Miracle-dz/UniFAR-FTUs huggingface dataset:
Dwarf Galaxies in the MATLAS Survey: Hubble Space Telescope Observations of the Globular Cluster System in the Ultra-diffuse Galaxy MATLAS-2019

Ultra-diffuse galaxies (UDGs) are very-low-surface-brightness galaxies with large effective radii. Spectroscopic measurements of a few UDGs have revealed a low dark-matter content based on the internal motion of stars or globular clusters (GCs). This is in contrast to the large number of GCs found for these systems, from which it would be expected to correspond to a large dark-matter halo mass. Here we present HST+ACS observations for the UDG MATLAS-2019 in the NGC 5846 group. Using the F606W and F814W filters, we trace the GC population two magnitudes below the peak of the GC luminosity function (GCLF). Employing Bayesian considerations, we identify 26 ± 6 GCs associated with the dwarf, yielding a large specific frequency of S N = 58 ± 14. We use the turnover of the GCLF to derive a distance of 21 ± 2 Mpc, which is consistent with the NGC 5846 group of galaxies. Due to the superior image quality of the HST, we are able to resolve the GCs and measure their sizes, which are consistent with the sizes of GCs around Local Group galaxies. Using the linear relation between the total mass of galaxies and of GCs, we derive a halo mass of 0.9 ± 0.2 × 1011 M ⊙ (M ⊙/L ⊙ > 1000). The high abundance of GCs, together with the small uncertainties, make MATLAS-2019 one of the most extreme UDGs, which likely sets an upper limit of the number of GCs for UDGs.
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting

Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most previous work either makes simple distribution assumptions or abandons modeling cross-series correlations. A promising line of work exploits scalable matrix factorization for latent-space forecasting, but is limited to linear embeddings, unable to model distributions, and not trainable end-to-end when using deep learning forecasting. We introduce a novel temporal latent auto-encoder method which enables nonlinear factorization of multivariate time series, learned end-to-end with a temporal deep learning latent space forecast model. By imposing a probabilistic latent space model, complex distributions of the input series are modeled via the decoder. Extensive experiments demonstrate that our model achieves state-of-the-art performance on many popular multivariate datasets, with gains sometimes as high as 50% for several standard metrics.
Symmetric Monoidal Categories with Attributes

When designing plans in engineering, it is often necessary to consider attributes associated to objects, e.g. the location of a robot. Our aim in this paper is to incorporate attributes into existing categorical formalisms for planning, namely those based on symmetric monoidal categories and string diagrams. To accomplish this, we define a notion of a"symmetric monoidal category with attributes. "This is a symmetric monoidal category in which objects are equipped with retrievable information and where the interactions between objects and information are governed by an"attribute structure. "We discuss examples and semantics of such categories in the context of robotics to illustrate our definition.
Streamer Self-Focusing in External Longitudinal Magnetic Field

Based on the results of numerical simulation of the development of a streamer discharge in CO 2 in a gap with an external longitudinal magnetic field, the possibility of self-focusing of such discharges is demonstrated. The self-focusing is caused by a sharp slowdown in the speed of the radial ionization wave due to a change in the EEDF, a decrease in the average electron energy, electron mobility, and the rate of electron impact ionization in the crossed electric and magnetic fields as compared with the case of the discharge development without magnetic field. Simultaneously with the deceleration of the radial ionization wave, the ionization wave accelerates along the axis of the discharge gap due to a decrease in the radius of the streamer head and an increase in the electric field on it. Since the electric and magnetic fields are parallel to each other on the axis of symmetry, for the longitudinal wave there is no decrease in the average electron energy and ionization rate with an increase in the magnetic field value.
How do some Bayesian Network machine learned graphs compare to causal knowledge?

