Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Image Retrieval: A Survey

—In recent years a vast amount of visual content has been generated and shared from various ﬁelds, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i . e . , content based image retrieval (CBIR), is a long-established research area, and more efﬁcient and accurate methods are needed for real time retrieval. Artiﬁcial intelligence has made progress in CBIR and has signiﬁcantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used databases, benchmarks, and evaluation methods used in the ﬁeld. We collect common challenges and propose promising future directions. More speciﬁcally, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network ﬁne-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the ﬁeld of category-based CBIR.
Neural networks in quantum many-body physics: a hands-on tutorial

Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificial neural networks have been successfully deployed to mitigate the exponential complexity often encountered in quantum many-body physics, the study of properties of quantum systems built out of a large number of interacting particles. In this Article, we overview some applications of machine learning in condensed matter physics and quantum information, with particular emphasis on hands-on tutorials serving as a quick-start for a newcomer to the field. We present supervised machine learning with convolutional neural networks to learn a phase transition, unsupervised learning with restricted Boltzmann machines to perform quantum tomography, and variational Monte Carlo with recurrent neural-networks for approximating the ground state of a many-body Hamiltonian. We briefly review the key ingredients of each algorithm and their corresponding neural-network implementation, and show numerical experiments for a system of interacting Rydberg atoms in two dimensions.
Stability of Control Systems under Extended Weakly-Hard Constraints

—Control systems can show robustness to many events, like disturbances and model inaccuracies. It is natural to speculate that they are also robust to alterations of the control signal pattern, due to sporadic late completions (called deadline misses ) when implemented as a digital task on an embedded platform. Recent research analysed stability when imposing constraints on the maximum number of consecutive deadlines that can be missed. This is only one type of characterization, and results in a pessimistic analysis when applied to more general cases. To overcome this limitation, this paper proposes a comprehensive stability analysis for control systems subject to a set of generic constraints, describing the possible sequences of correct completions and deadline misses. The proposed analysis brings the assessment of control systems robustness to computational problems one step closer to the controller implementation.
Robust Instability Radius for Multi-agent Dynamical Systems with Cyclic Structure

This paper is concerned with robust instability analysis for linear multi-agent dynamical systems with cyclic structure. This relates to interesting and important periodic oscillation phenomena in biology and neuronal science, since the nonlinear phenomena often occur when the linearized model around an equilibrium point is unstable. We first make a problem setting on the analysis and define the notion of robust instability radius (RIR) as a quantitative measure for maximum allowable stable dynamic perturbation in terms of the H-infinity norm. After showing lower bounds of the RIR, we derive the exact RIR, which is analytic and scalable, for first order time-lag agents. Finally, we make a remark on the potential applicability to some classes of higher order systems.
Long directed paths in Eulerian digraphs

An old conjecture of Bollobás and Scott asserts that every Eulerian directed graph with average degree d contains a directed cycle of length at least Ω(d). The best known lower bound for this problem is Ω(d) by Huang, Ma, Shapira, Sudakov and Yuster. They asked whether this estimate can be improved at least for directed paths instead of cycles and whether one can find a long path starting from any vertex if the host digraph is connected. In this paper we break the √ d barrier, showing how to find a path of length Ω(d) from any vertex of a connected Eulerian digraph.
The Galactic Chemical Evolution of Chlorine

We measured 35Cl abundances in 52 M giants with metallicities in the range −0.5 < [Fe/H] < 0.12. Abundances and atmospheric parameters were derived using infrared spectra from CSHELL on the NASA Infrared Telescope Facility and from optical echelle spectra. We measured Cl abundances by fitting a H35Cl molecular feature at 3.6985 μm with synthetic spectra. We also measured the abundances of O, Ca, Ti, and Fe using atomic absorption lines. We find that the [Cl/Fe] ratio for our stars agrees with chemical evolution models of Cl, and the [Cl/Ca] ratio is broadly consistent with the solar ratio over our metallicity range. Both indicate that Cl is primarily made in core-collapse supernovae with some contributions from Type Ia supernovae. We suggest that other potential nucleosynthesis processes, such as the ν-process, are not significant producers of Cl. Finally, we also find our Cl abundances are consistent with H ii and planetary nebular abundances at a given oxygen abundance, although there is scatter in the data.
Property Inference from Poisoning

