October 2023 arXiv papers — page 105

A. Kievsky, E. Garrido, M. Viviani, L. E. Marcucci

Scattering experiments with three free nucleons in the ingoing channel are extremely challenging in terrestrial laboratories. Recently, the ALICE Collaboration has successfully measured the scattering of three protons indirectly, by using the femtoscopy method in high-energy proton-proton collisions at the Large Hadron Collider. In order to establish a conne

Zhibo Jin, Zhiyu Zhu, Xinyi Wang, Jiayu Zhang

While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attacks are one of the effective attack types, which targets the learnt features in the hidden layers to improve its transferability across different models. Yet it is observed that the

Long Ju, Faiza Afzal, Yufeng Zhang

In this paper, we mainly study the integrability of 1+1 dimensional dispersive long-wave equation. Firstly, the Lie symmetry analysis of the equation is carried out in the first part. And the optimal system of the equation is obtained according to the symmetry, and the invariant solution and the reduced form of the target equation are solved according to the

Dominik Happel, Philipp Brendel, Andreas Rosskopf, Stefan Ditze

To date, power electronics parameter design tasks are usually tackled using detailed optimization approaches with detailed simulations or using brute force grid search grid search with very fast simulations. A new method, named "Continuously Adapting Random Sampling" (CARS) is proposed, which provides a continuous method in between. This allows for very fast

Amir Rasouli

Predicting pedestrian behavior is one of the main challenges for intelligent driving systems. In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory prediction algorithms. Based on various contextual information, we extract driving scenarios for a meaningful and systematic approach to identifying challenges for prediction mo

Petr Baroň, Jiří Kvita, Radek Přívara, Jan Tomeček

We apply gradient boosting machine learning techniques to the problem of hadronic jet substructure recognition using classical subjettiness variables available within a common parameterized detector simulation package DELPHES. Per-jet tagging classification is being explored. Jets produced in simulated proton-proton collisions are identified as consistent wi

Camila Laranjeira, Daniel Andrade, Jefersson A. dos Santos

With the looming threat of climate change, neglected tropical diseases such as dengue, zika, and chikungunya have the potential to become an even greater global concern. Remote sensing technologies can aid in controlling the spread of Aedes Aegypti, the transmission vector of such diseases, by automating the detection and mapping of mosquito breeding sites,

Minwoo Kim, Marc G Genton, Raphael Huser, Stefano Castruccio

There is a wide availability of methods for testing normality under the assumption of independent and identically distributed data. When data are dependent in space and/or time, however, assessing and testing the marginal behavior is considerably more challenging, as the marginal behavior is impacted by the degree of dependence. We propose a new approach to

Laura Eisenberger, Thomas Siegert, Karl Mannheim, Werner Porod

The indirect search for dark matter is typically restricted to individual photon bands and instruments. In the context of multiwavelength observations, finding a weak signal in large fore- and backgrounds at only one wavelength band is hampered by systematic uncertainties dominating the signal strength. Dark matter particle annihilation is producing Standard

Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li

Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression toward earlier and better patient-specific pathology management. However, conventional approaches rarely take advantage of longitudinal information for detection and prediction purposes, especially for Diabetic Retinopathy (DR). In the past ye

Bo-Wen Fan, Run-Qiu Yang

This paper shows that the bulk metric of a planar/spherically/hyperbolically symmetric asymptotically anti-de Sitter static black brane/hole can be reconstructed from its boundary frequency 2-point correlation functions of two probe scalar operators by solving Gel'fand-Levitan-Marchenko integral equation. Since the frequency correlation function is easily ha

Seungju Han, Junhyeok Kim, Jack Hessel, Liwei Jiang

Commonsense norms are defeasible by context: reading books is usually great, but not when driving a car. While contexts can be explicitly described in language, in embodied scenarios, contexts are often provided visually. This type of visually grounded reasoning about defeasible commonsense norms is generally easy for humans, but (as we show) poses a challen

Tao Zhuo, Zhiyong Cheng, Hehe Fan, Mohan Kankanhalli

Continual Learning (CL) aims to incrementally update a trained model on new tasks without forgetting the acquired knowledge of old ones. Existing CL methods usually reduce forgetting with task priors, \ie using task identity or a subset of previously seen samples for model training. However, these methods would be infeasible when such priors are unknown in r

