February 2024 arXiv papers — page 12

Olga Goulko, Hsing-Ta Chen, Moshe Goldstein, Guy Cohen

We investigate the real-time dynamics of the sub-Ohmic spin-boson model across a broad range of coupling strengths, using the numerically exact inchworm quantum Monte Carlo algorithm. From short- and intermediate-time dynamics starting from an initially decoupled state, we extract signatures of the zero-temperature quantum phase transition between localized

Maicol A. Ochoa

We report on the anharmonic signatures in dissipative polaritons' stationary energy distribution and thermodynamics under external periodic driving. First, we introduce a dynamic model for the dissipative anharmonic Jaynes-Cummings polariton with a generic time-periodic interaction representing modulations of the polariton's energy due to an external force o

Patrick Hiatt, Sorin Popa

We prove that, under the continuum hypothesis $\frak c=\aleph_1$, any ultraproduct II$_1$ factor $M= \prod_{\omega} M_n$ of separable finite factors $M_n$ contains more than $\frak c$ many mutually disjoint singular MASAs, in other words the {\it singular abelian rank of} $M$, $\text{\rm r}(M)$, is larger than $ \frak c$. Moreover, if the strong continuum hy

Benjamin David Evans, Raphael Trumpp, Marco Caccamo, Felix Jahncke

The F1TENTH autonomous driving platform, consisting of 1:10-scale remote-controlled cars, has evolved into a well-established education and research platform. The many publications and real-world competitions span many domains, from classical path planning to novel learning-based algorithms. Consequently, the field is wide and disjointed, hindering direct co

Malik Nafa, Tigran Mansuryan, Vincent Couderc, Alessandro Tonello

We propose a novel approach using Multiplex-Coherent Anti-Stokes Raman Scattering (M-CARS) for la-bel-free discriminations in biomedical tissues. The strategy is based on the evaluation of the contrast be-tween resonant and nonresonant contributions in a M-CARS hyperspectral dataset, and tested to identify and differentiate thin actin filaments from thick my

Rouzbeh Allahverdi, Ngo Phuc Duc Loc, Jacek K. Osiński

We study dark matter production from mediator decays in scenarios with an epoch of early matter domination. Particles that mediate interactions between dark matter and the standard model particles are kinematically accessible to the thermal bath as long as their mass is below the reheating temperature of the Universe after inflation. Decay of on-shell mediat

Nicolas Barros, Sergio Ciliberto, Ludovic Bellon

We demonstrate experimentally that, applying optimal protocols which drive the system between two equilibrium states characterized by a free energy difference $\Delta F$, we can maximize the probability of performing the transition between the two states with a work $W$ smaller than $\Delta F$. The second law holds only on average, resulting in the inequalit

Tatiana Novikova, Alexey Ovchinnikov, Gleb Pogudin, Jessica C. Ramella-Roman

Imaging Mueller polarimetry has already proved its potential for metrology, remote sensing and biomedicine. The real-time applications of this modality require both video rate image acquisition and fast data post-processing algorithms. First, one must check the physical realizability of the experimental Mueller matrices in order to filter out non-physical da

Mohammad S. Ramadan, Mihai Anitescu

The theory of dual control was introduced more than seven decades ago. Although it has provided rich insights to the fields of control, estimation, and system identification, dual control is generally computationally prohibitive. In recent years, however, the use of Koopman operator theory for control applications has been emerging. This paper presents a new

Vaishali Swaminathan, J. Alex Thomasson, Robert G. Hardin, Nithya Rajan

Radiometric accuracy of data is crucial in quantitative precision agriculture, to produce reliable and repeatable data for modeling and decision making. The effect of exposure time and gain settings on the radiometric accuracy of multispectral images was not explored enough. The goal of this study was to determine if having a fixed exposure (FE) time during

Yuan Fang, Mounica Mahankali, Yiming Wang, Lei Chen

Strong correlations in matter promote a landscape of quantum phases and associated quantum critical points. For metallic systems, there is increasing recognition that the quantum criticality goes beyond the Landau framework and, thus, novel means are needed to characterize the quantum critical fluid. Here we do so by studying an entanglement quantity, the qu

