December 2024 arXiv papers — page 41

Mathias Kuschick

To meet the precision targets of upcoming LHC runs in the simulation of top pair production events it is essential to also consider off-shell effects. Due to their great computational cost I propose to encode them in neural networks. For that I use a combination of neural networks that take events with approximate off-shell effects and transform them into ev

Gianluca Stellin, Karl-Heinz Speidel

In the framework of a macroscopic $\alpha$-cluster model, the structural properties and the spectroscopy of the $^{24}\mathrm{Mg}$ nucleus are investigated. Special attention is devoted to the electromagnetic selection rules imposed by the point-symmetry group $\mathcal{D}_{4h}$ that leaves invariant the adopted $6\alpha$ equilibrium configuration, a square

Ananda Roy

Anyonic chains provide lattice realizations of a rich set of quantum field theories in two space-time dimensions. The latter play a central role in the investigation of generalized symmetries, renormalization group flows and numerous exotic phases of strongly-correlated systems. Here, a variational quantum simulation scheme is presented for the analysis of t

Sara Riazi, Pedram Rooshenas

The integration of LLM-generated feedback into educational settings has shown promise in enhancing student learning outcomes. This paper presents a novel LLM-driven system that provides targeted feedback for conceptual designs in a Database Systems course. The system converts student-created entity-relationship diagrams (ERDs) into JSON format, allows the st

Sophia Tang, Yinuo Zhang, Pranam Chatterjee

We present PepTune, a multi-objective discrete diffusion model for simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with a novel bond-dependent masking schedule and invalid loss function. To guide the diffusion process, we introduce M

Francesco Iafrate, Stefano Iacus

We propose a novel framework for Network Stochastic Differential Equations (N-SDE), where each node in a network is governed by an SDE influenced by interactions with its neighbors. The evolution of each node is driven by the interplay of three key components: the node's intrinsic dynamics (\emph{momentum effect}), feedback from neighboring nodes (\emph{netw

Haoyang Li, Yuchen Hu, Chen Chen, Sabato Marco Siniscalchi

Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures needed for high-fidelity SE. Group-Rational KAN (GR-KAN), a variant of Kolmogorov-Arnold Networks (KAN), retains KAN's expressiveness while improving scalability on complex tasks. We ad

Brian Batell, Yechan Kim, Hye-Sung Lee, Jiheon Lee

We study dark gauge-mediated supersymmetry breaking (dark GMSB) in a theory with a new unbroken $U(1)_{D}$ local symmetry and massless dark photon. Messenger fields charged under both Standard Model and dark gauge symmetries produce new soft supersymmetry-breaking terms due to gauge kinetic mixing between $U(1)_Y$ hypercharge and $U(1)_D$. We show that large

Davide Bilò, Keerti Choudhary, Sarel Cohen, Tobias Friedrich

We design sensitivity oracles for error-prone networks. For a network problem $\Pi$, the data structure preprocesses a network $G=(V,E)$ and sensitivity parameter $f$ such that, for any set $F\subseteq V\cup E$ of up to $f$ link or node failures, it can report a solution for $\Pi$ in $G{-}F$. We study three network problems $\Pi$. $L$-Hop Shortest Path: Give

Bastian Harrach, Yi-Hsuan Lin, Tobias Weth

We study the Calder\'on problem for a logarithmic Schr\"odinger type operator of the form $L_{\Delta} +q$, where $L_{\Delta}$ denotes the logarithmic Laplacian, which arises as formal derivative $\frac{d}{ds} \big|_{s=0}(-\Delta)^s$ of the family of fractional Laplacian operators. This operator enjoys remarkable nonlocal properties, such as the unique contin

Yang Yang, Gia-Wei Chern

The relaxation dynamics of the Kitaev honeycomb model under a thermal quench is dominated by the quasi-stochastic diffusion and pair annihilation of visions, which are gapped flux excitations of an emergent $\mathbb{Z}_2$ gauge field of the Kitaev spin liquid. Both the diffusion energy barrier as well as the effective interactions between visons are mediated

Saumya Gupta, Theresa Jean Tanenbaum

There is growing interest in designing playful interactions with food, but food based tangible interactive narratives have received less attention. We introduce Gummy's Way Out, an interactive tangible narrative experience where interactors eat a gummy bear and help him find his way out of their bodies by eating various food items. By consuming different thi

