Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic Advances in Neurological Disorders

Objectives: The cumulative safety and efficacy measures of percutaneous transluminal angioplasty and stenting (PTAS) for secondary stroke prevention in patients with symptomatic intracranial arterial stenosis (sICAS) have not previously been evaluated using a metaanalytical approach. Methods: We conducted a systematic review and random effects meta-analysis of all available randomized controlled trials (RCTs) evaluating the safety and efficacy of PTAS (in comparison with medical therapy) for sICAS. Results: Three RCTs (678 total patients) were included in the quantitative analysis. PTAS was associated with a higher risk of recurrent ischemic stroke in the territory of qualifying artery both within 30 days [risk ratio (RR) = 2.21, 95% confidence interval (CI) 1.10–4.43] and 1 year (RR = 1.92, 95% CI 1.10–3.36). PTAS was also related to a higher risk of any ischemic stroke within 30 days from the index event (RR = 2.08, 95% CI 1.17–3.71). The risk for intracranial hemorrhage was found to be higher in PTAS patients both within 30 days (RR = 10.60, 95% CI 1.98–56.62) and 1 year (RR = 8.15, 95% CI 1.50–44.34). The composite outcome of any stroke or death within 1 year (RR = 2.29, 95% CI 1.13–4.66) and 2 years (RR = 1.52, 95% CI 1.04–2.21) was higher in PTAS than in medical therapy. PTAS was associated with a higher risk of any stroke or death within 2 years in the sICAS subgroup located in posterior circulation (RR = 2.37, 95% CI 1.27–4.42). Conclusions: PTAS is associated with adverse early and long-term outcomes and should not be recommended in patients with sICAS. Further research to identify subgroups of patients who could also serve as candidates for future interventional trials along with efforts to reduce procedure-related complications are needed.
A Comprehensive Characterization of the Honeybees in Siberia (Russia)

A comprehensive study of some populations of honeybee (332 colonies) in Siberia (Tomsk region, Krasnoyarsk Krai (Yenisei population), Altai) using morphometric and molecu‐ lar genetic methods was conducted. Infestation of bees (132 colonies) by Nosema has also been studied. Three variants of the COI-COII mtDNA locus were registered: PQQ, PQQQ (typical for Apis m. mellifera), and Q (specific for southern races). It was established that 64% of bee colonies from the Tomsk region and all colonies studied from the Kras‐ noyarsk and the Altai territories originate from Apis m. mellifera on the maternal line. According to the morphometric study, the majority of bee colonies of the Tomsk region are hybrids; in some colonies the mismatch of morphometric and mtDNA data was observed. Moreover, the majority of bee colonies infected by Nosema were hybrids. Yenisei population may be considered as a unique Apis m. mellifera population. Microsatellite analysis (loci А008, Ap049, AC117, AC216, Ap243, H110, A024, A113) showed the specific distribution of genotypes and alleles for some loci in the bees, which differ by geograph‐ ical location. Loci A024 and Ap049 are of considerable interest for further study as candidate markers for differentiation of subspecies; locus A008 can be considered informative for determining of different ecotypes of Apis m. mellifera.
Quantifying Attrition in Science: A Cohort-Based, Longitudinal Study of Scientists in 38 OECD Countries

In the present research, we explore how members of the global scientific community leave academic science and how attrition differs across genders, academic disciplines, and over time. Our approach is global, cohort-based, and longitudinal: we track individual male and female scientists over time and quantify the phenomenon traditionally referred to as"leaving science. "Using publication metadata from Scopus - a global bibliometric database of publications and citations - we follow the details of the publishing careers of scientists who started publishing in 2000 (N=142,776) and 2010 (N=232,843). Our study is restricted to 16 STEMM disciplines (science, technology, engineering, mathematics, and medicine), and we track the individual scholarly output of the two cohorts until 2022. Survival analyses show that attrition becomes ever less gendered, while regression models show that publication quantity is more consequential than publication quality for careers. Behind the aggregated changes at the level of all STEMM disciplines combined, widely nuanced changes were found to occur at the level of disciplines and over time. Attrition in science means different things for men versus women depending on the discipline; moreover, it means different things for scientists from different cohorts entering the scientific workforce. Finally, global bibliometric datasets were tested in this study, opening new opportunities to explore gender and disciplinary differences in attrition.
Collective behavior of squirmers in thin films

