Miracle-dz/UniFAR-FTUs huggingface dataset:
Bandits with many optimal arms

We consider a stochastic bandit problem with a possibly infinite number of arms. We write $p^*$ for the proportion of optimal arms and $\Delta$ for the minimal mean-gap between optimal and sub-optimal arms. We characterize the optimal learning rates both in the cumulative regret setting, and in the best-arm identification setting in terms of the problem parameters $T$ (the budget), $p^*$ and $\Delta$. For the objective of minimizing the cumulative regret, we provide a lower bound of order $\Omega(\log(T)/(p^*\Delta))$ and a UCB-style algorithm with matching upper bound up to a factor of $\log(1/\Delta)$. Our algorithm needs $p^*$ to calibrate its parameters, and we prove that this knowledge is necessary, since adapting to $p^*$ in this setting is impossible. For best-arm identification we also provide a lower bound of order $\Omega(\exp(-cT\Delta^2 p^*))$ on the probability of outputting a sub-optimal arm where $c>0$ is an absolute constant. We also provide an elimination algorithm with an upper bound matching the lower bound up to a factor of order $\log(T)$ in the exponential, and that does not need $p^*$ or $\Delta$ as parameter. Our results apply directly to the three related problems of competing against the $j$-th best arm, identifying an $\epsilon$ good arm, and finding an arm with mean larger than a quantile of a known order.
On Convolutional Precoding in PAC Codes

In polarization-adjusted convolutional (PAC) codes, a convolutional transform is employed as a precoding stage before the polar transform. It turned out that the precoding stage improves the weight distribution of polar codes according. That could be the main reason for the superiority of error correction performance of PAC codes relative to polar codes. In this work, we explicitly show why the convolutional precoding reduces the number of minimum-weight codewords. Furthermore, we show where the precoding stage is not effective in the improvement of weight distribution. Eventually, to evaluate the FER gain obtainable from reducing the number of min-weight codewords, we assess the performance and the number of minimum-weight codewords resulted from an irregular convolutional precoding process.
GISE-51: A scalable isolated sound events dataset

Most of the existing isolated sound event datasets comprise a small number of sound event classes, usually 10 to 15, restricted to a small domain, such as domestic and urban sound events. In this work, we introduce GISE-51, a dataset spanning 51 isolated sound events belonging to a broad domain of event types. We also release GISE-51-Mixtures, a dataset of 5-second soundscapes with hard-labelled event boundaries synthesized from GISE-51 isolated sound events. We conduct baseline sound event recognition (SER) experiments on the GISE-51-Mixtures dataset, benchmarking prominent convolutional neural networks, and models trained with the dataset demonstrate strong transfer learning performance on existing audio recognition benchmarks. Together, GISE-51 and GISE-51-Mixtures attempt to address some of the shortcomings of recent sound event datasets, providing an open, reproducible benchmark for future research along with the freedom to adapt the included isolated sound events for domain-specific applications.
Normal transport and excitonic condensation in an incoherent semimetal

We study a two-band dispersive SYK model in 1 + 1 dimension at half filling. We suggest a model that describes a semimetal with quadratic dispersion at half-filling. We compute the Green’s function at the saddle point using a combination of analytical and numerical methods. Employing a scaling symmetry of the Schwinger Dyson equations that becomes transparent in the strongly dispersive limit, we show that the exact solution of the problem yields a distinct type of nonFermi liquid with sublinear ρ ∝ T 2/5 temperature dependence of the resistivity. A scaling analysis indicates that this state corresponds to the fixed point of the dispersive SYK model for a quadratic band touching semimetal. We examine the formation of indirect exciton condensation in a bilayer system constructed from the above model. We find that the condensation temperature scales as a fast power-law Tc ∝ g, with g the strength of the repulsive coupling between the layers.
Out-of-Distribution Detection of Melanoma using Normalizing Flows

Generative modelling has been a topic at the forefront of machine learning research for a substantial amount of time. With the recent success in the field of machine learning, especially in deep learning, there has been an increased interest in explainable and interpretable machine learning. The ability to model distributions and provide insight in the density estimation and exact data likelihood is an example of such a feature. Normalizing Flows (NFs), a relatively new research field of generative modelling, has received substantial attention since it is able to do exactly this at a relatively low cost whilst enabling competitive generative results. While the generative abilities of NFs are typically explored, we focus on exploring the data distribution modelling for Out-of-Distribution (OOD) detection. Using one of the state-of-the-art NF models, GLOW, we attempt to detect OOD examples in the ISIC dataset. We notice that this model under performs in conform related research. To improve the OOD detection, we explore the masking methods to inhibit co-adaptation of the coupling layers however find no substantial improvement. Furthermore, we utilize Wavelet Flow which uses wavelets that can filter particular frequency components, thus simplifying the modeling process to data-driven conditional wavelet coefficients instead of complete images. This enables us to efficiently model larger resolution images in the hopes that it would capture more relevant features for OOD. The paper that introduced Wavelet Flow mainly focuses on its ability of sampling high resolution images and did not treat OOD detection. We present the results and propose several ideas for improvement such as controlling frequency components, using different wavelets and using other state-of-the-art NF architectures.
The HETDEX Survey: The Lyα Escape Fraction from 3D-HST Emission-Line Galaxies at z ∼ 2

