Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarize, Catalyze, Stabilize: How a minority of norm internalizers amplify group selection and punishment

Many mechanisms behind the evolution of cooperation, such as reciprocity, indirect reciprocity, and altruistic punishment, require group knowledge of individual actions. But what keeps people cooperating when no one is looking? Conformist norm internalization, the tendency to abide by the behavior of the majority of the group, even when it is individually harmful, could be the answer. In this paper, we analyze a world where (1) there is group selection and punishment by indirect reciprocity but (2) many actions (half) go unobserved, and therefore unpunished. Can norm internalization fill this"observation gap"and lead to high levels of cooperation, even when agents may in principle cooperate only when likely to be caught and punished? Specifically, we seek to understand whether adding norm internalization to the strategy space in a public goods game can lead to higher levels of cooperation when both norm internalization and cooperation start out rare. We found the answer to be positive, but, interestingly, not because norm internalizers end up making up a substantial fraction of the population, nor because they cooperate much more than other agent types. Instead, norm internalizers, by polarizing, catalyzing, and stabilizing cooperation, can increase levels of cooperation of other agent types, while only making up a minority of the population themselves.
Energy Deposition and Shielding Requirements for All Concepts of the Blanket Comparison and Selection Study

The Blanket Comparison and Selection Study (BCSS) carried out 16 blanket concepts (7 tokamaks and 9 tandem mirror reactors (TMRs)) for the final evaluation process. This process requires all the blanket design parameters, including the energy multiplication factors, the atomic displacement rate in the first wall, and the shielding definition for the reactor. A shielding assessment is performed to determine shielding materials, compositions, arrangements, and thicknesses for each concept. Two shielding criteria are adopted for this assessment: (a) workers are permitted in the reactor hall 1 day after shutdown, and (b) superconductor coils are required to function for a 150 MW x yr/m/sup 2/ deuterium-tritium neutron exposure at the first wall without a change in their performances. For the design purpose, the occupational exposure is 0.5 mrem/h, based on working 8 h/day and 40 h/week. This dose level is used to concur with current practice in the nuclear industry and the exposure policy of the U.S. Department of Energy, which limits the on-site personnel exposure level to less than onefifth of the maximum permissible dose equivalent limits. The personnel exposure criterion is used to size the outboard bulk shield for tokamak reactors and the shield thickness between the central cellmore » coils for TMRs. In the fusion power environment the insulator materials are the most sensitive components in the superconductor coils from the radiation damage point of view. A maximum insulator dose of 10/sup 10/ rad in the thermal insulator is used to size the bulk shield in the inboard section of the tokamak reactors and the central cell sections under the coils for TMRs. As a result of this criterion, all other nuclear responses do not exceed any design limit for the superconductor materials or the copper stabilizer. « less
Experimental observation of a Rindler horizon

In this manuscript we confirm the presence of a Rindler horizon at CERN-NA63 by exploring its thermodynamics induced by the Unruh effect in their high energy channeling radiation experiments. By linking the entropy of the emitted radiation to the photon number, we find the measured spectrum to be a simple manifestation of the second law of Rindler horizon thermodynamics and thus a direct measurement of the recoil Fulling-Davies-Unruh (FDU) temperature. Moreover, since the experiment is born out of an ultra-relativistic positron, and the FDU temperature is defined in the proper frame, we find that temperature boosts as a length and thus fast objects appear colder. The spectrum also provides us with a simple setting to measure fundamental constants, and we employ it to measure the positron mass.
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic feature and prototype representation suffer from coarse segmentation granularity and train-set overfitting. In this work, we design Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support correlation based on the transformer architecture. The self-attention modules are used to assist in establishing hierarchical dense features, as a means to accomplish the cascade matching between query and support features. Moreover, we propose a matching module to reduce train-set overfitting and introduce correlation distillation leveraging semantic correspondence from coarse resolution to boost fine-grained segmentation. Our method performs decently in experiments. We achieve 50.0% mIoU on COCO-20i dataset one-shot setting and 56.0% on five-shot segmentation, respectively. The code is available on the project website11https://github.com/Pbihao/HDMNet.
General hyperplane sections of log canonical threefolds in positive characteristic

