Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Constructions of Dual-Failure Fault-Tolerant Distance Preservers

Fault tolerant distance preservers (spanners) are sparse subgraphs that preserve (approximate) distances between given pairs of vertices under edge or vertex failures. So-far, these structures have been studied mainly from a centralized viewpoint. Despite the fact fault tolerant preservers are mainly motivated by the error-prone nature of distributed networks, not much is known on the distributed computational aspects of these structures. In this paper, we present distributed algorithms for constructing fault tolerant distance preservers and $+2$ additive spanners that are resilient to at most \emph{two edge} faults. Prior to our work, the only non-trivial constructions known were for the \emph{single} fault and \emph{single source} setting by [Ghaffari and Parter SPAA'16]. Our key technical contribution is a distributed algorithm for computing distance preservers w.r. t. a subset $S$ of source vertices, resilient to two edge faults. The output structure contains a BFS tree $BFS(s,G \setminus \{e_1,e_2\})$ for every $s \in S$ and every $e_1,e_2 \in G$. The distributed construction of this structure is based on a delicate balance between the edge congestion (formed by running multiple BFS trees simultaneously) and the sparsity of the output subgraph. No sublinear-round algorithms for constructing these structures have been known before.
Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach

Search engines often follow a two-phase paradigm where in the first stage (the retrieval stage) an initial set of documents is retrieved and in the second stage (the re-ranking stage) the documents are re-ranked to obtain the final result list. While deep neural networks were shown to improve the performance of the re-ranking stage in previous works, there is little literature about using deep neural networks to improve the retrieval stage. In this paper, we study the merits of combining deep neural network models and lexical models for the retrieval stage. A hybrid approach, which leverages both semantic (deep neural network-based) and lexical (keyword matching-based) retrieval models, is proposed. We perform an empirical study, using a publicly available TREC collection, which demonstrates the effectiveness of our approach and sheds light on the different characteristics of the semantic approach, the lexical approach, and their combination.
Derived heat trace asymptotics for the de Rham and Dolbeault complexes.

We examine the derived heat trace asymptotics in both the real and the complex settings for a generalized Witten perturbation. If the dimension is even, in the real context we show the integral of the local density for the derived heat trace asymptotics is half the Euler characteristic of the underlying manifold. In the complex context, we assume the underlying geometry is K\"ahler and show the integral of the local density for the derived heat trace asymptotics defined by the Dolbeault complex is a characteristic number of the complex tangent bundle and the twisting vector bundle. We identify this characteristic number if the real dimension is $2$ or $4$. In both the real and complex settings, the local density differs from the corresponding characteristic class by a divergence term.
A pilot search for extragalactic OH absorption with FAST

OH absorption is currently the only viable way to detect OH molecules in non-masing galaxies at cosmological distances. There have been only six such detections at z > 0.05 to date and so it is hard to put a statistically robust constraint on OH column densities in distant galaxies. We carried out a pilot OH absorption survey towards eight associated and one intervening H i 21-cm absorbers using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We were able to constrain the OH abundance relative to H i ([OH]/[H i]) to be lower than 10−6 ∼ 10−8 for redshifts z∈ [0.1919, 0.2241]. Although no individual detection was made, stacking three associated absorbers free of RFI provides a sensitive OH column density 3σ upper-limit $\sim 1.57 \times 10^{14} (T_x^{\rm OH}/10\,\mathrm{ K})(1/f_\mathrm{ c}^{\rm OH})\mathrm{ cm}^{-2}$, which corresponds to a [OH]/[H i] < 5.45 × 10−8. Combining with archival data, we show that associated absorbers have a slightly lower OH abundance than intervening absorbers. Our results are consistent with a trend of decreasing OH abundance with decreasing redshift.
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?

Task-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models. In NLP similar results are reported most commonly for low data regimes, non-pretrained models, or situationally for pretrained models. In this paper we ask how effective these techniques really are when applied to pretrained transformers. Using two popular varieties of task-agnostic data augmentation (not tailored to any particular task), Easy Data Augmentation (Wei andZou, 2019) and Back-Translation (Sennrichet al., 2015), we conduct a systematic examination of their effects across 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers, including BERT, XLNet, and RoBERTa. We observe a negative result, finding that techniques which previously reported strong improvements for non-pretrained models fail to consistently improve performance for pretrained transformers, even when training data is limited. We hope this empirical analysis helps inform practitioners where data augmentation techniques may confer improvements.
Convective heat transfer of a model emulsion at the droplet scale

