April 2024 arXiv papers — page 164

David Blanc, Surojit Ghosh, Aziz Kharoof

We study the homotopy theory of diagrams of chain complexes over a field indexed by a finite poset, and show that it can be completely described in terms of appropriate diagrams of graded vector spaces.

Constantia Alexandrou, Jacob Finkenrath, Theodoros Leontiou, Stefan Meinel

We study the $\bar b \bar b u d$ tetraquark with quantum numbers $I(J^P) = 0(1^+)$ as well as the $\bar b \bar b u s$ tetraquark with quantum numbers $J^P = 1^+$ using lattice QCD. We improve on existing work by including both local and scattering interpolating operators on both sides of the correlation functions and use symmetric correlation matrices. This

Diana Costa, Raymond Hu

This volume contains the proceedings of PLACES 2024, the 15th edition of the Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software. The PLACES workshop series offers a forum for researchers from different fields to exchange new ideas about the challenges of modern and future programming, where concurrency and distribut

Shivam Singh, Karthik Swaminathan, Raghav Arora, Ramandeep Singh

An agent assisting humans in daily living activities can collaborate more effectively by anticipating upcoming tasks. Data-driven methods represent the state of the art in task anticipation, planning, and related problems, but these methods are resource-hungry and opaque. Our prior work introduced a proof of concept framework that used an LLM to anticipate 3

Tyler Chang, Andrew Gillette, Romit Maulik

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of a

Lennart Bittel, Antonio A. Mele, Jens Eisert, Lorenzo Leone

Free-fermionic states, also known as matchgates or Gaussian states, are a fundamental class of quantum states due to their efficient classical simulability and their crucial role across various domains of Physics. With the advent of quantum devices, experiments now yield data from quantum states, including estimates of expectation values. We establish that d

Pengxiang Ding, Jianqin Yin

Joint relation modeling is a curial component in human motion prediction. Most existing methods rely on skeletal-based graphs to build the joint relations, where local interactive relations between joint pairs are well learned. However, the motion coordination, a global joint relation reflecting the simultaneous cooperation of all joints, is usually weakened

Isaac Vidaña, Jérôme Margueron, Hans-Josef Schulze

The equation of state of asymmetric nuclear matter as well as the neutron and proton effective masses and their partial-wave and spin-isospin decomposition are analyzed within the Brueckner--Hartree--Fock approach. Theoretical uncertainties for all these quantities are estimated by using several phase-shift-equivalent nucleon-nucleon forces together with two

Kaveh Rahbardar Mojaver, Seyed Mohammad Reza Safaee, Sunami Sajjanam Morrison, Odile Liboiron-Ladouceur

Mode Division Multiplexing (MDM) is a technique used over the past decade in Silicon Photonics (SiPh) to incorporate more data into communication links by employing higher-order transverse electric or transverse magnetic modes. MDM was primarily used in optical communication; however, in recent years, there have been several applications of MDM in optical co

Alexander Erlei, Mattheus Brenig, Nils Engelbrecht

Extensive research shows that consumers are generally averse to price discrimination. However, instruments of differential pricing can benefit consumer surplus and alleviate inequity through targeted price discounts. This paper examines how these outcome considerations influence consumer reactions to price discrimination. Six studies with 3951 participants s

The SoLid collaboration

The SoLid experiment is a very-short-baseline experiment aimed at searching for nuclear reactor-produced active to sterile antineutrino oscillations. The detection principle is based on the pairing of two types of solid scintillators: polyvinyl toluene and $^6$LiF:ZnS(Ag), which is a new technology used in this field of Physics. In addition to good neutron-g

Karthik Shankar, Meddelin Setiawan, Katherine S. Shanks, Matthew E. Krug

In this study, we employ high-energy X-ray characterization to examine the role of relatively large amounts of intragranular lattice misorientation -- present after many thermomechanical processes -- on the micromechanical response of Al-7085 with a modified T7452 temper. We utilize near-field high energy X-ray diffraction microscopy (HEDM) to measure three-