Christos Thrampoulidis

We initiate an investigation into the optimization properties of next-token prediction (NTP), the dominant training paradigm for modern language models. Specifically, we study the structural properties of the solutions selected by gradient-based optimizers among the many possible minimizers of the NTP objective. By framing NTP as cross-entropy minimization a

J. T. Quartuccio, P. H. R. S. Moraes, J. D. V. Arbañil

We present solutions for non-spherically symmetric neutron stars. We begin by deriving the Tolman-Oppenheimer-Volkoff equations from a parameterized metric that takes into account the deformation of the star due to differences in equatorial and polar pressures, expressed in terms of a parameter D, which is the ratio between polar and equatorial radius. The s

Binayyak B. Roy, Rimika Jaiswal, Tudor D. Stanescu, Sumanta Tewari

We investigate theoretically a one-dimensional semiconductor-superconductor (SM-SC) heterostructure with Rashba spin-orbit coupling and parallel Zeeman field in the presence of disorder generated by random charged impurities and identify the optimal regimes for realizing topological superconductivity and Majorana zero modes. Using a Green's function approach

Su-un Lee, Changhun Oh, Yat Wong, Senrui Chen

We study the evolution of conditional mutual information in generic open quantum systems, focusing on one-dimensional random circuits with interspersed local noise. Unlike in noiseless circuits, where conditional mutual information spreads linearly while being bounded by the lightcone, we find that noisy random circuits with an error rate $p$ exhibit superli

Simone Marciano, Davide Meloni, Matteo Parriciatu

We propose a model for leptons based on the smallest modular finite group $\Gamma_2\cong S_3$ that, for the first time, accounts for both the hints of large low-energy CP-violation in the lepton sector and the matter-antimatter asymmetry of the Universe, generated by only two heavy right-handed neutrinos. These same states are also employed in a Minimal sees

Geeling Chau, Yujin An, Ahamed Raffey Iqbal, Soon-Jo Chung

A major goal in neuroscience is to discover neural data representations that generalize. This goal is challenged by variability along recording sessions (e.g. environment), subjects (e.g. varying neural structures), and sensors (e.g. sensor noise), among others. Recent work has begun to address generalization across sessions and subjects, but few study robus

Abbi Ward, Jimmy Li, Julie Wang, Sriram Lakshminarasimhan

Background: Health datasets from clinical sources do not reflect the breadth and diversity of disease in the real world, impacting research, medical education, and artificial intelligence (AI) tool development. Dermatology is a suitable area to develop and test a new and scalable method to create representative health datasets. Methods: We used Google Search

Claudio de Castro Pellegrini

This paper addresses the problem of determining the optimum shape for a beer glass that minimizes the heat transfer while the liquid is consumed, thereby keeping it cold for as long as possible. The proposed solution avoids the use of insulating materials. The glass is modelled as a body of revolution generated by a smooth curve S, constructed from a materia

S. Fujimoto, M. Ouchi, K. Kohno, F. Valentino

Early galaxy formation, initiated by the dark matter and gas assembly, evolves through frequent mergers and feedback processes into dynamically hot, chaotic structures. In contrast, dynamically cold, smooth rotating disks have been observed in massive evolved galaxies merely 1.4 billion years after the Big Bang, suggesting rapid morphological and dynamical e

Aymeric Delteil, Stéphanie Buil, Jean-Pierre Hermier

In the solid state, a large variety of single-photon emitters present high quality photophysical properties together with a potential for integration. However, in many cases, the host matrix induces fluctuations of the emission wavelength in time, limiting the potential applications based on indistinguishable photons. A deep understanding of the underlying s

Bernhard Haeupler, Yaowei Long, Thatchaphol Saranurak

We present a new distance oracle in the fully dynamic setting: given a weighted undirected graph $G=(V,E)$ with $n$ vertices undergoing both edge insertions and deletions, and an arbitrary parameter $\epsilon$ where $\epsilon\in[1/\log^{c} n,1]$ and $c>0$ is a small constant, we can deterministically maintain a data structure with $n^{\epsilon}$ worst-case u

Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu

Public LLMs such as the Llama 2-Chat underwent alignment training and were considered safe. Recently Qi et al. [2024] reported that even benign fine-tuning on seemingly safe datasets can give rise to unsafe behaviors in the models. The current paper is about methods and best practices to mitigate such loss of alignment. We focus on the setting where a public

