Miracle-dz/UniFAR-FTUs huggingface dataset:
Lip-reading with Densely Connected Temporal Convolutional Networks

In this work, we present the Densely Connected Temporal Convolutional Network (DC-TCN) for lip-reading of isolated words. Although Temporal Convolutional Networks (TCN) have recently demonstrated great potential in many vision tasks, its receptive fields are not dense enough to model the complex temporal dynamics in lip-reading scenarios. To address this problem, we introduce dense connections into the network to capture more robust temporal features. Moreover, our approach utilises the Squeeze-and-Excitation block, a light-weight attention mechanism, to further enhance the model’s classification power. Without bells and whistles, our DC-TCN method has achieved 88.36% accuracy on the Lip Reading in the Wild (LRW) dataset and 43.65% on the LRW-1000 dataset, which has surpassed all the baseline methods and is the new state-of-the-art on both datasets.
The brown dwarf population in the star-forming region NGC 2264

The brown dwarf population in the canonical star forming region NGC2264 is so far poorly explored. We present a deep, multi-wavelength, multi-epoch survey of the star forming cluster NGC2264, aimed to identify young brown dwarf candidates in this region. Using criteria including optical/near-infrared colours, variability, Spitzer mid-infrared colour excess, extinction, and Gaia parallax and proper motion (in order of relevance), we select 902 faint red sources with indicators of youth. Within this sample we identify 429 brown dwarf candidates based on their infrared colours. The brown dwarf candidates are estimated to span a mass range from 0.01 to 0.08$M_{\odot}$. We find rotation periods for 44 sources, 15 of which are brown dwarf candidates, ranging from 3.6 hours to 6.5 days. A subset of 38 brown dwarf candidates show high level irregular variability indicative of ongoing disc accretion, similar to the behaviour of young stars.
AttendNets: Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers

While significant advances in deep learning has resulted in state-of-the-art performance across a large number of complex visual perception tasks, the widespread deployment of deep neural networks for TinyML applications involving on-device, low-power image recognition remains a big challenge given the complexity of deep neural networks. In this study, we introduce AttendNets, low-precision, highly compact deep neural networks tailored for on-device image recognition. More specifically, AttendNets possess deep self-attention architectures based on visual attention condensers, which extends on the recently introduced stand-alone attention condensers to improve spatial-channel selective attention. Furthermore, AttendNets have unique machine-designed macroarchitecture and microarchitecture designs achieved via a machine-driven design exploration strategy. Experimental results on ImageNet$_{50}$ benchmark dataset for the task of on-device image recognition showed that AttendNets have significantly lower architectural and computational complexity when compared to several deep neural networks in research literature designed for efficiency while achieving highest accuracies (with the smallest AttendNet achieving $\sim$7.2% higher accuracy, while requiring $\sim$3$\times$ fewer multiply-add operations, $\sim$4.17$\times$ fewer parameters, and $\sim$16.7$\times$ lower weight memory requirements than MobileNet-V1). Based on these promising results, AttendNets illustrate the effectiveness of visual attention condensers as building blocks for enabling various on-device visual perception tasks for TinyML applications.
AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators

Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized computing, but the fact that FPGAs are hard to program creates a steep learning curve for software programmers. Even with the help of high-level synthesis (HLS), accelerator designers still have to manually perform code reconstruction and cumbersome parameter tuning to achieve optimal performance. While many learning models have been leveraged by existing work to automate the design of efficient accelerators, the unpredictability of modern HLS tools becomes a major obstacle for them to maintain high accuracy. To address this problem, we propose an automated DSE framework—AutoDSE—that leverages a bottleneck-guided coordinate optimizer to systematically find a better design point. AutoDSE detects the bottleneck of the design in each step and focuses on high-impact parameters to overcome it. The experimental results show that AutoDSE is able to identify the design point that achieves, on the geometric mean, 19.9× speedup over one CPU core for MachSuite and Rodinia benchmarks. Compared to the manually optimized HLS vision kernels in Xilinx Vitis libraries, AutoDSE can reduce their optimization pragmas by 26.38× while achieving similar performance. With less than one optimization pragma per design on average, we are making progress towards democratizing customizable computing by enabling software programmers to design efficient FPGA accelerators.
Detection of Pb II in the Ultraviolet Spectra of Three Metal-poor Stars