Irene Bouw, Nirvana Coppola, Elisa Lorenzo García, Anna Somoza

In this paper we determine the conductor exponent of non-special Ciani quartics at primes of potentially good reduction in terms of the Ciani invariants. As an intermediate step in order to do so, we provide a reconstruction algorithm to construct Ciani quartics with given invariants. We also discuss how to descend the provided model to be defined over the s

Michael Pandazis

A Riemann surface equipped with its conformal hyperbolic metric is parabolic if and only if the geodesic flow on its unit tangent bundle is ergodic. Let X be a Cantor tree or a blooming Cantor tree Riemann surface. Fix a geodesic pants decomposition of X and call the boundary geodesics in the decomposition cuffs. Basmajian, Hakobyan, and \vSari\'c proved tha

Monika Pytlarz, Adrian Onicas, Alessandro Crimi

Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising because it can allow histopathological analysis in the absence of an underlying invasive biopsy procedure. Here, we tested a method for generating microscopic histological images from MRI scans of the human corpus callosum using

Chunwei Tian, Xuanyu Zhang, Qi Zhang, Mingming Yang

Convolutional neural networks (CNNs) depend on deep network architectures to extract accurate information for image super-resolution. However, obtained information of these CNNs cannot completely express predicted high-quality images for complex scenes. In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual

Gino Bishop, Dmitry Bagrets, Frank K. Wilhelm

In the framework of the hybrid quantum-classical variational cluster approach (VCA) to strongly correlated electron systems one of the goals of a quantum subroutine is to find single-particle correlation functions of lattice fermions in polynomial time. Previous works suggested to use variants of the Hadamard test for this purpose, which requires an implemen

Ethan Abraham, Mohammadhasan Dinpajooh, Clàudia Climent, Abraham Nitzan

Despite the desirability of polymers for use in many products due to their flexibility, light weight, and durability, their status as thermal insulators has precluded their use in applications where thermal conductors are required. However, recent results suggest that the thermal conductance of polymers can be enhanced and that their heat transport behaviors

Manuel Traub, Frederic Becker, Adrian Sauter, Sebastian Otte

Current slot-oriented approaches for compositional scene segmentation from images and videos rely on provided background information or slot assignments. We present a segmented location and identity tracking system, Loci-Segmented (Loci-s), which does not require either of this information. It learns to dynamically segment scenes into interpretable backgroun

Wathela Alhassan, T. Bulik, M. Suchenek

We present PyMerger, a Python tool for detecting binary black hole (BBH) mergers from the Einstein Telescope (ET), based on a Deep Residual Neural Network model (ResNet). ResNet was trained on data combined from all three proposed sub-detectors of ET (TSDCD) to detect BBH mergers. Five different lower frequency cutoffs ($F_{\text{low}}$): 5 Hz, 10 Hz, 15 Hz,

Chunwei Tian, Menghua Zheng, Wangmeng Zuo, Shichao Zhang

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a

Yaowu Liu, Zhonghua Liu, Xihong Lin

Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly

Thomas J. Radley, Paul Houston, Matthew E. Hubbard

In this article we consider the application of Euler's homogeneous function theorem together with Stokes' theorem to exactly integrate families of polynomial spaces over general polygonal and polyhedral (polytopic) domains in two- and three-dimensions, respectively. This approach allows for the integrals to be evaluated based on only computing the values of

Pratik Chattopadhyay

In chiral Einstein-Cartan gravity, a new gauge fixing procedure is implemented recently, leading to a very economical perturbation expansion of the action. Using this formulation and the relevant gauge-fixing, we develop the ghost Lagrangian on an arbitrary Einstein background using the BRST formalism. The novelty is the appearance of a new term quadratic in

Kibum Kim, Kanghoon Yoon, Jaehyeong Jeon, Yeonjun In

Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard, studies on WSSGG have utilized image captions to obtain unlocalized triplets while primarily focusing on grounding the unlocalized triplets over image regions. However, th

Gengming Liu, Violet Workman, Jiho Noh, Yuhao Ma

Topological crystalline insulators (TCIs) are classified by topological invariants defined with respect to the crystalline symmetries of their gapped bulk. The bulk-boundary correspondence then links the topological properties of the bulk to robust observables on the edges, e.g., the existence of robust edge modes or fractional charge. In one dimension, TCIs

