December 2024 arXiv papers — page 119

Jinhao Lin, Yifei Wang, Yanwu Xu, Qi Liu

Despite multimodal sentiment analysis being a fertile research ground that merits further investigation, current approaches take up high annotation cost and suffer from label ambiguity, non-amicable to high-quality labeled data acquisition. Furthermore, choosing the right interactions is essential because the significance of intra- or inter-modal interaction

C. Clontz, A. C. Seth, Z. Wang, S. O. Souza

Constraining the helium enhancement in stars is critical for understanding the formation mechanisms of multiple populations in star clusters. However, measuring helium variations for many stars within a cluster remains observationally challenging. We use Hubble Space Telescope photometry combined with MUSE spectroscopic data for over 7,200 red-giant branch s

Amanda Rios, Ibrahima Ndiour, Parual Datta, Omesh Tickoo

In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD ("COntinuous Novel CLAss Detector"), a comprehensive solution to the under-explored problem of continual novel class detection in post-deployment data. At each new task, our approach employs an iterative unce

Hanchu Zhou, Edward Xie, Wei Shao, Dechen Gao

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive, an

Gabrielle Tournaire, Marvin Schwiering, Robert Raussendorf, Sven Bachmann

We describe an efficient, fully fault-tolerant implementation of Measurement-Based Quantum Computation (MBQC) in the 3D cluster state. The two key novelties are (i) the introduction of a lattice defect in the underlying cluster state and (ii) the use of the Rudolph-Grover rebit encoding. Concretely, (i) allows for a topological implementation of the Hadamard

Zhenpu Zhang, Ting-Wei Hsu, Ting You Tan, Daniel H. Slichter

We present an optical tweezer array of $^{87}$Rb atoms housed in an cryogenic environment that successfully combines a 4 K cryopumping surface, a <50 K cold box surrounding the atoms, and a room-temperature high-numerical-aperture objective lens. We demonstrate a 3000 s atom trap lifetime, which enables us to optimize and measure losses at the $10^{-4}$ leve

Saptarshi Chakraborty, Peter L. Bartlett

Recent advances have revealed that the rate of convergence of the expected test error in deep supervised learning decays as a function of the intrinsic dimension and not the dimension $d$ of the input space. Existing literature defines this intrinsic dimension as the Minkowski dimension or the manifold dimension of the support of the underlying probability m

Wenyu Zhang, Mohammad J. Khojasteh, Nikolay A. Atanasov, Florian Meyer

Particle flow (PFL) is an effective method for overcoming particle degeneracy, the main limitation of particle filtering. In PFL, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. Recently proposed stochastic PFL introduces a diffusion term in the ordinary differential equation (ODE) that desc

Leonard Jung, Alexander Estornell, Michael Everett

For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance between nominal and contingency behavior, or plan for a singular contingency plan; however, this does not guarantee that the resulting plan is safe for all time. To address this re

Yue Li, Yang Wu, Yuqi Zhou, Mengxiang Zhang

The nontrivial degeneracies in non-Hermitian systems, exceptional points (EPs), have attracted extensive attention due to intriguing phenomena. Compared with commonly observed second-order EPs, high-order EPs show rich physics due to their extended dimension and parameter space, ranging from the coalescence of EPs into higher order to potential applications

Talon Chandler, Ivan E. Ivanov, Gabriel Sturm, Sheng Xiao

Correlative computational microscopy can accelerate imaging and modeling of cellular dynamics by relaxing trade-offs inherent to dynamic imaging. Existing computational microscopy frameworks are either specialized or overly generic, limiting use to fixed configurations or domain experts. We introduce WaveOrder, a generalist wave-optical framework for imaging

Chi-Jui Ho, Yash Belhe, Steve Rotenberg, Ravi Ramamoorthi

End-to-end optimization, which simultaneously optimizes optics and algorithms, has emerged as a powerful data-driven method for computational imaging system design. This method achieves joint optimization through backpropagation by incorporating differentiable optics simulators to generate measurements and algorithms to extract information from measurements.

