Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary investigation into how limb choice affects kinesthetic perception

We have a limited understanding of how we integrate haptic information in real-time from our upper limbs to perform complex bimanual tasks, an ability that humans routinely employ to perform tasks of varying levels of difficulty. In order to understand how information from both limbs is used to create a unified percept, it is important to study both the limbs separately first. Prevalent theories highlighting the role of central nervous system (CNS) in accounting for internal body dynamics seem to suggest that both upper limbs should be equally sensitive to external stimuli. However, there is empirical proof demonstrating a perceptual difference in our upper limbs for tasks like shape discrimination, prompting the need to study effects of limb choice on kinesthetic perception. In this manuscript, we start evaluating Just Noticeable Difference (JND) for stiffness for both forearms separately. Early results validate the need for a more thorough investigation of limb choice on kinesthetic perception.
Towards a Human Values Dashboard for Software Development: An Exploratory Study

Background: There is a growing awareness of the importance of human values (e.g., inclusiveness, privacy) in software systems. However, there are no practical tools to support the integration of human values during software development. We argue that a tool that can identify human values from software development artefacts and present them to varying software development roles can (partially) address this gap. We refer to such a tool as human values dashboard. Further to this, our understanding of such a tool is limited. Aims: This study aims to (1) investigate the possibility of using a human values dashboard to help address human values during software development, (2) identify possible benefits of using a human values dashboard, and (3) elicit practitioners' needs from a human values dashboard. Method: We conducted an exploratory study by interviewing 15 software practitioners. A dashboard prototype was developed to support the interview process. We applied thematic analysis to analyse the collected data. Results: Our study finds that a human values dashboard would be useful for the development team (e.g., project manager, developer, tester). Our participants acknowledge that development artefacts, especially requirements documents and issue discussions, are the most suitable source for identifying values for the dashboard. Our study also yields a set of high-level user requirements for a human values dashboard (e.g., it shall allow determining values priority of a project). Conclusions: Our study suggests that a values dashboard is potentially used to raise awareness of values and support values-based decision-making in software development. Future work will focus on addressing the requirements and using issue discussions as potential artefacts for the dashboard.
Comprehensive Broadband X-Ray and Multiwavelength Study of Active Galactic Nuclei in 57 Local Luminous and Ultraluminous Infrared Galaxies Observed with NuSTAR and/or Swift/BAT

We perform a systematic X-ray spectroscopic analysis of 57 local luminous and ultraluminous infrared galaxy systems (containing 84 individual galaxies) observed with the Nuclear Spectroscopic Telescope Array and/or Swift/BAT. Combining soft X-ray data obtained with Chandra, XMM-Newton, Suzaku, and/or Swift/XRT, we identify 40 hard (>10 keV) X-ray–detected active galactic nuclei (AGNs) and constrain their torus parameters with the X-ray clumpy torus model XCLUMPY. Among the AGNs at z < 0.03, for which sample biases are minimized, the fraction of Compton-thick (N H ≥ 1024 cm−2) AGNs reaches 64−15+14 % (6/9 sources) in late mergers, while it is 24−10+12 % (3/14 sources) in early mergers, consistent with the tendency reported by Ricci et al. We find that the bolometric AGN luminosities derived from the infrared data increase but the X-ray to bolometric luminosity ratios decrease with merger stage. The X-ray-weak AGNs in late mergers ubiquitously show massive outflows at subparsec to kiloparsec scales. Among them, the most luminous AGNs (L bol,AGN ∼ 1046 erg s−1) have relatively small column densities of ≲1023 cm−2 and almost super-Eddington ratios (λ Edd ∼ 1.0). Their torus covering factors (C T (22) ∼ 0.6) are larger than those of Swift/BAT-selected AGNs with similarly high Eddington ratios. These results suggest a scenario where, in the final stage of mergers, multiphase strong outflows are produced due to chaotic quasi-spherical inflows, and the AGN becomes extremely X-ray weak and deeply buried due to obscuration by inflowing and/or outflowing material.
Making Waves: Mirror Mode Structures Around Mars Observed by the MAVEN Spacecraft

