Miracle-dz/UniFAR-FTUs huggingface dataset:
A Nearly Linear-Time Distributed Algorithm for Exact Maximum Matching

In this paper, we propose a randomized $\tilde{O}(\mu(G))$-round algorithm for the maximum cardinality matching problem in the CONGEST model, where $\mu(G)$ means the maximum size of a matching of the input graph $G$. The proposed algorithm substantially improves the current best worst-case running time. The key technical ingredient is a new randomized algorithm of finding an augmenting path of length $\ell$ with high probability within $\tilde{O}(\ell)$ rounds, which positively settles an open problem left in the prior work by Ahmadi and Kuhn [DISC'20]. The idea of our augmenting path algorithm is based on a recent result by Kitamura and Izumi [IEICE Trans.'22], which efficiently identifies a sparse substructure of the input graph containing an augmenting path, following a new concept called \emph{alternating base trees}. Their algorithm, however, resorts to a centralized approach of collecting the entire information of the substructure into a single vertex for constructing an augmenting path. The technical highlight of this paper is to provide a fully-decentralized counterpart of such a centralized method. To develop the algorithm, we prove several new structural properties of alternating base trees, which are of independent interest.
Why Fair Automated Hiring Systems Breach EU Non-Discrimination Law

Employment selection processes that use automated hiring systems based on machine learning are becoming increasingly commonplace. Meanwhile, concerns about algorithmic direct and indirect discrimination that result from such systems are front-and-center, and the technical solutions provided by the research community often systematically deviate from the principle of equal treatment to combat disparate or adverse impacts on groups based on protected attributes. Those technical solutions are now being used in commercially available automated hiring systems, potentially engaging in real-world discrimination. Algorithmic fairness and algorithmic non-discrimination are not the same. This article examines a conflict between the two: whether such hiring systems are compliant with EU non-discrimination law.
Factors promoting coral- algal transition in coral transplantation experiments

useful platform by which to examine the overgrowth of coral by algae under different environmental conditions. The proliferation of algae on coral reefs is a serious concern at present. Under optimal conditions for coral survival and growth in the field, algal overgrowth was made possible only after extensive damage to coral tissue by gastropod predation. This was not the case, however, in land-based tanks where conditions were suboptimal for coral growth and survival, and where there was an absence of both predators and herbivores. As coral tissue deteriorated, high nutrient conditions appeared to promote a greater abundance as well as diversity of algae, notably Enteromorpha, Cladophora, Ceramium and Centroceras. In contrast, algal cover on coral transplants in the field was dominated by the genus Hypnea. Composition and percentage C cover of algae did not differ significantly between previously live transplants and dead coral skeletons in most treatments (two different coral species, field versus land-based tanks). There was also no significant difference in algal colonization of previously live transplants of two coral species, Porites cylindrica and P. rus. The above findings are relevant to understanding coral-algal dynamics under conditions of human induced perturbation (e.g., the depletion of herbivores, high nutrient loading). In addition, these results suggest developmental trajectories a coral community can undergo following such perturbation because different algal genera presumably are grazed on by different species of herbivores, and hence support different trophic pathways.
Reinforcement Learning Fine-tuning of Language Models is Biased Towards More Extractable Features

Many capable large language models (LLMs) are developed via self-supervised pre-training followed by a reinforcement-learning fine-tuning phase, often based on human or AI feedback. During this stage, models may be guided by their inductive biases to rely on simpler features which may be easier to extract, at a cost to robustness and generalisation. We investigate whether principles governing inductive biases in the supervised fine-tuning of LLMs also apply when the fine-tuning process uses reinforcement learning. Following Lovering et al (2021), we test two hypotheses: that features more $\textit{extractable}$ after pre-training are more likely to be utilised by the final policy, and that the evidence for/against a feature predicts whether it will be utilised. Through controlled experiments on synthetic and natural language tasks, we find statistically significant correlations which constitute strong evidence for these hypotheses.
Lightweight Portrait Matting via Regional Attention and Refinement

We present a lightweight model for high resolution portrait matting. The model does not use any auxiliary inputs such as trimaps or background captures and achieves real time performance for HD videos and near real time for 4K. Our model is built upon a two-stage framework with a low resolution network for coarse alpha estimation followed by a refinement network for local region improvement. However, a naive implementation of the two-stage model suffers from poor matting quality if not utilizing any auxiliary inputs. We address the performance gap by leveraging the vision transformer (ViT) as the backbone of the low resolution network, motivated by the observation that the tokenization step of ViT can reduce spatial resolution while retain as much pixel information as possible. To inform local regions of the context, we propose a novel cross region attention (CRA) module in the refinement network to propagate the contextual information across the neighboring regions. We demonstrate that our method achieves superior results and outperforms other baselines on three benchmark datasets while only uses $1/20$ of the FLOPS compared to the existing state-of-the-art model.
Hypergraphs with node attributes: structure and inference

Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace. Here we show how these node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We consider the problem of community detection in hypergraphs and develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions and to detect communities more accurately than using either of these types of information alone. The method learns automatically from the input data the extent to which structure and attributes contribute to explain the data, down weighing or discarding attributes if not informative. Our algorithmic implementation is efficient and scales to large hypergraphs and interactions of large numbers of units. We apply our method to a variety of systems, showing strong performance in hyperedge prediction tasks and in selecting community divisions that correlate with attributes when these are informative, but discarding them otherwise. Our approach illustrates the advantage of using informative node attributes when available with higher-order data.
Measurement of the neutrino-oxygen neutral-current interaction cross section by observing nuclear deexcitation γ rays

We report the ﬁrst measurement of the neutrino-oxygen neutral-current quasielastic (NCQE) cross section. It is obtained by observing nuclear deexcitation γ -rays which follow neutrino-oxygen interactions at the Super-Kamiokande water Cherenkov detector. We use T2K data corresponding to 3 . 01 × 10 20 protons on target. By selecting only events during the T2K beam window and with well-reconstructed vertices in the ﬁducial volume, the large background rate from natural radioactivity is dramatically reduced. We observe 43 events in the 4 − 30 MeV reconstructed energy window, compared with an expectation of 51.0, which includes an estimated 16.2 background events. The background is primarily nonquasielastic neutral-current interactions and has only 1.2 events from natural radioactivity. The ﬂux-averaged NCQE cross section we measure is 1 . 55 × 10 − 38 cm 2 with a 68% conﬁdence interval of (1 . 22 , 2 . 20) × 10 − 38 cm 2 at a median neutrino energy of 630 MeV, compared with the theoretical prediction of 2 . 01 × 10 − 38 cm 2 .
Coal-derived rates of atmospheric dust deposition during the Permian. Gondwana

Despite widespread evidence for atmospheric dust deposition prior to the Quaternary, quantitative rate data remains sparse. As dust influences both climate and biological productivity, the absence of quantitative dust data limits the comprehensiveness of models of pre-Quaternary climate and biogeochemical cycles. Here, we propose that inorganic matter contained in coal primarily records atmospheric dust deposition. To test this, we use the average concentration of inorganic matter in Permian coal to map global patterns and deposition rates of atmospheric dust over Pangea. The dust accumulation rate is calculated assuming Permian peat carbon accumulation rates in temperate climates were similar to Holocene rates and accounting for the loss of carbon during coalification. Coal-derived rates vary from 0.02 to 25 g m -2 yr -1 , values that fall within the present-day global range. A well constrained East-West pattern of dust deposition corresponding to expected palaeoclimate gradients extends across Gondwana with maximum dust deposition rates occurring close to arid regions. A similar pattern is partially defined over the northern hemisphere. Patterns are consistent with the presence of two large global dust plumes centred on the tropics. The spatial patterns of dust deposition also were compared to dust cycle simulations for the Permian made with the Community Climate System Model version 3 (CCSM3). Key differences between the simulations and the coal data are the lack of evidence for an Antarctic dust source, higher than expected dust deposition over N and S China and greater dust deposition rates over Western Gondwana. This new coal-based dust accumulation rate data expands the pre-Neogene quantitative record of atmospheric dust and can help to inform and validate models of global circulation and biogeochemical cycles over the past 350 Myr.
Local Convergence of Gradient Methods for Min-Max Games: Partial Curvature Generically Suffices

We study the convergence to local Nash equilibria of gradient methods for two-player zero-sum differentiable games. It is well-known that such dynamics converge locally when S ≻ 0 and may diverge when S = 0 , where S ⪰ 0 is the symmetric part of the Jacobian at equilibrium that accounts for the “potential” component of the game. We show that these dynamics also converge as soon as S is nonzero ( partial curvature ) and the eigenvectors of the antisymmetric part A are in general position with respect to the kernel of S . We then study the convergence rates when S ≪ A and prove that they typically depend on the average of the eigenvalues of S , instead of the minimum as an analogy with minimization problems would suggest. To illustrate our results, we consider the problem of computing mixed Nash equilibria of continuous games. We show that, thanks to partial curvature, conic particle methods – which optimize over both weights and supports of the mixed strategies – generically converge faster than fixed-support methods. For min-max games, it is thus beneficial to add degrees of freedom “with curvature”: this can be interpreted as yet another benefit of over-parameterization.
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects

With the widespread application of causal inference, it is increasingly important to have tools which can test for the presence of causal effects in a diverse array of circumstances. In this vein we focus on the problem of testing for distributional causal effects, where the treatment affects not just the mean, but also higher order moments of the distribution, as well as multidimensional or structured outcomes. We build upon a previously introduced framework, Counterfactual Mean Embeddings, for representing causal distributions within Reproducing Kernel Hilbert Spaces (RKHS) by proposing new, improved, estimators for the distributional embeddings. These improved estimators are inspired by doubly robust estimators of the causal mean, using a similar form within the kernel space. We analyse these estimators, proving they retain the doubly robust property and have improved convergence rates compared to the original estimators. This leads to new permutation based tests for distributional causal effects, using the estimators we propose as tests statistics. We experimentally and theoretically demonstrate the validity of our tests.
Biodiversity of free-living marine nematodes in the Yangtze River estuary and its adjacent waters

Abstract In this study, biodiversity patterns of free-living marine nematode communities were assessed in the Yangtze River estuary and its adjacent waters by certain diversity measures. The studied area exhibited spatial variation in nematode biodiversity patterns. According to habitat heterogeneity, two station groups, shallow water near shore area and shallow water continental shelf area, were observed in the Yangtze River estuary and its adjacent waters. In general, nematode abundance and species diversity in the near shore area were significantly lower than the continental shelf area. Salinity, sediment Chlorophyll-a (Chl-a) and Phaeophytin-a (Phaeo-a) concentrations were responsible for the discrimination of species diversity measures. They reflect the reverin influence to nematode species diversity. By contrast, our results showed that the average taxonomic distinctness with presence/absence data (Δ+), and the variation in taxonomic distinctness (Λ+) were not influenced by natural environmental variability, nor pollutant and anthropogenic disturbances. The funnel plots for Δ+ and Λ+ also indicate none of the studied stations were subjected to environmental stress and perturbation. Possibly because of nematodes' strong resistance to environmental stress, the taxonomic indices did not discriminate perturbed from unperturbed conditions in the present study. We should be cautious to regard the taxonomic distinctness indices as surrogates for other traditional indices such as the number of species or Shannon diversity.
Crossroads of assembling a moss genome: navigating contaminants and horizontal gene transfer in the moss Physcomitrellopsis africana

The first reference genome assembly of the moss Physcomitrellopsis africana, a rare narrow endemic terricolous species from southeastern coastal forests of South Africa, is presented here. Phylogenetically, Physcomitrellopsis africana bridges the major evo-devo moss models Physcomitrium patens and Funaria hygrometrica, which diverged from their common ancestor 60-80 million years ago. The Physcomitrellopsis africana genome was assembled with both long Nanopore reads (73x) and short Illumina reads (163x). The 440 Mb assembly comprises 2,396 contigs and 23,493 protein-coding genes (BUSCO: C:96.0%[D:13.9%]), including two unique genes of putative microbial origin absent in close relatives. While the informatic approaches to genome assembly are becoming more standardized, best practices for contamination detection are less defined. The long reads sequenced for the Physcomitrellopsis africana genome contained approximately 12% contamination originating from microbial sources. This study describes the informatic processes employed to distinguish contaminants from candidate horizontal gene transfer events. Following the assembly and annotation, examination of whole genome duplication, and patterns of gene family expansion and contraction, were conducted. The genome bears signatures of two whole genome duplications shared with Physcomitrium patens and F. hygrometrica. Comparative analyses of gene family evolution revealed contractions associated with the light harvesting regulatory network in Physcomitrellopsis africana in comparison to Physcomitrium patens and F. hygrometrica. This first high-quality African bryophyte genome provides insights into genome evolution and HGT in an understudied moss lineage. Article Summary The first draft genome of the rare moss, Physcomitrellopsis africana, endemic to South Africa’s southeastern coastal forests, is presented here. The 440 Mb genome fills a critical phylogenetic gap, enabling the first comparative analysis of the genomes of three moss species that diverge over the last 60-80 million years. The analysis uncovered 23,493 genes and provides new insights into genome evolution and gene family expansion/contraction in mosses. Rigorous contaminant filtering also identified two genes uniquely acquired through horizontal gene transfer.
Weakly alkaline conditions degrade aflatoxins through lactone ring opening

