March 2024 arXiv papers — page 57

Miguel Montero, Michelangelo Tartaglia

In six dimensions, there is an exotic $\mathcal{N}=(4,0)$ supermultiplet that contains only fields of spin $\leq 2$, but no graviton, and that on a circle reduces to 5D $\mathcal{N}=4$ supergravity. It has been proposed that, if suitable interactions exist, the $(4,0)$ theory might provide a consistent alternative UV completion for $\mathcal{N}=4$ 5D supergr

Sviatoslav Borisov, Corinne Charbonnel, Nikos Prantzos, Thibaut Dumont

Unraveling the cosmological Li problem - the discrepancy between Big Bang nucleosynthesis predictions and observed values in the Spite plateau - requires a comprehensive exploration of stellar evolution. In this study, we utilized the code STAREVOL to compute the stellar evolution models with atomic diffusion, rotation-induced processes, parametric turbulenc

Niko Koivunen, Oleg Lebedev, Martti Raidal

The regime of dark matter (DM) freeze-in at stronger coupling interpolates between freeze-in and freeze-out. It relies on Boltzmann-suppressed dark matter production, implying that the Standard Model bath temperature never exceeds the dark matter mass. In this work, we study this regime in the context of sterile neutrino dark matter, which can be sufficientl

Ian Padilla-Gay, Shashank Shalgar, Irene Tamborra

Neutron star merger remnants are unique sites for exploring neutrino flavor conversion in dense media. Because of the natural excess of $\bar{\nu}_e$ over $\nu_e$, the neutrino-neutrino potential can cancel the matter potential, giving rise to matter-neutrino resonant flavor conversion. Under the assumption of two (anti)neutrino flavors and spatial homogenei

Leonardo Tinti, Arthur Vereijken, Shahriyar Jafarzade, Francesco Giacosa

Historically it has been believed that a time-independent classical source has no effect on the scattering of relativistic uncharged field, in contrast with single particle quantum mechanics. In this work we show that the dynamics is not trivial. We solve exactly for the scattering amplitudes and find that a key ingredient is the production of particles from

Hanrong Ye, Dan Xu

Recently, there has been an increased interest in the practical problem of learning multiple dense scene understanding tasks from partially annotated data, where each training sample is only labeled for a subset of the tasks. The missing of task labels in training leads to low-quality and noisy predictions, as can be observed from state-of-the-art methods. T

Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee

Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model. LMMs typically take in a fixed and large amount of visual tokens, such as the penultimate layer features in the CLIP visual encoder, as the prefix content. Recent LMMs incorporate more complex visual inputs, such as h

Fernando Arias-Aragón, Luc Darmé, Giovanni Grilli di Cortona, Enrico Nardi

Resonant positron annihilation on atomic electrons provides a powerful method to search for light new particles coupled to $e^+e^-$. Reliable estimates of production rates require a detailed characterization of electron momentum distributions. We describe a general method that harnesses the target material Compton profile to properly include electron velocit

Max Casebolt, Chunhan Feng, Richard T. Scalettar, Steven Johnston

Most nonperturbative numerical studies of electron-phonon interactions focus on model Hamiltonians where the electrons interact with a phonon branch via a single type of microscopic mechanism. Two commonly explored couplings in this context are the Holstein and Su-Schrieffer-Heeger (SSH) interactions, which describe phonons modulating the on-site energy and

Kevin Xie, Jonathan Lorraine, Tianshi Cao, Jun Gao

Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D optimize multiple prompts simultaneously to improve efficiency, enabling fast text-to-3D synthesis. However, they cannot capture high-frequency geometry and texture details and stru

William Acero, Isabel Molina, J. Miguel Marín

When estimating area means, direct estimators based on area-specific data, are usually consistent under the sampling design without model assumptions. However, they are inefficient if the area sample size is small. In small area estimation, model assumptions linking the areas are used to "borrow strength" from other areas. The basic area-level model provides

Zheng Zhang, Wenbo Hu, Yixing Lao, Tong He

3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results while advancing real-time rendering performance. However, it relies heavily on the quality of the initial point cloud, resulting in blurring and needle-like artifacts in areas with insufficient initializing points. This is mainly attributed to the point cloud growth conditi