Arthur J. Parzygnat, Tai-Danae Bradley, Andrew Vlasic, Anh Pham

Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are commonly referred to as quantum encodings. The choice of such encodings may vary substantially from one task to another, and there exist only a few cases where structure has provide

Kuntal Talit, David A. Strubbe

The mechanical properties of hybrid perovskite materials are important for device flexibility, resistance to fracture, epitaxial growth, surface energetics of quantum dots, and induction or relief of stress in thin films due to thermal expansion and phase changes. These issues are particularly salient for solar cells in space applications. Nonetheless, few s

Davide Mesto, Michele Orza, Brunella Bardi, Angela Punzi

The tris(2,4,6-trichlorophenyl)methyl radical (TTM) has inspired the synthesis of several luminescent diradicals and diradicaloids, providing an extraordinary opportunity to control the nature of the low-lying excited states by fine-tuning the diradical character. However, the photophysical properties of TTM-derived diradicals remain not fully understood yet

Petar Mitrić, Vladimir Dobrosavljević, Darko Tanasković

We calculate the frequency-dependent mobility of the Holstein polaron in one dimension near adiabatic limit using the method based on dynamical quantum tipicality, as well as the quantum-classical method. The agreement between fully quantum and quantum-classical solutions is very good. The most prominent feature is the appearance of a zero-frequency peak in

Liren Jin, Xingguang Zhong, Yue Pan, Jens Behley

Robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene using an RGB-D camera on a mobile platform. We propose a hybrid map representation that combines a Gaussian splatting map with a coarse voxel map, leveraging the strengths of both

Shirshendu Ganguly, Kaihao Jing

The intrinsic geometry of the critical percolation cluster induced by the level set of the metric Gaussian free field on $\mathbb{Z}^{d}$ has been the subject of much recent activity. (Lupu, 2016) established that the critical percolation cluster has the same law as that in a Poisson loop soup where the intensity is dictated by the Green's function of the us

Haofei Yu, Zhaochen Hong, Zirui Cheng, Kunlun Zhu

Large Language Models (LLMs) have demonstrated remarkable potential in scientific domains, yet a fundamental question remains unanswered: Can we simulate human research communities with LLMs? Addressing this question can deepen our understanding of the processes behind idea brainstorming and inspire the automatic discovery of novel scientific insights. In th

E. Zubieta, F. García, S. del Palacio, C. M. Espinoza

The radio pulsar PSR J0742-2822 is known to exhibit rapid changes between different pulse profile states that correlate with changes in its spin-down rate. However, the connection between these variations and the glitch activity of the pulsar remains unclear. We aim to study the evolution of the pulse profile and spin-down rate of PSR J0742-2822 in the perio

Md. Tarek Hasan

Hyperparameter optimization (HPO) is critical for enhancing the performance of machine learning models, yet it often involves a computationally intensive search across a large parameter space. Traditional approaches such as Grid Search and Random Search suffer from inefficiency and limited scalability, while surrogate models like Sequential Model-based Bayes

Raf Cluckers, François Loeser, Kien Huu Nguyen, Floris Vermeulen

This work brings Mellin transforms into the realm of motivic integration. The new, larger class of motivic functions is stable under motivic Mellin and Fourier transforms, with general Fubini results and change of variables formulas. It specializes to $p$-adic integrals and $p$-adic Mellin transforms uniformly in $p$, with transfer principles between zero an

O. Benhaida, E. H. Saidi, L. B. Drissi, R. Ahl Laamara

We investigate the topological properties of photon-dressed energy bands in bilayer $\alpha-T_{3}$ lattices under off-resonant circularly polarized light, focusing on aligned and cyclic stacking configurations. Analytical expressions for quasi-energy bands are derived for aligned stacking, while numerical results address cyclic stacking at Dirac points. Circ

Marta Skreta, Lazar Atanackovic, Avishek Joey Bose, Alexander Tong

The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurring the significant computational burden of re-training a larger combined model. In this paper, we cast the problem of combining multiple pre-trained diffusion models at the generati

Kristan Jensen, Amir Raz

We show that a modified version of Son's Dirac composite fermion theory proposed by Seiberg et al gives a candidate unified description of the gapped and gapless fractional quantum Hall states within a single Landau level. Our main tool is the successive application of three-dimensional dualities to partially filled Landau levels of composite fermions, which