In this paper, we prove that if a $3$-dimensional quasi-projective variety $X$ over an algebraically closed field of characteristic $p>3$ has only log canonical singularities, then so does a general hyperplane section $H$ of $X$. We also show that the same is true for klt singularities, which is a slight extension of \cite{ST20}. In the course of the proof, we provide a sufficient condition for log canonical (resp.~klt) surface singularities to be geometrically log canonical (resp.~geometrically klt) over a field.
Turbulent boundary layers over smooth and rough forward-facing steps

The present work explores the impact of the roughness on the turbulent boundary layers over forward-facing steps. The roughness topography on the top surface of the rough step is replicated from a realistic turbine blade and embodies three-dimensional and highly irregular topographical features. High spatial resolution particle image velocimetry measurements are performed in the x−y planes at two different spanwise positions in turbulent boundary layers over both smooth and rough steps of the same mean heights at Reh=3450 and δ/h=8. Comparison of mean flow structures, Reynolds normal and shear stresses, quadrant analysis of instantaneous shear stress contributing events, and average spanwise vorticity reveals that the separated flow after the step is weakened by the surface roughness on top of the step while the flow ahead of the step is invariant to the surface conditions. The characteristics of the coherent spanwise vortices such as the numbers, size, and circulation distributions are also found to be s.
Exploring the use of deep learning in task-flexible ILC*

Growing demands in today’s industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.
Federated Learning without Full Labels: A Survey

Data privacy has become an increasingly important concern in real-world big data applications such as machine learning. To address the problem, federated learning (FL) has been a promising solution to building effective machine learning models from decentralized and private data. Existing federated learning algorithms mainly tackle the supervised learning problem, where data are assumed to be fully labeled. However, in practice, fully labeled data is often hard to obtain, as the participants may not have sufficient domain expertise, or they lack the motivation and tools to label data. Therefore, the problem of federated learning without full labels is important in real-world FL applications. In this paper, we discuss how the problem can be solved with machine learning techniques that leverage unlabeled data. We present a survey of methods that combine FL with semi-supervised learning, self-supervised learning, and transfer learning methods. We also summarize the datasets used to evaluate FL methods without full labels. Finally, we highlight future directions in the context of FL without full labels.
Spatially-Aware Car-Sharing Demand Prediction

In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms that offer high predictive performance as well as interpretability. In particular, we compare the spatially-implicit Random Forest model with spatially-aware methods for predicting average monthly per-station demand. The study utilizes a rich set of socio-demographic, location-based (e.g., POIs), and car-sharing-specific features as input, extracted from a large proprietary car-sharing dataset and publicly available datasets. We show that the global Random Forest model with geo-coordinates as an input feature achieves the highest predictive performance with an R-squared score of 0.87, while local methods such as Geographically Weighted Regression perform almost on par and additionally yield exciting insights into the heterogeneous spatial distributions of factors influencing car-sharing behaviour. Additionally, our study offers effective as well as highly interpretable methods for diagnosing and planning the placement of car-sharing stations.
Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities

This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia.Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for various NLP tasks in these languages. This repository can be updated periodically with contributions from other researchers.Our objective is to disseminate information to NLP researchers interested in Ethiopian languages and encourage future research in this domain.
Interpolation-based decoding of folded variants of linearized and skew Reed–Solomon codes

The sum-rank metric is a hybrid between the Hamming metric and the rank metric and suitable for error correction in multishot network coding and distributed storage as well as for the design of quantum-resistant cryptosystems. In this work, we consider the construction and decoding of folded linearized Reed–Solomon (FLRS) codes, which are shown to be maximum sum-rank distance (MSRD) for appropriate parameter choices. We derive an efficient interpolation-based decoding algorithm for FLRS codes that can be used as a list decoder or as a probabilistic unique decoder. The proposed decoding scheme can correct sum-rank errors beyond the unique decoding radius with a computational complexity that is quadratic in the length of the unfolded code. We show how the error-correction capability can be optimized for high-rate codes by an alternative choice of interpolation points. We derive a heuristic upper bound on the decoding failure probability of the probabilistic unique decoder and verify its tightness by Monte Carlo simulations. Further, we study the construction and decoding of folded skew Reed﻿-Solomon codes in the skew metric. Up to our knowledge, FLRS codes are the first MSRD codes with different block sizes that come along with an efficient decoding algorithm.
INTERSECTIONS OF LOOPS IN TWO-DIMENSIONAL MANIFOLDS