In this work, we measure the Lyα escape fraction of 935 [O iii]-emitting galaxies between 1.9 < z < 2.35 by comparing stacked spectra from the Hubble Space Telescope/WFC3's near-IR grism to corresponding stacks from the Hobby–Eberly Telescope Dark Energy Experiment’s Internal Data Release 2. By measuring the stacks’ Hβ to Lyα ratios, we determine the Lyα escape fraction as a function of stellar mass, star-formation rate, internal reddening, size, and [O iii]/Hβ ratio. We show that the escape fraction of Lyα correlates with a number of parameters, such as galaxy size, star-formation rate, and nebular excitation. However, we also demonstrate that most of these relations are indirect, and that the primary variables controlling the escape of Lyα are likely to be stellar mass and internal extinction. Overall, the escape of Lyα declines from ≳16% in galaxies with to ≲1% for systems with , with the sample’s mean escape fraction being .
The dust mass in Cassiopeia A from infrared and optical line flux differences

The large quantities of dust that have been found in a number of high-redshift galaxies have led to suggestions that core-collapse supernovae (CCSNe) are the main sources of their dust and have motivated the measurement of the dust masses formed by local CCSNe. For Cassiopeia A (Cas A), an oxygen-rich remnant of a Type IIb CCSN, a dust mass of 0.6–1.1 M⊙ has already been determined by two different methods, namely (a) from its far-infrared spectral energy distribution and (b) from analysis of the red–blue emission line asymmetries in its integrated optical spectrum. We present a third, independent, method for determining the mass of dust contained within Cas A. This compares the relative fluxes measured in similar apertures from [O iii] far-infrared and visual-region emission lines, taking into account foreground dust extinction, in order to determine internal dust optical depths, from which corresponding dust masses can be obtained. Using this method, we determine a dust mass within Cas A of at least 0.99$^{+0.10}_{-0.09}$ M⊙.
Bayesian imputation of COVID‐19 positive test counts for nowcasting under reporting lag

Obtaining up to date information on the number of UK COVID‐19 regional infections is hampered by the reporting lag in positive test results for people with COVID‐19 symptoms. In the UK, for ‘Pillar 2’ swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time‐series representation nowcasting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo.
Mass-ratio and Magnetic Flux Dependence of Modulated Accretion from Circumbinary Disks

Accreting supermassive binary black holes (SMBBHs) are potential multimessenger sources because they emit both gravitational-wave and electromagnetic (EM) radiation. Past work has shown that their EM output may be periodically modulated by an asymmetric density distribution in the circumbinary disk, often called an “overdensity” or “lump;” this modulation could possibly be used to identify a source as a binary. We explore the sensitivity of the overdensity to SMBBH mass ratio and magnetic flux through the accretion disk. We find that the relative amplitude of the overdensity and its associated EM periodic signal both degrade with diminishing mass ratio, vanishing altogether somewhere between 1:2 and 1:5. Greater magnetization also weakens the lump and any modulation of the light output. We develop a model to describe how lump formation results from internal stress degrading faster in the lump region than it can be rejuvenated through accretion inflow, and predicts a threshold value in specific internal stress below which lump formation should occur and which all our lump-forming simulations satisfy. Thus, detection of such a modulation would provide a constraint on both mass ratio and magnetic flux piercing the accretion flow.
SAFEval: Summarization Asks for Fact-based Evaluation

Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate this issue, recent work has proposed evaluation metrics which rely on question answering models to assess whether a summary contains all the relevant information in its source document. Though promising, the proposed approaches have so far failed to correlate better than ROUGE with human judgments. In this paper, we extend previous approaches and propose a uniﬁed framework, named SAFEval . In contrast to established metrics such as ROUGE or BERTScore, SAFEval does not require any ground-truth reference. Nonetheless, SAFEval substantially improves the correlation with human judgments over four evaluation dimensions (consistency, coherence, ﬂuency, and relevance), as shown in the extensive experiments we report.
GelSlim 3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Finger

