April 2024 arXiv papers — page 100

Lu Ping, Nicholas Russo, Zifan Wang, Ching-Hsiang Yao

Wide bandgap (WBG) semiconductors (Eg >2.0 eV) are integral to the advancement of next generation electronics, optoelectronics, and power industries, owing to their capability for high temperature operation, high breakdown voltage and efficient light emission. Enhanced power efficiency and functional performance can be attained through miniaturization, speci

Muna Naik, Swagato K. Ray, Jayanta Sarkar

For Riemannian symmetric spaces $X=G/K$ of noncompact type, we show that for all left $K$-invariant $f\in L^1(X)$, the functions $\|h_t\|_{L^p(X)}^{-1}(f\ast h_t-M_p(f)h_t)$ (with $h_t$ being the heat kernel of $X$) converges to zero in $L^p(X)$, $p\in [1,\infty]$, as $t\to\infty$, with the constant $M_p(f)$ depending only on $p$ and $f$. We also prove an an

Kanako Narita, Seiichi Sakamoto, Jin Koda, Yuki Yoshimura

ALMA data toward QSO J1851+0035 ($l$=$33.498^{\circ}$, $b$=$+0.194^{\circ}$) were used to study absorption lines by Galactic molecular gas. We detected 17 species (CO, $^{13}$CO, C$^{18}$O, HCO$^+$, H$^{13}$CO$^+$, HCO, H$_2$CO, C$_2$H, $c$-C$_3$H, $c$-C$_3$H$_2$, CN, HCN, HNC, CS, SO, SiO, and C) and set upper limits to 18 species as reference values for ch

Dron Khanna, Emily Laue Christensen, Saagarika Gosu, Xiaofeng Wang

Hybrid work, a fusion of different work environments that allow employees to work in and outside their offices, represents a new frontier for agile researchers to explore. However, due to the nascent nature of the research phenomena, we are yet to achieve a good understanding of the research terrain formulated when hybrid work meets agile software developmen

Hang Gao, Yongfeng Zhang

While Large Language Model (LLM) based agents excel at complex tasks, their performance in open-ended scenarios is often constrained by isolated operation and reliance on static databases, missing the dynamic knowledge exchange of human dialogue. To bridge this gap, we propose the INteractive Memory Sharing (INMS) framework, an asynchronous interaction parad

Chung Shue Chen, Peter Keevash, Sean Kennedy, Élie de Panafieu

We consider the design of a positioning system where a robot determines its position from local observations. This is a well-studied problem of considerable practical importance and mathematical interest. The dominant paradigm derives from the classical theory of de Bruijn sequences, where the robot has access to a window within a larger code and can determi

Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke

Crowdsourced labels play a crucial role in evaluating task-oriented dialogue systems (TDSs). Obtaining high-quality and consistent ground-truth labels from annotators presents challenges. When evaluating a TDS, annotators must fully comprehend the dialogue before providing judgments. Previous studies suggest using only a portion of the dialogue context in th

Yueyu Hu, Onur G. Guleryuz, Philip A. Chou, Danhang Tang

Stereoscopic video conferencing is still challenging due to the need to compress stereo RGB-D video in real-time. Though hardware implementations of standard video codecs such as H.264 / AVC and HEVC are widely available, they are not designed for stereoscopic videos and suffer from reduced quality and performance. Specific multiview or 3D extensions of thes

Lin Su, Alexander Douglas, Michal Szurek, Anne H. Hebert

High-resolution fluorescence imaging of ultracold atoms and molecules is paramount to performing quantum simulation and computation in optical lattices and tweezers. Imaging durations in these experiments typically range from a millisecond to a second, significantly limiting the cycle time. In this work, we present fast, 2.4 microseconds single-atom imaging

Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M Patel

Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation. For most applications, we can train the model end-toend with paired data to obtain photorealistic generation quality. However, to add an additional task, one often needs to retrain the model

Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M. Patel

Recently, diffusion transformers have gained wide attention with its excellent performance in text-to-image and text-to-vidoe models, emphasizing the need for transformers as backbone for diffusion models. Transformer-based models have shown better generalization capability compared to CNN-based models for general vision tasks. However, much less has been ex