Yantao Liu, Zijun Yao, Xin Lv, Yuchen Fan

Providing knowledge documents for large language models (LLMs) has emerged as a promising solution to update the static knowledge inherent in their parameters. However, knowledge in the document may conflict with the memory of LLMs due to outdated or incorrect knowledge in the LLMs' parameters. This leads to the necessity of examining the capability of LLMs

Dale D. Kocevski, Steven L. Finkelstein, Guillermo Barro, Anthony J. Taylor

We present a sample of 341 "little red dots" (LRDs) spanning the redshift range $z\sim2-11$ using data from the CEERS, PRIMER, JADES, UNCOVER and NGDEEP surveys. Unlike past use of color indices to identify LRDs, we employ continuum slope fitting using shifting bandpasses to sample the same rest-frame emission blueward and redward of the Balmer break. This e

Haoran Li, Haolin Shi, Wenli Zhang, Wenjun Wu

Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors. Despite significant progress, existing methods struggle with maintaining high quality, consistency, and editing flexibility. In this paper, we propose DreamScene, a 3D Gaussian-based novel text-to-3D scene generation framework, to tackle the aforementioned thr

Hasib-Al Rashid, Argho Sarkar, Aryya Gangopadhyay, Maryam Rahnemoonfar

Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged as a promising approach for running machine learning models on these devices, but integrating multiple data modalities into tinyML models still remains a challenge due to increased

Yaroslav V. Kartashov, Dmitry A. Zezyulin

We predict that one- and two-dimensional self-bound quantum droplets, forming in Bose-Einstein condensates in the presence of Lee-Huang-Yang (LHY) quantum corrections to the mean-field energy, may demonstrate exceptional mobility in periodic optical lattices and that they may exhibit considerable displacements across the lattice, remaining dynamically stable

Yizhou Xie, Xiangning Xie, Yuran Wang, Yanci Zhang

The rapid development of 3D acquisition technology has made it possible to obtain point clouds of real-world terrains. However, due to limitations in sensor acquisition technology or specific requirements, point clouds often contain defects such as holes with missing data. Inpainting algorithms are widely used to patch these holes. However, existing traditio

Junquan Tao

Many searches for additional Higgs bosons, which are predicted by a lot of interesting models beyond the standard model, have been performed at the LHC. Some selected latest results of the searches for extra Higgs bosons at the LHC are presented. These additional Higgs bosons could be produced either directly from the parton interactions or from the decays o

Jake Varley, Sumeet Singh, Deepali Jain, Krzysztof Choromanski

We present an embodied AI system which receives open-ended natural language instructions from a human, and controls two arms to collaboratively accomplish potentially long-horizon tasks over a large workspace. Our system is modular: it deploys state of the art Large Language Models for task planning,Vision-Language models for semantic perception, and Point C

Vanina Ruhlmann-Kleider, Christophe Yèche, Christophe Magneville, Henri Coquinot

Lyman break galaxies (LBGs) are promising probes for clustering measurements at high redshift, $z>2$, a region only covered so far by Lyman-$\alpha$ forest measurements. In this paper, we investigate the feasibility of selecting LBGs by exploiting the existence of a strong deficit of flux shortward of the Lyman limit, due to various absorption processes alon

Amin Esfahani, Achenef Tesfahun

This paper investigates the nonlinear Schr\"{o}dinger equation with a singular convolution potential. It demonstrates the local well-posedness of this equation in a modified Sobolev space linked to the energy. Additionally, we derive conditions under which the solutions are uniformly bounded in the energy space. This finding is closely linked to the existenc

Joaquim Ortiz-Haro

In this thesis, we aim to improve the performance of TAMP algorithms from three complementary perspectives. First, we investigate the integration of discrete task planning with continuous trajectory optimization. Our main contribution is a conflict-based solver that automatically discovers why a task plan might fail when considering the constraints of the ph

Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg

We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be generated during inference. More importantly,

Kai Zhang, Yejin Kim, Xiaozhong Liu

Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, personalized LLM response generation holds the potential to offer substantial benefits for individuals in critical areas such as medical. Existing research has explored memory-augmented methods to prompt the LLM with pre-sto

