Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling Limits of Planar Symplectic Ensembles

We consider various asymptotic scaling limits N →∞ for the 2N complex eigenvalues of non-Hermitian random matrices in the symmetry class of the symplectic Ginibre ensemble. These are known to be integrable, forming Pfaffian point processes, and we obtain limiting expressions for the corresponding kernel for different potentials. The first part is devoted to the symplectic Ginibre ensemble with the Gaussian potential. We obtain the asymptotic at the edge of the spectrum in the vicinity of the real line. The unifying form of the kernel allows us to make contact with the bulk scaling along the real line and with the edge scaling away from the real line, where we recover the known determinantal process of the complex Ginibre ensemble. Part two covers ensembles of Mittag-Leffler type with a singularity at the origin. For potentials Q(ζ) = |ζ| − (2c/N) log |ζ|, with λ > 0 and c > −1, the limiting kernel obeys a linear differential equation of fractional order 1/λ at the origin. For integer m = 1/λ it can be solved in terms of Mittag-Leffler functions. In the last part, we derive Ward’s equation for planar symplectic ensembles for a general class of potentials. It serves as a tool to investigate the Gaussian and singular Mittag-Leffler universality class. This allows us to determine the functional form of all possible limiting kernels (if they exist) that are translation invariant, up to their integration domain.
Transductive Few-Shot Learning: Clustering is All You Need?

We investigate a general formulation for clustering and transductive few-shot learning, which integrates prototype-based objectives, Laplacian regularization and supervision constraints from a few labeled data points. We propose a concave-convex relaxation of the problem, and derive a computationally efficient block-coordinate bound optimizer, with convergence guarantee. At each iteration,our optimizer computes independent (parallel) updates for each point-to-cluster assignment. Therefore, it could be trivially distributed for large-scale clustering and few-shot tasks. Furthermore, we provides a thorough convergence analysis based on point-to-set maps. Were port comprehensive clustering and few-shot learning experiments over various data sets, showing that our method yields competitive performances, in term of accuracy and optimization quality, while scaling up to large problems. Using standard training on the base classes, without resorting to complex meta-learning and episodic-training strategies, our approach outperforms state-of-the-art few-shot methods by significant margins, across various models, settings and data sets. Surprisingly, we found that even standard clustering procedures (e.g., K-means), which correspond to particular, non-regularized cases of our general model, already achieve competitive performances in comparison to the state-of-the-art in few-shot learning. These surprising results point to the limitations of the current few-shot benchmarks, and question the viability of a large body of convoluted few-shot learning techniques in the recent literature.
Data lake concept and systems: a survey

Although big data has been discussed for some years, it still has many research challenges, especially the variety of data. It poses a huge diﬃculty to eﬃciently integrate, access, and query the large volume of diverse data in information silos with the traditional ‘schema-on-write’ approaches such as data warehouses. Data lakes have been proposed as a solution to this problem. They are repositories storing raw data in its original formats and providing a common access interface. This survey reviews the development, deﬁnition, and architectures of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing data lake systems based on their provided functions, which makes this survey a useful technical reference for designing, implementing and applying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey would motivate the future development of data lake research and practice.
On the Power of Preconditioning in Sparse Linear Regression

Sparse linear regression is a fundamental problem in high-dimensional statistics, but strikingly little is known about how to efficiently solve it without restrictive conditions on the design matrix. We consider the (correlated) random design setting, where the covariates are independently drawn from a multivariate Gaussian $N(0,\ \Sigma)$, for some $n\times n$ positive semi-definite matrix $\Sigma$, and seek estimators $\hat{w}$ minimizing $(\hat{w}-w^{\ast})^{T}\Sigma(\hat{w}-w^{\ast})$, where $w^{\ast}$ is the k-sparse ground truth. Information theoretically, one can achieve strong error bounds with only $O(k\log n)$ samples for arbitrary $\Sigma$ and $w^{\ast}$; however, no efficient algorithms are known to match these guarantees even with $o(n)$ samples, without further assumptions on $\Sigma$ or $w^{\ast}$. Yet there is little evidence for this gap in the random design setting: computational lower bounds are only known for worst-case design matrices. To date, random-design instances (i.e. specific covariance matrices $\Sigma$) have only been proven hard against the Lasso program and variants. More precisely, these “hard” instances can often be solved by Lasso after a simple change-of-basis (i.e. preconditioning). In this work, we give both upper and lower bounds clarifying the power of preconditioning as a tool for solving sparse linear regression problems. On the one hand, we show that the preconditioned Lasso can solve a large class of sparse linear regression problems nearly optimally: it succeeds whenever the dependency structure of the covariates, in the sense of the Markov property, has low treewidth - even if $\Sigma$ is highly ill-conditioned. This upper bound builds on ideas from the wavelet and signal processing literature. As a special case of this result, we give an algorithm for sparse linear regression with covariates from an autoregressive time series model, where we also show that the (usual) Lasso provably fails. On the other hand, we construct (for the first time) random-design instances which are provably hard even for an optimally preconditioned Lasso. In fact, we complete our treewidth classification by proving that for any treewidth-t graph, there exists a Gaussian Markov Random Field on this graph such that the preconditioned Lasso, with any choice of preconditioner, requires $\Omega(t^{1/20})$ samples to recover $O(\log n)$ -sparse signals when covariates are drawn from this model.
Fingerprints of possible even-parity superconducting states in Sr$_2$RuO$_4$ detected by planar tunneling spectroscopy

