May 2023 arXiv papers — page 33

Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang

Large language models(LLMs) have sparked a new wave of exciting AI applications. Hosting these models at scale requires significant memory resources. One crucial memory bottleneck for the deployment stems from the context window. It is commonly recognized that model weights are memory hungry; however, the size of key-value embedding stored during the generat

Zekun Li, Nadia Benabdallah, Richard Laforest, Richard L. Wahl

Thorium-227-based alpha-particle radiopharmaceutical therapies ({\alpha}-RPTs) are being investigated in several clinical and pre-clinical studies. After administration, Thorium-227 decays to Radium-223, another alpha-particle-emitting isotope, which redistributes within the patient. Reliable dose quantification of both Thorium-227 and Radium-223 is clinical

David Soong, Sriram Sridhar, Han Si, Jan-Samuel Wagner

Large language models (LLMs) have made significant advancements in natural language processing (NLP). Broad corpora capture diverse patterns but can introduce irrelevance, while focused corpora enhance reliability by reducing misleading information. Training LLMs on focused corpora poses computational challenges. An alternative approach is to use a retrieval

Rajeev Alur, Osbert Bastani, Kishor Jothimurugan, Mateo Perez

The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objectives for reinforcement learning (RL). However, LTL has the downside that it is sensitive to small perturbations in the transition probabilities, which prevents probably approximately correct (PAC) learning without additional ass

Jian-fei Wang, Hao-jie Xu, Fuqiang Wang

Relativistic isobar ($^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr) collisions have revealed intricate differences in their nuclear size and shape, inspiring unconventional studies of nuclear structure using relativistic heavy ion collisions. In this study, we investigate the relative differences in the mean multiplicity ($R_{\langle N_{\rm

Ningzhi Xie, Quentin A. A. Tanguy, Johannes E. Fröch, Karl F. Böhringer

With the advent of neuroimaging and microsurgery, there is a rising need for capturing images through an optical fiber. We present an approach of imaging through a single fiber without mechanical scanning by implementing spatial-spectral encoding. The spectral encoding is achieved through a microfabricated spectral filter array, where light from different sp

Jiaxin Yuan, Amar Shah, Channing Bentz, Maria Cameron

Many processes in nature such as conformal changes in biomolecules and clusters of interacting particles, genetic switches, mechanical or electromechanical oscillators with added noise, and many others are modeled using stochastic differential equations with small white noise. The study of rare transitions between metastable states in such systems is of grea

Md Ashequr Rahman, Zekun Li, Zitong Yu, Richard Laforest

SPECT provides a mechanism to perform absorbed-dose quantification tasks for $\alpha$-particle radiopharmaceutical therapies ($\alpha$-RPTs). However, quantitative SPECT for $\alpha$-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose

Bingjie Tang, Michael A. Lin, Iretiayo Akinola, Ankur Handa

Robotic assembly is a longstanding challenge, requiring contact-rich interaction and high precision and accuracy. Many applications also require adaptivity to diverse parts, poses, and environments, as well as low cycle times. In other areas of robotics, simulation is a powerful tool to develop algorithms, generate datasets, and train agents. However, simula

Tankred Saanum, Noémi Éltető, Peter Dayan, Marcel Binz

Everything else being equal, simpler models should be preferred over more complex ones. In reinforcement learning (RL), simplicity is typically quantified on an action-by-action basis -- but this timescale ignores temporal regularities, like repetitions, often present in sequential strategies. We therefore propose an RL algorithm that learns to solve tasks w

Andreu Puy, Elisabet Gimeno, Jordi Torrents, Palina Bartashevich

Animals moving together in groups are believed to interact among each other with effective social forces, such as attraction, repulsion and alignment. Such forces can be inferred using 'force maps', i.e. by analysing the dependency of the acceleration of a focal individual on relevant variables. Here we introduce a force map technique suitable for the analys