This work presents a new version of tactile-sensing finger, GelSlim 3.0, which integrates the ability to sense high-resolution shape, force, and slip in a more compact form factor than previous implementations, designed for cluttered bin-picking scenarios. The novel design integrates real-time model-based algorithms to measure shape, estimate the 3-D contact force distribution, and detect incipient slip. The constraints imposed by the photometric stereo algorithm used for depth reconstruction and the implementation of a planar sensing surface make the miniaturization of previous designs nontrivial. To achieve a compact integration, we optimize the optical path from illumination source to camera. Using an optical simulation environment, we develop an illumination shaping lens and position the source LEDs and camera. The optimized optical configuration is integrated into a finger design composed of a robust and easily replaceable snap-to-fit fingertip module that facilitates manufacture, assembly, use, and repair. To stimulate future research in tactile-sensing and provide the robotics community access to a reliable and easily reproducible tactile finger with a diversity of sensing modalities, we open-source the design, fabrication methods, and software at https://github.com/mcubelab/gelslim.
Current operators in integrable models: a review

We consider the current operators of one-dimensional integrable models. These operators describe the flow of the conserved charges of the models, and they play a central role in generalized hydrodynamics. We present key statements about the mean currents in finite volume and in the thermodynamic limit, and we review the various proofs of the exact formulas. We also present a few new results in this review. New contributions include a computation of the currents of the Heisenberg spin chains using the string hypothesis, and simplified formulas in the thermodynamic limit. We also discuss implications of our results for the asymptotic behavior of dynamical correlation functions.
Revisiting Dockerfiles in Open Source Software Over Time

Docker is becoming ubiquitous with containerization for developing and deploying applications. Previous studies have analyzed Dockerfiles that are used to create container images in order to better understand how to improve Docker tooling. These studies obtain Dockerfiles using either Docker Hub or Github. In this paper, we revisit the findings of previous studies using the largest set of Dockerfiles known to date with over 9.4 million unique Dockerfiles found in the World of Code infrastructure spanning from 2013-2020. We contribute a historical view of the Dockerfile format by analyzing the Docker engine changelogs and use the history to enhance our analysis of Dockerfiles. We also reconfirm previous findings of a downward trend in using OS images and an upward trend of using language images. As well, we reconfirm that Dockerfile smell counts are slightly decreasing meaning that Dockerfile authors are likely getting better at following best practices. Based on these findings, it indicates that previous analyses from prior works have been correct in many of their findings and their suggestions to build better tools for Docker image creation are further substantiated.
Insight into delay based reservoir computing via eigenvalue analysis

In this paper we give a profound insight into the computation capability of delay based reservoir computing via an eigenvalue analysis. We concentrate on the task-independent memory capacity to quantify the reservoir performance and compare these with the eigenvalue spectrum of the dynamical system. We show that these two quantities are deeply connected, and thus the reservoir computing performance is predictable by analysing the small signal response of the reservoir. Our results suggest that any dynamical system used as a reservoir can be analysed in this way. We apply our method exemplarily to a photonic laser system with feedback and compare the numerically computed recall capabilities with the eigenvalue spectrum. Optimal performance is found for a system with the eigenvalues having real parts close to zero and off-resonant imaginary parts.
A Frustratingly Easy Approach for Joint Entity and Relation Extraction

End-to-end relation extraction aims to identify named entities and extract relations between them simultaneously. Most recent work models these two subtasks jointly, either by unifying them in one structured prediction framework, or multi-task learning through shared representations. In this work, we describe a very simple approach for joint entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05, and SciERC). Our approach essentially builds on two independent pre-trained encoders and merely uses the entity model to provide input features for the relation model. Through a series of careful examinations, we validate the importance of learning distinct contextual representations for entities and relations, fusing entity information at the input layer of the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both encoders at inference time, obtaining a 8-16$\times$ speedup with a small accuracy drop.
Study on Fluorescent Staining of Bacteria for Total Direct Counts in Polycyclic Aromatic Hydrocarbons (PAHs) Contaminated Soil with 5-(4,6-DichloroTriazin-2-yl) Amino Fluorescein (DTAF)

Using DTAF fluorescent staining technique, the number of bacteria and the distributions of ΣPAHs in two different drilling vertical depths of a field contaminated by PAHs are counted and analyzed. The result showed that the distributions of the number of bacteria and the ΣPAHs in the vertical depth have obvious corresponding relations, and the higher the ΣPAHs is, the more the number of bacteria would be.
Pleiotropic Properties of Valsartan: Do They Result from the Antiglycooxidant Activity? Literature Review and In Vitro Study

