December 2024 arXiv papers — page 131

Abdelrahman Elnenaey, Marwan Torki

Image reflection removal is crucial for restoring image quality. Distorted images can negatively impact tasks like object detection and image segmentation. In this paper, we present a novel approach for image reflection removal using a single image. Instead of focusing on model architecture, we introduce a new training technique that can be generalized to im

Yanqi Su, Zhenchang Xing, Chong Wang, Chunyang Chen

Exploratory testing (ET) harnesses tester's knowledge, creativity, and experience to create varying tests that uncover unexpected bugs from the end-user's perspective. Although ET has proven effective in system-level testing of interactive systems, the need for manual execution has hindered large-scale adoption. In this work, we explore the feasibility, chal

Zejian Li, Chenye Meng, Yize Li, Ling Yang

Recent advances in text-to-image (T2I) generation have shown remarkable success in producing high-quality images from text. However, existing T2I models show decayed performance in compositional image generation involving multiple objects and intricate relationships. We attribute this problem to limitations in existing datasets of image-text pairs, which lac

Daniil Shmelev, Cristopher Salvi

Central to rough path theory is the signature transform of a path, an infinite series of tensors given by the iterated integrals of the underlying path. The signature poses an effective way to capture sequentially ordered information, thanks both to its rich analytic and algebraic properties as well as its universality when used as a basis to approximate fun

Iman Munire Bilal, Zheng Fang, Miguel Arana-Catania, Felix-Anselm van Lier

As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language

Hongming Guo, Ruibo Fu, Yizhong Geng, Shuai Liu

Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face challenges in producing audio with rich content. The intricate details and texture required in Mel-spectrograms for such audio often surpass the models' capacity, leading to outpu

Alaa Ibrahim, Bruno Salvy

Deciding the positivity of a sequence defined by a linear recurrence with polynomial coefficients and initial condition is difficult in general. Even in the case of recurrences with constant coefficients, it is known to be decidable only for order up to~5. We consider a large class of linear recurrences of arbitrary order, with polynomial coefficients, for w

Tyler Ward, Abdullah-Al-Zubaer Imran

Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. This signals a need for accurate, automati

Michael Aichmüller, Hector Geffner

In planning and reinforcement learning, the identification of common subgoal structures across problems is important when goals are to be achieved over long horizons. Recently, it has been shown that such structures can be expressed as feature-based rules, called sketches, over a number of classical planning domains. These sketches split problems into subpro

Ioannis Xarchakos, Theodoros Koukopoulos

The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challenge of generating high-fidelity tiled garment images essential for personalized recommendations, outfit composition, and virtual try-on systems from photos of g

J. Cepila, J. G. Contreras, M. Matas, M. Vaculciak

A stable numerical solution of the impact-parameter-dependent next-to-leading order Balitsky-Kovchegov equation is presented for the first time. The rapidity evolution of the dipole amplitude is discussed in detail. Dipole amplitude properties, such as the evolution speed or anomalous dimension behaviour, are studied as a function of the impact parameter and

Mikhail Mamamev, Arkadiy Taranenko, Alexander Demanov, Petr Parfenov

In this note, we present the directed flow $v_1$ measurements of protons from Xe+Cs(I) collisions at 3.8 AGeV (BM@N run8). We show the datasets, event and track selection cuts, centrality definition, event plane reconstruction and resolution. The $v_1$ results are presented as function of transverse momentum ($p_T$) and rapidity ($y_{cm}$) for 10-30\% centra

Elies Ben Achour, Cenk Beydeda, Gabriele Untereiner, Martin Dressel

The overall, loaded quality factor $Q_\mathrm{L}$ quantifies the loss of energy stored in a resonator. Here we discuss on general grounds how $Q_\mathrm{L}$ of a planar microwave resonator made of a conventional superconductor should depend on temperature and frequency. We consider contributions to $Q_\mathrm{L}$ due to dissipation by thermal quasiparticles

Akua Dickson, Juan C. Pacheco Garcia, Ran Jing, Meredith L. Anderson

Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate due to their deformable nature and nonlinear dynamics. This article introduces a fast real-time trajectory generation approach for soft robot manipulators, whic

Estefanía Dalmasso, Gabriela R. Lezama, Marisa Toschi

In this work we obtain weighted boundedness results for singular integral operators with kernels exhibiting exponential decay. We also show that the classes of weights are characterized by a suitable maximal operator. Additionally, we study the boundedness of various operators associated with the generalized Schr\"odinger operator $-\Delta + \mu$, where $\mu