G. Bruce Berriman, John C. Good, Ian Heywood, R. Moseley

The Montage Image Mosaic Engine, first released in 2002, has found applicability across the electromagnetic spectrum to support data processing and visualization. This broad applicability has come about through its design as an Open Source ANSI-C toolkit (and Python binary extensions), with independent components to perform each step in the creation of a mos

Yinhao Dong, Pan Peng, Ali Vakilian

We study learning-augmented streaming algorithms for estimating the value of MAX-CUT in a graph. In the classical streaming model, while a $1/2$-approximation for estimating the value of MAX-CUT can be trivially achieved with $O(1)$ words of space, Kapralov and Krachun [STOC'19] showed that this is essentially the best possible: for any $\epsilon > 0$, any (

Jing Yang, Pratusha Bhuvana Prasad, Qing Zhang, Yajie Zhao

Accurately measuring the geometry and spatially-varying reflectance of real-world objects is a complex task due to their intricate shapes formed by concave features, hollow engravings and diverse surfaces, resulting in inter-reflection and occlusion when photographed. Moreover, issues like lens flare and overexposure can arise from interference from secondar

Viviana Gomez, Gabriel Tellez

Self-organized criticality is a dynamical system property where, without external tuning, a system naturally evolves towards its critical state, characterized by scale-invariant patterns and power-law distributions. In this paper, we explored a self-organized critical dynamic on the Sierpinski carpet lattice, a scale-invariant structure whose dimension is de

Emily F. Kerrison, Ron D. Ekers, John Morgan, Rajan Chhetri

Recent observations of interplanetary scintillation (IPS) at radio frequencies have proved to be a powerful tool for probing the solar environment from the ground. But how far back does this tradition really extend? Our survey of the literature to date has revealed a long history of scintillating observations, beginning with the oral traditions of Indigenous

Jonghyuk Park, Alex Lascarides, Subramanian Ramamoorthy

In this paper, we offer a learning framework in which the agent's knowledge gaps are overcome through corrective feedback from a teacher whenever the agent explains its (incorrect) predictions. We test it in a low-resource visual processing scenario, in which the agent must learn to recognize distinct types of toy truck. The agent starts the learning process

Yu Shao, Jiawen Bai, Yingze Hou, Xia'an Zhou

The credit spread is a key indicator in bond investments, offering valuable insights for fixed-income investors to devise effective trading strategies. This study proposes a novel credit spread forecasting model leveraging ensemble learning techniques. To enhance predictive accuracy, a feature selection method based on mutual information is incorporated. Emp

Dilip Paneru, Francesco Di Colandrea, Alessio D'Errico, Ebrahim Karimi

Highly correlated biphoton states are powerful resources in quantum optics, both for fundamental tests of the theory and practical applications. In particular, high-dimensional spatial correlation has been used in several quantum information processing and sensing tasks, for instance, in ghost imaging experiments along with several quantum key distribution p

Alexandro Luna, Weiran Yang

We prove a generalized non-stationary version of the fiber contraction mapping theorem. It was originally used in [HirschPugh70] to prove that the stable foliation of a $C^2$ Anosov diffeomorphism of a surface is $C^1$. Our generalized principle is used in [Luna24], where an analogous regularity result for stable foliations of non-stationary systems is prove

Jiajian Zhang, Wenhui Huang, Ji Chu, Jiawei Qiu

Fock-state lattices (FSLs), composed of photon number states with infinite Hilbert space, have emerged as a promising platform for simulating high-dimensional physics due to their potential to extend into arbitrarily high dimensions. Here, we demonstrate the construction of multi-dimensional FSLs using superconducting quantum circuits. By controlling artific

Weitang Liu, Yuelei Li, Ying Wai Li, Zihan Wang

Comparing two (large) language models (LMs) side-by-side and pinpointing their prediction similarities and differences on the same set of inputs are crucial in many real-world scenarios, e.g., one can test if a licensed model was potentially plagiarized by another. Traditional analysis compares the LMs' outputs on some benchmark datasets, which only cover a

Sonja Bjelobaba, Lorna Waddington, Mike Perkins, Tomáš Foltýnek

Background: The rapid development and use of generative AI (GenAI) tools in academia presents complex and multifaceted ethical challenges for its users. Earlier research primarily focused on academic integrity concerns related to students&#39; use of AI tools. However, limited information is available on the impact of GenAI on academic research. This study a

Thijmen van Gend, Donald Jay Bertulfo, Seda Gürses

Recent applications of Privacy Enhancing Technologies (PETs) reveal a paradox. PETs aim to alleviate power asymmetries, but can actually entrench the infrastructural power of companies implementing them vis-à-vis other public and private organisations. We investigate whether and how this contradiction manifests with an empirical study of Amazon&#39;s cloud c

