Miracle-dz/UniFAR-FTUs huggingface dataset:
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection

Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real-world. By facilitating labeled links of the graph as the training data, numerous deep learning based link prediction methods have been studied, which have dominant prediction accuracy compared with non-deep methods. However,the threats of maliciously crafted training graph will leave a specific backdoor in the deep model, thus when some specific examples are fed into the model, it will make wrong prediction, defined as backdoor attack. It is an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of backdoor attack on link prediction, and propose Link-Backdoor to reveal the training vulnerability of the existing link prediction methods. Specifically, the Link-Backdoor combines the fake nodes with the nodes of the target link to form a trigger. Moreover, it optimizes the trigger by the gradient information from the target model. Consequently, the link prediction model trained on the backdoored dataset will predict the link with trigger to the target state. Extensive experiments on five benchmark datasets and five well-performing link prediction models demonstrate that the Link-Backdoor achieves the state-of-the-art attack success rate under both white-box (i.e., available of the target model parameter)and black-box (i.e., unavailable of the target model parameter) scenarios. Additionally, we testify the attack under defensive circumstance, and the results indicate that the Link-Backdoor still can construct successful attack on the well-performing link prediction methods. The code and data are available at https://github.com/Seaocn/Link-Backdoor.
Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification

Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled data via contrasting different augmented views of data. In this work, we propose a novel Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC) that learns representations from unlabeled data with contrastive learning. Specifically, we propose time-series-specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module. Additionally, we conduct a systematic study of time-series data augmentation selection, which is a key part of contrastive learning. We also extend TS-TCC to the semi-supervised learning settings and propose a Class-Aware TS-TCC (CA-TCC) that benefits from the available few labeled data to further improve representations learned by TS-TCC. Specifically, we leverage the robust pseudo labels produced by TS-TCC to realize a class-aware contrastive loss. Extensive experiments show that the linear evaluation of the features learned by our proposed framework performs comparably with the fully supervised training. Additionally, our framework shows high efficiency in few labeled data and transfer learning scenarios.
An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs

We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun conversational question answering (ConvQA). However, in a real-world scenario (e.g., voice assistants such as Siri, Alexa, and Google Assistant), users prefer verbalized answers. This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with multiple paraphrased verbalized answers. We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness. We additionally perform an error analysis that details the rates of models' mispredictions in specified categories. Our proposed dataset extended with answer verbalization is publicly available with detailed documentation on its usage for wider utility.
Discrete sets definable in strong expansions of ordered Abelian groups

We study the structure of infinite discrete sets D definable in expansions of ordered Abelian groups whose theories are strong and definably complete, with particular emphasis on the set D' comprised of differences between successive elements. In particular, if the burden of the structure is at most n, then the result of applying the operation mapping D to D' n times must be a finite set (Theorem 2.13). In the case when the structure is densely ordered and has burden 2, we show that any definable unary discrete set must be definable in some elementary extension of the structure (R;<, +, Z) (Theorem 3.1).
Entropy Induced Pruning Framework for Convolutional Neural Networks

Structured pruning techniques have achieved great compression performance on convolutional neural networks for image classification tasks. However, the majority of existing methods are sensitive with respect to the model parameters, and their pruning results may be unsatisfactory when the original model is trained poorly. That is, they need the original model to be fully trained, to obtain useful weight information. This is time-consuming, and makes the effectiveness of the pruning results dependent on the degree of model optimization. To address the above issue, we propose a novel metric named Average Filter Information Entropy (AFIE). It decomposes the weight matrix of each layer into a low-rank space, and quantifies the filter importance based on the distribution of the normalized eigenvalues. Intuitively, the eigenvalues capture the covariance among filters, and therefore could be a good guide for pruning. Since the distribution of eigenvalues is robust to the updating of parameters, AFIE can yield a stable evaluation for the importance of each filter no matter whether the original model is trained fully. We implement our AFIE-based pruning method for three popular CNN models of AlexNet, VGG-16, and ResNet-50, and test them on three widely-used image datasets MNIST, CIFAR-10, and ImageNet, respectively. The experimental results are encouraging. We surprisingly observe that for our methods, even when the original model is trained with only one epoch, the AFIE score of each filter keeps identical to the results when the model is fully-trained. This fully indicates the effectiveness of the proposed pruning method.
Efficient quantum algorithms for solving quantum linear system problems