We report the first detection of the Pb ii line at 2203.534 Å in three metal-poor stars, using ultraviolet spectra obtained with the Space Telescope Imaging Spectrograph on board the Hubble Space Telescope. We perform a standard abundance analysis assuming local thermodynamic equilibrium (LTE) to derive lead (Pb, Z = 82) abundances. The Pb ii line yields a higher abundance than Pb i lines by +0.36 ± 0.34 dex and +0.49 ± 0.28 dex in the stars HD 94028 and HD 196944, where Pb i lines had been detected previously. The Pb ii line is likely formed in LTE, and these offsets affirm previous calculations showing that Pb i lines commonly used as abundance indicators underestimate the Pb abundance in LTE. Pb is enhanced in the s-process-enriched stars HD 94028 ([Pb/Fe] = +0.95 ± 0.14) and HD 196944 ([Pb/Fe] = +2.28 ± 0.23), and we show that 208Pb is the dominant Pb isotope in these two stars. The (Pb/Eu) ratio in the r-process-enhanced star HD 222925 is 0.76 ± 0.14, which matches the solar system r-process ratio and indicates that the solar system r-process residuals for Pb are, in aggregate, correct. The Th/Pb chronometer in HD 222925 yields an age of 8.2 ± 5.8 Gyr, and we highlight the potential of the Th/Pb chronometer as a relatively model-insensitive age indicator in r-process-enhanced stars.
INSPIRED: Toward Sociable Recommendation Dialog Systems

In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations. Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels. In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model. This work is a first step for building sociable recommendation dialog systems with a basis of social science theories.
Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games

Solution concepts of traditional game theory assume entirely rational players; therefore, their ability to exploit subrational opponents is limited. One type of subrationality that describes human behavior well is the quantal response. While there exist algorithms for computing solutions against quantal opponents, they either do not scale or may provide strategies that are even worse than the entirely-rational Nash strategies. This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games. Our contributions are: (1) we define two different solution concepts related to exploiting quantal opponents and analyze their properties; (2) we prove that computing these solutions is computationally hard; (3) therefore, we evaluate several heuristic approximations based on scalable counterfactual regret minimization (CFR); and (4) we identify a CFR variant that exploits the bounded opponents better than the previously used variants while being less exploitable by the worst-case perfectly-rational opponent.
Electronic structure and finite temperature magnetism of yttrium iron garnet

Yttrium iron garnet is a complex ferrimagnetic insulator with 20 magnon modes which is used extensively in fundamental experimental studies of magnetisation dynamics. As a transition metal oxide with moderate gap (2.8 eV), yttrium iron garnet requires a careful treatment of electronic correlation. We have applied quasiparticle self-consistent GW to provide a fully ab initio description of the electronic structure and resulting magnetic properties, including the parameterisation of a Heisenberg model for magnetic exchange interactions. Subsequent spin dynamical modelling with quantum statistics extends our description to the magnon spectrum and thermodynamic properties such as the Curie temperature, finding favourable agreement with experimental measurements. This work provides a snapshot of the state-of-the art in modelling of complex magnetic insulators.
Plasticity in Root/Shoot Partitioning: Optimal, Ontogenetic, or Both?

1. We tested whether plants increase root :shoot ratios to compensate for limitations of below-ground resources in a manner consistent with optimal partitioning theory or whether the relative production of roots and shoots is controlled by species-specific developmental patterns. Individuals of two annual plant species, Abutilon theophrasti and Chenopodium album, were grown from seed in controlled greenhouse conditions under high- or low-nutrient regimes. Mid-way through the experiment, a sub-set of low-nutrient-grown plants were given high nutrient availability and a sub-set of high-nutrient-grown plants were transferred to a low nutrient environment. 2. Under continuous nutrient regimes : (1) high-nutrient-grown plants of both species grew faster and had a lower root :shoot ratio than low-nutrient-grown plants, consistent with optimal partitioning theory ; (2) both species exhibited a substantial amount of ontogenetic drift as root :shoot ratios decreased through ontogeny (subsequent to an initial increase in R/S shortly after germination) ; (3) allometric analyses revealed that increased allocation to roots occurred very early in ontogeny for both species, after which the relative growth of shoots exceeded that of roots in low-nutrient-grown plants compared to their high nutrient-grown counterparts - a result inconsistent with optimal partitioning theory. 3. Under temporally varying nutrient regimes : (1) growth substantially increased in low-nutrient-grown plants that were switched to a high-nutrient environment without a change in root :shoot partitioning ; (2) there was no change in growth or partitioning when plants were switched from a high- to a low-nutrient regime. 4. We conclude that, for these annual species, root/shoot partitioning is partially consistent with optimal partitioning theory but that is also highly ontogenetically constrained. This constraint is evident both in substantive ontogenetic drift in partitioning and in the period during development that plasticity in partitioning can be expressed.
Facilitating Connected Autonomous Vehicle Operations Using Space-weighted Information Fusion and Deep Reinforcement Learning Based Control