The graph of a Bayesian Network (BN) can be machine learned, determined by causal knowledge, or a combination of both. In disciplines like bioinformatics, applying BN structure learning algorithms can reveal new insights that would otherwise remain unknown. However, these algorithms are less effective when the input data are limited in terms of sample size, which is often the case when working with real data. This paper focuses on purely machine learned and purely knowledge-based BNs and investigates their differences in terms of graphical structure and how well the implied statistical models explain the data. The tests are based on four previous case studies whose BN structure was determined by domain knowledge. Using various metrics, we compare the knowledge-based graphs to the machine learned graphs generated from various algorithms implemented in TETRAD spanning all three classes of learning. The results show that, while the algorithms produce graphs with much higher model selection score, the knowledge-based graphs are more accurate predictors of variables of interest. Maximising score fitting is ineffective in the presence of limited sample size because the fitting becomes increasingly distorted with limited data, guiding algorithms towards graphical patterns that share higher fitting scores and yet deviate considerably from the true graph. This highlights the value of causal knowledge in these cases, as well as the need for more appropriate fitting scores suitable for limited data. Lastly, the experiments also provide new evidence that support the notion that results from simulated data tell us little about actual real-world performance. Index Terms — Bayesian network structure learning, causal discovery, causal models, directed acyclic graph, limited data, probabilistic graphical models. —————————— ◆ ——————————
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training

Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper aims to address this problem by revisiting the locally supervised learning, where a network is split into gradient-isolated modules and trained with local supervision. We experimentally show that simply training local modules with E2E loss tends to collapse task-relevant information at early layers, and hence hurts the performance of the full model. To avoid this issue, we propose an information propagation (InfoPro) loss, which encourages local modules to preserve as much useful information as possible, while progressively discard task-irrelevant information. As InfoPro loss is difficult to compute in its original form, we derive a feasible upper bound as a surrogate optimization objective, yielding a simple but effective algorithm. In fact, we show that the proposed method boils down to minimizing the combination of a reconstruction loss and a normal cross-entropy/contrastive term. Extensive empirical results on five datasets (i.e., CIFAR, SVHN, STL-10, ImageNet and Cityscapes) validate that InfoPro is capable of achieving competitive performance with less than 40% memory footprint compared to E2E training, while allowing using training data with higher-resolution or larger batch sizes under the same GPU memory constraint. Our method also enables training local modules asynchronously for potential training acceleration. Code is available at: https://github.com/blackfeather-wang/InfoPro-Pytorch.
Cloud, Fog, or Edge: Where to Compute?

The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application management. These include where to offload an application—from the cloud to the edge—to meet its computation and communication requirements. To support these decisions, we provide, in this article, a detailed performance and carbon footprint analysis of a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.
Generic Event Boundary Detection: A Benchmark for Event Segmentation

This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. Conventional work in temporal video segmentation and action detection focuses on localizing pre-defined action categories and thus does not scale to generic videos. Cognitive Science has known since last century that humans consistently segment videos into meaningful temporal chunks. This segmentation happens naturally, without pre-defined event categories and without being explicitly asked to do so. Here, we repeat these cognitive experiments on mainstream CV datasets; with our novel annotation guideline which addresses the complexities of taxonomy-free event boundary annotation, we introduce the task of Generic Event Boundary Detection (GEBD) and the new benchmark Kinetics-GEBD. We view GEBD as an important stepping stone towards understanding the video as a whole, and believe it has been previously neglected due to a lack of proper task definition and annotations. Through experiment and human study we demonstrate the value of the annotations. Further, we benchmark supervised and un-supervised GEBD approaches on the TAPOS dataset and our Kinetics-GEBD. We release our annotations and baseline codes at CVPR’21 LOVEU Challenge: https://sites.google. com/view/loveucvpr21.
Meta-Learning for Effective Multi-task and Multilingual Modelling

Natural language processing (NLP) tasks (e.g. question-answering in English) benefit from knowledge of other tasks (e.g., named entity recognition in English) and knowledge of other languages (e.g., question-answering in Spanish). Such shared representations are typically learned in isolation, either across tasks or across languages. In this work, we propose a meta-learning approach to learn the interactions between both tasks and languages. We also investigate the role of different sampling strategies used during meta-learning. We present experiments on five different tasks and six different languages from the XTREME multilingual benchmark dataset. Our meta-learned model clearly improves in performance compared to competitive baseline models that also include multi-task baselines. We also present zero-shot evaluations on unseen target languages to demonstrate the utility of our proposed model.
[C ii] and CO Emission along the Bar and Counter-arms of NGC 7479