Mingjie Jian, Xiaoting Fu, Noriyuki Matsunaga, Valentina D'Orazi

The precise measurement of stellar abundances plays a pivotal role in providing constraints on the chemical evolution of the Galaxy. However, before spectral lines can be employed as reliable abundance indicators, particularly for challenging elements such as helium, they must undergo thorough scrutiny. Galactic open clusters, representing well-defined singl

Milan Malcic, Rustam Steingart, Otmar Venjakob, Max Witzelsperger

Inspired by Nakamura's work (arXiv:1305.0880) on $\epsilon$-isomorphisms for $(\varphi,\Gamma)$-modules over (relative) Robba rings with respect to the cyclotomic theory, we formulate an analogous conjecture for $L$-analytic Lubin-Tate $(\varphi_L,\Gamma_L)$-modules over (relative) Robba rings for any finite extension $L$ of $\mathbb{Q}_p.$ In contrast to Ka

Bo Yang, Elham Kashefi, Dominik Leichtle, Harold Ollivier

Quantum state purification is the functionality that, given multiple copies of an unknown state, outputs a state with increased purity. This will be an essential building block for near- and middle-term quantum ecosystems before the availability of full fault tolerance, where one may want to suppress errors not only in expectation values but also in quantum

Mizuki Oikawa

We define the notion of the $(G,\Gamma)$-crossed center of a $(G,\Gamma)$-crossed tensor category in the sense of Natale. We show that the $(G, \Gamma)$-crossed center is a $(G \bowtie \Gamma, G \times \Gamma)$-braided tensor category. This construction generalizes the graded center construction for graded tensor categories and the equivariant center constru

Jinhee Kim, Taesung Kim, Jaegul Choo

Large language models (LLMs) have demonstrated remarkable in-context learning capabilities across diverse applications. In this work, we explore the effectiveness of LLMs for generating realistic synthetic tabular data, identifying key prompt design elements to optimize performance. We introduce EPIC, a novel approach that leverages balanced, grouped data sa

Adi Simhi, Jonathan Herzig, Idan Szpektor, Yonatan Belinkov

Large language models (LLMs) are prone to hallucinations, which sparked a widespread effort to detect and prevent them. Recent work attempts to mitigate hallucinations by intervening in the model's generation, typically computing representative vectors of hallucinations vs. grounded generations, for steering the model's hidden states away from a hallucinator

Mihaela Ifrim, Daniel Tataru

In recent work the authors proposed a broad global well-posedness conjecture for cubic defocusing dispersive equations in one space dimension, and then proved this conjecture in two cases, namely for one dimensional semilinear and quasilinear Schr\"odinger flows. Inspired by the circle of ideas developed in the proof of the above conjecture, in this paper we

Renke Wang, Francien G. Bossema, Thierry Blu, Pier Luigi Dragotti

Traditional sampling schemes often assume that the sampling locations are known. Motivated by the recent bioimaging technique known as cryogenic electron microscopy (cryoEM), we consider the problem of reconstructing an unknown 3D structure from samples of its 2D tomographic projections at unknown angles. We focus on 3D convex bilevel polyhedra and 3D point

Elvis Cantelli, Ramachrisna Teixeira

The formation and structure of the Milky Way has a fundamental role in our understanding of the universe and its evolution, and thanks to the Gaia mission and large spectroscopic surveys, we live an exceptional moment of data availability, allowing us to trace the building blocks of the Galactic disk and their relations. In this sense, we propose here the ex

Han Lin, Jaemin Cho, Abhay Zala, Mohit Bansal

ControlNets are widely used for adding spatial control to text-to-image diffusion models with different conditions, such as depth maps, scribbles/sketches, and human poses. However, when it comes to controllable video generation, ControlNets cannot be directly integrated into new backbones due to feature space mismatches, and training ControlNets for new bac

Harlan Campbell, Tim Morris, Paul Gustafson

Derived variables are variables that are constructed from one or more source variables through established mathematical operations or algorithms. For example, body mass index (BMI) is a derived variable constructed from two source variables: weight and height. When using a derived variable as the outcome in a statistical model, complications arise when some