Giorgio Gubbiotti, Yang Shi

We determine the affine Weyl symmetries of some two-dimensional birational maps known as QRT roots arising from Kahan--Hirota--Kimura discretisation of two different reduced Nahm systems. The main finding is that the symmetry types of these discrete systems are subgroups of the Weyl groups for Sakai's discrete Painlev\'e equations to which the QRT maps are t

Danilo Monteiro Ribeiro, Rayfran Rocha Lima, César França, Alberto de Souza

CONTEXT: Self-efficacy is a concept researched in various areas of knowledge that impacts various factors such as performance, satisfaction, and motivation. In Software Engineering, it has mainly been studied in the academic context, presenting results similar to other areas of knowledge. However, it is also important to understand its impact in the industri

Karthik Vaidyanathan, Marco Salvi, Bartlomiej Wronski, Tomas Akenine-Möller

The continuous advancement of photorealism in rendering is accompanied by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression technique specifically designed for material textures. We unlock two more levels of detail, i.e., 16x more texels, using low bitrate compressi

Emiel Hoogeboom, Eirikur Agustsson, Fabian Mentzer, Luca Versari

Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly improve perceptual quality at a given bit-rate, outperforming state-of-the-art approaches PO-ELIC and HiFiC as measured by F

Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang

Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks. To adopt prompt learning in the NER task, two kinds of methods have been explored from a pair of symmetric perspectives, populating the template by enumerating spans to predict their entity types or constructing type-specific prompts to l

Angela Aguglia, Bence Csajbók, Luca Giuzzi

In this paper, we consider point sets of finite Desarguesian planes whose multisets of intersection numbers with lines is the same for all but one exceptional parallel class of lines. We call such sets regular of affine type. When the lines of the exceptional parallel class have the same intersection numbers, then we call these sets regular of pointed type.

Jingyang Huo, Qiang Sun, Boyan Jiang, Haitao Lin

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this

K. Czerski, R. Dubey, M. Kaczmarski, A. Kowalska

Electron emission in the deuteron-deuteron reaction supporting existence of the single-particle threshold resonance in 4 He has been observed for the first time. The measured electron energy spectrum and the electron-proton branching ratio agree very well with the assumed electron-positron pair creation decay of the 0+ resonance state to the ground state and

Kai Zhang, Rong Zhou, Eashan Adhikarla, Zhiling Yan

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outpu

Jeffrey Marshall, Namit Anand

We introduce a framework for simulating quantum optics by decomposing the system into a finite rank (number of terms) superposition of coherent states. This allows us to define a resource theory, where linear optical operations are 'free' (i.e., do not increase the rank), and the simulation complexity for an $m$-mode system scales quadratically in $m$, in st

Min Zhao, Rongzhen Wang, Fan Bao, Chongxuan Li

This paper presents \emph{ControlVideo} for text-driven video editing -- generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions (such as edge maps), and fine-

Boyuan Shi, Florian Mintert

Existing approaches to analogue quantum simulations of time-dependent quantum systems rely on perturbative corrections to quantum simulations of time-independent quantum systems. We overcome this restriction to perturbative treatments with an approach based on flow equations and a multi-mode Fourier expansion. The potential of the quantum simulations that ca

Tanveer Hannan, Rajat Koner, Maximilian Bernhard, Suprosanna Shit

Recent trends in Video Instance Segmentation (VIS) have seen a growing reliance on online methods to model complex and lengthy video sequences. However, the degradation of representation and noise accumulation of the online methods, especially during occlusion and abrupt changes, pose substantial challenges. Transformer-based query propagation provides promi

Minjia Chen, Yizhi Wang, Chunhui Yao, Adrian Wonfor

Photonic integrated circuits have been extensively explored for optical processing with the aim of breaking the speed bottleneck of digital electronics. However, the input/output (IO) bottleneck remains one of the key barriers. Here we report a novel photonic iterative processor (PIP) for matrix-inversion-intensive applications. The direct reuse of inputted

