Miracle-dz/UniFAR-FTUs huggingface dataset:
Blind Inference of Centrality Rankings from Graph Signals

We study the blind centrality ranking problem, where our goal is to infer the eigenvector centrality ranking of nodes solely from nodal observations, i.e., without information about the topology of the network. We formalize these nodal observations as graph signals and model them as the outputs of a network process on the underlying (unobserved) network. A simple spectral algorithm is proposed to estimate the leading eigenvector of the associated adjacency matrix, thus serving as a proxy for the centrality ranking. A finite rate performance analysis of the algorithm is provided, where we find a lower bound on the number of graph signals needed to correctly rank (with high probability) two nodes of interest. We then specialize our general analysis for the particular case of dense Erdős-Rényi graphs, where existing graph-theoretical results can be leveraged. Finally, we illustrate the proposed algorithm via numerical experiments on synthetic and real-world networks, with special emphasis on how the network features influence the performance.
Asynchronous Distributed Event-Triggered Coordination for Multiagent Coverage Control

This article revisits a multiagent deployment problem where agents are restricted from requesting information from other agents as well as sending acknowledgments when information is received. These communication constraints relax the assumptions of instantaneous communication and synchronous actions by agents (request and response actions). In this article, we propose a fully asynchronous communication aware solution to the multiagent deployment problem that uses an event-triggered broadcasting strategy. Unlike all existing triggered solutions, our event-triggered broadcasting algorithm relies on agents to decide when to broadcast (push) information to others in the network without the need for a response from other agents. In addition, the proposed strategy determines how best to move when up-to-date information is unavailable and cannot be requested. The algorithm is capable of achieving similar levels of performance to that of a continuous or periodic strategy. Our solution is proven to achieve asymptotic convergence, and simulation results are provided to demonstrate that the proposed event-triggered broadcasting algorithm can achieve an adequate level of performance under the communication constraints.
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

Vision-based sign language recognition aims at helping the deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words. Due to the limited vocabulary size, models learned from those datasets cannot be applied in practice. In this paper, we introduce a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers. This dataset will be made publicly available to the research community. To our knowledge,it is by far the largest public ASL dataset to facilitate word-level sign recognition research.Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios. Specifically we implement and compare two different models,i.e., (i) holistic visual appearance based approach, and (ii) 2D human pose based approach. Both models are valuable baselines that will benefit the community for method benchmarking. Moreover, we also propose a novel pose-based temporal graph convolution networks (Pose-TGCN) that model spatial and temporal dependencies in human pose trajectories simultaneously, which has further boosted the performance of the pose-based method. Our results show that pose-based and appearance-based models achieve comparable performances up to 62.63% at top-10 accuracy on 2,000 words/glosses, demonstrating the validity and challenges of our dataset. Our dataset and baseline deep models are available at https://dxli94.github. io/WLASL/.
Implications of the search for optical counterparts during the first six months of the Advanced LIGO’s and Advanced Virgo’s third observing run: possible limits on the ejecta mass and binary properties

GW170817 showed that neutron star mergers not only emit gravitational waves but also can release electromagnetic signatures in multiple wavelengths. Within the first half of the third observing run of the Advanced LIGO and Virgo detectors, there have been a number of gravitational wave candidates of compact binary systems for which at least one component is potentially a neutron star. In this article, we look at the candidates S190425z, S190426c, S190510g, S190901ap, and S190910h, predicted to have potentially a non-zero remnant mass, in more detail. All these triggers have been followed up with extensive campaigns by the astronomical community doing electromagnetic searches for their optical counterparts; however, according to the released classification, there is a high probability that some of these events might not be of extraterrestrial origin. Assuming that the triggers are caused by a compact binary coalescence and that the individual source locations have been covered during the EM follow-up campaigns, we employ three different kilonova models and apply them to derive possible constraints on the matter ejection consistent with the publicly available gravitational-wave trigger information and the lack of a kilonova detection. These upper bounds on the ejecta mass can be related to limits on the maximum mass of the binary neutron star candidate S190425z and to constraints on the mass-ratio, spin, and NS compactness for the potential black hole–neutron star candidate S190426c. Our results show that deeper electromagnetic observations for future gravitational wave events near the horizon limit of the advanced detectors are essential.
Set Partition Modulation