We transform the problem of solving linear system of equations A x = b to a problem of ﬁnding the right singular vector with singular value zero of an augmented matrix C , and present two quantum algorithms for solving this problem. The ﬁrst algorithm solves the problem directly by applying the quantum eigenstate ﬁltering algorithm with query complexity of O ( sκ log (1 /ǫ )) for a s -sparse matrix C , where κ is the condition number of the matrix A , and ǫ is the desired precision. The second algorithm uses the quantum resonant transition approach, the query complexity scales as O [ sκ + log (1 /ǫ ) / log log (1 /ǫ )]. Both algorithms meet the optimal query complexity in κ , and are simpler than previous algorithms.
Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves

Cosmological shock waves are essential to understanding the formation of cosmological structures. To study them, scientists run computationally expensive high-resolution 3D hydrodynamic simulations. Interpreting the simulation results is challenging because the resulting data sets are enormous, and the shock wave surfaces are hard to separate and classify due to their complex morphologies and multiple shock fronts intersecting. We introduce a novel pipeline, Virgo, combining physical motivation, scalability, and probabilistic robustness to tackle this unsolved unsupervised classiﬁcation problem. To this end, we employ kernel principal component analysis with low-rank matrix approximations to denoise data sets of shocked particles and create labeled subsets. We perform supervised classiﬁcation to recover full data resolution with stochastic variational deep kernel learning. We evaluate on three state-of-the-art data sets with varying complexity and achieve good results. The proposed pipeline runs automatically, has only a few hyperparameters, and performs well on all tested data sets. Our results are promising for large-scale applications, and we highlight now enabled future scientiﬁc work.
Flexible Bayesian Multiple Comparison Adjustment Using Dirichlet Process and Beta-Binomial Model Priors

Researchers frequently wish to assess the equality or inequality of groups, but this comes with the challenge of adequately adjusting for multiple comparisons. Statistically, all possible configurations of equality and inequality constraints can be uniquely represented as partitions of the groups, where any number of groups are equal if they are in the same partition. In a Bayesian framework, one can adjust for multiple comparisons by constructing a suitable prior distribution over all possible partitions. Inspired by work on variable selection in regression, we propose a class of flexible beta-binomial priors for Bayesian multiple comparison adjustment. We compare this prior setup to the Dirichlet process prior suggested by Gopalan and Berry (1998) and multiple comparison adjustment methods that do not specify a prior over partitions directly. Our approach to multiple comparison adjustment not only allows researchers to assess all pairwise (in)equalities, but in fact all possible (in)equalities among all groups. As a consequence, the space of possible partitions grows quickly - for ten groups, there are already 115,975 possible partitions - and we set up a stochastic search algorithm to efficiently explore the space. Our method is implemented in the Julia package EqualitySampler, and we illustrate it on examples related to the comparison of means, variances, and proportions.
Complete numerical description of the laser-induced thermal profile in a liquid, to explain complex self-induced diffraction patterns

In the past, laser propagation in a fluid with heat transfer has been modeled using simplistic conduction and convection conditions yielding inaccurate predictions. Here we present a detailed numerical study describing the thermal profile of the fluid and its interaction with the laser. Furthermore, we evaluate the diffraction field in the far field produced by a pump beam impinging in the fluid and the interferometric pattern obtained normal to the propagation direction to test our model. Direct comparison between experimental results and numerical simulation allows for a complete understanding of the energy transfer from the laser to the liquid and the subsequent effect on the laser propagation. Spatial self-phase modulation and propagation control from small-phase diffraction to aberration-controlled diffraction and up to diffraction oscillation are observed and explained with our modeling.
Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression

One of the major open problems in machine learning is to characterize generalization in the overparameterized regime, where most traditional generalization bounds become inconsistent even for overparameterized linear regression. In many scenarios, this failure can be attributed to obscuring the crucial interplay between the training algorithm and the underlying data distribution. This paper demonstrate that the generalization behavior of overparameterized model should be analyzed in a both data-relevant and algorithm-relevant manner. To make a formal characterization, We introduce a notion called data-algorithm compatibility, which considers the generalization behavior of the entire data-dependent training trajectory, instead of traditional last-iterate analysis. We validate our claim by studying the setting of solving overparameterized linear regression with gradient descent. Specifically, we perform a data-dependent trajectory analysis and derive a sufficient condition for compatibility in such a setting. Our theoretical results demonstrate that if we take early stopping iterates into consideration, generalization can hold with significantly weaker restrictions on the problem instance than the previous last-iterate analysis.
DAOS as HPC Storage: a View From Numerical Weather Prediction

Object storage solutions potentially address long-standing performance issues with POSIX file systems for certain I/O workloads, and new storage technologies offer promising performance characteristics for data-intensive use cases.In this work, we present a preliminary assessment of Intel’s Distributed Asynchronous Object Store (DAOS), an emerging high-performance object store, in conjunction with non-volatile storage and evaluate its potential use for HPC storage. We demonstrate DAOS can provide the required performance, with bandwidth scaling linearly with additional DAOS server nodes in most cases, although choices in configuration and application design can impact achievable bandwidth. We describe a new I/O benchmark and associated metrics that address object storage performance from application-derived workloads.
On the intersection form of fillings