Jianhao Yuan, Jie Zhang, Shuyang Sun, Philip Torr

Synthetic training data has gained prominence in numerous learning tasks and scenarios, offering advantages such as dataset augmentation, generalization evaluation, and privacy preservation. Despite these benefits, the efficiency of synthetic data generated by current methodologies remains inferior when training advanced deep models exclusively, limiting its

Gabrielle Menet, Duc-Manh Nguyen

Let $d \geq 2$ and $n\geq 3$ be two natural numbers. Given any sequence $\kappa=(k_1,\dots,k_n) \in \mathbb{Z}^n$ such that $\gcd(k_1,\dots,k_n,d)=1$, we consider the family of Riemann surfaces obtained from the plane curves defined by $y^d=\prod_{i=1}^n(x-b_i)^{k_i}$, where $\{b_1,\dots,b_n\}$ are $n$ distinct points in $\mathbb{C}$. The monodromy of the co

Xiaohang Tang, Yi Zhou, Taichi Aida, Procheta Sen

Semantic Change Detection (SCD) of words is an important task for various NLP applications that must make time-sensitive predictions. Some words are used over time in novel ways to express new meanings, and these new meanings establish themselves as novel senses of existing words. On the other hand, Word Sense Disambiguation (WSD) methods associate ambiguous

Anand Brahmbhatt, Vipul Rathore, Mausam, Parag Singla

Recent literature has seen a significant focus on building machine learning models with specific properties such as fairness, i.e., being non-biased with respect to a given set of attributes, calibration i.e., model confidence being aligned with its predictive accuracy, and explainability, i.e., ability to be understandable to humans. While there has been wo

Elisabetta Colombo, Paola Frediani, Juan Carlos Naranjo, Gian Pietro Pirola

We study the local geometry of the moduli space of intermediate Jacobians of $(2,2)$-threefolds in ${\mathbb P}^2 \times {\mathbb P}^2$. More precisely, we prove that a composition of the second fundamental form of the Siegel metric in $\mathcal A_9$ restricted to this moduli space, with a natural multiplication map is a nonzero holomorphic section of a vect

Yagmur Yigit, Leandros Maglaras, Mohamed Amine Ferrag, Naghmeh Moradpoor

This paper delves into a comprehensive analysis of fault-tolerant memory systems, focusing on recovery techniques modeled using Markov chains to address transient errors. The study revolves around the application of scrubbing methods in conjunction with Single Error Correction and Double Error Detection (SEC-DED) codes. It explores three primary models: 1) E

Taichi Aida, Danushka Bollegala

Meanings of words change over time and across domains. Detecting the semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. We consider the problem of predicting whether a given target word, $w$, changes its meaning between two different text corpora, $\mathcal{C}_1$ and $\mathcal{C}_2$. For thi

Andrew Weng, Hamidreza Movahedi, Clement Wong, Jason B. Siegel

This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations for current and SOC imbalance dynamics within a charge or discharge cycle. These dynamics are represented by a

Seisho Abe

Nuclear deexcitation associated with neutrino interactions and nucleon decays has a significant impact on a recent key observable for neutrino detectors: neutron multiplicity. However, most existing neutrino Monte Carlo event generators do not consider this process. For a comprehensive prediction of the neutron multiplicity, a nuclear deexcitation simulator

Junhui Yang, Rohit Bhattacharya, Youjin Lee, Ted Westling

Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model that permits identification of a desired causal parameter. However, a fundamental challenge is correct specification of such a model, which usually involves making untestable assumpti

Hossein B. Jond

This paper studies cooperative control of noncooperative double-integrator multi-agent systems (MASs) with input delay on connected directed graphs in the context of a differential graphical game (DGG). In the distributed DGG, each agent seeks a distributed information control policy by optimizing an individual local performance index (PI) of distributed inf

Zhuoxiao Chen, Yadan Luo, Zixin Wang, Zijian Wang

LiDAR-based 3D object detection has recently seen significant advancements through active learning (AL), attaining satisfactory performance by training on a small fraction of strategically selected point clouds. However, in real-world deployments where streaming point clouds may include unknown or novel objects, the ability of current AL methods to capture s