In this paper, a novel modulation scheme called set partition modulation (SPM) is proposed. In this scheme, set partitioning and ordered subsets in the set partitions are used to form codewords. We define different SPM variants and depict a practical model for using SPM with orthogonal frequency division multiplexing (OFDM). For the OFDM-SPM schemes, different constellations are used to distinguish between different subsets in a set partition. To achieve good distance properties as well as better error performance for the OFDM-SPM codewords, we define a codebook selection problem and formulate such a problem as a clique problem in graph theory. In this regard, we propose a fast and efficient codebook selection algorithm. We analyze error and achievable rate performance of the proposed schemes and provide asymptotic results for the performance. It is shown that the proposed SPM variants are general schemes, which encompass multi-mode OFDM with index modulation (MM-OFDM-IM) and dual-mode OFDM with index modulation (DM-OFDM-IM) as special cases. It is also shown that OFDM-SPM schemes are capable of exhibiting better error performance and improved achievable rate than conventional OFDM, OFDM-IM, DM-OFDM-IM, and MM-OFDM-IM.
A Comprehensive Chandra Study of the Disk Wind in the Black Hole Candidate 4U 1630-472

The mechanisms that drive disk winds are a window into the physical processes that underlie the disk. Stellar-mass black holes are an ideal setting in which to explore these mechanisms, in part because their outbursts span a broad range in mass accretion rate. We performed a spectral analysis of the disk wind found in six Chandra/HETG observations of the black hole candidate 4U 1630−472, covering a range of luminosities over two distinct spectral states. We modeled both wind absorption and extended wind re-emission components using PION, a self-consistent photoionized absorption model. In all but one case, two photoionization zones were required in order to obtain acceptable fits. Two independent constraints on launching radii, obtained via the ionization parameter formalism and the dynamical broadening of the re-emission, helped characterize the geometry of the wind. The innermost wind components ( ) tend toward small volume filling factors, high ionization, densities up to , and outflow velocities of ∼0.003c. These small launching radii and large densities require magnetic driving, as they are inconsistent with numerical and analytical treatments of thermally driven winds. Outer wind components ( ) are significantly less ionized and have filling factors near unity. Their larger launching radii, lower densities ( ), and outflow velocities (∼0.0007c) are nominally consistent with thermally driven winds. The overall wind structure suggests that these components may also be part of a broader MHD outflow and perhaps better described as magneto-thermal hybrid winds.
A Hybrid Semantic Parsing Approach for Tabular Data Analysis

This paper presents a novel approach to translating natural language questions to SQL queries for given tables, which meets three requirements as a real-world data analysis application: cross-domain, multilingualism and enabling quick-start. Our proposed approach consists of: (1) a novel data abstraction step before the parser to make parsing table-agnosticism; (2) a set of semantic rules for parsing abstracted data-analysis questions to intermediate logic forms as tree derivations to reduce the search space; (3) a neural-based model as a local scoring function on a span-based semantic parser for structured optimization and efficient inference. Experiments show that our approach outperforms state-of-the-art algorithms on a large open benchmark dataset WikiSQL. We also achieve promising results on a small dataset for more complex queries in both English and Chinese, which demonstrates our language expansion and quick-start ability.
Rethinking Exposure Bias In Language Modeling

Exposure bias describes the phenomenon that a language model trained under the teacher forcing schema may perform poorly at the inference stage when its predictions are conditioned on its previous predictions unseen from the training corpus. Recently, several generative adversarial networks (GANs) and reinforcement learning (RL) methods have been introduced to alleviate this problem. Nonetheless, a common issue in RL and GANs training is the sparsity of reward signals. In this paper, we adopt two simple strategies, multi-range reinforcing, and multi-entropy sampling, to amplify and denoise the reward signal. Our model produces an improvement over competing models with regards to BLEU scores and road exam, a new metric we designed to measure the robustness against exposure bias in language models.
Stochastic Reachability for Systems up to a Million Dimensions

We present a solution to the first-hitting time stochastic reachability problem for extremely high-dimensional stochastic dynamical systems. Our approach takes advantage of a nonparametric learning technique known as conditional distribution embeddings to model the stochastic kernel using a data-driven approach. By embedding the dynamics and uncertainty within a reproducing kernel Hilbert space, it becomes possible to compute the safety probabilities for stochastic reachability problems as simple matrix operations and inner products. We employ a convergent approximation technique, random Fourier features, in order to accommodate the large sample sets needed for high-dimensional systems. This technique avoids the curse of dimensionality, and enables the computation of safety probabilities for high-dimensional systems without prior knowledge of the structure of the dynamics or uncertainty. We validate this approach on a double integrator system, and demonstrate its capabilities on a million-dimensional, nonlinear, non-Gaussian, repeated planar quadrotor system.
Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction

This extended abstract describes our solution for the Traffic4Cast Challenge 2019. The key problem we addressed is to properly model both low-level (pixel based) and high-level spatial information while still preserve the temporal relations among the frames. Our approach is inspired by the recent adoption of convolutional features into a recurrent neural networks such as LSTM to jointly capture the spatio-temporal dependency. While this approach has been proven to surpass the traditional stacked CNNs (using 2D or 3D kernels) in action recognition, we observe suboptimal performance in traffic prediction setting. Therefore, we apply a number of adaptations in the frame encoder-decoder layers and in sampling procedure to better capture the high-resolution trajectories, and to increase the training efficiency.
Holographic spectral functions with momentum relaxation

We study (fermionic) spectral functions in two holographic models, the Gubser-Rocha-linear axion model and the linear axion model, where translational symmetry is broken by axion fields linear to the boundary coordinates ($\psi_{I}=\beta \delta_{Ii} x^{i}$). Here, $\beta$ corresponds to the strength of momentum relaxation. The spectral function is computed by the fermionic Green's function of the bulk Dirac equation, where a fermion mass, $m$, and a dipole coupling, $p$, are introduced as input parameters. By classifying the shape of spectral functions, we construct complete phase diagrams in ($m,p,\beta$) space for both models. We find that two phase diagrams are similar even though their background geometries are different. We also find that the effect of momentum relaxation on the (spectral function) phases of two models are similar even though the effect of momentum relaxation on the DC conductivities of two models are very different. We suspect that this is because holographic fermion does not back-react to geometry in our framework.
6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints

We present 6-PACK, a deep learning approach to category-level 6D object pose tracking on RGB-D data. Our method tracks in real time novel object instances of known object categories such as bowls, laptops, and mugs. 6-PACK learns to compactly represent an object by a handful of 3D keypoints, based on which the interframe motion of an object instance can be estimated through keypoint matching. These keypoints are learned end-to-end without manual supervision in order to be most effective for tracking. Our experiments show that our method substantially outperforms existing methods on the NOCS category-level 6D pose estimation benchmark and supports a physical robot to perform simple vision-based closed-loop manipulation tasks. Our code and video are available at https://sites.google. com/view/6packtracking.
ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries

Zero-shot learning (ZSL) has received extensive attention recently especially in areas of fine-grained object recognition, retrieval, and image captioning. Due to the complete lack of training samples and high requirement of defense transferability, the ZSL model learned is particularly vulnerable against adversarial attacks. Recent work also showed adversarially robust generalization requires more data. This may significantly affect the robustness of ZSL. However, very few efforts have been devoted towards this direction. In this paper, we take an initial attempt, and propose a generic formulation to provide a systematical solution (named ATZSL) for learning a defensive ZSL model. It is capable of achieving better generalization on various adversarial objects recognition while only losing a negligible performance on clean images for unseen classes, by casting ZSL into a min-max optimization problem. To address it, we design a defensive relation prediction network, which can bridge the seen and unseen class domains via attributes to generalize prediction and defense strategy. Additionally, our framework can be extended to deal with the poisoned scenario of unseen class attributes. An extensive group of experiments are then presented, demonstrating that ATZSL obtains remarkably more favorable trade-off between model transferability and robustness, over currently available alternatives under various settings.
Scaling Betweenness Approximation to Billions of Edges by MPI-based Adaptive Sampling

Betweenness centrality is one of the most popular vertex centrality measures in network analysis. Hence, many (sequential and parallel) algorithms to compute or approximate betweenness have been devised. Recent algorithmic advances have made it possible to approximate betweenness very efficiently on shared-memory architectures. Yet, the best shared-memory algorithms can still take hours of running time for large graphs, especially for graphs with a high diameter or when a small relative error is required.In this work, we present an MPI-based generalization of the state-of-the-art shared-memory algorithm for betweenness approximation. This algorithm is based on adaptive sampling; our parallelization strategy can be applied in the same manner to adaptive sampling algorithms for other problems. In experiments on a 16-node cluster, our MPI-based implementation is by a factor of 16.1x faster than the state-of-the-art shared-memory implementation when considering our parallelization focus – the adaptive sampling phase – only. For the complete algorithm, we obtain an average (geom. mean) speedup factor of 7.4x over the state of the art. For some previously very challenging inputs, this speedup is much higher. As a result, our algorithm is the first to approximate betweenness centrality on graphs with several billion edges in less than ten minutes with high accuracy.
Face Behavior à la carte: Expressions, Affect and Action Units in a Single Network