C. Reichhardt, C. J. O. Reichhardt

We examine the ordering, pinning, and dynamics of two-dimensional pattern forming systems interacting with a periodic one-dimensional substrate. In the absence of the substrate, particles with competing long-range repulsion and short-range attraction form anisotropic crystal, stripe, and bubble states. When the system is tuned across the stripe transition in

Abigail R. Chriss, Guy Worthey

In this study, we extend the dust-independent Hatzidimitriou (1991) relation between cluster age and $d_{B-R}$ color difference between the red giant branch (RGB) and red clump to younger cluster ages. We perform membership analysis on fourteen galactic open clusters using Gaia DR3 astrometry, then compute the difference in color of the RGB and red clump $d_

Nadia Creignou, Oscar Defrain, Frédéric Olive, Simon Vilmin

Given a CNF formula $\varphi$ with clauses $C_1, \dots, C_m$ over a set of variables $V$, a truth assignment $\mathbf{a} : V \to \{0, 1\}$ generates a binary sequence $\sigma_\varphi(\mathbf{a})=(C_1(\mathbf{a}), \ldots, C_m(\mathbf{a}))$, called a signature of $\varphi$, where $C_i(\mathbf{a})=1$ if clause $C_i$ evaluates to 1 under assignment $\mathbf{a}$,

Paul Over, Sergio Bengoechea, Thomas Rung, Francesco Clerici

The paper presents a variational quantum algorithm to solve initial-boundary value problems described by second-order partial differential equations. The approach uses hybrid classical/quantum hardware that is well suited for quantum computers of the current noisy intermediate-scale quantum era. The partial differential equation is initially translated into

Shahn Majid

The turn of the millennium was a time of optimism about an approach to noncommutative geometry inspired by rich mathematical objects called `quantum groups' and its applications to quantum spacetime. This would model quantum gravity effects as noncommutativity of spacetime coordinates and was arguably going to solve quantum gravity itself. It took a further

Xiaoyong Chu, Raghuveer Garani, Camilo García-Cely, Thomas Hambye

The Sun may capture asymmetric dark matter (DM), which can subsequently form bound-states through the radiative emission of a sub-GeV scalar. This process enables generation of scalars without requiring DM annihilation. In addition to DM capture on nucleons, the DM-scalar coupling responsible for bound-state formation also induces capture from self-scatterin

Evan Sheridan, Lana Mineh, Raul A. Santos, Toby Cubitt

Quantum computers open up new avenues for modelling the physical properties of materials and molecules. Density Functional Theory (DFT) is the gold standard classical algorithm for predicting these properties, but relies on approximations of the unknown universal functional, limiting its general applicability for many fundamental and technologically relevant

Yicheng Mao, Roselinde Kessels, Tom van der Zanden

Discrete choice experiments (DCEs) investigate the attributes that influence individuals' choices when selecting among various options. To enhance the quality of the estimated choice models, researchers opt for Bayesian optimal designs that utilize existing information about the attributes' preferences. Given the nonlinear nature of choice models, the constr

Marian Necula, Tudorel Andrei, Bogdan Oancea, Mihaela Păun

The modernization of offi cial statistics involves the use of new data sources, such as data collected through remote sensing. The document contains a description of how an urban green index, derived from the SDG 11.7 objective, was obtained for Romania's 41 county seat cities based on free data sets collected by remote sensing from the European and North Am

Oscar Kremer, Igor Califrer, Daniel Tandeitnik, Jean Pierre von der Weid

We implement an all electrical controller for 3D feedback cooling of an optically levitated nanoparticle capable of reaching sub-Kelvin temperatures for the center of mass motion. The controller is based on an optimal policy where state estimation is made by delayed position measurements. The method offers a simplified path for pre-cooling and decoupling the

David Hathcock, James P. Sethna

Universal scaling laws only apply asymptotically near critical phase transitions. We propose a general scheme, based on normal form theory of renormalization group flows, for incorporating corrections to scaling that quantitatively describe the entire neighboring phases. Expanding Onsager's exact solution of the 2D Ising model about the critical point, we id

Mehdi Ramezani, Sadegh Salami, Mehdi Shokhmkar, Morteza Moradi

In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing the requisite qubit count from $n^2$ to $n\log_{2} (n)$, our QAOA-based algorithm not only contributes to the on