Matteo Tomasetto, Francesco Braghin, Andrea Manzoni

Continuous monitoring and real-time control of high-dimensional distributed systems are often crucial in applications to ensure a desired physical behavior, without degrading stability and system performances. Traditional feedback control design that relies on full-order models, such as high-dimensional state-space representations or partial differential equ

Xuefang Xu, Junzhi Wang, Qian Gou, Juan Li

Broadband spectroscopic observations with high sensitivity provide an unbiased way to detect emissions of molecules in space. We present deep observations from ~ 105.8 GHz to 113.6 GHz toward 50 Galactic massive star-forming regions using IRAM 30-m millimeter telescope, with noise levels ranging from 6 to 29 at frequency channel spacing of 195 kHz, which cor

Tao Qian, Mingyu Fang, Qinran Hu, Chengcheng Shao

This paper proposes V2Sim, an open source Pythonbased simulation platform designed for advanced vehicle-to-grid (V2G) analysis in coupled urban power and transportation networks. By integrating a microscopic urban transportation network (MUTN) with a power distribution network (PDN), V2Sim enables precise modeling of electric vehicle charging loads (EVCL) an

Claire Voisin

We introduce and study the notion of universally defined cycles of smooth varieties of dimension $d$, and prove that they are given by polynomials in the Chern classes. A similar result is proved for universally defined cycles on products of smooth varieties. We also state a conjectural explicit form for universally defined cycles on powers of smooth varieti

Morgan B. Talbot, Gabriel Kreiman, James J. DiCarlo, Guy Gaziv

The currently leading artificial neural network models of the visual ventral stream - which are derived from a combination of performance optimization and robustification methods - have demonstrated a remarkable degree of behavioral alignment with humans on visual categorization tasks. We show that image perturbations generated by these models can enhance th

Vincent-Pierre Berges, Barlas Oğuz, Daniel Haziza, Wen-tau Yih

Memory layers use a trainable key-value lookup mechanism to add extra parameters to a model without increasing FLOPs. Conceptually, sparsely activated memory layers complement compute-heavy dense feed-forward layers, providing dedicated capacity to store and retrieve information cheaply. This work takes memory layers beyond proof-of-concept, proving their ut

Xinyu Li, Yizhou Fan, Tongguang Li, Mladen Rakovic

The focus of education is increasingly set on learners' ability to regulate their own learning within technology-enhanced learning environments (TELs). Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to self-regulate their learning productively, as they typically need to naviga

Xiaofan Mu, Merna Bibars, Salman Seyedi, Iris Zheng

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 older adults with normal cognition or Mild Cognitive Impairment (MCI), derived

Manon Soulier, Bouchra Lekbaby, Imane Houari, Henri Decauchy

Cold piezoelectric plasma (CPP) is a novel approach in cancer therapy, enabling the development of portable treatment devices capable of triggering cancer cell death. While its effectiveness remains underexplored, this research focuses on its application against cholangiocarcinoma (CCA), an aggressive cancer of the biliary tract. A CPP device is utilized to

Anatoly A. Svidzinsky, Wenzhuo Zhang

We introduce a technique for calculating the density operator time evolution along the lines of Heisenberg representation of quantum mechanics. Using this technique, we find the exact solution for the quantum evolution of two and three coupled harmonic oscillators initially prepared in thermal states at different temperatures. We show that such systems exhib

Zeyuan Yang, Delin Chen, Xueyang Yu, Maohao Shen

Long video understanding poses unique challenges due to their temporal complexity and low information density. Recent works address this task by sampling numerous frames or incorporating auxiliary tools using LLMs, both of which result in high computational costs. In this work, we introduce a curiosity-driven video agent with self-exploration capability, dub

Matías Carrasco, Franz Mayr, Sergio Yovine

This work studies the question of learning probabilistic deterministic automata from language models. For this purpose, it focuses on analyzing the relations defined on algebraic structures over strings by equivalences and similarities on probability distributions. We introduce a congruence that extends the classical Myhill-Nerode congruence for formal langu

Jean-Paul Kamseu-Mogo, Manon Soulier, Georges Kamgang-Youbi, Hortense Noëlle Apala Mafouasson

