Miracle-dz/UniFAR-FTUs huggingface dataset:
The winner takes it all but one

We study competing first passage percolation on graphs generated by the configuration model with infinite-mean degrees. Initially, two uniformly chosen vertices are infected with a type 1 and type 2 infection, respectively, and the infection then spreads via nearest neighbors in the graph. The time it takes for the type 1 (resp. 2) infection to traverse an edge e is given by a random variable $X_1(e)$ (resp. $X_2(e)$ ) and, if the vertex at the other end of the edge is still uninfected, it then becomes type 1 (resp. 2) infected and immune to the other type. Assuming that the degrees follow a power-law distribution with exponent $\tau \in (1,2)$ , we show that with high probability as the number of vertices tends to infinity, one of the infection types occupies all vertices except for the starting point of the other type. Moreover, both infections have a positive probability of winning regardless of the passage-time distribution. The result is also shown to hold for the erased configuration model, where self-loops are erased and multiple edges are merged, and when the degrees are conditioned to be smaller than $n^\alpha$ for some $\alpha\gt 0$ .
Pseudo-Anosov subgroups of general fibered 3–manifold groups

We show that finitely generated and purely pseudo-Anosov subgroups of fundamental groups of fibered 3–manifolds with reducible monodromy are convex cocompact as subgroups of the mapping class group via the Birman exact sequence. Combined with results of Dowdall–Kent–Leininger and Kent–Leininger–Schleimer, this establishes the result for the image of all such fibered 3–manifold groups in the mapping class group.
"Am I Private and If So, how Many?" - Using Risk Communication Formats for Making Differential Privacy Understandable

Mobility data is essential for cities and communities to identify areas for necessary improvement. Data collected by mobility providers already contains all the information necessary, but privacy of the individuals needs to be preserved. Differential privacy (DP) defines a mathematical property which guarantees that certain limits of privacy are preserved while sharing such data, but its functionality and privacy protection are difficult to explain to laypeople. In this paper, we adapt risk communication formats in conjunction with a model for the privacy risks of DP. The result are privacy notifications which explain the risk to an individual's privacy when using DP, rather than DP's functionality. We evaluate these novel privacy communication formats in a crowdsourced study. We find that they perform similarly to the best performing DP communications used currently in terms of objective understanding, but did not make our participants as confident in their understanding. We also discovered an influence, similar to the Dunning-Kruger effect, of the statistical numeracy on the effectiveness of some of our privacy communication formats and the DP communication format used currently. These results generate hypotheses in multiple directions, for example, toward the use of risk visualization to improve the understandability of our formats or toward adaptive user interfaces which tailor the risk communication to the characteristics of the reader.
Underwater Image Enhancement Using Pre-trained Transformer

The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical example of the important role of the machine learning algorithms to support marine exploration and monitoring, reducing the need for human intervention like the manual processing of the images, thus saving time, effort, and cost. This paper is the first application of the image transformer-based approach called"Pre-Trained Image Processing Transformer"to underwater images. This approach is tested on the UFO-120 dataset, containing 1500 images with the corresponding clean images.
A Credible and Robust Approach to Ego-Motion Estimation Using an Automotive Radar

Consistent motion estimation is fundamental for all mobile autonomous systems. While this sounds like an easy task, often, it is not the case because of changing environmental conditions affecting odometry obtained from vision, Lidar, or the wheels themselves. Unsusceptible to challenging lighting and weather conditions, radar sensors are an obvious alternative. Usually, automotive radars return a sparse point cloud, representing the surroundings. Utilizing this information to motion estimation is challenging due to unstable and phantom measurements, which result in a high rate of outliers. We introduce a credible and robust probabilistic approach to estimate the ego-motion based on these challenging radar measurements; intended to be used within a loosely-coupled sensor fusion framework. Compared to existing solutions, evaluated on the popular nuScenes dataset and others, we show that our proposed algorithm is more credible while not depending on explicit correspondence calculation.
Does the Market of Citations Reward Reproducible Work?