M. M. Botana, M. V. Ramallo

We study the critical fluctuations near the resistive transition of very thin films of high-temperature cuprate superconductors composed of a number $N$ of only a few unit cells of superconducting bilayers. For that, we solve the fluctuation spectrum of a Gaussian-Ginzburg-Landau model for few-bilayers superconductors considering two alternating Josephson in

Priyaranjan Pattnayak, Hitesh Laxmichand Patel, Bhargava Kumar, Amit Agarwal

Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and analyzing diverse types of data, including text, images, audio, and video. Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video convers

Łukasz Borchmann

ARC Challenge appears more difficult than ARC Easy for modern LLMs primarily due to an evaluation setup that prevents direct comparison of answer choices rather than inherent complexity. Although some researchers have quietly shifted to a more appropriate scheme over the last year, the implications of this change have yet to be widely acknowledged. We highli

Carina Betken, Erik Broman, Anna Gusakova, Christoph Thäle

In this paper we study Poisson processes of so-called "fat" cylinders in hyperbolic space. As our main result we show that this model undergoes a percolation phase transition. We prove this by establishing a novel link between the fat Poisson cylinder process and semi-scale invariant random fractal models on the unit sphere and in $\mathbb{R}^d.$ As a second

Maria Chudnovsky, Sepehr Hajebi, Sophie Spirkl

The pathwidth of a graph $G$ is the smallest $w\in \mathbb{N}$ such that $G$ can be constructed from a sequence of graphs, each on at most $w+1$ vertices, by gluing them together in a linear fashion. We provide a full classification of the unavoidable induced subgraphs of graphs with large pathwidth.

Jorge Pinochet

Research into educational physics is a field of study that has undergone sustained growth in recent decades. Among the topics addressed in educational physics, there is a relatively new field of research that seeks to make advanced physics accessible to teachers in this area, that is, physics that, due to its novelty and complexity, is beyond the reach of a

Wei Zhang, Yi Zhang, Li Zhu, Qianghuai Jia

Large Language Models (LLMs) have made significant strides in Natural Language Processing and coding, yet they struggle with robustness and accuracy in complex function calls. To tackle these challenges, this paper introduces ADC, an innovative approach that enhances LLMs' ability to follow function formats and match complex parameters. ADC utilizes a high-q

Masahiro Kato

This study investigates an asymptotically minimax optimal algorithm in the two-armed fixed-budget best-arm identification (BAI) problem. Given two treatment arms, the objective is to identify the arm with the highest expected outcome through an adaptive experiment. We focus on the Neyman allocation, where treatment arms are allocated following the ratio of t

Eric A. Bergshoeff, Patrick Concha, Octavio Fierro, Evelyn Rodríguez

We show how to take the first step in the conformal program for constructing general matter couplings to Carroll gravity. In particular, we couple a single massless electric/magnetic scalar to conformal Carroll gravity with isotropic dilatations and show how, upon gauge-fixing, we obtain a (non-conformal version of) electric/magnetic Carroll gravity. We dete

Hesam Nikpey, Saswati Sarkar, Shirin Saeedi Bidokhti

Group testing, a problem with diverse applications across multiple disciplines, traditionally assumes independence across nodes' states. Recent research, however, focuses on real-world scenarios that often involve correlations among nodes, challenging the simplifying assumptions made in existing models. In this work, we consider a comprehensive model for arb

Zeeshan Ahmad

The electrical double layer (EDL) is fundamental to the operation of devices for electrochemical energy storage and conversion. Existing models of EDL in solid electrolytes focus predominantly on the space charge layer and lack a complete treatment of the core layer which is an integral part of the EDL. The core layer exhibits significant variations in defec

Nayel Bettache

This study explores the estimation of parameters in a matrix-valued linear regression model, where the $T$ responses $(Y_t)_{t=1}^T \in \mathbb{R}^{n \times p}$ and predictors $(X_t)_{t=1}^T \in \mathbb{R}^{m \times q}$ satisfy the relationship $Y_t = A^* X_t B^* + E_t$ for all $t = 1, \ldots, T$. In this model, $A^* \in \mathbb{R}_+^{n \times m}$ has $L_1$-

Hussein Naser, Hashim A. Hashim, Mojtaba Ahmadi

This paper presents a novel approach to utilizing underactuated quadrotor Unmanned Aerial Vehicles (UAVs) as assistive devices in cooperative payload transportation task through human guidance and physical interaction. The proposed system consists of two underactuated UAVs rigidly connected to the transported payload. This task involves the collaboration bet