In this study, we investigate the effects of pre-germinative and post-germinative plasma treatments, applied separately or in combination, to improve maize germination and early seedling development. Pre-germinative treatment consists of priming the seeds with a dry atmospheric plasma (DAP) generated by a dielectric barrier device (DBD), characterized by min

Yunfei Luo, Yuliang Chen, Asif Salekin, Tauhidur Rahman

Time-series foundation models excel at tasks like forecasting across diverse data types by leveraging informative waveform representations. Wearable sensing data, however, pose unique challenges due to their variability in patterns and frequency bands, especially for healthcare-related outcomes. The main obstacle lies in crafting generalizable representation

E. Shchukin, P. van Loock

Continuous-variable Gaussian entanglement is an attractive notion, both as a fundamental concept in quantum information theory, based on the well-established Gaussian formalism for phase-space variables, and as a practical resource in quantum technology, exploiting in particular, unconditional room-temperature squeezed-light quantum optics. The readily avail

Rayne Holland, Seyit Camtepe, Chandra Thapa, Minhui Xue

We propose $\mathtt{PrivHP}$, a lightweight synthetic data generator with \textit{differential privacy} guarantees. $\mathtt{PrivHP}$ uses a novel hierarchical decomposition that approximates the input's cumulative distribution function (CDF) in bounded memory. It balances hierarchy depth, noise addition, and pruning of low-frequency subdomains while preserv

Chang Han, Li-Yang Chen, Zu-Cheng Chen, Chengjie Fu

We study an inflation model with nonminimal derivative coupling that features a coupling between the derivative of the inflaton field and the Einstein tensor. This model naturally amplifies curvature perturbations at small scales via gravitationally enhanced friction, a mechanism critical for the formation of primordial black holes and the associated product

Ali Athar, Xueqing Deng, Liang-Chieh Chen

Recent advances in multimodal large language models (MLLMs) have expanded research in video understanding, primarily focusing on high-level tasks such as video captioning and question-answering. Meanwhile, a smaller body of work addresses dense, pixel-precise segmentation tasks, which typically involve category-guided or referral-based object segmentation. A

Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega

We explore the problem of sampling graph signals in scenarios where the graph structure is not predefined and must be inferred from data. In this scenario, existing approaches rely on a two-step process, where a graph is learned first, followed by sampling. More generally, graph learning and graph signal sampling have been studied as two independent problems

Anatoly A. Svidzinsky, Marlan O. Scully, William Unruh

Minkowski vacuum is empty from the perspective of Unruh-Minkowski photons, however, in the Rindler picture, it is filled with entangled pairs of Rindler photons. A ground-state atom uniformly accelerated through Minkowski vacuum can become excited by absorbing a Rindler photon (Unruh effect) or, in the alternative description, by emitting an Unruh-Minkowski

Kyle R. Chickering

The use of neural networks for solving differential equations is practically difficult due to the exponentially increasing runtime of autodifferentiation when computing high-order derivatives. We propose $n$-TangentProp, the natural extension of the TangentProp formalism \cite{simard1991tangent} to arbitrarily many derivatives. $n$-TangentProp computes the e

Idoko Agbo, Dr Hoda El-Sayed, M. D Kamruzzan Sarker

The intersection of technology and mental health has spurred innovative approaches to assessing emotional well-being, particularly through computational techniques applied to audio data analysis. This study explores the application of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models on wavelet extracted features and Mel-frequency C

Tarleton Gillespie, Ryland Shaw, Mary L. Gray, Jina Suh

As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety seems paramount. "Red-teaming" has quickly become the primary approach to test AI models--prioritized by AI companies, and enshrined in AI policy and regulation. Members of red teams act as adversaries, probing AI systems to test t

Kiran K. Kethineni, Saraju P. Mohanty, Elias Kougianos, Sanjukta Bhowmick

To efficiently manage plant diseases, Agriculture Cyber-Physical Systems (A-CPS) have been developed to detect and localize disease infestations by integrating the Internet of Agro-Things (IoAT). By the nature of plant and pathogen interactions, the spread of a disease appears as a focus with density of infected plants and intensity of infection diminishing

Justin Cheigh, Guilherme Zeus Dantas e Moura, Jacob Lehmann Duke, Annika Mauro

The purpose of this short note is to show the interplay between math outreach and conducting original research, in particular how each can build off the other.