Patrick Chi-Kit Cheong, Nishad Muhammed, Pavan Chawhan, Matthew D. Duez

The conformal flatness approximation to the Einstein equations has been successfully used in many astrophysical applications such as initial data constructions and dynamical simulations. Although it has been shown that full general relativistic strongly differentially rotating equilibrium models deviate by at most a few percent from their conformally flat co

Jiangpeng He, Fengqing Zhu

Class-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data while retaining learned knowledge. A major challenge of CIL arises when applying to real-world data characterized by non-uniform distribution, which introduces a dual imbalance problem involving (i) disparities between stored exemplars of old tasks a

Andrei Cozma, Landon Harris, Hairong Qi, Ping Ji

This paper introduces a robust approach for automated defect detection in tire X-ray images by harnessing traditional feature extraction methods such as Local Binary Pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) features, as well as Fourier and Wavelet-based features, complemented by advanced machine learning techniques. Recognizing the challenges

Ava Elizabeth Scott, Lev Tankelevitch, Sean Rintel

Ineffective meetings due to unclear goals are major obstacles to productivity, yet support for intentionality is surprisingly scant in our meeting and allied workflow technologies. To design for intentionality, we need to understand workers' attitudes and practices around goals. We interviewed 21 employees of a global technology company and identified contra

Frederik De Ceuster, Thomas Ceulemans, Leen Decin, Taïssa Danilovich

Spectral line observations encode a wealth of information. A key challenge, therefore, lies in the interpretation of these observations in terms of models to derive the physical and chemical properties of the astronomical environments from which they arise. In this paper, we present pomme: an open-source Python package that allows users to retrieve 1D or 3D

Zbigniew Błaszczyk, Arturo Espinosa Baro, Antonio Viruel

The notion of effective topological complexity, introduced by B{\l}aszczyk and Kaluba, deals with using group actions in the configuration space in order to reduce the complexity of the motion planning algorithm. In this article we focus on studying several properties of such notion of topological complexity. We introduce a notion of effective LS-category wh

M. Susits, J. Tóth

This study addresses a longstanding question regarding the mathematical proof of chaotic behavior in kinetic differential equations. Following the numerous numerical and experimental results in the past 50 years, we introduce two formal chemical reactions that rigorously demonstrate this behavior. Our approach involves transforming chaotic equations into kin

Shubhayan Sarkar, Alexandre C. Orthey,, Gautam Sharma, Saronath Halder

Device-independent certification of quantum states enables the characterization of states within a device under minimal physical assumptions. A major problem in this regard is to certify quantum states using minimal resources. Aiming to address this problem, we consider a multipartite quantum steering scenario involving an arbitrary number of parties, of whi

Valery I. Kovalchuk

General analytical expressions for the observables in $dA$-scattering reaction have been derived in the diffraction approximation. The resulting formulas describe the cross section and polarization states of the deuteron when it scattering by nuclei with zero spin in the ground state. The tabulated distributions of the target nucleus density and the realisti

U. Sureshkumar, A. Durkalec, A. Pollo, W. J. Pearson

Galaxy mergers play a crucial role in galaxy evolution. However, the correlation between mergers and the local environment of galaxies is not fully understood. We aim to address the question of whether galaxy mergers prefer denser or less dense environments by quantifying the spatial clustering of mergers and non-mergers. We use two different indicators to c

Y-H. Nian, I. Vinograd, C. Chaffey, Y. Li

The suppression of ferroquadrupolar order in TmVO$_4$ in a magnetic field is well-described by the transverse field Ising model, enabling detailed studies of critical dynamics near the quantum phase transition. We describe nuclear magnetic resonance measurements in pure and Y-doped single crystals. The non-Kramers nature of the ground state doublet leads to

Kiyoto Nakamura, Joachim Ankerhold

While the accuracy of qubit operations has been greatly improved in the last decade, further development is demanded to achieve the ultimate goal: a fault-tolerant quantum computer that can solve real-world problems more efficiently than classical computers. With growing fidelities even subtle effects of environmental noise such as qubit-reservoir correlatio

J. Andrade, Rodolfo Casana, E. da Hora

We investigate the existence of BPS structures in a Maxwell-Higgs electrodynamics immersed within a chiral medium, whose electromagnetic properties are described by both the Chern-Simons term and a neutral scalar field. The implementation of the Bogomol'nyi-Prasad-Sommerfield's technique provides the BPS potential and the self-dual equations whose solutions