Akash Karthikeyan, Yash Vardhan Pant

Sequence models have demonstrated remarkable success in behavioral planning by leveraging previously collected demonstrations. However, solving multi-task missions remains a significant challenge, particularly when the planner must adapt to unseen constraints and tasks, such as discovering goals and unlocking doors. Such behavioral planning problems are chal

Michal Shlapentokh-Rothman, Yu-Xiong Wang, Derek Hoiem

Adapting visual programming or prompting large language models (LLMs) to generate executable code for visual tasks like visual question answering (VQA) for specialized tasks or domains remains challenging due to high annotation and inference costs. We propose a low-cost visual program distillation method that can be used for models with at most 1 billion par

Preetish Kakkar, Srijani Mukherjee, Hariharan Ragothaman, Vishal Mehta

Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be generated from perceptions which can be applied to enhance object attributes like geometry, substances, and lighting by adding physical models of light propagat

Leandro Parada, Hanlin Tian, Jose Escribano, Panagiotis Angeloudis

Collaborative navigation becomes essential in situations of occluded scenarios in autonomous driving where independent driving policies are likely to lead to collisions. One promising approach to address this issue is through the use of Vehicle-to-Vehicle (V2V) networks that allow for the sharing of perception information with nearby agents, preventing catas

Chengchang Liu, Luo Luo, John C. S. Lui

This paper studied the problem of solving the system of nonlinear equations ${\bf F}({\bf x})={\bf 0}$, where ${\bf F}:{\mathbb R}^{d}\to{\mathbb R}^d$. We propose Gram-Reduced Levenberg--Marquardt method which updates the Gram matrix ${\bf J}(\cdot)^\top{\bf J}(\cdot)$ in every $m$ iterations, where ${\bf J}(\cdot)$ is the Jacobian of ${\bf F}(\cdot)$. Our

Camille Horbez, Jingyin Huang

Given a right-angled Artin group $G$ with finite outer automorphism group, we determine which right-angled Artin groups are measure equivalent (or orbit equivalent) to $G$.

Rongzhe Wei, Mufei Li, Mohsen Ghassemi, Eleonora Kreačić

Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its restrictive assumptions make it unsuitable for LLMs, giving rise to various heuristic approaches typically assessed through empirical evaluations. These standard evaluations randoml

Eric C. Rowell, Yuze Ruan

In this note we give a complete classification of all indecomposable yet reducible representations of $B_3$ for dimensions $2$ and $3$ over an algebraically closed field $K$ with characteristic $0$, up to equivalence. We illustrate their utility with an example.

A. M. S. Smith, Sz. Csizmadia, V. Van Grootel, M. Lendl

Aims: We aim to observe the transits and occultations of WASP-33b, which orbits a rapidly-rotating $\delta$ Scuti pulsator, with the goal of measuring the orbital obliquity via the gravity-darkening effect, and constraining the geometric albedo via the occultation depth. Methods: We observed four transits and four occultations with CHEOPS, and employ a varie

Foivos Fioravantes, Dušan Knop, Jan Matyáš Křišťan, Nikolaos Melissinos

Consider the scenario where multiple agents have to move in an optimal way through a network, each one towards their ending position while avoiding collisions. By optimal, we mean as fast as possible, which is evaluated by a measure known as the makespan of the proposed solution. This is the setting studied in the Multiagent Path Finding problem. In this wor

Ao Liu, Wenshan Li, Beibei Li, Wengang Ma

Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial poisoning attacks on node classification tasks. Current defensive methods require substituting the original GNNs with defense models, regardless of the original's type. This approach, while targeting adversarial robustness, compromises the enhancements developed in

Michael J. Quin, Antonino Di Piazza, Matteo Tamburini

Highly coherent and powerful light sources capable of generating soft x-ray frequency combs are essential for high precision measurements and rigorous tests of fundamental physics. In this work, we derive the analytical conditions required for the emission of coherent radiation from an electron beam colliding with a laser pulse, modeled as a plane wave. Thes

Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen

It is well known that intra-life learning, defined as an additional controller optimization loop, is beneficial for evolving robot morphologies for locomotion. In this work, we investigate this further by comparing it in two different environments: an easy flat environment and a more challenging hills environment. We show that learning is significantly more

