December 2024 arXiv papers — page 9

João Bravo, Jacopo Bono, Pedro Saleiro, Hugo Ferreira

Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage the scale and complexity of these graph datasets, machine learning (ML) approaches have become essential. However, CTDGs pose challenges for ML because traditional static graph me

Chengcheng Han, Hong-Jian He, Linghao Song, Jingtao You

The tiny neutrino masses are most naturally explained by seesaw mechanism through singlet right-handed neutrinos, which can further explain the matter-antimatter asymmetry in the Universe. In this Letter, we propose a new approach to study cosmological signatures of neutrino seesaw through the interaction between inflaton and right-handed neutrinos that resp

Zhiyu Tan, WenXu Qian, Hesen Chen, Mengping Yang

Diffusion models have established themselves as the de facto primary paradigm in visual generative modeling, revolutionizing the field through remarkable success across various diverse applications ranging from high-quality image synthesis to temporal aware video generation. Despite these advancements, three fundamental limitations persist, including 1) disc

Camille Aron, Manas Kulkarni

We study how spatiotemporal chaos in dynamical systems can be controlled by stochastically returning them to their initial conditions. Focusing on discrete nonlinear maps, we analyze how key measures of chaos -- the Lyapunov exponent and butterfly velocity, which quantify sensitivity to initial perturbations and the ballistic spread of information, respectiv

Wanglong Lu, Jikai Wang, Tao Wang, Kaihao Zhang

Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements in face restoration but often fall short

Shilpak Banerjee, Divya Khurana, Philipp Kunde

In both smooth and analytic categories, we construct examples of diffeomorphisms of topological entropy zero with intricate ergodic properties. On any smooth compact connected manifold of dimension 2 admitting a nontrivial circle action, we construct a smooth diffeomorphism whose differential is weakly mixing with respect to a smooth measure in the projectiv

Isaac Neal, Steve Shkoller, Vlad Vicol

We establish an infinite hierarchy of finite-time gradient catastrophes for smooth solutions of the 1D Euler equations of gas dynamics with non-constant entropy. Specifically, for all integers $n\geq 1$, we prove that there exist classical solutions, emanating from smooth, compressive, and non-vacuous initial data, which form a cusp-type gradient singularity

Guosheng Fu, Brendan Keith, Rami Masri

We introduce the first-order system proximal Galerkin (FOSPG) method, a locally mass-conserving, hybridizable finite element method for solving heterogeneous anisotropic diffusion and obstacle problems. Like other proximal Galerkin methods, FOSPG finds solutions by solving a recursive sequence of smooth, discretized, nonlinear subproblems. We establish the w

Michael J. Feldman, Theodor Misiakiewicz, Elad Romanov

This work studies estimation of sparse principal components in high dimensions. Specifically, we consider a class of estimators based on kernel PCA, generalizing the covariance thresholding algorithm proposed by Krauthgamer et al. (2015). Focusing on Johnstone's spiked covariance model, we investigate the "critical" sparsity regime, where the spa

Chia-Yu Hung, Navonil Majumder, Zhifeng Kong, Ambuj Mehrish

We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models lies in the difficulty of creating preference pairs, as TTA lacks structured mechanisms like verifiable rewards or gold-standard

Heejin Choi, Minji Lee, Chang Hyeong Lee, Jaeho Yang

In this work, we propose a new method for ordering nets during the process of layer assignment in global routing problems. The global routing problems that we focus on in this work are based on routing problems that occur in the design of substrates in multilayered semiconductor packages. The proposed new method is based on machine learning techniques and we

Prabal Saxena, Zeynep Dilli, Peter Snapp, Tilak Hewagama

In this paper, we detail recent and current work that is being carried out to fabricate and advance novel SiC UV instrumentation that is aimed at enabling more sensitive measurements across numerous disciplines, with a short discussion of the promise such detectors may hold for the Habitable Worlds Observatory. We discuss SiC instrument development progress

Gautier Dagan, Frank Keller, Alex Lascarides

We present Plancraft, a multi-modal evaluation dataset for LLM agents. Plancraft has both a text-only and multi-modal interface, based on the Minecraft crafting GUI. We include the Minecraft Wiki to evaluate tool use and Retrieval Augmented Generation (RAG), as well as a handcrafted planner and Oracle Retriever, to ablate the different components of a modern

Tijana Radenkovic, Marko Vojinovic

We study the path integral quantization of the topological 3BF theory, whose gauge symmetry is described by a 3-group. This theory is relevant for the quantization of general relativity coupled to Standard Model of elementary particles. We explicitly construct a state sum corresponding to the discretized path integral of a 3BF action. Being a topological inv