We prove, by an ad hoc method, that exact fillings with vanishing rational first Chern class of flexibly fillable contact manifolds have unique integral intersection forms. We appeal to the special Reeb dynamics (stronger than ADC \`a la Lazarev) on the contact boundary, while a more systematic approach working for general ADC manifolds is developed independently by Eliashberg, Ganatra and Lazarev. We also discuss cases where the vanishing rational first Chern class assumption can be removed. We derive the uniqueness of diffeomorphism types of exact fillings of certain flexibly fillable contact manifolds and obstructions to contact embeddings, which are not necessarily exact.
Preparation, Characterization and Microwave Dielectric Properties of A2BWO6 (A=Sr, Ba; B=Co, Ni, Zn) Double Perovskite Ceramics

The A2BWO6 (A=Sr, Ba; B=Co, Ni, Zn) microwave dielectric ceramics were prepared by conventional solid-state ceramic route. According to the X-ray diffraction (XRD) results, the ceramics have cubic perovskite structure for all compounds. In addition, the Ni-containing compounds showed apparent supperlattice reflections at low angles, whereas the others didn't. The results of Raman spectroscopy show that the degree of B-site ordering is different with different B2+ ions. The dielectric properties of the ceramics were measured in the frequency range 6.5 to 8.5 GHz using resonance methods. The ceramics have dielectric constant in the range 18 to 30, high quality factor (Q×f value up to 56000 GHz) and negative temperature coefficient of resonant frequencies in the range -72.9 to -31.1 ppm/°C. The dielectric constant increased with increasing B2+ ionic polarizability, and the quality factor was enhanced due to the increase in B-site ordering. The relationship between temperature coefficient of dielectric constant and tolerance factor was accordant in comparison with the other perovskites.
Hellings and Downs correlation of an arbitrary set of pulsars

Pulsar timing arrays (PTAs) detect gravitational waves (GWs) via the correlations they induce in the arrival times of pulses from different pulsars. We assume that the GWs are described by a Gaussian ensemble. The mean correlation $h^2 \mu_{\rm u}(\gamma)$ as a function of the angle $\gamma$ between the directions to two pulsars was predicted by Hellings and Downs (HD) in 1983. The variance $\sigma^2_{\rm tot}(\gamma)$ in this correlation was recently calculated by Allen[11] for a single noise-free pulsar pair at angle $\gamma$, which shows that after averaging over many pairs, the variance reduces to an intrinsic cosmic variance $\sigma^2_{\rm cos}(\gamma)$. Here, we extend this to an $arbitrary$ set of pulsars at specific sky locations, with pulsar pairs binned by $\gamma$. We derive the linear combination of pulsar-pair correlations which is the optimal estimator of the HD correlation for each bin, illustrating our methods with plots of the expected range of variation away from the HD curve, for the sets of pulsars monitored by three active PTA collaborations. We compute the variance of and the covariance between these binned estimates, and show that these reduce to the cosmic variance and covariance $s(\gamma,\gamma')$ respectively, in the many-pulsar limit. The likely fluctuations away from the HD curve $\mu_{\rm u}(\gamma)$ are strongly correlated/anticorrelated in the three angular regions where $\mu_{\rm u}(\gamma)$ is successively positive, negative, and positive. We also construct the optimal estimator of the squared strain $h^2$. When there are very many pulsar pairs, this determines $h^2$ with arbitrary precision because PTAs probe an infinite set of GW modes. To assess observed deviations away from the HD curve, we characterize several $\chi^2$ goodness-of-fit statistics. We also show how pulsar noise and measurement noise can be included.
Class-attention Video Transformer for Engagement Intensity Prediction

In order to deal with variant-length long videos, prior works extract multi-modal features and fuse them to predict students' engagement intensity. In this paper, we present a new end-to-end method Class Attention in Video Transformer (CavT), which involves a single vector to process class embedding and to uniformly perform end-to-end learning on variant-length long videos and fixed-length short videos. Furthermore, to address the lack of sufficient samples, we propose a binary-order representatives sampling method (BorS) to add multiple video sequences of each video to augment the training set. BorS+CavT not only achieves the state-of-the-art MSE (0.0495) on the EmotiW-EP dataset, but also obtains the state-of-the-art MSE (0.0377) on the DAiSEE dataset. The code and models have been made publicly available at https://github.com/mountainai/cavt.
A Pipeline for DNS-Based Software Fingerprinting