Leonardo de la Cruz

Feynman integrals appropriately generalized are $\mathsf A$-hypergeometric functions. Among the properties of $\mathsf A$-hypergeometric functions are symmetries associated with the Newton polytope. In ordinary hypergeometric functions these symmetries lead to linear transformations. Combining tools of $\mathsf A$-hypergeometric systems and the computation o

Jerret Ross, Brian Belgodere, Samuel C. Hoffman, Vijil Chenthamarakshan

Transformer-based models trained on large and general purpose datasets consisting of molecular strings have recently emerged as a powerful tool for successfully modeling various structure-property relations. Inspired by this success, we extend the paradigm of training chemical language transformers on large-scale chemical datasets to generative tasks in this

Regina Stodden

In this work, we propose EASSE-multi, a framework for easier automatic sentence evaluation for languages other than English. Compared to the original EASSE framework, EASSE-multi does not focus only on English. It contains tokenizers and versions of text simplification evaluation metrics which are suitable for multiple languages. In this paper, we exemplify

Vineeth Francis Thalakottoor Jose Chacko, Alain Louis-Joseph, Daniel Abergel

We present experimental single and multimode sustained 1H NMR masers in solution on thermally polarized spins at room temperature and 9.4 T achieved through the electronic control of radiation feedback (radiation damping). Our observations illustrate the breakdown of the usual three-dimensional Maxwell-Bloch equations for radiation feedback and a simple toy

Rohit Saxena, Frank Keller

Abstractive summarization for long-form narrative texts such as movie scripts is challenging due to the computational and memory constraints of current language models. A movie script typically comprises a large number of scenes; however, only a fraction of these scenes are salient, i.e., important for understanding the overall narrative. The salience of a s

Mohamed Sabba

Pascal's triangle is widely used as a pedagogical tool to explain the "first-order" multiplet patterns that arise in the spectra of $I_N S$ coupled spin-1/2 systems in magnetic resonance. Various other combinatorial structures, which may be well-known in the broader field of quantum dynamics, appear to have largely escaped the attention of the magnetic reson

Alexandre Trilles

We introduce the Feldman-Katok pseudometric (FK-pseudometric for short) for flows. We then provide a characterization of zero entropy loosely Bernoulli measures for continuous flows via the FK-pseudometric extending the result known for discrete-time dynamical systems. We also provide a purely topological characterization of uniquely ergodic continuous flows

Harmon Bhasin, Timothy Ossowski, Yiqiao Zhong, Junjie Hu

Large language models (LLM) have recently shown the extraordinary ability to perform unseen tasks based on few-shot examples provided as text, also known as in-context learning (ICL). While recent works have attempted to understand the mechanisms driving ICL, few have explored training strategies that incentivize these models to generalize to multiple tasks.

Fiona McCarthy, J. Colin Hill, William R. Coulton, David W. Hogg

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation with multi-frequency methods, where different signals are isolated by their frequency behaviors. Many so-called "blind" methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or

Karnik Ram, Shobhit Aggarwal, Robert Tamburo, Siddharth Ancha

As factories continue to evolve into collaborative spaces with multiple robots working together with human supervisors in the loop, ensuring safety for all actors involved becomes critical. Currently, laser-based light curtain sensors are widely used in factories for safety monitoring. While these conventional safety sensors meet high accuracy standards, the

Botond Barta, Dorina Lakatos, Attila Nagy, Milán Konor Nyist

Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce. To address this gap our paper introduces HunSum-2 an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is ass

Weizhe Chen, Zhihan Wang, Jiaoyang Li, Sven Koenig

Since more and more algorithms are proposed for multi-agent path finding (MAPF) and each of them has its strengths, choosing the correct one for a specific scenario that fulfills some specified requirements is an important task. Previous research in algorithm selection for MAPF built a standard workflow and showed that machine learning can help. In this pape