Zhenwei Wang, Tengfei Wang, Gerhard Hancke, Ziwei Liu

Real-world applications often require a large gallery of 3D assets that share a consistent theme. While remarkable advances have been made in general 3D content creation from text or image, synthesizing customized 3D assets following the shared theme of input 3D exemplars remains an open and challenging problem. In this work, we present ThemeStation, a novel

Ruining Li, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi

We introduce DragAPart, a method that, given an image and a set of drags as input, generates a new image of the same object that responds to the action of the drags. Differently from prior works that focused on repositioning objects, DragAPart predicts part-level interactions, such as opening and closing a drawer. We study this problem as a proxy for learnin

Hakim Boumaza, Sylvain Zalczer

We consider a random family of Dirac operators on $N$ parallel real lines, modelling for example a graphene nanoribbon. We establish a localization criterion involving properties on the group generated by transfer matrices. In particular, we consider not only the case where this group is the symplectic group but also a strict subgroup of it. We establish und

Jaesang Park, Alireza Askarian, Srinivasa Salapaka

This article introduces two control frameworks: one for Grid-Following (GFL) inverters aiding Grid-Forming (GFM) inverters in voltage regulation during large contingency events and optimizing power transactions under normal conditions; and another for seamless transitions between grid-tied and grid-isolated setups, managing voltage transient characteristics.

Hongyan Liu, Edwin Versteeg, Miha Fuderer, Oscar van der Heide

Purpose: Current 3D Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) protocols use transient-state, gradient-spoiled gradient-echo sequences that are prone to cerebrospinal fluid (CSF) pulsation artifacts when applied to the brain. This study aims at developing a 3D MR-STAT protocol for whole-brain relaxometry that overcomes the challenges posed b

Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang

Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input. The length of the text token is restricted to 77, and an emp

Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu

We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue. Our core design is a progressive training approach that unifies the masked video modeling, crossmodal contrastive learning, and next token prediction, scaling up the video enco

Ali Chouman, Dimitrios Michael Manias, Abdallah Shami

Experimentation in practical, end-to-end (E2E) next-generation networks deployments is becoming increasingly prevalent and significant in the realm of modern networking and wireless communications research. The prevalence of fifth-generation technology (5G) testbeds and the emergence of developing networks systems, for the purposes of research and testing, f

Volker Koch, Larry McLerran, Gerald A. Miller, Volodymyr Vovchenko

The possibility that nuclear matter might be Quarkyonic is considered. Quarkyonic matter is high baryon density matter that is confined but can be approximately thought of as a filled Fermi sea of quarks surrounded by a shell of nucleons. Here, nuclear matter is described by the IdylliQ sigma model for Quarkyonic matter, generalizing the non-interacting Idyl

Nadia Alshahwan, Arianna Blasi, Kinga Bojarczuk, Andrea Ciancone

This paper reports the results of the deployment of Rich-State Simulated Populations at Meta for both automated and manual testing. We use simulated users (aka test users) to mimic user interactions and acquire state in much the same way that real user accounts acquire state. For automated testing, we present empirical results from deployment on the Facebook

Matteo Califano, Rocco D'Agostino, Daniele Vernieri

The theory of inflation provides an elegant explanation for the nearly flat universe observed today, which represents one of the pillars of the standard cosmological model. However, recent studies have reported some deviations from a flat geometry, arguing that a closed universe would be instead favored by observations. Given its central role played in the c

Jiawei Zang, Matija Medvidović, Dominik Kiese, Domenico Di Sante

Characterizing complex many-body phases of matter has been a central question in quantum physics for decades. Numerical methods built around approximations of the renormalization group (RG) flow equations have offered reliable and systematically improvable answers to the initial question -- what simple physics drives quantum order and disorder? The flow equa

Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang

We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on native performance of existing LLMs, without training interventions. We deploy LLMs as agents in simple multi-armed bandit environments, specifying the environment description and

Aqeel Anwar, Tae Eun Choe, Zian Wang, Sanja Fidler

Detecting a diverse range of objects under various driving scenarios is essential for the effectiveness of autonomous driving systems. However, the real-world data collected often lacks the necessary diversity presenting a long-tail distribution. Although synthetic data has been utilized to overcome this issue by generating virtual scenes, it faces hurdles s

