Miracle-dz/UniFAR-FTUs huggingface dataset:
BraidNet: Braiding Semantics and Details for Accurate Human Parsing

This paper focuses on fine-grained human parsing in images. This is a very challenging task due to the diverse person appearance, semantic ambiguity of different body parts and clothing, and extremely small parsing targets. Although existing approaches can achieve significant improvement by pyramid feature learning, multi-level supervision, and joint learning with pose estimation, human parsing is still far from being solved. Different from existing approaches, we propose a Braiding Network, named as BraidNet, to learn complementary semantics and details for fine-grained human parsing. The BraidNet contains a two-stream braid-like architecture. The first stream is a semantic abstracting net with a deep yet narrow structure which can learn semantic knowledge by a hierarchy of fully convolution layers to overcome the challenges of diverse person appearance. To capture low-level details of small targets, the detail-preserving net is designed to exploit a shallow yet wide network without down-sampling, which can retain sufficient local structures for small objects. Moreover, we design a group of braiding modules across the two sub-nets, by which complementary information can be exchanged during end-to-end training. Besides, in the end of BraidNet, a Pairwise Hard Region Embedding strategy is propose to eliminate the semantic ambiguity of different body parts and clothing. Extensive experiments show that the proposed BraidNet achieves better performance than the state-of-the-art methods for fine-grained human parsing.
A kinematically unbiased, all-sky search for nearby, young, low-mass stars

The past two decades have seen dramatic progress in our knowledge of the population of young stars of age $\lt \! 200\,$ Myr that lie within $150\,$ pc of the Sun. These nearby, young stars, most of which are found in loose, comoving groups, provide the opportunity to explore (among many other things) the dissolution of stellar clusters and their diffusion into the field star population. Here, we exploit the combination of astrometric and photometric data from Gaia and photometric data from GALEX (UV) and 2MASS (near-IR) in an attempt to identify additional nearby, young, late-type stars. Specifically, we present a sample of 146 GALEX UV-selected late-type (predominantly K-type) field stars with Gaia-based distances $\lt \! 125\,$ pc (based on Gaia Data Release 1) that have isochronal ages $\lt \! 80\,$ Myr even if equal-components binaries. We investigate the spectroscopic and kinematic properties of this sample. Despite their young isochronal ages, only ∼10 per cent of stars among this sample can be confidently associated with established nearby, young moving groups (MGs). These candidate MG members include five stars newly identified in this study. The vast majority of our sample of 146 nearby young star candidates have anomalous kinematics relative to the known MGs. These stars may hence represent a previously unrecognized population of young stars that has recently mixed into the older field star population. We discuss the implications and caveats of such a hypothesis – including the intriguing fact that, in addition to their non-young-star-like kinematics, the majority of the UV-selected, isochronally young field stars within $50\,$ pc appear surprisingly X-ray faint.
Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation

Image-to-image translation architectures may have limited effectiveness in some circumstances. For example, while generating rainy scenarios, they may fail to model typical traits of rain as water drops, and this ultimately impacts the synthetic images realism. With our method, called domain bridge, web-crawled data are exploited to reduce the domain gap, leading to the inclusion of previously ignored elements in the generated images. We make use of a network for clear to rain translation trained with the domain bridge to extend our work to Unsupervised Domain Adaptation (UDA). In that context, we introduce an online multimodal style-sampling strategy, where image translation multimodality is exploited at training time to improve performances. Finally, a novel approach for self-supervised learning is presented, and used to further align the domains. With our contributions, we simultaneously increase the realism of the generated images, while reaching on par performances with respect to the UDA state-of-the-art, with a simpler approach.
Direct Detection of Solar Angular Momentum Loss with the Wind Spacecraft

The rate at which the solar wind extracts angular momentum (AM) from the Sun has been predicted by theoretical models for many decades, and yet we lack a conclusive measurement from in situ observations. In this Letter we present a new estimate of the time-varying AM flux in the equatorial solar wind, as observed by the Wind spacecraft from 1994 to 2019. We separate the AM flux into contributions from the protons, alpha particles, and magnetic stresses, showing that the mechanical flux in the protons is ∼3 times larger than the magnetic field stresses. We observe the tendency for the AM flux of fast wind streams to be oppositely signed to the slow wind streams, as noted by previous authors. From the average total flux, we estimate the global AM loss rate of the Sun to be 3.3 × 1030 erg, which lies within the range of various magnetohydrodynamic wind models in the literature. This AM loss rate is a factor of ∼2 weaker than required for a Skumanich-like rotation period evolution ( stellar age−1/2), which should be considered in studies of the rotation period evolution of Sun-like stars.
Doubly robust treatment effect estimation with missing attributes

