Miracle-dz/UniFAR-FTUs huggingface dataset:
Universality of cutoff for graphs with an added random matching

We establish universality of cutoff for simple random walk on a class of random graphs defined as follows. Given a finite graph $G=(V,E)$ with $|V|$ even we define a random graph $ G^*=(V,E \cup E')$ obtained by picking $E'$ to be the (unordered) pairs of a random perfect matching of $V$. We show that for a sequence of such graphs $G_n$ of diverging sizes and of uniformly bounded degree, if the minimal size of a connected component of $G_n$ is at least 3 for all $n$, then the random walk on $G_n^*$ exhibits cutoff w.h. p. This provides a simple generic operation of adding some randomness to a given graph, which results in cutoff.
DIRECT-Net: a unified mutual-domain material decomposition network for quantitative dual-energy CT imaging.

PURPOSE The purpose of this paper is to present an end-to-end deep convolutional neural network to improve the dual-energy CT (DECT) material decomposition performance. METHODS In this study, we propose a unified mutual-domain (sinogram domain and CT domain) material decomposition network (DIRECT-Net) for DECT imaging. By design, the DIRECT-Net has immediate access to mutual-domain data, and utilizes stacked convolution neural network layers for noise reduction and material decomposition. The training data are numerically generated following the fundamental DECT imaging physics. Numerical simulation of the XCAT digital phantom, experiments of a biological specimen, a calcium chloride phantom and an iodine solution phantom are carried out to evaluate the performance of DIRECT-Net. Comparisons are performed with different DECT decomposition algorithms. RESULTS Results demonstrate that the proposed DIRECT-Net can generate water and bone basis images with less artifacts compared to the other decomposition methods. Additionally, the quantification errors of the calcium chloride (75-375 mg/cm3 ) and the iodine (2-20 mg/cm3 ) are less than 4%. CONCLUSIONS An end-to-end material decomposition network is proposed for quantitative DECT imaging. The qualitative and quantitative results demonstrate that this new DIRECT-Net has promising benefits in improving the DECT image quality. This article is protected by copyright. All rights reserved.
Shallow Water Moment models for bedload transport problems

In this work a simple but accurate shallow model for bedload sediment transport is proposed. The model is based on applying the moment approach to the Shallow Water Exner model, making it possible to recover the vertical structure of the flow. This approach allows us to obtain a better approximation of the fluid velocity close to the bottom, which is the relevant velocity for the sediment transport. A general Shallow Water Exner moment model allowing for polynomial velocity profiles of arbitrary order is obtained. A regularization ensures hyperbolicity and easy computation of the eigenvalues. The system is solved by means of an adapted IFCP scheme proposed here. The improvement of this IFCP type scheme is based on the approximation of the eigenvalue associated to the sediment transport. Numerical tests are presented which deal with large and short time scales. The proposed model allows to obtain the vertical structure of the fluid, which results in a better description on the bedload transport of the sediment layer.
Prevalence of neural collapse during the terminal phase of deep learning training

Significance Modern deep neural networks for image classification have achieved superhuman performance. Yet, the complex details of trained networks have forced most practitioners and researchers to regard them as black boxes with little that could be understood. This paper considers in detail a now-standard training methodology: driving the cross-entropy loss to zero, continuing long after the classification error is already zero. Applying this methodology to an authoritative collection of standard deepnets and datasets, we observe the emergence of a simple and highly symmetric geometry of the deepnet features and of the deepnet classifier, and we document important benefits that the geometry conveys—thereby helping us understand an important component of the modern deep learning training paradigm. Modern practice for training classification deepnets involves a terminal phase of training (TPT), which begins at the epoch where training error first vanishes. During TPT, the training error stays effectively zero, while training loss is pushed toward zero. Direct measurements of TPT, for three prototypical deepnet architectures and across seven canonical classification datasets, expose a pervasive inductive bias we call neural collapse (NC), involving four deeply interconnected phenomena. (NC1) Cross-example within-class variability of last-layer training activations collapses to zero, as the individual activations themselves collapse to their class means. (NC2) The class means collapse to the vertices of a simplex equiangular tight frame (ETF). (NC3) Up to rescaling, the last-layer classifiers collapse to the class means or in other words, to the simplex ETF (i.e., to a self-dual configuration). (NC4) For a given activation, the classifier’s decision collapses to simply choosing whichever class has the closest train class mean (i.e., the nearest class center [NCC] decision rule). The symmetric and very simple geometry induced by the TPT confers important benefits, including better generalization performance, better robustness, and better interpretability.
Model-independent Constraints on Cosmic Curvature: Implication from Updated Hubble Diagram of High-redshift Standard Candles