Property inference attacks consider an adversary who has access to a trained ML model and tries to extract some global statistics of the training data. In this work, we study property inference in scenarios where the adversary can maliciously control a part of the training data (poisoning data) with the goal of increasing the leakage. Previous works on poisoning attacks focused on trying to decrease the accuracy of models. Here, for the first time, we study poisoning attacks where the goal of the adversary is to increase the information leakage of the model. We show that poisoning attacks can boost the information leakage significantly and should be considered as a stronger threat model in sensitive applications where some of the data sources may be malicious.We theoretically prove that our attack can always succeed as long as the learning algorithm used has good generalization properties. Then we experimentally evaluate our on different datasets (Census dataset, Enron email dataset, MNIST and CelebA), properties (that are present in the training data as features, that are not present as features, and properties that are uncorrelated with the rest of the training data or classification task) and model architectures (including Resnet-18 and Resnet-50). We were able to achieve high attack accuracy with relatively low poisoning rate, namely, 2–3% poisoning in most of our experiments. We also evaluated our attacks on models trained with DP and we show that even with very small values for $\epsilon$, the attack is still quite successful1.1Code is available at https://github.com/smahloujifar/PropertyInferenceFromPoisoning.git
Can a patchy model describe the potential spread of West Nile virus in Germany?

In 2018, West Nile Virus (WNV) was detected for the first time in Germany. Since the first detection, 36 human cases and 175 cases in horses and birds are detected. The transmission cycle of West Nile Virus includes birds and mosquitoes and – as dead-end hosts – people and horses. Spatial dissemination of the disease is caused by the movements of birds and mosquitoes. While the activity and movement of mosquitoes are depending mainly on temperature, in the birds there is a complex movement pattern caused by local birds and long range dispersal birds. To this end, we have developed a metapopulation network model framework to delineate the potential spatial distribution and spread of WNV across Germany as well as to evaluate the risk throughout our proposed network model. Our model facilitates the interconnection amongst the vector, local birds and long range dispersal birds contact networks. We have assumed different distance dispersal kernels models for the vector and avian populations with the intention to include short and long range dispersal. The model includes spatial variation of mosquito abundance and the movements to resemble the reality.
Recent Trends in Named Entity Recognition (NER)

The availability of large amounts of computer-readable textual data and hardware that can process the data has shifted the focus of knowledge projects towards deep learning architecture. Natural Language Processing, particularly the task of Named Entity Recognition is no exception. The bulk of the learning methods that have produced state-of-the-art results have changed the deep learning model, the training method used, the training data itself or the encoding of the output of the NER system. In this paper, we review significant learning methods that have been employed for NER in the recent past and how they came about from the linear learning methods of the past. We also cover the progress of related tasks that are upstream or downstream to NER, e.g., sequence tagging, entity linking, etc., wherever the processes in question have also improved NER results.
Quantum machine learning models are kernel methods

With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a machine learning model with a quantum circuit. While such"quantum models"are sometimes called"quantum neural networks", it has been repeatedly noted that their mathematical structure is actually much more closely related to kernel methods: they analyse data in high-dimensional Hilbert spaces to which we only have access through inner products revealed by measurements. This technical manuscript summarises, formalises and extends the link by systematically rephrasing quantum models as a kernel method. It shows that most near-term and fault-tolerant quantum models can be replaced by a general support vector machine whose kernel computes distances between data-encoding quantum states. In particular, kernel-based training is guaranteed to find better or equally good quantum models than variational circuit training. Overall, the kernel perspective of quantum machine learning tells us that the way that data is encoded into quantum states is the main ingredient that can potentially set quantum models apart from classical machine learning models.
Logical-Combinatorial Approaches in Dynamic Recognition Problems

A pattern recognition scenario, where instead of object classification into the classes by the learning set, the algorithm aims to allocate all objects to the same, the so-called "normal" class, is the research objective. Given the learning set L; the class K0 is called “normal”, and the reminder l classes K1, K2, . , Kl from the environment K are “deviated”. The classification algorithm is for a recurrent use in a "classification, action" format. Actions Ai are defined for each “deviated” class Ki. Applied to an object x ∈ Ki, the action delivers update Ai(x) of the object. The goal is in constructing a classification algorithm A that applied repeatedly (small number of times) to the objects of L, moves the objects (correspondingly, the elements of K) to the “normal” class. In this way, the static recognition action is transferred to a dynamic domain. This paper is continuing the discussion on the “normal” class classification problem, its theoretical postulations, possible use cases, and advantages of using logical-combinatorial approaches in solving these dynamic recognition problems. Some light relation to the topics like reinforcement learning, and recurrent neural networks are also provided.
Calibrating and Improving Graph Contrastive Learning