The field of bibliometrics, studying citations and behavior, is critical to the discussion of reproducibility. Citations are one of the primary incentive and reward systems for academic work, and so we desire to know if this incentive rewards reproducible work. Yet to the best of our knowledge, only one work has attempted to look at this combined space, concluding that non-reproducible work is more highly cited. We show that answering this question is more challenging than first proposed, and subtle issues can inhibit a robust conclusion. To make inferences with more robust behavior, we propose a hierarchical Bayesian model that incorporates the citation rate over time, rather than the total number of citations after a fixed amount of time. In doing so we show that, under current evidence the answer is more likely that certain fields of study such as Medicine and Machine Learning (ML) do reward reproducible works with more citations, but other fields appear to have no relationship. Further, we find that making code available and thoroughly referencing prior works appear to also positively correlate with increased citations. Our code can be found at the following link: https://github.com/EdwardRaff/ReproducibleCitations/
Detonation modeling with the particles on demand method

A kinetic model based on the particles on demand method is introduced for gas phase detonation hydrodynamics in conjunction with the Lee–Tarver reaction model. The proposed model is realized on two- and three-dimensional lattices and is validated with a set of benchmarks. Quantitative validation is performed with the Chapman–Jouguet theory up to a detonation wave speed of Mach 20 in one dimension. Two-dimensional outward expanding circular detonation is tested for isotropy of the model as well as for the asymptotic detonation wave speed. Mach reflection angles are verified in setups consisting of interacting strong bow shocks emanating from detonation. Spherical detonation is computed to show the viability of the proposed model for three-dimensional simulations.
SoundBeam: Target Sound Extraction Conditioned on Sound-Class Labels and Enrollment Clues for Increased Performance and Continuous Learning

In many situations, we would like to hear desired sound events (SEs) while being able to ignore interference. Target sound extraction (TSE) tackles this problem by estimating the audio signal of the sounds of target SE classes in a mixture of sounds while suppressing all other sounds. We can achieve this with a neural network that extracts the target SEs by conditioning it on clues representing the target SE classes. Two types of clues have been proposed, i.e., target SE class labels and enrollment audio samples (or audio queries), which are pre-recorded audio samples of sounds from the target SE classes. Systems based on SE class labels can directly optimize embedding vectors representing the SE classes, resulting in high extraction performance. However, extending these systems to extract new SE classes not encountered during training is not easy. Enrollment-based approaches extract SEs by finding sounds in the mixtures that share similar characteristics to the enrollment audio samples. These approaches do not explicitly rely on SE class definitions and can thus handle new SE classes. In this paper, we introduce a TSE framework, SoundBeam, that combines the advantages of both approaches. We also perform an extensive evaluation of the different TSE schemes using synthesized and real mixtures, which shows the potential of SoundBeam.
ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation

ECGs have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits. However, the lack of public data and standard experimental protocols makes the evaluation and comparison of novel ECG methods difficult. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. We consider verification and identification tasks, single- and multi-session settings, and single- and multi-lead ECGs recorded with traditional and user-friendly devices. We also present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database, and evaluate it with detailed experimental protocol and public databases. With the popular PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in single- and multi-session verification. The results achieved prove the soundness of ECGXtractor across multiple scenarios and databases. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field.
Physics-assisted Generative Adversarial Network for X-Ray Tomography

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model

Pretrained language models have served as important backbones for natural language processing. Recently, in-domain pretraining has been shown to benefit various domain-specific downstream tasks. In the biomedical domain, natural language generation (NLG) tasks are of critical importance, while understudied. Approaching natural language understanding (NLU) tasks as NLG achieves satisfying performance in the general domain through constrained language generation or language prompting. We emphasize the lack of in-domain generative language models and the unsystematic generative downstream benchmarks in the biomedical domain, hindering the development of the research community. In this work, we introduce the generative language model BioBART that adapts BART to the biomedical domain. We collate various biomedical language generation tasks including dialogue, summarization, entity linking, and named entity recognition. BioBART pretrained on PubMed abstracts has enhanced performance compared to BART and set strong baselines on several tasks. Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.
Quantum Routing with Teleportation

