamming in practice, both barrage jamming and burst jamming are included and a two-step “Go Decomposition” (GoDec) method via alternating minimization is devised for such mixed jamming signal model, where the $a$ $priori$ rank information is exploited to suppress these two kinds of jammers and extract the desired target. Simulation results verify the robust performance of the devised scheme.
All-in-One Image-Grounded Conversational Agents

As single-task accuracy on individual language and image tasks has improved substantially in the last few years, the long-term goal of a generally skilled agent that can both see and talk becomes more feasible to explore. In this work, we focus on leveraging individual language and image tasks, along with resources that incorporate both vision and language towards that objective. We design an architecture that combines state-of-the-art Transformer and ResNeXt modules fed into a novel attentive multimodal module to produce a combined model trained on many tasks. We provide a thorough analysis of the components of the model, and transfer performance when training on one, some, or all of the tasks. Our final models provide a single system that obtains good results on all vision and language tasks considered, and improves the state-of-the-art in image-grounded conversational applications.
Hunting dark matter in galaxy clusters with non-thermal electrons

The electron population inferred to be responsible for the mini-halo within the Ophiuchus galaxy cluster is a steep power law in energy with a slope of 3.8. This is substantially different to that predicted by dark matter (DM) annihilation models. In this work, we present a method of indirect comparison between the observed electron spectrum and that predicted for indirect DM emissions. This method utilizes differences in the consequences of a given electron distribution on the subsequent spectral features of synchrotron emissions. To fully exploit this difference, by leveraging the fact that the peak and cut-off synchrotron frequencies are substantially different to hard power-law cases for WIMP masses above ∼50 GeV, we find that we need μJy sensitivities at frequencies above 10 GHz while being sensitive to arcminute scales. We explore the extent to which this electron spectrum comparison can be validated with the up-coming next-generation Very Large Array (ngVLA) instrument. We show that, with the ngVLA, this method allows us to produce far stronger constraints than existing VLA data, indeed these exceed the Fermi-LAT dwarf searches in a wide variety of annihilation channels and for all studied magnetic field scenarios.
Classification of Douglas (α,β)-metrics on five-dimensional nilpotent Lie groups

In this paper, we classify all simply connected five-dimensional nilpotent Lie groups which admit [Formula: see text]-metrics of Berwald and Douglas type defined by a left invariant Riemannian metric and a left invariant vector field. During this classification, we give the geodesic vectors, Levi-Civita connection, curvature tensor, sectional curvature and [Formula: see text]-curvature.
Second post-Newtonian order radiative dynamics of inspiralling compact binaries in the effective field theory approach

We use the Effective Field Theory (EFT) framework to compute the mass quadrupole moment, the equation of motion, and the power loss of inspiralling compact binaries at the second order in the Post-Newtonian (PN) approximation. We present expressions for the stress-energy pseudo-tensor components of the binary system in higher PN orders. The 2PN correction to the mass quadrupole moment as well as to the acceleration computed in the linearized harmonic gauge presented here are the ingredients needed for the calculation of the next-to-next-to leading order radiation reaction force, which will be presented elsewhere. While this paper reproduces known results, it supplies the building blocks necessary for future higher order calculations in the EFT methodology.
Spatial risk estimation in Tweedie compound Poisson double generalized linear models

Tweedie exponential dispersion family constitutes a fairly rich sub-class of the celebrated exponential family. In particular, a member, compound Poisson gamma (CP-g) model has seen extensive use over the past decade for modeling mixed response featuring exact zeros with a continuous response from a gamma distribution. This paper proposes a framework to perform residual analysis on CP-g double generalized linear models for spatial uncertainty quantification. Approximations are introduced to proposed framework making the procedure scalable, without compromise in accuracy of estimation and model complexity; accompanied by sensitivity analysis to model mis-specification. Proposed framework is applied to modeling spatial uncertainty in insurance loss costs arising from automobile collision coverage. Scalability is demonstrated by choosing sizable spatial reference domains comprised of groups of states within the United States of America.
Recognizing Instagram Filtered Images with Feature De-stylization