Maximilien Gadouleau, Loïc Paulevé, Sara Riva

Boolean networks are extensively applied as models of complex dynamical systems, aiming at capturing essential features related to causality and synchronicity of the state changes of components along time. Dynamics of Boolean networks result from the application of their Boolean map according to a so-called update mode, specifying the possible transitions be

Angelos Arelakis, Nilesh Shah, Yiannis Nikolakopoulos, Dimitrios Palyvos-Giannas

In our exploration of Composable Memory systems utilizing CXL, we focus on overcoming adoption barriers at Hyperscale, underscored by economic models demonstrating Total Cost of Ownership (TCO). While CXL addresses the pressing memory capacity needs of emerging Hyperscale applications, the escalating demands from evolving use cases such as AI outpace the cap

Lorena Gallego-Viñarás, Juan Miguel Mira-Tomás, Anna Michela-Gaeta, Gerard Pinol-Ripoll

Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals acquired through polysomnography (PSG) for the early detection of AD.

Péter Kevei, László Viharos

In R\'enyi's representation for exponential order statistics, we replace the iid exponential sequence with any iid sequence, and call the resulting order statistic generalized R\'enyi statistic. We prove that by randomly reordering the variables in the generalized R\'enyi statistic, we obtain in the limit a sequence of iid exponentials. This result allows us

Paul Xing, Vincent Perrot, Adan Ulises Dominguez-Vargas, Stephan Quessy

Hemodynamic changes occur in stroke and neurodegenerative diseases. Developing imaging techniques allowing the in vivo visualization and quantification of cerebral blood flow would help better understand the underlying mechanism of those cerebrovascular diseases. 3D ultrasound localization microscopy (ULM) is a novel technology that can map the microvasculat

Matteo Giordano

I derive constraints on the Dirac spectrum in the chirally symmetric phase of a gauge theory with two massless fermion flavors. Using only general properties of correlation functions of scalar and pseudoscalar bilinears, I prove that in the chiral limit of vanishing fermion mass $m$ the corresponding susceptibilities and all their derivatives with respect to

Kisung Kang, David G. Cahill, André Schleife

Optical and magneto-optical properties of magnetic materials have been widely exploited to characterize magnetic structures and phenomena, however, their temperature dependence is not well understood. This study implements the supercell approach with thermal lattice and magnetic disorders to obtain optical and magneto-optical spectra at finite temperatures b

Selomit Ramírez-Uribe, Andrés E. Rentería-Olivo, Germán Rodrigo

Quantum algorithms are a promising framework for unfolding the causal configurations of multiloop Feynman diagrams, which is equivalent to querying the \textit{directed acyclic graph} (DAG) configurations of undirected graphs in graph theory. In this paper, we present a quantum algorithm for querying in both types of applications, using a systematic and spar

Jiawei Guo, Ziming Li, Xueling Liu, Kaijing Ma

Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to rigorously assess the performance of LLMs in code editing tasks, including debugging, translating, polishing, and requirement switching. Unlike existing benchmarks focusing solely o

Siddharth Raghu, Rajdip Nayek, Vamsi Chalamalla

The transformative impact of machine learning, particularly Deep Learning (DL), on scientific and engineering domains is evident. In the context of computational fluid dynamics (CFD), Physics-Informed Neural Networks (PINNs) represent a significant innovation, enabling data-driven fluid simulations while incorporating physics-based laws described by partial

Siyuan Mei, Fuxin Fan, Fabian Wagner, Mareike Thies

Deep learning-based medical image processing algorithms require representative data during development. In particular, surgical data might be difficult to obtain, and high-quality public datasets are limited. To overcome this limitation and augment datasets, a widely adopted solution is the generation of synthetic images. In this work, we employ conditional

Sabarish V. Narayanan, Donald L. Koch, Sarah Hormozi

We analyse the motion of a flagellated bacterium in a two-fluid medium using slender body theory. The two-fluid model is useful for describing a body moving through a complex fluid with a microstructure whose length scale is comparable to the characteristic scale of the body. This is true for bacterial motion in biological fluids (entangled polymer solutions