After more than 25 years of research, three even-parity superconducting states—the d+ id-wave, d + ig-wave, and s + id-wave states—have emerged as leading candidates for the superconducting states of Sr2RuO4. In the present work, we propose a tunneling spectroscopy experiment for distinguishing among these three superconducting states. The key component of our proposal is that we examine the conductance spectra of normal-metal/Sr2RuO4 junctions with various angles between the junction interface and the crystal axis of the Sr2RuO4. The angle dependence of the conductance spectra shows a unique pattern in each superconducting state, which can function as a fingerprint for verifying the pairing symmetry of Sr2RuO4.
Trilateral Attention Network for Real-time Medical Image Segmentation

Accurate segmentation of medical images into anatomically meaningful regions is critical for the extraction of quantitative indices or biomarkers. The common pipeline for segmentation comprises regions of interest detection stage and segmentation stage, which are independent of each other and typically performed using separate deep learning networks. The performance of the segmentation stage highly relies on the extracted set of spatial features and the receptive fields. In this work, we propose an end-to-end network, called Trilateral Attention Network (TaNet), for real-time detection and segmentation in medical images. TaNet has a module for region localization, and three segmentation pathways: 1) handcrafted pathway with hand-designed convolutional kernels, 2) detail pathway with regular convolutional kernels, and 3) a global pathway to enlarge the receptive field. The first two pathways encode rich handcrafted and low-level features extracted by hand-designed and regular kernels while the global pathway encodes high-level context information. By jointly training the network for localization and segmentation using different sets of features, TaNet achieved superior performance, in terms of accuracy and speed, when evaluated on an echocardiography dataset for cardiac segmentation. The code and models will be made publicly available in TaNet Github page.
Autobots: Latent Variable Sequential Set Transformers

Robust multi-agent trajectory prediction is essential for the safe control of robots and vehicles that interact with humans. Many existing methods treat social and temporal information separately and therefore fall short of modelling the joint future trajectories of all agents in a socially consistent way. To address this, we propose a new class of Latent Variable Sequential Set Transformers which autoregressively model multi-agent trajectories. We refer to these architectures as “AutoBots”. AutoBots model the contents of sets (e.g. representing the properties of agents in a scene) over time and employ multi-head self-attention blocks over these sequences of sets to encode the sociotemporal relationships between the different actors of a scene. This produces either the trajectory of one ego-agent or a distribution over the future trajectories for all agents under consideration. Our approach works for general sequences of sets and we provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. For the single-agent prediction case, we validate our model on the NuScenes motion prediction task and achieve competitive results on the global leaderboard. In the multi-agent forecasting setting, we validate our model on TrajNet. We ﬁnd that our method outperforms physical extrapolation and recurrent network baselines and generates scene-consistent trajectories.
WHAT LEADS GUESTS TO SATISFACTION AND LOYALTY IN UAE FIVE-STAR HOTELS? AHP ANALYSIS TO SERVICE QUALITY DIMENSIONS

The hospitality industry has witnessed an impressive growth in the recent past. However, this has also led to fierce competition resulting in an enhanced focus on customer satisfaction. This study investigates and identifies critical service dimensions for five-star hotels in United Arab Emirates (UAE). The objective of the study is to examine the intricacies of customer satisfaction and loyalty with regard to service quality in the fivestar hotels of UAE. Following a quantitative and empirical approach, the data was collected by a structured questionnaire and tested on Analytic Hierarchy H.H. Alzoubi; M. Vij; A. Vij; J.R. Hanaysha 103 Enlightening Tourism. A Pathmaking Journal, Vol. 11, No 1 (2021), pp. 102-135 ISSN 2174-548X Process (AHP). Results support the impact of service quality dimensions on both Customer Satisfaction and Customer Loyalty. The "Personal Contact” dimensions (Responsiveness and Empathy) and the "Service Performance” dimensions (Reliability and Assurance) were found to be significant factor impacting the perception of the hotels guests towards Service Quality. The findings are useful particularly to the hotel industry to analyze the gaps in service quality and thereafter put more efforts into developing appropriate strategies for achieving customer satisfaction and customer loyalty.
Loneliness and Mobile Phone Addiction Among Chinese College Students: The Mediating Roles of Boredom Proneness and Self-Control