Samuel Valiquette, Gwladys Toulemonde, Jean Peyhardi, Éric Marchand

Count data are omnipresent in many applied fields, often with overdispersion. With mixtures of Poisson distributions representing an elegant and appealing modelling strategy, we focus here on how the tail behaviour of the mixing distribution is related to the tail of the resulting Poisson mixture. We define five sets of mixing distributions and we identify f

Piotr Florek, Adam Zagdański

This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost, LightGBM and CatBoost), have been thoroughly compared on several publicly available real-world datasets of sufficient diversity. In the study

Diata Traore, Emmanuel Giner, Julien Toulouse

The basis-set correction method based on density-functional theory consists in correcting the energy calculated by a wave-function method with a given basis set by a density functional. This basis-set correction density functional incorporates the short-range electron correlation effects missing in the basis set. This results in accelerated basis convergence

Aurélien Delphin, Fabien Boux, Clément Brossard, Thomas Coudert

MR vascular Fingerprinting proposes to use the MR Fingerprinting framework to quantitatively and simultaneously map several microvascular characteristics at a sub-voxel scale. The initial implementation assessed the local blood oxygenation saturation (SO 2), blood volume fraction (BVf) and vessel averaged radius (R) in humans and rodent brains using simple 2

Zhenchao Jin

This paper presents SSSegmenation, which is an open source supervised semantic image segmentation toolbox based on PyTorch. The design of this toolbox is motivated by MMSegmentation while it is easier to use because of fewer dependencies and achieves superior segmentation performance under a comparable training and testing setup. Moreover, the toolbox also p

Mohannad Abu-Romoh, Nelson Costa, Yves Jaouën, Antonio Napoli

In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using the stochastic gradient descent. We evaluate DBP and LDBP in a simulated WDM dual-polarization fiber transmiss

Mauricio Garcia-Vergara, Guillaume Demésy, André Nicolet, Frédéric Zolla

In this paper, the electromagnetic radiation from an oscillating particle placed in the vicinity of an object of size comparable to the wavelength is studied. Although this problem may seem academic at first sight, the details of the calculations are presented throughout without any detail left under the carpet. A polyharmonic decomposition of the radiation

Ioannis Dalianis, Fotis Farakos, Alex Kehagias

We note that the typical gauge mediation of supersymmetry breaking is in tension with the global limit of the festina lente swampland bound. The alternatives are mediation/breaking schemes that decouple together with gravity, as for example gravity mediation, for which we highlight some basic phenomenological properties. Gauge mediation remains instead a via

Femke Bangma, Lev Levitin, Marijn Lucas, Andrew Casey

The motivation to develop materials for quantum technologies has put exploration of novel quantum states of matter at the focus of several research fields, with particular efforts towards understanding and controlling the behaviour of quantum entangled and other strongly interacting electronic states. Experimental investigation is of primary importance, but

Ehsan Latif, WenZhan Song, Ramviyas Parasuraman

Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots. It is made ef

Prabir Banik, Arunava Bhadra, Sanjay K. Ghosh

Very recently, HAWC observatory discovered the high-energy gamma ray emission from the solar disk during the quiescent stage of the Sun, extending the Fermi-LAT detection of intense, hard emission between 0.1 - 200 GeV to TeV energies. The flux of these observed gamma-rays is significantly higher than that theoretically expected from hadronic interactions of

Yevhen Kushnirenko, Brinda Kuthanazhi, Lin-Lin Wang, Benjamin Schrunk

The recent discovery of unconventional surface state pairs, which give rise to Fermi arcs and spin textures, in antiferromagnetically ordered NdBi raised the interest in rare-earth monopnictides. Several scenarios of antiferromagnetic order have been suggested to explain the origin of these states with some of them being consistent with the presence of non-t

Jeremias Aguilera Damia, Riccardo Argurio, Francesco Benini, Sergio Benvenuti

We explore novel examples of RG flows preserving a non-invertible self-duality symmetry. Our main focus is on $\mathcal{N}=1$ quadratic superpotential deformations of 4d $\mathcal{N}=4$ super-Yang-Mills theory with gauge algebra $\mathfrak{su}(N)$. A theory that can be obtained in this way is the so-called $\mathcal{N}=1^*$ SYM where all adjoint chiral multi

