Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tale of Two Cities: Software Developers Working from Home during the COVID-19 Pandemic

The COVID-19 pandemic has shaken the world to its core and has provoked an overnight exodus of developers who normally worked in an office setting to working from home. The magnitude of this shift and the factors that have accompanied this new unplanned work setting go beyond what the software engineering community has previously understood to be remote work. To find out how developers and their productivity were affected, we distributed two surveys (with a combined total of 3,634 responses that answered all required questions) weeks apart to understand the presence and prevalence of the benefits, challenges, and opportunities to improve this special circumstance of remote work. From our thematic qualitative analysis and statistical quantitative analysis, we find that there is a dichotomy of developer experiences influenced by many different factors (that for some are a benefit, while for others a challenge). For example, a benefit for some was being close to family members but for others having family members share their working space and interrupting their focus, was a challenge. Our surveys led to powerful narratives from respondents and revealed the scale at which these experiences exist to provide insights as to how the future of (pandemic) remote work can evolve.
Efficient Mass Estimate at the Core of Strong Lensing Galaxy Clusters Using the Einstein Radius

In the era of large surveys, yielding thousands of galaxy clusters, efficient mass proxies at all scales are necessary in order to fully utilize clusters as cosmological probes. At the cores of strong lensing clusters, the Einstein radius can be turned into a mass estimate. This efficient method has been routinely used in literature, in lieu of detailed mass models; however, its scatter, assumed to be , has not yet been quantified. Here, we assess this method by testing it against ray-traced images of cluster-scale halos from the Outer Rim N-body cosmological simulation. We measure a scatter of 13.9% and a positive bias of 8.8% in , with no systematic correlation with total cluster mass, concentration, or lens or source redshifts. We find that increased deviation from spherical symmetry increases the scatter; conversely, where the lens produces arcs that cover a large fraction of its Einstein circle, both the scatter and the bias decrease. While spectroscopic redshifts of the lensed sources are critical for accurate magnifications and time delays, we show that for the purpose of estimating the total enclosed mass, the scatter introduced by source redshift uncertainty is negligible compared to other sources of error. Finally, we derive and apply an empirical correction that eliminates the bias, and reduces the scatter to 10.1% without introducing new correlations with mass, redshifts, or concentration. Our analysis provides the first quantitative assessment of the uncertainties in , and enables its effective use as a core mass estimator of strong lensing galaxy clusters.
A new era of fine structure constant measurements at high redshift

New observations of the quasar HE0515$-$4414 have been made using the HARPS spectrograph on the ESO 3.6m telescope, aided by the Laser Frequency Comb (LFC). We present three important advances for $\alpha$ measurements in quasar absorption spectra from these observations. Firstly, the data have been wavelength calibrated using LFC and ThAr methods. The LFC wavelength calibration residuals are six times smaller than when using the standard ThAr calibration. We give a direct comparison between $\alpha$ measurements made using the two methods. Secondly, spectral modelling was performed using Artificial Intelligence (fully automated, all human bias eliminated), including a temperature parameter for each absorption component. Thirdly, in contrast to previous work, additional model parameters were assigned to measure $\alpha$ for each individual absorption component. The increase in statistical uncertainty from the larger number of model parameters is small and the method allows a substantial advantage; outliers that would otherwise contribute a significant systematic, possibly corrupting the entire measurement, are identified and removed, permitting a more robust overall result. The $z_{abs} = 1.15$ absorption system along the HE0515$-$4414 sightline yields 40 new $\alpha$ measurements. We constrain spatial fluctuations in $\alpha$ to be $\Delta\alpha/\alpha \leq 9 \times 10^{-5}$ on scales $\approx 20\;{\rm km\,s}^{-1}$, corresponding to $\approx25\;$kpc if the $z_{abs} = 1.15$ system arises in a $1\;$Mpc cluster. Collectively, the 40 measurements yield $\Delta\alpha/\alpha=-0.27\pm2.41\times10^{-6}$, consistent with no variation.
Probing Symmetries of Quantum Many-Body Systems through Gap Ratio Statistics

