Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of quarkonium production with ALICE at the LHC

ALICE is a general purpose experiment designed to investigate nucleus-nucleus collisions at the Large Hadron Collider (LHC), located at CERN. The ALICE detector is optimized for the reconstruction of quarkonia through the dimuon decay channel at foward rapidity as well as the dielectron decay channel at midrapidity. In this contribution, quarkonium measurements performed by the ALICE collaboration at both midrapidity and forward rapidity for various energies and colliding systems (pp, p-Pb and Pb-Pb), will be discussed and compared to theory.
Circumstellar Medium Interaction in SN 2018lab, A Low-luminosity Type IIP Supernova Observed with TESS

We present photometric and spectroscopic data of SN 2018lab, a low-luminosity Type IIP supernova (LLSN) with a V-band peak luminosity of −15.1 ± 0.1 mag. SN 2018lab was discovered by the Distance Less Than 40 Mpc (DLT40) SN survey only 0.73 days post-explosion, as determined by observations from the Transiting Exoplanet Survey Satellite (TESS). TESS observations of SN 2018lab yield a densely sampled, fast-rising, early-time light curve likely powered by ejecta–circumstellar medium (CSM) interaction. The blueshifted, broadened flash feature in the earliest spectra (<2 days) of SN 2018lab provides further evidence for ejecta–CSM interaction. The early emission features in the spectra of SN 2018lab are well described by models of a red supergiant progenitor with an extended envelope and a close-in CSM. As one of the few LLSNe with observed flash features, SN 2018lab highlights the need for more early spectra to explain the diversity of the flash feature morphology of Type II SNe.
Style-Agnostic Reinforcement Learning

We present a novel method of learning style-agnostic representation using both style transfer and adversarial learning in the reinforcement learning framework. The style, here, refers to task-irrelevant details such as the color of the background in the images, where generalizing the learned policy across environments with different styles is still a challenge. Focusing on learning style-agnostic representations, our method trains the actor with diverse image styles generated from an inherent adversarial style perturbation generator, which plays a min-max game between the actor and the generator, without demanding expert knowledge for data augmentation or additional class labels for adversarial training. We verify that our method achieves competitive or better performances than the state-of-the-art approaches on Procgen and Distracting Control Suite benchmarks, and further investigate the features extracted from our model, showing that the model better captures the invariants and is less distracted by the shifted style. The code is available at https://github.com/POSTECH-CVLab/style-agnostic-RL.
GOALS-JWST: Hidden Star Formation and Extended PAH Emission in the Luminous Infrared Galaxy VV 114

James Webb Space Telescope (JWST) Mid-Infrared Instrument (MIRI) images of the luminous infrared (IR) galaxy VV 114 are presented. This redshift ∼0.020 merger has a western component (VV 114W) rich in optical star clusters and an eastern component (VV 114E) hosting a luminous mid-IR nucleus hidden at UV and optical wavelengths by dust lanes. With MIRI, the VV 114E nucleus resolves primarily into bright NE and SW cores separated by 630 pc. This nucleus comprises 45% of the 15 μm light of VV 114, with the NE and SW cores having IR luminosities, L IR(8 − 1000 μm) ∼ 8 ± 0.8 × 1010 L ⊙ and ∼ 5 ± 0.5 × 1010 L ⊙, respectively, and IR densities, ΣIR ≳ 2 ± 0.2 × 1013 L ⊙ kpc−2 and ≳ 7 ± 0.7 × 1012 L ⊙ kpc−2, respectively—in the range of ΣIR for the Orion star-forming core and the nuclei of Arp 220. The NE core, previously speculated to have an active galactic nucleus (AGN), has starburst-like mid-IR colors. In contrast, the VV 114E SW core has AGN-like colors. Approximately 40 star-forming knots with L IR ∼ 0.02–5 × 1010 L ⊙ are identified, 28% of which have no optical counterpart. Finally, diffuse emission accounts for 40%–60% of the mid-IR emission. Mostly notably, filamentary polycyclic aromatic hydrocarbon (PAH) emission stochastically excited by UV and optical photons accounts for half of the 7.7 μm light of VV 114. This study illustrates the ability of JWST to detect obscured compact activity and distributed PAH emission in the most extreme starburst galaxies in the local universe.
Continuous differentiability of a weak solution to very singular elliptic equations involving anisotropic diffusivity

Abstract In this paper we consider a very singular elliptic equation that involves an anisotropic diffusion operator, including the one-Laplacian, and is perturbed by a p-Laplacian-type diffusion operator with 1 < p < ∞ {1<p<\infty} . This equation seems analytically difficult to handle near a facet, the place where the gradient vanishes. Our main purpose is to prove that weak solutions are continuously differentiable even across the facet. Here it is of interest to know whether a gradient is continuous when it is truncated near a facet. To answer this affirmatively, we consider an approximation problem, and use standard methods including De Giorgi’s truncation and freezing coefficient methods.
A High Performance Scintillator Ion Beam Monitor