Missing attributes are ubiquitous in causal inference, as they are in most applied statistical work. In this paper, we consider various sets of assumptions under which causal inference is possible despite missing attributes and discuss corresponding approaches to average treatment effect estimation, including generalized propensity score methods and multiple imputation. Across an extensive simulation study, we show that no single method systematically out-performs others. We find, however, that doubly robust modifications of standard methods for average treatment effect estimation with missing data repeatedly perform better than their non-doubly robust baselines; for example, doubly robust generalized propensity score methods beat inverse-weighting with the generalized propensity score. This finding is reinforced in an analysis of an observations study on the effect on mortality of tranexamic acid administration among patients with traumatic brain injury in the context of critical care management. Here, doubly robust estimators recover confidence intervals that are consistent with evidence from randomized trials, whereas non-doubly robust estimators do not.
Accurate 6D Object Pose Estimation by Pose Conditioned Mesh Reconstruction

Current 6D object pose estimation methods consist of Deep Convo-lutional Neural Networks fully optimized for a single object but with its architecture standardized among objects with different shapes. In contrast to previous works, we explicitly exploit each object’s distinct topological information with an automated process and prior to any post-processing refinement stage. In order to achieve this, we propose a learning framework in which a Graph Convolutional Neural Network reconstructs a Pose Conditioned 3D mesh of the object. A robust estimation of the allocentric orientation of the target object is recovered by computing, in a differentiable manner, the Procrustes’ alignment between the canonical and reconstructed dense 3D meshes. Our method is capable of self validating its pose estimation by measuring the quality of the reconstructed mesh, which is invaluable in real life applications. In our experiments on the LINEMOD, OCCLUSION and YCB-Video benchmarks, the proposed method outperforms state-of-the-arts.
Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access

Sparsely spread code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In this paper, we propose a novel trainable multiuser detector called sparse trainable projected gradient (STPG) detector, which is based on the notion of deep unfolding. In the STPG detector, trainable parameters are embedded to a projected gradient descent algorithm, which can be trained by standard deep learning techniques such as back propagation and stochastic gradient descent. Advantages of the detector are its low computational cost and small number of trainable parameters, which enables us to treat massive SCDMA systems. In particular, its computational cost is smaller than a conventional belief propagation (BP) detector while the STPG detector exhibits nearly same detection performance with a BP detector. We also propose a scalable joint learning of signature sequences and the STPG detector for signature design. Numerical results show that the joint learning improves multiuser detection performance particularly in the low SNR regime.
Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts

Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present an empirical evaluation of 9 state-of-the-art automated analysis tools using two new datasets: i) a dataset of 69 annotated vulnerable smart contracts that can be used to evaluate the precision of analysis tools; and ii) a dataset with all the smart contracts in the Ethereum Blockchain that have Solidity source code available on Etherscan (a total of 47,518 contracts). The datasets are part of SmartBugs, a new extendable execution framework that we created to facilitate the integration and comparison between multiple analysis tools and the analysis of Ethereum smart contracts. We used SmartBugs to execute the 9 automated analysis tools on the two datasets. In total, we ran 428,337 analyses that took approximately 564 days and 3 hours, being the largest experimental setup to date both in the number of tools and in execution time. We found that only 42% of the vulnerabilities from our annotated dataset are detected by all the tools, with the tool Mythril having the higher accuracy (27%). When considering the largest dataset, we observed that 97% of contracts are tagged as vulnerable, thus suggesting a considerable number of false positives. Indeed, only a small number of vulnerabilities (and of only two categories) were detected simultaneously by four or more tools.
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules

Predicting the relationship between a molecule's structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR) modeling, is an important challenge in chemistry, impacting human nutrition, manufacture of synthetic fragrance, the environment, and sensory neuroscience. We propose the use of graph neural networks for QSOR, and show they significantly out-perform prior methods on a novel data set labeled by olfactory experts. Additional analysis shows that the learned embeddings from graph neural networks capture a meaningful odor space representation of the underlying relationship between structure and odor, as demonstrated by strong performance on two challenging transfer learning tasks. Machine learning has already had a large impact on the senses of sight and sound. Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.
A stackelberg game of backward stochastic differential equations with partial information

