Miracle-dz/UniFAR-FTUs huggingface dataset:
A lack of constraints on the cold opaque H i mass: H i spectra in M31 and M33 prefer multicomponent models over a single cold opaque component

Previous work has argued that atomic gas mass estimates of galaxies from 21-cmH i emission are systematically low due to a cold opaque atomic gas component. If true, this opaque component necessitates a ∼ 35% correction factor relative to the mass from assuming optically-thin H i emission. These mass corrections are based on fitting H i spectra with a single opaque component model that produces a distinct “top-hat” shaped line profile. Here, we investigate this issue using deep, high spectral resolution H i VLA observations of M31 and M33 to test if these top-hat profiles are instead superpositions of multiple H i components along the line-of-sight. We fit both models and find that > 80% of the spectra strongly prefer a multi-component Gaussian model while < 2% prefer the single opacity-corrected component model. This strong preference for multiple components argues against previous findings of lines-of-sight dominated by only cold H i. Our findings are enabled by the improved spectral resolution (0.42 km s−1), whereas coarser spectral resolution blends multiple components together. We also show that the inferred opaque atomic ISM mass strongly depends on the goodness-of-fit definition and is highly uncertain when the inferred spin temperature has a large uncertainty. Finally, we find that the relation of the H i surface density with the dust surface density and extinction has significantly more scatter when the inferredH i opacity correction is applied. These variations are difficult to explain without additionally requiring large variations in the dust properties. Based on these findings, we suggest that the opaque H imass is best constrained by H i absorption studies.
Magnetic Properties of Star-forming Dense Cores

Magnetic and energetic properties are presented for 17 dense cores within a few hundred parsecs of the Sun. Their plane-of-sky field strengths B pos are estimated from the dispersion of polarization directions, following Davis, Chandrasekhar, and Fermi (DCF). Their ratio of mass to magnetic critical mass is 0.5≲M/MB≲3 , indicating nearly critical field strengths. The field strength B pos is correlated with column density N as Bpos∝Np , where p=1.05±0.08 , and with density n as Bpos∝nq , where q=0.66±0.05 . These magnetic properties are consistent with those derived from Zeeman studies, with less scatter. Relations between virial mass M V , magnetic critical mass M B , and Alfvén amplitude σB/B match the observed range of M/M B for cores observed to be nearly virial, 0.5≲M/MV≲2 , with moderate Alfvén amplitudes, 0.1≲σB/B≲0.4 . The B − N and B − n correlations in the DCF and Zeeman samples can be explained when such bound, Alfvénic, and nearly critical cores have central concentration and spheroidal shape. For these properties, B∝N because M/M B is nearly constant compared to the range of N, and B∝n2/3 because M 1/3 is nearly constant compared to the range of n 2/3. The observed core fields that follow B∝n2/3 need not be much weaker than gravity, in contrast to core fields that follow B∝n2/3 owing to spherical contraction at constant mass. Instead, the nearly critical values of M/M B suggest that the observed core fields are nearly as strong as possible, among values that allow gravitational contraction.
Unified Detection of Digital and Physical Face Attacks

State-of-the-art defense mechanisms against face attacks achieve near perfect accuracies within one of three attack categories, namely adversarial, digital manipulation, or physical spoofs, however, they fail to generalize well when tested across all three categories. Poor generalization can be attributed to learning incoherent attacks jointly. To over-come this shortcoming, we propose a unified attack detection framework, namely UniFAD, that can automatically cluster 25 coherent attack types belonging to the three categories. Using a multi-task learning framework along with k-means clustering, UniFAD learns joint representations for coherent attacks, while uncorrelated attack types are learned separately. Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94.73% @ 0.2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories. Proposed method can detect an attack within 3 milliseconds on a Nvidia 2080Ti. UniFAD can also identify the attack categories with 97.37% accuracy. Code and dataset will be publicly available.
Jet Transport Coefficient at the Large Hadron Collider Energies in a Color String Percolation Approach

Within the color string percolation model (CSPM), jet transport coefficient, q^, is calculated for various multiplicity classes in proton-proton and centrality classes in nucleus-nucleus collisions at the Large Hadron Collider energies for a better understanding of the matter formed in ultra-relativistic collisions. q^ is studied as a function of final state charged particle multiplicity (pseudorapidity density at midrapidity), initial state percolation temperature and energy density. The CSPM results are then compared with different theoretical calculations from the JET Collaboration those incorporate particle energy loss in the medium.
Mobile Robot Yielding Cues for Human-Robot Spatial Interaction*