Dirk HR Spennemann, Sharnie Hurford

While three-dimensional visualization has become a common tool in various cultural heritage applications, the emphasis has been on high fidelity representation, essentially the generation of digital twins or digital reconstructions. Overlooked appears to be the utility of 3D in research collaboration where one of the researchers has access to the original, p

Zenon Gniazdowski

This paper proposes a new method for similarity analysis and, consequently, a new algorithm for clustering different types of random attributes, both numerical and nominal. However, in order for nominal attributes to be clustered, their values must be properly encoded. In the encoding process, nominal attributes obtain a new representation in numerical form.

Abdalla Hussein, Patrick Mitran, Catherine Rosenberg

Motivated by the need to use spectrum more efficiently, this paper investigates fine grained spectrum sharing (FGSS) in Multi-User massive MIMO (MU-mMIMO) systems where a neutral host enables users from different operators to share the same resource blocks. To be accepted by operators, FGSS must i) guarantee isolation so that the load of one operator does no

Paul Schwerdtner, Serkan Gugercin, Benjamin Peherstorfer

Approximating field variables and data vectors from sparse samples is a key challenge in computational science. Widely used methods such as gappy proper orthogonal decomposition and empirical interpolation rely on linear approximation spaces, limiting their effectiveness for data representing transport-dominated and wave-like dynamics. To address this limita

Aditya Patra, Saroj Rout, Arun Ravindran

The paper addresses advancements in Generative Artificial Intelligence (GenAI) and digital chip design, highlighting the integration of Large Language Models (LLMs) in automating hardware description and design. LLMs, known for generating human-like content, are now being explored for creating hardware description languages (HDLs) like Verilog from natural l

Clément Fortin, Kai Wang, T. Pereg-Barnea

The bosonic Kitaev chain is known to have extraordinary properties distinct from its fermionic counterpart. For example, it exhibits the non-Hermitian skin effect -- its eigenmodes are exponentially localized to the edges of the chain -- even when the system is Hermitian. Such non-Hermitian effects originate from the fact that the dynamics of bosonic quadrat

Huaijiang Zhu, Tong Zhao, Xinpei Ni, Jiuguang Wang

The tremendous success of behavior cloning (BC) in robotic manipulation has been largely confined to tasks where demonstrations can be effectively collected through human teleoperation. However, demonstrations for contact-rich manipulation tasks that require complex coordination of multiple contacts are difficult to collect due to the limitations of current

Martin Stanek

This short communication shows that the Chessography encryption scheme is incorrect, redundant, and the the security claims based on the complexity of chess games are unjustified. It also demonstrates an insufficient randomness in the final chess game positions, which could be of separate interest.

Serap A. Savari

Superresolution theory and techniques seek to recover signals from samples in the presence of blur and noise. Discrete image registration can be an approach to fuse information from different sets of samples of the same signal. Quantization errors in the spatial domain are inherent to digital images. We consider superresolution and discrete image registratio

Jiyao Hu, Zhenyu Zhou, Xiaowei Yang

Two types of radio frequency (RF) impairments frequently occur in a cable broadband network: impairments that occur inside a cable network and impairments occur at the edge of the broadband network, i.e., in a subscriber's premise. Differentiating these two types of faults is important, as different faults require different types of technical personnel to re

Faith Johnson, Ryan Meegan, Jack Lowry, Peter Oudemans

Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation. Using aerial and ground imaging over a time series, we develop a framework for characterizing the ripening process of cranberry crops, a crucial component for precision agriculture tasks such as comparing crop breeds (high-thro

Nagarjuna Chary Addanki

Let $F \in S_{k_1}(\Gamma^{(2)}(N_1))$ and $G \in S_{k_2}(\Gamma^{(2)}(N_2))$ be two Siegel cusp forms over the congruence subgroups $\Gamma^{(2)}(N_1)$ and $\Gamma^{(2)}(N_2)$ respectively. Assume that they are Hecke eigenforms in different eigenspaces and satisfy the Generalized Ramanujan Conjecture. Let $\lambda_F(p)$ denote the eigenvalue of $F$ with res

Ryosuke Uematsu, Yoshihiro Ueda, David M. Alexander, A. M. Swinbank

We investigate the properties of active galactic nuclei (AGNs) in the brightest submillimeter galaxies (SMGs) in the COSMOS field. We utilize the bright sample of ALMA/SCUBA-2 COSMOS Survey (AS2COSMOS), which consists of 260 SMGs with $S_{\mathrm{870}\, \mu \mathrm{m}}=0.7\text{--}19.2\,\mathrm{mJy}$ at $z=0\text{--}6$. We perform optical to millimeter spect