Ruochu Yang, Fumin Zhang, Mengxue Hou

We develop a hierarchical LLM-task-motion planning and replanning framework to efficiently ground an abstracted human command into tangible Autonomous Underwater Vehicle (AUV) control through enhanced representations of the world. We also incorporate a holistic replanner to provide real-world feedback with all planners for robust AUV operation. While there h

Gretchen R. Stahlman

Unidentified Anomalous Phenomena (UAP), also known as Unidentified Flying Objects (UFOs), has shifted from being a stigmatized topic on the fringes of scientific inquiry to a legitimate subject of scientific interest with a need for high quality, curated data, and rigorous scientific investigation. This paper presents a preliminary scoping review and analysi

Anirban Das, Tim Herbermann, Manibrata Sen, Volodymyr Takhistov

Diffuse neutrinos from past supernovae in the Universe present us with a unique opportunity to test dark matter (DM) interactions. These neutrinos can scatter and boost the DM particles in the Milky Way halo to relativistic energies allowing us to detect them in terrestrial laboratories. Focusing on generic models of DM-neutrino and electron interactions, me

Dingyu Wang

In the turnstile streaming model, a dynamic vector $\mathbf{x}=(\mathbf{x}_1,\ldots,\mathbf{x}_n)\in \mathbb{Z}^n$ is updated by a stream of entry-wise increments/decrements. Let $f\colon\mathbb{Z}\to \mathbb{R}_+$ be a symmetric function with $f(0)=0$. The \emph{$f$-moment} of $\mathbf{x}$ is defined to be $f(\mathbf{x}) := \sum_{v\in[n]}f(\mathbf{x}_v)$. W

Yuepeng Hu, Zhengyuan Jiang, Moyang Guo, Neil Zhenqiang Gong

Watermark has been widely deployed by industry to detect AI-generated images. The robustness of such watermark-based detector against evasion attacks in the white-box and black-box settings is well understood in the literature. However, the robustness in the no-box setting is much less understood. In this work, we propose a new transfer evasion attack to ima

Chenxi Whitehouse

This thesis investigates how natural language understanding and generation with transformer models can benefit from grounding the models with knowledge representations and addresses the following key research questions: (i) Can knowledge of entities extend its benefits beyond entity-centric tasks, such as entity linking? (ii) How can we faithfully and effect

Abdur Rahman Bin Md Faizullah, Ashok Urlana, Rahul Mishra

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers.

Joe Gorka, Tim Hsu, Wenting Li, Yury Maximov

Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures. Traditional power-flow-based tools for assessing cascading blackout risk are too slow to properly explore the space of

Yiliang Zhou, Hanley Ong, Patrick Kennedy, Carol Wu

The study examines the application of GPT-4V, a multi-modal large language model equipped with visual recognition, in detecting radiological findings from a set of 100 chest radiographs and suggests that GPT-4V is currently not ready for real-world diagnostic usage in interpreting chest radiographs.

Ryan Teehan, Brenden Lake, Mengye Ren

Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly. Prompting in-context is not robust to context distractions, and often fails to confer much information about the new concepts. Classic methods for few-shot word learning in NLP, relying on global word vectors, are

Xiaoling Hu

In many scenarios, especially biomedical applications, the correct delineation of complex fine-scaled structures such as neurons, tissues, and vessels is critical for downstream analysis. Despite the strong predictive power of deep learning methods, they do not provide a satisfactory representation of these structures, thus creating significant barriers in s

Badri N. Patro, Vijay S. Agneeswaran

Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, recent literature highlights issues with attention networks, including low inductive bias and quadratic complexity concerning input sequence length. State Space Models (SSMs) like S4 a

Olivier Pinaud

This work is concerned with the high contrast stochastic homogenization of the Helmholtz equation. Our goal is to characterize the second order moments of the scaling limit of the fluctuations of the wavefield. We show that these moments are those of a random wavefield solution to a homogenized Helmholtz equation with a white noise source term and obtain exp

Simon De Kockere, Dieder Van den Broeck, Uzair Abdul Latif, Krijn D. de Vries

A new generation of neutrino observatories will search for PeV-EeV neutrinos interacting in the ice by detecting radio pulses. Extended air showers propagating into the ice will form an important background and could be a valuable calibration signal. We present results from a Monte-Carlo simulation framework developed to fully simulate radio emission from co

Dario Cordero-Erausquin, Nathael Gozlan, Shohei Nakamura, Hiroshi Tsuji

We reveal the relation between the Legendre transform of convex functions and heat flow evolution, and how it applies to the functional Blaschke-Santalo inequality. We also describe local maximizers in this inequality.