We numerically study the Rayleigh-Benard (RB) convection in two-dimensional model emulsions confined between two parallel walls at fixed temperatures. The systems under study are heterogeneous, with finite-size droplets dispersed in a continuous phase. The droplet concentration is chosen to explore the convective heat transfer of both Newtonian (low droplet concentration) and non-Newtonian (high droplet concentration) emulsions, the latter exhibiting shear-thinning rheology, with a noticeable increase of viscosity at low shear rates. It is well known that the transition to convection of a homogeneous Newtonian system is accompanied by the onset of steady flow and time-independent heat flux; in marked contrast, the heterogeneity of emulsions brings in an additional and previously unexplored phenomenology. As a matter of fact, when the droplet concentration increases, we observe that the heat transfer process is mediated by a non-steady flow, with neat heat-flux fluctuations, obeying a non-Gaussian statistics. The observed findings are ascribed to the emergence of space correlations among distant droplets, which we highlight via direct measurements of the droplets displacement and the characterisation of the associated correlation functions.
EqCo: Equivalent Rules for Self-supervised Contrastive Learning

In this paper, we propose a method, named EqCo (Equivalent Rules for Contrastive Learning), to make self-supervised learning irrelevant to the number of negative samples in the contrastive learning framework. Inspired by the infomax principle, we point that the margin term in contrastive loss needs to be adaptively scaled according to the number of negative pairs in order to keep steady mutual information bound and gradient magnitude. EqCo bridges the performance gap among a wide range of negative sample sizes, so that for the first time, we can perform self-supervised contrastive training using only a few negative pairs (e.g.smaller than 256 per query) on large-scale vision tasks like ImageNet, while with little accuracy drop. This is quite a contrast to the widely used large batch training or memory bank mechanism in current practices. Equipped with EqCo, our simplified MoCo (SiMo) achieves comparable accuracy with MoCo v2 on ImageNet (linear evaluation protocol) while only involves 16 negative pairs per query instead of 65536, suggesting that large quantities of negative samples might not be a critical factor in contrastive learning frameworks.
Quantitative K-theory, positive scalar curvature, and band width

We develop two connections between the quantitative framework of operator $K$-theory for geometric $C^*$-algebras and the problem of positive scalar curvature. First, we introduce a quantitative notion of higher index and use it to give a refinement of the well-known obstruction of Rosenberg to positive scalar curvature on closed spin manifolds coming from the higher index of the Dirac operator. We show that on a manifold with uniformly positive scalar curvature, the propagation at which the index of the Dirac operator vanishes is related inversely to the curvature lower bound. Second, we give an approach, using related techniques, to Gromov's band width conjecture, which has been the subject of recent work by Zeidler and Cecchini from a different point of view.
Improving AMR Parsing with Sequence-to-Sequence Pre-training

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance. To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing. However, previous pre-trained models, like BERT, are implemented for general purpose which may not work as expected for the specific task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the vanilla fine-tuning method to a multi-task learning fine-tuning method that optimizes for the performance of AMR parsing while endeavors to preserve the response of pre-trained models. Extensive experimental results on two English benchmark datasets show that both the single and joint pre-trained models significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0), which reaches the state of the art. The result is very encouraging since we achieve this with seq2seq models rather than complex models. We make our code and model available at this https URL.
On almost quasi-negative holomorphic sectional curvature

A recent celebrated theorem of Diverio-Trapani and Wu-Yau states that a compact K\"ahler manifold admitting a K\"ahler metric of quasi-negative holomorphic sectional curvature has an ample canonical line bundle, confirming a conjecture of Yau. In this note we shall introduce a natural notion of \emph{almost quasi-negative holomorphic sectional curvature} and extend this theorem to compact K\"ahler manifolds of almost quasi-negative holomorphic sectional curvature. We also obtain a gap-type theorem for the inequality $\int_Xc_1(K_X)^n>0$ in terms of the holomorphic sectional curvature. The $\alpha$-invariant plays a key role in the discussions.
Dynamics and Allocation of Transaction Cost in Multiagent Industrial Symbiosis

This paper discusses the dynamics of Transaction Cost (TC) in Industrial Symbiosis Institutions (ISI) and provides a fair and stable mechanism for TC allocation among the involved firms in a given ISI. In principle, industrial symbiosis, as an implementation of the circular economy paradigm in the context of industrial relation, is a practice aiming at reducing the material/energy footprint of the firm. The well-engineered form of this practice is proved to decrease the transaction costs at a collective level. This can be achieved using information systems for: identifying potential synergies, evaluating mutually beneficial ones, implementing the contracts, and governing the behavior of the established relations. Then the question is "how to distribute the costs for maintaining such an information system in a fair and stable manner? " We see such a cost as a collective transaction cost and employ an integrated method rooted in cooperative game theory and multiagent systems research to develop a fair and stable allocation mechanism for it. The novelty is twofold: in developing analytical multiagent methods for capturing the dynamics of transaction costs in industrial symbiosis and in presenting a novel game-theoretic mechanism for its allocation in industrial symbiosis institutions. While the former contributes to the theories of industrial symbiosis (methodological contribution), the latter supports decision makers aiming to specify fair and stable industrial symbiosis contracts (practical contribution).
K2-111: an old system with two planets in near-resonance†