Luyang Liu, Jonas Pfeiffer, Jiaxing Wu, Jun Xie

Techniques enabling large language models (LLMs) to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However, the standard approaches generate sequences of discrete tokens immediately before responding, and so they can incur significant latency costs and be challenging to optimize. In th

Yuri A. Kordyukov, Vladimir M. Manuilov

We consider the Schr\"odinger operator $H(\mu) = \nabla_{\bf A}^*\nabla_{\bf A} + \mu V$ on a Riemannian manifold $M$ of bounded geometry, where $\mu>0$ is a coupling parameter, the magnetic field ${\bf B}=d{\bf A}$ and the electric potential $V$ are uniformly $C^\infty$-bounded, $V\geq 0$. We assume that, for some $E_0>0$, each connected component of the su

Faridun N. Jalolov, Alexander G. Kvashnin

The hardness of materials plays an important role in material design. There are numerous experimental methods to measure the hardness of materials, but theoretical prediction of hardness is challenging. By investigating the correlation between hardness and the elastic properties of materials, namely shear and bulk moduli, the pressure derivative of bulk modu

Yanli Wang, Yanlin Wang, Suiquan Wang, Daya Guo

Repository-level code translation refers to translating an entire code repository from one programming language to another while preserving the functionality of the source repository. Many benchmarks have been proposed to evaluate the performance of such code translators. However, previous benchmarks mostly provide fine-grained samples, focusing at either co

Yiwen Hu, Huatong Song, Jia Deng, Jiapeng Wang

Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved. This paper presents a detailed technical report on YuLan-Mini, a highly capable base model with 2.42B parameters that achieves top-tier performance among models of similar parameter scale. Our

Jacob F. F. Bulmer, Javier Martínez-Cifuentes, Bryn A. Bell, Nicolás Quesada

To understand quantum optics experiments, we must perform calculations that consider the principal sources of noise, such as losses, spectral impurity and partial distinguishability. In both discrete and continuous variable systems, these can be modeled as mixed Gaussian states over multiple modes. The modes are not all resolved by photon-number measurements

Rui Qian, Xin Yin, Dejing Dou

Current Large Multimodal Models (LMMs) empowered visual grounding typically rely on $\texttt{<SEG>}$ tokens as a text prompt to jointly optimize the vision-language model (e.g., LLaVA) and the downstream task-specific model (e.g., SAM). However, we observe that little research has looked into how it works.In this work, we first visualize the similarity maps,

Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir

In distributed learning agents aim at collaboratively solving a global learning problem. It becomes more and more likely that individual agents are malicious or faulty with an increasing size of the network. This leads to a degeneration or complete breakdown of the learning process. Classical aggregation schemes are prone to breakdown at small contamination

Ermo Hua, Che Jiang, Xingtai Lv, Kaiyan Zhang

Extending the context length of Language Models (LMs) by improving Rotary Position Embedding (RoPE) has become a trend. While prior works mainly address RoPE's limitations within attention, this paper uncovers the adverse effects on length generalization from nearly all parts of LMs. Using Discrete Signal Processing theory, we show that RoPE enables periodic

Ioannis D. Gialamas, Antonio Racioppi

We study symmetry-breaking inflation within the framework of metric-affine gravity. By introducing a non-minimal coupling, $\beta(\phi)\tilde{\cal R}$, between the Holst invariant and the inflaton, both small-field and large-field inflationary predictions can be brought into agreement with the latest observational constraints. Remarkably, even for sub-Planck

Jacob Fein-Ashley, Rajgopal Kannan, Viktor Prasanna

Conventional deep networks rely on one-way backpropagation that overlooks reconciling high-level predictions with lower-level representations. We propose \emph{Contextual Feedback Loops} (CFLs), a lightweight mechanism that re-injects top-down context into earlier layers for iterative refinement. Concretely, CFLs map the network's prediction to a compact \em

Antonio Fernando Bertachini de Almeida Prado

The main goal of the present research is to make a summary of analytical equations that can be found to calculate a swing-by maneuver in the three-dimensional space. Analytical equations based in the patched conics approximation are showed and they allow to calculate the variation in velocity, angular momentum, energy and inclination of the spacecraft that i

