May 2024 arXiv papers — page 22

Naoto Dainobu

Let $E$ be an elliptic curve defined over $\mathbb{Q}$ and $F$ be $\mathbb{Q}$ or an imaginary quadratic field with certain conditions. In this article, we study the ideal class group $\mathrm{Cl}(F_E)$ of the $p$-division field $F_E:=F(E[p])$ of $E$ over $F$ for an odd prime number $p$. More precisely, we investigate the non-vanishing of the $E[p]$-componen

Pasquale Casaburi, Lorenzo Dall'Amico, Nicolò Gozzi, Kyriaki Kalimeri

The COVID19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic characteristics shaped population response during restrictions and how repeated interventions eroded adherence over time.

Yi-Ying Sung, Lachlan Oberg, Rebecca Griffin, Alex K. Schenk

Near-surface nitrogen-vacancy centres are critical to many diamond-based quantum technologies such as information processors and nanosensors. Surface defects play an important role in the design and performance of these devices. The targeted creation of defects is central to proposed bottom-up approaches to nanofabrication of quantum diamond processors, and

Runfeng Lin, Dacheng Xu, Huijiang Wang, Zebiao Chen

When evaluating a learner's knowledge proficiency, the multiple-choice question is an efficient and widely used format in standardized tests. Nevertheless, generating these questions, particularly plausible distractors (incorrect options), poses a considerable challenge. Generally, the distractor generation can be classified into cloze-style distractor gener

Guangliang Pan, Jie Li, Minglei Li

Spectrum prediction is considered as a key technology to assist spectrum decision. Despite the great efforts that have been put on the construction of spectrum prediction, achieving accurate spectrum prediction emphasizes the need for more advanced solutions. In this paper, we propose a new multichannel multi-step spectrum prediction method using Transformer

J. Altmann, A. Dubla, V. Greco, A. Rossi

The formation of hadrons is a fundamental process in nature that can be investigated at particle colliders. As several recent findings demonstrate, with $\mathrm{e^+e^-}$ collisions as a "vacuum-like" reference at one extreme, and central nucleus--nucleus as a dense, extended-size system characterized by flow and local equilibrium at the opposite extreme, di

Yuvraj Sahni, Jiannong Cao, Lei Yang, Shengwei Wang

Collaborative edge computing has become a popular paradigm where edge devices collaborate by sharing resources. Data dissemination is a fundamental problem in CEC to decide what data is transmitted from which device and how. Existing works on data dissemination have not focused on coflow scheduling in CEC, which involves deciding the order of flows within an

William E. Harris, Joshua S. Speagle

In almost any study involving optical/NIR photometry, understanding the completeness of detection and recovery is an essential part of the work. The recovery fraction is, in general, a function of several variables including magnitude, color, background sky noise, and crowding. We explore how completeness can be modelled, {with the use of artificial-star tes

Haide Wang, Ji Zhou, Jinyang Yang, Zhiyang Liu

Burst-mode DSP based on 10ns preamble is proposed for upstream reception of 200G coherent TDM-PON. The 128-symbol tone preamble is used for SOP, frequency offset, and sampling phase estimation, while the 192-symbol CAZAC preamble is used for frame synchronization and channel estimation.

Andreas Scholl, Daniel Schiffner, Natalie Kiesler

Large Language Models (LLMs) have taken the world by storm, and students are assumed to use related tools at a great scale. In this research paper we aim to gain an understanding of how introductory programming students chat with LLMs and related tools, e.g., ChatGPT-3.5. To address this goal, computing students at a large German university were motivated to

Zhibo Hu, Chen Wang, Helen, Paik

Workloads in data processing clusters are often represented in the form of DAG (Directed Acyclic Graph) jobs. Scheduling DAG jobs is challenging. Simple heuristic scheduling algorithms are often adopted in practice in production data centres. There is much room for scheduling performance optimisation for cost saving. Recently, reinforcement learning approach

Filippo Agnesi, Lucia Carlucci, Gia Burjanadze, Fabio Bernini

The objective of this study was to characterize hemodynamic changes during trans-vascular stimulation of the renal nerve and their dependence on stimulation parameters. We employed a stimulation catheter inserted in the right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic and pulse blood pressure and heart rate were recorded during st

Daniel Halpern, Ariel D. Procaccia, Ehud Shapiro, Nimrod Talmon

A citizens' assembly is a group of people who are randomly selected to represent a larger population in a deliberation. While this approach has successfully strengthened democracy, it has certain limitations that suggest the need for assemblies to form and associate more organically. In response, we propose federated assemblies, where assemblies are intercon

Arifa Khan, Saravanan P, Venkatesan S. K.