Mao Hong, Zhengling Qi, Yanxun Xu

In this paper, we propose a policy gradient method for confounded partially observable Markov decision processes (POMDPs) with continuous state and observation spaces in the offline setting. We first establish a novel identification result to non-parametrically estimate any history-dependent policy gradient under POMDPs using the offline data. The identifica

Marco Berritta, Stefano Scali, Federico Cerisola, Janet Anders

Atomistic spin dynamics (ASD) is a standard tool to model the magnetization dynamics of a variety of materials. The fundamental dynamical model underlying ASD is entirely classical. In this paper, we present two approaches to effectively incorporate quantum effects into ASD simulations, thus enhancing their low temperature predictions. The first allows to si

Wolf-Jürgen Beyn

In these notes we generalize the notion of a (pseudo) metric measuring the distance of two points, to a (pseudo) n-metric which assigns a value to a tuple of n points. We present two principles of constructing pseudo n-metrics. The first one uses the Vandermonde determinant while the second one uses exterior products and is related to the volume of the simpl

Yung-Sung Chuang, Wei Fang, Shang-Wen Li, Wen-tau Yih

We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering. EAR first applies a query expansion model to generate a diverse set of queries, and then uses a query reranker to select the ones that could lead to better retrieval results. Motivated by the observation that the b

Md. Simul Hasan Talukder, Sharmin Akter

Fetal health is a critical concern during pregnancy as it can impact the well-being of both the mother and the baby. Regular monitoring and timely interventions are necessary to ensure the best possible outcomes. While there are various methods to monitor fetal health in the mother's womb, the use of artificial intelligence (AI) can improve the accuracy, eff

Sara Saghafi, Kourosh Nozari

In this paper, we aim to explore the impact of the Planck scale corrections and the Holographic quintessence on the shadow behavior of non-rotating black holes. To do this, we consider the quantum-corrected Schwarzschild black hole surrounded by the quintessence field inspired by the Kazakov-Solodukhin and the Kiselev ideas, and we call this combination the

Fei Kong, Jinhao Duan, RuiPeng Ma, Hengtao Shen

Recently, diffusion models have achieved remarkable success in generating tasks, including image and audio generation. However, like other generative models, diffusion models are prone to privacy issues. In this paper, we propose an efficient query-based membership inference attack (MIA), namely Proximal Initialization Attack (PIA), which utilizes groundtrut

Elaine Li, Felix Stutz, Thomas Wies, Damien Zufferey

Multiparty session types (MSTs) are a type-based approach to verifying communication protocols. Central to MSTs is a projection operator: a partial function that maps protocols represented as global types to correct-by-construction implementations for each participant, represented as a communicating state machine. Existing projection operators are syntactic

K. Lévêque-Simon, A. Camper, R. Taïeb, J. Caillat

We present cross sections for the formation of positronium chloride (PsCl) in its ground state from the charge exchange between positronium (Ps) and chloride (Cl$^-$) in the range of 10 meV - 100 eV Ps energy. We have used theoretical models based on the first Born approximation in its three-body formulation. We simulated the collisions between Ps and Cl$^-$

Daman Arora, Subbarao Kambhampati

There have been wide spread claims in the literature about the emergent reasoning capabilities of Pretrained Large Language Models. However, recent studies, have found that their ability to plan remains questionable. Through our experiments using GPT-2, we empirically demonstrate that the performance of a finetuned baseline remains poor because it violates p

Waïss Azizian, Franck Iutzeler, Jérôme Malick

Wasserstein distributionally robust estimators have emerged as powerful models for prediction and decision-making under uncertainty. These estimators provide attractive generalization guarantees: the robust objective obtained from the training distribution is an exact upper bound on the true risk with high probability. However, existing guarantees either suf