Raffaele Giuseppe Cestari, Stefano Longari, Stefano Zanero, Simone Formentin

In recent years, SCADA (Supervisory Control and Data Acquisition) systems have increasingly become the target of cyber attacks. SCADAs are no longer isolated, as web-based applications expose strategic infrastructures to the outside world connection. In a cyber-warfare context, we propose a Model Predictive Control (MPC) architecture with adaptive resilience

Yohan Dubois, Francisco Rodríguez Montero, Corentin Guerra, Maxime Trebitsch

We introduce a model for dust evolution in the RAMSES code for simulations of galaxies with a resolved multiphase interstellar medium. Dust is modelled as a fluid transported with the gas component, and is decomposed into two sizes, 5 nm and 0.1 $\mu\rm m$, and two chemical compositions for carbonaceous and silicate grains. Using a suite of isolated disc sim

Shiqi Lei, Kanghoon Lee, Linjing Li, Jinkyoo Park

Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly. Recent research has explored various ways to enhance offline learning efficiency by considering the characteristics (e.g., expertise level or multiple demonstrators)

Matthew Hough, Stephen A. Vavasis

We present two first-order primal-dual algorithms for solving saddle point formulations of linear programs, namely FWLP (Frank-Wolfe Linear Programming) and FWLP-P. The former iteratively applies the Frank-Wolfe algorithm to both the primal and dual of the saddle point formulation of a standard-form LP. The latter is a modification of FWLP in which regulariz

Aporva Varshney

We study an example of a projective threefold with a non-isolated singularity and its derived category. The singular locus can be locally described as a line of surface nodes compounded with a threefold node at the origin. We construct a semiorthogonal decomposition where one component absorbs the singularity in the sense of Kuznetsov--Shinder, and the other

Benjamin Walker, Andrew D. McLeod, Tiexin Qin, Yichuan Cheng

The vector field of a controlled differential equation (CDE) describes the relationship between a control path and the evolution of a solution path. Neural CDEs (NCDEs) treat time series data as observations from a control path, parameterise a CDE's vector field using a neural network, and use the solution path as a continuously evolving hidden state. As the

Laurent Yves Emile Ramos Cheret, Vinicius Prado da Fonseca, Thiago Eustaquio Alves de Oliveira

Non-flat surfaces pose difficulties for robots operating in unstructured environments. Reconstructions of uneven surfaces may only be partially possible due to non-compliant end-effectors and limitations on vision systems such as transparency, reflections, and occlusions. This study achieves blind surface reconstruction by harnessing the robotic manipulator'

Kaiyue Wen, Xingyu Dang, Kaifeng Lyu

This paper investigates the gap in representation powers of Recurrent Neural Networks (RNNs) and Transformers in the context of solving algorithmic problems. We focus on understanding whether RNNs, known for their memory efficiency in handling long sequences, can match the performance of Transformers, particularly when enhanced with Chain-of-Thought (CoT) pr

Aurora Ramírez, José Raúl Romero, Carlos García-Martínez, Sebastián Ventura

Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheuristic optimization frameworks are a practical alternative as they provide a variety of algorithms composed of

Frank G. Garvan, James A. Sellers, Nicolas Allen Smoot

In 2012 Paule and Radu proved a difficult family of congruences modulo powers of 5 for Andrews' 2-colored generalized Frobenius partition function. The family is associated with the classical modular curve of level 20. We demonstrate the existence of a congruence family for a related generalized Frobenius partition function associated with the same curve. We

Mahdi Karami, Ali Ghodsi

In the rapidly evolving field of deep learning, the demand for models that are both expressive and computationally efficient has never been more critical. This paper introduces Orchid, a novel architecture designed to address the quadratic complexity of traditional attention mechanisms without compromising the ability to capture long-range dependencies and i

Jiarui Xing, Nian Wu, Kenneth Bilchick, Frederick Epstein

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from

Pierluigi Colli, Jürgen Sprekels, Fredi Tröltzsch

In this paper we study the optimal control of a parabolic initial-boundary value problem of viscous Cahn-Hilliard type with zero Neumann boundary conditions. Phase field systems of this type govern the evolution of diffusive phase transition processes with conserved order parameter. It is assumed that the nonlinear functions driving the physical processes wi