We study the biological evolution of low-latency natural neural networks for short-term survival, and its parallels in the development of low latency high-performance Central Processing Unit in computer design and architecture. The necessity of accurate high-quality display of motion picture led to the special processing units known as the GPU, just as how s

Emily Dong, Sarah Marzen

Interest in how democracies form consensus has increased recently, with statistical physics and economics approaches both suggesting that there is convergence to a fixed point in belief networks, but with fluctuations in opinions when there are ``stubborn'' voters. We modify a model of opinion dynamics in which agents are fully Bayesian to account for two co

Bradley Dirks

We give a generalization to bi-filtered $\mathcal D$-modules underlying mixed Hodge modules of the relation between microlocalization along $f_1,...,f_r \in \mathcal O_X(X)$ and vanishing cycles along $g = \sum_{i=1}^r y_i f_i$. This leads to an interesting isomorphism between localization triangles. As an application, we use these results to compare the $k$

Lawrence M. Krauss, Francesco Marino, Samuel L. Braunstein, Mir Faizal

Recent experimental progresses in controlling classical and quantum fluids have made it possible to realize acoustic analogues of gravitational black holes, where a flowing fluid provides an effective spacetime on which sound waves propagate, demonstrating Hawking-like radiation and superradiance. We propose the exciting possibility that new hydrodynamic sys

Jakub Iwański, Krzysztof P. Korona, Mateusz Tokarczyk, Grzegorz Kowalski

Boron nitride exhibits diverse crystal structures, predominantly a layered arrangement with strong intraplanar covalent bonds and weak interplanar van der Waals bonds. While commonly referred to as hexagonal BN (hBN), the sp$^2$-bonded BN atomic planes can also arrange in other configurations like Bernal (bBN) or rhombohedral (rBN) stacking orders. Variation

Pierre Lemaire, Angelo Furno, Stefania Rubrichi, Alexis Bondu

Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by individuals. Such information is invaluable for monitoring large urban areas and supporting the implementation of decision-making services. When analyzed in real time, NSD can enable the

David Wilkening, Omar Moured, Thorsten Schwarz, Karin Muller

Exam documents are essential educational materials for exam preparation. However, they pose a significant academic barrier for blind and visually impaired students, as they are often created without accessibility considerations. Typically, these documents are incompatible with screen readers, contain excessive white space, and lack alternative text for visua

Osama A. Marzouk

A complete model for estimating the electric conductivity of combustion product gases, with added cesium (Cs) or potassium (K) vapor for ionization, is presented. Neutral carrier gases serve as the bulk fluid that carries the seed material, as well as the electrons generated by the partial thermal (equilibrium) ionization of the seed alkali metal. The model

Thomas Mädler, Olaf Baake, Hamideh Hosseini, Jeffrey Winicour

Choptuik's critical phenomena in general relativity is revisited in the affine-null metric formulation of Einstein's equations for a massless scalar field in spherical symmetry. Numerical solutions are obtained by evolution of initial data using pseudo-spectral methods. The underlying system consists of differential equations along the outgoing null rays whi

Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann

Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive field" is typically limited to the l-hop neighborhood of a node and that information exchange between distant nodes is limited by over-squashing. Motivated by these limitations, we p

Grzegorz Plebanek, Jakub Rondoš, Damian Sobota

We prove that, for every compact spaces $K_1,K_2$ and compact group $G$, if both $K_1$ and $K_2$ map continuously onto $G$, then the Banach space $C(K_1 \times K_2)$ contains a complemented subspace isometric to the Banach space $C(G)$. Consequently, $C(K_1\times K_2)$ contains a complemented copy of $C([0,1])$ for every non-scattered $K_1,K_2$. Also, answer

Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song

Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural language into solvable sub-tasks, thereby fulfilling the original requests. In this context, the sub-tasks can be naturally viewed as a graph, where the nodes represent t

Joseph P. Conlon

This is a short account, based on a talk given at the 2024 Moriond Cosmology Conference, of where and why string theory matters in early universe cosmology. It is written for a cosmology audience predisposed to be at best sceptical, and at worst contemptuous, of the notion that either quantum gravity or string theory has any relevance for their discipline. I

Omar Moured, Sara Alzalabny, Anas Osman, Thorsten Schwarz

Visualizations, such as charts, are crucial for interpreting complex data. However, they are often provided as raster images, which are not compatible with assistive technologies for people with blindness and visual impairments, such as embossed papers or tactile displays. At the same time, creating accessible vector graphics requires a skilled sighted perso

Luis Peña-Moñino, Miguel Pérez-Torres, Jacobo Varela, Philippe Zarka

The habitability of exoplanets hosted by M-dwarf stars dramatically depends on their space weather. We present 3D magneto-hydrodynamic simulations to characterise the magneto-plasma environment and thus the habitability of the Earth-like planet Proxima b when it is subject to both calm and extreme (CME-like) space weather conditions. We study the role of the

Wen-Gao Long, Yun-Jiang Jiang, Yu-Tian Li

We study the full asymptotic expansion of the monodromy data ({\it i.e.}, Stokes multipliers) for the first Painlev\'{e} transcendent (PI) with large initial data or large pole parameters. Our primary approach involves refining the complex WKB method, also known as the method of uniform asymptotics, to approximate the second-order ODEs derived from PI's Lax

Evgeny A. Stepanov

The interplay between strong electronic correlations and the inherent frustration of certain lattice geometries is a common mechanism for the formation of nontrivial states of matter. In this work, we theoretically explore the collective electronic effects in the monolayer Nb$_3$Cl$_8$, a recently discovered triangular lattice Mott insulator. Our advanced ma

Andrea Freschi, Robert Hancock, Andrew Treglown

Given a matrix $A$ with integer entries, a subset $S$ of an abelian group and $r \in \mathbb N$, we say that $S$ is $(A,r)$-Rado if any $r$-colouring of $S$ yields a monochromatic solution to the system of equations $Ax=0$. A classical result of Rado characterises all those matrices $A$ such that $\mathbb N$ is $(A,r)$-Rado for all $r \in \mathbb N$. R\"odl

Anna Broms, Alex H. Barnett, Anna-Karin Tornberg

Stokes flows with near-touching rigid particles induce near-singular lubrication forces under relative motion, making their accurate numerical treatment challenging. With the aim of controlling the accuracy with a computationally cheap method, we present a new technique that combines the method of fundamental solutions (MFS) with the method of images. For ri

Marzieh Gheisari, Auguste Genovesio

Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based o

Omar Moured, Shahid Ali Farooqui, Karin Muller, Sharifeh Fadaeijouybari

Alternative Texts (Alt-Text) for chart images are essential for making graphics accessible to people with blindness and visual impairments. Traditionally, Alt-Text is manually written by authors but often encounters issues such as oversimplification or complication. Recent trends have seen the use of AI for Alt-Text generation. However, existing models are s

Sk Kalimuddin, Biswajit Das, Sudipta Chatterjee, Arnab Bera

Topological insulators (TIs) with symmetry-protected surface states, offer exciting opportunities for next-generation photonic and optoelectronic device applications. The heterojunctions of TIs and semiconductors (e.g. Si, Ge) have been observed to excellent photo-responsive characteristics. However, the realization of high-frequency operations in these hete

Fangzhi Xu, Qika Lin, Tianzhe Zhao, Jiawei Han

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transfo

Ranieri Nery, Nadja K. Bernardes, Daniel Cavalcanti, Rafael Chaves

The understanding of open quantum systems is crucial for the development of quantum technologies. Of particular relevance is the characterisation of divisible quantum dynamics, seen as a generalisation of Markovian processes to the quantum setting. Here, we propose a way to detect divisibility and quantify how non-divisible a quantum channel is through the c