Rui Sun, Andi Zhang, Haiming Zhang, Jinke Ren

Out-of-distribution (OOD) detection is a crucial task for ensuring the reliability and robustness of machine learning models. Recent works have shown that generative models often assign high confidence scores to OOD samples, indicating that they fail to capture the semantic information of the data. To tackle this problem, we take advantage of sample repairin

Marcos Treviso, Alexis Ross, Nuno M. Guerreiro, André F. T. Martins

Selective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models. However, prior work has not explored how these methods can be integrated to combine their complementary advantages. We overcome this limitation by introducing CREST (ContRastive Edits with Sp

Spiros Zafeiris, George Papadakis

In this study, we present a parallel topology algorithm with a suitable interpolation method for chimera simulations in CFD. The implementation is done in the unstructured Finite Volume (FV) framework and special attention is given to the numerical algorithm. The aim of the proposed algorithm is to approximate fields with discontinuities with application to

Fahim Dalvi, Hassan Sajjad, Nadir Durrani

Neuron analysis provides insights into how knowledge is structured in representations and discovers the role of neurons in the network. In addition to developing an understanding of our models, neuron analysis enables various applications such as debiasing, domain adaptation and architectural search. We present NeuroX, a comprehensive open-source toolkit to

Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner

Can a Large Language Model (LLM) solve simple abstract reasoning problems? We explore this broad question through a systematic analysis of GPT on the Abstraction and Reasoning Corpus (ARC), a representative benchmark of abstract reasoning ability from limited examples in which solutions require some "core knowledge" of concepts such as objects, goal states,

Eddie L. Ungless, Björn Ross, Anne Lauscher

Cutting-edge image generation has been praised for producing high-quality images, suggesting a ubiquitous future in a variety of applications. However, initial studies have pointed to the potential for harm due to predictive bias, reflecting and potentially reinforcing cultural stereotypes. In this work, we are the first to investigate how multimodal models

Jinhang Zuo, Zhiyao Zhang, Zhiyong Wang, Shuai Li

Online learning to rank (OLTR) is a sequential decision-making problem where a learning agent selects an ordered list of items and receives feedback through user clicks. Although potential attacks against OLTR algorithms may cause serious losses in real-world applications, little is known about adversarial attacks on OLTR. This paper studies attack strategie

Nguyen-Thi Dang, Jialun Li

Let G be a semisimple Lie group without compact factor and $\Gamma$ < G a torsion-free, cocompact, irreducible lattice. According to Selberg, periodic orbits of regular Weyl chamber flows live on tori. We prove that these periodic tori equidistribute exponentially fast towards the quotient of the Haar measure. From the equidistribution formula, we deduce a h

Luke Lippstreu, Marcus Spradlin, Akshay Yelleshpur Srikant, Anastasia Volovich

We solve the Landau equations to find the singularities of nine three-loop 7-point graphs that arise as relaxations of the graph studied in arXiv:2211.16425. Along the way we establish that $Y{-}\Delta$ equivalence fails for certain branches of solutions to the Landau equations. We find two graphs with singularities outside the heptagon symbol alphabet; in p

Olalla A. Castro-Alvaredo, Stefano Negro, Fabio Sailis

In recent years a considerable amount of attention has been devoted to the investigation of 2D quantum field theories perturbed by certain types of irrelevant operators. These are the composite field $\mathrm{T}\bar{\mathrm{T}}$ - constructed out of the components of the stress-energy tensor - and its generalisations - built from higher-spin conserved curren

Ana Trisovic

Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs

Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley

Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a "mindstorm." Recent implementations of NN-based societies of minds consist of large language models (LLMs) and other NN-based experts communicating through a natural lang

Alexander Chaushev, Steph Sallum, Julien Lozi, Frantz Martinache

Kernel phase interferometry (KPI) is a data processing technique that allows for the detection of asymmetries (such as companions or disks) in high-Strehl images, close to and within the classical diffraction limit. We show that KPI can successfully be applied to hyperspectral image cubes generated from integral field spectrographs (IFSs). We demonstrate thi