Graph contrastive learning algorithms have demonstrated remarkable success in various applications such as node classification, link prediction, and graph clustering. However, in unsupervised graph contrastive learning, some contrastive pairs may contradict the truths in downstream tasks and thus the decrease of losses on these pairs undesirably harms the performance in the downstream tasks. To assess the discrepancy between the prediction and the ground-truth in the downstream tasks for these contrastive pairs, we adapt the expected calibration error (ECE) to graph contrastive learning. The analysis of ECE motivates us to propose a novel regularization method, Contrast-Reg, to ensure that decreasing the contrastive loss leads to better performance in the downstream tasks. As a plug-in regularizer, Contrast-Reg effectively improves the performance of existing graph contrastive learning algorithms. We provide both theoretical and empirical results to demonstrate the effectiveness of Contrast-Reg in enhancing the generalizability of the Graph Neural Network(GNN) model and improving the performance of graph contrastive algorithms with different similarity definitions and encoder backbones across various downstream tasks.
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation

Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision. Simple ‘direct transfer’ of a learned model based on a multilingual input encoding has provided a strong benchmark. This paper presents a method for unsupervised cross-lingual transfer that improves over direct transfer systems by using their output as implicit supervision as part of self-training on unlabelled text in the target language. The method assumes minimal resources and provides maximal flexibility by (a) accepting any pre-trained arc-factored dependency parser; (b) assuming no access to source language data; (c) supporting both projective and non-projective parsing; and (d) supporting multi-source transfer. With English as the source language, we show significant improvements over state-of-the-art transfer models on both distant and nearby languages, despite our conceptually simpler approach. We provide analyses of the choice of source languages for multi-source transfer, and the advantage of non-projective parsing. Our code is available online.
Tracing Young Star-forming Clumps in the Nearby Flocculent Spiral Galaxy NGC 7793 with UVIT Imaging

Star formation in galaxies is a hierarchical process with a wide range of scales, from smaller clusters to larger stellar complexes. Here, we present an ultra-violet imaging study of the nearby flocculent spiral galaxy NGC 7793, observed using the Ultra-Violet Imaging Telescope (UVIT). We find that the disk scale length estimated in Far-UV (2.64 ± 0.16 kpc) is larger than that in Near-UV (2.21 ± 0.21 Kpc) and optical (1.08 Kpc), which supports the inside-out growth scenario of the galaxy disk. The star-forming UV disk is also found to be contained within the extent of H i gas, with a column density greater than 1021 cm−2. With the spatial resolution of UVIT (1 pixel ∼ 6.8 pc), we identified 2046 young star-forming clumps in the galaxy with radii between ∼12 and 70 pc, which matches well with the size of giant molecular clouds (GMCs) detected in the galaxy. Around 61% of the regions identified in our study are younger than 20 Myr, which points to a recent enhancement in star formation across the galaxy. We also note that the youngest star-forming regions, with an age of <10 Myr, distinctly trace the flocculent arms of the galaxy. The estimated mass of the clumps covers a range between 103 and 106 M ⊙. We notice a gradient in the mass distribution of identified clumps along the spiral arms. We also study the nuclear star cluster of the galaxy, finding that the stellar populations in the cluster outskirts are younger than the inner part.
A Two-Functional-Network Framework of Opinion Dynamics

A common trait involving the opinion dynamics in social networks is an anchor on interacting network to characterize the opinion formation process among participating social actors, such as information flow, cooperative and antagonistic influence, etc. Nevertheless, interacting networks are generally public for social groups, as well as other individuals who may be interested in. This blocks a more precise interpretation of the opinion formation process since social actors always have complex feeling, motivation and behavior, even beliefs that are personally private. In this paper, we formulate a general configuration on describing how individual's opinion evolves in a distinct fashion. It consists of two functional networks: interacting network and appraisal network. Interacting network inherits the operational properties as DeGroot iterative opinion pooling scheme while appraisal network, forming a belief system, quantifies certain cognitive orientation to interested individuals' beliefs, over which the adhered attitudes may have the potential to be antagonistic. We explicitly show that cooperative appraisal network always leads to consensus in opinions. Antagonistic appraisal network, however, causes opinion cluster. It is verified that antagonistic appraisal network affords to guarantee consensus by imposing some extra restrictions. They hence bridge a gap between the consensus and the clusters in opinion dynamics. We further attain a gauge on the appraisal network by means of the random convex optimization approach. Moreover, we extend our results to the case of mutually interdependent issues.
Fate of a bulge in an inflated hyperelastic tube: theory and experiment