Lorenzo Bianchi, Fabio Carrara, Nicola Messina, Fabrizio Falchi

Modern applications increasingly demand flexible computer vision models that adapt to novel concepts not encountered during training. This necessity is pivotal in emerging domains like extended reality, robotics, and autonomous driving, which require the ability to respond to open-world stimuli. A key ingredient is the ability to identify objects based on fr

Nancy Holincheck, Jessica L. Rosenberg, Xiaolu Zhang, Tiffany Butler

Quantum science and computing represent a vital intersection between science and technology, gaining increasing importance in modern society. There is a pressing need to incorporate these concepts into the K-12 curriculum, equipping new generations with the tools to navigate and thrive in an evolving technological landscape. This study explores the professio

Andrea Atzori, Fadi Boutros, Naser Damer, Gianni Fenu

Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concerns. Large face datasets are primarily sourced from web-based images, lacking explicit user consent. In this pa

Fabien Caubet, Marc Dambrine, Jérémi Dardé

This paper is devoted to the understanding of regularisation process in the shape optimization approach to the so-called Dirichlet inverse obstacle problem for elliptic operators. More precisely, we study two different regularisations of the very classical shape optimization approach consisting in minimizing a mismatched functional. The first one is an impli

Andreas Blommaert, Thomas G. Mertens, Jacopo Papalini

We work out a precise holographic duality between sine dilaton gravity, and DSSYK. More precisely, canonical quantization of sine dilaton gravity reproduces q-Schwarzian quantum mechanics, which is the auxiliary system that arises from the chord diagrams of DSSYK. The role of the chord number in DSSYK is played by the (Weyl rescaled) geodesic length in the b

Witold Bednorz, Piotr Godlewski

In this paper we improve the best known constant for the discrepancy formulated in the Komlos Conjecture. The result is based on the improvement of the subgaussian bound for the random vector constructed in the Gram-Schmidt Random Walk algorithm. Moreover, we present detailed argument for the smoothed analysis of this random vector. The analysis concerns a m

The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta

The flux of ultra-high energy cosmic rays reaching Earth above the ankle energy (5 EeV) can be described as a mixture of nuclei injected by extragalactic sources with very hard spectra and a low rigidity cutoff. Extragalactic magnetic fields existing between the Earth and the closest sources can affect the observed CR spectrum by reducing the flux of low-rig

Yuchen Fan, Yantao Liu, Zijun Yao, Jifan Yu

Modern Large Language Models (LLMs) have showcased remarkable prowess in various tasks necessitating sophisticated cognitive behaviors. Nevertheless, a paradoxical performance discrepancy is observed, where these models underperform in seemingly elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation. (1) T

Eric Dexheimer, Andrew J. Davison

We present COMO, a real-time monocular mapping and odometry system that encodes dense geometry via a compact set of 3D anchor points. Decoding anchor point projections into dense geometry via per-keyframe depth covariance functions guarantees that depth maps are joined together at visible anchor points. The representation enables joint optimization of camera

Momonari Kudo, Kazuhiro Yokoyama

Determining the complexity of computing Gröbner bases is an important problem in both theory and practice, and solving degrees provide a central measure of this complexity. We study solving degrees and Gröbner basis computations for affine polynomial systems, with particular emphasis on semi-regular sequences. We first derive two upper bounds for the maximum

Eoin Carolan, Anthony Kiely, Steve Campbell, Sebastian Deffner

Commonly, the notion of "quantum chaos'' refers to the fast scrambling of information throughout complex quantum systems undergoing unitary evolution. Motivated by the Krylov complexity and the operator growth hypothesis, we demonstrate that the entropy of the population distribution for an operator in time is a useful way to capture the complexity of the in

Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Dong-Kyu Chae

Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting information flow in a very effective manner. Despite being widely used globally, Bangla is relatively underrepres

Jiahang Li, Peng Yun, Yang Xu, Ye Zhang

Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, paying inadequate attention to the inherent differences between RGB and thermal modalities. Recent progress in vision foundation models (VFMs) tr