Mobile robots are increasingly being deployed in public spaces such as shopping malls, airports, and urban sidewalks. Most of these robots are designed with human-aware motion planning capabilities but are not designed to communicate with pedestrians. Pedestrians encounter these robots without prior understanding of the robots’ behaviour, which can cause discomfort, confusion, and delayed social acceptance. In this research, we explore the common human-robot interaction at a doorway or bottleneck in a structured environment. We designed and evaluated communication cues used by a robot when yielding to a pedestrian in this scenario. We conducted an online user study with 102 participants using videos of a set of robot-to-human yielding cues. Results show that a Robot Retreating cue was the most socially acceptable cue. Repeated measures and Friedman’s ANOVAs on components of social acceptability were statistically significant (p = .01) and had small and medium effect sizes (ηp2 = .04, ηp2 = .08). The results of this work help guide the development of mobile robots for public spaces.
SILVERRUSH X: Machine Learning-aided Selection of 9318 LAEs at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS Survey Data

We present a new catalog of 9318 Lyα emitter (LAE) candidates at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 that are photometrically selected by the SILVERRUSH program with a machine learning technique from large area (up to 25.0 deg2) imaging data with six narrowband filters taken by the Subaru Strategic Program with Hyper Suprime-Cam and a Subaru intensive program, Cosmic HydrOgen Reionization Unveiled with Subaru. We construct a convolutional neural network that distinguishes between real LAEs and contaminants with a completeness of 94% and a contamination rate of 1%, enabling us to efficiently remove contaminants from the photometrically selected LAE candidates. We confirm that our LAE catalogs include 177 LAEs that have been spectroscopically identified in our SILVERRUSH programs and previous studies, ensuring the validity of our machine learning selection. In addition, we find that the object-matching rates between our LAE catalogs and our previous results are ≃80%–100% at bright NB magnitudes of ≲24 mag. We also confirm that the surface number densities of our LAE candidates are consistent with previous results. Our LAE catalogs will be made public on our project webpage.
Hypothesis-driven Stream Learning with Augmented Memory

Stream learning refers to the ability to acquire and transfer knowledge across a continuous stream of data without forgetting and without repeated passes over the data. A common way to avoid catastrophic forgetting is to intersperse new examples with replays of old examples stored as image pixels or reproduced by generative models. Here, we consider stream learning in image classiﬁcation tasks and propose a novel hypotheses-driven Augmented Memory Network, which efﬁ-ciently consolidates previous knowledge with a limited number of hypotheses in the augmented memory and replays relevant hypotheses to avoid catastrophic forgetting. The advantages of hypothesis-driven replay over pixel-level replay and generative replay are two-fold. First, hypothesis-based knowledge consolidation avoids redundant information in the image pixel space and makes memory usage more ef-ﬁcient. Second, hypotheses in the augmented memory can be re-used for learning new tasks, improving generalization and transfer learning ability. We evaluated our method on three stream learning object recognition datasets. Our method performs comparably well or better than state-of-the-art methods, while offering more efﬁcient memory usage. All source code and data are publicly available https: //github.com/kreimanlab/AugMem.
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark

Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art advances in each family are measured largely in isolation of each other. As a result of diverging evaluation norms, a direct or thorough comparison of different approaches is challenging. To bridge this gap, we perform a cross-family study of the best transfer and meta learners on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, large-scale transfer methods (Big Transfer, BiT) outperform competing approaches on MD, even when trained only on ImageNet. In contrast, meta-learning approaches struggle to compete on VTAB when trained and validated on MD. However, BiT is not without limitations, and pushing for scale does not improve performance on highly out-of-distribution MD tasks. In performing this study, we reveal a number of discrepancies in evaluation norms and study some of these in light of the performance gap. We hope that this work facilitates sharing of insights from each community, and accelerates progress on few-shot learning.
Searching Efficient Model-Guided Deep Network for Image Denoising

Unlike the success of neural architecture search (NAS) in high-level vision tasks, it remains challenging to find computationally efficient and memory-efficient solutions to low-level vision problems such as image restoration through NAS. One of the fundamental barriers to differential NAS-based image restoration is the optimization gap between the super-network and the sub-architectures, causing instability during the searching process. In this paper, we present a novel approach to fill this gap in image denoising application by connecting model-guided design (MoD) with NAS (MoD-NAS). Specifically, we propose to construct a new search space under a model-guided framework and develop more stable and efficient differential search strategies. MoD-NAS employs a highly reusable width search strategy and a densely connected search block to automatically select the operations of each layer as well as network width and depth via gradient descent. During the search process, the proposed MoD-NAS remains stable because of the smoother search space designed under the model-guided framework. Experimental results on several popular datasets show that our MoD-NAS method has achieved at least comparable even better PSNR performance than current state-of-the-art methods with fewer parameters, fewer flops, and less testing time. “The code associate with this paper is available at: https://see.xidian. edu.cn/faculty/wsdong/Projects/Mod-NAS.htm”.
Near-Precise Parameter Approximation for Multiple Multiplications on a Single DSP Block