Gangqiang Chen

Let ${\mathcal S}$ be the class of analytic functions $f$ in the unit disk ${\mathbb D}$ with $f({\mathbb D}) \subset \overline{\mathbb D}$. Fix pairwise distinct points $z_1,\ldots,z_{n+1}\in \mathbb{D}$ and corresponding interpolation values $w_1,\ldots,w_{n+1}\in \overline{\mathbb{D}}$. Suppose that $f\in{\mathcal S}$ and $f(z_j)=w_j$, $j=1,\ldots,n+1$. T

Masato Tamura

Social group activity recognition is a challenging task extended from group activity recognition, where social groups must be recognized with their activities and group members. Existing methods tackle this task by leveraging region features of individuals following existing group activity recognition methods. However, the effectiveness of region features is

Jorge Luiz Deolindo-Silva

A smooth ruled surface in 4-space has only parabolic points or inflection points of real type. We show, by means of contact with transverse planes, that at a parabolic point, there exist two tangent directions determining two planes along which the parallel projection exhibits $\mathcal A$-singularities of type butterfly or worse. In particular, such parabol

Margherita Lazzaretto, Jonas Peters, Niklas Pfister

We consider the task of predicting a response Y from a set of covariates X in settings where the conditional distribution of Y given X changes over time. For this to be feasible, assumptions on how the conditional distribution changes over time are required. Existing approaches assume, for example, that changes occur smoothly over time so that short-term pre

Victoria Leonenkova, Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin

Objective no-reference image- and video-quality metrics are crucial in many computer vision tasks. However, state-of-the-art no-reference metrics have become learning-based and are vulnerable to adversarial attacks. The vulnerability of quality metrics imposes restrictions on using such metrics in quality control systems and comparing objective algorithms. A

Bing Han, Margo A. Sidell

Assessing covariate balance (CB) is a common practice in various types of evaluation studies. Two-sample descriptive statistics, such as the standardized mean difference, have been widely applied in the scientific literature to assess the goodness of CB. Studies in health policy, health services research, built and social environment research, and many other

Saurav Goyal, Roman N. Lee, Sven-Olaf Moch, Vaibhav Pathak

Polarized semi-inclusive deep-inelastic scattering (SIDIS) is a key process in the quest for a resolution of the proton spin puzzle. We present the complete results for the polarized SIDIS process at next-to-next-to-leading order (NNLO) in perturbative quantum chromodynamics. Our analytical results include all partonic channels for the scattering of polarize

J. Dubois, M. Siudek, D. Fraix-Burnet, J. Moultaka

Aims: This study aims to trace the chronological evolution of galaxy spectra over cosmic time. Focusing on the VIPERS dataset, we seek to understand the diverse population of galaxies within narrow redshift bins, comparing our findings with the previously mapped diversity of SDSS galaxies. Methods: We use Fisher-EM, an unsupervised subspace model-based class

Diganta Rabha, Vimod Kumar, Akshay Kumar, Dinesh Saini

Background-oriented schlieren (BOS) is a powerful technique for flow visualization. Nevertheless, the widespread dissemination of BOS is impeded by its dependence on scientific cameras, computing hardware, and dedicated analysis software. In this work, we aim to democratize BOS by providing a smartphone based scientific tool called "Pocket Schlieren". Pocket

Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski

Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning. While foundation models have been useful in natural language processing and some vision tasks for some time, the foundation model developed with image segmentation in mind - Segment Anything Model (SAM) - has been develop

Navonil Majumder, Chia-Yu Hung, Deepanway Ghosal, Wei-Ning Hsu

Generative multimodal content is increasingly prevalent in much of the content creation arena, as it has the potential to allow artists and media personnel to create pre-production mockups by quickly bringing their ideas to life. The generation of audio from text prompts is an important aspect of such processes in the music and film industry. Many of the rec

Priyanka Sarmah, Himanshu Verma, Kingman Cheung, Joseph Silk

A supermassive black hole (SMBH) at the core of an active galactic nucleus (AGN) provides room for the elusive ultra-light scalar particles (ULSP) to be produced through a phenomenon called \textit{superradiance}. This phenomenon produces a cloud of scalar particles around the black hole by draining its spin angular momentum. In this work, we present a study

Michael N. Martinez, Yjan A. Gordon, Keith Bechtol, Gillian Cartwright

Radio observations of strongly lensed objects are valuable as cosmological probes. Lensed radio sources have proven difficult to identify in large part due to the limited depth and angular resolution of the previous generation of radio sky surveys, and as such, only a few dozen lensed radio sources are known. In this work we present the results of a pilot st

