Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Domain Randomization

Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. In addition, when domain randomization and policy transfer fail, Active Domain Randomization offers more insight into the deficiencies of both the chosen parameter ranges and the learned policy, allowing for more focused debugging. Our experiments across various physics-based simulated and a real-robot task show that this enhancement leads to more robust, consistent policies.
Radial abundance gradients in the outer Galactic disk as traced by main-sequence OB stars

Context. Elemental abundance gradients in galactic disks are important constraints for models of how spiral galaxies form and evolve. However, the abundance structure of the outer disk region of the Milky Way is poorly known, which hampers our understanding of the spiral galaxy that is closest to us and that can be studied in greatest detail. Young OB stars are good tracers of the present-day chemical abundance distribution of a stellar population and because of their high luminosities they can easily be observed at large distances, making them suitable to explore and map the abundance structure and gradients in the outer regions of the Galactic disk. Aims. Using a sample of 31 main-sequence OB stars located between galactocentric distances 8.4−15.6 kpc, we aim to probe the present-day radial abundance gradients of the Galactic disk. Methods. The analysis is based on high-resolution spectra obtained with the MIKE spectrograph on the Magellan Clay 6.5-m telescope on Las Campanas. We used a non-NLTE analysis in a self-consistent semi-automatic routine based on TLUSTY and SYNSPEC to determine atmospheric parameters and chemical abundances. Results. Stellar parameters (effective temperature, surface gravity, projected rotational velocity, microturbulence, and macroturbulence) and silicon and oxygen abundances are presented for 28 stars located beyond 9 kpc from the Galactic center plus three stars in the solar neighborhood. The stars of our sample are mostly on the main-sequence, with effective temperatures between 20 800−31 300 K, and surface gravities between 3.23−4.45 dex. The radial oxygen and silicon abundance gradients are negative and have slopes of −0.07 dex kpc−1 and −0.09 dex kpc−1, respectively, in the region 8.4 ≤ RG ≤ 15.6 kpc. Conclusions. The obtained gradients are compatible with the present-day oxygen and silicon abundances measured in the solar neighborhood and are consistent with radial metallicity gradients predicted by chemodynamical models of Galaxy Evolution for a subsample of young stars located close to the Galactic plane.
A Randomized Coordinate Descent Method with Volume Sampling

We analyze the coordinate descent method with a new coordinate selection strategy, called volume sampling. This strategy prescribes selecting subsets of variables of certain size proportionally to the determinants of principal submatrices of the matrix, that bounds the curvature of the objective function. In the particular case, when the size of the subsets equals one, volume sampling coincides with the well-known strategy of sampling coordinates proportionally to their Lipschitz constants. For the coordinate descent with volume sampling, we establish the convergence rates both for convex and strongly convex problems. Our theoretical results show that, by increasing the size of the subsets, it is possible to accelerate the method up to the factor which depends on the spectral gap between the corresponding largest eigenvalues of the curvature matrix. Several numerical experiments confirm our theoretical conclusions.
Quizbowl: The Case for Incremental Question Answering

Quizbowl is a scholastic trivia competition that tests human knowledge and intelligence; additionally, it supports diverse research in question answering (QA). A Quizbowl question consists of multiple sentences whose clues are arranged by difficulty (from obscure to obvious) and uniquely identify a well-known entity such as those found on Wikipedia. Since players can answer the question at any time, an elite player (human or machine) demonstrates its superiority by answering correctly given as few clues as possible. We make two key contributions to machine learning research through Quizbowl: (1) collecting and curating a large factoid QA dataset and an accompanying gameplay dataset, and (2) developing a computational approach to playing Quizbowl that involves determining both what to answer and when to answer. Our Quizbowl system has defeated some of the best trivia players in the world over a multi-year series of exhibition matches. Throughout this paper, we show that collaborations with the vibrant Quizbowl community have contributed to the high quality of our dataset, led to new research directions, and doubled as an exciting way to engage the public with research in machine learning and natural language processing.
CAFE-R: A Code That Solves the Special Relativistic Radiation Hydrodynamics Equations