DSP blocks are one of the efficient solutions to implement multiply-accumulate (MAC) operations on FPGA’s. However, since the DSP blocks have wide multiplier and adder blocks, MAC operations using low bit-length parameters lead to an underutilization. Hence, an efficient approximation technique is introduced. The technique includes manipulation and approximation of the low bit-length parameters based upon a Single DSP - Multiple Multiplication (SDMM) execution. The accuracy of the developed optimization technique was evaluated for different CNN weight bit precisions using the Alexnet and VGG-16 networks and the ImageNet ILSVRC-2012 dataset. The optimization can be implemented without loss of accuracy in almost all cases, while it causes slight accuracy losses in a few cases. Through these optimizations, multiple parameter multiplications are performed in a single DSP block at the cost of a small hardware overhead. As a result of our optimizations, the parameters are represented in a different format on off-chip memory, providing up to 33% compression without any hardware cost. A prototype systolic array architecture was implemented employing our optimizations on a Xilinx Zynq FPGA. It reduced the number of DSP blocks by 66.6%, 75%, and 83.3% for 8, 6, and 4-bit input variables, respectively.
Constraining planetesimal stirring: how sharp are debris disc edges?

The dust production in debris discs by grinding collisions of planetesimals requires their orbits to be stirred. However, stirring levels remain largely unconstrained, and consequently the stirring mechanisms as well. This work shows how the sharpness of the outer edge of discs can be used to constrain the stirring levels. Namely, the sharper the edge is the lower the eccentricity dispersion must be. For a Rayleigh distribution of eccentricities (e), I find that the disc surface density near the outer edge can be parametrised as tanh[(rmax−r)/lout], where rmax approximates the maximum semi-major axis and lout defines the edge smoothness. If the semimajor axis distribution has sharp edges erms is roughly 1.2lout/rmax, or erms = 0.77lout/rmax if semi-major axes have diffused due to self-stirring. This model is fitted to ALMA data of five wide discs: HD 107146, HD 92945, HD 206893, AUMic and HR 8799. The results show that HD 107146, HD 92945 and AU Mic have the sharpest outer edges, corresponding to erms values of 0.121 ± 0.05, 0.15+0.07 −0.05 and 0.10 ± 0.02 if their discs are self-stirred, suggesting the presence of Pluto-sized objects embedded in the disc. Although these stirring values are larger than typically assumed, the radial stirring of HD 92945 is in good agreement with its vertical stirring constrained by the disc height. HD 206893 and HR 8799, on the other hand, have smooth outer edges that are indicative of scattered discs since both systems have massive inner companions.
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation

Point cloud semantic segmentation often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to train a 3D network with small percentages of point labels, we take the approach to an extreme and propose "One Thing One Click," meaning that the annotator only needs to label one point per object. To leverage these extremely sparse labels in network training, we design a novel self-training approach, in which we iteratively conduct the training and label propagation, facilitated by a graph propagation module. Also, we adopt a relation network to generate the per-category prototype and explicitly model the similarity among graph nodes to generate pseudo labels to guide the iterative training. Experimental results on both ScanNet-v2 and S3DIS show that our self-training approach, with extremely-sparse annotations, outperforms all existing weakly supervised methods for 3D semantic segmentation by a large margin, and our results are also comparable to those of the fully supervised counterparts.
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios

Often, the storage and computational constraints of embeddeddevices demand that a single on-device ASR model serve multiple use-cases / domains. In this paper, we propose aFlexibleTransducer(FlexiT) for on-device automatic speech recognition to flexibly deal with multiple use-cases / domains with different accuracy and latency requirements. Specifically, using a single compact model, FlexiT provides a fast response for voice commands, and accurate transcription but with more latency for dictation. In order to achieve flexible and better accuracy and latency trade-offs, the following techniques are used. Firstly, we propose using domain-specific altering of segment size for Emformer encoder that enables FlexiT to achieve flexible de-coding. Secondly, we use Alignment Restricted RNNT loss to achieve flexible fine-grained control on token emission latency for different domains. Finally, we add a domain indicator vector as an additional input to the FlexiT model. Using the combination of techniques, we show that a single model can be used to improve WERs and real time factor for dictation scenarios while maintaining optimal latency for voice commands use-cases
Multi-hierarchical Convolutional Network for Efficient Remote Photoplethysmograph Signal and Heart Rate Estimation from Face Video Clips