Abhishek Chaudhary

We propose an {\em implementable} numerical scheme for the discretization of linear-quadratic optimal control problems involving SDEs in higher dimensions with {\em control constraint}. For time discretization, we employ the implicit Euler scheme, deriving discrete optimality conditions that involve time discretization of a backward stochastic differential e

Deepshikha Mishra, A. Swaminathan

The purpose of this manuscript is to derive two distinct integral representations of the generalized polylogarithm using two different techniques. The first approach involves the Dirichlet series and its Laplace representation, which leads to a single integral representation. The second approach utilizes the Hadamard convolution, resulting in a double integr

Daniel Kapec, Ahmed Sheta

There are two interesting classes of trapped null geodesics in any black hole spacetime: those that lie on the photon ring and those that generate the horizon. Recent work introduced a "near-ring" scaling limit that exhibits the emergent symmetries of the eikonal quasinormal mode (QNM) spectrum associated to the photon ring. This analysis was reformulated ge

Alister Burr, Ana Garcia Armada, Carsten Smeenk, Yang Miao

The upcoming next generation of wireless communication is anticipated to revolutionize the conventional functionalities of the network by adding sensing and localization capabilities, low-power communication, wireless brain computer interactions, massive robotics and autonomous systems connection. Furthermore, the key performance indicators expected for the

Hugo Flores García, Oriol Nieto, Justin Salamon, Bryan Pardo

We present Sketch2Sound, a generative audio model capable of creating high-quality sounds from a set of interpretable time-varying control signals: loudness, brightness, and pitch, as well as text prompts. Sketch2Sound can synthesize arbitrary sounds from sonic imitations (i.e.,~a vocal imitation or a reference sound-shape). Sketch2Sound can be implemented o

Pascal Epple, Igor Shilov, Bozhidar Stevanoski, Yves-Alexandre de Montjoye

Generative Artificial Intelligence (Gen-AI) models are increasingly used to produce content across domains, including text, images, and audio. While these models represent a major technical breakthrough, they gain their generative capabilities from being trained on enormous amounts of human-generated content, which often includes copyrighted material. In thi

Xiaodong Cui, A F M Saif, Songtao Lu, Lisha Chen

In this paper, we propose a bilevel joint unsupervised and supervised training (BL-JUST) framework for automatic speech recognition. Compared to the conventional pre-training and fine-tuning strategy which is a disconnected two-stage process, BL-JUST tries to optimize an acoustic model such that it simultaneously minimizes both the unsupervised and supervise

Véronique Bruyère, Jean-François Raskin, Alexis Reynouard, Marie Van Den Bogaard

This paper studies the rational synthesis problem for multi-player games played on graphs when rational players are following subgame perfect equilibria. In these games, one player, the system, declares his strategy upfront, and the other players, composing the environment, then rationally respond by playing strategies forming a subgame perfect equilibrium.

Paolo Comaron, Nathan Goldman, Atac Imamoglu, Ivan Amelio

Detecting vortices in neutral superfluids represents an outstanding experimental challenge. Using stochastic classical-field methods, we theoretically show that a quantum impurity repulsively coupled to a weakly-interacting Bose gas at finite temperature carries direct spectroscopic signatures of vortex proliferation. In two dimensions, we find that a low-en

Rangel Daroya, Luisa Vieira Lucchese, Travis Simmons, Punwath Prum

Many remote sensing applications employ masking of pixels in satellite imagery for subsequent measurements. For example, estimating water quality variables, such as Suspended Sediment Concentration (SSC) requires isolating pixels depicting water bodies unaffected by clouds, their shadows, terrain shadows, and snow and ice formation. A significant bottleneck

Christina Runkel, Kanchana Vaishnavi Gandikota, Jonas Geiping, Carola-Bibiane Schönlieb

Being able to reconstruct training data from the parameters of a neural network is a major privacy concern. Previous works have shown that reconstructing training data, under certain circumstances, is possible. In this work, we analyse such reconstructions empirically and propose a new formulation of the reconstruction as a solution to a bilevel optimisation

Abdullah Hyder, Will Fox, Kirill Lezhnin, Samuel Totorica

We develop a ray-tracing model for laser-plasma interaction suitable for coupling in-line into kinetic particle-in-cell plasma simulation. The model is based on inverse Bremsstrahlung absorption and includes oblique incidence effects and reflection at the critical surface. The energy deposition is given to electrons by randomized kicks to momentum. The model