Madeleine Kubasch

We study a multilayer SIR model with two levels of mixing, namely a global level which is uniformly mixing, and a local level with two layers distinguishing household and workplace contacts, respectively. We establish the large population convergence of the corresponding stochastic process. For this purpose, we use an individual-based model whose state space

Felix Jimenez, Matthias Katzfuss

For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification (UQ), while deep neural networks (DNNs) excel at representation learning. Deterministic UQ methods for neural networks have successfully combined the two and require only a single pass through the neural network. However, current methods necessitate changes to

Yoshihiko Abe, Toshifumi Noumi, Kaho Yoshimura

We study black hole extremality in nonlinear electrodynamics motivated by the Weak Gravity Conjecture (WGC) and the Festina Lente (FL) bound. For illustration, we consider the Euler-Heisenberg model and the Dirac-Born-Infeld model in asymptotically flat spacetime, de Sitter spacetime, and anti-de Sitter spacetime. We find that in all cases the extremal condi

Lucas Brivadis, Antoine Chaillet, Jean Auriol

An adaptive observer is proposed to estimate the synaptic distribution between neurons asymptotically from the measurement of a part of the neuronal activity and a delayed neural field evolution model. The convergence of the observer is proved under a persistency of excitation condition. Then, the observer is used to derive a feedback law ensuring asymptotic

Ann E. Wehrle, Michael Carini, Paul J. Wiita, Joshua Pepper

We present second observations by K2 of OJ~287 and 7 other $\gamma$-ray AGNs obtained in 2017-2018, second and third observations of the lobe-dominated, steep spectrum quasar 3C~207, and observations of 9 additional blazars not previously observed with K2. The AGN were observed simultaneously with K2 and the Fermi Large Area Telescope for 51-81 days. Our ful

Serhii Bardyla, Fortunato Maesano, Lyubomyr Zdomskyy

In this paper we study the behaviour of selective separability properties in the class of Frech\'{e}t-Urysohn spaces. We present two examples, the first one given in ZFC proves the existence of a countable Frech\'{e}t-Urysohn (hence $R$-separable and selectively separable) space which is not $H$-separable; assuming $\mathfrak{p}=\mathfrak{c}$, we construct s

Fabian Zaiser, Andrzej S. Murawski, Luke Ong

We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors. To express such models, we introduce a probabilistic programming language that supports discrete and continuous sampling, discrete observations, affine

Julien Berestycki, Cole Graham, Yujin H. Kim, Bastien Mallein

We study traveling waves of the KPP equation in the half-space with Dirichlet boundary conditions. We show that minimal-speed waves are unique up to translation and rotation but faster waves are not. We represent our waves as Laplace transforms of martingales associated to branching Brownian motion in the half-plane with killing on the boundary. We thereby i

Samuel Abreu, Giuseppe De Laurentis, Harald Ita, Maximillian Klinkert

We complete the computation of the two-loop helicity amplitudes for the production of three photons at hadron colliders, including all contributions beyond the leading-color approximation. We reconstruct the analytic form of the amplitudes from numerical finite-field samples obtained with the numerical unitarity method. This method requires as input surface

Giorgos Filandrianos, Edmund Dervakos, Orfeas Menis-Mastromichalakis, Chrysoula Zerva

In the wake of responsible AI, interpretability methods, which attempt to provide an explanation for the predictions of neural models have seen rapid progress. In this work, we are concerned with explanations that are applicable to natural language processing (NLP) models and tasks, and we focus specifically on the analysis of counterfactual, contrastive exp

Peidi Xu, Olga Sosnovtseva, Charlotte Mehlin Sørensen, Kenny Erleben

Accurate analysis and modeling of renal functions require a precise segmentation of the renal blood vessels. Micro-CT scans provide image data at higher resolutions, making more small vessels near the renal cortex visible. Although deep-learning-based methods have shown state-of-the-art performance in automatic blood vessel segmentations, they require a larg