Kunal Sawarkar, Abhilasha Mangal, Shivam Raj Solanki

Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q\&A (Question-Answering) systems. However, RAG accuracy becomes increasingly challenging as the corpus of documents scales up, with Retrievers playing an outsized role in the overall RAG accuracy b

Zhitong Xiong, Yi Wang, Fahong Zhang, Adam J. Stewart

Earth observation (EO) in open-world settings presents a unique challenge: different applications rely on diverse sensor modalities, each with varying ground sampling distances, spectral ranges, and numbers of spectral bands. However, existing EO foundation models are typically tailored to specific sensor types, making them inflexible when generalizing acros

Zhi-Chao Wang, Xian Gao

We investigate a class of gravity theories respecting only spatial covariance, termed spatially covariant gravity, in the presence of an auxiliary scalar field. We examine the conditions on the Lagrangian required to eliminate scalar degrees of freedom, allowing only two tensorial degrees of freedom to propagate. Instead of strict constraint analysis, in thi

Ruben Flores-Mendieta, Sergio Alejandro Garcia-Monreal, Luis Roman Ruiz-Robles, Francisco Alberto Torres-Bautista

The baryon mass operator is studied within a combined expansion in $1/N_c$ and perturbative $SU(3)$ flavor symmetry breaking, where $N_c$ denotes the number of quark charges. Flavor projection operators are used to classify the baryon operators involved in the expansion, which fall into the flavor representations $1$, $8$, $10+\overline{10}$, $27$, $35+\over

Aziliz Guezou-Philippe, Arnaud Clavé, Ehouarn Maguet, Ludivine Maintier

Background. Osteoarthritis affects about 528 million people worldwide, causing pain and stiffness in the joints. Arthroplasty is commonly performed to treat joint osteoarthritis, reducing pain and improving mobility. Nevertheless, a significant share of patients remain unsatisfied with their surgery. Personalised arthroplasty was introduced to improve surgic

Paolo Rissone, Aurelien Severino, Isabel Pastor, Felix Ritort

RNA's diversity of structures and functions impacts all life forms since primordia. We use calorimetric force spectroscopy to investigate RNA folding landscapes in previously unexplored low-temperature conditions. We find that Watson-Crick RNA hairpins, the most basic secondary structure elements, undergo a glass-like transition below $\mathbf{T_G\sim 20 ^{\

Aviv Slobodkin, Ori Shapira, Ran Levy, Ido Dagan

Grounded text generation, encompassing tasks such as long-form question-answering and summarization, necessitates both content selection and content consolidation. Current end-to-end methods are difficult to control and interpret due to their opaqueness. Accordingly, recent works have proposed a modular approach, with separate components for each step. Speci

Dionyssios Mantzavinos, Türker Ozsarı

The Hadamard well-posedness of the nonlinear Schr\"odinger equation with power nonlinearity formulated on the spatial quarter-plane is established in a low-regularity setting with Sobolev initial data and Dirichlet boundary data in appropriate Bourgain-type spaces. As both of the spatial variables are restricted to the half-line, a different approach is need

Mitch Hamidi

We characterize when a C*-cover admits a C*-dynamical extension of dynamics on an operator algebra in terms of the boundary ideal structure for the operator algebra in its maximal representation and show that the C*-covers that admit such an extension form a complete lattice. We study dynamical systems arising from groups acting via inner automorphisms in a

N. N. Orlova, A. A. Avakyants, A. V. Timonina, N. N. Kolesnikov

We experimentally study magnetization reversal curves for MnTe single crystals, which is the altermagnetic candidate. Above 85~K temperature, we confirm the antiferromagnetic behavior of magnetization $M$, which is known for $\alpha$--MnTe. Below 85~K, we observe anomalous low-field magnetization behavior, which is accompanied by the sophisticated $M(\alpha)