Martin Klissarov, Mikael Henaff, Roberta Raileanu, Shagun Sodhani

Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create

J. Thorben Frank, Stefan Chmiela, Klaus-Robert Müller, Oliver T. Unke

Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions. Self-attention offers a compelling mechanism for capturing these global effects, but its quadratic complexity presents a

Sana Mahmood, Jinqi Lu, Soudeh Ghorbani

To fulfill the low latency requirements of today's applications, deployment of RDMA in datacenters has become prevalent over the recent years. However, the in-order delivery requirement of RDMAs prevents them from leveraging powerful techniques that help improve the performance of datacenters, ranging from fine-grained load balancers to throughput-optimal ex

Nariel Monteiro, Alexander Stasinski

The conjugation representation of a finite group $G$ is the complex permutation module defined by the action of $G$ on itself by conjugation. Addressing a problem raised by Hain motivated by the study of a Hecke action on iterated Shimura integrals, Tiep proved that for $G=\operatorname{SL}_{2}(\mathbb{Z}/p^{r})$, where $r\geq1$ and $p\geq5$ is a prime, any

Alfonso Ballon-Bayona, Jonathan P. Shock, Dimitrios Zoakos

We investigate a strongly coupled finite-density anisotropic fluid in $2+1$ dimensions dual to an asymptotically AdS black brane that is a solution of Einstein-Maxwell-Axion theory in $3+1$ dimensions. Despite the anisotropy, the fluid thermodynamic properties align with those of a conformal fluid. Moreover, we show that the fluid is stable under the increas

Po-Yan Tseng, Yu-Min Yeh

We investigate a neutrino-scalar dark matter (DM) $\nu\phi$ interaction encountering distinctive neutrino sources, namely Diffuse Supernova Neutrino Background (DSNB) and Active Galactic Nuclei (AGN). The interaction is mediated by a fermionic particle $F$, in which the $\nu\phi$ scattering cross section characterizes different energy dependent with respect

Pallavi Jain, Dino Ienco, Roberto Interdonato, Tristan Berchoux

Pre-trained vision-language models (VLMs), such as CLIP, demonstrate impressive zero-shot classification capabilities with free-form prompts and even show some generalization in specialized domains. However, their performance on satellite imagery is limited due to the underrepresentation of such data in their training sets, which predominantly consist of gro

José A. Carrillo, Tommaso Lorenzi, Fiona R. Macfarlane

We consider a partial differential equation model for the growth of heterogeneous cell populations subdivided into multiple distinct discrete phenotypes. In this model, cells preferentially move towards regions where they feel less compressed, and thus their movement occurs down the gradient of the cellular pressure, which is defined as a weighted sum of the

Dong Chen, Alice Dethise, Istemi Ekin Akkus, Ivica Rimac

A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and model owners want to protect the confidentiality of their respective assets (i.e., datasets, models and training code), with the dataset owners also caring about the privacy of indivi

Kamal Acharya, Mehul Lad, Houbing Song, Liang Sun

Advanced Air Mobility (AAM), encompassing Urban Air Mobility (UAM) and Regional Air Mobility (RAM), offers innovative solutions to mitigate the issues related to ground transportation like traffic congestion, environmental pollution etc. RAM addresses transportation inefficiencies over medium-distance trips (50-500 miles), which are often underserved by both

Valentin Patilea, Sunny G. W. Wang

The computation of integrals is a fundamental task in the analysis of functional data, which are typically considered as random elements in a space of squared integrable functions. Borrowing ideas from recent advances in the Monte Carlo integration literature, we propose effective unbiased estimation and inference procedures for integrals of uni- and multiva

Preet Mishra, Sapna Ratan Shah, R. K. Brojen Singh

Spatio-temporal dynamics of the evolution of population involving growth and diffusion processes can be modeled by class of partial diffusion equations (PDEs) known as reaction-diffusion systems. In this work, we developed a nonlinear transformations method that converts the original nonlinear Fisher-KPP class of PDEs into an exactly solvable class. We then

Luke Mortimer

Bell inequalities are an important tool for studying non-locality, however quickly become computationally intractable as the system size grows. We consider a novel method for finding an upper bound for the quantum violation of such inequalities by combining the NPA hierarchy, the method of alternating projections, and the memory-efficient optimisation algori