Pierre Harvey Richemond, Yunhao Tang, Daniel Guo, Daniele Calandriello

The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is a quadruplet composed of a prompt, two independent responses (completions of the prompt) and a human preferen

Duncan R. Lorimer, Maura A. McLaughlin, Matthew Bailes

In 2007 we were part of a team that discovered the so-called ``Lorimer Burst'', the first example of a new class of objects now known as fast radio bursts (FRBs). These enigmatic events are only a few ms in duration and occur at random locations on the sky at a rate of a few thousand per day. Several thousand FRBs are currently known. While it is now well es

Andrea Albano, Marzia Mazzotta, Paola Stefanelli

The main aim of this paper is to determine reflections to bijective and non-degenerate solutions of the Yang-Baxter equation, by exploring their connections with their derived solutions. This is motivated by a recent description of left non-degenerate solutions in terms of a family of automorphisms of their associated left rack. In some cases, we show that t

Sabrina Aufiero, Preben Forer, Pierpaolo Vivo, Fabio Caccioli

Debt recycling is an aggressive equity extraction strategy that potentially permits faster repayment of a mortgage. While equity progressively builds up as the mortgage is repaid monthly, mortgage holders may obtain another loan they could use to invest on a risky asset. The wealth produced by a successful investment is then used to repay the mortgage faster

Xinyue Shen, Yixin Wu, Michael Backes, Yang Zhang

Recently, the concept of artificial assistants has evolved from science fiction into real-world applications. GPT-4o, the newest multimodal large language model (MLLM) across audio, vision, and text, has further blurred the line between fiction and reality by enabling more natural human-computer interactions. However, the advent of GPT-4o's voice mode may al

Li Wang, Qiang Xu

Concerned with elliptic operators with stationary random coefficients governed by linear or nonlinear mixing conditions and bounded (or unbounded) $C^1$ domains, this paper mainly studies (weighted) annealed Calder\'on-Zygmund estimates, some of which are new even in a periodic setting. Stronger than some classical results derived by a perturbation argument

Maximilian Herde, Bogdan Raonić, Tobias Rohner, Roger Käppeli

We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enable continuous-in-time evaluations. A novel training strategy leveraging the semi-group property of time-dependent PDEs to allow for significant scaling-up of the training data is a

Zengqun Zhao, Yu Cao, Shaogang Gong, Ioannis Patras

Current facial expression recognition (FER) models are often designed in a supervised learning manner and thus are constrained by the lack of large-scale facial expression images with high-quality annotations. Consequently, these models often fail to generalize well, performing poorly on unseen images in inference. Vision-language-based zero-shot models demo

Xiaolong Xue, Guangyong Shang, Zhen Ma, Minghui Xu

Digital watermarking methods are commonly used to safeguard digital media copyrights by confirming ownership and deterring unauthorized use. However, without reliable third-party oversight, these methods risk security vulnerabilities during watermark extraction. Furthermore, digital media lacks tangible ownership attributes, posing challenges for secure copy

Shuyu Cheng, Yibo Miao, Yinpeng Dong, Xiao Yang

This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to input queries. Some previous methods improve the query efficiency by incorporating the gradient of a surrogate white-box model into query-based attacks due to the adversarial transf

Anna Breger, Ander Biguri, Malena Sabaté Landman, Ian Selby

Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on medical images. The popular full reference (FR) IQA measures PSNR and SSIM are known and tested for working successfully in many natural imaging tasks, but discrepancies in medical s

Stefan Borgwardt, Filippo De Bortoli, Patrick Koopmann

Concrete domains have been introduced in the context of Description Logics to allow references to qualitative and quantitative values. In particular, the class of $\omega$-admissible concrete domains, which includes Allen's interval algebra, the region connection calculus (RCC8), and the rational numbers with ordering and equality, has been shown to yield ex