Clotilde Fermanian Kammerer, Caroline Lasser, Didier Robert

This paper is devoted to the construction of approximations of the propagator associated with a semi-classical matrix-valued Schr\"odinger operator with symbol presenting smooth eigenvalues crossings. Inspired by the approach of the theoretical chemists Herman and Kluk who propagated continuous superpositions of Gaussian wave-packets for scalar equations, we

Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding

Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that cannot be accomplished by a single entity alone. This is likely due to factors such as security constraints, privacy concerns, and limitations in computation resources. As a result, c

Hwiyeon Kim, Joohee Kim, Yunha Han, Hwajung Hong

As online news increasingly include data journalism, there is a corresponding increase in the incorporation of visualization in article thumbnail images. However, little research exists on the design rationale for visualization thumbnails, such as resizing, cropping, simplifying, and embellishing charts that appear within the body of the associated article.

Cunxiang Wang, Zhikun Xu, Qipeng Guo, Xiangkun Hu

The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems leverage Pretrained Language Models (PLMs) to model the relationship between questions and passages. However, the diversity in surface form expressions can hinder the model's ability to

Emilio N. M. Cirillo, Vanessa Jacquier, Cristian Spitoni

The metastable behavior of the stochastic Blume--Capel model with Glauber dynamics is studied when zero-boundary conditions are considered. The presence of zero-boundary conditions changes drastically the metastability scenarios of the model: \emph{heterogeneous nucleation} will be proven in the region of the parameter space where the chemical potential is l

Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez

Benchmark datasets in computer vision often contain off-topic images, near duplicates, and label errors, leading to inaccurate estimates of model performance. In this paper, we revisit the task of data cleaning and formalize it as either a ranking problem, which significantly reduces human inspection effort, or a scoring problem, which allows for automated d

Zhongxin Liu, Kui Liu, Xin Xia, Xiaohu Yang

Effective unit tests can help guard and improve software quality but require a substantial amount of time and effort to write and maintain. A unit test consists of a test prefix and a test oracle. Synthesizing test oracles, especially functional oracles, is a well-known challenging problem. Recent studies proposed to leverage neural models to generate test o

G. M. Viswanathan

Enumerating polygons on regular lattices is a classic problem in rigorous statistical mechanics. The goal of enumerating polygons on the square lattice via fermionic path integration was achieved using a free-fermion quadratic action in the late 1970s. Given that polygon edges only link 2 vertices, it is considered plausible, if not natural, that an action o

Melanie Rupflin

As the energy of any map $v$ from $S^2$ to $S^2$ is at least $4\pi \vert deg(v)\vert$ with equality if and only if $v$ is a rational map one might ask whether maps with small energy defect $\delta_v=E(v)-4\pi \vert deg(v)\vert$ are necessarily close to a rational map. While such a rigidity statement turns out to be false for maps of general degree, we will p

Yasmeen Abdrabou, Felix Dietz, Ahmed Shams, Pascal Knierim

Users are the last line of defense as phishing emails pass filter mechanisms. At the same time, phishing emails are designed so that they are challenging to identify by users. To this end, attackers employ techniques, such as eliciting stress, targeting helpfulness, or exercising authority, due to which users often miss being manipulated out of malicious int

Patrick Wagner, Temesgen Mehari, Wilhelm Haverkamp, Nils Strodthoff

Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we p

Gian Luca Orlandi

In a linac driven Free Electron Laser (FEL), the shot-to-shot and non-invasive monitoring of the electron bunch length is normally ensured by Bunch Compressor Monitors (BCMs). The bunch-length dependent signal of a BCM results from the detection and integration - over a given frequency band - of the temporal coherent enhancement of the radiation spectral ene

Cunxiang Wang, Haofei Yu, Yue Zhang

Open-Domain Question Answering (ODQA) systems necessitate a reader model capable of generating answers by simultaneously referring to multiple passages. Although representative models like Fusion-in-Decoder (FiD) have been proposed to address this challenge, these systems can inadvertently rely on spurious features instead of genuine causal relationships bet