Nikolaos E. Palaiodimopoulos, Simon Ohler, Michael Fleischhauer, David Petrosyan

We exploit controlled breaking of time-reversal symmetry to realize coherent routing of quantum information in spin networks. The key component of our scheme is a spin triangle whose chirality is determined by the quantum state of a control qubit which thus defines the propagation direction, or a superposition thereof, of the quantum information. We then con

Vittorio Martino, Giulio Tralli

In this paper we aim at characterizing the gauge balls in the Heisenberg group $\mathbb{H}^n$ as the only domains where suitable overdetermined problems of Serrin type can be solved. We discuss a one parameter family of overdetermined problems where both the source functions and the Neumann-like data are non-constant and they are related to the geometry of t

Weimi Zhou, Yong-Jin Liu

Solving the distributional worst-case in the distributionally robust optimization problem is equivalent to finding the projection onto the intersection of simplex and singly linear inequality constraint. This projection is a key component in the design of efficient first-order algorithms. This paper focuses on developing efficient algorithms for computing th

Jinyu Li

Due to the powerful edge-preserving ability and low computational complexity, Guided image filter (GIF) and its improved versions has been widely applied in computer vision and image processing. However, all of them are suffered halo artifacts to some degree, as the regularization parameter increase. In the case of inconsistent structure of guidance image an

Hsuan-Fu Hua, Ching-Ju Chang, Tse-Ching Lin, Ruby Chiu-Hsing Weng

The Elo rating system is a simple and widely used method for calculating players' skills from paired comparisons data. Many have extended it in various ways. Yet the question of updating players' variances remains to be further explored. In this paper, we address the issue of variance update by using the Laplace approximation for posterior distribution, toge

Shaomu Tan, Christof Monz

Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing but often suffers from poor zero-shot (ZS) translation qualities. While prior work has explored the causes of overall low ZS performance, our work introduces a fresh perspective: the presence of high variations in ZS performance. This suggests that MNMT does not uniformly exhibit po

S. S. Agaev, K. Azizi, B. Barsbay, H. Sundu

Decays of the fully beauty four-quark structures $X_{\mathrm{4b}}$ and $T_{ \mathrm{4b}}$ to $B$ meson pairs are investigated in the framework of QCD three-point sum rule method. We model the scalar exotic mesons $X_{\mathrm{4b }}$ and $T_{\mathrm{4b}}$ as diquark-antidiquark systems composed of the axial-vector and pseudoscalar diquarks, respectively. The m

Haoran Li, Yulin Chen, Jinglong Luo, Jiecong Wang

The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines. On the one hand, powerful language models, trained on massive textual data, have brought unparalleled accessibility and usability for both models and users. On the other han

Krzysztof Lech, Anna Zdunik

We consider sequences of compositions of quadratic polynomials $f_{c_n} (z) = z^2 + c_n$. For such sequences one can naturally generalize the definitions of the Julia set and basin of infinity from the autonomous case. In this setting the Julia set depends on a sequence $\omega = (c_0, c_1, ...)$. We study the equilibrium (harmonic) measure on such Julia set

V. V. Kovtyukh, S. M. Andrievsky, K. Werner, S. A. Korotin

While looking for recently discovered distant Cepheids with an interesting chemical composition, we noticed one star (OGLE GD-CEP-1353) with extremely large equivalent widths of spectral lines of heavy elements. The aim of this work is to perform an abundance analysis, and to find a possible explanation for the found chemical anomaly. Quantitative analysis o

Dustin Axman, Avik Ray, Shubham Garg, Jing Huang

Collection of annotated dialogs for training task-oriented dialog systems have been one of the key bottlenecks in improving current models. While dialog response generation has been widely studied on the agent side, it is not evident if similar generative models can be used to generate a large variety of, and often unexpected, user inputs that real dialog sy

Tianjun Ke, Haoqun Cao, Zenan Ling, Feng Zhou

Meta-learning has demonstrated promising results in few-shot classification (FSC) by learning to solve new problems using prior knowledge. Bayesian methods are effective at characterizing uncertainty in FSC, which is crucial in high-risk fields. In this context, the logistic-softmax likelihood is often employed as an alternative to the softmax likelihood in