The connectivity aspect of connected autonomous vehicles (CAV) is beneficial because it facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External (V2X) communication. Onboard sensing equipment including LiDAR and camera can reasonably characterize the traffic environment in the immediate locality of the CAV. However, their performance is limited by their sensor range (SR). On the other hand, longer-range information is helpful for characterizing imminent conditions downstream. By contemporaneously coalescing the short- and long-range information, the CAV can construct comprehensively its surrounding environment and thereby facilitate informed, safe, and effective movement planning in the short-term (local decisions including lane change) and long-term (route choice). In this paper, we describe a Deep Reinforcement Learning based approach that integrates the data collected through sensing and connectivity capabilities from other vehicles located in the proximity of the CAV and from those located further downstream, and we use the fused data to guide lane changing, a specific context of CAV operations. In addition, recognizing the importance of the connectivity range (CR) to the performance of not only the algorithm but also of the vehicle in the actual driving environment, the paper carried out a case study. The case study demonstrates the application of the proposed algorithm and duly identifies the appropriate CR for each level of prevailing traffic density. It is expected that implementation of the algorithm in CAVs can enhance the safety and mobility associated with CAV driving operations. From a general perspective, its implementation can provide guidance to connectivity equipment manufacturers and CAV operators, regarding the default CR settings for CAVs or the recommended CR setting in a given traffic environment.
Uncertainty Reasoning for Probabilistic Petri Nets via Bayesian Networks

This paper exploits extended Bayesian networks for uncertainty reasoning on Petri nets, where firing of transitions is probabilistic. In particular, Bayesian networks are used as symbolic representations of probability distributions, modelling the observer's knowledge about the tokens in the net. The observer can study the net by monitoring successful and failed steps. An update mechanism for Bayesian nets is enabled by relaxing some of their restrictions, leading to modular Bayesian nets that can conveniently be represented and modified. As for every symbolic representation, the question is how to derive information - in this case marginal probability distributions - from a modular Bayesian net. We show how to do this by generalizing the known method of variable elimination. The approach is illustrated by examples about the spreading of diseases (SIR model) and information diffusion in social networks. We have implemented our approach and provide runtime results.
The nature of the symbiotic candidate 2MASS J07363415+6538548 in the field of NGC 2403

New Online Database of Symbiotic Variables includes several poorly characterised objects and candidate symbiotic stars, not only in the Milky Way but also in other galaxies. The goal of the research presented in this paper was to reveal the nature of 2MASS J07363415+6538548, the object discovered as an X-ray source in the field of NGC 2403, and tentatively classified as a symbiotic candidate or a cataclysmic variable. By analysis of available photometric data from ground-based surveys, together with high precision photometry from TESS, remarkable astrometric measurements of the Gaia satellite, and observations of other surveys spanning from X-rays to infrared, we have found that the object is not a symbiotic star nor a cataclysmic variable but rather an active K-type dwarf. The star is located in the distance of 415 pc, has an effective temperature of 4 275 K, luminosity of 0.14 L$\odot$, a mass of 0.7 M$\odot$, and radius of 0.7 R$\odot$. It has a rotational period ~ 3 days and is a strong X-ray source with the X-ray luminosity of ~ $10^{30}$ erg/s. Gyrochronology and isochrone fitting confirmed that the star is young.
Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Time-division-duplexing massive multiple-input multiple-output (MIMO) systems estimate the channel state information (CSI) by leveraging the uplink-downlink channel reciprocity, which is no longer valid when the mismatch arises from the asymmetric uplink and downlink radio frequency (RF) chains. Existing works treat the reciprocity mismatch as constant for simplicity. However, the practical RF chain consists of nonlinear components, which leads to nonlinear reciprocity mismatch. In this work, we examine the impact and the calibration approach of the nonlinear reciprocity mismatch in massive MIMO systems. To evaluate the impact of the nonlinear mismatch, we first derive the closed-form expression of the ergodic achievable rate. Then, we analyze the performance loss caused by the nonlinear mismatch to show that the impact of the mismatch at the base station (BS) side is much larger than that at the user equipment side. Therefore, we propose a calibration method for the BS. During the calibration, polynomial function is applied to approximate the nonlinear mismatch factor, and over-the-air training is employed to estimate the polynomial coefficients. After that, the calibration coefficients are computed by maximizing the downlink achievable rate. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.
RDSGAN: Rank-based Distant Supervision Relation Extraction with Generative Adversarial Framework