Chunli Tang, Gaihua Ye, Cynthia Nnokwe, Mengqi Fang

Optical excitation of chiral phonons plays a vital role in studying the phonon-driven magnetic phenomena in solids. Transition metal dichalcogenides host chiral phonons at high symmetry points of the Brillouin zone, providing an ideal platform to explore the interplay between chiral phonons and valley degree of freedom. Here, we investigate the helicity-reso

Jakub Iwański, Jakub Kierdaszuk, Arkadiusz Ciesielski, Johannes Binder

A common solution for precise magnetic field sensing is to employ spin-active defects in semiconductors, with the NV center in diamond as prominent example. However, the three-dimensional nature of diamond limits the obtainable proximity of the defect to the sample. Two-dimensional boron nitride, which can host spin-active defects, can be used to overcome th

Mehtab Ahmed Khan, Dries Allaerts, Simon Watson, Matthew Churchfield

Wind farms, particularly offshore clusters, are becoming larger than ever before. Besides influencing wind farms and local meteorology downstream, large wind farms can trigger atmospheric gravity waves in the inversion layer and the free atmosphere aloft. Wind farm-induced gravity waves can cause adverse pressure gradients upstream of the wind farm, that con

Haechan An, Ali Najjar Amiri, Dominic P. Goronzy, David A. Garcia Wetten

Detecting electronic hot spots is important for understanding the heat dissipation and thermal management of electronic and semiconductor devices. Optical thermoreflective imaging is being used to perform precise temporal and spatial imaging of heat on wires and semiconductor materials. We apply quantum squeezed light to perform thermoreflective imaging on m

Ying Wang, Mirko Pasquini, Kévin Colin, Håkan Hjalmarsson

We study the problem of determining an effective exploration strategy in static and non-linear optimization problems, which depend on an unknown scalar parameter to be learned from online collected noisy data. An optimal trade-off between exploration and exploitation is crucial for effective optimization under uncertainties, and to achieve this we consider a

L. Álvaro-Gómez, J. Hurst, S. Hegde, S. Ruiz-Gómez

Topology is a powerful tool for categorizing magnetization textures by defining a topological index in both two-dimensional (2D) systems, such as thin films or curved surfaces, and in 3D bulk systems. In the emerging field of 3D nanomagnetism, both volume and surface topological numbers must be considered, requiring the identification of a proper global topo

Tim Heib, Paul Lageyre, Alessandro Ferreri, Frank K. Wilhelm

In this work we study the validity of the rotating wave approximation of an ideal system composed of two harmonic oscillators evolving with a quadratic Hamiltonian and arbitrarily strong interaction. We prove its validity for arbitrary states by bounding the error introduced. We then restrict ourselves to the dynamics of Gaussian states and are able to fully

Zuyuan Zhang, Hanhan Zhou, Mahdi Imani, Taeyoung Lee

With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand. However, existing approaches for training collaborative agents often require defined and known reward signals and cannot address the problem of teaming with unknown age

Lena Bittermann, Fernando Dominguez, Patrik Recher

We propose a route to detect Majorana bound states (MBSs) by coupling a topological superconductor to quantum dots (QDs) in a $pnp$ junction. Here, two MBSs are coherently coupled to electrons on two QDs, which recombine with holes to photons. We focus on the spectroscopy of cross-correlated shot noise and the polarization of the emitted photons. Our detecti

Javier Martínez-Cifuentes, Hubert de Guise, Nicolás Quesada

Validation of quantum advantage claims in the context of Gaussian Boson Sampling (GBS) currently relies on providing evidence that the experimental samples genuinely follow their corresponding ground truth, i.e., the theoretical model of the experiment that includes all the possible losses that the experimenters can account for. This approach to verification

Ivan Di Liberti, Morgan Rogers

We extend Deligne's original argument showing that locally coherent topoi have enough points, clarified using collage diagrams. We show that our refinement of Deligne's technique can be adapted to recover every existing result of this kind, including the most recent results about $\kappa$-coherent $\kappa$-topoi. Our presentation allows us to relax the cardi

Andrei Rasputnyi, Zhaopin Chen, Michael Birk, Oren Cohen

We observe non-perturbative high harmonic generation in solids driven by a macroscopic quantum state of light, bright squeezed vacuum (BSV), which we generate in a single spatiotemporal mode. The BSV-driven process is considerably more efficient in the generation of high harmonics than classical light of the same mean intensity. Due to its broad photon-numbe