Aflatoxins (AFs) are fungal metabolites that ubiquitously contaminate many common food crops and contribute to major foodborne diseases in humans and animals. The ability to remove AFs from common food and feed commodities will improve health standards and limit the economic impact inflicted by AF food contamination. Known chemical strategies have used strong acids and bases to remove contaminating AF, but these methods often lead to ecological waste issues downstream. In this study, we explore the application of weaker acidic and alkaline conditions to removes two types of AFs, AFB1 and AFG2. We find that a pH 9 buffered environment reduces AFB1 and AFG2 by more than 50% and 95%, respectively, within 24 hours. We show that AF degradation is through lactone ring opening, which is a known cause of AF toxicity, and provide a potential structure of the AFG2 degradation byproduct. Further, we confirm that incubation in the pH 9 environment reduces the genotoxicity of AFB1. Our findings indicate that a weakly alkaline environment may adequately detoxify AF-contaminated food or feed without the need to apply stronger or harsher basic conditions.
The intrinsically disordered transcriptional activation domain of CIITA is functionally tuneable by single substitutions: An exception or a new rule?

During protein evolution, some amino acid substitutions modulate protein function (“tuneability”) . In most proteins, the accessible tuneable range is wide and can be sampled by a set of protein variants that each contain multiple amino acid substitutions. In other proteins, the full range can be accessed by a set of variants that each contains a single substitution. Indeed, site-saturating substitutions at individual “rheostat” positions can sample the full range for some folded globular proteins. In proteins with intrinsically disordered regions (IDRs), many functional studies – which would also detect tuneability – used multiple substitutions or deleted small regions. These results have led to proposed mechanisms such as the "acidic exposure" model for transcriptional activation domains (AD). Only a few IDRs have been assessed with single substitutions. Results have been mixed: (i) The disordered ADs of two full-length transcription factors did not show tuneability, yet (ii) a fragment of another AD was tuneable by single substitutions. Here, we tested tuneability in the AD of a full-length, non-DNA-binding transcription factor, human class II transactivator (CIITA). Sequence analyses and experiments showed that CIITA’s AD is an IDR. Functional assays of singly-substituted variants showed that this IDR was highly tuneable, and some outcomes were not predicted by the acidic exposure model. Instead, four tested positions showed rheostat behaviour, with a wide range of effects on transcriptional activation. Thus, tuneability of different IDRs can vary widely; future studies are needed to illuminate the biophysical features that govern whether an IDR can be tuned by single substitutions.
Binding in visual working memory is task-dependent

Working memory is a neurocognitive system for maintaining and manipulating information online for a short period after the source of information disappeared. What information is held in working memory was shown to flexibly match current functional goal. Considering this, we revisited the question of whether information is held in WM as separate features or as bound objects. We conjectured that rather than having a fixed answer, the format in which information is maintained in working memory is also task-dependent. In two studies focusing on binding between two non-spatial features (color and orientation), or between a spatial and a non-spatial feature (location and color), we manipulated the relative relevance of conjunctions and separate features, in a delayed (Yes/No) recognition task. We found that memory for feature conjunctions was better when required by the task for both types of binding. Moreover, we found that the memory of the spatial feature was impaired when conjunction was not encoded or maintained, while memory for non-spatial features was found to be relatively independent of the task. We conclude that the representation format of objects in working memory is influenced by task requirements.
ReptiLearn: A Smart Home Cage for Behavioral Experiments in Reptiles

Understanding behavior and its evolutionary underpinnings is crucial for unraveling the complexities of brain function. Traditional approaches strive to reduce behavioral complexity by designing short-term, highly constrained behavioral tasks with dichotomous choices in which animals respond to defined external perturbation. In contrast, natural behaviors evolve over multiple time scales and under minimally constrained conditions in which actions are selected through bi-directional interactions with the environment and without human intervention. Recent technological advancements have opened up new possibilities for more natural experimental designs by replacing stringent experimental control with accurate multidimensional behavioral analysis. However, these approaches have been tailored to fit only a small number of species. This specificity limits the experimental opportunities offered by species diversity. Further, it hampers comparative analyses which are essential for extracting overarching behavioral principles and for examining behavior from an evolutionary perspective. To address this limitation, we developed ReptiLearn - a versatile, low-cost, Python-based solution, optimized for conducting automated long-term experiments in the home cage of reptiles, without human intervention. In addition, this system offers unique features such as precise temperature measurement and control, live prey reward dispensers, engagement with touch screens, and remote control through a user-friendly web interface. Finally, ReptiLearn incorporates low-latency closed-loop feedback allowing bi-directional interactions between animals and their environments. Thus, ReptiLearn provides a comprehensive solution for researchers studying behavior in ectotherms and beyond, bridging the gap between constrained laboratory settings and natural behavior in non-conventional model systems. We demonstrate the capabilities of ReptiLearn by automatically training the lizard Pogona vitticeps on a complex spatial learning task requiring association learning, displaced reward learning and reversal learning.