Introduction Mobile phones bring convenience to people’s lives, but also affect their physical and mental health (especially in college students). Previous studies have revealed that mobile phone addiction is positively related to loneliness. This study further explored the mediating effects of boredom proneness and self-control on the relationship between loneliness and mobile phone addiction. Methods A cross-sectional design was conducted in the present study. The investigation employed the Chinese version of the UCLA Loneliness Self-report Scale, Boredom Proneness Scale-Short Form, the Chinese version of the Self-control scale, and the Mobile Phone Addiction Index. Out of 1122 college students, 1078 completed the questionnaire survey and became our final subjects. PROCESS macro of SPSS 21 was used to conduct explore the mediating roles of boredom proneness and self-control in the relationship between loneliness and mobile phone addiction. Results Loneliness, boredom proneness, and mobile phone addiction were significantly and positively correlated with each other, as well as significantly negatively correlated with self-control. Boredom proneness and self-control, when operating in parallel, partially mediated the relationship between loneliness and mobile phone addiction. By contrast, when occurring sequentially, they fully mediated the relationship between loneliness and mobile phone addiction. Conclusion Mobile phone addiction among lonely college students can be eliminated by exercising self-control and alleviating boredom proneness.
Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.
Identifying “vital attributes” for assessing disturbance–recovery potential of seafloor communities

Abstract Despite a long history of disturbance–recovery research, we still lack a generalizable understanding of the attributes that drive community recovery potential in seafloor ecosystems. Marine soft‐sediment ecosystems encompass a range of heterogeneity from simple low‐diversity habitats with limited biogenic structure, to species‐rich systems with complex biogenic habitat structure. These differences in biological heterogeneity are a product of natural conditions and disturbance regimes. To search for unifying attributes, we explore whether a set of simple traits can characterize community disturbance–recovery potential using seafloor patch‐disturbance experiments conducted in two different soft‐sediment landscapes. The two landscapes represent two ends of a spectrum of landscape biotic heterogeneity in order to consider multi‐scale disturbance–recovery processes. We consider traits at different levels of biological organization, from the biological traits of individual species, to the traits of species at the landscape scale associated with their occurrence across the landscape and their ability to be dominant. We show that in a biotically heterogeneous landscape (Kawau Bay, New Zealand), seafloor community recovery is stochastic, there is high species turnover, and the landscape‐scale traits are good predictors of recovery. In contrast, in a biotically homogeneous landscape (Baltic Sea), the options for recovery are constrained, the recovery pathway is thus more deterministic and the scale of recovery traits important for determining recovery switches to the individual species biological traits within the disturbed patch. Our results imply that these simple, yet sophisticated, traits can be effectively used to characterize community recovery potential and highlight the role of landscapes in providing resilience to patch‐scale disturbances.
Impacts of COVID‐19 and Price Transmission in U.S. Meat Markets

Abstract Coronavirus 2019 (COVID‐19) has caused ongoing disruptions to U.S. meat markets via demand and supply‐side shocks. Abnormally high prices have been reported at retail outlets and meat packers have been accused of unfair business practices because of widening price spreads. Processing facilities have experienced COVID‐19 outbreaks resulting in shutdowns. Using weekly data on wholesale and retail prices of beef, pork, and poultry, we characterize the time series behavior and dynamic linkages of U.S. meat prices before the COVID‐19 pandemic. We model vertical price transmission using both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. With the estimated models, we then compare price movements under COVID‐19 to model predictions. All three meat markets are well‐integrated and we observe unexpected, large price movements in April and May of 2020. Early COVID‐19 related shocks appear to be transitory with prices returning to expected levels at a pace consistent with the speed of transmission prior to the pandemic. This well‐functioning market process suggests a degree of resilience in U.S. meat supply chains.
Reporting of activated partial thromboplastin time (aPTT): Could we achieve better comparability of the results?