The statistics of gap ratios between consecutive energy levels is a widely used tool, in particular in the context of many-body physics, to distinguish between chaotic and integrable systems, described respectively by Gaussian ensembles of random matrices and Poisson statistics. In this work we extend the study of the gap ratio distribution $P(r)$ to the case where discrete symmetries are present. This is important, since in certain situations it may be very impractical, or impossible, to split the model into symmetry sectors, let alone in cases where the symmetry is not known in the first place. Starting from the known expressions for surmises in the Gaussian ensembles, we derive analytical surmises for random matrices comprised of several independent blocks. We check our formulae against simulations from large random matrices, showing excellent agreement. We then present a large set of applications in many-body physics, ranging from quantum clock models and anyonic chains to periodically-driven spin systems. In all these models the existence of a (sometimes hidden) symmetry can be diagnosed through the study of the spectral gap ratios, and our approach furnishes an efficient way to characterize the number and size of independent symmetry subspaces. We finally discuss the relevance of our analysis for existing results in the literature, as well as point out possible future applications and extensions.
Block-Wise Minimization-Majorization Algorithm for Huber'S Criterion: Sparse Learning and Applications

Huber's criterion can be used for robust joint estimation of regression and scale parameters in the linear model. Huber's [1] motivation for introducing the criterion stemmed from nonconvexity of the joint maximum likelihood objective function as well as non-robustness (unbounded influence function) of the associated ML-estimate of scale. In this paper, we illustrate how the original algorithm proposed by Huber can be set within the block-wise minimization majorization framework. In addition, we propose novel data-adaptive step sizes for both the location and scale, which are further improving the convergence. We then illustrate how Huber's criterion can be used for sparse learning of underdetermined linear model using the iterative hard thresholding approach. We illustrate the usefulness of the algorithms in an image denoising application and simulation studies.
Faster Reachability in Static Graphs

One of the most fundamental problems in computer science is the reachability problem: Given a directed graph and two vertices s and t, can s reach t via a path? We revisit existing techniques and combine them with new approaches to support a large portion of reachability queries in constant time using a linear-sized reachability index. In an experimental study, we compare a variety of algorithms with respect to their index-building and query times as well as their memory footprint. All of them yield a time/space trade-off for queries. Surprisingly, due to cache effects, a higher investment in space doesn't necessarily pay off: Reachability queries can often be answered even significantly faster than single memory accesses in a precomputed full reachability matrix.
An improved parametric method for evaluating radiative accelerations in stellar interiors

The single-valued parameter (SVP) method is a parametric method that offers the possibility of computing radiative accelerations in stellar interiors much faster than other methods. It has been implemented in a few stellar evolution numerical codes for about a decade. In the present paper, we describe improvements we have recently brought in the process of preparing, from atomic/opacity databases, the SVP tables that are needed to use the method, and their extension to a larger stellar mass domain (from 1 to 10 solar mass) on the main-sequence. We discuss the validity domain of the method. We also present the website from where new tables and codes can be freely accessed and implemented in stellar evolution codes.
Improving Deep Stereo Network Generalization with Geometric Priors

End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar. However, large datasets of diverse real-world scenes with dense ground truth are difficult to obtain and currently not publicly available to the research community. As a result, many algorithms rely on small real-world datasets of similar scenes or synthetic datasets, but end-to-end algorithms trained on such datasets often generalize poorly to different images that arise in real-world applications. As a step towards addressing this problem, we propose to incorporate prior knowledge of scene geometry into an end-to-end stereo network to help networks generalize better. For a given network, we explicitly add a gradient-domain smoothness prior and occlusion reasoning into the network training, while the architecture remains unchanged during inference. Experimentally, we show consistent improvements if we train on synthetic datasets and test on the Middlebury (real images) dataset. Noticeably, we improve PSM-Net accuracy on Middlebury from 5.37 MAE to 3.21 MAE without sacrificing speed.
Polarization of GRB prompt emission and its application to POLAR’s data

Synchrotron emission polarization is very sensitive to the magnetic field configuration. Recently, polarization of synchrotron emission with a mixed (SM) magnetic field in the gamma-ray burst (GRB) afterglow phase has been developed. Here, we apply these SM models to the GRB prompt phase and compare their polarization properties with that of synchrotron emission in purely ordered (SO) magnetic field. We find that the polarization properties in an SM model are very similar to these in a corresponding SO model (e.g., synchrotron emission in a mixed magnetic field with an aligned ordered part (SMA) and synchrotron emission with a purely ordered aligned magnetic field (SOA)), only with a lower polarization degree (PD). We also discuss the statistical properties of the models. We find PDs of the simulated bursts are concentrated around 25% for both SOA and synchrotron emission in a purely ordered toroidal magnetic field (SOT), while they can range from 0% to 25% for SMA and synchrotron emission in a mixed magnetic field with a toroidal ordered part (SMT), depending on ξB value, i.e., the ratio of magnetic reduction of the ordered magnetic field over that of random magnetic field. From statistics, if PDs of majority GRBs are non-zero, then it favors SO and SM models. Further, if there are some bright GRBs with prominently lower PDs than that of the majority GRBs, it favors SOT (SMT) models; if all the bright GRBs have comparable PDs with the majority ones, it favors SOA (SMA) models. Finally, we apply our results to POLAR’s data and find that ∼ 10% time-integrated PDs of the observed bursts favor SMA and SMT models, and the ξB parameter of these bursts is constrained to be around 1.135.
Multiple-Source Adaptation with Domain Classifiers