We present new SOFIA [C ii] and ALMA CO J=1→0 observations of the nearby asymmetric barred spiral galaxy NGC 7479. The data, which cover the whole bar of the galaxy and the counter-arms visible in the radio continuum, are analyzed in conjunction with a wealth of existing visible, infrared, radio, and X-ray data. As in most normal galaxies, the [C ii] emission is generally consistent with emission from cooling gas excited by photoelectric heating in photodissociation regions. However, anomalously high [C ii]/CO ratios are seen at the two ends of the counter-arms. Both ends show shell-like structures, possibly bubbles, in Hα emission. In addition, the southern end has [C ii]-to-infrared emission ratios inconsistent with normal star formation. Because there is little H i emission at this location, the [C ii] emission probably originates in warm shocked molecular gas heated by the interaction of the radio jet forming the counter-arms with the interstellar medium in the galaxy. At two other locations, the high [C ii]/CO ratios provide evidence for the existence of patches of CO-dark molecular gas. The [C ii] and CO observations also reveal resolved velocity components along the bar. In particular, the CO emission can be separated into two components associated with gas along the leading edge of the bar and gas trailing the bar. The trailing gas component that amounts to approximately 40% of the gas around the bar region may be related to a minor merger.
Scaling of the disorder operator at 
(2+1)d
 U(1) quantum criticality

Yan-Cheng Wang,1 Meng Cheng,2, ∗ and Zi Yang Meng3, † 1School of Materials Science and Physics, China University of Mining and Technology, Xuzhou 221116, China 2Department of Physics, Yale University, New Haven, CT 06520-8120, U.S.A 3Department of Physics and HKU-UCAS Joint Institute of Theoretical and Computational Physics, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China (Dated: August 19, 2021)
ResLT: Residual Learning for Long-Tailed Recognition

Deep learning algorithms face great challenges with long-tailed data distribution which, however, is quite a common case in real-world scenarios. Previous methods tackle the problem from either the aspect of input space (re-sampling classes with different frequencies) or loss space (re-weighting classes with different weights), suffering from heavy over-fitting to tail classes or hard optimization during training. To alleviate these issues, we propose a more fundamental perspective for long-tailed recognition, i.e., from the aspect of parameter space, and aims to preserve specific capacity for classes with low frequencies. From this perspective, the trivial solution utilizes different branches for the head, medium, tail classes respectively, and then sums their outputs as the final results is not feasible. Instead, we design the effective residual fusion mechanism – with one main branch optimized to recognize images from all classes, another two residual branches are gradually fused and optimized to enhance images from medium+tail classes and tail classes respectively. Then the branches are aggregated into final results by additive shortcuts. We test our method on several benchmarks, i.e., long-tailed version of CIFAR-10, CIFAR-100, Places, ImageNet, and iNaturalist 2018. Experimental results manifest the effectiveness of our method. Our code is available at https://github.com/jiequancui/ResLT.
Bent-core liquid crystals in a route to efficient organic nonlinear optical materials

The design, synthesis and nonlinear optical (NLO) response of some bent compounds, at both the molecular and macroscopic level, are reported. Based on a bent-core NLO-phore with a μβ0 in the range of 390 × 10−48 esu (1907 nm), the second order susceptibility tensor for second harmonic generation is evaluated at excitation wavelengths of 1064 and 1600 nm in a mesogenic material with a SmCP mesophase. The NLO coefficients are in the range 10–100 pm V−1. Thus it is deduced that a molecular design leading to bent-core liquid crystals with large β values is a correct strategy to achieve NLO-materials for bulk second order effects.
Galvanic Vestibular Stimulation Produces Cross-Modal Improvements in Visual Thresholds

Background Stochastic resonance (SR) refers to a faint signal being enhanced with the addition of white noise. Previous studies have found that vestibular perceptual thresholds are lowered with noisy galvanic vestibular stimulation (i.e., “in-channel” SR). Auditory white noise has been shown to improve tactile and visual thresholds, suggesting “cross-modal” SR. Objective We investigated galvanic vestibular white noise (nGVS) (n = 9 subjects) to determine the cross-modal effects on visual and auditory thresholds. Methods We measured auditory and visual perceptual thresholds of human subjects across a swath of different nGVS levels in order to determine if some individual-subject determined best nGVS level elicited a reduction in thresholds as compared the no noise condition (sham). Results We found improvement in visual thresholds (by an average of 18%, p = 0.014). Subjects with higher (worse) visual thresholds with no stimulation (sham) improved more than those with lower thresholds (p = 0.04). Auditory thresholds were unchanged by vestibular stimulation. Conclusion These results are the first demonstration of cross-modal improvement with galvanic vestibular stimulation, indicating galvanic vestibular white noise can produce cross-modal improvements in some sensory channels, but not all.
EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a crucial non-invasive application in brain–computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods. Approach. The proposed CNN model, namely EEG-inception, is built on the backbone of the inception-time network, which has showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets. Main results. The proposed model outperforms all state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 s to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust. Significance. From the experiment results, it can be inferred that the EEG-inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks.
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms

The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a multitude of model-agnostic, nonparametric meta-learners have been proposed in recent years. Such learners decompose the treatment effect estimation problem into separate sub-problems, each solvable using standard supervised learning methods. Choosing between different meta-learners in a data-driven manner is difficult, as it requires access to counterfactual information. Therefore, with the ultimate goal of building better understanding of the conditions under which some learners can be expected to perform better than others a priori, we theoretically analyze four broad meta-learning strategies which rely on plug-in estimation and pseudo-outcome regression. We highlight how this theoretical reasoning can be used to guide principled algorithm design and translate our analyses into practice by considering a variety of neural network architectures as base-learners for the discussed meta-learning strategies. In a simulation study, we showcase the relative strengths of the learners under different data-generating processes.
First principles supercurrent calculation in realistic magnetic Josephson junctions

We investigate the transport properties of magnetic Josephson junctions. In order to capture realistic material band structure effects, we develop a numerical method combining density functional theory and Bogoliubov-de Gennes model. We demonstrate the capabilities of this method by studying Nb/Ni/Nb junctions in the clean limit. The supercurrent through the junctions is calculated as a function of the ferromagnetic Ni thickness, magnetization, and crystal orientation. We identify two generic mechanisms for the supercurrent decay with ferromagnet thickness: (i) large exchange splitting may gap out minority or majority carriers leading to the suppression of Andreev reflection in the junction, (ii) loss of synchronization between different modes due to the significant dispersion of the quasiparticle velocity with the transverse momentum. Our results are in good agreement with recent experimental studies of Nb/Ni/Nb junctions. The present approach opens a path for material composition optimization in magnetic Josephson junctions and superconducting magnetic spin valves.
Generative Adversarial Network using Perturbed-Convolutions

—Despite growing insights into the GAN training, it still suffers from instability during the training procedure. To alleviate this problem, this paper presents a novel convolutional layer, called perturbed-convolution (PConv), which focuses on achieving two goals simultaneously: penalize the discriminator for training GAN stably and prevent the overﬁtting problem in the discriminator. PConv generates perturbed features by randomly disturbing an input tensor before performing the convolution operation. This approach is simple but surprisingly effective. First, to reliably classify real and generated samples using the disturbed input tensor, the intermediate layers in the discriminator should learn features having a small local Lipschitz value. Second, due to the perturbed features in PConv, the discriminator is difﬁcult to memorize the real images; this makes the discriminator avoid the overﬁtting problem. To show the generalization ability of the proposed method, we conducted extensive experiments with various loss functions and datasets including CIFAR-10, CelebA-HQ, LSUN, and tiny-ImageNet. Quantitative evaluations demonstrate that WCL signiﬁcantly improves the performance of GAN and conditional GAN in terms of Frechet inception distance (FID). For instance, the proposed method improves FID scores on the tiny-ImageNet dataset from 58.59 to 50.42.
Separation of Control and Data Transmissions in 5G Networks May Not be Beneficial

The logical separation of control signaling from data transmission in a mobile cellular network has been shown to have significant energy saving potential compared with the legacy systems. As a result, there has been a lot of focus in recent years on development and realization of separation architectures. Our study, however, shows that the energy savings of separation architecture remain under 16–17% when compared with legacy systems and this gain falls to a mere 7% when both architectures are realized under a CloudRAN (CRAN) setting. Moreover, when we strategically place some small base-stations (SBSs) to cover the area in a densely deployed scenario and allow all other base-stations (BSs) to be used only on-demand, the system consumes much less energy than the separation architecture. While we expected that most equipment would be shut down during nights, our study shows that around 70% of the small cells are required to be active to serve randomly distributed minimum data load, i.e., active mobile equipment. Contemporary mobile traffic is predominantly data which does not go to extremely low levels during nights. We discuss, in detail, the assumptions, their implications, and the effects of system parameter values on our conclusions.