Introduction Activated partial thromboplastin time (aPTT) is determined and reported as clotting time in seconds aPTT(s), but it is presumed that reporting results as patient-to-normal clotting time ratio, aPTT(r), could minimize within-laboratory variability. The aim of study was to investigate differences in reporting aPTT results that can affect comparability of the results among Croatian laboratories and suggest further steps for its harmonization. Materials and methods The questionnaire on aPTT reporting practice was distributed to 83 laboratories through Survey Monkey application in March 2019 as the part of the first regular round of Croatian Centre for Quality Assessment in Laboratory Medicine proficiency testing. Results The survey response rate was 0.49. Majority of laboratories report aPTT results as both, seconds and ratio. Participants reported use of 23 different aPTT(s) reference intervals along with 17 different combinations of reagent/coagulometer and 25 aPTT(s) denominators of different origin for aPTT(r) calculation. Despite the same aPTT(s) results, the use of different denominators caused a dispersion of aPTT(r) results that can lead to exceeding external quality assessment performance criteria of 7%, particularly when results were compared for the same reagent group only. By applying aPTT(s) reference interval mean as denominator for calculation of aPTT(r) reference interval better concordance to harmonized one was obtained (17 vs. 27; χ2 = 3.972; P = 0.046). Conclusion In order to improve comparability of the results, laboratories are advised to use mean of aPTT(s) reference interval as denominator for aPTT(r) calculation. Type of coagulometer need to be considered when evaluating aPTT proficiency test results and its currently acceptable limit of performance evaluated accordingly.
Volatilome of Aleppo Pine litter over decomposition process

Abstract Biogenic Volatile Organic Compounds (BVOC) are largely accepted to contribute to both atmospheric chemistry and ecosystem functioning. While the forest canopy is recognized as a major source of BVOC, emissions from plant litter have scarcely been explored with just a couple of studies being focused on emission patterns over litter decomposition process. The aim of this study was to quantitatively and qualitatively characterize BVOC emissions (C1–C15) from Pinus halepensis litter, one of the major Mediterranean conifer species, over a 15‐month litter decomposition experiment. Senescent needles of P. halepensis were collected and placed in 42 litterbags where they underwent in situ decomposition. Litterbags were collected every 3 months and litter BVOC emissions were studied in vitro using both online (PTR‐ToF‐MS) and offline analyses (GC‐MS). Results showed a large diversity of BVOC (58 compounds detected), with a strong variation over time. Maximum total BVOC emissions were observed after 3 months of decomposition with 9.18 µg gDM −1 hr−1 mainly composed by terpene emissions (e.g., α‐pinene, terpinolene, β‐caryophyllene). At this stage, methanol, acetone, and acetic acid were the most important nonterpenic volatiles representing, respectively, up to 26%, 10%, and 26% of total emissions. This study gives an overview of the evolution of BVOC emissions from litter along with decomposition process and will thus contribute to better understand the dynamics and sources of BVOC emission in Mediterranean pine forests.
Habitat fragmentation and vegetation structure impact gastrointestinal parasites of small mammalian hosts in Madagascar

Abstract Deleterious effects of habitat loss and fragmentation on biodiversity have been demonstrated in numerous taxa. Although parasites represent a large part of worldwide biodiversity, they are mostly neglected in this context. We investigated the effects of various anthropogenic environmental changes on gastrointestinal parasite infections in four small mammal hosts inhabiting two landscapes of fragmented dry forest in northwestern Madagascar. Coproscopical examinations were performed on 1,418 fecal samples from 903 individuals of two mouse lemur species, Microcebus murinus (n = 199) and M. ravelobensis (n = 421), and two rodent species, the native Eliurus myoxinus (n = 102) and the invasive Rattus rattus (n = 181). Overall, sixteen parasite morphotypes were detected and significant prevalence differences between host species regarding the most common five parasites may be explained by parasite–host specificity or host behavior, diet, and socioecology. Ten host‐ and habitat‐related ecological variables were evaluated by generalized linear mixed modeling for significant impacts on the prevalence of the most abundant gastrointestinal parasites and on gastrointestinal parasite species richness (GPSR). Forest maturation affected homoxenous parasites (direct life cycle) by increasing Lemuricola, but decreasing Enterobiinae gen. sp. prevalence, while habitat fragmentation and vegetation clearance negatively affected the prevalence of parasites with heterogenic environment (i.e., Strongyloides spp.) or heteroxenous (indirect cycle with intermediate host) cycles, and consequently reduced GPSR. Forest edges and forest degradation likely change abiotic conditions which may reduce habitat suitability for soil‐transmitted helminths or required intermediate hosts. The fragility of complex parasite life cycles suggests understudied and potentially severe effects of decreasing habitat quality by fragmentation and degradation on hidden ecological networks that involve parasites. Since parasites can provide indispensable ecological services and ensure stability of ecosystems by modulating animal population dynamics and nutrient pathways, our study underlines the importance of habitat quality and integrity as key aspects of conservation.