We present a 3D special relativistic radiation hydrodynamics code. It uses the radiative inversion scheme with the M1 closure relation for the radiation equations, which allows the treatment of a wide range of optical depth, temperature, and opacity. The radiation field is treated in the graybody approximation. We present the standard 1D and 2D tests that include both optically thin and thick scenarios, as well as hydrodynamical and radiation-pressure-dominated configurations. As an application in 3D, we show the evolution of a jet driven by radiation hydrodynamics with a helical perturbation. The code is expected to allow the exploration of scenarios in high-energy astrophysics where the radiation is important, like sources of gamma-ray bursts.
The Communicative Modes Analysis System in Psychotherapy From Mixed Methods Framework: Introducing a New Observation System for Classifying Verbal and Non-verbal Communication

Communication represents the core of psychotherapy. The dynamic interaction between verbal and non-verbal components during patient-therapist exchanges, indeed, promotes the co-construction of meanings bringing about change within a process of reciprocal influence of participants. Our paper aims to illustrate the building of a new observational instrument of the therapeutic discourse, the Communicative Modes Analysis System in Psychotherapy (CMASP), and its reliability study from Mixed Methods framework. The CMASP is a single classification system analyzing the communication features within therapeutic exchanges. Born to overcome the limits of traditional psychotherapy research which considers verbal and non-verbal dimensions of communication as in polar opposition, the CMASP building was based on the performative function derived from the Speech Act Theory. We used this function as a comprehensive theorization to interpret the communication components in psychotherapy as an integrated and interacting system. In fact, the instrument detects and classifies, at the overall and dimension level, the verbal and extra-linguistic components of psychotherapeutic communication implemented by the therapist and patients in the form of communicative modes. From the observational methodology framework, it was built an instrument able to record and analyze verbal, vocal and interruption behaviors by combining elements of qualitative and quantitative research approaches. The sample consisted of 30 psychotherapy audio recordings and verbatim transcripts of psychotherapy sessions (for a total of 8327 speaking turns). Four main dimensions were elaborated (Verbal Mode-Structural Form, Verbal Mode-Communicative Intent, Vocal Mode, and Interruption Mode) according to the agency role of communication components. The instrument is a field format combined with category systems. For each dimension, we built a category system that is exhaustive and mutually exclusive. From all dimensions, we have a total of 33 categories. Intra-and inter-judge reliability among four independent judges was computed on a total of 503 speaking turns coded through Cohen’s κ and Krippendorff’s canonical agreement coefficients (Cc), respectively. The CMASP showed high intra-and inter-judge agreement at the global, dimensional, and categorical level providing researchers and professionals with a single and flexible classification system, able to give multiple and concurrent information about the psychotherapy process.
New Phenomena in Large-Scale Internet Traffic

The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest publicly available Internet traffic data containing 50 billion packets. Analysis of this streaming data using 10,000 processors in the MIT SuperCloud reveals a new phenomena: the importance of otherwise unseen leaf nodes and isolated links in Internet traffic. Our analysis further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The measured model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies.
Towards Universal Object Detection by Domain Attention

Despite increasing efforts on universal representations for visual recognition, few have addressed object detection. In this paper, we develop an effective and efficient universal object detection system that is capable of working on various image domains, from human faces and traffic signs to medical CT images. Unlike multi-domain models, this universal model does not require prior knowledge of the domain of interest. This is achieved by the introduction of a new family of adaptation layers, based on the principles of squeeze and excitation, and a new domain-attention mechanism. In the proposed universal detector, all parameters and computations are shared across domains, and a single network processes all domains all the time. Experiments, on a newly established universal object detection benchmark of 11 diverse datasets, show that the proposed detector outperforms a bank of individual detectors, a multi-domain detector, and a baseline universal detector, with a 1.3x parameter increase over a single-domain baseline detector. The code and benchmark are available at http://www.svcl. ucsd.edu/projects/universal-detection/.
Cross-Modal Self-Attention Network for Referring Image Segmentation

We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expression in the image. Existing works in this area treat the language expression and the input image separately in their representations. They do not sufficiently capture long-range correlations between these two modalities. In this paper, we propose a cross-modal self-attention (CMSA) module that effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the input image. In addition, we propose a gated multi-level fusion module to selectively integrate self-attentive cross-modal features corresponding to different levels in the image. This module controls the information flow of features at different levels. We validate the proposed approach on four evaluation datasets. Our proposed approach consistently outperforms existing state-of-the-art methods.
Functional Isolation Forest