Ashna Jose, Emilie Devijver, Massih-Reza Amini, Noel Jakse

Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes crucial to explore active learning methods for reducing the size of training sets while maintaining high accuracy. The aim is to select the optimal subset of data for labeling from an i

Frank Tip, Jonathan Bell, Max Schaefer

In mutation testing, the quality of a test suite is evaluated by introducing faults into a program and determining whether the program's tests detect them. Most existing approaches for mutation testing involve the application of a fixed set of mutation operators, e.g., replacing a "+" with a "-", or removing a function's body. However, certain types of real-

Kim Hoang Tran, Phuc Vuong Do, Ngoc Quoc Ly, Ngan Le

Sports videos pose complex challenges, including cluttered backgrounds, camera angle changes, small action-representing objects, and imbalanced action class distribution. Existing methods for detecting actions in sports videos heavily rely on global features, utilizing a backbone network as a black box that encompasses the entire spatial frame. However, thes

Yixuan Sun, Elizabeth Cucuzzella, Steven Brus, Sri Hari Krishna Narayanan

Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena such as hurricanes, extreme precipitation, and droughts. Ocean models rely on subgrid-scale parameterizations that require calibration and often significantly affect model skill. Wh

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

We present a measurement of neutral pion production in charged-current interactions using data recorded with the MicroBooNE detector exposed to Fermilab's booster neutrino beam. The signal comprises one muon, one neutral pion, any number of nucleons, and no charged pions. Studying neutral pion production in the MicroBooNE detector provides an opportunity to

Ziyu Zhan, Hao Wang, Qiang Liu, Xing Fu

Photonic computing, with potentials of high parallelism, low latency and high energy efficiency, have gained progressive interest at the forefront of neural network (NN) accelerators. However, most existing photonic computing accelerators concentrate on discriminative NNs. Large-scale generative photonic computing machines remain largely unexplored, partly d

D. Josset, S. Cayula, B. Concannon, S. Sova

This paper presents the NRL shipboard lidar and the first lidar dataset of underwater bubbles. The meaning of these lidar observations, the algorithms used and their current limitations are discussed. The derivation of the lidar multiple scattering regime is derived from the lidar observations and theory. The detection of the underwater bubble presence and t

Benjamin Hafner, Katherine Meyer

Dispersal of propagules (seeds, spores) from a geographically isolated population into an uninhabitable matrix can threaten population persistence if it prevents new growth from keeping pace with mortality. Quantifying propagule loss can thus inform restoration and conservation of vulnerable populations in fragmented landscapes. To model propagule loss in de

Mahnoor N. Gondal, Saad Ur Rehman Shah, Arul M. Chinnaiyan, Marcin Cieslik

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general,

Nan Jiang

This note clarifies some confusions (and perhaps throws out more) around model-based reinforcement learning and their theoretical understanding in the context of deep RL. Main topics of discussion are (1) how to reconcile model-based RL's bad empirical reputation on error compounding with its superior theoretical properties, and (2) the limitations of empiri

Jonas Köppl, Nicolas Lanchier, Max Mercer

To study later spatial evolutionary games based on the multitype contact process, we first focus in this paper on the conditions for survival/extinction in the presence of only one strategy, in which case our model consists of a variant of the contact process with a density-dependent birth rate. The players are located on the $d$-dimensional integer lattice,

Mody Sy, Raymond Knopp

This paper presents Bit-Interleaved Coded Modulation metrics for joint estimation detection using training or reference signal transmission strategies for short to long block length channels. We show that it is possible to enhance the performance and sensitivity through joint detection-estimation compared to standard receivers, especially when the channel st

Xiao Zhou, Xiaoman Zhang, Chaoyi Wu, Ya Zhang

In this paper, we consider the problem of visual representation learning for computational pathology, by exploiting large-scale image-text pairs gathered from public resources, along with the domain-specific knowledge in pathology. Specifically, we make the following contributions: (i) We curate a pathology knowledge tree that consists of 50,470 informative

Mia Chiquier, Utkarsh Mall, Carl Vondrick

Multimodal pre-trained models, such as CLIP, are popular for zero-shot classification due to their open-vocabulary flexibility and high performance. However, vision-language models, which compute similarity scores between images and class labels, are largely black-box, with limited interpretability, risk for bias, and inability to discover new visual concept