Antonino Ficarra, Ayesha Asloob Qureshi

Let $S=K[x_1,\dots,x_n]$ be the polynomial ring over a field $K$, and let $I\subset S$ be a monomial ideal. In this paper, we introduce the $i$th \textit{homological shift algebras} $\text{HS}_i(\mathcal{R}(I))=\bigoplus_{k\ge1}\text{HS}_i(I^k)$ of $I$. If $I$ has linear powers, these algebras have the structure of a finitely generated bigraded module over t

Tun-Chieh Lou, Chung-Che Wang, Jyh-Shing Roger Jang, Henian Li

This paper proposes the use of iterative transfer learning applied to deep learning models for side-channel attacks. Currently, most of the side-channel attack methods train a model for each individual byte, without considering the correlation between bytes. However, since the models' parameters for attacking different bytes may be similar, we can leverage t

V. Del Duca, C. Duhr, L. Fekésházy, F. Guadagni

We present NNLOCAL, a proof-of-concept parton-level Monte Carlo program implementing the extension of the completely local subtraction scheme CoLoRFulNNLO to the case of color-singlet production in hadron collisions. We have built general local subtraction terms that regularize all single and double unresolved infrared singularities in real radiation phase s

Joseph P. Molnar, Jiangnan Xia, Rui Zhang, Samuel J. Grauer

This paper presents a novel neural-implicit approach to laser absorption tomography (LAT) with an experimental demonstration. A coordinate neural network is used to represent thermochemical state variables as continuous functions of space and time. Unlike most existing neural methods for LAT, which rely on prior simulations and supervised training, our appro

Nima Arkani-Hamed, Hadleigh Frost, Giulio Salvatori

Scattering amplitudes for colored theories have recently been formulated in a new way, in terms of curves on surfaces. In this note we describe a canonical set of functions we call surface functions, associated to all orders in the topological expansion, that are naturally suggested by this point of view. Surface functions are generating functions for all in

Jun-Xiang Zhang, Yuan-De Jin, Chu-Dan Qiu, Wen-Long Ma

Metastability is a ubiquitous phenomenon in non-equilibrium physics and classical stochastic dynamics.It arises when the system dynamics settles in long-lived states before eventually decaying to true equilibria. Remarkably, it has been predicted that quantum metastability can also occur in continuous-time and discrete-time open quantum dynamics. However, th

Keisha J. Cook, Nathan Rayens, Linh Do, Christine K. Payne

The movement of intracellular cargo transported by molecular motors is commonly marked by switches between directed motion and stationary pauses. The predominant measure for assessing movement is effective diffusivity, which predicts the mean-squared displacement of particles over long time scales. In this work, we consider an alternative analysis regime tha

Dmitri Bykov, Viacheslav Krivorol, Andrew Kuzovchikov

We consider quantum mechanical systems of spin chain type, with finite-dimensional Hilbert spaces and $\mathcal{N}=2$ or $\mathcal{N}=4$ supersymmetry, described in $\mathcal{N}=2$ superspace in terms of nonlinear chiral multiplets. We prove that they are natural truncations of 1D sigma models, whose target spaces are $\mathsf{SU}(n)$ (co)adjoint orbits. As

Korakit Seemakhupt, Sihang Liu, Samira Khan

Deploying Retrieval Augmented Generation (RAG) on resource-constrained edge devices is challenging due to limited memory and processing power. In this work, we propose EdgeRAG which addresses the memory constraint by pruning embeddings within clusters and generating embeddings on-demand during retrieval. To avoid the latency of generating embeddings for larg

Luis G. Crespo, Bret Stanford, Natalia Alexandrov

This paper proposes risk-averse and risk-agnostic formulations to robust design in which solutions that satisfy the system requirements for a set of scenarios are pursued. These scenarios, which correspond to realizations of uncertain parameters or varying operating conditions, can be obtained either experimentally or synthetically. The proposed designs are

Christos Petridis

Text classification is a very common task nowadays and there are many efficient methods and algorithms that we can employ to accomplish it. Transformers have revolutionized the field of deep learning, particularly in Natural Language Processing (NLP) and have rapidly expanded to other domains such as computer vision, time-series analysis and more. The transf

X. Song, C. Dalfó, M. A. Fiol, S. Zhang

Given a graph $G=(V,E)$ on $n$ vertices and an integer $k$ between 1 and $n-1$, the $k$-token graph $F_k(G)$ has vertices representing the $k$-subsets of $V$, and two vertices are adjacent if their symmetric difference is the two end-vertices of an edge in $E$. Using the theory of Markov chains of random walks and the interchange process, it was proved that

Seonguk Yoo, Aljaz Zalar

When the algebraic variety associated with a truncated moment sequence is finite, solving the moment problem follows a well-defined procedure. However, moment problems involving infinite algebraic varieties are more complex and less well-understood. Recent studies suggest that certain bivariate moment sequences can be transformed into equivalent univariate s