Maria Chudnovsky, Linda Cook, James Davies, Sang-il Oum

Perfect graphs can be described as the graphs whose stable set polytopes are defined by their non-negativity and clique inequalities (including edge inequalities). In 1975, Chv\'{a}tal defined an analogous class of t-perfect graphs, which are the graphs whose stable set polytopes are defined by their non-negativity, edge inequalities, and odd circuit inequal

Sofía Pérez Casulo, Marcelo Fiori, Federico Larroca, Gonzalo Mateos

We put forth a principled design of a neural architecture to learn nodal Adjacency Spectral Embeddings (ASE) from graph inputs. By bringing to bear the gradient descent (GD) method and leveraging the principle of algorithm unrolling, we truncate and re-interpret each GD iteration as a layer in a graph neural network (GNN) that is trained to approximate the A

Timothy E. Faver, Hermen Jan Hupkes, J. Douglas Wright

We prove the existence of small-amplitude periodic traveling waves in dimer Fermi-Pasta-Ulam-Tsingou (FPUT) lattices without assumptions of physical symmetry. Such lattices are infinite, one-dimensional chains of coupled particles in which the particle masses and/or the potentials of the coupling springs can alternate. Previously, periodic traveling waves we

Victor O. Costa, Mauren D'Ávila, Douglas Arena, Vinicius Schreiner

CubeSats require robust OBDH solutions in harsh environments. The Demoiselle OBC, featuring a radiation-tolerant APSoC and layered FSW, supports reuse, in-orbit updates, and secure operations. To be validated through ITASAT2 and SelenITA, it ensures fault tolerance, flexibility, and compatibility with emerging technologies. This architecture establishes a fo

Alice Meda, Alberto Mura, Salvatore Virzì, Alessio Avella

In this study, we demonstrate the possibility to protect, with Quantum Key Distribution (QKD), a critical infrastructure as the fiber-based one used for time and frequency (TF) dissemination service. The proposed technique allows to disseminate secure and precise TF signals between two fiber-opticconnected locations, on a critical infrastructure, using both

Yun Liu, Bowen Yang, Licheng Zhong, He Wang

Learning generic skills for humanoid robots interacting with 3D scenes by mimicking human data is a key research challenge with significant implications for robotics and real-world applications. However, existing methodologies and benchmarks are constrained by the use of small-scale, manually collected demonstrations, lacking the general dataset and benchmar

Ruiqi He, Yushu He, Longju Bai, Jiarui Liu

Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese. To address this gap, we construct Chumor, the first Chinese humor explanation dataset that exceeds the size of existing humor datasets. Chumor is sourced from Ruo Zhi Ba, a Chinese Reddit-like p

Yue-Chang Peng, Jian-Min Wang, Pu Du, Shuo Zhai

It has been found that some quasars are undergoing quasi-periodic variations (most of them with damped amplitudes) in optical bands from long-term monitoring campaigns, but how to explain the origin of such light curve variations still remains an open question. In this paper, we use the warped accretion disks model to explain the quasi-periodical variations.

Changyue Wang, Weihang Su, Qingyao Ai, Yichen Tang

Knowledge Editing is a technique that updates large language models (LLMs) with new information to maintain their world knowledge. This approach avoids the need to rebuild the model from scratch, thereby addressing the high costs associated with frequent retraining. Among these, the in-context editing paradigm stands out for its effectiveness in integrating

Yuchi Wang, Junliang Guo, Xinyi Xie, Tianyu He

Recent advancements in video autoencoders (Video AEs) have significantly improved the quality and efficiency of video generation. In this paper, we propose a novel and compact video autoencoder, VidTwin, that decouples video into two distinct latent spaces: Structure latent vectors, which capture overall content and global movement, and Dynamics latent vecto

Yue-Chang Peng, Jian-Min Wang, Yu Zhao, Luis C. Ho

There is growing evidence for star formation inside outflows of active galactic nuclei (AGNs). The formed stars are injected into bulges and give rise to perturbation of bulges. In this paper, we investigate the issues of non-rotating, spherically symmetric bulges under the perturbation of fast, massive outflows with stars formed inside. We show that the pot

Mingjun Zhao, Leah Dickstein, Akshay S. Nadig, Wenjun Zhou

It has been shown that light speckle fluctuations provide a means for noninvasive measurements of cerebral blood flow index (CBFi). While conventional Diffuse Correlation Spectroscopy (DCS) provides marginal brain sensitivity for CBFi in adult humans, new techniques have recently emerged to improve diffuse light throughput and thus, brain sensitivity. Here w