Omar Ikne, Benjamin Allaert, Ioan Marius Bilasco, Hazem Wannous

Many existing facial expression recognition (FER) systems encounter substantial performance degradation when faced with variations in head pose. Numerous frontalization methods have been proposed to enhance these systems' performance under such conditions. However, they often introduce undesirable deformations, rendering them less suitable for precise facial

Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su

Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in natural language to rigorous derivations in formal systems. In recent years, the advancement of deep learning, especially the emergence of large language models, has sparked a notable surge of research exploring these techniques to enhance the process of theorem provi

Armando Sosthène Kali Balogoun, Guy Martial Nkiet

This paper adresses the problem of testing for the equality of $k$ probability distributions on Hilbert spaces, with $k\geqslant 2$. We introduce a generalization of the maximum variance discrepancy called multiple maximum variance discrepancy (MMVD). Then, a consistent estimator of this measure is proposed as test statistic, and its asymptotic distribution

Yuzhen Huang, Jinghan Zhang, Zifei Shan, Junxian He

There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the success of large language models (LLMs): the development of more advanced language models is essentially enhancing compression which facilitates intelligence. Despi

R. D. B. Fontana, Angel Rincon

We investigate the propagation of a scalar field in a $(2+1)$-dimensional accelerated black hole, recently revisited in \cite{Arenas_Henriquez_2022}. We briefly describe the minimally-coupled configuration as rendering a trivial scalar perturbation with a rescale of the field mass. On the contrary, the free scalar field propagation presents an intricate dyna

Yannick Meurice

We discuss the possibility of estimating experimentally the von Neumann entanglement entropy $S_{A}^{vN}$ of a symmetric bi-partite quantum system $AB$ by using the basic measurement counts (bitstrings) for a $single$ copy of a prepared state. Using exact diagonalization and analog simulations performed with the publicly available QuEra facilities for chains

Aleksei M. Aleshin, Vladimir V. Nikitin, Petr I. Pronin

In quantum mechanics, randomness is postulated as a separate axiom. De Broglie's theory allows one to reproduce quantum phenomena from completely deterministic formalism. But the question of the quantum randomness emergency in the de Broglie-Bohm theory needs special attention. In the work [G. Tastevin, F. Lalo\"e, Comptes Rendus. Physique, 2021, 22, 1, pp.

Siddhant Bansal, Michael Wray, Dima Damen

Large Vision Language Models (VLMs) are now the de facto state-of-the-art for a number of tasks including visual question answering, recognising objects, and spatial referral. In this work, we propose the HOI-Ref task for egocentric images that aims to understand interactions between hands and objects using VLMs. To enable HOI-Ref, we curate the HOI-QA datas

Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research question

June Moh Goo, Zichao Zeng, Jan Boehm

Recent advances have demonstrated that Language Vision Models (LVMs) surpass the existing State-of-the-Art (SOTA) in two-dimensional (2D) computer vision tasks, motivating attempts to apply LVMs to three-dimensional (3D) data. While LVMs are efficient and effective in addressing various downstream 2D vision tasks without training, they face significant chall

Seok Hyun Byun, Mihai Ciucu

A number which is either the square of an integer or two times the square of an integer is called squarish. There are two main results in the literature on graphs whose number of perfect matchings is squarish: one due to Jockusch (for planar graphs invariant under rotation by 90 degrees) and the other due to the second author (concerning planar graphs with t

H Thienpondt, JM García-Regaña, I Calvo, G Acton

Efficient control of turbulent heat transport is crucial for magnetic confinement fusion reactors. This work discusses the complex interplay between density gradients and micro-instabilities, shedding light on their impact on turbulent heat transport in different fusion devices. In particular, the influence of density gradients on turbulent heat transport is

Ilya Chevyrev, Christophe Garban

We prove that Villain interaction applied to lattice gauge theory can be obtained as the limit of both Wilson and Manton interactions on a larger graph which we call the {\em carpet graph.} This is the lattice gauge theory analog of a well-known property for spin $O(N)$ models where Villain type interactions are the limit of $\mathbb{S}^{N-1}$ spin systems d