We study the problem of implementing arbitrary permutations of qubits under interaction constraints in quantum systems that allow for arbitrarily fast local operations and classical communication (LOCC). In particular, we show examples of speedups over swap-based and more general unitary routing methods by distributing entanglement and using LOCC to perform quantum teleportation. We further describe an example of an interaction graph for which teleportation gives a logarithmic speedup in the worst-case routing time over swap-based routing. We also study limits on the speedup aﬀorded by quantum teleportation—showing an O ( √ N log N ) upper bound on the separation in routing time for any interaction graph—and give tighter bounds for some common classes of graphs.
FOURTH-ORDER SCHR ¨ODINGER TYPE OPERATOR WITH UNBOUNDED COEFFICIENTS IN L 2 ( R N )

In this paper we study generation results in L 2 ( R N ) for the fourth order Schr¨odinger type operator with unbounded coeﬃcients of the form A = a 2 ∆ 2 + V 2 where a ( x ) = 1 + | x | α and V = | x | β with α > 0 and β > ( α − 2) + . We obtain that ( − A, D ( A )) generates an analytic strongly continuous semigroup in L 2 ( R N ) for N ≥ 5. Moreover, the maximal domain D ( A ) can be characterized for N > 8 by the weighted Sobolev space D 2 ( A ) = { u ∈ H 4 ( R N ) : V 2 u ∈ L 2 ( R N ) , | x | 2 α − h D 4 − h u ∈ L 2 ( R N ) for h = 0 , 1 , 2 , 3 , 4 } .
Qade: solving differential equations on quantum annealers

We present a general method, called Qade, for solving differential equations using a quantum annealer. One of the main advantages of this method is its flexibility and reliability. On current devices, Qade can solve systems of coupled partial differential equations that depend linearly on the solution and its derivatives, with non-linear variable coefficients and arbitrary inhomogeneous terms. We test this through several examples that we implement in state-of-the-art quantum annealers. The examples include a partial differential equation and a system of coupled equations. This is the first time that equations of these types have been solved in such devices. We find that the solution can be obtained accurately for problems requiring a small enough function basis. We provide a Python package implementing the method at gitlab.com/jccriado/qade.
Multilevel memetic hypergraph partitioning with greedy recombination

The Hypergraph Partitioning (HGP) problem is a well-studied problem that finds applications in a variety of domains. In several application domains, such as the VLSI design and database migration planning, the quality of the solution is more of a concern than the running time of the algorithm. In this work, we introduce novel problem-specific recombination and mutation operators and develop a new multilevel memetic algorithm by combining these operators with kKaHyPar-E. The performance of our algorithm is compared with the state-of-the-art HGP algorithms on 150 real-life instances taken from the benchmark sets used in the literature. The experiments reveal that our algorithm outperforms all others, and finds the best solutions in 112, 115, and 125 instances in 2, 4, and 8 hours, respectively.
Galaxy clustering in the VVV Near-IR Galaxy Catalogue

Mapping galaxies at low Galactic latitudes and determining their clustering status are fundamental steps in defining the large-scale structure in the nearby Universe. The VVV Near-IR Galaxy Catalogue (VVV NIRGC) allows us to explore this region in great detail. Our goal is to identify galaxy overdensities and characterize galaxy clustering in the Zone of Avoidance. We use different clustering algorithms to identify galaxy overdensities: the Voronoi tessellations, the Minimum Spanning Tree and the Ordering Points To Identify the Clustering Structure. We studied the membership, isolation, compactness, and flux limits to identify compact groups of galaxies. Each method identified a variety of galaxy systems across the Galactic Plane that are publicly available. We also explore the probability that these systems are formed by concordant galaxies using mock catalogues. Nineteen galaxy systems were identified in all of the four methods. They have the highest probability to be real overdensities. We stress the need for spectroscopic follow-up observations to confirm and characterize these new structures.
Drivers' attention detection: a systematic literature review

Countless traffic accidents often occur because of the inattention of the drivers. Many factors can contribute to distractions while driving, since objects or events to physiological conditions, as drowsiness and fatigue, do not allow the driver to stay attentive. The technological progress allowed the development and application of many solutions to detect the attention in real situations, promoting the interest of the scientific community in these last years. Commonly, these solutions identify the lack of attention and alert the driver, in order to help her/him to recover the attention, avoiding serious accidents and preserving lives. Our work presents a Systematic Literature Review (SLR) of the methods and criteria used to detect attention of drivers at the wheel, focusing on those methods based on images. As results, 50 studies were selected from the literature on drivers' attention detection, in which 22 contain solutions in the desired context. The results of SLR can be used as a resource in the preparation of new research projects in drivers' attention detection.
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings

One intriguing property of adversarial attacks is their “transferability” – an adversarial example crafted with respect to one deep neural network (DNN) model is often found effective against other DNNs as well. Intensive research has been conducted on this phenomenon under simplistic controlled conditions. Yet, thus far there is still a lack of comprehensive understanding about transferability-based attacks (“transfer attacks”) in real-world environments.To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account. The study leads to a number of interesting findings which are inconsistent to the existing ones, including: (i) Simple surrogates do not necessarily improve real transfer attacks. (ii) No dominant surrogate architecture is found in real transfer attacks. (iii) It is the gap between posterior (output of the softmax layer) rather than the gap between logit (so-called κ value) that increases transferability. Moreover, by comparing with prior works, we demonstrate that transfer attacks possess many previously unknown properties in real-world environments, such as (i) Model similarity is not a well-defined concept. (ii) L2 norm of perturbation can generate high transferability without usage of gradient and is a more powerful source than L∞ norm. We believe this work sheds light on the vulnerabilities of popular MLaaS platforms and points to a few promising research directions.1
Measurement of static light scattering of bio-particles

Light scattering by small particles is one of the most prevailing and non-invasive technique for examining the properties of particulate systems chiefly of biological origin. The sub-micron particles including the bio-particles were so chosen because of their importance in biology and biomedical sciences. Light scattering investigation from homogenous, pathogenic Pseudomonas aeruginosa and non-pathogenic Mycobacterium smegmatis was carried out at two different wavelength of incident light, by using an original designed and fabricated polar and azimuth-dependent light scattering setup. Theoretical scattering plots were generated by using T-matrix approach to validate our analyses. Simulations of light scattering of these particles were also carried out using a novel Monte- Carlo simulation technique. The relation between experimental, theoretical and simulated result is presented in this paper. We have studied Pseudomonas aeruginosa and Mycobacterium smegmatis , and attempted for prospect of observing its morphological property by using light scattering tool.
Engagement Detection with Multi-Task Training in E-Learning Environments

Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes mean squared error and triplet loss together to determine the engagement level of students in an e-learning environment. The performance of this system is evaluated and compared against the state-of-the-art on a publicly available dataset as well as videos collected from real-life scenarios. The results show that ED-MTT achieves 6 % lower MSE than the best state-of-the-art performance with highly acceptable training time and lightweight feature extraction. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Generative Adversarial Method Based on Neural Tangent Kernels

The recent development of Generative adversarial networks (GANs) has driven many computer vision applica-tions. Despite the great synthesis quality, training GANs often confronts several issues, including non-convergence, mode collapse, and gradient vanishing. There exist several workarounds, for example, regularizing Lipschitz continuity and adopting Wasserstein distance. Although these methods can partially solve the problems, we argue that the problems are result from modeling the discriminator with deep neural networks. In this paper, we base on newly derived deep neural network theories called Neural Tangent Kernel (NTK) and propose a new generative algorithm called generative adversarial NTK (GA-NTK). The GA-NTK models the discriminator as a Gaussian Process (GP). With the help of the NTK theories, the training dynamics of GA-NTK can be described with a closed-form formula. To synthesize data with the closed-form formula, the objectives can be simpliﬁed into a single-level adversarial optimization problem. We conduct extensive experiments on real-world datasets, and the results show that GA-NTK can generate images comparable to those by GANs but is much easier to train under various conditions. We also study the current limitations of GA-NTK and propose some workarounds to make GA-NTK more practical.
Quantum encoding is suitable for matched filtering

Matched ﬁltering is a powerful signal searching technique used in several employments from radar and communications applications to gravitational-wave detection. Here we devise a method for matched ﬁltering with the use of quantum bits. Our method’s asymptotic time complexity does not depend on template length and, including encoding, is O ( L (log 2 L ) 2 ) for a data with length L and a template with length N , which is classically O ( NL ). Hence our method has superior time complexity over the classical computation for long templates. We demonstrate our method with real quantum hardware on 4 qubits and also with simulations.