Juan Schmidt, Aashish Sapkota, Carsyn L. Mueller, Shuyang Xiao

SrNi$_2$P$_2$ is unique among the ThCr$_2$Si$_2$ class since it exhibits a temperature induced transition upon cooling from an uncollapsed tetragonal (ucT) state to a one-third-collapsed orthorhombic (tcO) state where one out of every three P-rows bond across the Sr layers. This compound is also known for exhibiting bulk superconductivity below 1.4 K at ambi

Guillaume Huber, Michael Bottom, Charles-Antoine Claveau, Shane Jacobson

Spectroscopy and direct-imaging of ultra-faint targets such as Earth-like exoplanets and high redshift galaxies are among the primary goals of upcoming large scale astronomy projects like the Habitable World Observatory (HWO). Such objectives pose extreme instrumental challenges, in particular on detectors where dark currents lower than 1 e-/pixel/kilosecond

Haihao Lu, Zedong Peng, Jinwen Yang

We present MPAX (Mathematical Programming in JAX), an open-source first-order solver for large-scale linear programming (LP) and convex quadratic programming (QP) built natively in JAX. The primary goal of MPAX is to exploit modern machine learning infrastructure for large-scale mathematical programming, while also providing advanced mathematical programming

Varsha Subramanyan, Shi-Zeng Lin, Avadh Saxena

The minimal coupling of strain to Dirac and Weyl semimetals, and its modeling as a pseudo-gauge field has been extensively studied, resulting in several proposed topological transport signatures. In this work, we study the effects of strain on higher winding number Weyl semimetals and show that strain is not a pseudo-gauge field for any winding number larger

H. M. Fausey, A. J. van der Horst, N. R. Tanvir, K. Wiersema

Gamma-ray bursts (GRBs) are excellent probes of the high-redshift Universe due to their high luminosities and the relatively simple intrinsic spectra of their afterglows. They can be used to estimate the fraction of neutral hydrogen (i.e., neutral fraction) in the intergalactic medium at different redshifts through the examination of their Lyman-alpha dampin

Zeyu Yang, Karel Adamek, Wesley Armour

Deep learning models in computer vision have achieved significant success but pose increasing concerns about energy consumption and sustainability. Despite these concerns, there is a lack of comprehensive understanding of their energy efficiency during inference. In this study, we conduct a comprehensive analysis of the inference energy consumption of 1,200

Graham M. Shore

The extremely narrow natural linewidths of rovibrational energy levels in the molecular hydrogen ion $\textrm{H}_2^{\,+}$, and the prospect of synthesising its antimatter counterpart $\overline{\textrm{H}}_2^{\,-}$, make it a promising candidate for high-precision tests of fundamental symmetries such as Lorentz and CPT invariance. In this paper, we present a

Matteo Sesia, Vladimir Svetnik

We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrictive type-I censoring scenarios. The proposed method imputes unobserved censoring times using a machine learning model, and then analyzes the imputed data using a survival model cal

Laura De Carli, Andrew Echezabal, Ismael Morell

We explore a novel link between two seemingly disparate mathematical concepts: Egyptian fractions and fractals. By examining the decomposition of rationals into sums of distinct unit fractions, a practice rooted in ancient Egyptian mathematics, and the arithmetic operations that can be performed using this decomposition, we uncover fractal structures that em

Junjie Luo, Abhimanyu Kumbara, Mansur Shomali, Rui Han

Continuous glucose monitoring (CGM) combined with AI offers new opportunities for proactive diabetes management through real-time glucose forecasting. However, most existing models are task-specific and lack generalization across patient populations. Inspired by the autoregressive paradigm of large language models, we introduce CGM-LSM, a Transformer decoder

Michael te Vrugt

Neural networks based on soft and biological matter constitute an interesting potential alternative to traditional implementations based on electric circuits. DNA is a particularly promising system in this context due its natural ability to store information. In recent years, researchers have started to construct neural networks that are based on DNA. In thi

Binxu Wang, John J. Vastola

By learning the gradient of smoothed data distributions, diffusion models can iteratively generate samples from complex distributions. The learned score function enables their generalization capabilities, but how the learned score relates to the score of the underlying data manifold remains largely unclear. Here, we aim to elucidate this relationship by comp