We present an in‐depth analysis of a time interval when quasi‐linear mirror mode structures were detected by magnetic field and plasma measurements as observed by the NASA/Mars Atmosphere and Volatile EvolutioN spacecraft. We employ ion and electron spectrometers in tandem to support the magnetic field measurements and confirm that the signatures are indeed mirror modes. Wedged against the magnetic pile‐up boundary, the low‐frequency signatures last on average ∼10 $\sim 10$ s with corresponding sizes of the order of 15–30 upstream solar wind proton thermal gyroradii, or 10–20 proton gyroradii in the immediate wake of the quasi‐perpendicular bow shock. Their peak‐to‐peak amplitudes are of the order of 30–35 nT with respect to the background field, and appear as a mixture of dips and peaks, suggesting that they may have been at different stages in their evolution. Situated in a marginally stable plasma with β‖ ∼ 1, we hypothesize that these so‐called magnetic bottles, containing a relatively higher energy and denser ion population with respect to the background plasma, are formed upstream of the spacecraft behind the quasi‐perpendicular shock. These signatures are very reminiscent of magnetic bottles found at other unmagnetized objects such as Venus and comets, also interpreted as mirror modes. Our case study constitutes the first unmistakable identification and characterization of mirror modes at Mars from the joint points of view of magnetic field, electron and ion measurements. Up until now, the lack of high‐temporal resolution plasma measurements has prevented such an in‐depth study.
Probing Protoplanetary Disk Winds with C ii Absorption

We present an analysis of wind absorption in the C ii λ1335 doublet toward 40 classical T Tauri stars with archival far-ultraviolet (FUV) spectra obtained by the Hubble Space Telescope. Absorption features produced by fast or slow winds are commonly detected (36 out of 40 targets) in our sample. The wind velocity of the fast wind decreases with disk inclination, which is consistent with expectations for a collimated jet. Slow wind absorption is mostly detected in disks with intermediate or high inclination, without a significant dependence of wind velocity on disk inclination. Both the fast and slow wind absorption are preferentially detected in FUV lines of neutral or singly ionized atoms. The Mg ii λ λ2796, 2804 lines show wind absorption consistent with the absorption in the C ii lines. We develop simplified semi-analytical disk/wind models to interpret the observational disk wind absorption. Both fast and slow winds are consistent with expectations from a thermal-magnetized disk wind model and are generally inconsistent with a purely thermal wind. Both the models and the observational analysis indicate that wind absorption occurs preferentially from the inner disk, which offers a wind diagnostic in complement to optical forbidden line emission that traces the wind in larger volumes.
The Diagonal Distance of CWS Codes

Quantum degeneracy in error correction is a feature unique to quantum error correcting codes, unlike their classical counterpart. It allows a quantum error correcting code to correct errors even when they can not uniquely pinpoint the error. The diagonal distance of a quantum code is an important parameter that characterizes if the quantum code is degenerate or not. If code has a distance more than the diagonal distance, then it is degenerate, whereas if it is below the diagonal distance, then it is nondegenerate. We show that most of the CWS codes without a cycle of length four attain the upper bound of diagonal distance d+1, where d is the minimum vertex degree of the associated graph. Addressing the question of degeneracy, we give necessary conditions on CWS codes to be degenerate. We show that any degenerate CWS code with graph $G$ and classical code C will either have a short cycle in the graph $G$ or will be such that the classical code C has one of the coordinates trivially zero for all codewords.
Wavelet Design in a Learning Framework

Wavelets have proven to be highly successful in several signal and image processing applications. Wavelet design has been an active field of research for over two decades, with the problem often being approached from an analytical perspective. In this paper, we introduce a learning based approach to wavelet design. We draw a parallel between convolutional autoencoders and wavelet multiresolution approximation, and show how the learning angle provides a coherent computational framework for addressing the design problem. We aim at designing data-independent wavelets by training filterbank autoencoders, which precludes the need for customized datasets. In fact, we use high-dimensional Gaussian vectors for training filterbank autoencoders, and show that a near-zero training loss implies that the learnt filters satisfy the perfect reconstruction property with very high probability. Properties of a wavelet such as orthogonality, compact support, smoothness, symmetry, and vanishing moments can be incorporated by designing the autoencoder architecture appropriately and with a suitable regularization term added to the mean-squared error cost used in the learning process. Our approach not only recovers the well known Daubechies family of orthogonal wavelets and the Cohen-Daubechies-Feauveau family of symmetric biorthogonal wavelets, but also learns wavelets outside these families.
Radio continuum sources behind the Large Magellanic Cloud

We present a comprehensive multifrequency catalogue of radio sources behind the Large Magellanic Cloud (LMC) between 0.2 and 20 GHz, gathered from a combination of new and legacy radio continuum surveys. This catalogue covers an area of ∼144 deg2 at angular resolutions from 45 arcsec to ∼3 arcmin. We find 6434 discrete radio sources in total, of which 3789 are detected at two or more radio frequencies. We estimate the median spectral index (α; where Sv ∼ να) of α = −0.89 and mean of −0.88 ± 0.48 for 3636 sources detected exclusively at two frequencies (0.843 and 1.384 GHz) with similar resolution [full width at half-maximum (FWHM) ∼40–45 arcsec]. The large frequency range of the surveys makes it an effective tool to investigate Gigahertz Peak Spectrum (GPS), Compact Steep Spectrum (CSS), and Infrared Faint Radio Source (IFRS) populations within our sample. We find 10 GPS candidates with peak frequencies near 5 GHz, from which we estimate their linear size. 1866 sources from our catalogue are CSS candidates with α < −0.8. We found six candidates for High Frequency Peaker (HFP) sources, whose radio fluxes peak above 5 GHz and no sources with unconstrained peaks and α > 0.5. We found optical counterparts for 343 of the radio continuum sources, of which 128 have a redshift measurement. Finally, we investigate the population of 123 IFRSs found in this study.
Lower Bounds for Symmetric Circuits for the Determinant