This paper reports on the detailed characterization of the K2-111 planetary system with K2, WASP, and ASAS-SN photometry, as well as high-resolution spectroscopic data from HARPS-N and ESPRESSO. The host, K2-111, is confirmed to be a mildly evolved (log g = 4.17), iron-poor ([Fe/H] = −0.46), but alpha-enhanced ([α/Fe]=0.27), chromospherically quiet, very old thick disc G2 star. A global fit, performed by using PyORBIT, shows that the transiting planet, K2-111 b, orbits with a period Pb = 5.3518 ± 0.0004 d and has a planet radius of $1.82^{+0.11}_{-0.09}$ R⊕ and a mass of $5.29^{+0.76}_{-0.77}$ M⊕, resulting in a bulk density slightly lower than that of the Earth. The stellar chemical composition and the planet properties are consistent with K2-111 b being a terrestrial planet with an iron core mass fraction lower than the Earth. We announce the existence of a second signal in the radial velocity data that we attribute to a non-transiting planet, K2-111 c, with an orbital period of 15.6785 ± 0.0064 d, orbiting in near-3:1 mean motion resonance with the transiting planet, and a minimum planet mass of 11.3 ± 1.1 M⊕. Both planet signals are independently detected in the HARPS-N and ESPRESSO data when fitted separately. There are potentially more planets in this resonant system, but more well-sampled data are required to confirm their presence and physical parameters.
The Ascent of Water in Trees.

In the earlier discussions of this problem, it has been tacitly assumed that it was only necessary to find forces sufficient to balance a head of water equal to the height of the loftiest tree to explain the ascent of sap in it. The problem is, however, rather one of dynamics than of statics, for we have to find forces sufficient not only to balance the head of water, but also to keep this water moving upwards in narrow tubes with a velocity varying from a few centimetres to as much as 6 metres per hour. Janse has in fact shown empirically, and his results have been confirmed by Strasburger, that to drive water through the stem of a Conifer at the transpiration rate requires a head of water several times the length of the stem. Water is a liquid of definite viscosity, and the resistance offered even to its slow flow through tubes of small diameter and considerable length is a factor of great importance. The purpose of the following research has been to find the amount of this resistance in definite cases, the forces required to overcome it, and hence the total force required to raise the water with sufficient rapidity to the summit of an actively transpiring tall tree. Furthermore, an attempt has been made to determine the possible means by which this force could be generated, and propagated in the conducting wood.
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision

User modeling is critical for many personalized web services. Many existing methods model users based on their behaviors and the labeled data of target tasks. However, these methods cannot exploit useful information in unlabeled user behavior data, and their performance may be not optimal when labeled data is scarce. Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data. We propose two self-supervision tasks for user model pre-training. The first one is masked behavior prediction, which can model the relatedness between historical behaviors. The second one is next K behavior prediction, which can model the relatedness between past and future behaviors. The pre-trained user models are finetuned in downstream tasks to learn task-specific user representations. Experimental results on two real-world datasets validate the effectiveness of our proposed user model pre-training method.
Automata-based Quantitative Verification

Quantitative analysis of computing systems is an emerging area in automated formal analysis. Such properties address aspects such as costs and rewards, quality measures, resource consumption, distance metrics, etc. Existing solutions for problems in quantitative analysis face two challenges, namely lack of generalizability and separation-of-techniques. Lack of generalizability refers to the issue that solution approaches are specialized to cost model. Different cost models deploy such disparate algorithms that there is no transfer of knowledge from one cost model to another. Separation-of-techniques refers to the inherent dichotomy in solving problems in quantitative analysis. Most algorithms comprise of a structural phase which reasons about the structure of the quantitative system(s) using techniques from automata or graphs, and a numerical phase, which reasons about the quantitative dimension/cost model using numerical methods. The techniques used in both phases are so unlike each other that they are difficult to combine, thereby impacting scalability. This thesis contributes to a novel framework that addresses these challenges. The introduced framework, called comparator automata or comparators in short, builds on automata-theoretic foundations to generalize across a variety of cost models. Comparators enable automata-based methods in the numerical phase, hence eradicating the dependence on numerical methods. In doing so, comparators are able to integrate the structural and numerical phases. On the theoretical front, we demonstrate that these have the advantage of generalizable results, and yield complexity-theoretic improvements over a range of problems in quantitative analysis. On the empirical front, we demonstrate that comparator-based solutions render more efficient, scalable, and robust performance, and are able to integrate quantitative with qualitative objectives.
Distinction entre Spodoptera latifascia (Walker) et Spodoptera cosmioides (Walker), bona species [Lepidoptera, Noctuidae]