Tom Bridgeland, Fabrizio Del Monte, Luca Giovenzana

We apply results on inducing stability conditions to local Calabi-Yau threefolds and obtain applications to Donaldson-Thomas (DT) theory. A basic example is the total space of the canonical bundle of $Z=\mathbb{P}^1\times \mathbb{P}^1$. We use a result of Dell to construct stability conditions on the derived category of $X$ for which all stable objects can b

Lucas Tsunaki, Bernd Bauerhenne, Malwin Xibraku, Martin E. Garcia

Quantum tokens envision to store unclonable quantum states in a physical device, with the goal of being used for personal authentication protocols, as required by banks. Still, the experimental realization of such devices faces many technical challenges, which can be partially mitigated using ensembles instead of single qubits. In this work, we thus propose

Quynh-Mai Thi Nguyen, Lan-Nhi Thi Nguyen, Cam-Van Thi Nguyen

The objective of multimodal intent recognition (MIR) is to leverage various modalities-such as text, video, and audio-to detect user intentions, which is crucial for understanding human language and context in dialogue systems. Despite advances in this field, two main challenges persist: (1) effectively extracting and utilizing semantic information from robu

Andor Diera, Lukas Galke, Fabian Karl, Ansgar Scherp

Continual learning remains a challenge across various natural language processing (NLP) tasks, as models updated with new training data often risk catastrophic forgetting of previously acquired knowledge. We introduce a discrete key-value bottleneck (DKVB) for encoder-only language models, enabling efficient continual learning through localized updates. Insp

N. Heidari, G. H'ebrard, E. Martioli, J. D. Eastman

We present the study of seven systems, three of which TOI-2295, TOI-2537, and TOI-5110 are newly discovered planetary systems. Through the analysis of TESS photometry, SOPHIE radial velocities, and high-spatial resolution imaging, we found that TOI-2295b, TOI-2537b, and TOI-5110b are transiting warm Jupiters with orbital periods ranging from 30 to 94 d, mass

Daniel Geissler, Bo Zhou, Sungho Suh, Paul Lukowicz

A fundamental step in the development of machine learning models commonly involves the tuning of hyperparameters, often leading to multiple model training runs to work out the best-performing configuration. As machine learning tasks and models grow in complexity, there is an escalating need for solutions that not only improve performance but also address sus

Meng-Zi Huang, Philipp Fabritius, Jeffrey Mohan, Mohsen Talebi

Fermionic superfluid junctions typically exhibit suppressed thermal and spin transport due to the presence of a pairing gap but allow coherent particle transport. While dissipation generally weakens coherent transport, it can also induce excitations that open other transport channels. In this work, we experimentally study a one-dimensional superfluid junctio

Tianxin Huang, Zhenyu Zhang, Ying Tai, Gim Hee Lee

Existing research has made impressive strides in reconstructing human facial shapes and textures from images with well-illuminated faces and minimal external occlusions. Nevertheless, it remains challenging to recover accurate facial textures from scenarios with complicated illumination affected by external occlusions, e.g. a face that is partially obscured

Gauhar Abbas, Neelam Singh

We investigate the collider phenomenology of the standard Hierarchical VEVs Model by proposing a new version, which avoids large flavor changing neutral current interactions, thus, rendering the scale of new physics as low as the electroweak scale. The resulting collider signatures are distinctive and testable at the High-Luminosity LHC, the High-Energy LHC,

Yujin Kim, Sol Choi, Bum-Jae You, Keunwoo Jang

Articulated object manipulation is a challenging task, requiring constrained motion and adaptive control to handle the unknown dynamics of the manipulated objects. While reinforcement learning (RL) has been widely employed to tackle various scenarios and types of articulated objects, the complexity of these tasks, stemming from multiple intertwined objective

Yingxin Li, Ye Li, Yuan Meng, Xinzhu Ma

As large language models (LLMs) continue to advance, the demand for higher quality and faster processing of long contexts across various applications is growing. KV cache is widely adopted as it stores previously generated key and value tokens, effectively reducing redundant computations during inference. However, as memory overhead becomes a significant con

Lefteris Loukas, Nikolaos Smyrnioudis, Chrysa Dikonomaki, Spyros Barbakos

We present GR-NLP-TOOLKIT, an open-source natural language processing (NLP) toolkit developed specifically for modern Greek. The toolkit provides state-of-the-art performance in five core NLP tasks, namely part-of-speech tagging, morphological tagging, dependency parsing, named entity recognition, and Greeklishto-Greek transliteration. The toolkit is based o