Rafael Barbudo, Aurora Ramírez, José Raúl Romero

Automatic workflow composition (AWC) is a relevant problem in automated machine learning (AutoML) that allows finding suitable sequences of preprocessing and prediction models together with their optimal hyperparameters. This problem can be solved using evolutionary algorithms and, in particular, grammar-guided genetic programming (G3P). Current G3P approach

Fabio Berra, Gladis Pradolini, Pablo Quijano

We prove mixed inequalities for the Hardy-Littlewood maximal function $M^{\rho,\sigma}$, where $\rho$ is a critical radius function and $\sigma\geq 0$. We also exhibit and prove an extension of Cruz-Uribe, Martell and P\'erez extrapolation result in \cite{CruzUribe-Martell-Perez} to the setting of Muckenhoupt weights associated to a critical radius function

Khalil Sabri, Célia Djilali, Guillaume-Alexandre Bilodeau, Nicolas Saunier

Urban traffic environments present unique challenges for object detection, particularly with the increasing presence of micromobility vehicles like e-scooters and bikes. To address this object detection problem, this work introduces an adapted detection model that combines the accuracy and speed of single-frame object detection with the richer features offer

Garima Chhikara, Anurag Sharma, Kripabandhu Ghosh, Abhijnan Chakraborty

Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources required for fine-tuning a model. Fairness in LLMs helps ensure inclusivity, equal representation based on factors such as race, gender and promotes responsible AI deployment. As the

Maxim Nazarov

We study in detail the Yangian of the periplectic Lie superalgebra. For this Yangian we verify an analogue of the Poincar\'e-Birkhoff-Witt Theorem. Moreover we introduce a family of free generators of the centre of this Yangian.

Álvaro M. Alhambra, Ángela Capel, Paul Gondolf, Alberto Ruiz-de-Alarcón

We show that spin chains in thermal equilibrium have a correlation structure in which individual regions are strongly correlated at most with their near vicinity. We quantify this with alternative notions of the conditional mutual information, defined through the so-called Belavkin-Staszewski relative entropy. We prove that these measures decay superexponent

Yair Mulian

For almost 75 years, the general solution for the Schr\"odinger equation was assumed to be generated by an exponential or a time-ordered exponential known as the Dyson series. We study the unitarity of a solution in the case of a singular Hamiltonian and provide a new methodology that is not based on the assumption that the underlying space is $L^{2}(\mathbb

Crystal Qian, James Wexler

Although recent developments in generative AI have greatly enhanced the capabilities of conversational agents such as Google's Gemini (formerly Bard) or OpenAI's ChatGPT, it's unclear whether the usage of these agents aids users across various contexts. To better understand how access to conversational AI affects productivity and trust, we conducted a mixed-

A. I. Hernández-Juárez, R. Gaitán, G. Tavares-Velasco

An analysis of the off-shell $H^\ast\rightarrow ZZ \rightarrow \overline{\ell}_1\ell_1\overline{\ell}_2\ell_2$ decay width is presented for both unpolarized and polarized $Z$ gauge bosons in the scenario with the most general $H^*ZZ$ vertex function, which is given in terms of two $CP$-even ($\hat b_Z$ and $\hat c_Z$) and one $CP$-odd ($\tilde b_Z$) anomalou

Wentao Zhu, Zhining Zhang, Yizhou Wang

Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social reasoning. While Large Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms underlying these capabilities remain elusive. In this study, we discover that it is possible to linearly decode the belief status

Qin Zhang, Xiaowei Li, Jiexin Lu, Liping Qiu

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are

Abbas Tinwala, Ashish Narang, Subhendra Mohanty, Sukanta Panda

Investigating the thermal inflationary model, we introduce stochastic effects, incorporating a cutoff parameter $\sigma$ which distinguishes between quantum and classical modes. Testing the model against Planck 2018 data, we observe a preference for a non-zero $\sigma$ at least at 68\% C.L., suggesting the classicalization of most modes and providing a theor