A high-performance Scintillator Ion Beam Monitor (SBM) provides diagnostics across an extremely wide range of isotopes, energies, and intensities employing a machine-vision camera with two novel scintillator materials moveable into/out of the beam without breaking vacuum. Scintillators are: 1) a semicrystalline polymer material (PM), film, 1-200 µm thick; and 2) a 100-400 µ m thick opaque sheet consisting of a hybrid of an in-organic-polymer (HM) hybrid matrix. The SBM was demonstrated at the Facility for Rare Isotope Beams (FRIB, East Lansing, MI) providing real-time beam profile and rate analysis spanning more than five orders of magnitude including visualization of single ion signals . It may replace FRIB reference detectors: a phosphorescent beam viewer, a Faraday cup, a microchannel plate, and a silicon detector.
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results

We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on"cross-domain"meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve"any-way"and"any-shot"problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of"ways"(within the range 2-20) and any number of"shots"(within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains.
CEERS Key Paper. II. A First Look at the Resolved Host Properties of AGN at 3 < z < 5 with JWST

We report on the host properties of five X-ray-luminous active galactic nuclei (AGN) identified at 3 < z < 5 in the first epoch of imaging from the Cosmic Evolution Early Release Science Survey. Each galaxy has been imaged with the JWST Near-Infrared Camera, which provides rest-frame optical morphologies at these redshifts. We also derive stellar masses and star formation rates for each host by fitting its spectral energy distribution using a combination of galaxy and AGN templates. We find that three of the AGN hosts have spheroidal morphologies, one is a bulge-dominated disk, and one is dominated by pointlike emission. None are found to show strong morphological disturbances that might indicate a recent interaction or merger event. When compared to a sample of mass-matched inactive galaxies, we find that the AGN hosts have morphologies that are less disturbed and more bulge-dominated. Notably, all four of the resolved hosts have rest-frame optical colors consistent with a quenched or poststarburst stellar population. The presence of AGN in passively evolving galaxies at z > 3 is significant because a rapid feedback mechanism is required in most semianalytic models and cosmological simulations to explain the growing population of massive quiescent galaxies observed at these redshifts. Our findings show that AGN can continue to inject energy into these systems after their star formation is curtailed, potentially heating their halos and preventing renewed star formation. Additional observations will be needed to determine what role this feedback may play in helping to quench these systems and/or maintain their quiescent state.
A risk measurement approach from risk-averse stochastic optimization of score functions

We propose a risk measurement approach for a risk-averse stochastic problem. We provide results that guarantee that our problem has a solution. We characterize and explore the properties of the argmin as a risk measure and the minimum as a deviation measure. We provide a connection between linear regression models and our framework. Based on this conception, we consider conditional risk and provide a connection between the minimum deviation portfolio and linear regression. Moreover, we also link the optimal replication hedging to our framework.
Microwalk-CI: Practical Side-Channel Analysis for JavaScript Applications

Secret-dependent timing behavior in cryptographic implementations has resulted in exploitable vulnerabilities, undermining their security. Over the years, numerous tools to automatically detect timing leakage or even to prove their absence have been proposed. However, a recent study at IEEE S&P 2022 showed that, while many developers are aware of one or more analysis tools, they have major difficulties integrating these into their workflow, as existing tools are tedious to use and mapping discovered leakages to their originating code segments requires expert knowledge. In addition, existing tools focus on compiled languages like C, or analyze binaries, while the industry and open-source community moved to interpreted languages, most notably JavaScript. In this work, we introduce Microwalk-CI, a novel side-channel analysis framework for easy integration into a JavaScript development workflow. First, we extend existing dynamic approaches with a new analysis algorithm, that allows efficient localization and quantification of leakages, making it suitable for use in practical development. We then present a technique for generating execution traces from JavaScript applications, which can be further analyzed with our and other algorithms originally designed for binary analysis. Finally, we discuss how Microwalk-CI can be integrated into a continuous integration (CI) pipeline for efficient and ongoing monitoring. We evaluate our analysis framework by conducting a thorough evaluation of several popular JavaScript cryptographic libraries, and uncover a number of critical leakages.
Simulating BFT Protocol Implementations at Scale

The novel blockchain generation of Byzantine fault-tolerant (BFT) state machine replication (SMR) protocols focuses on scalability and performance to meet requirements of distributed ledger technology (DLT), e.g., decentralization and geographic dispersion. Validating scalability and performance of BFT protocol implementations requires careful evaluation. While experiments with real protocol deployments usually offer the best realism, they are costly and time-consuming. In this paper, we explore simulation of unmodified BFT protocol implementations as as a method for cheap and rapid protocol evaluation: We can accurately forecast the performance of a BFT protocol while experimentally scaling its environment, i.e., by varying the number of nodes or geographic dispersion. Our approach is resource-friendly and preserves application-realism, since existing BFT frameworks can be simply plugged into the simulation engine without requiring code modifications or re-implementation.
Metrics of News Audience Polarization: Same or Different?