Benedict Schlüter, Supraja Sridhara, Andrin Bertschi, Shweta Shinde

AMD SEV-SNP offers VM-level trusted execution environments (TEEs) to protect the confidentiality and integrity for sensitive cloud workloads from untrusted hypervisor controlled by the cloud provider. AMD introduced a new exception, #VC, to facilitate the communication between the VM and the untrusted hypervisor. We present WeSee attack, where the hypervisor

Alicia Flórez Berdasco, María Elena de Cos Gómez, Jaime Laviada, Fernando Las-Heras

An ultra-compact wearable antenna, for electronic travel aid (ETA) applications, is presented. An AMC-backed twin arrow antenna, operative in the 24.05-24.25 GHz frequency band, has been designed for imaging systems supporting ETA. Artificial Magnetic Conductor (AMC) is combined with the antenna with the aim of reducing the backward radiation to the wearing

Okko Makkonen, Sampo Niemelä, Camilla Hollanti, Serge Kas Hanna

This work focuses on the challenges of non-IID data and stragglers/dropouts in federated learning. We introduce and explore a privacy-flexible paradigm that models parts of the clients' local data as non-private, offering a more versatile and business-oriented perspective on privacy. Within this framework, we propose a data-driven strategy for mitigating the

Maurizio Consoli, George Rupp

As an alternative to the metastability of the electroweak vacuum, resulting from perturbative calculations, one can consider a non-perturbative effective potential which, as at the beginning of the Standard Model, is restricted to the pure $\Phi^4$ sector yet consistent with the known analytical and numerical studies. In this approach, where the electroweak

Chang Che, Zengyi Huang, Chen Li, Haotian Zheng

This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the

Reinhard Genzel, Frank Eisenhauer, Stefan Gillessen

More than a century ago, Albert Einstein presented his general theory of gravitation (GR) to the Prussian Academy of Sciences. One of the predictions of the theory is that not only particles and objects with mass, but also the quanta of light, photons, are tied to the curvature of space-time, and thus to gravity. There must be a critical compactness, above w

Xiaotong Liu

We propose a joint model that links the strategic level location and capacity decisions with the operational level routing and hub assignment decisions to solve hub network design problem with congestion and heterogeneous economics of scale. We also develop a novel flow-based mixed-integer second-order cone programming (MISOCP) formulation. We perform numeri

Luk R. Arnaut, John M. Ladbury

In part I, spectral moments and kurtosis were established as parameters in analytic models of correlation and spectral density functions for dynamic reverberation fields. In this part II, several practical limitations affecting the accuracy of estimating these parameters from measured stir sweep data are investigated. For sampled fields, the contributions of

Adam Keilthy, Martin Raum

We relate analytically defined deformations of modular curves and modular forms from the literature to motivic periods via cohomological descriptions of deformation theory. Leveraging cohomological vanishing results, we prove the existence and essential uniqueness of deformations, which we make constructive via established Lie algebraic arguments and a notio

Sichen Chen, Yingyi Zhang, Siming Huang, Ran Yi

Recently, transformer-based methods have achieved state-of-the-art prediction quality on human pose estimation(HPE). Nonetheless, most of these top-performing transformer-based models are too computation-consuming and storage-demanding to deploy on edge computing platforms. Those transformer-based models that require fewer resources are prone to under-fittin

Tobias Huber, Tobias Hurth, Jack Jenkins, Enrico Lunghi

We present theoretical predictions for observables in inclusive $\bar{B}\to X_s \ell^+\ell^-$ suitable for measurements at hadron colliders through a sum-over-exclusive approach. At low $q^2$ we calculate the branching ratio and three angular observables. At high $q^2$ we provide the branching ratio and the ratio of the $\bar B \to X_s \ell^+\ell^-$ rate wit

Francesco Pontiggia, Ezio Bartocci, Michele Chiari

We address the problem of model checking context-free specifications for probabilistic pushdown automata, which has relevant applications in the verification of recursive probabilistic programs. Operator Precedence Languages (OPLs) are an expressive subclass of context-free languages suitable for model checking recursive programs. The derived Precedence Orie