Sam Speight

We develop realizability models of intensional type theory, based on groupoids, wherein realizers themselves carry non-trivial (non-discrete) homotopical structure. In the spirit of realizability, this is intended to formalize a homotopical BHK interpretation, whereby evidence for an identification is a path. Specifically, we study partitioned groupoidal ass

Syrine Krichene, Francesco Piccinno, Fangyu Liu, Julian Martin Eisenschlos

Chart-to-summary generation can help explore data, communicate insights, and help the visually impaired people. Multi-modal generative models have been used to produce fluent summaries, but they can suffer from factual and perceptual errors. In this work we present CHATS-CRITIC, a reference-free chart summarization metric for scoring faithfulness. CHATS-CRIT

Xindi Wang, Robert E. Mercer, Frank Rudzicz

The International Classification of Diseases (ICD) serves as a definitive medical classification system encompassing a wide range of diseases and conditions. The primary objective of ICD indexing is to allocate a subset of ICD codes to a medical record, which facilitates standardized documentation and management of various health conditions. Most existing ap

Hongyuan Dong, Jiawen Li, Bohong Wu, Jiacong Wang

Image captioning has long been regarded as a fundamental task in visual understanding. Recently, however, few large vision-language model (LVLM) research discusses model's image captioning performance because of the outdated short-caption benchmarks and unreliable evaluation metrics. In this work, we propose to benchmark detail image caption task by curating

Xiangcheng Zheng, Shangqin Zhu, Yiqun Li

The Sonine kernel described by the classical Sonine condition of convolution form is an important class of kernels used in integral equations and nonlocal differential equations. This work extends this idea to introduce weighted Sonine conditions where the non-convolutional weight functions accommodate the inhomogeneity in practical applications. We characte

Victor G. Kac, Pierluigi Möseneder Frajria, Paolo Papi

In this paper we study unitary Ramond twisted representations of minimal $W$-algebras. We classify all such irreducible highest weight representations with a non-Ramond extremal highest weight (unitarity in the Ramond extremal case, as well as in the untwisted extremal case, remains open). We compute the characters of these representations and deduce from th

Christian Lehmann, Sofia Feltzing, Diane Feuillet, Georges Kordopatis

Making use of the APOGEE DR17 catalogue with high quality data for 143,509 red giant branch stars we explore the strength of different mechanisms that causes a star to radially migrate in the Milky Way stellar disk. At any position in the disk we find stars that are more metal-rich than the local interstellar medium. This is surprising and normally attribute

Zhe Hu, Tuo Liang, Jing Li, Yiren Lu

Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge

Chuan-Ren Chen, Yuan-Feng Hsieh, Chrisna Setyo Nugroho

Dark sector may exist and interact with Standard Model (SM) through the $U(1)$ kinetic mixing. Through this portal-type interaction, dark photon from dark sector couples to SM fermions, and may explain the discrepancy between experimental data and SM calculations on muon anomalous magnetic moment, muon $g-2$. However, current searches for dark photon impose

Renzhi Wang, Piji Li

Model editing aims to efficiently alter the behavior of Large Language Models (LLMs) within a desired scope, while ensuring no adverse impact on other inputs. Recent years have witnessed various model editing methods been proposed. However, these methods either exhibit poor overall performance or struggle to strike a balance between generalization and locali

Shusong Xu, Peiye Liu

Artificial Intelligence Generated Content(AIGC), known for its superior visual results, represents a promising mitigation method for high-cost advertising applications. Numerous approaches have been developed to manipulate generated content under different conditions. However, a crucial limitation lies in the accurate description of products in advertising a

Xindi Wang, Robert E. Mercer, Frank Rudzicz

The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD indexing assigns a subset of ICD codes to a medical record. Since human coding is labour-intensive and error-prone, many studies employ machine learning to automate the coding proc

Hesham El Faham, Giovanni Pelliccioli, Eleni Vryonidou

This study explores fully leptonic WZ and WW production at the LHC within the SMEFT framework at NLO in QCD, focusing on both CP-even and CP-odd triple-gauge-coupling dimension-six operators. We investigate the off-shell processes, contrasting our findings in inclusive setups with those in the presence of realistic fiducial selections. Alongside the conventi