Automatic facial behavior analysis has a long history of studies in the intersection of computer vision, physiology and psychology. However it is only recently, with the collection of large-scale datasets and powerful machine learning methods such as deep neural networks, that automatic facial behavior analysis started to thrive. Three of its iconic tasks are automatic recognition of basic expressions (e.g. happy, sad, surprised), estimation of continuous emotions (e.g., valence and arousal), and detection of facial action units (activations of e.g. upper/inner eyebrows, nose wrinkles). Up until now these tasks have been mostly studied independently collecting a dataset for the task. We present the first and the largest study of all facial behaviour tasks learned jointly in a single multi-task, multi-domain and multi-label network, which we call FaceBehaviorNet. For this we utilize all publicly available datasets in the community (around 5M images) that study facial behaviour tasks in-the-wild. We demonstrate that training jointly an end-to-end network for all tasks has consistently better performance than training each of the single-task networks. Furthermore, we propose two simple strategies for coupling the tasks during training, co-annotation and distribution matching, and show the advantages of this approach. Finally we show that FaceBehaviorNet has learned features that encapsulate all aspects of facial behaviour, and can be successfully applied to perform tasks (compound emotion recognition) beyond the ones that it has been trained in a zero- and few-shot learning setting.
The Metal Abundances across Cosmic Time (MACT) Survey. III – The relationship between stellar mass and star formation rate in extremely low-mass galaxies

Extragalactic studies have demonstrated that there is a moderately tight (≈0.3 dex) relationship between galaxy stellar mass (M⋆) and star formation rate (SFR) that holds for star-forming galaxies at M⋆ ∼ 3 × 108–1011 M⊙, i.e. the ‘star formation main sequence’. However, it has yet to be determined whether such a relationship extends to even lower mass galaxies, particularly at intermediate or higher redshifts. We present new results using observations for 714 narrow-band H α-selected galaxies with stellar masses between 106 and 1010 M⊙ (average of 108.2 M⊙) at z ≈ 0.07–0.5. These galaxies have sensitive ultraviolet (UV) to near-infrared photometric measurements and optical spectroscopy. The latter allows us to correct our H α SFRs for dust attenuation using Balmer decrements. Our study reveals that: (1) for low-SFR galaxies, our H α SFRs systematically underpredict compared to far-UV measurements, consistent with other studies; (2) at a given stellar mass (≈108 M⊙), log (specific SFR) evolves as A log (1 + z) with A = 5.26 ± 0.75, and on average, specific SFR increases with decreasing stellar mass; (3) the SFR–M⋆ relation holds for galaxies down to ∼106 M⊙ (∼1.5 dex below previous studies), and over lookback times of up to 5 Gyr, follows a redshift-dependent relation of log (SFR) ∝ α log (M⋆/M⊙) + β z with α = 0.60 ± 0.01 and β = 1.86 ± 0.07; and (4) the observed dispersion in the SFR–M⋆ relation at low stellar masses is ≈0.3 dex. Accounting for survey selection effects using simulated galaxies, we estimate that the true dispersion is ≈0.5 dex.
High-Confidence Policy Optimization: Reshaping Ambiguity Sets in Robust MDPs

Robust MDPs are a promising framework for computing robust policies in reinforcement learning. Ambiguity sets, which represent the plausible errors in transition probabilities, determine the trade-off between robustness and average-case performance. The standard practice of defining ambiguity sets using the $L_1$ norm leads, unfortunately, to loose and impractical guarantees. This paper describes new methods for optimizing the shape of ambiguity sets beyond the $L_1$ norm. We derive new high-confidence sampling bounds for weighted $L_1$ and weighted $L_\infty$ ambiguity sets and describe how to compute near-optimal weights from rough value function estimates. Experimental results on a diverse set of benchmarks show that optimized ambiguity sets provide significantly tighter robustness guarantees.
Relation Module for Non-answerable Prediction on Question Answering

Machine reading comprehension(MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). Our solution is a relation module that is adaptable to any MRC model. The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both BiDAF and BERT models as baseline readers. We obtain 1.8% gain of F1 on top of the BiDAF reader, and 1.0% on top of the BERT base model. These results show the effectiveness of our relation module on MRC