Ali Forootani, Raffaele Iervolino

Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data. However, traditional FL approaches often face limitations in scalability and efficiency due to their reliance on synchronous client updates, which can result in significant delay

M. V. Ramallo

We reason that various recent works by different groups reporting new phenomenologies in superconductors can be understood in a unifying way as instances of the appearance of novel competitions (or synergies in some cases) between the coexisting orders at play in superconducting materials. In particular, we argue that the main common feature of such phenomen

Vasileios Kouvakis, Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos, Hongwu Liu

Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage expansion

Kiryong Chung, Jaehyun Kim, Jeong-Seop Kim

Let $X$ be the Fano threefold of index one, degree $22$, and $\mathrm{Pic}(X)\cong\mathbb{Z}$. Such a threefold $X$ can be realized by a regular zero section $\mathbf{s}$ of $(\bigwedge^2\mathcal{F}^{*})^{\oplus 3}$ over Grassmannian variety $\mathrm{Gr}(3,V)$, $\dim V=7$ with the universal subbundle $\mathcal{F}$. When the section $\mathbf{s}$ is given by t

J. A. Hernández, A. A. Eberharter, M. Schuler, J. Lass

Although the search for quantum many-body phenomena in magnetic materials has a strong focus on highly frustrated systems, even unfrustrated quantum magnets show a multitude of unconventional phenomena in their spin excitation spectra. YbBr$_3$ is an excellent realization of the $S = 1/2$ antiferromagnetic Heisenberg model on the honeycomb lattice, and we ha

Dana R. Hamad, Tarik A. Rashid

This study introduces the LPBSA, an advanced optimization algorithm that combines Learner Performance-based Behavior (LPB) and Simulated Annealing (SA) in a hybrid approach. Emphasizing metaheuristics, the LPBSA addresses and mitigates the challenges associated with traditional LPB methodologies, enhancing convergence, robustness, and adaptability in solving

Prasanta Kumar Das, Shyamashish Dey, Saumyen Kundu, Santosh Kumar Rai

The inert doublet model (IDM), a minimal extension of the Standard Model (SM), provides a scalar dark matter (DM) candidate that belongs to the additional Higgs doublet. The model faces challenges in achieving the correct relic abundance for compressed spectra and DM masses in the high-mass range. In this work we introduce a $Z_2$-odd singlet vector-like qua

Shaull Almagor, Guy Avni, Neta Dafni

Two-player graph games are a fundamental model for reasoning about the interaction of agents. These games are played between two players who move a token along a graph. In bidding games, the players have some monetary budget, and at each step they bid for the privilege of moving the token. Typically, the winner of the bid either pays the loser or the bank, o

Yixin Ren, Haocheng Zhang, Yewei Xia, Hao Zhang

Score-based causal discovery methods can effectively identify causal relationships by evaluating candidate graphs and selecting the one with the highest score. One popular class of scores is kernel-based generalized score functions, which can adapt to a wide range of scenarios and work well in practice because they circumvent assumptions about causal mechani

Jintai Wu, Keping Qiu, Frederick Poidevin, Pierre Bastien

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi

Jingqiu Zhou, Lue Fan, Xuesong Chen, Linjiang Huang

In this paper, we present GaussianPainter, the first method to paint a point cloud into 3D Gaussians given a reference image. GaussianPainter introduces an innovative feed-forward approach to overcome the limitations of time-consuming test-time optimization in 3D Gaussian splatting. Our method addresses a critical challenge in the field: the non-uniqueness p

Hoyoung D. Kang, Rosalba Perna, Davide Lazzati, Yi-Han Wang

With the discovery of gravitational waves (GWs), Active Galactic Nuclei (AGN) disks have emerged as an interesting environment for hosting a fraction of their sources. AGN disks are conducive to forming both long and short Gamma-Ray Bursts (GRBs), and their anticipated cosmological occurrence within these disks has potential to serve as an independent tool f

Nicolas Boulanger, Guillaume Lhost, Sylvain Thomée

We analyse the couplings of a partially massless spin-2 field with a doublet of massless, real spin-3/2 fields. In the flat limit, this spectrum coincides with the spectrum of ${\cal N}=2$ pure supergravity around anti-de Sitter spacetime AdS$_4$. We classify all the possible parity-invariant, non-Abelian deformations of the free theory that lead to a deform