Deep neural networks have been shown to suffer from poor generalization when small perturbations are added (like Gaussian noise), yet little work has been done to evaluate their robustness to more natural image transformations like photo filters. This paper presents a study on how popular pretrained models are affected by commonly used Instagram filters. To this end, we introduce ImageNet-Instagram, a filtered version of ImageNet, where 20 popular Instagram filters are applied to each image in ImageNet. Our analysis suggests that simple structure preserving filters which only alter the global appearance of an image can lead to large differences in the convolutional feature space. To improve generalization, we introduce a lightweight de-stylization module that predicts parameters used for scaling and shifting feature maps to “undo” the changes incurred by filters, inverting the process of style transfer tasks. We further demonstrate the module can be readily plugged into modern CNN architectures together with skip connections. We conduct extensive studies on ImageNet-Instagram, and show quantitatively and qualitatively, that the proposed module, among other things, can effectively improve generalization by simply learning normalization parameters without retraining the entire network, thus recovering the alterations in the feature space caused by the filters.
Extended Experimental Inferential Structure Determination Method for Evaluating the Structural Ensembles of Disordered Protein States

Characterization of proteins with intrinsic or unfolded state disorder comprises a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a protein structural ensemble by accounting for the uncertainties of a wide range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects, paramagnetic relaxation enhancements, residual dipolar couplings, and hydrodynamic radii, single molecule fluorescence Forster resonance energy transfer efficiencies and small angle X-ray scattering intensity curves. We apply X-EISD to the drkN SH3 unfolded state domain and show that certain experimental data types are more influential than others for both eliminating structural ensemble models, while also finding equally probable disordered ensembles that have alternative structural properties that will stimulate further experiments to discriminate between them.
Quasinormal modes of black holes surrounded by dark matter and their connection with the shadow radius

The purpose of this article is twofold. First, we highlight a simple connection between the real part of quasinormal modes (QNMs) in the eikonal limit and the shadow radius of black holes (BHs) and then explore the effect of dark matter on the QNMs of massless scalar field and electromagnetic field perturbations in a BH spacetime surrounded by perfect fluid dark matter (BHPFDM). Using the Wentzel--Kramers--Brillouin (WKB) approximation, we show that the quasinormal mode spectra of BHPFDM deviate from those of Schwarzschild black hole due to the presence of the perfect fluid dark matter (PFDM) encoded by the parameter $k$. Moreover, it is shown that for any $kg0$ the real part and the absolute value of the imaginary part of QNM frequencies increases, and this means that the field perturbations in the presence of PFDM decay more rapidly compared to the Schwarzschild vacuum BH. We point out that there exists a reflecting point ${k}_{0}$ corresponding to maximal values for the real part of QNM frequencies. Namely, as the PFDM parameter $k$ increases in the interval $kl{k}_{0}$, the QNM frequencies increases and reach their maximum values at $k={k}_{0}$. Finally, we show that ${k}_{0}$ is also a reflecting point for the shadow radius, while this conclusion can be deduced directly from the inverse relation between the real part of QNMs and the shadow radius.
Hour-Ahead Load Forecasting Using AMI Data

—Accurate short-term load forecasting is essential for efﬁcient operation of the power sector. Predicting load at a ﬁne granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and ﬂuctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called Forecasting using Matrix Factorization (FMF). FMF does not use any consumers’ demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that FMF signiﬁcantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to 26 . 5% and 24 . 4% improvement in RMSE over Regression Tree and Support Vector Machine, respectively and up to 36% and 73 . 2% improvement in MAPE over Random Forest and Long Short-Term Memory neural network, respectively.
A Novel Machine Learning Based Framework for Detection of Autism Spectrum Disorder (ASD)

ABSTRACT Autism Spectrum Disorder (ASD) is linked with abridged ability in social behavior. Scientists working in the broader domain of cognitive sciences have done a lot of research to discover the root cause of ASD, but its biomarkers are still unknown. Some studies from the domain of neuroscience have highlighted the fact that corpus callosum and intracranial brain volume hold significant information for the detection of ASD. Taking inspiration from such findings, in this article, we have proposed a machine learning based framework for automatic detection of ASD using features extracted from corpus callosum and intracranial brain volume. Our proposed framework has not only achieved good recognition accuracy but has also reduced the complexity of training machine learning model by selecting features that are most significant in terms of discriminative capabilities for classification of ASD. Second, for benchmarking and to verify potential of deep learning on analyzing neuroimaging data, in this article, we have presented results achieved by using the transfer learning approach. For this purpose, we have used the pre-trained VGG16 model for the classification of ASD.