We consider the multiple-source adaptation (MSA) problem and improve a previously proposed MSA solution, where accurate density estimation per domain is required to obtain favorable learning guarantees. In this work, we replace the difficult task of density estimation per domain with a much easier task of domain classification, and show that the two solutions are equivalent given the true densities and domain classifier, yet the newer approach benefits from more favorable guarantees when densities and domain classifier are estimated from finite samples. Our experiments with real-world applications demonstrate that the new discriminative MSA solution outperforms the previous solution with density estimation, as well as other domain adaptation baselines.
CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images

Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (cnec-xet) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. We empirically evaluate CDeC-Net on the publicly available benchmark datasets with extensive experiments. Our solution has three important properties: (i) a single trained model CDeC-Net‡that performs well across all the popular benchmark datasets; (ii) we report excellent performances across multiple, including higher, thresholds of IoU; (iii) by following the same protocol of the recent papers for each of the benchmarks, we consistently demonstrate the superior quantitative performance. Our code and models are publicly available at https://github.com/mdv3101/CDeCNet for enabling reproducibility of the results.
A Baseline Analysis for Podcast Abstractive Summarization

Podcast summary, an important factor affecting end-users' listening decisions, has often been considered a critical feature in podcast recommendation systems, as well as many downstream applications. Existing abstractive summarization approaches are mainly built on fine-tuned models on professionally edited texts such as CNN and DailyMail news. Different from news, podcasts are often longer, more colloquial and conversational, and noisier with contents on commercials and sponsorship, which makes automatic podcast summarization extremely challenging. This paper presents a baseline analysis of podcast summarization using the Spotify Podcast Dataset provided by TREC 2020. It aims to help researchers understand current state-of-the-art pre-trained models and hence build a foundation for creating better models.
Channel-Directed Gradients for Optimization of Convolutional Neural Networks

We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error. The method requires only simple processing of existing stochastic gradients, can be used in conjunction with any optimizer, and has only a linear overhead (in the number of parameters) compared to computation of the stochastic gradient. The method works by computing the gradient of the loss function with respect to output-channel directed re-weighted L2 or Sobolev metrics, which has the effect of smoothing components of the gradient across a certain direction of the parameter tensor. We show that defining the gradients along the output channel direction leads to a performance boost, while other directions can be detrimental. We present the continuum theory of such gradients, its discretization, and application to deep networks. Experiments on benchmark datasets, several networks and baseline optimizers show that optimizers can be improved in generalization error by simply computing the stochastic gradient with respect to output-channel directed metrics.
Bias-Awareness for Zero-Shot Learning the Seen and Unseen

Generalized zero-shot learning recognizes inputs from both seen and unseen classes. Yet, existing methods tend to be biased towards the classes seen during training. In this paper, we strive to mitigate this bias. We propose a bias-aware learner to map inputs to a semantic embedding space for generalized zero-shot learning. During training, the model learns to regress to real-valued class prototypes in the embedding space with temperature scaling, while a margin-based bidirectional entropy term regularizes seen and unseen probabilities. Relying on a real-valued semantic embedding space provides a versatile approach, as the model can operate on different types of semantic information for both seen and unseen classes. Experiments are carried out on four benchmarks for generalized zero-shot learning and demonstrate the benefits of the proposed bias-aware classifier, both as a stand-alone method or in combination with generated features.
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

In this paper, we propose a novel stochastic gradient estimator---ProbAbilistic Gradient Estimator (PAGE)---for nonconvex optimization. PAGE is easy to implement as it is designed via a small adjustment to vanilla SGD: in each iteration, PAGE uses the vanilla minibatch SGD update with probability $p$ or reuses the previous gradient with a small adjustment, at a much lower computational cost, with probability $1-p$. We give a simple formula for the optimal choice of $p$. We prove tight lower bounds for nonconvex problems, which are of independent interest. Moreover, we prove matching upper bounds both in the finite-sum and online regimes, which establish that PAGE is an optimal method. Besides, we show that for nonconvex functions satisfying the Polyak-Łojasiewicz (PL) condition, PAGE can automatically switch to a faster linear convergence rate. Finally, we conduct several deep learning experiments (e.g., LeNet, VGG, ResNet) on real datasets in PyTorch, and the results demonstrate that PAGE not only converges much faster than SGD in training but also achieves the higher test accuracy, validating our theoretical results and confirming the practical superiority of PAGE.
Angular Momentum about the Contact Point for Control of Bipedal Locomotion: Validation in a LIP-based Controller