Helard Becerra Martinez, Alessandro Ragano, Diptasree Debnath, Asad Ullah

Watching movies and TV shows with subtitles enabled is not simply down to audibility or speech intelligibility. A variety of evolving factors related to technological advances, cinema production and social behaviour challenge our perception and understanding. This study seeks to formalise and give context to these influential factors under a wider and novel

Dawei Zhang, Roberto Tron

We present a novel approach that aims to address both safety and stability of a haptic teleoperation system within a framework of Haptic Shared Autonomy (HSA). We use Control Barrier Functions (CBFs) to generate the control input that follows the user's input as closely as possible while guaranteeing safety. In the context of stability of the human-in-the-lo

Wojciech G. Stark, Cas van der Oord, Ilyes Batatia, Yaolong Zhang

Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations

Vít Krátký, Giuseppe Silano, Matouš Vrba, Christos Papaioannidis

This paper presents a formation control approach for contactless gesture-based Human-Swarm Interaction (HSI) between a team of multi-rotor Unmanned Aerial Vehicles (UAVs) and a human worker. The approach is designed to monitor the safety of human workers, particularly those operating at heights. In the proposed dynamic formation scheme, one UAV acts as the f

ATLAS Collaboration

A search for long-lived particles (LLPs) using 140 fb$^{-1}$ of $pp$ collision data with $\sqrt{s} = 13$ TeV recorded by the ATLAS experiment at the LHC is presented. The search targets LLPs with masses between $5$ and $55$ GeV that decay hadronically in the ATLAS inner detector. Benchmark models with LLP pair production from exotic decays of the Higgs boson

Stefano Gogioso, Nicola Pinzani

In this Letter, we introduce a notion of local fraction for experiments taking place against arbitrary static causal backgrounds -- greatly generalising previous results on no-signalling scenarios -- and we explicitly formulate a linear program to compute this quantity. We derive a free characterisation of causal functions which allows us to efficiently cons

Jimyeong Kim, Jungwon Park, Wonjong Rhee

In text-to-image personalization, a timely and crucial challenge is the tendency of generated images overfitting to the biases present in the reference images. We initiate our study with a comprehensive categorization of the biases into background, nearby-object, tied-object, substance (in style re-contextualization), and pose biases. These biases manifest i

Roy S. Smith, Mohamed Abdalmoaty, Mingzhou Yin

Data-driven control uses a past signal trajectory to characterise the input-output behaviour of a system. Willems' lemma provides a data-based prediction model allowing a control designer to bypass the step of identifying a state-space or transfer function model. This paper provides a more parsimonious formulation of Willems' lemma that separates the model i

Siri Narla, Piyush Kumar, Mohammad Adnaan, Azad Naeemi

In this paper we present a comprehensive design and benchmarking study of Content Addressable Memory (CAM) at the 7nm technology node in the context of similarity search applications. We design CAM cells based on SRAM, spin-orbit torque, and ferroelectric field effect transistor devices and from their layouts extract cell parasitics using state of the art ED

Yijin Zeng, Niall M. Adams, Dean A. Bodenham

We develop a new rank-based approach for univariate two-sample testing in the presence of missing data which makes no assumptions about the missingness mechanism. This approach is a theoretical extension of the Wilcoxon-Mann-Whitney test that controls the Type I error by providing exact bounds for the test statistic after accounting for the number of missing

Paul Merrikin

Multi-parameter families of $\mathcal{N}=0$ Type IIA and Type IIB AdS$_5$ solutions are presented, promoting to $\mathcal{N}=1$ in some special cases. The G-Structure description of each $\mathcal{N}=1$ solution is given, requiring an Abelian T-Duality of the G-Structure conditions and Pure Spinors. Investigations at the boundaries are performed for a two-pa

Jisu Kim, Kirak Kim

"The Hall of Singularity" is an immersive art that creates personalized experiences of receiving prophecies from an AI deity through an integration of Artificial Intelligence (AI) and Virtual Reality (VR). As a metaphor for the mythologizing of AI in our society, "The Hall of Singularity" offers an immersive quasi-religious experience where individuals can e