Given an arbitrary smooth two-dimensional manifold with nonempty boundary and a point , mappings and are constructed. In terms of them the author formulates and proves necessary and sufficient conditions for realizability of an element of the group by a simple loop, conditions for the realizability of a few elements of by nonintersecting loops and conditions for realizability of an automorphism of this group by a diffeomorphism . Bibliography: 14 titles.
Cross-Utterance ASR Rescoring with Graph-Based Label Propagation

We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts the rescoring collaboratively among utterances, instead of individually. Experiments on the VCTK dataset demonstrate that our approach consistently improves ASR performance, as well as fairness across speaker groups with different accents. Our approach provides a low-cost solution for mitigating the majoritarian bias of ASR systems, without the need to train new domain- or accent-specific models.
Clustering multivariate time series using energy distance

A novel methodology is proposed for clustering multivariate time series data using energy distance defined in Székely and Rizzo (2013). Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure the separation between the finite‐dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is calculated, a hierarchical clustering method is then applied to obtain the dendrogram. This procedure is completely nonparametric as the dissimilarities between stationary distributions are directly calculated without making any model assumptions. In order to justify this procedure, asymptotic properties of the energy distance estimates are derived for general stationary and ergodic time series. The method is illustrated in a simulation study for various component time series that are either linear or nonlinear. Finally, the methodology is applied to two examples; one involves the GDP of selected countries and the other is the population size of various states in the U.S.A. in the years 1900–1999.
The collective dynamics of a stochastic port-Hamiltonian self-driven agent model in one dimension

This paper studies the collective motion of self-driven agents in a one-dimensional space with periodic boundaries, using a stochastic Port-Hamiltonian system (PHS) with symmetric nearest-neighbor interactions and additive Brownian noise as an external input. In the case of a quadratic potential the PHS is an Ornstein-Uhlenbeck process for which we explicitly determine the distribution for any time $t\ge 0$ and in the limit $t\to\infty$. In particular, we characterize the collective motion by showing that the agents' positions tend to build exactly one cluster. This is confirmed in simulations that show rapid and coordinated motion among agents, driven by noise, despite the absence of a preferred direction of motion in the model. Remarkably, the theoretical properties observed in the Ornstein-Uhlenbeck process also emerge in simulations of the nonlinear model incorporating a general interaction potential.
Three-Dimensional Variational Data Analysis to Retrieve Thermodynamical and Dynamical Fields from Various Nested Wind Measurements

Abstract The present study is devoted to a new analysis of wind measurements from dropsonding and/or radiosonding of Doppler information from multiple Doppler radar scanning and of other wind measurements (sodar, dynamical sensors on board aircraft, and instruments at ground) aimed at retrieving three-dimensional thermodynamical and dynamical fields both in clear air and in precipitating areas of mesoscale phenomena. This analysis, well suited to assimilate data from differing platforms specified at differing spatial/temporal resolutions, is based on the analytical and variational concept of the Multiple Analytical Doppler (MANDOP) analysis and thus is an extension of it. This new analysis presents many advantages, including the same as MANDOP and others well adapted for the verification or the initialization of a mesoscale cloud model. An application to simulated and to real data extracted from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment database is presented in the .
The lift on an aerofoil in grid-generated turbulence

The three-dimensional effects of turbulence cannot be neglected when the spanwise wavelength of the incident turbulence is not effectively infinite with respect to the chord, which may invalidate the strip assumption. Based on three-dimensional theory, a general approach, expressed in terms of a two-dimensional Fourier transform of the correlation of the buffeting force, is proposed to identify the two-wavenumber spectrum and aerodynamic admittance of the lift force on an aerofoil. It is essential that the approach presented can be validated in wind tunnel experiments. The coherence of the lift force on an aerofoil in grid-generated turbulence is obtained by simultaneous measurements of unsteady surface pressures around several chordwise strips on a stiff sectional model, which controls the accuracy of results. For the purpose of the Fourier transform, three empirical coherence models of the lift force are presented to fit the experimental results. Compared with the linearized theory, the two-wavenumber aerodynamic admittance can describe well the pressure distribution and the pattern of energy transition in an isotropic turbulence field. Thus, the failure mechanism of the traditional strip assumption can be demonstrated explicitly. In addition, the results obtained also validate the theory proposed by Graham (Aeronaut. Q., vol. 21, 1970, pp. 182–198; Aeronaut. Q., vol. 22, 1971, pp. 83–100). The present approach can be extended to study the three-dimensionality of the buffeting force on line-like structures with arbitrary cross-configurations, such as long-span bridges and high-rise buildings.
Clinical determinants of hospital mortality in liver failure: a comprehensive analysis of 62,717 patients