Bacteria in biofilms form complex structures and can collectively migrate within mobile aggregates, which is referred to as swarming. This behavior is influenced by a combination of various factors, including morphological characteristics and propulsive forces of swimmers, their volume fraction within a confined environment, and hydrodynamic and steric interactions between them. In our study, we employ the squirmer model for microswimmers and the dissipative particle dynamics method for fluid modeling to investigate the collective motion of swimmers in thin films. The film thickness permits a free orientation of non-spherical squirmers, but constraints them to form a two-layered structure at maximum. Structural and dynamic properties of squirmer suspensions confined within the slit are analyzed for different volume fractions of swimmers, motility types (e.g., pusher, neutral squirmer, puller), and the presence of a rotlet dipolar flow field, which mimics the counter-rotating flow generated by flagellated bacteria. Different states are characterized, including a gas-like phase, swarming, and motility-induced phase separation, as a function of increasing volume fraction. Our study highlights the importance of an anisotropic swimmer shape, hydrodynamic interactions between squirmers, and their interaction with the walls for the emergence of different collective behaviors. Interestingly, the formation of collective structures may not be symmetric with respect to the two walls. Furthermore, the presence of a rotlet dipole significantly mitigates differences in the collective behavior between various swimmer types. These results contribute to a better understanding of the formation of bacterial biofilms and the emergence of collective states in confined active matter.
FSSH-2: Fewest Switches Surface Hopping with robust switching probability

This study introduces the FSSH-2 scheme, a redefined and numerically stable adiabatic Fewest Switches Surface Hopping (FSSH) method for mixed quantum-classical dynamics. It reformulates the standard FSSH hopping probability without non-adiabatic coupling vectors and allows for numerical time integration with larger step sizes. The advantages of FSSH-2 are demonstrated by numerical experiments for five different model systems in one and two spatial dimensions with up to three electronic states.
Suppression of metal-to-insulator transition and stabilization of superconductivity by pressure in Re3Ge7

The effect of pressure on the low-temperature states of the Re3Ge7 is investigated by both electrical and Hall resistance and magnetization measurements. At ambient pressure, the temperature dependent resistance of Re3Ge7 behaves quasi-linearly from room temperature down to 60 K, then undergoes a two-step metal-to-insulator transitions (MIT) at temperatures T1 = 59.4 K and T2 = 58.7 K which may be related to a structural phase transition or occurrence of charge density wave ordering. Upon applying pressure, the two-step (T1, T2) MIT splits into three steps (T1, T2 and T3) above 1 GPa, and all traces of MITs are fully suppressed by ~8 GPa. Subsequently, the onset of bulk superconductivity (SC) occurs between 10.8 and 12.2 GPa and persists to our highest pressure of 26.8 GPa. At 12.2 GPa the superconducting transition temperature, Tc, and upper critical field, Hc2 reach the maximum of Tc (onset) ~5.9 K and Hc2 (1.8 K) ~ 14 kOe. Our results not only present the observation of SC under high pressure in Re3Ge7 but also delineate the interplay between SC and other competing electronic states by creating a T - p phase diagram for this potentially topologically nontrivial system Re3Ge7.
Optimizing Visible Light Communication Efficiency Through Reinforcement Learning-Based NOMA-CSK Integration

In this paper, we explore the use of Non-Orthogonal Multiple Access (NOMA) and Color Shift Keying (CSK) for Visible Light Communication (VLC) systems. VLC is a wireless communication technology that uses visible light as the carrier signal to transmit information. It has several advantages over traditional radio frequency communication, including higher bandwidth, lower interference, and greater security. We first provide an introduction to NOMA and CSK and explain how they can be applied to VLC systems. NOMA is a technique that allows multiple users to share the same frequency channel by allocating different power levels to each user. This enables more users to connect to a single VLC transmitter simultaneously, thereby improving system capacity and spectral efficiency. CSK, on the other hand, is a modulation technique that uses different colors of light to represent digital information. By changing the color of the transmitted signal, information can be encoded and decoded at the receiver. Next, we discuss how NOMA and CSK can be combined in VLC systems by using different power levels to represent different users. This allows for more efficient use of the frequency spectrum, as multiple users can share the same channel at the same time. Additionally, we examine the potential benefits of using NOMA and CSK together in VLC systems to increase data rate. Finally, we discuss how reinforcement learning, a machine learning technique used to train agents to make decisions based on environmental feedback, can be used to optimize NOMA-CSK-VLC networks by allowing agents to learn and adapt to changing network conditions. Overall, our paper provides insights into the benefits of combining NOMA and CSK for VLC systems, highlighting the potential for improving communication efficiency and performance.
Entanglement cost for infinite-dimensional physical systems