Melanie Sclar, Jane Yu, Maryam Fazel-Zarandi, Yulia Tsvetkov

Do large language models (LLMs) have theory of mind? A plethora of papers and benchmarks have been introduced to evaluate if current models have been able to develop this key ability of social intelligence. However, all rely on limited datasets with simple patterns that can potentially lead to problematic blind spots in evaluation and an overestimation of mo

Nathan T. Goodfriend, Inam Mirza, Alexander V. Bulgakov, Eleanor E. B. Campbell

The two-dimensional nanomaterial, hexagonal boron nitride (hBN) was cleanly transferred via a blister-based laser-induced forward-transfer method. The transfer was performed utilizing femtosecond and nanosecond laser pulses for separation distances of ~16 and ~200 micrometers between a titanium donor film deposited on a glass substrate and a silicon/silicon

Michael te Vrugt

There is a growing interest in the development of artificial neural networks that are implemented in a physical system. A major challenge in this context is that these networks are difficult to train since training here would require a change of physical parameters rather than simply of coefficients in a computer program. For this reason, reservoir computing

Yanki Lekili, Jenia Tevelev

As observed by Kawamata, a $\mathbb{Q}$-Gorenstein smoothing of a Wahl singularity gives rise to a one-parameter flat degeneration of a matrix algebra. A similar result holds for a general smoothing of any two-dimensional cyclic quotient singularity, where the matrix algebra is replaced by a hereditary algebra. From a categorical perspective, these one-param

Xiaohao Xu, Feng Xue, Shibo Zhao, Yike Pan

Real-time multi-agent collaboration for ego-motion estimation and high-fidelity 3D reconstruction is vital for scalable spatial intelligence. However, traditional methods produce sparse, low-detail maps, while recent dense mapping approaches struggle with high latency. To overcome these challenges, we present MAC-Ego3D, a novel framework for real-time collab

Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang, Yusen Zhang

The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range of tasks. Many existing approaches to automating prompt engineering rely exclusively on textual feedback, refining prompts based solely on inference errors identified by large, co

Jeremy McCormick, Gregory P. Dubois-Felsmann, Andrei Salnikov, Brian Van Klaveren

The Data Management team of the Vera C. Rubin Observatory has developed a data description language and toolset, Felis, for defining the semantics and metadata of its public-facing data catalogs. Felis uses a rich Pydantic data model for describing and validating catalog metadata, expressed as a human-readable and editable YAML format. Felis also provides a

Th. Rivinius, R. Klement, S. D. Chojnowski, D. Baade

Many classical Be stars acquire their very rapid rotation by mass and angular-momentum transfer in massive binaries. Short-lived intermediate-phase objects have only been discovered recently. Data archives and the literature have been searched for additional candidates exhibiting this patterns. Thirteen candidates were identified at various confidence levels

Johann Schmidt, Frank Dreyer, Sayed Abid Hashimi, Sebastian Stober

Traffic signal control plays a crucial role in urban mobility. However, existing methods often struggle to generalize beyond their training environments to unseen scenarios with varying traffic dynamics. We present TransferLight, a novel framework designed for robust generalization across road-networks, diverse traffic conditions and intersection geometries.

Pablo Morales-Álvarez, Stergios Christodoulidis, Maria Vakalopoulou, Pablo Piantanida

The emergence of large pre-trained vision-language models (VLMs) represents a paradigm shift in machine learning, with unprecedented results in a broad span of visual recognition tasks. CLIP, one of the most popular VLMs, has exhibited remarkable zero-shot and transfer learning capabilities in classification. To transfer CLIP to downstream tasks, adapters co

Neeldhara Misra, Harshil Mittal, Ashutosh Rai

We study the Boolean Satisfiability problem (SAT) in the framework of diversity, where one asks for multiple solutions that are mutually far apart (i.e., sufficiently dissimilar from each other) for a suitable notion of distance/dissimilarity between solutions. Interpreting assignments as bit vectors, we take their Hamming distance to quantify dissimilarity,

Ilijas Farah

We give another bit of evidence that forcing axioms provide proper framework for rigidity of quotient structures, by improving the OCA lifting theorem proved by the author in late 20th century and greatly simplifying its proof. In the assumptions of this theorem. We also extend the conclusion of author's 2004 lifting theorem from a lifting result for countab