Distant supervision has been widely used for relation extraction but suffers from noise labeling problem. Neural network models are proposed to denoise with attention mechanism but cannot eliminate noisy data due to its non-zero weights. Hard decision is proposed to remove wrongly-labeled instances from the positive set though causes loss of useful information contained in removed instances. In this paper, we propose a novel generative neural framework named RDSGAN (Rank-based Distant Supervision GAN) which automatically generates valid instances for distant supervision relation extraction. Our framework combines soft attention and hard decision to learn the distribution of true positive instances via adversarial training and selects valid instances conforming to the distribution via rank-based distant supervision, which addresses the false positive problem. Experimental results show the superiority of our framework over strong baselines.
Rethinking Evaluation Methodology for Audio-to-Score Alignment

This paper offers a precise, formal definition of an audio-to-score alignment. While the concept of an alignment is intuitively grasped, this precision affords us new insight into the evaluation of audio-to-score alignment algorithms. Motivated by these insights, we introduce new evaluation metrics for audio-to-score alignment. Using an alignment evaluation dataset derived from pairs of KernScores and MAESTRO performances, we study the behavior of our new metrics and the standard metrics on several classical alignment algorithms.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso

Detecting where and when brain regions activate in a cognitive task or in a given clinical condition is the promise of non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG). This problem, referred to as source localization, or source imaging, poses however a high-dimensional statistical inference challenge. While sparsity promoting regularizations have been proposed to address the regression problem, it remains unclear how to ensure statistical control of false detections. Moreover, M/EEG source imaging requires to work with spatio-temporal data and autocorrelated noise. To deal with this, we adapt the desparsified Lasso estimator -- an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions -- to temporal data corrupted with autocorrelated noise. We call it the desparsified multi-task Lasso (d-MTLasso). We combine d-MTLasso with spatially constrained clustering to reduce data dimension and with ensembling to mitigate the arbitrary choice of clustering; the resulting estimator is called ensemble of clustered desparsified multi-task Lasso (ecd-MTLasso). With respect to the current procedures, the two advantages of ecd-MTLasso are that i)it offers statistical guarantees and ii)it allows to trade spatial specificity for sensitivity, leading to a powerful adaptive method. Extensive simulations on realistic head geometries, as well as empirical results on various MEG datasets, demonstrate the high recovery performance of ecd-MTLasso and its primary practical benefit: offer a statistically principled way to threshold MEG/EEG source maps.
Learning Object Detection from Captions via Textual Scene Attributes

Object detection is a fundamental task in computer vision, requiring large annotated datasets that are difficult to collect, as annotators need to label objects and their bounding boxes. Thus, it is a significant challenge to use cheaper forms of supervision effectively. Recent work has begun to explore image captions as a source for weak supervision, but to date, in the context of object detection, captions have only been used to infer the categories of the objects in the image. In this work, we argue that captions contain much richer information about the image, including attributes of objects and their relations. Namely, the text represents a scene of the image, as described recently in the literature. We present a method that uses the attributes in this "textual scene graph" to train object detectors. We empirically demonstrate that the resulting model achieves state-of-the-art results on several challenging object detection datasets, outperforming recent approaches.
Couplings for Andersen dynamics

Andersen dynamics is a standard method for molecular simulations, and a precursor of the Hamiltonian Monte Carlo algorithm used in MCMC inference. The stochastic process corresponding to Andersen dynamics is a PDMP (piecewise deterministic Markov process) that iterates between Hamiltonian flows and velocity randomizations of randomly selected particles. Both from the viewpoint of molecular dynamics and MCMC inference, a basic question is to understand the convergence to equilibrium of this PDMP particularly in high dimension. Here we present couplings to obtain sharp convergence bounds in the Wasserstein sense that do not require global convexity of the underlying potential energy.
Wavenumber-space band clipping in nonlinear periodic structures

In weakly nonlinear systems, the main effect of cubic nonlinearity on wave propagation is an amplitude-dependent correction of the dispersion relation. This phenomenon can manifest either as a frequency shift or as a wavenumber shift depending on whether the excitation is prescribed as an initial condition or as a boundary condition, respectively. Several models have been proposed to capture the frequency shifts observed when the system is subjected to harmonic initial excitations. However, these models are not compatible with harmonic boundary excitations, which represent the conditions encountered in most practical applications. To overcome this limitation, we present a multiple scales framework to analytically capture the wavenumber shift experienced by dispersion relation of nonlinear monatomic chains under harmonic boundary excitations. We demonstrate that the wavenumber shifts result in an unusual dispersion correction effect, which we term wavenumber-space band clipping. We then extend the framework to locally resonant periodic structures to explore the implications of this phenomenon on bandgap tunability. We show that the tuning capability is available if the cubic nonlinearity is deployed in the internal springs supporting the resonators.