Xuchen Wu, Sebastian Jaimungal

We study partial information Nash equilibrium between a broker and an informed trader. In this setting, the informed trader, who possesses knowledge of a trading signal, trades multiple assets with the broker in a dealer market. Simultaneously, the broker offloads these assets in a lit exchange where their actions impact the asset prices. The broker, however

Florian Knoop, Nimrod Benshalom, Matan Menahem, Paul Gartner

We present an ab initio theory of the non-resonant Raman scattering process in crystals at finite temperature in direct comparison with experiments. The theory incorporates the scattering geometry and polarization dependence of the Raman process and the small but finite wave vectors of the phonons for correctly describing the scattering with longitudinal opt

Leonardo Cefalo, Alessio Pollice, Virgilio Gómez-Rubio

This paper studies the relationship between the student's abilities in the second year of high school and the infrastructural endowment in all Italian municipalities, using spatial Bayesian modelling. Municipal student scores are obtained by averaging standardized and spatially homogeneous indicators of student outcomes provided by the Invalsi Institute for

Shivani Aggarwal, Saumya Singh, Dinkar Mishra, Bhupesh Kumar

The paper presents a study of wakefield generation and electron injection via propagation of radially polarized laser pulses in homogeneous pre-ionized plasma. The analytical study is based on Lorentz force and continuity equations. Perturbation technique and quasi-static approximation are used for evaluating the generated longitudinal wakefields. Trapping a

Martin Houde, Liam Beaudoin, Robert Kwolek, Kazuki Hirota

We present design principles for ultrashort-pulse, type-0 phase-matched optical parametric amplifiers to generate and measure spectrally pure degenerate squeezed light. We consider a fundamental signal (second-harmonic) mode at 2090 (1045) nm and show that our proposed design achieves a Schmidt number of $K \approx 1.02$ with squeezing levels greater than 15

Yue Deng, Yan Yu, Weiyu Ma, Zirui Wang

The availability of challenging simulation environments is pivotal for advancing the field of Multi-Agent Reinforcement Learning (MARL). In cooperative MARL settings, the StarCraft Multi-Agent Challenge (SMAC) has gained prominence as a benchmark for algorithms following centralized training with decentralized execution paradigm. However, with continual adva

Eric Brunner, Luuk Coopmans, Gabriel Matos, Matthias Rosenkranz

Building upon recent progress in Lindblad engineering for quantum Gibbs state preparation algorithms, we propose a simplified protocol that is shown to be efficient under the eigenstate thermalization hypothesis (ETH). The ETH reduces circuit overheads of the Lindblad simulation algorithm and ensures a fast convergence toward the target Gibbs state. Moreover

Yu-Jun Liu, Ka Kwan Pak, Peng Ren, Mengbo Guo

Exceptional points (EPs) has seen substantial advances in both experiment and theory. However, in quantum systems, higher-order exceptional points remain of great interest and possess numerous intriguing properties yet to be fully explored. Here, we describe a \emph{PT} symmetry-protected three-level non-Hermitian system with the dissipative spin-orbit-coupl

Po-Hung Chen, Dah-Wei Chiou, Bo-Hung Chen, Jie-Hong Roland Jiang

The recently developed quantum circuit cutting technique greatly extends the capabilities of current noisy intermediate-scale quantum (NISQ) hardware. However, it introduces substantial overhead in both classical postprocessing and quantum resources, as the postprocessing complexity and sampling cost scale exponentially with the number of circuit cuts. In th

Juan-Pablo Llerena-Córdova

In 1987, Mazur and Tate stated conjectures which, in some cases, resemble the classical Birch-Swinnerton-Dyer conjecture and its $p$-adic analog. We study experimentally three conjectures stated by Mazur and Tate using SageMath. Our findings indicate discrepancies in some of the original statements of some of the conjectures presented by Mazur and Tate. Howe

Edoardo Alviani, Adam Falkowski

We propose a Lorentz invariant and little group covariant description of beta decay amplitudes relying on on-shell amplitude methods and the spinor variables for massive particles. The framework is employed to calculate Coulomb corrections to the decay amplitude and their contribution to T -odd correlation coefficients, including the D parameter. In the SM l

Nathaniel Weir, Bhavana Dalvi Mishra, Orion Weller, Oyvind Tafjord

Recent reasoning methods (e.g., chain-of-thought, entailment reasoning) help users understand how language models (LMs) answer a single question, but they do little to reveal the LM's overall understanding, or "theory," about the question's topic, making it still hard to trust the model. Our goal is to materialize such theories - here called microtheories (a