The cosmic curvature (Ωk) is a fundamental parameter for cosmology. In this paper, we propose an improved model-independent method to constrain the cosmic curvature, which is geometrically related to the Hubble parameter H(z) and luminosity distance DL(z). Using the currently largest H(z) sample from the well-known cosmic chronometers, as well as the luminosity distance DL(z) from the relation between the UV and X-ray luminosities of 1598 quasars and the newly compiled Pantheon sample including 1048 SNe Ia, 31 independent measurements of the cosmic curvature Ωk(z) can be expected covering the redshift range of 0.07 < z < 2. Our estimation of Ωk(z) is fully compatible with flat universe at the current level of observational precision. Meanwhile, we find that, for the Hubble diagram of 1598 quasars as a new type of standard candle, the spatial curvature is constrained to be Ωk = 0.08 ± 0.31. For the latest Pantheon sample of SNe Ia observations, we obtain Ωk = − 0.02 ± 0.14. Compared to other approaches aiming for model-independent estimations of spatial curvature, our analysis also achieves constraints with competitive precision. More interestingly, it is suggested that the reconstructed curvature Ωk is negative in the high-redshift region, which is also consistent with the results from the model-dependent constraints in the literature. Such findings are confirmed by our reconstructed evolution of Ωk(z), in the framework of a model-independent method of Gaussian processes (GP) without assuming a specific form.
Transformer based Multilingual document Embedding model

One of the current state-of-the-art multilingual document embedding model LASER is based on the bidirectional LSTM neural machine translation model. This paper presents a transformer-based sentence/document embedding model, T-LASER, which makes three significant improvements. Firstly, the BiLSTM layers is replaced by the attention-based transformer layers, which is more capable of learning sequential patterns in longer texts. Secondly, due to the absence of recurrence, T-LASER enables faster parallel computations in the encoder to generate the text embedding. Thirdly, we augment the NMT translation loss function with an additional novel distance constraint loss. This distance constraint loss would further bring the embeddings of parallel sentences close together in the vector space; we call the T-LASER model trained with distance constraint, cT-LASER. Our cT-LASER model significantly outperforms both BiLSTM-based LASER and the simpler transformer-based T-LASER.
A study on thick pearls produced by the third generation shell color lines of pearl oyster(Pinctada martensii)

Two experiments were designed to evaluate the pearl production traits of the third generation red(RS3),yellow(YS3),white(WS3)and black(BS3)shell colored lines.Different colored lines were used as host and donor oysters respectively.The pearl in Experiment Ⅰ,16 combinations were established by sampling the RS3,YS3,WS3 and BS3 lines as honor and donor oysters.Another combination was established by using the common cultured stock and YS3 as honor and donor oysters,respectively.And a total of seventeen combinations were established.The pearls were harvested after the honor oysters were cultured for twenty-four months.In Experiment Ⅱ,sixteen combinations were established by sampling the RS3,YS3,WS3 and BS3 lines as honor and donor oysters.The pearls were harvested after the honor oysters were cultured for eighteen months.After harvesting pearls,shell height growth and survival of the RS3,YS3,WS3 and BS3 lines were compared in both experiments.Retention rate,commercial pearl rate,better quality pearl rate and pearl thickness of each combination in Experiment Ⅰ and Ⅱ were compared.The results showed that there existed significant differences in mean shell height and survival among the RS3,YS3,WS3 and BS3 lines in Experiment Ⅰ and Ⅱ(P0.05). The BS3 line had the largest mean shell height compared with the RS3,YS3 and WS3 lines in Experiment Ⅰ and Ⅱ. The YS3 line had the largest survival in Experiment Ⅰ and the BS3 line had the largest survival in Experiment Ⅱ. In Experiment Ⅰ,there were significant differences in retention rate,commercial rate,perfect quality pearl rate and pearl performance values among the 17 combinations(P0.05);There were insignificant differences in pearl thickness among the 17 combinations(P0.05);The BW combination had the highest retention rate,commercial pearl rate,pearl thickness and pearl performance value,and the RB had the highest perfect quality pearl rate.In Experiment Ⅱ,there were significant differences in retention rate,commercial rate,pearl thickness,perfect quality pearl rate and pearl performance values among the 16 combinations(P0.05). A combined analysis on host or donor oysters showed that the BS3 line is desirable for host oyster to produce better quality pearl and is promising in the commercial pearl production.
Role of the chiral anomaly in polarized deeply inelastic scattering: Finding the triangle graph inside the box diagram in Bjorken and Regge asymptotics