Xun Huang, Hai Wu, Xin Li, Xiaoliang Fan

LiDAR-based 3D object detection models have traditionally struggled under rainy conditions due to the degraded and noisy scanning signals. Previous research has attempted to address this by simulating the noise from rain to improve the robustness of detection models. However, significant disparities exist between simulated and actual rain-impacted data point

Andrea Stefani, Agnese Giacomino, Sara Morandi, Andrea Marchetti

This work presents an original concept directed to implement an unconventional methodology where a device is produced by integrating a solid-state circuitry concept and an electrochemistry cell. In our experimental system an organic semiconductor, (PCPDT-BT), serves both as the gate and working electrode. Gating is obtained via electrochemical polarization e

Giulio Biroli, Tony Bonnaire, Valentin de Bortoli, Marc Mézard

Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been trained optimally. Our analysis reveals three distinct dynamical regimes during the backward generative diffusion process. The generative dynamics, starting from pure noise, encou

Zhihao Zhang, Shengcao Cao, Yu-Xiong Wang

The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However, even though the image and language representations have been aligned by cross-modal models like CLIP, we fi

Om Roy, Yashar Moshfeghi, Agustin Ibanez, Francisco Lopera

Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows.

Jun Yang

We propose a global Jacquet-Langlands correspondence for the modules over the von Neumann algebras of $S$-arithmetic subgroups of $\rm{GL}(2)$ and of a quaternion algebra $D$, which are both defined over a totally real number field $F$. If a representation $\pi'=\otimes\pi'_v$ of $D^{\times}(\mathbb{A}_F)$ corresponds to a representation $\pi=\otimes \pi_v$

Anika Tabassum Raisa, Syed Nazmus Sakib, Mohammad Jobayer Hossain, Kaiser Ahmed Rocky

The advanced multijunction solar cell (MJSC) has emerged as a frontrunner in photovoltaic literature due to its superior photoconversion efficiency (PCE) owing to its complex fabrication procedure and high costs. This article aims to systematically review the advancements of III-V MJSCs by focusing on computational modelling and experimental fabrication meth

Mahya Ramezani, Jose Luis Sanchez-Lopez

The integration of Unmanned Aerial Vehicles (UAVs) into Search and Rescue (SAR) missions presents a promising avenue for enhancing operational efficiency and effectiveness. However, the success of these missions is not solely dependent on the technical capabilities of the drones but also on their acceptance and interaction with humans on the ground. This pap

Michał Boczek, Tomasz Józefiak, Marek Kaluszka, Andrzej Okolewski

In this paper, we propose a new generalization of the classical discrete Choquet integral to the multivalued framework in terms of an admissible order that refines the natural partial order on the considered value set. The new Choquet-like operator takes as input a finite number of values of a given type, in particular real numbers, intervals, and vectors, a

Adolfo Ballester-Bolinches, Ramón Esteban-Romero, Maria Ferrara, Vicent Pérez-Calabuig

The aim of this paper is to study supersoluble skew braces, a class of skew braces that encompasses all finite skew braces of square-free order. It turns out that finite supersoluble skew braces have Sylow towers, and that in an arbitrary supersoluble skew brace $B$ many relevant skew brace-theoretical properties are easier to identify: for example, a centra

Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun

Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in f

Benedikt Barthel Sorensen, Alexis Charalampopoulos, Shixuan Zhang, Bryce Harrop

Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully-resolved climate simulations remain computationally intractable, policy makers must rely on coarse-models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored "sub-grid" sca

Tommaso Cortopassi, Vladimir Georgiev

We study the existence of positive solutions of a particular elliptic system in $\mathbb{R}^3$ composed of two coupled non linear stationary Schr\"odinger equations (NLSEs), that is $-\epsilon^2 \Delta u + V(x) u= h_v(u,v), - \epsilon^2 \Delta v + V(x) v=h_u (u,v)$. Under certain hypotheses on the potential $V$ and the non linearity $h$, we manage to prove t

Andrés Anabalón, Horatiu Nastase, Marcelo Oyarzo

We find AdS soliton solutions in 5-dimensional gauged supergravity, obtained from the $S^5$ compactification of type IIB, with a dilaton saturating the Breitenlohner-Freedman bound. The solutions depend on the value of the periodicity of an $S^1$ cycle and the boundary values for two $U(1)$ gauge fields, and give a scalar VEV in the dual field theory. At cer