We prove that the entanglement cost equals the regularized entanglement of formation for any infinite-dimensional quantum state $\rho_{AB}$ with finite quantum entropy on at least one of the subsystems $A$ or $B$. This generalizes a foundational result in quantum information theory that was previously formulated only for operations and states on finite-dimensional systems. The extension to infinite dimensions is nontrivial because the conventional tools for establishing both the direct and converse bounds, i.e., strong typically, monotonicity, and asymptotic continuity, are no longer directly applicable. To address this problem, we construct a new entanglement dilution protocol for infinite-dimensional states implementable by local operations and a finite amount of one-way classical communication (one-way LOCC), using weak and strong typicality multiple times. We also prove the optimality of this protocol among all protocols even under infinite-dimensional separable operations by developing an argument based on alternative forms of monotonicity and asymptotic continuity of the entanglement of formation for infinite-dimensional states. Along the way, we derive a new integral representation for the quantum entropy of infinite-dimensional states, which we believe to be of independent interest. Our results allow us to fully characterize an important operational entanglement measure -- the entanglement cost -- for all infinite-dimensional physical systems.
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks

We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates. In this paper, we take a novel approach by replacing the standard linear Markovian token by one which follows a nonlinear Markov chain - namely the Self-Repellent Radom Walk (SRRW). Defined for any given 'base' Markov chain, the SRRW, parameterized by a positive scalar {\alpha}, is less likely to transition to states that were highly visited in the past, thus the name. In the context of MCMC sampling on a graph, a recent breakthrough in Doshi et al. (2023) shows that the SRRW achieves O(1/{\alpha}) decrease in the asymptotic variance for sampling. We propose the use of a 'generalized' version of the SRRW to drive token algorithms for distributed stochastic optimization in the form of stochastic approximation, termed SA-SRRW. We prove that the optimization iterate errors of the resulting SA-SRRW converge to zero almost surely and prove a central limit theorem, deriving the explicit form of the resulting asymptotic covariance matrix corresponding to iterate errors. This asymptotic covariance is always smaller than that of an algorithm driven by the base Markov chain and decreases at rate O(1/{\alpha}^2) - the performance benefit of using SRRW thereby amplified in the stochastic optimization context. Empirical results support our theoretical findings.
Thermal Properties of the Hot Core Population in Sagittarius B2 Deep South

We report the discovery of nine new hot molecular cores in the Deep South (DS) region of Sagittarius B2 using Atacama Large Millimeter/submillimeter Array Band 6 observations. We measure the rotational temperature of CH3OH and derive the physical conditions present within these cores and the hot core Sgr B2(S). The cores show heterogeneous temperature structure, with peak temperatures between 252 and 662 K. We find that the cores span a range of masses (203–4842 M ⊙) and radii (3587–9436 au). CH3OH abundances consistently increase with temperature across the sample. Our measurements show the DS hot cores are structurally similar to Galactic disk hot cores, with radii and temperature gradients that are comparable to sources in the disk. They also show shallower density gradients than disk hot cores, which may arise from the Central Molecular Zone’s higher density threshold for star formation. The hot cores have properties which are consistent with those of Sgr B2(N), with three associated with Class II CH3OH masers and one associated with an ultra-compact H ii region. Our sample nearly doubles the high-mass star-forming gas mass near Sgr B2(S) and suggests the region may be a younger, comparably massive counterpart to Sgr B2(N) and (M). The relationship between peak CH3OH abundance and rotational temperature traced by our sample and a selection of comparable hot cores is qualitatively consistent with predictions from chemical modeling. However, we observe constant peak abundances at higher temperatures (T ≳ 250 K), which may indicate mechanisms for methanol survival that are not yet accounted for in models.
Online Handbook of Argumentation for AI: Volume 2

This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.
Distantly Supervised Morpho-Syntactic Model for Relation Extraction

The task of Information Extraction (IE) involves automatically converting unstructured textual content into structured data. Most research in this field concentrates on extracting all facts or a specific set of relationships from documents. In this paper, we present a method for the extraction and categorisation of an unrestricted set of relationships from text. Our method relies on morpho-syntactic extraction patterns obtained by a distant supervision method, and creates Syntactic and Semantic Indices to extract and classify candidate graphs. We evaluate our approach on six datasets built on Wikidata and Wikipedia. The evaluation shows that our approach can achieve Precision scores of up to 0.85, but with lower Recall and F1 scores. Our approach allows to quickly create rule-based systems for Information Extraction and to build annotated datasets to train machine-learning and deep-learning based classifiers.
Self Supervised Vision for Climate Downscaling

Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already bringing noticeable changes to Earth's weather and climate patterns with an increased frequency of unpredictable and extreme weather events. Future projections for climate change research are based on Earth System Models (ESMs), the computer models that simulate the Earth's climate system. ESMs provide a framework to integrate various physical systems, but their output is bound by the enormous computational resources required for running and archiving higher-resolution simulations. For a given resource budget, the ESMs are generally run on a coarser grid, followed by a computationally lighter $downscaling$ process to obtain a finer-resolution output. In this work, we present a deep-learning model for downscaling ESM simulation data that does not require high-resolution ground truth data for model optimization. This is realized by leveraging salient data distribution patterns and the hidden dependencies between weather variables for an $\textit{individual}$ data point at $\textit{runtime}$. Extensive evaluation with $2$x, $3$x, and $4$x scaling factors demonstrates that the proposed model consistently obtains superior performance over that of various baselines. The improved downscaling performance and no dependence on high-resolution ground truth data make the proposed method a valuable tool for climate research and mark it as a promising direction for future research.
GPU Acceleration of a Conjugate Exponential Model for Cancer Tissue Heterogeneity

Heterogeneity in the cell population of cancer tissues poses many challenges in cancer diagnosis and treatment. Studying the heterogeneity in cell populations from gene expression measurement data in the context of cancer research is a problem of paramount importance. In addition, reducing the computation time of the algorithms that deal with high volumes of data has its obvious merits. Parallelizable models using Markov chain Monte Carlo methods are typically slow. This paper shows a novel, computationally efficient, and parallelizable model to analyze heterogeneity in cancer tissues using GPUs. Because our model is parallelizable, the input data size does not affect the computation time much, provided the hardware resources are not exhausted. Our model uses qPCR (quantitative polymerase chain reaction) gene expression measurements to study heterogeneity in cancer tissue. We compute the cell proportion breakup by accelerating variational methods on a GPU. We test this model on synthetic and real-world gene expression data collected from fibroblasts and compare the performance of our algorithm with those of MCMC and Expectation Maximization. Our new model is computationally less complex and faster than existing Bayesian models for cancer tissue heterogeneity.
Magic distances for flat bands in twisted bilayer graphene

Twisted bilayer graphene is known to host isolated and relatively flat bands near charge neutrality, when tuned to specific magic angles. Nonetheless, these rotational misalignments, lying below 1.1 degrees, result in long-period moir\'e crystals, whose anomalous electronic properties are hardly accessible to reliable atomistic simulations. Here, we present a map of differently stacked graphene sheets, at arbitrary rotation angles corresponding to precise interplanar distances, into an equivalence class represented by magic-angle twisted bilayer graphene. We determine the equivalence relation in the class within a continuum model, and extend its definition to a tight-binding approach. Then, we use density functional theory to suggest that the magic-angle physics may be characterized by costly computational strategies on a twisted bilayer geometry, with conveniently large stacking angles. Our results may pave the way for an ab initio characterization of the unconventional topological phases and related excitations, associated with currently observed low-energy quasi-flat bands.
The Role of the Third Dredge-up and Mass Loss in Shaping the Initial–Final Mass Relation of White Dwarfs

The initial–final mass relation (IFMR) plays a crucial role in understanding stellar structure and evolution by linking a star’s initial mass to the mass of the resulting white dwarf. This study explores the IFMR in the initial mass range 0.8 ≤ M ini/M ⊙ ≤ 4 using full PARSEC evolutionary calculations supplemented with COLIBRI computations to complete the ejection of the envelope and obtain the final core mass. Recent works have shown that the supposed monotonicity of the IFMR is interrupted by a kink in the initial mass range M ini ≈ 1.65–2.10 M ⊙, due to the interaction between recurrent dredge-up episodes and stellar winds in carbon stars evolving on the thermally pulsing asymptotic giant branch phase. To reproduce the IFMR nonmonotonic behavior we investigate the role of convective overshooting efficiency applied to the base of the convective envelope (f env) and to the borders of the pulse-driven convective zone (f pdcz), as well as its interplay with mass loss. We compare our models to observational data and find that f env must vary with initial mass in order to accurately reproduce the IFMR’s observed kink and slopes. We find some degeneracy between the overshooting parameters when only the IFMR information is used. Nonetheless, this analysis provides valuable insights into the internal mixing processes during the TP-AGB phase.
Species Scale and Primordial Gravitational Waves

The species scale is a field-dependent UV cut-off for any effective field theory weakly coupled to gravity. In this letter we show that, in the context of inflationary cosmology, a detection of primordial gravitational waves will set an upper bound on the decay rate $|\Lambda'_s/\Lambda_s|$ of the species scale. Specifically, we derive this in terms of the tensor-to-scalar ratio $r$ of power spectra of primordial perturbations. Given the targets of current and next generation experiments, we show that any successful detection would signify that this upper limit is of the order of unity, which is consistent with recent discussions in the literature.