Federico Zullo, Maria Grazia Naso, Elena Vuk

We present certain general structures related to the solutions of Painlev\'e equation II and to the solutions of the differential equation satisfied by the corresponding Hamiltonian equations, together with the tau functions. By taking advantage of the B\"acklund transformations we find different explicit rational expressions linking the solutions of Painlev

Mikkel Abrahamsen, Kevin Buchin, Maike Buchin, Linda Kleist

We study two well-known reconfiguration problems. Given a start and a target configuration of geometric objects in a polygon, we wonder whether we can move the objects from the start configuration to the target configuration while avoiding collisions between the objects and staying within the polygon. Problems of this type have been considered since the earl

Mingxuan Xiao, Yan Xiao, Shunhui Ji, Hanbo Cai

Benefiting from the advancements in LLMs, NLP software has undergone rapid development. Such software is widely employed in various safety-critical tasks, such as financial sentiment analysis, toxic content moderation, and log generation. Unlike traditional software, LLM-based NLP software relies on prompts and examples as inputs. Given the complexity of LLM

Mahir Labib Dihan, Mohammed Eunus Ali, Md Rizwan Parvez

Mapping and navigation services like Google Maps, Apple Maps, OpenStreetMap, are essential for accessing various location-based data, yet they often struggle to handle natural language geospatial queries. Recent advancements in Large Language Models (LLMs) show promise in question answering (QA), but creating reliable geospatial QA datasets from map services

Luciana Angluli, Simone Ferrari, Luca Lorenzi

We study Cauchy problems associated to elliptic operators acting on vector-valued functions and coupled up to the first-order. We prove pointwise estimates for the spatial derivatives of the semigroup associated to these problems in the space of bounded and continuous functions over $\R^d$. Consequently, we deduce relevant regularity results both in H\"older

Shu Luo

In this study, we investigate the thermodynamic law of accelerating and rotating black hole described by ro- tating C-metric, as well as holography properties in Nariai limit, which are related to Nariai-CFT and Kerr-CFT correspondence. In order to achieve this goal we define a regularized Komar mass with physical interpretation of varying the horizon area f

David Green, Yoyo Jiang, Sean Sanford

Non-split Real Tambara-Yamagami categories are a family of fusion categories over the real numbers that were recently introduced and classified by Plavnik, Sanford, and Sconce. We consider which of these categories admit braidings, and classify the resulting braided equivalence classes. We also prove some new results about the split real and split complex Ta

Levente Bodnár, Jared León, Xizhi Liu, Oleg Pikhurko

Let the tight $\ell$-cycle minus one edge $C_\ell^{3-}$ be the $3$-graph on $\{1,\dots,\ell\}$ consisting of $\ell-1$ consecutive triples in the cyclic order. We show that, for every $\ell\ge 5$ not divisible by $3$, the Tur\'an density of $C_{\ell}^{3-}$ is $1/4$ and also prove some finer structure results. This proves a conjecture of Mubayi--Sudakov--Pikhu

Ali Taqi, Karel Adámek, Quentin Gueuning, Maciej Serylak

With the emergence of new radio telescopes promising larger fields of view at lower observation frequencies (e.g., SKA), addressing direction-dependent effects (DDE) (e.g., direction-specific beam responses), polarisation leakage, and pointing errors has become all the more important. Be it through A-projection or otherwise, addressing said effects often req

Nicola Messina, Lucia Vadicamo, Leo Maltese, Claudio Gennaro

Recent advancements in deep learning have significantly enhanced content-based retrieval methods, notably through models like CLIP that map images and texts into a shared embedding space. However, these methods often struggle with domain-specific entities and long-tail concepts absent from their training data, particularly in identifying specific individuals

Bobo Hua, Yang Shen

In this paper, we study the Steklov eigenvalue of a Riemannian manifold (M, g) with smooth boundary. For compact M , we establish a Cheeger-type inequality for the first Steklov eigenvalue by the isocapacitary constant. For non-compact M , we estimate the bottom of the spectrum of the Dirichlet-to-Neumann operator by the isocapacitary constant.

Sakura Schafer-Nameki

These proceedings discuss some of the highlights of recent research in Formal Theory. The topics covered range from recent progress in scattering amplitudes, quantum gravity constraints on effective field theories, AdS/CFT, flat space holography, to generalized symmetries.