Jirui Qi, Raquel Fernández, Arianna Bisazza

Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but large variations are observed across languages. With the ultimate goal of ensuring that users with different language backgrounds obtain consistent feedback from the same model, we study the cross-lingual consistency (CLC) of fac

Xi Jie Yeo, Eva Ernst, Alvin Leow, Jaesuk Hwang

We present a technique to estimate the proportion of coherent emission in the light emitted by a practical laser source without spectral filtering. The technique is based on measuring interferometric photon correlations between the output ports of an asymmetric Mach-Zehnder interferometer. With this, we characterize the fraction of coherent emission in the l

Yinchao Dong, Linhai Zhao

At present, the shunting process of train to track circuit is usually studied by taking the shunting resistance of the first wheel set of train as the equivalent model, which ignores the shunting effect of other wheel sets and cannot study the fault conditions such as "pool shunting". Especially for the jointless track circuit (JTC), the compensation capacit

Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger

As transformers are equivariant to the permutation of input tokens, encoding the positional information of tokens is necessary for many tasks. However, since existing positional encoding schemes have been initially designed for NLP tasks, their suitability for vision tasks, which typically exhibit different structural properties in their data, is questionabl

Jiahao Ji, Jingyuan Wang, Yu Mou, Cheng Long

Spatio-temporal (ST) prediction is an important and widely used technique in data mining and analytics, especially for ST data in urban systems such as transportation data. In practice, the ST data generation is usually influenced by various latent factors tied to natural phenomena or human socioeconomic activities, impacting specific spatial areas selective

Alexandre Blain, Bertrand Thirion, Olivier Grisel, Pierre Neuvial

Controlled variable selection is an important analytical step in various scientific fields, such as brain imaging or genomics. In these high-dimensional data settings, considering too many variables leads to poor models and high costs, hence the need for statistical guarantees on false positives. Knockoffs are a popular statistical tool for conditional varia

Ka My Dang, Yi Jia Zhang, Tianchen Zhang, Chao Wang

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracell

Manuel Traub, Frederic Becker, Sebastian Otte, Martin V. Butz

While human infants exhibit knowledge about object permanence from two months of age onwards, deep-learning approaches still largely fail to recognize objects' continued existence. We introduce a slot-based autoregressive deep learning system, the looped location and identity tracking model Loci-Looped, which learns to adaptively fuse latent imaginations wit

J. C. Bernauer, E. W. Cline, H. Atac, W. J. Briscoe

Human bias is capable of changing the analysis of measured data sufficiently to alter the results of an experiment. It is incumbent upon modern experiments, especially those investigating quantities considered contentious in the broader community, to blind their analysis in an effort to minimize bias. The choice of a blinding model is experiment specific, bu

Yan Pan, Jiapeng Xie, Jiajie Wu, Bo Zhou

Although significant progress has been made, achieving place recognition in environments with perspective changes, seasonal variations, and scene transformations remains challenging. Relying solely on perception information from a single sensor is insufficient to address these issues. Recognizing the complementarity between cameras and LiDAR, multi-modal fus

Valery V. Ryzhikov

We present spectrally disjoint Sidon automorphisms whose tensor squares are isomorphic to a planar shift. Spectra of such automorphisms do not possess the group property. To check the singularity of spectrum, we use polynomial rigidity of operators associated with Kolmogorov linear determinism. In the class of mixing Gaussian and Poisson suspensions we reali

Stephen F. King, Xin Wang

In a class of modular-invariant models with multiple moduli fields, the viable lepton flavour mixing pattern can be realised if the values of moduli are selected to be at the fixed points. In this paper, we investigate a modulus stabilisation mechanism in the multiple-modulus framework which is capable of providing de Sitter (dS) minima precisely at the fixe

Francisco A. Rodrigues

Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, an

Mohamed El Kadiri

Our aim in this paper is to prove that if plurisubharmonic functions $u_1,. . . , u_n$, $v_1,. . ., v_n$ in the domain of definition of the complex Monge-Amp\`ere operator on a domain set $D\subset \mathbb{C}^n$ ($n\geq 1$) are such that $u_1= v_1, . . ., u_n=v_n$ on a Borel plurifinely open set $\Omega\subset D$, then $$dd^cu_1\wedge ...\wedge dd^cu_n=dd^cv