Philipp Moeßner, Heike Adel

With the advent of publicly available AI-based text-to-image systems, the process of creating photorealistic but fully synthetic images has been largely democratized. This can pose a threat to the public through a simplified spread of disinformation. Machine detectors and human media expertise can help to differentiate between AI-generated (fake) and real im

Anish Giri, David Hyde, Kalman Varga

This paper introduces a robust and scalable framework for implementing nested affine transformations in quantum circuits. Utilizing Hadamard-supported conditional initialization and block encoding, the proposed method systematically applies sequential affine transformations while preserving state normalization. This approach provides an effective method for

Renato Huzak, Ansfried Janssens, Otavio Henrique Perez, Goran Radunović

The main goal of this paper is to give a complete fractal analysis of piecewise smooth (PWS) slow-fast Li\'{e}nard equations. For the analysis, we use the notion of Minkowski dimension of one-dimensional orbits generated by slow relation functions. More precisely, we find all possible values for the Minkowski dimension near PWS slow-fast Hopf points and near

Mustafa Cavus, Przemyslaw Biecek

The Rashomon effect presents a significant challenge in model selection. It occurs when multiple models achieve similar performance on a dataset but produce different predictions, resulting in predictive multiplicity. This is especially problematic in high-stakes environments, where arbitrary model outcomes can have serious consequences. Traditional model se

Guillermo Baltra, Tarang Saluja, Yuri Pradkin, John Heidemann

The Internet provides global connectivity by virtue of a public core -- the routable public IP addresses that host services and to which cloud, enterprise, and home networks connect. Today the public core faces many challenges to uniform, global reachability: firewalls and access control lists, commercial disputes that stretch for days or years, and governme

Michelle Delcourt, Tom Kelly, Luke Postle

We codify a short self-contained proof of the existence of $K_q^r$-absorbers implicit in Keevash's original proof of the Existence Conjecture. Combining this with the work of the first and third authors in yields a proof of the Existence Conjecture for Combinatorial Designs that is not reliant on the construction of $K_q^r$-absorbers by Glock, K\"uhn, Lo, an

Ahmed J. Abdelmaksoud, Shady Agwa, Themis Prodromakis

Transformers are gaining increasing attention across Natural Language Processing (NLP) application domains due to their outstanding accuracy. However, these data-intensive models add significant performance demands to the existing computing architectures. Systolic array architectures, adopted by commercial AI computing platforms like Google TPUs, offer energ

Benjamin Hinrichs, Fumio Hiroshima

We present a simple functional integration based proof that the semigroups generated by the ultraviolet-renormalized translation-invariant non- and semi-relativistic Nelson Hamiltonians are positivity improving (and hence ergodic) with respect to the Fr\"ohlich cone for arbitrary values of the total momentum. Our argument simplifies known proofs for ergodici

Rytis Jursenas

The aim of this brief note is to demonstrate that the boundary pair of a dissipative operator is determined by the unitary boundary pair of its symmetric part.

Chun-Mei Feng, Yuanyang He, Jian Zou, Salman Khan

Existing test-time prompt tuning (TPT) methods focus on single-modality data, primarily enhancing images and using confidence ratings to filter out inaccurate images. However, while image generation models can produce visually diverse images, single-modality data enhancement techniques still fail to capture the comprehensive knowledge provided by different m

Alison L. Crisp, Jonas Klüter, Marz L. Newman, Matthew T. Penny

The first generation of transiting planet searches in globular clusters yielded no detections, and in hindsight, only placed occurrence rate limits slightly higher than the measured occurrence rate in the higher-metallicity Galactic thick disk. To improve these limits, we present the first results of a new wide field search for transiting hot Jupiters in the

C. Heins, V. Iurchuk, O. Gladii, L. Körber

Nonreciprocal spin-wave propagation in bilayer ferromagnetic systems has attracted significant attention due to its potential to precisely quantify material parameters as well as for applications in magnonic logic and information processing. In this study we investigate the nonreciprocity of spin-wave dispersions in heterostructures consisting of two distinc

A. Deltuva, D. Jurčiukonis

Deuteron-${}^{3}\mathrm{He}$ reactions in the 15 to 40 MeV range are studied using a three-body model where the constructed nonlocal optical potentials rely on rigorous nucleon-${}^{3}\mathrm{He}$ scattering calculations. The differential cross section for the elastic scattering and neutron transfer reaction is predicted quite well up to 90 deg scattering an