For the purpose of monitoring the behavior of complex infrastructures (e.g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest. The statistical analysis of such massive data of functional nature raises many challenging methodological questions. The primary goal of this paper is to extend the popular Isolation Forest (IF) approach to Anomaly Detection, originally dedicated to finite dimensional observations, to functional data. The major difficulty lies in the wide variety of topological structures that may equip a space of functions and the great variety of patterns that may characterize abnormal curves. We address the issue of (randomly) splitting the functional space in a flexible manner in order to isolate progressively any trajectory from the others, a key ingredient to the efficiency of the algorithm. Beyond a detailed description of the algorithm, computational complexity and stability issues are investigated at length. From the scoring function measuring the degree of abnormality of an observation provided by the proposed variant of the IF algorithm, a Functional Statistical Depth function is defined and discussed as well as a multivariate functional extension. Numerical experiments provide strong empirical evidence of the accuracy of the extension proposed.
A Contextual $\psi$-Epistemic Model of the $n$-Qubit Stabilizer Formalism

Contextuality, a generalization of non-locality, has been proposed as the resource that provides the computational speed-up for quantum computation. For universal quantum computation using qudits, of odd-prime dimension, contextuality has been shown to be a necessary and possibly sufficient resource. However, the role of contextuality in quantum computation with qubits remains open. The $n$-qubit stabilizer formalism, which by itself cannot provide a quantum computer super-polynomial computational advantage over a classical counterpart, is contextual. Therefore contextuality cannot be identified as a sufficient resource for quantum computation. However it can be identified as a necessary resource. In this paper we construct a contextual $\psi$-epistemic ontological model of the $n$-qubit stabilizer formalism, to investigate the contextuality present in the formalism. We demonstrate it is possible for such a model to be outcome deterministic, and therefore have a value assignment to all Pauli observables. In our model the value assignment must be updated after a measurement of any stabilizer observable. Remarkably, this includes the value assignments for observables that commute with the measurement. A stronger manifestation of contextuality than is required by known no-go theorems.
Slow motion for one-dimensional nonlinear damped hyperbolic Allen-Cahn systems

We consider a nonlinear damped hyperbolic reaction-diffusion system in a bounded interval of the real line with homogeneous Neumann boundary conditions and we study the metastable dynamics of the solutions. Using an "energy approach" introduced by Bronsard and Kohn [CPAM 1990] to study slow motion for Allen-Cahn equation and improved by Grant [SIAM J. Math. Anal. 1995] in the study of Cahn-Morral systems, we improve and extend to the case of systems the results valid for the hyperbolic Allen-Cahn equation. In particular, we study the limiting behavior of the solutions as $\varepsilon\to0^+$, where $\varepsilon^2$ is the diffusion coefficient, and we prove existence and persistence of metastable states for a time $T_\varepsilon>\exp(A/\varepsilon)$. Such metastable states have a transition layer structure and the transition layers move with exponentially small velocity.
On Kaczmarz Signal Processing Technique in Massive MIMO

To exploit the benefits of massive multiple-input multiple-output (M-MIMO) technology in scenarios where base stations need to be inexpensive, the computational complexity of classical signal processing schemes for spatial multiplexing must be reduced. This calls for suboptimal or "relaxed" schemes that perform well the required combining/precoding steps while achieve low computational complexities. Herein, modifications are proposed aiming to improve its rate of convergence with a rising performance-computational complexity trade-off solution for the M-MIMO combining/precoding problems. Such improvements are motivated by observed negative effects of pathloss and shadowing, originally presented here, over the rate of convergence granted by the KA, which consequently worsens its achievable spectral efficiency (SE). Specifically, the randomized version of KA (rKA) is used when conceiving the signal processing schemes due to its global convergence properties, where our adaptations can be seen as a fairness policy for algorithm initialization that guarantees the converge of all combining/precoding vectors, whether they be from center located users or those located at the edge of the cell. Two different rKA-based schemes are in fact discussed, of which only one is considered applicable, given the constraints placed by the underlying scenario. To ensure that the proposed method outclasses the original proposal, a mathematical characterization is presented along with numerical results that employ more realistic system and channel conditions. The simulations embrace the least-squares and minimum mean-squared error estimators of the channel responses, also taking into account uncorrelated and correlated Rayleigh fading channels. The spatial correlation of the latter is modeled using the exponential correlation model combined with large-scale fading variations over the array.
Extracting the longitudinal structure function 
FL(x,Q2)
 at small 
x
 from a Froissart-bounded parametrization of 
F2(x,Q2)