Luis Crespo, Álvaro Pelayo, Francisco Santos

We solve several open problems concerning integer points of polytopes arising in symplectic and algebraic geometry. In this direction we give the first proof of a broad case of Ewald's Conjecture (1988) concerning symmetric integral points of monotone lattice polytopes in arbitrary dimension. We also include an asymptotic quantitative study of the set of poi

Amirsadegh Roshanzamir

Supervised Machine Learning is an innovative method that aims to mimic human learning by using past experiences. In this study, we utilize supervised machine learning algorithms to analyze the factors that contribute to the punctuality of Tehran BRT bus system. We gather publicly available datasets of 2020 to 2022 from Municipality of Tehran to train and tes

Chao Chen, Run-Ze Liu, Jizhou Wu, Zu-En Su

Berry curvature is a fundamental element to characterize topological quantum physics, while a full measurement of Berry curvature in momentum space was not reported for topological states. Here we achieve two-dimensional Berry curvature reconstruction in a photonic quantum anomalous Hall system via Hall transport measurement of a momentum-resolved wave packe

Juncai He, Xinliang Liu, Jinchao Xu

In this work, we propose a concise neural operator architecture for operator learning. Drawing an analogy with a conventional fully connected neural network, we define the neural operator as follows: the output of the $i$-th neuron in a nonlinear operator layer is defined by $O_i(u) = \sigma\left( \sum_j W_{ij} u + B_{ij}\right)$. Here, $ W_{ij}$ denotes the

Morsal Babayan, Evgeniy Redekop, Esko Kokkonen, Unni Olsbye

The Methanol-To-Hydrocarbons (MTH) process involves the conversion of methanol, a C1 feedstock that can be produced from green sources, into hydrocarbons using shape-selective microporous acidic catalysts - zeolite and zeotypes \cite{olsbye2012}. This reaction yields a complex mixture of species, some of which are highly reactive and/or present in several is

Massimiliano Fessina, Andrea Zaccaria, Giulio Cimini, Tiziano Squartini

Production networks arise from supply and customer relations among firms. These systems are gaining growing attention as a consequence of disruptions due to natural or man-made disasters that happened in the last years, such as the Covid-19 pandemic or the Russia-Ukraine war. However, data constraints force the few, available studies to consider only country

Chenghua Gong, Xiang Li, Jianxiang Yu, Cheng Yao

Graphs have become an important modeling tool for web applications, and Graph Neural Networks (GNNs) have achieved great success in graph representation learning. However, the performance of traditional GNNs heavily relies on a large amount of supervision. Recently, ``pre-train, fine-tune'' has become the paradigm to address the issues of label dependency an

Shamil Asgarli, Dragos Ghioca, Zinovy Reichstein

Let $d$ and $n$ be positive integers, and $E/F$ be a separable field extension of degree $m=\binom{n+d}{n}$. We show that if $|F| > 2$, then there exists a point $P\in \mathbb{P}^n(E)$ which does not lie on any degree $d$ hypersurface defined over $F$. In other words, the $m$ Galois conjugates of $P$ impose independent conditions on the $m$-dimensional $F$-v

G. V. Paradezhenko, A. A. Pervishko, D. Yudin

We propose a technique for optimizing parameterized circuits in variational quantum algorithms based on the probabilistic tensor sampling optimization. This method allows one to relax random initialization issues or heuristics for generating initial guess of variational parameters, and can be used to avoid local minima. We illustrate our approach on the exam

Marc Jourdan, Andrée Delahaye-Duriez, Clémence Réda

In good arm identification (GAI), the goal is to identify one arm whose average performance exceeds a given threshold, referred to as a good arm, if it exists. Few works have studied GAI in the fixed-budget setting when the sampling budget is fixed beforehand, or in the anytime setting, when a recommendation can be asked at any time. We propose APGAI, an any

Ananya Singha, José Cambronero, Sumit Gulwani, Vu Le

Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to process the table. Inspired by prior work, we generate a collection of self-supervised structural tasks (e.g. navigate to a cell and row; transpose the table) and evaluate the performa