Reese E. Jones, Craig M. Hamel, Dan Bolintineanu, Kyle Johnson

When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for these effects using a variational autoencoder to encode the structure-property map of the stochastic volume elements mak

Jose J. Quintana, Miguel A. Ferrer, Moises Diaz, Jose J. Feo

Collaborative robots or cobots interact with humans in a common work environment. In cobots, one under investigated but important issue is related to their movement and how it is perceived by humans. This paper tries to analyze whether humans prefer a robot moving in a human or in a robotic fashion. To this end, the present work lays out what differentiates

Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang

Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge. Existing works often overlook the distribution discrepancies induced by policy or dynamics shifts, or rely on specialized algorithms with task priors, thus often resulting in suboptimal policy performances

Mareike Thies, Fabian Wagner, Noah Maul, Siyuan Mei

Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often incorporating temporal smoothness constraints on the estimated motion patterns. This study analyzes the influence

Aditya Gulati, Marina Martinez-Garcia, Daniel Fernandez, Miguel Angel Lozano

The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beauty filters. We conduct a large-scale online user study involving 2,748 participants who rated facial images fro

Yi-Zheng Fan, Hui-Feng Wu, Yi Wang

Let $K$ be a simplical complex, and let $\mathcal{L}_i^{up}(K), \mathcal{Q}_i^{up}(K)$ be the $i$-th up Laplacian and signless Laplacian of $K$, respectively. In this paper we proved that the largest eigenvalue of $\mathcal{L}_i^{up}(K)$ is not greater than the largest eigenvalue of $\mathcal{Q}_i^{up}(K)$; furthermore, if $K$ is $(i+1)$-path connected, then

Yizhou Huang, Zhi-Feng Zhang, Peng Ye

In spacetime dimensions of 4 (i.e., 3+1) and higher, topological orders exhibit spatially extended excitations like loops and membranes, which support diverse topological data characterizing braiding, fusion, and shrinking processes, despite the absence of anyons. Our understanding of these topological data remains less mature compared to 3D, where anyons ha

Markus J. Buehler

We present Cephalo, a series of multimodal vision large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding. A key innovation of Cephalo is its advanced dataset generation method. Cephalo is trained on integrated image and text data from thousands of scientific papers and sci

Baishuai Zuo, Chuancun Yin

In this paper, we discuss the worst-case of distortion riskmetrics for general distributions when only partial information (mean and variance) is known. This result is applicable to general class of distortion risk measures and variability measures. Furthermore, we also consider worst-case of weighted entropy for general distributions when only partial infor

Dipam Goswami, Albin Soutif--Cormerais, Yuyang Liu, Sandesh Kamath

Continual learning methods are known to suffer from catastrophic forgetting, a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore, to reduce potential drift in the feature extractor, existing exemplar-free methods are typically evaluated in settings where the first task is significantly larger

Celestine Mendler-Dünner, Gabriele Carovano, Moritz Hardt

The power of digital platforms is at the center of major ongoing policy and regulatory efforts. To advance existing debates, we designed and executed an experiment to measure the performative power of online search providers. Instantiated in our setting, performative power quantifies the ability of a search engine to steer web traffic by rearranging results.

Dongwhi Kim, Nuno Moniz

As machine learning continues to gain prominence, transparency and explainability are increasingly critical. Without an understanding of these models, they can replicate and worsen human bias, adversely affecting marginalized communities. Algorithmic recourse emerges as a tool for clarifying decisions made by predictive models, providing actionable insights

Antoine Lhomme, Nicolas Catusse, Nadia Brauner

A frequently studied performance measure in online optimization is competitive analysis. It corresponds to the worst-case ratio, over all possible inputs of an algorithm, between the performance of the algorithm and the optimal offline performance. However, this analysis may be too pessimistic to give valuable insight on a problem. Several workarounds exist,