In this paper, we present the modular design and implementation of DONUT, a novel tool for identifying software running on a device. Our tool uses a rule-based approach to detect software-specific DNS fingerprints (stored in an easily extendable database) in passively monitored DNS traffic. We automated the rule extraction process for DONUT with the help of ATLAS, a novel tool we developed for labeling network traffic by the software that created it. We demonstrate the functionality of our pipeline by generating rules for a number of applications, evaluate the performance as well as scalability of the analysis, and confirm the functional correctness of DONUT using an artificial data set for which the ground-truth is known. In addition, we evaluate DONUT's analysis results on a large real-world data set with unknown ground truth.
Is the Distribution of Income Compatible with a Stable Distribution

Mandelbrot (1961) proposed to apply the class of Pareto-Levy distributions - which belong to the Stable distributions - as a framework for modelling income distributions. He also presented theoretic arguments in favor of the Pareto-Levy distributions. In this paper we provide additional theoretical justification for this class of distributions. We also use micro data on individual market income to estimate the parameters of a Pareto-Levy distribution. Several estimation methods have been applied. The estimated Pareto-Levy distribution appears to fit the data well.
SKYSURF-3: Testing Crowded Object Catalogs in the Hubble eXtreme Deep Field Mosaics to Study Sample Incompleteness from an Extragalactic Background Light Perspective

Extragalactic background light (EBL) studies have revealed a significant discrepancy between direct measurements—via instruments measuring “bare” sky from which Zodiacal and Galactic light models are subtracted—and measurements of the integrated galaxy light (IGL). This discrepancy could lie in either method, whether it be an incomplete Zodiacal model or missed faint galaxies in the IGL calculations. It has been proposed that the discrepancy is due to deep galaxy surveys, such as those with the Hubble Space Telescope, missing up to half of the faint galaxies with 24 ≲ m AB ≲ 29 mag. We address this possibility by simulating higher number densities of galaxies, and so assess incompleteness due to object overlap, with three replications of the Hubble UltraDeep Field (HUDF). SourceExtractor is used to compare the recovered counts and photometry to the original HUDF, allowing us to assess how many galaxies may have been missed due to confusion, i.e., due to blending with neighboring faint galaxies. This exercise reveals that, while up to 50% of faint galaxies with 28 ≲ m AB ≲ 29 mag were missed or blended with neighboring objects in certain filters, not enough were missed to account for the EBL discrepancy alone in any of the replications.
Look Both Ways: Bidirectional Visual Sensing for Automatic Multi-Camera Registration

This work describes the automatic registration of a large network (approximately 40) of fixed, ceiling-mounted environment cameras spread over a large area (approximately 800 squared meters) using a mobile calibration robot equipped with a single upward-facing fisheye camera and a backlit ArUco marker for easy detection. The fisheye camera is used to do visual odometry (VO), and the ArUco marker facilitates easy detection of the calibration robot in the environment cameras. In addition, the fisheye camera is also able to detect the environment cameras. This two-way, bidirectional detection constrains the pose of the environment cameras to solve an optimization problem. Such an approach can be used to automatically register a large-scale multi-camera system used for surveillance, automated parking, or robotic applications. This VO based multi-camera registration method has been extensively validated using real-world experiments, and also compared against a similar approach which uses a LiDAR - an expensive, heavier and power hungry sensor.
Antipodal two-weight rank metric codes

We consider the class of linear antipodal two-weight rank metric codes and discuss their properties and characterization in terms of $t$-spreads. It is shown that the dimension of such codes is $2$ and the minimum rank distance is at least half of the length. We construct antipodal two-weight rank metric codes from certain MRD codes. A complete classification of such codes is obtained, when the minimum rank distance is equal to half of the length. As a consequence of our construction of two-weight rank metric codes, we get some explicit two-weight Hamming metric codes.
Bias amplification in experimental social networks is reduced by resampling

Large-scale social networks are thought to contribute to polarization by amplifying people's biases. However, the complexity of these technologies makes it difficult to identify the mechanisms responsible and to evaluate mitigation strategies. Here we show under controlled laboratory conditions that information transmission through social networks amplifies motivational biases on a simple perceptual decision-making task. Participants in a large behavioral experiment showed increased rates of biased decision-making when part of a social network relative to asocial participants, across 40 independently evolving populations. Drawing on techniques from machine learning and Bayesian statistics, we identify a simple adjustment to content-selection algorithms that is predicted to mitigate bias amplification. This algorithm generates a sample of perspectives from within an individual's network that is more representative of the population as a whole. In a second large experiment, this strategy reduced bias amplification while maintaining the benefits of information sharing.