Yuan Bi, Cheng Qian, Zhicheng Zhang, Nassir Navab

Ultrasound (US) has been widely used in daily clinical practice for screening internal organs and guiding interventions. However, due to the acoustic shadow cast by the subcutaneous rib cage, the US examination for thoracic application is still challenging. To fully cover and reconstruct the region of interest in US for diagnosis, an intercostal scanning pat

Rupert L. Frank, Hynek Kovařík

By the Aharonov-Casher theorem, the Pauli operator $P$ has no zero eigenvalue when the normalized magnetic flux $\alpha$ satisfies $|\alpha|<1$, but it does have a zero energy resonance. We prove that in this case a Lieb-Thirring inequality for the $\gamma$-th moment of the eigenvalues of $P+V$ is valid under the optimal restrictions $\gamma\geq |\alpha|$ an

L. Nakazono, R. R. Valença, G. Soares, R. Izbicki

The advent of massive broad-band photometric surveys enabled photometric redshift estimates for unprecedented numbers of galaxies and quasars. These estimates can be improved using better algorithms or by obtaining complementary data such as narrow-band photometry, and broad-band photometry over an extended wavelength range. We investigate the impact of both

Bing-Jyun Tsao, Bhavesh Khamesra, Miguel Gracia-Linares, Pablo Laguna

Recent gravitational wave observations include possible detections of black hole - neutron star binary mergers. As with binary black hole mergers, numerical simulations help characterize the sources. For binary systems with neutron star components, the simulations help to predict the imprint of tidal deformations and disruptions on the gravitational wave sig

G. O. Heymans, G. Scorza, N. F. Svaiter, C. D. Rodríguez-Camargo

We discuss the Bekenstein generalized entropy of a Schwarzschild black hole, with the contribution of an external matter field affected by degrees of freedom near the event horizon. In the Euclidean section of the Schwarzschild manifold, we consider an Euclidean quantum effective model, a scalar theory in the presence of an additive disorder field. The avera

Arjun Panickssery, Samuel R. Bowman, Shi Feng

Self-evaluation using large language models (LLMs) has proven valuable not only in benchmarking but also methods like reward modeling, constitutional AI, and self-refinement. But new biases are introduced due to the same LLM acting as both the evaluator and the evaluatee. One such bias is self-preference, where an LLM evaluator scores its own outputs higher

Serhat Bakirtas, Matthieu R. Bloch, Elza Erkip

Hardware Trojans can inflict harm on wireless networks by exploiting the link margins inherent in communication systems. We investigate a setting in which, alongside a legitimate communication link, a hardware Trojan embedded in the legitimate transmitter attempts to establish communication with its intended rogue receiver. To illustrate the susceptibility o

Zichao Zeng, June Moh Goo, Xinglei Wang, Bin Chi

A building's age of construction is crucial for supporting many geospatial applications. Much current research focuses on estimating building age from facade images using deep learning. However, building an accurate deep learning model requires a considerable amount of labelled training data, and the trained models often have geographical constraints. Recent

KamLAND, Super-Kamiokande Collaborations, :, Seisho Abe

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. O

Giordano d'Aloisio, Claudio Di Sipio, Antinisca Di Marco, Davide Di Ruscio

Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in software systems, researchers have been developing techniques to automatically assess the fairness of software systems. Nonetheless, a significant proportion of these techniques rel

Min-Hui Lin, Mahesh Reddy, Guillaume Berger, Michel Sarkis

In this paper, we present EdgeRelight360, an approach for real-time video portrait relighting on mobile devices, utilizing text-conditioned generation of 360-degree high dynamic range image (HDRI) maps. Our method proposes a diffusion-based text-to-360-degree image generation in the HDR domain, taking advantage of the HDR10 standard. This technique facilitat

Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees

Due to the sensitive nature of medicine, it is particularly important and highly demanded that AI methods are explainable. This need has been recognised and there is great research interest in xAI solutions with medical applications. However, there is a lack of user-centred evaluation regarding the actual impact of the explanations. We evaluate attribute- an

Nico Meyer, Martin Röhn, Jakob Murauer, Axel Plinge

Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations ex

Aleks Kissinger, Neil J. Ross, John van de Wetering

A catalysis state is a quantum state that is used to make some desired operation possible or more efficient, while not being consumed in the process. Recent years have seen catalysis used in state-of-the-art protocols for implementing magic state distillation or small angle phase rotations. In this paper we will see that we can also use catalysis to prove th