Dawar and Wilsenach (ICALP 2020) introduce the model of symmetric arithmetic circuits and show an exponential separation between the sizes of symmetric circuits for computing the determinant and the permanent. The symmetry restriction is that the circuits which take a matrix input are unchanged by a permutation applied simultaneously to the rows and columns of the matrix. Under such restrictions we have polynomial-size circuits for computing the determinant but no subexponential size circuits for the permanent. Here, we consider a more stringent symmetry requirement, namely that the circuits are unchanged by arbitrary even permutations applied separately to rows and columns, and prove an exponential lower bound even for circuits computing the determinant. The result requires substantial new machinery. We develop a general framework for proving lower bounds for symmetric circuits with restricted symmetries, based on a new support theorem and new two-player restricted bijection games. These are applied to the determinant problem with a novel construction of matrices that are bi-adjacency matrices of graphs based on the CFI construction. Our general framework opens the way to exploring a variety of symmetry restrictions and studying trade-offs between symmetry and other resources used by arithmetic circuits.
Throttling Equilibria in Auction Markets

Throttling is a popular method of budget management for online ad auctions in which the platform modulates the participation probability of an advertiser in order to smoothly spend her budget across many auctions. In this work, we investigate the setting in which all of the advertisers simultaneously employ throttling to manage their budgets, and we do so for both first-price and second-price auctions. We analyze the structural and computational properties of the resulting equilibria. For first-price auctions, we show that a unique equilibrium always exists, is well-behaved and can be computed efficiently via tatonnement-style decentralized dynamics. In contrast, for second-price auctions, we prove that even though an equilibrium always exists, the problem of finding an equilibrium is PPAD-complete, there can be multiple equilibria, and it is NP-hard to find the revenue maximizing one. We also compare the equilibrium outcomes of throttling to those of multiplicative pacing, which is the other most popular and well-studied method of budget management. Finally, we characterize the Price of Anarchy of these equilibria for liquid welfare by showing that it is at most 2 for both first-price and second-price auctions, and demonstrating that our bound is tight.
Joint Shapley values: a measure of joint feature importance

The Shapley value is one of the most widely used measures of feature importance partly as it measures a feature's average effect on a model's prediction. We introduce joint Shapley values, which directly extend Shapley's axioms and intuitions: joint Shapley values measure a set of features' average contribution to a model's prediction. We prove the uniqueness of joint Shapley values, for any order of explanation. Results for games show that joint Shapley values present different insights from existing interaction indices, which assess the effect of a feature within a set of features. The joint Shapley values provide intuitive results in ML attribution problems. With binary features, we present a presence-adjusted global value that is more consistent with local intuitions than the usual approach.
Dynamic Detection of Mobile Malware Using Smartphone Data and Machine Learning

Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen with the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow, stressing the importance of research on the detection of mobile malware. Detection methods for mobile malware exist but are still limited. In this article, we provide an overview of the performance of machine learning (ML) techniques to detect malware on Android, without using privileged access. The ML-classifiers use device information such as the CPU usage, battery usage, and memory usage for the detection of 10 subtypes of Mobile Trojans on the Android Operating System. We use a real-life dataset containing device and malware data from 47 users for a year (2016). We examine which features, i.e., aspects, of a device, are most important to monitor to detect (subtypes of) Mobile Trojans. The focus of this article is on dynamic hardware features. Using these dynamic features we apply state-of-the-art machine learning classifiers: Random Forest, K-Nearest Neighbour, and AdaBoost. We show classification results on different feature sets, making a distinction between global device features, and specific app features. None of the measured feature sets require privileged access. Our results show that the Random Forest classifier performs best as a general malware classifier: across 10 subtypes of Mobile Trojans, it achieves an F1 score of 0.73 with a False Positive Rate (FPR) of 0.009 and a False Negative Rate (FNR) of 0.380. The Random Forest, K-Nearest Neighbours, and AdaBoost classifiers achieve F1 scores above 0.72, an FPR below 0.02 and, an FNR below 0.33, when trained separately to detect each subtype of Mobile Trojans.
DeepTitle - Leveraging BERT to generate Search Engine Optimized Headlines