Pengyue Jia, Derong Xu, Xiaopeng Li, Zhaocheng Du

The reranker and generator are two critical components in the Retrieval-Augmented Generation (i.e., RAG) pipeline, responsible for ranking relevant documents and generating responses. However, due to differences in pre-training data and objectives, there is an inevitable gap between the documents ranked as relevant by the reranker and those required by the g

Grigor Adamyan

We developed a conformal map technique to analyze the attenuation of edge modes propagating along imperfect boundaries. In systems where the potential energy exhibits conformal invariance, the conformal transformation can straighten the boundary, simplifying the boundary conditions. Using the example of edge modes in a simple field-theoretical model, we exam

Jiayue Yang, Niayesh Afshordi, Mahdi Torabian, Seyed Akbar Jafari

In analogue gravity studies, the goal is to replicate black hole phenomena, such as Hawking radiation, within controlled laboratory settings. In the realm of condensed matter systems, this may happen in 2D tilted Dirac cone materials based on honeycomb lattice. In particular, we compute the entropy of this system, and find it has the same form as black hole

Pengyue Jia, Zhaocheng Du, Yichao Wang, Xiangyu Zhao

Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Conventional approaches typically employ surrogate models-such as decision trees or neural networks-to estimate feature importance. However, their effectiveness is inherently constrained, as these models may struggle under suboptimal training condit

Rouven Essig, Peiran Li, Zhen Liu, Megan McDuffie

We propose to search for millicharged particles produced in high-intensity electron beam dumps using small ultralow-threshold sensors. As a concrete example, we consider a Skipper-CCD placed behind the beam dump in Hall A at Jefferson Lab. We compute the millicharged particle flux, including both electromagnetic cascade and meson productions emanating from a

Luke Mortimer

Since the early 2000s there has existed the meme that "DOOM can run on anything". Whether it be an ATM or a calculator, someone at some point has recompiled DOOM to run on it. Now the quantum computer finally joins the list. More specifically, this project represents the first level of DOOM loosely rewritten using Hadamards and Toffolis which, despite being

Daniel Geissler, Bo Zhou, Mengxi Liu, Paul Lukowicz

Supervised machine learning often operates on the data-driven paradigm, wherein internal model parameters are autonomously optimized to converge predicted outputs with the ground truth, devoid of explicitly programming rules or a priori assumptions. Although data-driven methods have yielded notable successes across various benchmark datasets, they inherently

Atharva Khadilkar, Mark Stamp

In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as Convolutional Neural Networks (CNN) in classifying images derived from executable files. In this paper, we consider an innovative method that relies on an image conversion process

Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer, Robert Jenssen

Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly important in the unsupervised field of representation learning explainable artificial intelligence (R-XAI). Current R-XAI methods provide uncertainty by measuring variability in the impo

Indibar Debnath, Habibul Islam, Edgar Martínez-Moro, Om Prakash

Let $\mathcal A$ the affine algebra given by the ring $\mathbb{F}_q[X_1,X_2,\ldots,X_\ell]/ I$, where $I$ is the ideal $\langle t_1(X_1), t_2(X_2), \ldots, t_\ell(X_\ell) \rangle$ with each $t_i(X_i)$, $1\leq i\leq \ell$, being a square-free polynomial over $\mathbb{F}_q$. This paper studies the $k$-Galois hulls of $\lambda$-constacyclic codes over $\mathcal

Aymen Merrouche, Stefanie Wuhrer, Edmond Boyer

We introduce a novel, data-driven approach for reconstructing temporally coherent 3D motion from unstructured and potentially partial observations of non-rigidly deforming shapes. Our goal is to achieve high-fidelity motion reconstructions for shapes that undergo near-isometric deformations, such as humans wearing loose clothing. The key novelty of our work

Chengliang Tan, Risto Korhonen

In this paper, an Askey-Wilson version of the Wronskian-Casorati determinant $\mathcal{W}(f_{0}, \dots, f_{n})(x)$ for meromorphic functions $f_{0}, \dots, f_{n}$ is introduced to establish an Askey-Wilson version of the general form of the Second Main Theorem in projective space. This improves upon the original Second Main Theorem for the Askey-Wilson opera