Jacob Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth

We study the phenomenon of \textit{in-context learning} (ICL) exhibited by large language models, where they can adapt to a new learning task, given a handful of labeled examples, without any explicit parameter optimization. Our goal is to explain how a pre-trained transformer model is able to perform ICL under reasonable assumptions on the pre-training proc

Ole Delzer, Ingo Weber, Richard Hobeck, Stefan Schulte

Blockchain technology has become one of the most popular trends in IT over the last few years. Its increasing popularity and the discovery of ever more use cases raises the question of how to improve scalability. While researchers are exploring ways to scale the on-chain processing of transactions, the scalability of the off-chain creation of transactions ha

Jonas Schuett

This article argues that frontier artificial intelligence (AI) developers need an internal audit function. First, it describes the role of internal audit in corporate governance: internal audit evaluates the adequacy and effectiveness of a company's risk management, control, and governance processes. It is organizationally independent from senior management

Bahar Taşkesen, Dan A. Iancu, Çağıl Koçyiğit, Daniel Kuhn

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations, subject to additive noise, with the goal of minimizing a quadratic cost function for the state and con

A. S. Makarov, G. V. Afonin, R. A. Konchakov, J. C. Qiao

On the basis of shear modulus measurements on a Pt-based glass, we calculated temperature dependence of the defect concentration c using the Interstitialcy theory. This temperature dependence is compared with temperature dependence of the normalized full width at half maximum (FWHM) gamma of the first peak of the structure factor S(q) for the same glass avai

Javier Sivianes, Julen Ibañez-Azpiroz

The shift current is part of the second-order optical response of materials with a close connection to topology. Here we report a sign inversion in the band-edge shift photoconductivity of the Haldane model when the system undergoes a topological phase transition. This result is obtained following two complementary schemes. On one hand, we derive an analytic

Mouadh Guesmi, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain

Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with the RS. Justification and transparency represent two crucial goals in explainable recommendation. Different from transparency, which faithfully exposes the reasoning behind the

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu, Debanjan Haldar

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi

James F. Cass, Hermes Bloomfield-Gadelha

We show that the flagellar beat of bull spermatozoa and Chlamydomonas Reinhardtii can be modelled by a minimal, geometrically nonlinear reaction-diffusion system. Model solutions are spatio-temporally animated patterns describing flagellar bending waves, connecting beating patterns of cilia and flagella with chemical patterns from classical reaction-diffusio

Dario Siebenkotten, Bernd Kaestner, Arne Hoehl, Shuhei Amakawa

Scattering-type scanning near-field optical microscopy (s-SNOM) enables sub-diffraction spectroscopy, featuring high sensitivity to small spatial permittivity variations of the sample surface. However, due to the near-field probe-sample interaction, the quantitative extraction of the complex permittivity leads to a computationally demanding inverse problem,

Zhen Cao, F. Aharonian, Q. An, Axikegu

We present the first catalog of very-high energy and ultra-high energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory (LHAASO). The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array (WCDA) from March 2021 to September 2022 and 933 days of data recorded by the Kilometer Squared Array (KM2

Xin Zhou, Adam J. Spiers

Fabricating existing and popular open-source adaptive robotic grippers commonly involves using multiple professional machines, purchasing a wide range of parts, and tedious, time-consuming assembly processes. This poses a significant barrier to entry for some robotics researchers and drives others to opt for expensive commercial alternatives. To provide both

Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun

Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making. However, existing models often oversimplify the problem by assuming a time-independent error process and overlooking serial correlation. To overcome this limitation, we propose an inno

Joe A. Smith, Dandan Zhang, Krishna C. Balram

Developing practical quantum technologies will require the exquisite manipulation of fragile systems in a robust and repeatable way. As quantum technologies move towards real world applications, from biological sensing to communication in space, increasing experimental complexity introduces constraints that can be alleviated by the introduction of new techno