ABSTRACT Although media and communication scholars have suggested various analytical methods for measuring and comparing news audience polarization across countries, we lack a systematic assessment of the metrics produced by these techniques. Using survey data from the 2016 Reuters Institute Digital News Report on news use in 26 countries, we address this gap through a resampling simulation experiment. Our simulation revealed a strong impact of analytical choices, which invited disparate interpretations in terms of how polarized news audiences are, how strongly audience polarization structurally varies between news environments, and how news audience polarization is distributed cross-nationally. Alternative choices led to profound differences in the compatibility, consistency, and validity of the empirical news audience polarization estimates. We conclude from these results that a more precise methodological understanding of news audience polarization metrics informs our capability to draw meaningful inferences from empirical work.
Implicit fractional differential equations with Φ–Caputo fractional derivative in Banach spaces

Abstract In the present paper, we consider a class of initial value problem for nonlinear implicit fractional differential equations involving the Φ–Caputo fractional derivative in Banach spaces. A new minimal condition on the parameters and the relationship between them is used to show the existence, uniqueness, and Mittag-Leffler–Ulam–Hyers stability of solutions by using the Mönch’s fixed point theorem combined with a classical technique for measures of noncompactness. Moreover, the Mittag-Leffler–Ulam–Hyers stability of proposed problem is discussed. Finally, two examples are presented to illustrate the results.
Variations in Oocyte Maturation and Lysis of the Common Toad Rhinella arenarum (Anura: Bufonidae): Is Climate Change Affecting Oocyte Functionality?

Abstract. Many recent studies have shown that amphibian populations have been affected by a variety of causes including climate change, pollution, habitat destruction, and disease. Such drivers could also influence the physiological and behavioral reproductive events of individuals. Rhinella arenarum is an amphibian species that has been extensively used as an experimental model for biological studies. This is an ecologically important species due to its abundance and wide distribution in South America. Our research on the female gonad of R. arenarum conducted in the last three decades has allowed us to observe that the competence of the ovary of this species has been slowly modified over the years, manifested by an increase in oocyte lysis during in vitro assays. To better understand the changes, a retrospective analysis of data from the last three decades was performed in order to quantify oocyte maturation and lysis in the presence of different incubation mediums. Data from three sampling years (1993, 2005, and 2018) were selected, and percentages of oocyte maturation and lysis were compared. Results show a significant decrease in oocyte maturation and an increase in oocyte lysis in the last sampling year. We also examined the climate conditions of the habitat of R. arenarum in each sampling year and their variation as a possible driver of decreased ovarian functionality. A significant increase in minimum temperatures, solar radiation, and relative humidity is reported. Alterations in the climate conditions of the natural habitat of R. arenarum might be one of many factors responsible for the decrease in ovarian functionality. Further analysis of the impact of the ongoing climate change on several aspects of amphibian phenology is needed to strengthen understanding of population declines and promote species conservation.
BV-GAN: 3D time-of-flight magnetic resonance angiography cerebrovascular vessel segmentation using adversarial CNNs

Abstract. Purpose: Cerebrovascular vessel segmentation is a key step in the detection of vessel pathology. Brain time-of-flight magnetic resonance angiography (TOF-MRA) is a main method used clinically for imaging of blood vessels using magnetic resonance imaging. This method is primarily used to detect narrowing, blockage of the arteries, and aneurysms. Despite its importance, TOF-MRA interpretation relies mostly on visual, subjective assessment performed by a neuroradiologist and is mostly based on maximum intensity projections reconstruction of the three-dimensional (3D) scan, thus reducing the acquired spatial resolution. Works tackling the central problem of automatically segmenting brain blood vessels typically suffer from memory and imbalance related issues. To address these issues, the spatial context of the segmentation consider by neural networks is typically restricted (e.g., by resolution reduction or analysis of environments of lower dimensions). Although efficient, such solutions hinder the ability of the neural networks to understand the complex 3D structures typical of the cerebrovascular system and to leverage this understanding for decision making. Approach: We propose a brain-vessels generative-adversarial-network (BV-GAN) segmentation model, that better considers connectivity and structural integrity, using prior based attention and adversarial learning techniques. Results: For evaluations, fivefold cross-validation experiments were performed on two datasets. BV-GAN demonstrates consistent improvement of up to 10% in vessel Dice score with each additive designed component to the baseline state-of-the-art models. Conclusions: Potentially, this automated 3D-approach could shorten analysis time, allow for quantitative characterization of vascular structures, and reduce the need to decrease resolution, overall improving diagnosis cerebrovascular vessel disorders.
An Intermediate-Centric Dataflow for Transposed Convolution Acceleration on FPGA