We revisit the role of the chiral "triangle" anomaly in deeply inelastic scattering (DIS) of electrons off polarized protons employing a powerful worldline formalism. We demonstrate how the triangle anomaly appears at high energies in the DIS box diagram for the polarized proton structure function $g_1(x_B,Q^2)$ in both the Bjorken limit of large $Q^2$ and in the Regge limit of small $x_B$. We show that the operator product expansion is not required to extract the anomaly in either asymptotics though it is sufficient in the Bjorken limit. Likewise, the infrared pole in the anomaly arises in both limits. The leading contribution to $g_1$, in both Bjorken and Regge asymptotics, is therefore given by the expectation value of the topological charge density, generalizing a result previously argued by Jaffe and Manohar to hold for the first moment of $g_1$. In follow-up work, we will show how our results motivate the derivation of a helicity-dependent effective action incorporating the physics of the anomaly at small $x_B$ and shall discuss the QCD evolution of $g_1(x_B,Q^2)$ in this framework.
MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation

Recently, self-attention based models have achieved state-of-the-art performance in sequential recommendation task. Following the custom from language processing, most of these models rely on a simple positional embedding to exploit the sequential nature of the user’s history. However, there are some limitations regarding the current approaches. First, sequential recommendation is different from language processing in that timestamp information is available. Previous models have not made good use of it to extract additional contextual information. Second, using a simple embedding scheme can lead to information bottleneck since the same embedding has to represent all possible contextual biases. Third, since previous models use the same positional embedding in each attention head, they can wastefully learn overlapping patterns. To address these limitations, we propose MEANTIME (MixturE of AtteNTIon mechanisms with Multi-temporal Embeddings) which employs multiple types of temporal embeddings designed to capture various patterns from the user’s behavior sequence, and an attention structure that fully leverages such diversity. Experiments on real-world data show that our proposed method outperforms current state-of-the-art sequential recommendation methods, and we provide an extensive ablation study to analyze how the model gains from the diverse positional information.
BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models

This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.
Karyotypic characterization of Muscina stabulans (Fallen) (Diptera: Muscidae) using conventional staining, silver staining and C-banding

SUMMARYChromosome analysis of Muscina stabulans with Giemsa and silver nitrate staining and C-banding revealed an example of an exception among Muscidae. M. stabulans also presents the diploid chromosomal set consisting of 10 biarmed chromosomes, being 4 pairs metacentric and one pair submetacentric. None of the pairs was found heteromorphic. Positive but small C-band regions were always located on the centromeric regions of all chromosomes; an intersticial C + band appears only on the short arm of chromosome 2. In meiotic plates only chromosome 2 was observed bearing one Ag-NOR site on the short arm too.
Improving predictions of Bayesian neural nets via local linearization

The generalized Gauss-Newton (ggn) approximation is often used to make practical Bayesian deep learning approaches scalable by replacing a second order derivative with a product of first order derivatives. In this paper we argue that the ggn approximation should be understood as a local linearization of the underlying Bayesian neural network (bnn), which turns the bnn into a generalized linear model (glm). Because we use this linearized model for posterior inference, we should also predict using this modified model instead of the original one. We refer to this modified predictive as “glm predictive” and show that it effectively resolves common underfitting problems of the Laplace approximation. It extends previous results in this vein to general likelihoods and has an equivalent Gaussian process formulation, which enables alternative inference schemes for bnns in function space. We demonstrate the effectiveness of our approach on several standard classification datasets and on out-of-distribution detection. We provide an implementation at https://github.com/ AlexImmer/BNN-predictions.
Endoscopic decompositions and the Hausel–Thaddeus conjecture

Abstract We construct natural operators connecting the cohomology of the moduli spaces of stable Higgs bundles with different ranks and genera which, after numerical specialisation, recover the topological mirror symmetry conjecture of Hausel and Thaddeus concerning $\mathrm {SL}_n$- and $\mathrm {PGL}_n$-Higgs bundles. This provides a complete description of the cohomology of the moduli space of stable $\mathrm {SL}_n$-Higgs bundles in terms of the tautological classes, and gives a new proof of the Hausel–Thaddeus conjecture, which was also proven recently by Gröchenig, Wyss and Ziegler via p-adic integration. Our method is to relate the decomposition theorem for the Hitchin fibration, using vanishing cycle functors, to the decomposition theorem for the twisted Hitchin fibration, whose supports are simpler.
Factors influencing killing rates of white-tailed deer by coyotes in eastern Canada

Predation affects the dynamics of many ungulate species. Until recently, little attention has been given to understanding the underlying processes and relationships in predator-prey systems. We examined factors affecting killing rates of white-tailed deer (Odocoileus virginianus) by coyotes (Canis latrans) in Nova Scotia, Canada. Snowshoe hare (Lepus americanus) and deer abundance, distribution and relative vulnerability of deer, and coyote group size all significantly influenced killing rates of deer by coyotes in winter. Groups of coyotes initiated proportionately more chases than single coyotes but chase success differed little among groups of 1-4 coyotes. Snow depth had a positive influence on success of pursuits. More kills were observed in areas of low deer density relative to areas with high deer densities. The mean distance of deer kills to recent clearcuts was significantly shorter than expected in an area where deer yarded during winter, but not in an area where deer did not aggregate during winter. Predator-prey ratios may not be a reliable indicator of predation rates of deer by coyotes because factors such as relative abundance and vulnerability of alternate prey, winter severity, and coyote social behavior also influence killing rates.
Weakly Supervised Learning with Region and Box-level Annotations for Salient Instance Segmentation