Automated headline generation for online news articles is not a trivial task - machine generated titles need to be grammatically correct, informative, capture attention and generate search traffic without being"click baits"or"fake news". In this paper we showcase how a pre-trained language model can be leveraged to create an abstractive news headline generator for German language. We incorporate state of the art fine-tuning techniques for abstractive text summarization, i.e. we use different optimizers for the encoder and decoder where the former is pre-trained and the latter is trained from scratch. We modify the headline generation to incorporate frequently sought keywords relevant for search engine optimization. We conduct experiments on a German news data set and achieve a ROUGE-L-gram F-score of 40.02. Furthermore, we address the limitations of ROUGE for measuring the quality of text summarization by introducing a sentence similarity metric and human evaluation.
On the topologies of the exponential

Factorization algebras have been defined using three different topologies on the Ran space. We study these three different topologies on the exponential, which is the union of the Ran space and the empty configuration, and show that an exponential property is satisfied in each case. As a consequence we describe the weak homotopy type of the exponential Exp(X) for each topology, in the case where X is not connected. We also study these exponentials as stratified spaces and show that the metric exponential is conically stratified for a general class of spaces. As a corollary, we obtain that locally constant factorization algebras defined by Beilinson-Drinfeld are equivalent to locally constant factorization algebras defined by Lurie.
Modelling Latent Translations for Cross-Lingual Transfer

While achieving state-of-the-art results in multiple tasks and languages, translation-based cross-lingual transfer is often overlooked in favour of massively multilingual pre-trained encoders. Arguably, this is due to its main limitations: 1) translation errors percolating to the classification phase and 2) the insufficient expressiveness of the maximum-likelihood translation. To remedy this, we propose a new technique that integrates both steps of the traditional pipeline (translation and classification) into a single model, by treating the intermediate translations as a latent random variable. As a result, 1) the neural machine translation system can be fine-tuned with a variant of Minimum Risk Training where the reward is the accuracy of the downstream task classifier. Moreover, 2) multiple samples can be drawn to approximate the expected loss across all possible translations during inference. We evaluate our novel latent translation-based model on a series of multilingual NLU tasks, including commonsense reasoning, paraphrase identification, and natural language inference. We report gains for both zero-shot and few-shot learning setups, up to 2.7 accuracy points on average, which are even more prominent for low-resource languages (e.g., Haitian Creole). Finally, we carry out in-depth analyses comparing different underlying NMT models and assessing the impact of alternative translations on the downstream performance.
Bootstrapping Whittle Estimators

Fitting parametric models by optimizing frequency domain objective functions is an attractive approach of parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the assumption that the true spectral density of the underlying process does not necessarily belong to the parametric class of spectral densities fitted, the distribution of Whittle estimators typically depends on difficult to estimate characteristics of the underlying process. This makes the implementation of asymptotic results for the construction of confidence intervals or for assessing the variability of estimators, difficult in practice. This paper proposes a frequency domain bootstrap method to estimate the distribution of Whittle estimators which is asymptotically valid under assumptions that not only allow for possible model misspecification but also for weak dependence conditions which are satisfied by a wide range of stationary stochastic processes. Adaptions of the bootstrap procedure developed to incorporate different modifications of Whittle estimators proposed in the literature, like for instance, tapered, de-biased or boundary extended Whittle estimators, are also considered. Simulations demonstrate the capabilities of the bootstrap method proposed and its good finite sample performance. A real-life data analysis on sunspots is also presented.
Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring

Over the years, several approaches have tried to tackle the problem of performing an automatic scoring of the sleeping stages. Although any polysomnography usually collects over a dozen of different signals, this particular problem has been mainly tackled by using only the Electroencephalograms presented in those records. On the other hand, the other recorded signals have been mainly ignored by most works. This paper explores and compares the convenience of using additional signals apart from electroencephalograms. More specifically, this work uses the SHHS-1 dataset with 5,804 patients containing an electromyogram recorded simultaneously as two electroencephalograms. To compare the results, first, the same architecture has been evaluated with different input signals and all their possible combinations. These tests show how, using more than one signal especially if they are from different sources, improves the results of the classification. Additionally, the best models obtained for each combination of one or more signals have been used in ensemble models and, its performance has been compared showing the convenience of using these multi-signal models to improve the classification. The best overall model, an ensemble of Depth-wise Separational Convolutional Neural Networks, has achieved an accuracy of 86.06\% with a Cohen's Kappa of 0.80 and a $F_{1}$ of 0.77. Up to date, those are the best results on the complete dataset and it shows a significant improvement in the precision and recall for the most uncommon class in the dataset.