This paper is concerned with a Stackelberg game of backward stochastic differential equations (BSDEs) with partial information, where the information of the follower is a sub-$\sigma$-algebra of that of the leader. Necessary and sufficient conditions of the optimality for the follower and the leader are first given for the general problem, by the partial information stochastic maximum principles of BSDEs and forward-backward stochastic differential equations (FBSDEs), respectively. Then a linear-quadratic (LQ) Stackelberg game of BSDEs with partial information is investigated. The state estimate feedback representation for the optimal control of the follower is first given via two Riccati equations. Then the leader's problem is formulated as an optimal control problem of FBSDE. Four high-dimensional Riccati equations are introduced to represent the state estimate feedback for the optimal control of the leader. Theoretic results are applied to a pension fund management problem of two players in the financial market.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
New Construction of Complementary Sequence (or Array) Sets and Complete Complementary Codes

A new method to construct <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-ary complementary sequence sets (CSSs) and complete complementary codes (CCCs) of size <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is proposed by using desired para-unitary (PU) matrices. The concept of seed PU matrices is introduced and a systematic approach on how to compute the explicit forms of the functions in constructed CSSs and CCCs from the seed PU matrices is given. A general form of these functions only depends on a basis of the functions from <inline-formula> <tex-math notation="LaTeX">${\mathbb Z}_{N}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">${\mathbb Z}_{q}$ </tex-math></inline-formula> and representatives in the equivalent class of Butson-type Hadamard (BH) matrices. Especially, the realization of Golay pairs from our general form exactly coincides with the standard Golay pairs. The realization of ternary complementary sequences of size 3 is first reported here. For the realization of the quaternary complementary sequences of size 4, almost all the sequences derived here are never reported before. Generalized seed PU matrices and the recursive constructions of the desired PU matrices are also studied, and a large number of new constructions of CSSs and CCCs are given accordingly. From the perspective of this paper, all the known results of CSSs and CCCs with explicit GBF form in the literature (except non-standard Golay pairs) are constructed from the WHT matrices. This suggests that the proposed method with other BH matrices will yield a large number of new CSSs and CCCs with the significantly increasing number of the sequences of low peak-to-mean envelope power ratio.
Quantitative MRI of the brain—measuring changes caused by disease

P Tofts. Chichester: John Wiley & Sons, 2003, pp 617, £175.00. ISBN 0-470-84721-2 We have waited for a long time for a comprehensive book on magnetic resonance (MR) techniques that will appeal to the neurologist/neuroradiologist as well as the physicist and researcher. A book that is right up to date and is relevant across the board for all who are interested in the technique and that deals with quantification. Paul Tofts has produced a book that is in the coffee table style, in the best sense of …
Incremental Online Spoken Language Understanding

Spoken Language Understanding (SLU) typically comprises of an automatic speech recognition (ASR) followed by a natural language understanding (NLU) module. The two modules process signals in a blocking sequential fashion, i.e., the NLU often has to wait for the ASR to finish processing on an utterance basis, potentially leading to high latencies that render the spoken interaction less natural. In this paper, we propose recurrent neural network (RNN) based incremental processing towards the SLU task of intent detection. The proposed methodology offers lower latencies than a typical SLU system, without any significant reduction in system accuracy. We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems. A lexical End-of-Sentence (EOS) detector is proposed for segmenting the stream of transcript into sentences for intent classification. Intent detection experiments are conducted on benchmark ATIS dataset modified to emulate a continuous incremental stream of words with no utterance demarcation. We also analyze the prospects of early intent detection, before EOS, with our proposed system.
Injecting Hierarchy with U-Net Transformers

The Transformer architecture has become increasingly popular over the past couple of years, owing to its impressive performance on a number of natural language processing (NLP) tasks. However, it may be argued that the Transformer architecture lacks an explicit hierarchical representation, as all computations occur on word-level representations alone, and therefore, learning structure poses a challenge for Transformer models. In the present work, we introduce hierarchical processing into the Transformer model, taking inspiration from the U-Net architecture, popular in computer vision for its hierarchical view of natural images. We propose a novel architecture that combines ideas from Transformer and U-Net models to incorporate hierarchy at multiple levels of abstraction. We empirically demonstrate that the proposed architecture outperforms the vanilla Transformer and strong baselines in the chit-chat dialogue and machine translation domains.
Ideal magnetohydrodynamic equations on a sphere and elliptic-hyperbolic property