Spodoptera cosmioides, described by Walker in 1858 from Para, Brazil, has hitherto been regarded as a junior synonym of S. latifascia, described by Walker in 1856 from Jamaica. We observed several molecular, morphological, physiological and behavioural differences between latifascia moths collected in North America and West Indies, and moths coming from South America. The latter correspond to the type of cosmioides, which must be considered a distinct species.
Unsupervised Shadow Removal Using Target Consistency Generative Adversarial Network

Unsupervised shadow removal aims to learn a non-linear function to map the original image from shadow domain to non-shadow domain in the absence of paired shadow and non-shadow data. In this paper, we develop a simple yet efficient target-consistency generative adversarial network (TC-GAN) for the shadow removal task in the unsupervised manner. Compared with the bidirectional mapping in cycle-consistency GAN based methods for shadow removal, TC-GAN tries to learn a one-sided mapping to cast shadow images into shadow-free ones. With the proposed target-consistency constraint, the correlations between shadow images and the output shadow-free image are strictly confined. Extensive comparison experiments results show that TC-GAN outperforms the state-of-the-art unsupervised shadow removal methods by 14.9% in terms of FID and 31.5% in terms of KID. It is rather remarkable that TC-GAN achieves comparable performance with supervised shadow removal methods.
A Separation Method for Multicomponent Nonstationary Signals with Crossover Instantaneous Frequencies

In nature and engineering world, the acquired signals are usually affected by multiple complicated factors and appear as multicomponent nonstationary modes. In such and many other situations, it is necessary to separate these signals into a finite number of monocomponents to represent the intrinsic modes and underlying dynamics implicated in the source signals. In this paper, we consider the separation of a multicomponent signal which has crossing instantaneous frequencies (IFs), meaning that some of the components of the signal overlap in the time-frequency domain. We use a kernel function-based complex quadratic phase function to represent a multicomponent signal in the three-dimensional space of time, frequency and chirp rate, to be called the localized quadratic-phase Fourier transform (LQFT). We analyze the error bounds for IF estimation and component recovery with LQFT. In addition, we propose a matched-filter along certain specific time-frequency lines with respect to the chirp rate to make nonstationary signals be further separated and more concentrated in the three-dimensional space of LQFT. Based on the approximation of source signals with linear frequency modulation modes at any local time, we introduce an innovative signal reconstruction algorithm which is suitable for signals with crossing IFs. Moreover, this algorithm decreases component recovery errors when the IFs curves of different components are not crossover, but fast-varying and close to one and other. Numerical experiments on synthetic and real signals show our method is more accurate and consistent in signal separation than the empirical mode decomposition, synchrosqueezing transform, and other approaches.
Suitability of Poplar Plantations for a Cavity-Nesting Specialist, the Lesser Spotted Woodpecker Dendrocopos minor, in the Mediterranean Mosaic Landscape

Abstract. Monocultures of even-aged trees in short rotation are a forest system of low ecological complexity that has been described as unsuitable for the establishment of stable populations of forest birds. However, key habitat quality cues could make them attractive to forest specialists. This paper assesses the suitability of poplar plantations in the northeast of the Iberian Peninsula for a forest specialist, the Lesser Spotted Woodpecker Dendrocopos minor. Poplar stands occupy a small area of an agroforestry mosaic landscape where semi-natural Mediterranean woodland is predominant. Population size, nesting success, home ranges and habitat selection were studied by radio-tracking and monitoring during the breeding season and the winter. Poplar plantations were preferentially selected for breeding and foraging in the spring and the winter. Home ranges in the breeding season and the winter (32.4 and 438.5 ha, respectively) were similar to those observed in semi-natural woodlands that have been studied in Europe. However, population density (0.25 territories/100 ha) was lower than that described in most European semi-natural woodlands. Nesting success was low (0.54), due to strong competition with other cavity nesters, predation of nests by the Greater Spotted Woodpecker, and loss of one of the adults. Fifty per cent of the foraging activity during the breeding season took place in an area of 180 metres around the nest. The amount of standing dead wood in poplar stands was much higher than in the surrounding habitats and source areas. The moderate breeding success and the high rate of adult predation may suggest that poplar plantations act as an ecological trap, in which standing dead wood may be a habitat quality cue that attracts birds to this non-ideal habitat. Poplar plantations become even less suitable when most of the available habitat is felled at the same time. Suitable planning of poplar plantation rotations and recovery of riparian forest is the best way to ensure the survival of Lesser Spotted Woodpecker populations in the long term.