Anton Halaski, Matthias G. Krauss, Daniel Basilewitsch, Christiane P. Koch

Squeezing is a non-classical feature of quantum states that is a useful resource, for example in quantum sensing of mechanical forces. Here, we show how to use optimal control theory to maximize squeezing in an optomechanical setup with two external drives and determine how fast the mechanical mode can be squeezed. For the autonomous drives considered here,

Anisa Khatun

The ALICE experiment has undergone a major detector upgrade for Run 3, expanding its detection capabilities for a wide variety of studies. The new continuous readout has significantly enhanced the physics potential for ultra-peripheral collision analyses. In this talk, we discussed some of the physics analyses that can be carried out in ultra-peripheral coll

Shikhamoni Nath, Arpan Chandra Mazumder, Dhiren Kumar Basnet

Let $q$ be a positive integral power of some prime $p$ and $\mathbb{F}_{q^m}$ be a finite field with $q^m$ elements for some $m \in \mathbb{N}$. Here we establish a sufficient condition for the existence of primitive normal pairs of the type $(\epsilon, f(\epsilon))$ in $\mathbb{F}_{q^m}$ over $\mathbb{F}_{q}$ with two prescribed traces, $Tr_{{\mathbb{F}_{q^

Fumiya Hanamura, Warit Asavanant, Hironari Nagayoshi, Atsushi Sakaguchi

Non-Gaussian quantum gates are essential components for optical quantum information processing. However, the efficient implementation of practically important multi-mode higher-order non-Gaussian gates has not been comprehensively studied. We propose a measurement-based method to directly implement general, multi-mode, and higher-order non-Gaussian gates usi

Hans Ulrich Simon

In the year 1990, B\'ela Bollob\'as, Imre Leader and Andrew Radcliffe considered the following combinatorial problem: given three parameters k, n and q, find a set of k vertices in the binary n-cube which contains a maximal number of q-dimensional subcubes. It was shown that an optimal solution is given by the k vertices which coincide with the binary repres

Georg Rutishauser, Joan Mihali, Moritz Scherer, Luca Benini

Ternary neural networks (TNNs) offer a superior accuracy-energy trade-off compared to binary neural networks. However, until now, they have required specialized accelerators to realize their efficiency potential, which has hindered widespread adoption. To address this, we present xTern, a lightweight extension of the RISC-V instruction set architecture (ISA)

Tang-Kai Lee, Jingze Zhu

Following \L ojasiewicz's uniqueness theorem and Thom's gradient conjecture, Arnold proposed a stronger version about the existence of limit tangents of gradient flow lines for analytic functions. We prove \L ojasiewicz's theorem and Arnold's conjecture in the context of arrival time functions for mean curvature flows in $\mathbb R^{n+1}$ with neck or non-de

Kaisa Matomäki, Sebastian Zuniga Alterman

Weighted sieves are used to detect numbers with at most $S$ prime factors with $S \in \mathbb{N}$ as small as possible. When one studies problems with two variables in somewhat symmetric roles (such as Chen primes, that is primes $p$ such that $p+2$ has at most two prime factors), one can utilize the switching principle. Here we discuss how different sieve w

Lucie Domino, Mariam Beaure d'Augères, Jian Zhang, Shahram Janbaz

In many scenarios -- when we bite food or during a crash -- fracture is inevitable. Finding solutions to steer fracture to mitigate its impact or turn it into a purposeful functionality, is therefore crucial. Strategies using composites, changes in chemical composition or crystal orientation, have proven to be very efficient, but the crack path control remai

Emmanuel Ramasso, Rafael de O. Teloli, Romain Marcel

This paper investigates the use of deep transfer learning based on convolutional neural networks (CNNs) to monitor the condition of bolted joints using acoustic emissions. Bolted structures are critical components in many mechanical systems, and the ability to monitor their condition status is crucial for effective structural health monitoring. We evaluated

Carlos Cotta, José E. Gallardo

We consider an operational model of suicide bombing attacks -- an increasingly prevalent form of terrorism -- against specific targets, and the use of protective countermeasures based on the deployment of detectors over the area under threat. These detectors have to be carefully located in order to minimize the expected number of casualties or the economic d