We present a method to extract, in the leading and next-to-leading order approximations, the longitudinal deep-inelastic scattering structure function FL(x,Q2) from the experimental data by relying on a Froissart-bounded parametrization of the transversal structure function F2(x,Q2) and, partially, on the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi equations. Particular attention is paid on kinematics of low and ultra low values of the Bjorken variable x. Analytical expressions for FL(x,Q2) in terms of the effective parameters of the parametrization of F2(x,Q2) are presented explicitly. We argue that the obtained structure functions FL(x,Q2) within both, the leading and next-to-leading order approximations, manifestly obey the Froissart boundary conditions. Numerical calculations and comparison with available data from ZEUS and H1-Collaborations at HERA demonstrate that the suggested method provides reliable structure functions FL(x,Q2) at low x in a wide range of the momentum transfer (1 GeV2 < Q2 < 3000 GeV2) and can be applied as well in analyses of ultra-high energy processes with cosmic neutrinos.
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering

In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with self-updated attention. Our VideoQA model firstly generates the global context-aware visual and textual features respectively by interacting current inputs with memory contents. After that, it makes the attentional fusion of the multimodal visual and textual representations to infer the correct answer. Multiple cycles of reasoning can be made to iteratively refine attention weights of the multimodal data and improve the final representation of the QA pair. Experimental results demonstrate our approach achieves state-of-the-art performance on four VideoQA benchmark datasets.
$\mathcal{G}$ -Softmax: Improving Intraclass Compactness and Interclass Separability of Features

Intraclass compactness and interclass separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intraclass compactness indicates how close the features with the same label are to each other and interclass separability indicates how far away the features with different labels are. In this paper, we investigate intraclass compactness and interclass separability of features learned by convolutional networks and propose a Gaussian-based softmax (<inline-formula> <tex-math notation="LaTeX">$\mathcal {G}$ </tex-math></inline-formula>-softmax) function that can effectively improve intraclass compactness and interclass separability. The proposed function is simple to implement and can easily replace the softmax function. We evaluate the proposed <inline-formula> <tex-math notation="LaTeX">$\mathcal {G}$ </tex-math></inline-formula>-softmax function on classification data sets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) and on multilabel classification data sets (i.e., MS COCO and NUS-WIDE). The experimental results show that the proposed <inline-formula> <tex-math notation="LaTeX">$\mathcal {G}$ </tex-math></inline-formula>-softmax function improves the state-of-the-art models across all evaluated data sets. In addition, the analysis of the intraclass compactness and interclass separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement. More importantly, we observe that high intraclass compactness and interclass separability are linearly correlated with average precision on MS COCO and NUS-WIDE. This implies that the improvement of intraclass compactness and interclass separability would lead to the improvement of average precision.
3DPeople: Modeling the Geometry of Dressed Humans

Recent advances in 3D human shape estimation build upon parametric representations that model very well the shape of the naked body, but are not appropriate to represent the clothing geometry. In this paper, we present an approach to model dressed humans and predict their geometry from single images. We contribute in three fundamental aspects of the problem, namely, a new dataset, a novel shape parameterization algorithm and an end-to-end deep generative network for predicting shape. First, we present 3DPeople, a large-scale synthetic dataset with 2 Million photo-realistic images of 80 subjects performing 70 activities and wearing diverse outfits. Besides providing textured 3D meshes for clothes and body we annotated the dataset with segmentation masks, skeletons, depth, normal maps and optical flow. All this together makes 3DPeople suitable for a plethora of tasks. We then represent the 3D shapes using 2D geometry images. To build these images we propose a novel spherical area-preserving parameterization algorithm based on the optimal mass transportation method. We show this approach to improve existing spherical maps which tend to shrink the elongated parts of the full body models such as the arms and legs, making the geometry images incomplete. Finally, we design a multi-resolution deep generative network that, given an input image of a dressed human, predicts his/her geometry image (and thus the clothed body shape) in an end-to-end manner. We obtain very promising results in jointly capturing body pose and clothing shape, both for synthetic validation and on the wild images.