Gerhard Jung, Giulio Biroli, Ludovic Berthier

Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic model for glass-forming liquids. This is a notoriously difficult task, as it amounts to ergodically exploring

Banasree Sadhukhan

Bulk photovoltaic effect is a non-linear response in noncentrosymmetric materials that converts light into DC current. In this work, we investigate the optical linear and non-linear responses in a chalcopyrite semiconductor ZnGeP$_2$. We report large bulk photovoltaics namely shift and circular photogalvanic current conductivities which are 4.46 $\mu$A/V$^2$

Nuzhat Prova

Recent advances in natural language processing (NLP) may enable artificial intelligence (AI) models to generate writing that is identical to human written form in the future. This might have profound ethical, legal, and social repercussions. This study aims to address this problem by offering an accurate AI detector model that can differentiate between elect

Jie Zheng, Linqiao Jiang, Jianfeng Tian

This paper introduces an astronomical image alignment algorithm. This algorithm uses the means of the rows and columns of the original image for alignment, and finds the optimal offset corresponding to the maximum similarity by comparing different offsets between images. The similarity is evaluated by the standard deviation of the quotient divided by the mea

Sanxing Chen, Sam Wiseman, Bhuwan Dhingra

The desire and ability to seek new information strategically are fundamental to human learning but often overlooked in current language agent evaluation. We analyze a popular web shopping task designed to test language agents' ability to perform strategic exploration and discover that it can be reformulated and solved as a single-turn retrieval task without

Dennis Mayer, Angelika Knothe

Electrostatically confined quantum dots in bilayer graphene have shown potential as building blocks for quantum technologies. To operate the dots, e.g., as qubits, a precise understanding and control of the confined states and their properties is required. Herein, a large-scale numerical characterization of confined quantum states in bilayer graphene dots is

Zekang Zhou, Jin Jiang, Paritosh Karnatak, Ziwei Wang

Graphene moir\'e systems are ideal environments for investigating complex phase diagrams and gaining fundamental insights into the mechanisms underlying exotic states of matter, as they permit controlled manipulation of electronic properties. Magic-angle twisted trilayer graphene (MATTG) has emerged as a key platform to explore moir\'e superconductivity, owi

Prajwal Padmanabha, Giorgio Nicoletti, Davide Bernardi, Samir Suweis

Metapopulation models have been instrumental in demonstrating the ecological impact of landscape structure on the survival of a focal species in complex environments. However, extensions to multiple species with arbitrary dispersal networks often rely on phenomenological assumptions limiting their scope. Here, we develop a multilayer network model of competi

Francesco A. B. Silva, Cecilia Pagliantini, Karen Veroy

The use of model order reduction techniques in combination with ensemble-based methods for estimating the state of systems described by nonlinear partial differential equations has been of great interest in recent years in the data assimilation community. Methods such as the multi-fidelity ensemble Kalman filter (MF-EnKF) and the multi-level ensemble Kalman

Y. Abdedou, A. Fuchs, P. Fuchs, J. Heiler

Silicon carbide (SiC) is the leading wide-bandgap semiconductor material, providing mature doping and device fabrication. Additionally, SiC hosts a multitude of optically active point defects (color centers) and is relevant for many applications in quantum technologies. A crucial step towards harnessing the full potential of the SiC platform includes technol

Guangjin Pan, Shugong Xu, Shunqing Zhang, Xiaojing Chen

Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes real-time XR video very sensitive to dynamic network environments. To improve the users' quality of experience

Sarthak Das, Ding Huang, Ivan Verzhbitskiy, Zi-En Ooi

Excitons are key to the optoelectronic applications of van der Waals semiconductors with the potential for versatile on-demand tuning of properties. Yet, their electrical manipulation is complicated by their inherent charge neutrality and the additional loss channels induced by electrical doping. We demonstrate the dynamic control of valley polarization in c

Vahagn Nersesyan, Manuel Rissel

We show that buoyancy driven flows can be steered in an arbitrary time towards any state by applying as control only an external temperature profile in a subset of small measure. More specifically, we prove that the 2D incompressible Boussinesq system on the torus is globally approximately controllable via physically localized heating or cooling. In addition