Abstract Background Liver failure (LF) is characterised by a loss of the synthetic and metabolic liver function and is associated with a high mortality. Large-scale data on recent developments and hospital mortality of LF in Germany are missing. A systematic analysis and careful interpretation of these datasets could help to optimise outcomes of LF. Methods We used standardised hospital discharge data of the Federal Statistical Office to evaluate current trends, hospital mortality and factors associated with an unfavourable course of LF in Germany between 2010 and 2019. Results A total of 62,717 hospitalised LF cases were identified. Annual LF frequency decreased from 6716 (2010) to 5855 (2019) cases and was higher among males (60.51%). Hospital mortality was 38.08% and significantly declined over the observation period. Mortality significantly correlated with patients’ age and was highest among individuals with (sub)acute LF (47.5%). Multivariate regression analyses revealed pulmonary (OR ARDS : 2.76, OR mechanical ventilation : 6.46) and renal complications (OR acute kidney failure : 2.04, OR hepatorenal syndrome : 2.92) and sepsis (OR: 1.92) as factors for increased mortality. Liver transplantation reduced mortality in patients with (sub)acute LF. Hospital mortality significantly decreased with the annual LF case volume and ranged from 47.46% to 29.87% in low- or high-case-volume hospitals, respectively. Conclusions Although incidence rates and hospital mortality of LF in Germany have constantly decreased, hospital mortality has remained at a very high level. We identified a number of variables associated with increased mortality that could help to improve framework conditions for the treatment of LF in the future.
Role of transduodenal endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/FNB) for diagnosis of retroperitoneal fibrosis (Ormond’s disease)

Abstract Background Retroperitoneal fibrosis (RPF), often referred to as Ormond’s disease when it is of idiopathic origin, is a rare disease characterized by the presence of inflammatory infiltrates and periaortic masses in the retroperitoneum. For a definite diagnosis, a biopsy and subsequent pathological examination is required. Currently accepted methods for retroperitoneal biopsy include open, laparoscopic, or CT-guided approaches. However, transduodenal endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/FNB) for diagnosis of RPF has attracted only little attention in the literature. Case Reports We report two male patient cases who presented with leukocytosis, elevated C-reactive protein, and a suspicious retroperitoneal mass of unknown origin on computed tomography. One patient also reported left lower quadrant pain, whereas the other patient suffered from back pain and weight loss. In both patients, idiopathic RPF was successfully diagnosed by using transduodenal EUS-FNA/FNB with 22- and 20-gauge aspiration needles. Histopathology revealed dense lymphocytic infiltrates and fibrosis. The procedures lasted approximately 25 and 20 minutes, respectively, and in both patients no serious adverse events occurred. Treatment included steroid therapy and administration of Azathioprine. Conclusion We demonstrate that using EUS-FNA/FNB to diagnose RPF is a feasible, fast, and safe method, which should always be considered as a first-line diagnostic modality. Hence, this case report emphasizes that gastrointestinal endoscopists are likely to play an important role in the setting of suspected RPF.
Analysing Syntactic and Semantic Features in Pre-trained Language Models in a Fully Unsupervised Setting

Transformer-based pre-trained language models (PLMs) have been used in all NLP tasks and resulted in a great success. This has led to the question of whether we can transfer this knowledge to syntactic or semantic parsing in a completely unsupervised setting. In this study, we leverage PLMs as a source of external knowledge to perform a fully unsupervised parser model for semantic, constituency and dependency parsing. We analyse the results for English, German, French, and Turkish to understand the impact of the PLMs on different languages for syntactic and semantic parsing. We visualize the attention layers and heads in PLMs for parsing to understand the information that can be learned throughout the layers and the attention heads in the PLMs both for different levels of parsing tasks. The results obtained from dependency, constituency, and semantic parsing are similar to each other, and the middle layers and the ones closer to the final layers have more syntactic and semantic information.