M. Q. Dong, Zhi-Xin Guo

Anisotropic magnetoresistance (AMR) is a well-known magnetoelectric coupling phenomenon, commonly exhibiting two-fold symmetry relative to the magnetic field. In this study, we reveal the existence of high-order AMRs in two-dimensional (2D) magnetic monolayers. Based on density functional theory (DFT) calculations of Fe3GeTe2 and CrTe2 monolayers, we find th

Hai-Yen Thi Nguyen, Cam-Van Thi Nguyen

Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions. Comparative Quintuple Extraction (COQE) aims to identify five key components in text: the target entity, compared entities, compared aspects, opinions on these aspects, and polarity. Extracting precise comparative information from product reviews is ch

Daniel Geissler, Bo Zhou, Paul Lukowicz

Human Activity Recognition using time-series data from wearable sensors poses unique challenges due to complex temporal dependencies, sensor noise, placement variability, and diverse human behaviors. These factors, combined with the nontransparent nature of black-box Machine Learning models impede interpretability and hinder human comprehension of model beha

Mingda Jia, Liming Zhao, Ge Li, Yun Zheng

Human-object interaction (HOI) detectors with popular query-transformer architecture have achieved promising performance. However, accurately identifying uncommon visual patterns and distinguishing between ambiguous HOIs continue to be difficult for them. We observe that these difficulties may arise from the limited capacity of traditional detector queries i

Kelsey M. Nelson, Maureen S. Golan, Matthew D. Bartos, Javad Mohammadi

Modern power grids are evolving to become more interconnected, include more electric vehicles (EVs), and utilize more renewable energy sources (RES). Increased interconnectivity provides an opportunity to manage EVs and RES by using price signaling to shift EV loads towards periods of high RES output. This work uses ERCOT's 2035 RES installation plans and pr

Yifan Xie, Tao Feng, Xin Zhang, Xiangyang Luo

Talking head synthesis with arbitrary speech audio is a crucial challenge in the field of digital humans. Recently, methods based on radiance fields have received increasing attention due to their ability to synthesize high-fidelity and identity-consistent talking heads from just a few minutes of training video. However, due to the limited scale of the train

Mingkun Lei, Xue Song, Beier Zhu, Hao Wang

Text-driven style transfer aims to merge the style of a reference image with content described by a text prompt. Recent advancements in text-to-image models have improved the nuance of style transformations, yet significant challenges remain, particularly with overfitting to reference styles, limiting stylistic control, and misaligning with textual content.

Leihui Li, Lixuepiao Wan, Xuping Zhang

The Next Best View (NBV) problem is a pivotal challenge in 3D robotic scanning, with the potential to significantly improve the efficiency of object capture and reconstruction. Existing methods for determining the NBV often overlook view overlap, assume a fixed virtual origin for the camera, and rely on voxel-based representations of 3D data. To address thes

Denis Denisov, Alexander Tarasov, Vitali Wachtel

We consider random walks conditioned to stay positive. When the mean of increments is zero and variance is finite it is known that they converge to the Rayleigh distribution. In the present paper we derive a Berry-Esseen type estimate and show that the rate of convergence is of order $n^{-1/2}$.

Elena Cardillo, Lucilla Frattura

Coding morbidity data using international standard diagnostic classifications is increasingly important and still challenging. Clinical coders and physicians assign codes to patient episodes based on their interpretation of case notes or electronic patient records. Therefore, accurate coding relies on the legibility of case notes and the coders' understandin

Yuang Zhang, Liping Wang, Yihong Huang, Yuanxing Zheng

Unsupervised Outlier Detection (UOD) is a critical task in data mining and machine learning, aiming to identify instances that significantly deviate from the majority. Without any label, deep UOD methods struggle with the misalignment between the model's direct optimization goal and the final performance goal of Outlier Detection (OD) task. Through the persp

Giovanni Catino, Dario Daniele Monticelli, Alberto Roncoroni, Xiaodong Wang

In this paper we study positive solutions to the CR Yamabe equation in noncompact $(2n+1)$-dimensional Sasakian manifolds with nonnegative curvature. In particular, we show that the Heisenberg group $\mathbb{H}^1$ is the only (complete) Sasakian space with nonnegative Tanaka-Webster scalar curvature admitting a (nontrivial) positive solution. Moreover, under