Diponkor Bala, S M Rakib Ul Karim, Rownak Ara Rasul

Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification from histopathological images. This includes the histopathological diagnosis, an importan

Yikang Zhang, Chuang-Wei Liu, Jiahang Li, Yingbing Chen

Road inspection is crucial for maintaining road serviceability and ensuring traffic safety, as road defects gradually develop and compromise functionality. Traditional inspection methods, which rely on manual evaluations, are labor-intensive, costly, and time-consuming. While data-driven approaches are gaining traction, the scarcity and spatial sparsity of r

Zhi-zhong Xing, Jing-yu Zhu

With the help of a full Euler-like block parametrization of the flavor structure for the canonical seesaw mechanism, we present the first general and explicit analytical calculations of the two neutrino mass-squared differences, three flavor mixing angles and the effective Dirac CP-violating phase responsible for the primary behaviors of neutrino oscillation

Yun-Chieh Cheng, Yu-Tong Shen, Guanqun Song, Ting Zhu

This study investigates how the new release of MacBook Pro I/O devices affects the obsolescence of related accessories. We also explore how these accessories will impact the environment and the economic consequences. As technology progresses, each new MacBook Pro releases outdated prior accessories, making more electronic waste. This phenomenon makes modern

Divya Acharya, Prasanna Poojary, Vadiraja Bhatta G R

This study aims to determine the algebraic structures of $\alpha$-constacyclic codes of length $3p^s$ over the finite commutative non-chain ring $\mathcal{R}=\frac{\mathbb{F}_{p^m}[u, v]}{\langle u^2, v^2, uv-vu\rangle}$, for a prime $p \neq 3.$ For the unit $\alpha$, we consider two different instances: when $\alpha$ is a cube in $\mathcal{R}$ and when it i

Kyle Richardson, Vivek Srikumar, Ashish Sabharwal

Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the development of many new variants of the original DPO loss, understanding the differences between these recent proposals, as well as developing new DPA loss functions, remains difficult

Philipp Weder, Paul Schwerdtner, Benjamin Peherstorfer

Leveraging nonlinear parametrizations for model reduction can overcome the Kolmogorov barrier that affects transport-dominated problems. In this work, we build on the reduced dynamics given by Neural Galerkin schemes and propose to parametrize the corresponding reduced solutions on quadratic manifolds. We show that the solutions of the proposed quadratic-man

Fabius Krämer, Tim Laux

We propose and study a novel efficient algorithm for clustering and classification tasks based on the famous MBO scheme. On the one hand, inspired by Jacobs et al. [J. Comp. Phys. 2018], we introduce constraints on the size of clusters leading to a linear integer problem. We prove that the solution to this problem is induced by a novel order statistic. This

Benjamin Berkels, Peter Binev

The problem of noise in a general data acquisition procedure can be resolved more accurately if it is based on a model that describes well the distortions of the data including both spatial and intensity changes. The focus of this article is the modeling of the position distortions during sequential data acquisitions. A guiding example is the data obtained b

Jong-Ik Park, Carlee Joe-Wong

Federated learning (FL) addresses privacy concerns in training language models by enabling multiple clients to contribute to the training, without sending their data to others. However, non-IID (identically and independently distributed) data across clients often limits FL's performance. This issue is especially challenging during model fine-tuning, as noise

Yu-Tong Liu

The notion of pointwise differentials for distributions is a way to extract local information of distributions by rescaling the distribution at a point. In this paper, we study the pointwise differentials for distributions corresponding to a negative order Sobolev functions. Our main results prove Borel regularity, Lusin approximation, rectifiability, and a

Rishiraj Saha Roy, Chris Hinze, Joel Schlotthauer, Farzad Naderi

Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions to SPARQL queries. However, SPARQL has certain shortcomings: (i) it is brittle for complex intents and conversational questions, and (ii) it is not suitable for more abstract need

Zhehu Yuan, Jinyang Liu, Guanqun Song, Ting Zhu

In satellite applications, managing thermal conditions is a significant challenge due to the extreme fluctuations in temperature during orbital cycles. One of the solutions is to heat the satellite when it is not exposed to sunlight, which could protect the satellites from extremely low temperatures. However, heat dissipation is necessary for Graphics Proces