Mechanical instability in a pre-tensioned finite hyperelastic tube subjected to a slowly increasing internal pressure produces a spatially localized bulge at a critical pressure. This instability is studied in controlled experiments on inflated latex rubber tubes, from the perspective of buckling observed in aneurysms and their rupture risk. The fate of the bulge under continued inflation is governed by the end-conditions and the initial tension in the tube. In a tube with one end fixed and a weight attached to the other freely moving end, the bulge propagates axially at low initial tension, growing in length, and the tube relaxes by extension without buckling. Rupture occurs when the tension is high. By contrast, the bulge formed in an initially stretched tube held fixed at both its ends can buckle or rupture, depending on the amount of initial tension. Experiments are reported for different initial tensions and boundary conditions (BCs). Failure maps in the stretch parameter space and in stretch–tension space are constructed by extending existing theories for bulge formation and buckling analyses to the experimentally relevant BCs. Failure maps deduced from the theory are compared against experiments, and the underlying assumptions are critically assessed. Experiments reveal that buckling provides an alternative route to relieve the stress built up during inflation. Hence, buckling, when it occurs, can be a protective fail-safe mechanism against the rupture of a bulge in an inflated elastic tube.
Convergence rates of Gibbs measures with degenerate minimum

We study convergence rates for Gibbs measures, with density proportional to $e^{-f(x)/t}$, as $t \rightarrow 0$ where $f : \mathbb{R}^d \rightarrow \mathbb{R}$ admits a unique global minimum at $x^\star$. We focus on the case where the Hessian is not definite at $x^\star$. We assume instead that the minimum is strictly polynomial and give a higher order nested expansion of $f$ at $x^\star$, which depends on every coordinate. We give an algorithm yielding such a decomposition if the polynomial order of $x^\star$ is no more than $8$, in connection with Hilbert's $17^{\text{th}}$ problem. However, we prove that the case where the order is $10$ or higher is fundamentally different and that further assumptions are needed. We then give the rate of convergence of Gibbs measures using this expansion. Finally we adapt our results to the multiple well case.
Bayesian Paired-Comparison with the bpcs Package

This article introduces the bpcs R package (Bayesian Paired Comparison in Stan) and the statistical models implemented in the package. The goal of this package is to facilitate the use of Bayesian models for paired comparison data in behavioral research. Historically, studies on preferences have relied on Likert scale assessments and the frequentist approach to analyze the data. As an alternative, this article proposes the use of Bayesian models for forced choices assessments. The advantages of forced-choice assessments are that they minimize common bias that Likert scales are susceptible to, they can increase criterion validity, control for faking responses, and are quite useful to assess preferences in studies with children and non-humans primates. Bayesian data analyses are less sensitive to optional stopping, have better control of type I error, have stronger evidence towards the null hypothesis, allows propagation of uncertainties, includes prior information, and perform well when handling models with many parameters and latent variables. The bpcs package provides a consistent interface for R users and several functions to evaluate the posterior distribution of all parameters, to estimate the posterior distribution of any contest between items, and to obtain the posterior distribution of the ranks. Three reanalyses of recent studies that used the frequentist Bradley-Terry model are presented. These reanalyses are conducted with the Bayesian models of the bpcs package and all the code used to fit the models, generate the figures and the tables are available in the online appendix. Funding: This work was partially supported by the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation. The authors would like to thank L. Hopper for revising the results of the second reanalysis and G. Marton for revising the results of the third reanalysis. ar X iv :2 10 1. 11 22 7v 2 [ st at . M E ] 2 9 Ja n 20 21 BAYESIAN PAIRED-COMPARISON WITH THE BPCS PACKAGE 2
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection

The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media does not exist independently in the form of an article. Many other entities, such as news creators, news subjects, and so on, exist on social media and have relationships with news articles. Different entities and relationships can be modeled as a heterogeneous information network (HIN). In this paper, we attempt to solve the fake news detection problem with the support of a news-oriented HIN. We propose a novel fake news detection framework, namely Adversarial Active Learning-based Heterogeneous Graph Neural Network (AA-HGNN) which employs a novel hierarchical attention mechanism to perform node representation learning in the HIN. AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data. An adversarial selector will be trained to query high-value candidates for the active learning framework. When the adversarial active learning is completed, AA-HGNN detects fake news by classifying news article nodes. Experiments with two real-world fake news datasets show that our model can outperform text-based models and other graph-based models when using less labeled data benefiting from the adversarial active learning. As a model with generalizability, AA-HGNN also has the ability to be widely used in other node classification-related applications on heterogeneous graphs.