Franca auf der Heiden, Markus Axer, Katrin Amunts, Miriam Menzel

Mapping the intricate network of nerve fibers is crucial for understanding brain function. Three-Dimensional Polarized Light Imaging (3D-PLI) and Computational Scattered Light Imaging (ComSLI) map dense nerve fibers in brain sections with micrometer resolution using visible light. 3D-PLI reconstructs 3D-fiber orientations, while ComSLI disentangles multiple

Rhoss Likibi Pellat, Emmanuel Che Fonka, Olivier Menoukeu Pamen

We investigate the convergence rate for the time discretization of a class of quadratic backward SDEs -- potentially involving path-dependent terminal values -- when coupled with non-standard Lipschitz-type forward SDEs. In our review of the explicit time-discretization schemes in the spirit of Pag\`es \& Sagna (see \cite{PaSa18}), we achieve an error contro

Roxane Merat, Giovanni Cioffi, Leonard Bauersfeld, Davide Scaramuzza

Globally-consistent localization in urban environments is crucial for autonomous systems such as self-driving vehicles and drones, as well as assistive technologies for visually impaired people. Traditional Visual-Inertial Odometry (VIO) and Visual Simultaneous Localization and Mapping (VSLAM) methods, though adequate for local pose estimation, suffer from d

Pim J. Dekker, Marjolein N. van der Linden, Detlef Lohse

The evaporation of multi-component sessile droplets is key in many physicochemical applications such as inkjet printing, spray cooling, and micro-fabrication. Past fundamental research has primarily concentrated on single drops, though in applications they are rarely isolated. Here, we experimentally explore the effect of neighbouring drops on the evaporatio

Hong Qin, Elijah J. Kolmes, Michael Updike, Nicholas Bohlsen

Phase space engineering by RF waves plays important roles in both thermal D-T fusion and non-thermal advanced fuel fusion. But not all phase space manipulation is allowed, certain fundamental limits exist. In addition to Liouville's theorem, which requires the manipulation to be volume-preserving, Gromov's non-squeezing theorem imposes another constraint. Th

Junjie Shu, Yixi Liao, Erxiao Wang

We classify edge-to-edge tilings of the sphere by congruent pentagons with the edge combination $a^4b$ and with any irrational angle in degree: they are three $1$-parameter families of pentagonal subdivisions of the Platonic solids, with $12, 24$ and $60$ tiles; and a sequence of $1$-parameter families of pentagons admitting non-symmetric $3$-layer earth map

Wladimir E. Banda-Barragán, Andrei Antipov, Daniel Villarruel

Connecting numerical simulations to observations is essential to understanding the physics of galactic winds. Our Galaxy hosts a large-scale, multi-phase nuclear wind, whose dense gas has been detected using HI and molecular line observations. In this paper, we summarise our recent numerical work devoted to producing synthetic HI observables and measuring th

P. Chris Broekema, Rob V. van Nieuwpoort

Currently, data-intensive scientific applications require vast amounts of compute resources to deliver world-leading science. The climate emergency has made it clear that unlimited use of resources (e.g., energy) for scientific discovery is no longer acceptable. Future computing hardware promises to be much more energy efficient, but without better optimized

Stéphane d'Ascoli, Corentin Bel, Jérémy Rapin, Hubert Banville

Deep learning has recently enabled the decoding of language from the neural activity of a few participants with electrodes implanted inside their brain. However, reliably decoding words from non-invasive recordings remains an open challenge. To tackle this issue, we introduce a novel deep learning pipeline to decode individual words from non-invasive electro

Nguyen Van Doan, Dat Tran Nguyen, Cam-Van Thi Nguyen

Multimodal Aspect-Based Sentiment Analysis (MABSA) combines text and images to perform sentiment analysis but often struggles with irrelevant or misleading visual information. Existing methodologies typically address either sentence-image denoising or aspect-image denoising but fail to comprehensively tackle both types of noise. To address these limitations,

Jun Wang, Zhaoyang Yin

In this paper, we study the existence of ground state standing waves and orbital stability, of prescribed mass, for the nonlinear critical Choquard equation \begin{equation*} \left\{\begin{array}{l} i \partial_t u+\Delta u -V(x)u+(I_{\alpha}\ast|u|^{q})|u|^{q-2}u+(I_{\alpha}\ast|u|^{2_{\alpha}^*})|u|^{2_{\alpha}^*-2}u=0,\ (x, t) \in \mathbb{R}^d \times \math