Lukas Pöhler, Valentin Schrader, Alexander Ladwein, Florian von Keller

Potential malicious misuse of civilian artificial intelligence (AI) poses serious threats to security on a national and international level. Besides defining autonomous systems from a technological viewpoint and explaining how AI development is characterized, we show how already existing and openly available AI technology could be misused. To underline this,

Liliane Kunstmann, Débora Pina, Daniel de Oliveira, Marta Mattoso

Many existing scientific workflows require High Performance Computing environments to produce results in a timely manner. These workflows have several software library components and use different environments, making the deployment and execution of the software stack not trivial. This complexity increases if the user needs to add provenance data capture ser

Tom Williams

In this paper I describe the design of an introductory course in Human-Robot Interaction. This project-driven course is designed to introduce undergraduate and graduate engineering students, especially those enrolled in Computer Science, Mechanical Engineering, and Robotics degree programs, to key theories and methods used in the field of Human-Robot Interac

Neda Foroutan, Markus Schröder, Andreas Dengel

The process of cyber mapping gives insights in relationships among financial entities and service providers. Centered around the outsourcing practices of companies within fund prospectuses in Germany, we introduce a dataset specifically designed for named entity recognition and relation extraction tasks. The labeling process on 948 sentences was carried out

Nina Doerr, Benjamin Lee, Katarina Baricova, Dieter Schmalstieg

Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques i

B. Borkovits, E. Kontsek, A. Pesti, P. Gordon

In this project, we used formalin-fixed paraffin-embedded (FFPE) tissue samples to measure thousands of spectra per tissue core with Fourier transform mid-infrared spectroscopy using an FT-IR imaging system. These cores varied between normal colon (NC) and colorectal primer carcinoma (CRC) tissues. We created a database to manage all the multivariate data ob

Álvaro Cendal, Iñaki Garay, Luis J. Garay

We study the notion of volume and its dynamics in the loop-quantum-gravity truncation known as the two-vertex model. We also show that its U(N)-symmetry reduction provides the old effective dynamics of loop quantum cosmology with an arbitrary perfect barotropic fluid content. A suitable modification of the Poisson bracket structure of the U(N)-symmetric mode

Lorenzo Bastianello, Vassili Vergopoulos

By embedding uncertainty into time, we obtain a conjoint axiomatic characterization of both Exponential Discounting and Subjective Expected Utility that accommodates arbitrary state and outcome spaces. In doing so, we provide a novel and simple time-interpretation of subjective probability. The subjective probability of an event is calibrated using time disc

Stefan H. Strub, Luigi Ferraioli, Cédric Schmelzbach, Simon C. Stähler

The Laser Interferometer Space Antenna (LISA) is a planned space-based observatory to measure gravitational waves in the millihertz frequency band. This frequency band is expected to be dominated by signals from millions of Galactic binaries and tens of merging massive black hole binaries. The LISA Data Challenge 2a is focused on robust signal extraction fro

Hongzhi Gao, Zheng Chen, Zehui Chen, Lin Chen

Training high-accuracy 3D detectors necessitates massive labeled 3D annotations with 7 degree-of-freedom, which is laborious and time-consuming. Therefore, the form of point annotations is proposed to offer significant prospects for practical applications in 3D detection, which is not only more accessible and less expensive but also provides strong spatial i

Yuxin Zhang, Clément Huneau, Jérôme Idier, Diana Mateus

Despite today's prevalence of ultrasound imaging in medicine, ultrasound signal-to-noise ratio is still affected by several sources of noise and artefacts. Moreover, enhancing ultrasound image quality involves balancing concurrent factors like contrast, resolution, and speckle preservation. Recently, there has been progress in both model-based and learning-b

Shiva Safari, William Bateman-Hemphill, Asimpunya Mitra, Félix Desrochers

Early efforts to realize exotic quantum ground states in frustrated magnets focused on frustration arising from the lattice geometry alone. Attention has shifted to bond-dependent anisotropic interactions, as well as further-neighbor interactions, on non-geometrically-frustrated lattices due to their greater versatility. The honeycomb magnet BaCo$_2$(AsO$_4$

Patryk Rygiel, Dieuwertje Alblas, Christoph Brune, Kak Khee Yeung

Personalized 3D vascular models can aid in a range of diagnostic, prognostic, and treatment-planning tasks relevant to cardiovascular disease management. Deep learning provides a means to obtain such models automatically from image data. Ideally, a user should have control over the included region in the vascular model. Additionally, the model should be wate