Salient instance segmentation is a new challenging task that received widespread attention in saliency detection area. Due to the limited scale of the existing dataset and the high mask annotations cost, it is difficult to train a salient instance neural network completely. In this paper, we appeal to train a salient instance segmentation framework by a weakly supervised source without resorting to laborious labeling. We present a cyclic global context salient instance segmentation network (CGCNet), which is supervised by the combination of the binary salient regions and bounding boxes from the existing saliency detection datasets. For a precise pixel-level location, a global feature refining layer is introduced that dilates the context features of each salient instance to the global context in the image. Meanwhile, a labeling updating scheme is embedded in the proposed framework to online update the weak annotations for next iteration. Experiment results demonstrate that the proposed end-to-end network trained by weakly supervised annotations can be competitive to the existing fully supervised salient instance segmentation methods. Without bells and whistles, our proposed method achieves a mask AP of 57.13%, which outperforms the best fully supervised methods and establishes new states of the art for weakly supervised salient instance segmentation.
Structure learning in inverse Ising problems using ℓ 2-regularized linear estimator

The inference performance of the pseudolikelihood method is discussed in the framework of the inverse Ising problem when the ℓ 2-regularized (ridge) linear regression is adopted. This setup is introduced for theoretically investigating the situation where the data generation model is different from the inference one, namely the model mismatch situation. In the teacher-student scenario under the assumption that the teacher couplings are sparse, the analysis is conducted using the replica and cavity methods, with a special focus on whether the presence/absence of teacher couplings is correctly inferred or not. The result indicates that despite the model mismatch, one can perfectly identify the network structure using naive linear regression without regularization when the number of spins N is smaller than the dataset size M, in the thermodynamic limit N → ∞. Further, to access the underdetermined region M < N, we examine the effect of the ℓ 2 regularization, and find that biases appear in all the coupling estimates, preventing the perfect identification of the network structure. We, however, find that the biases are shown to decay exponentially fast as the distance from the center spin chosen in the pseudolikelihood method grows. Based on this finding, we propose a two-stage estimator: in the first stage, the ridge regression is used and the estimates are pruned by a relatively small threshold; in the second stage the naive linear regression is conducted only on the remaining couplings, and the resultant estimates are again pruned by another relatively large threshold. This estimator with the appropriate regularization coefficient and thresholds is shown to achieve the perfect identification of the network structure even in 0 < M/N < 1. Results of extensive numerical experiments support these findings.
Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis

Timely access to accurate information is crucial during the COVID-19 pandemic. Prompted by key stakeholders' cautioning against an "infodemic", we study information sharing on Twitter from January through May 2020. We observe an overall surge in the volume of general as well as COVID-19-related tweets around peak lockdown in March/April 2020. With respect to engagement (retweets and likes), accounts related to healthcare, science, government and politics received by far the largest boosts, whereas accounts related to religion and sports saw a relative decrease in engagement. While the threat of an "infodemic" remains, our results show that social media also provide a platform for experts and public authorities to be widely heard during a global crisis.
Phase transition and microstructures of five-dimensional charged Gauss-Bonnet-AdS black holes in the grand canonical ensemble

In this paper, we study the small-large black hole phase transition and construct the Ruppeiner geometry for the five-dimensional charged Gauss-Bonnet-AdS black hole in the grand canonical ensemble. By making use of the equal area law, we obtain the analytical coexistence curve of the small and large black holes. Then the phase diagrams are examined. We also calculate the change of the thermodynamic volume during the small-large phase transition, which indicates that there exists a sudden change among the black hole microstructures. The corresponding normalized scalar curvature of the Ruppeiner geometry is also calculated. Combing with the empirical observation of scalar curvature, we find that for low electric potential, the attractive interaction dominates among the microstructures, while a high electric potential produces repulsive interactions. In the reduced parameter space, we observe that only attractive interaction is allowed when the coexistence region is excluded. The normalized scalar curvature also admits a critical exponent 2 and a universal constant $-\frac{1}{8}$. In particular, the value of the normalized scalar curvature keeps the same along the coexistence small and large black hole curves. So in the grand canonical ensemble, the interaction can keep constant at the phase transition where the black hole microstructures change. These results disclose the intriguing microstructures for the charged AdS black hole in the Gauss-Bonnet gravity.