Chengkai Huang, Yu Xia, Rui Wang, Kaige Xie

Retrieval-augmented large language models (LLMs) have been remarkably competent in various NLP tasks. However, it was observed by previous works that retrieval is not always helpful, especially when the LLM is already knowledgeable on the query to answer. Motivated by this, Adaptive Retrieval-Augmented Generation (ARAG) studies retrieving only when the knowl

Philipp Seitz, Manuel Geiger, Christian Ufrecht, Axel Plinge

With the increasing sophistication and capability of quantum hardware, its integration, and employment in high performance computing (HPC) infrastructure becomes relevant. This opens largely unexplored access models and scheduling questions in such quantum-classical computing environments, going beyond the current cloud access model. SCIM MILQ is a scheduler

Sasmita Rout, Gautam Kumar Das

Let $G=(V, E)$ be a simple undirected graph with no isolated vertex. A set $D_t\subseteq V$ is a total dominating set of $G$ if $(i)$ $D_t$ is a dominating set, and $(ii)$ the set $D_t$ induces a subgraph with no isolated vertex. The total dominating set of minimum cardinality is called the minimum total dominating set, and the size of the minimum total domi

Dhiren K. Pradhan, David C. Moore, A. Matt Francis, Jacob Kupernik

Silicon microelectronics, consisting of complementary metal oxide semiconductor (CMOS) technology, have changed nearly all aspects of human life from communication to transportation, entertainment, and healthcare. Despite the widespread and mainstream use, current silicon-based devices suffer significant reliability issues at temperatures exceeding 125 C. Th

Liv d'Aliberti, Evan Gronberg, Joseph Kovba

Artificial intelligence (AI) models introduce privacy vulnerabilities to systems. These vulnerabilities may impact model owners or system users; they exist during model development, deployment, and inference phases, and threats can be internal or external to the system. In this paper, we investigate potential threats and propose the use of several privacy-en

Rodolfo G. Campos, Benedikt Heid, Jacopo Timini

The Cold War was the defining episode of geopolitical fragmentation in the twentieth century. Trade between East and West across the Iron Curtain (a symbolical and physical barrier dividing Europe into two distinct areas) was restricted, but the severity of these restrictions varied over time. We quantify the trade and welfare effects of the Iron Curtain and

Yi-Xin Huang, Hou-I Liu, Hong-Han Shuai, Wen-Huang Cheng

Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional information of object queries is not customized for detecting tiny objects, whose scale is extraordinarily smaller than general objects. Also, DETR-like methods using a fixed number of q

Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König

Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavior, they hint users towards possible actions and give grounds for contesting decisions. As a crucial factor in achieving these goals, counterfactuals must be plausible, i.e., descri

Nunzia Cerrato, Giacomo De Palma, Vittorio Giovannetti

Adopting a statistical approach we study the degradation of entanglement of a quantum system under the action of an ensemble of randomly distributed Markovian noise. This enables us to address scenarios where only limited information is available on the mechanisms that rule the noisy evolution of the model. As an application, we characterize the statistic of

Alexander Grayver, Christopher C. Finlay, Nils Olsen

Tidal flow of seawater across the Earth's magnetic field induces electric currents and magnetic fields within the ocean and solid Earth. The amplitude and phase of the induced fields depends on electrical properties of both the seawater and the solid Earth, thus can be used as a proxy to study seabed properties or potentially for monitoring long-term trends

Georg Bergner, Vaibhav Gautam, Masanori Hanada, Jack Holden

Linear confinement with Casimir scaling of the string tension in confining gauge theories is a consequence of a certain property of the Polyakov loop related to random matrices. This mechanism does not depend on the details of the theories (neither the gauge group nor dimensions) and explains approximate Casimir scaling below string-breaking length. In this