Nicolas Baumann, Michael Baumgartner, Edoardo Ghignone, Jonas Kühne

To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of camera-only solutions lies in their cost-effectiveness. Notably, despite the prevalent use of Radio Detection and Ranging (RADAR) sensors in a

Simon Brown, Leonardo Bautista-Gomez

There have been several studies into measuring the level of decentralization in Ethereum through applying various indices to indicate the relative dominance of entities in different domains in the ecosystem. However, these indices do not capture any correlation between those different entities, that could potentially make them the subject of external coercio

Lea Kunkel, Mathias Trabs

The empirical success of Generative Adversarial Networks (GANs) caused an increasing interest in theoretical research. The statistical literature is mainly focused on Wasserstein GANs and generalizations thereof, which especially allow for good dimension reduction properties. Statistical results for Vanilla GANs, the original optimization problem, are still

Gabriel R. de Andrade Silva, Vagson Luiz de Carvalho Santos, Guilherme Henrique Rezende Bittencourt

We propose a physical mechanism for fine-tuning the phase of a domain wall around the equilibrium directions involving the application of an external field and a spin polarized current in opposite directions. In this way, the position of the domain wall remains unchanged, but its phase can be slightly modified around the initial equilibrium positions, applyi

Kirill Dubovitskii, Denis M. Basko, Julia S. Meyer, Manuel Houzet

Understanding the mechanisms of qubit decoherence is a crucial prerequisite for improving the qubit performance. In this work we discuss the effects of residual Bogolyubov quasiparticles in Schr\"odinger cat qubits, either of the dissipative or Kerr type. The major difference from previous studies of quasiparticles in superconducting qubits is that the Schr\

Teresa Yeo, Andrei Atanov, Harold Benoit, Aleksandr Alekseev

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach and pre-define prompts to generate new data using either a language model or human expertise, we develop an automated closed-loop system which involves two feedback mechanisms. The fi

Wooseop Hwang, Daniel K. Park, Israel F. Araujo, Carsten Blank

The Helstrom measurement (HM) is known to be the optimal strategy for distinguishing non-orthogonal quantum states with minimum error. Previously, a binary classifier based on classical simulation of the HM has been proposed. It was observed that using multiple copies of the sample data reduced the classification error. Nevertheless, the exponential growth i

Julian Berger, Tobias Friedrich, Pascal Lenzner, Paraskevi Machaira

Today we rely on networks that are created and maintained by smart devices. For such networks, there is no governing central authority but instead the network structure is shaped by the decisions of selfish intelligent agents. A key property of such communication networks is that they should be easy to navigate for routing data. For this, a common approach i

Christian Lenz, Rohit Menon, Michael Schreiber, Melvin Paul Jacob

Horticultural tasks such as pruning and selective harvesting are labor intensive and horticultural staff are hard to find. Automating these tasks is challenging due to the semi-structured greenhouse workspaces, changing environmental conditions such as lighting, dense plant growth with many occlusions, and the need for gentle manipulation of non-rigid plant

Shubham Yadav, M. Mishra, Tapomoy Guha Sarkar

Detecting axionic dark matter (DM) could be possible in an X-ray spectrum from strongly magnetized neutron stars (NSs). We examine the possibility of axion-photon conversion in the magnetospheres of strongly magnetized NSs. In the current work, we investigate how the modified Tolman Oppenheimer Volkoff (TOV) system of equations (in the presence of a magnetic

Yahya Badran, Christine Preisach

Knowledge Tracing (KT) is concerned with predicting students' future performance on learning items in intelligent tutoring systems. Learning items are tagged with skill labels called knowledge concepts (KCs). Many KT models expand the sequence of item-student interactions into KC-student interactions by replacing learning items with their constituting KCs. T

Harvinder Lehal, Natchanon Luangsomboon, Jörg Liebeherr

Models for the dynamics of congestion control generally involve systems of coupled differential equations. Universally, these models assume that traffic sources saturate the maximum transmissions allowed by the congestion control method. This is not suitable for studying congestion control of intermittent but bursty traffic sources. In this paper, we present