Dorina Weichert, Alexander Kister, Sebastian Houben, Patrick Link

The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on the other hand. We address the case of adversarial robustness, where all parameters are controllable during the optimization process, but a subset of them is uncontrollable or even

Shuang Song, Stefania Benonisdottir, Jun S. Liu, Augustine Kong

It is increasingly recognized that participation bias can pose problems for genetic studies. Recently, to overcome the challenge that genetic information of non-participants is unavailable, it is shown that by comparing the IBD (identity by descent) shared and not-shared segments among the participants, one can estimate the genetic component underlying parti

Alexander Rakowski, Remo Monti, Viktoriia Huryn, Marta Lemanczyk

With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consist

Ziyang Zhang, Edgar Simo-Serra

Neural rendering provides a fundamentally new way to render photorealistic images. Similar to traditional light-baking methods, neural rendering utilizes neural networks to bake representations of scenes, materials, and lights into latent vectors learned from path-tracing ground truths. However, existing neural rendering algorithms typically use G-buffers to

Shuai Yuan, Guancong Lin, Lixian Zhang, Runmin Dong

Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas. Although there have been advances in high-quality land cover datasets that reveal the physical features of urban landscapes, the lack of fine-grained land use datasets hinders a deeper understanding of how hu

Irem Portakal, Daniel Windisch

This paper is a significant step forward in understanding dependency equilibria within the framework of real algebraic geometry encompassing both pure and mixed equilibria. In alignment with Spohn's original definition of dependency equilibria, we propose two alternative definitions, allowing for an algebro-geometric comprehensive study of all dependency equ

Zongbao Zhang, Jiao Hao, Wenmeng Zhao, Yan Liu

The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal vari

S. Bellotti, D. Evensberget, A. A. Vidotto, A. Lavail

The accurate characterisation of the stellar magnetism of planetary host stars has been gaining momentum, especially in the context of transmission spectroscopy investigations of exoplanets. Indeed, the magnetic field regulates the amount of energetic radiation and stellar wind impinging on planets, as well as the presence of inhomogeneities on the stellar s

Daniel Murfet, William Troiani

We develop a point of view on reduction of multiplicative proof nets based on quantum error-correcting codes. To each proof net we associate a code, in such a way that cut-elimination corresponds to error correction.

Claudio Alexandre Piedade, Philippe Tranchida

Given a residually connected incidence geometry $\Gamma$ that satisfies two conditions, denoted $(B_1)$ and $(B_2)$, we construct a new geometry $H(\Gamma)$ with properties similar to those of $\Gamma$. This new geometry $H(\Gamma)$ is inspired by a construction of Percsy, Percsy and Leemans [1]. We show how $H(\Gamma)$ relates to the classical halving opera

Álvaro G. Iñesta, Hyeongrak Choi, Dirk Englund, Stephanie Wehner

Distributing quantum states reliably among distant locations is a key challenge in the field of quantum networks. One-way quantum networks address this by using one-way communication and quantum error correction. Here, we analyze quantum circuit switching as a protocol to distribute quantum states in one-way quantum networks. In quantum circuit switching, pa

Julian Sarpe, Andreas Klaedtke, Herbert De Gersem

This paper proposes the utilization of a periodic Parareal with a periodic coarse problem to efficiently perform adjoint sensitivity analysis for the steady state of time-periodic nonlinear circuits. In order to implement this method, a modified formulation for adjoint sensitivity analysis based on the transient approach is derived.

Jeffery Dick, Saptarshi Nath, Christos Peridis, Eseoghene Benjamin

Context detection involves labeling segments of an online stream of data as belonging to different tasks. Task labels are used in lifelong learning algorithms to perform consolidation or other procedures that prevent catastrophic forgetting. Inferring task labels from online experiences remains a challenging problem. Most approaches assume finite and low-dim

Gyuseok Lee, SeongKu Kang, Wonbin Kweon, Hwanjo Yu

Knowledge distillation (KD) has emerged as a promising technique for addressing the computational challenges associated with deploying large-scale recommender systems. KD transfers the knowledge of a massive teacher system to a compact student model, to reduce the huge computational burdens for inference while retaining high accuracy. The existing KD studies