Pengqin Wang, Meixin Zhu, Hongliang Lu, Hui Zhong

Prediction, decision-making, and motion planning are essential for autonomous driving. In most contemporary works, they are considered as individual modules or combined into a multi-task learning paradigm with a shared backbone but separate task heads. However, we argue that they should be integrated into a comprehensive framework. Although several recent ap

Antoine Honoré, Anubhab Ghosh, Saikat Chatterjee

We consider reconstruction of an ambient signal in a compressed sensing (CS) setup where the ambient signal has a neural network based generative model. The generative model has a sparse-latent input and we refer to the generated ambient signal as generative sparse-latent signal (GSL). The proposed sparsity inducing reconstruction algorithm is inherently non

F. Fontani, E. Roueff, L. Colzi, P. Caselli

To understand the chemistry of sulphur (S) in the interstellar medium, models need to be tested by observations of S-bearing molecules in different physical conditions. We analyse observations obtained with the IRAM 30m telescope towards 15 well-known cores classified in the three main evolutionary stages of the high-mass star-formation process: high-mass st

Prabhat Kumar, Josh Pinskier, David Howard, Matthijs Langelaar

Compliant mechanisms actuated by pneumatic loads are receiving increasing attention due to their direct applicability as soft robots that perform tasks using their flexible bodies. Using multiple materials to build them can further improve their performance and efficiency. Due to developments in additive manufacturing, the fabrication of multi-material soft

Yongkang Wang, Fujie Tang, Xiaoqing Yu, Kuo-Yang Chiang

Water plays a crucial role in geological, biological, and technological processes. Nanoscale water confinement occurs in many of these settings, including sedimentary rocks, water channel proteins, and applications like desalination and water purification membranes. The structure and properties of water in nanoconfinement can differ significantly from bulk w

Mathijs R. van Geerenstein, Felicia Ruppel, Klaus Dietmayer, Dariu M. Gavrila

3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network architecture used for this task, in which so-called object queries act as candidate objects. Initializing these object queries based on current sensor inputs is a common practice. Fo

Yifei Qian, Xiaopeng Hong, Zhongliang Guo, Ognjen Arandjelović

To alleviate the heavy annotation burden for training a reliable crowd counting model and thus make the model more practicable and accurate by being able to benefit from more data, this paper presents a new semi-supervised method based on the mean teacher framework. When there is a scarcity of labeled data available, the model is prone to overfit local patch

Haomian Huang, Li Chen, Enya Shen, Jianmin Wang

As the most essential part of CAD modeling operations, boolean operations on B-rep CAD models often suffer from errors. Errors caused by geometric precision or numerical uncertainty are hard to eliminate. They will reduce the reliability of boolean operations and damage the integrity of the resulting models. And it is difficult to repair false boolean result

Antonio Esposito, László Mikolás

We provide a well-posedness theory for a class of nonlocal continuity equations on co-evolving graphs. We describe the connection among vertices through an edge weight function and we let it evolve in time, coupling its dynamics with the dynamics on the graph. This is relevant in applications to opinion dynamics and transportation networks. Existence and uni

Junghyun Lee, Eunsang Lee, Young-Sik Kim, Yongwoo Lee

Recent studies have explored the deployment of privacy-preserving deep neural networks utilizing homomorphic encryption (HE), especially for private inference (PI). Many works have attempted the approximation-aware training (AAT) approach in PI, changing the activation functions of a model to low-degree polynomials that are easier to compute on HE by allowin

Aaquib Syed, Can Rager, Arthur Conmy

Automated interpretability research has recently attracted attention as a potential research direction that could scale explanations of neural network behavior to large models. Existing automated circuit discovery work applies activation patching to identify subnetworks responsible for solving specific tasks (circuits). In this work, we show that a simple me

Wenbo Yu, Bin Chen, Qinshan Zhang, Shu-Tao Xia

Different from data-oriented communication systems that primarily focus on how to accurately transmit every bit of data, task-oriented semantic communication systems only transmit the specific semantic information required by downstream tasks, strive to minimize the communication overhead and maintain competitive tasks execution performance in the presence o