: Heart beat rhythm and heart rate (HR) are important physiological parameters of the human body. This study presents an efficient multi-hierarchical spatio-temporal convolutional network that can quickly estimate remote physiological (rPPG) signal and HR from face video clips. First, the facial color distribution characteristics are extracted using a low-level face feature Generation (LFFG) module. Then, the three-dimensional (3D) spatio-temporal stack convolution module (STSC) and multi-hierarchical feature fusion module (MHFF) are used to strengthen the spatio-temporal correlation of multi-channel features. In the MHFF, sparse optical flow is used to capture the tiny motion information of faces between frames and generate a self-adaptive region of interest (ROI) skin mask. Finally, the signal prediction module (SP) is used to extract the estimated rPPG signal. The experimental results on the three datasets show that the proposed network outperforms the state-of-the-art methods.
A new perspective on the competent programmer hypothesis through the reproduction of bugs with repeated mutations

The competent programmer hypothesis states that most programmers are competent enough to create correct or almost correct source code. Because this implies that bugs should usually manifest through small variations of the correct code, the competent programmer hypothesis is one of the fundamental assumptions of mutation testing. Unfortunately, it is still unclear if the competent programmer hypothesis holds and past research presents contradictory claims. Within this article, we provide a new perspective on the competent programmer hypothesis and its relation to mutation testing. We try to re-create real-world bugs through chains of mutations to understand if there is a direct link between mutation testing and bugs. The lengths of these paths help us to understand if the source code is really almost correct, or if large variations are required. Our results indicate that while the competent programmer hypothesis seems to be true, mutation testing is missing important operators to generate representative real-world bugs.
Weakly-Supervised Audio-Visual Sound Source Detection and Separation

Learning how to localize and separate individual object sounds in the audio channel of the video is a difficult task. Current state-of-the-art methods predict audio masks from artificially mixed spectrograms, known as Mix-and-Separate framework. We propose an audio-visual co-segmentation, where the network learns both what individual objects look and sound like, from videos labeled with only object labels. Unlike other recent visually-guided audio source separation frameworks, our architecture can be learned in an end-to-end manner and requires no additional supervision or bounding box proposals. Specifically, we introduce weakly-supervised object segmentation in the context of sound separation. We also formulate spectrogram mask prediction using a set of learned mask bases, which combine using coefficients conditioned on the output of object segmentation — a design that facilitates separation. Extensive experiments on the MUSIC dataset show that our proposed approach outperforms state-of-the-art methods on visually guided sound source separation and sound denoising.
Differentiable simulation for system identification and visuomotor control ∇ Sim : D IFFERENTIABLE SIMULATION FOR SYSTEM IDENTIFICATION AND VISUOMOTOR CONTROL

We consider the problem of estimating an object’s physical properties such as mass, friction, and elasticity directly from video sequences. Such a system identification problem is fundamentally ill-posed due to the loss of information during image formation. Current solutions require precise 3D labels which are labor-intensive to gather, and infeasible to create for many systems such as deformable solids or cloth. We present ∇Sim, a framework that overcomes the dependence on 3D supervision by leveraging differentiable multiphysics simulation and differentiable rendering to jointly model the evolution of scene dynamics and image formation. This novel combination enables backpropagation from pixels in a video sequence through to the underlying physical attributes that generated them. Moreover, our unified computation graph – spanning from the dynamics and through the rendering process – enables learning in challenging visuomotor control tasks, without relying on state-based (3D) supervision, while obtaining performance competitive to or better than techniques that rely on precise 3D labels.
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog system. Most dialog systems are trained on a pool of annotated OOD data to achieve this goal. However, collecting the annotated OOD data for a given domain is an expensive process. To mitigate this issue, previous works have proposed generative adversarial networks (GAN) based models to generate OOD data for a given domain automatically. However, these proposed models do not work directly with the text. They work with the text’s latent space instead, enforcing these models to include components responsible for encoding text into latent space and decoding it back, such as auto-encoder. These components increase the model complexity, making it difficult to train. We propose OodGAN, a sequential generative adversarial network (SeqGAN) based model for OOD data generation. Our proposed model works directly on the text and hence eliminates the need to include an auto-encoder. OOD data generated using OodGAN model outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR 0.95)
Challenges in solving chiral hydrodynamics

We prove that ideal chiral hydrodynamics, as derived from chiral kinetic theory, is acausal and its initial-value problem is ill-posed. Therefore, such theory cannot be used to determine how the chiral anomaly affects the hydrodynamic evolution. We also prove that the standard first-order theory of viscous chiral hydrodynamics is acausal. We show that these fundamental issues can be cured by using different definitions (frames) for the hydrodynamic fields.