Andrew J. Peterson

While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large langu

Wilson R. M. Rabelo, Sandra D. Prado, Leonardo G. Brunnet

The quantum approximate optimization algorithm (QAOA) can require considerable processing time for developers to test and debug their codes on expensive quantum devices. One avenue to circumvent this difficulty is to use the error maps of quantum devices, where a local simulator can be automatically configured to mimic an actual device backend. In our work,

Lili Mu, Volkmar Welker

Let $\Delta$ be a $(d-1)$-dimensional simplicial complex and $h^ \Delta = (h_0^ \Delta ,\ldots, h_d^ \Delta)$ its $h$-vector. For a face uniform subdivision operation ${\mathcal F}$ we write $\Delta_{\mathcal F}$ for the subdivided complex and $H_{\mathcal F}$ for the matrix such that $h^ {\Delta_{\mathcal F}} = H_{\mathcal F} h^ \Delta$. In connection with

Simon Schramm, Christoph Wehner, Ute Schmid

Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the 21st century. However, in many applications, users require an explanation of the Artifici

Somin Park, Carol C. Menassa, Vineet R. Kamat

In the construction industry, where work environments are complex, unstructured and often dangerous, the implementation of Human-Robot Collaboration (HRC) is emerging as a promising advancement. This underlines the critical need for intuitive communication interfaces that enable construction workers to collaborate seamlessly with robotic assistants. This stu

Luk R. Arnaut, John M. Ladbury

Experimental auto- and cross-correlation functions and their corresponding spectral density functions are extracted from measured sweep data of mode-stirred fields. These are compared with theoretical models derived in part I, using estimated spectral moments from part II. The second-order Pad\'{e} approximant based model accounts for the main features of th

Uta Isabella Meyer, Ivan Šupić, Frédéric Grosshans, Damian Markham

Self-testing identifies quantum states and correlations that exhibit nonlocality, distinguishing them, up to local transformations, from other quantum states. Due to their strong nonlocality, it is known that all graph states can be self-tested in the standard setting - where parties are not allowed to communicate. Recently it has been shown that graph state

Simon Klüttermann, Emmanuel Müller

Outlier detection (OD) is one of the core challenges in machine learning. Transductive learning, which leverages test data during training, has shown promise in related machine learning tasks, yet remains largely unexplored for modern OD. We present Doust, the first end-to-end transductive deep learning algorithm for outlier detection, which explicitly lever

Pietro Sabelli

In the context of dependent type theory, we show that coinductive predicates have an equivalent topological counterpart in terms of coinductively generated positivity relations, introduced by G. Sambin to represent closed subsets in point-free topology. Our work is complementary to a previous one with M.E. Maietti, where we showed that, in dependent type the

Iqra Bano, Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique

Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Ne

Alexander Felski, Alireza Beygi, Christos Karapoulitidis, S. P. Klevansky

A comparative study of entropy dynamics as an indicator of physical behavior in an open two-state system with balanced gain and loss is presented. We distinguish the perspective taken in utilizing the conventional framework of Hermitian-adjoint states from an approach that is based on biorthogonal-adjoint states and a third case based on an isospectral mappi

Jifan Yu, Xiaohan Zhang, Yifan Xu, Xuanyu Lei

Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the hallucination problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models

Julia Lascar, Jérôme Bobin, Fabio Acero

Hyperspectral images are data cubes with two spatial dimensions and a third spectral dimension, providing a spectrum for each pixel, and thus allow the mapping of extended sources' physical properties. In this article, we present the Semi-blind Unmixing with Sparsity for Hyperspectral Images (SUSHI), an algorithm for non-stationary unmixing of hyperspectral

Trevor Smith, Madhav Rijal, Christopher Tatsch, R. Michael Butts

This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required para

Yi Huang, Sankar Das Sarma

Two recent electronic transport experiments from Columbia University and Harvard University have reported record high mobility and low channel densities in transition metal dichalcogenide (TMD) WSe$_2$ monolayers [J. Pack, et al., arXiv:2310.19782; A. Y. Joe, et al., Phys. Rev. Lett. 132, 056303 (2024)]. A two-dimensional (2D) metal-insulator transition (MIT