Valsartan belongs to angiotensin II type 1 (AT1) receptor blockers (ARB) used in cardiovascular diseases like heart failure and hypertension. Except for its AT1-antagonism, another mechanism of drug action has been suggested in recent research. One of the supposed actions refers to the positive impact on redox balance and reducing protein glycation. Our study is aimed at assessing the antiglycooxidant properties of valsartan in an in vitro model of oxidized bovine serum albumin (BSA). Glucose, fructose, ribose, glyoxal (GO), methylglyoxal (MGO), and chloramine T were used as glycation or oxidation agents. Protein oxidation products (total thiols, protein carbonyls (PC), and advanced oxidation protein products (AOPP)), glycooxidation products (tryptophan, kynurenine, N-formylkynurenine, and dityrosine), glycation products (amyloid-β structure, fructosamine, and advanced glycation end products (AGE)), and albumin antioxidant activity (total antioxidant capacity (TAC), DPPH assay, and ferric reducing antioxidant power (FRAP)) were measured in each sample. In the presence of valsartan, concentrations of protein oxidation and glycation products were significantly lower comparing to control. Moreover, albumin antioxidant activity was significantly higher in those samples. The drug's action was comparable to renowned antiglycation agents and antioxidants, e.g., aminoguanidine, metformin, Trolox, N-acetylcysteine, or alpha-lipoic acid. The conducted experiment proves that valsartan can ameliorate protein glycation and oxidation in vitro in various conditions. Available animal and clinical studies uphold this statement, but further research is needed to confirm it, as reduction of protein oxidation and glycation may prevent cardiovascular disease development.
Eyes-Open Versus Eyes-Closed Somatosensory Motor Balance in Professional Soccer Players With Chronic Ankle Instability: A Case-Control Study

Background: Chronic ankle instability (CAI) is a condition defined by certain structural and functional deficits in the ankle joint complex after acute ankle injury. These deficits include pathological joint laxity, impaired postural control, and decreased strength and neuromuscular control. Purpose: To compare an eyes-open versus an eyes-closed balance training protocol in professional soccer players with CAI. Study Design: Cohort study; Level of evidence, 2. Methods: For this study, we evaluated 19 players from 2 professional soccer teams in Madrid, Spain, all of whom had CAI. Participants from both teams were randomly assigned to an eyes-open group (n = 9) or eyes-closed group (n = 10). All participants completed 4 weeks of a supervised exercise protocol consisting of 3 sessions per week. Members of both the eyes-open and eyes-closed groups performed the same exercise protocol in the same order of execution. At the end of the protocol, the participants were assessed for pain (visual analog scale), ankle dorsiflexion range of motion (weightbearing lunge test), dynamic stability (Star Excursion Balance Test), and fear of movement and reinjury (Tampa Scale for Kinesiophobia). We compared results both before and after balance training and between the eyes-open and eyes-closed balance training groups. Results: Statistically significant differences were found for all of the assessed variables before and after balance training. No statistically significant differences were found between the eyes-closed and eyes-open groups on any variable. Conclusion: In the current study, eyes-closed balance training was not more effective than eyes-open balance training for CAI in professional soccer players.
Symptom cluster is associated with prolonged return-to-play in symptomatic athletes with acute respiratory illness (including COVID-19): a cross-sectional study—AWARE study I

Background There are no data relating symptoms of an acute respiratory illness (ARI) in general, and COVID-19 specifically, to return to play (RTP). Objective To determine if ARI symptoms are associated with more prolonged RTP, and if days to RTP and symptoms (number, type, duration and severity) differ in athletes with COVID-19 versus athletes with other ARI. Design Cross-sectional descriptive study. Setting Online survey. Participants Athletes with confirmed/suspected COVID-19 (ARICOV) (n=45) and athletes with other ARI (ARIOTH) (n=39). Methods Participants recorded days to RTP and completed an online survey detailing ARI symptoms (number, type, severity and duration) in three categories: ‘nose and throat’, ‘chest and neck’ and ‘whole body’. We report the association between symptoms and RTP (% chance over 40 days) and compare the days to RTP and symptoms (number, type, duration and severity) in ARICOV versus ARIOTH subgroups. Results The symptom cluster associated with more prolonged RTP (lower chance over 40 days; %) (univariate analysis) was ‘excessive fatigue’ (75%; p<0.0001), ‘chills’ (65%; p=0.004), ‘fever’ (64%; p=0.004), ‘headache’ (56%; p=0.006), ‘altered/loss sense of smell’ (51%; p=0.009), ‘Chest pain/pressure’ (48%; p=0.033), ‘difficulty in breathing’ (48%; p=0.022) and ‘loss of appetite’ (47%; p=0.022). ‘Excessive fatigue’ remained associated with prolonged RTP (p=0.0002) in a multiple model. Compared with ARIOTH, the ARICOV subgroup had more severe disease (greater number, more severe symptoms) and more days to RTP (p=0.0043). Conclusion Symptom clusters may be used by sport and exercise physicians to assist decision making for RTP in athletes with ARI (including COVID-19).