In the control of bipedal locomotion, linear velocity of the center of mass has been widely accepted as a primary variable for summarizing a robot's state vector. The ubiquitous massless-legged linear inverted pendulum (LIP) model is based on it. In this paper, we argue that angular momentum about the contact point has several properties that make it superior to linear velocity for feedback control. So as not to confuse the benefits of angular momentum with any other control design decisions, we first reformulate the standard LIP controller in terms of angular momentum. We then implement the resulting feedback controller on the 20 degree-of-freedom bipedal robot, Cassie Blue, where each leg accounts for nearly one-third of the robot's total mass of 35~Kg. Under this controller, the robot achieves fast walking, rapid turning while walking, large disturbance rejection, and locomotion on rough terrain. The reasoning developed in the paper is applicable to other control design philosophies, whether they be Hybrid Zero Dynamics or Reinforcement Learning.
Review of Graphene-based Thermal Polymer Nanocomposites: Current State of the Art and Future Prospects.

We review the current state of the art of graphene-enhanced thermal interface materials for the management of heat the next generation of electronics. Increased integration densities, speed, and power of electronic and optoelectronic devices require thermal interface materials with substantially higher thermal conductivity, improved reliability, and lower cost. Graphene has emerged as a promising filler material that can meet the demands of future high-speed and high-powered electronics. This review describes the use of graphene as a filler in curing and non-curing polymer matrices. Special attention is given to strategies for achieving the thermal percolation threshold with its corresponding characteristic increase in the overall thermal conductivity. Many applications require high thermal conductivity of the composites while simultaneously preserving electrical insulation. A hybrid filler -- graphene and boron nitride -- approach is presented as possible technology for independent control of electrical and thermal conduction. Reliability and lifespan performance of thermal interface materials is an important consideration towards the determination of appropriate practical applications. The present review addresses these issues in detail, demonstrating the promise of the graphene-enhanced thermal interface materials as compared to alternative technologies.
Table2Charts: Learning Shared Representations for Recommending Charts on Multi-dimensional Data

It is common for people to create different types of charts to explore a multi-dimensional dataset (table). However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification, imbalanced data and open vocabulary exist. In this paper, we propose Table2Charts framework which learns common patterns from a large corpus of (table, charts) pairs. Based on deep Q-learning with copying mechanism and heuristic searching, Table2Charts does table-to-sequence generation, where each sequence follows a chart template. On a large spreadsheet corpus with 196k tables and 306k charts, we show that Table2Charts could learn a shared representation of table fields so that tasks on different chart types could mutually enhance each other. Table2Charts has >0.61 recall at top-3 and >0.49 recall at top-1 for both single-type and multi-type chart recommendation tasks.
Designing adiabatic time evolution from high-frequency bichromatic sources

We investigate the quantum dynamics of a two-level system driven by a bichromatic field, using a non-perturbative analysis. We make special emphasis in the case of two large frequencies, where the Magnus expansion can fail, and in the case of a large and a small frequency, where resonances can dominate. In the first case, we show that two large frequencies can be combined to produce an effective adiabatic evolution. In the second case, we show that high frequency terms (which naturally arise as corrections to the adiabatic evolution obtained in the first case) can be used to produce a highly tunable adiabatic evolution over the whole Bloch sphere, controlled by multi-photon resonances.
ISBN: 978-989-20-7053-7 Typology of fashion-sharing business models

Purpose The aim of this study is to develop a conceptual framework of the sharing economy within the fashion industry. Design/methodology/approach – The methodological approach followed in this paper was to systematically review the academic literature on the sharing economy. A systematic and precise search was done, followed by an analysis and careful understanding. The next step was a synthesis and interpretation. Findings – After creating the first framework relating to the sharing economy within the fashion industry, the findings show that there are different strands of fashion-sharing business models. On the one hand, we have identified those which are “pure” fashion –sharing business models such as fashion libraries where the core of the business is based on the exchanges and participation of the users. On the other hand, there are some businesses which use sharing practices purely as a promotional tool. In addition to these opposite strands, we have identified another category which is hybrid businesses. These have been set up thanks to sharing economy models (i.e. crowdfunding) but their main activities are not based on a collaborative model. Originality/value The paper provides valuable strategic insights for the fashion industry to be able to solve two main problems: the lack of studies which explicitly differentiate between various forms of the sharing economy (Möhlmann 2015), and building a specific typology for the sharing economy within the fashion industry.