Vipin Kumar Sharma, Sreekanth Harikumar, Margherita Grespan, Marek Biesiada

We investigate the novel features of gravitational wave solutions in $f(R)$ gravity under proper gauge considerations in the shifted Ricci scalar background curvature ($R^{1+\epsilon}$). The solution is further explored to study the modified dispersion relations for massive modes at local scales and to derive constraints on $\epsilon$. Our analysis yields ne

Tommy Li, Maxim Breitkreiz

Weyl-semimetal superstructures with a spiraling position of a pair of Weyl nodes of opposite chirality can host a chiral-symmetry preserving Fermi-arc metal state, where the chirality is carried by cylindrical Fermi surfaces, electron- and hole-like depending on the chirality. The Fermi surfaces nest at vanishing momentum separation (zero nesting vector) at

Giovanni Chesi, Chiara Macchiavello, Massimiliano Federico Sacchi

We study the work fluctuations in ergotropic heat engines, namely two-strokes quantum Otto engines where the work stroke is designed to extract the ergotropy (the maximum amount of work by a cyclic unitary evolution) from a couple of quantum systems at canonical equilibrium at two different temperatures, whereas the heat stroke thermalizes back the systems t

Jiayu Yang, Ziang Cheng, Yunfei Duan, Pan Ji

Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that is able to generate multiple images of the same object, as if seen they are captured from different viewpoints, while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited. Central to our method is a multi-view consisten

Arko Bhaumik, Supratik Pal

Assuming a slow-roll inflationary model where conformal invariance of the Maxwell action is broken via a non-minimal kinetic coupling term, we investigate the non-Gaussian three-point cross-correlation function between the primordial curvature perturbation and the primordial magnetic field, under a fairly general choice of initial vacua for both the scalar a

D. A. Green, S. Roy

The X-ray source CXOU J163802.6-471358is thought to be a pulsar wind nebula (PWN), as it shows an extended, $\approx 40$ arcsec trail from a compact source. Here we present GMRT observations of this source at 330 and 1390 MHz, which reveal a remarkable linear radio trail $\approx 90$ arcsec in extent. Although the radio trail points back to the supernova rem

Olga Dikaia, Alessandra Luchini, Tommy Nylander, Alexei Grunin

This study introduces silicon substrates with a switchable magnetic contrast layer (MCL) for polarised neutron reflectometry experiments (PNR) at solid/liquid interface to study soft matter surface layers. The advantage with neutron reflectometry (NR) data is that structural and compositional information can be enhanced by using different isotopic contrast o

Enrico Meggiolaro

In this work, we analyze (using a chiral effective Lagrangian model) the scalar and pseudoscalar meson mass spectrum of QCD at finite temperature, above the chiral transition at $T_c$, in the realistic case with $N_f = 2 + 1$ light quark flavors (that is, with $m_{u,d} \to 0$ and $m_s \neq 0$), looking, in particular, for signatures of the breaking of the $U

Frank Fundel

Diffusion models excel in image generation but lack detailed semantic control using text prompts. Additional techniques have been developed to address this limitation. However, conditioning diffusion models solely on text-based descriptions is challenging due to ambiguity and lack of structure. In contrast, scene graphs offer a more precise representation of

A. Mehrnejat, M. Ciomaga Hatnean, M. C. Rosamond, N. Banerjee

In ferromagnet/superconductor bilayer systems, dipolar fields from the ferromagnet can create asymmetric energy barriers for the formation and dynamics of vortices through flux pinning. Conversely, the flux emanating from vortices can pin the domain walls of the ferromagnet, thereby creating asymmetric critical currents. Here, we report the observation of a

Timo Häckel, Philipp Meyer, Lukas Stahlbock, Falk Langer

Connected vehicles are vulnerable to manipulation and a broad attack surface can be used to intrude in-vehicle networks from anywhere on earth. In this work, we present an integrated security infrastructure comprising network protection, monitoring, incident management, and counteractions, which we built into a prototype based on a production car. Our vehicl

Junichi Baba, Takayuki R. Saitoh, Takuji Tsujimoto

We explore the influence of the Milky Way galaxy's chemical evolution on the formation, structure, and habitability of the Solar system. Using a multi-zone Galactic Chemical Evolution (GCE) model, we successfully reproduce key observational constraints, including the age-metallicity ([Fe/H]) relation, metallicity distribution functions, abundance gradients,