This work contains the derivation and type analysis of the conical ideal magnetohydrodynamic equations. The 3D ideal MHD equations with Powell source terms, subject to the assumption that the solution is conically invariant, are projected onto a unit sphere using tools from tensor calculus. Conical flows provide valuable insight into supersonic and hypersonic flow past bodies, but are simpler to analyze and solve numerically. Previously, work has been done on conical inviscid flows governed by the Euler equations with great success. It is known that some flight regimes involve flows of ionized gases, and thus there is motivation to extend the study of conical flows to the case where the gas is electrically conducting. To the authors’ knowledge, the conical magnetohydrodynamic equations have never been derived, and so this paper is the first investigation of that system. Among the results, we show that conical flows for this case do exist mathematically and that the governing system of partial differential equations is of mixed type. Throughout the domain it can be either hyperbolic or elliptic depending on the solution.
An approximate description of quantum states

We introduce an approximate description of an $N$-qubit state, which contains sufficient information to estimate the expectation value of any observable with precision independent of $N$. We show, in fact, that the error in the estimation of the observables' expectation values decreases as the inverse of the square root of the number of the system's identical preparations and increases, at most, linearly in a suitably defined, $N$-independent, seminorm of the observables. Building the approximate description of the $N$-qubit state only requires repetitions of single-qubit rotations followed by single-qubit measurements and can be considered for implementation on today's Noisy Intermediate-Scale Quantum (NISQ) computers. The access to the expectation values of all observables for a given state leads to an efficient variational method for the determination of the minimum eigenvalue of an observable. The method represents one example of the practical significance of the approximate description of the $N$-qubit state. We conclude by briefly discussing extensions to generative modelling and with fermionic operators.
The effects of population III radiation backgrounds on the cosmological 21-cm signal

We investigate the effects of Population III (Pop III) stars and their remnants on the cosmological 21-cm global signal. By combining a semi-analytic model of Pop III star formation with a global 21-cm simulation code, we investigate how X-ray and radio emission from accreting Pop III black holes may affect both the timing and depth of the 21-cm absorption feature that follows the initial onset of star formation during the Cosmic Dawn. We compare our results to the findings of the EDGES experiment, which has reported the first detection of a cosmic 21-cm signal. In general, we find that our fiducial Pop III models, which have peak star formation rate densities of ∼10−4 M⊙ yr−1 Mpc−3 between z ∼ 10 and z ∼ 15, are able to match the timing of the EDGES signal quite well, in contrast to models that ignore Pop III stars. To match the unexpectedly large depth of the EDGES signal without recourse to exotic physics, we vary the parameters of emission from accreting black holes (formed as Pop III remnants) including the intrinsic strength of X-ray and radio emission as well as the local column density of neutral gas. We find that models with strong radio emission and relatively weak X-ray emission can self-consistently match the EDGES signal, though this solution requires fine-tuning. We are only able to produce signals with sharp features similar to the EDGES signal if the Pop III IMF is peaked narrowly around $140 \, \mathrm{M}_\odot$.
Multiple Populations in Globular Clusters: Unified Efforts from Stellar Evolution and Chemical Evolution Models

Recent stellar evolution models for globular clusters (GCs) in a multiple population paradigm suggest that horizontal-branch (HB) morphology and the mean period of type ab RR Lyrae variables are mostly determined by He and CNO abundances and relative ages for subpopulations. These parameters are also provided by chemical evolution models constructed to reproduce the Na–O anticorrelation. Therefore, a consistency check is possible between the synthetic HB and chemical evolution models. Furthermore, by combining them, a better constraint might be attained for star formation history and chemical abundances of subpopulations in GCs. We find, from such efforts made for four GCs, M4, M5, M15, and M80, that consistent results can be obtained from these two independent studies. In our unified model, He and Na abundances gradually increase over the generation, and, therefore, the various extensions observed in both HB morphology and the Na–O chemical pattern depend on the presence of later generation stars after the second generation. It is schematically shown that this observed diversity, however, would not be naturally explained by the models requiring dilution. Further spectroscopic observations are required, for metal-poor GCs in particular, to obtain a more detailed constraint from this approach.