Marco Drewes, Yannis Georis, Michael Klasen, Luca Paolo Wiggering

We compute the dominant QED correction to the neutrino-electron interaction rate in the vicinity of neutrino decoupling in the early universe, and estimate its impact on the effective number of neutrino species $N_{\rm eff}$ in cosmic microwave background anisotropy observations. We find that the correction to the interaction rate is at the sub-percent level

Conor John Williams, James Elliott

Fully-strict fork-join parallelism is a powerful model for shared-memory programming due to its optimal time scaling and strong bounds on memory scaling. The latter is rarely achieved due to the difficulty of implementing continuation stealing in traditional High Performance Computing (HPC) languages -- where it is often impossible without modifying the comp

Alyssa Hwang, Kalpit Dixit, Miguel Ballesteros, Yassine Benajiba

We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents. To create NewsQs, we augment a traditional multi-document summarization dataset with questions automatically generated by a T5-Large model fine-tuned on FAQ-style news articles from the News On the Web corpus. We show that fine-tuning a model with contro

Mridusmita Das, Madhurjya P. Bora

In this work, an 1D electrostatic hybrid-Particle-in-Cell-Monte-Carlo-Collisionh-PIC-MCC) code is used to study the response of a plasma to a moving, external, charged perturbation (debris). We show that the so-called pinned solitons can form only under certain specific conditions through a turbulent regime of the ion-ion counter-streaming electrostatic inst

Georg Manten, Cecilia Casolo, Emilio Ferrucci, Søren Wengel Mogensen

Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance. Such processes can often be accurately modeled via stochastic differential equations (SDEs), which naturally imply causal relationships via "which variables enter the differential of which o

Lanyun Zhu, Deyi Ji, Tianrun Chen, Peng Xu

Despite achieving rapid developments and with widespread applications, Large Vision-Language Models (LVLMs) confront a serious challenge of being prone to generating hallucinations. An over-reliance on linguistic priors has been identified as a key factor leading to these hallucinations. In this paper, we propose to alleviate this problem by introducing a no

A. V. Belitsky, L. V. Bork, J. M. Grumski-Flores, V. A. Smirnov

We study the form factor of the lowest component of the stress-tensor multiplet away from the origin of the moduli space in the spontaneously broken, aka Coulomb, phase of the maximally supersymmetric Yang-Mills theory for decay into three massive W-bosons. The calculations are done at two-loop order by deriving and solving canonical differential equations i

Sneha N. Naik, Elsa D. Angelini, R. Graham Barr, Norrina Allen

High-resolution full lung CT scans now enable the detailed segmentation of airway trees up to the 6th branching generation. The airway binary masks display very complex tree structures that may encode biological information relevant to disease risk and yet remain challenging to exploit via traditional methods such as meshing or skeletonization. Recent clinic

Anirudh Sivakumar, Pankaj Kumar Mishra, Ahmad A. Hujeirat, Paulsamy Muruganandam

We present the simulation results of merging harmonically confined rotating Bose-Einstein condensates in two dimensions. Merging of the condensate is triggered by positioning the rotation axis at the trap minima and moving both condensates towards each other while slowly ramping their rotation frequency. We analyze the dynamics of the merged condensate by le

Hisham Sati, Urs Schreiber

Flux- and charge-quantization laws for higher gauge fields of Maxwell type -- e.g. the common electromagnetic field (the "A-field") but also the B-, RR-, and C-fields considered in string/M-theory -- specify non-perturbative completions of these fields by encoding their solitonic behaviour and hence by specifying the discrete charges carried by the individua

James E. Smith

An agent employing reinforcement learning takes inputs (state variables) from an environment and performs actions that affect the environment in order to achieve some objective. Rewards (positive or negative) guide the agent toward improved future actions. This paper builds on prior clustering neural network research by constructing an agent with biologicall

Alfie Robinson, Richard A. Booth, James E. Owen

We present a new 2D axisymmetric code, cuDisc, for studying protoplanetary discs, focusing on the self-consistent calculation of dust dynamics, grain size distribution and disc temperature. Self-consistently studying these physical processes is essential for many disc problems, such as structure formation and dust removal, given that the processes heavily de

Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós

One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2010). In this problem, we aim to track the number of events that occur over time, while hiding the existence of every single event. More specifically, in every time step $t\in[T]$ we learn