Transposed convolution has been prevailing in convolutional neural networks (CNNs), playing an important role in multiple scenarios such as image segmentation and back-propagation process of training CNNs. This mainly benefits from the ability to up-sample the input feature maps by interpolating new information from the input feature pixels. However, the backward-stencil computation constrains its performance and hindered its wide application in diverse platforms. Moreover, in contrast to the efforts on accelerating the convolution, there is a rare investigation on the acceleration of transposed convolution that is identically compute-intensive as the former. For acceleration of transposed convolution, we propose an intermediate-centric dataflow scheme, in which we decouple the generation of the intermediate patch from its further process, aim at efficiently performing the backward-stencil computation. The intermediate-centric dataflow breaks the transposed convolution into several phases/stages, achieving feeding the input feature maps and performing the backward-stencil computation in a pipelining manner. It also provides four-degree computation parallelism and efficient data reuse of input feature maps/weights. Furthermore, we also theoretically analyze the irregular data dependence leveraging the polyhedral model, which constrains the parallel computing of transposed convolution. Additionally, we devise an optimization problem to explore the design space and automatically generate the optimal design configurations for different transposed convolutional layers and hardware platforms. By selecting the representative transposed convolutional layers from DCGAN, FSRCNN, and FCN, we generate the corresponding accelerator arrays of intermediate-centric dataflow on the Xilinx Alveo U200 platform and reach the performance of 3.92 TOPS, 2.72 TOPS, and 4.76 TOPS, respectively.
Computing in School in the UK & Ireland: A Comparative Study

Many countries have increased their focus on computing in primary and secondary education in recent years and the UK and Ireland are no exception. The four nations of the UK have distinct and separate education systems, with England, Scotland, Wales, and Northern Ireland offering different national curricula, qualifications, and teacher education opportunities; this is the same for the Republic of Ireland. This paper describes computing education in these five jurisdictions and reports on the results of a survey conducted with computing teachers. A validated instrument was localised and used for this study, with 512 completed responses received from teachers across all five countries The results demonstrate distinct differences in the experiences of the computing teachers surveyed that align with the policy and provision for computing education in the UK and Ireland. This paper increases our understanding of the differences in computing education provision in schools across the UK and Ireland, and will be relevant to all those working to understand policy around computing education in school.
Research Challenges for the Design of Human-Artificial Intelligence Systems (HAIS)

Artificial intelligence (AI) capabilities are increasingly common components of all socio-technical information systems that integrate human and machine actions. The impacts of AI components on the design and use of application systems are evolving rapidly as improved deep learning techniques and fresh big data sources afford effective and efficient solutions for broad ranges of applications. New goals and requirements for Human-AI System (HAIS) functions and qualities are emerging, whereas the boundaries between human and machine behaviors continue to blur. This research commentary identifies and addresses the design science research (DSR) challenges facing the field of Information Systems as the demand for human-machine synergies in Human-Artificial Intelligence Systems surges in all application areas. The design challenges of HAIS are characterized by a taxonomy of eight C's - composition, complexity, creativity, confidence, controls, conscience, certification, and contribution. By applying a design science research frame to structure and investigate HAIS design, implementation, use, and evolution, we propose a forward-thinking agenda for relevant and rigorous information systems research contributions.
Fixed point theorems in uniform spaces

A mapping F of a metric space X into itself is said to satisfy a Lipschitz condition with Lipschitz constant K if d(F(x), F(y)) ≤ K d(x, y) , (x, y eX). If this condition is satisfied with a Lipschitz constant K such that 0 ≤ K 0 , V n ≥ k -- gn(V)0gn+1 (V) 0 . 0 gn+p (V) c W. -- We consider also sequences of contraction mappings in metric and uniform spaces. In metric spaces we prove a theorem for a sequence of contraction mapping of a complete e - chainable metric space. In uniform spaces we prove the following theorem and then show how it may be used to prove other results for sequences of mappings in uniform spaces. -- THEOREM: Let (E, U) be a complete uniform space and Fk a U-contractive mapping from E into itself, with fixed points Uk (k = 1, 2, . ). Suppose lim -- [special characters omitted] -- Fk(x) = F(x) for every x e E, where F is a U-contractive mapping from E into itself. Then lim -- [special characters omitted] -- Uk = U, where U is a fixed point of F.