Bart De Bruyn, Sergey Goryainov, Willem Haemers, Leonid Shalaginov

A divisible design graph is a graph whose adjacency matrix is an incidence matrix of a (group) divisible design. Divisible design graphs were introduced in 2011 as a generalization of $(v,k,\lambda)$-graphs. Here we describe four new infinite families that can be obtained from the symplectic strongly regular graph $Sp(2e,q)$ ($q$ odd, $e\geq 2$) by modifying

Peng Yu, Han Fu, William F. Schiela, William Strickland

Planar Josephson junctions (JJs) have emerged as a promising platform for the realization of topological superconductivity and Majorana zero modes. To obtain robust quasi one-dimensional (1D) topological superconducting states using planar JJs, limiting the number of 1D Andreev bound states' subbands that can be present, and increasing the size of the topolo

J. Decker, M. Hoppe, U. Sheikh, B. P. Duval

Runaway electrons (REs) are a concern for tokamak fusion reactors from discharge startup to termination. A sudden localized loss of a multi-megaampere RE beam can inflict severe damage to the first wall. Should a disruption occur, the existence of a RE seed may play a significant role in the formation of a RE beam and the magnitude of its current. The applic

Zhicheng Zhu, Xing Gao, Dominique Manchon

We propose a combinatorial formula for the coproduct in a Hopf algebra of decorated multi-indices that recently appeared in the literature, which can be briefly described as the graded dual of the enveloping algebra of the free Novikov algebra generated by the set of decorations. Similarly to what happens for the Hopf algebra of rooted forests, the formula c

Lakshmikanta Sau, Priyadarshi Mukherjee, Sasthi C. Ghosh

Reconfigurable intelligent surfaces (RISs) is a novel communication technology that has been recently presented as a potential candidate for beyond fifth-generation wireless communication networks. In this paper, we propose a priority-aware user traffic-dependent grouping-based multihop routing scheme for a RIS-assisted millimeter wave (mmWave) device-to-dev

Jiayi Li, Ruilin Luo, Jiaqi Sun, Jing Xiao

Knowledge Graph Completion (KGC) has emerged as a promising solution to address the issue of incompleteness within Knowledge Graphs (KGs). Traditional KGC research primarily centers on triple classification and link prediction. Nevertheless, we contend that these tasks do not align well with real-world scenarios and merely serve as surrogate benchmarks. In t

Vidit Agrawal, Shixin Zhang, Lane E. Schultz, Dane Morgan

Ensemble models can be used to estimate prediction uncertainties in machine learning models. However, an ensemble of N models is approximately N times more computationally demanding compared to a single model when it is used for inference. In this work, we explore fitting a single model to predicted ensemble error bar data, which allows us to estimate uncert

Jannik Albrecht, Sebastien Andreina, Frederik Armknecht, Ghassan Karame

Extensive research on Nakamoto-style consensus protocols has shown that network delays degrade the security of these protocols. Established results indicate that, perhaps surprisingly, maximal security is achieved when the network is as small as two nodes due to increased delays in larger networks. This contradicts the very foundation of blockchains, namely

Mufan Liu, Le Yang, Yiling Xu, Ye-kui Wang

Adaptive bitrate (ABR) using conventional codecs cannot further modify the bitrate once a decision has been made, exhibiting limited adaptation capability. This may result in either overly conservative or overly aggressive bitrate selection, which could cause either inefficient utilization of the network bandwidth or frequent re-buffering, respectively. Neur

Yuxi Li, Yi Liu, Gelei Deng, Ying Zhang

With the expanding application of Large Language Models (LLMs) in various domains, it becomes imperative to comprehensively investigate their unforeseen behaviors and consequent outcomes. In this study, we introduce and systematically explore the phenomenon of "glitch tokens", which are anomalous tokens produced by established tokenizers and could potentiall

Philipp Buschermöhle, Taouba Jouini, Torsten Lilge, Matthias A. Müller

This paper presents a robust MPC scheme for linear systems subject to time-varying, uncertain constraints that arise from uncertain environments. The predicted input sequence is parameterized over future environment states to guarantee constraint satisfaction despite an imprecise environment prediction and unknown evolution of the future constraints. We prov