Joonwon Jang, Jaehee Kim, Wonbin Kweon, Seonghyeon Lee

Large Language Models (LLMs) rely on generating extensive intermediate reasoning units (e.g., tokens, sentences) to enhance final answer quality across a wide range of complex tasks. While this approach has proven effective, it inevitably increases substantial inference costs. Previous methods adopting token-level reduction without clear criteria result in p

Kayna L. Mendoza, Haoyang Ni, Georgios Varnavides, Miaofang Chi

Magnetic materials phase reconstruction from Lorentz transmission electron microscopy (LTEM) measurements has traditionally been achieved using longstanding methods such as off-axis holography (OAH) and the transport-of-intensity equation (TIE). Amidst the increase in access to processing power and the development of advanced algorithms, phase retrieval of n

Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi, Takamitsu Matsubara

This paper investigates a novel nonlinear update rule based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in the standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without no bias. On the other hand, the recent biological studies revealed that there are non

Yiqing Gu, Yimeng Gu, Feiyang Liu, Seiko Ohira-Kawamura

Materials manifesting the Kitaev model, characterized by bond-dependent interactions on a honeycomb lattice, can host exotic phenomena like quantum spin liquid states and topological magnetic excitations. However, finding such materials remains a formidable challenge. Here, we report high-resolution inelastic neutron scattering measurements performed on VI3,

Robin Rajamäki, Piya Pal

This paper investigates array geometry and waveform design for integrated sensing and communications (ISAC) employing sensor selection. We consider ISAC via index modulation, where various subsets of transmit (Tx) sensors are used for both communications and monostatic active sensing. The set of Tx subarrays make up a codebook, whose cardinality we maximize

Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou, Zeng-Guang Hou

Offline preference-based reinforcement learning (PbRL) provides an effective way to overcome the challenges of designing reward and the high costs of online interaction. However, since labeling preference needs real-time human feedback, acquiring sufficient preference labels is challenging. To solve this, this paper proposes a offLine prEference-bAsed RL wit

Runshi Zhou, Fang Zhang, Linghang Kong, Feng Wu

As quantum chips scale up for large-scale computation, hardware defects become inevitable and must be carefully addressed. In this work, we introduce Halma, a defect mitigation technique empowered by an expanded native gate set that incorporates the iSWAP gate alongside the conventional CNOT gate. Halma emerges as a supplementary technique within the defect

Bert Lindenhovius, Vladimir Zamdzhiev

We show that the category OS of operator spaces, with complete contractions as morphisms, is locally countably presentable. This result, together with its symmetric monoidal closed structure with respect to the projective tensor product of operator spaces, implies the existence of cofree (cocommutative) coalgebras with respect to the projective tensor produc

David Blanco-Mulero, Yifei Dong, Julia Borras, Florian T. Pokorny

Robotic grasp and manipulation taxonomies, inspired by observing human manipulation strategies, can provide key guidance for tasks ranging from robotic gripper design to the development of manipulation algorithms. The existing grasp and manipulation taxonomies, however, often assume object rigidity, which limits their ability to reason about the complex inte

Erik Van Dyke, James Eills, Kirill Sheberstov, John Blanchard

Fundamental weak interactions have been shown to violate parity in both nuclear and atomic systems. However, observation of parity violation in a molecular system has proven an elusive target. Nuclear spin dependent contributions of the weak interaction are expected to result in energetic differences between enantiomers manifesting in nuclear magnetic resona

Jingyuan Ma, Rui Li, Zheng Li, Lei Sha

Recently, preference optimization methods such as DPO have significantly enhanced large language models (LLMs) in wide tasks including dialogue and question-answering. However, current methods fail to account for the varying difficulty levels of training samples during preference optimization, leading to mediocre performance in tasks with high accuracy requi

Siyuan Fang, Kaijing Ma, Tianyu Zheng, Xinrun Du

Large language models (LLMs) demonstrate exceptional performance across a variety of tasks, yet they are often affected by hallucinations and the timeliness of knowledge. Leveraging knowledge graphs (KGs) as external knowledge sources has emerged as a viable solution, but existing methods for LLM-based knowledge graph question answering (KGQA) are often limi

Yang Li, Yuri Mishin

We report on molecular dynamics simulations of the atomic structure and diffusion processes at Al(110)/Si(001) interphase boundary created by simulated vapor deposition of Al(Si) alloy onto Si(001) substrate. An array of parallel misfit dislocations of both full and partial types is observed at the interface. Si atoms segregate to the misfit dislocations, wi

Yichao Fu, Junda Chen, Siqi Zhu, Zheyu Fu

Test-time reasoning algorithms such as chain-of-thought, self-consistency, and MCTS enhance LLM problem-solving but can wastefully generate many tokens without improving accuracy. At the same time, we observe that these algorithms exhibit answer stabilization: their intermediate solutions often cease to change after a certain point, and further investment of

Qi Zhan, Xing Hu, Xin Xia, Shanping Li

Deep learning operators are fundamental components of modern deep learning frameworks. With the growing demand for customized operators, it has become increasingly common for developers to create their own. However, designing and implementing operators is complex and error-prone, due to hardware-specific optimizations and the need for numerical stability. Th

Andrey V. Gorbach

Equidistant 1D arrays of thin film lithium niobate waveguides can exhibit non-trivial topology due to a specific interplay between inter- and intra-modal couplings of two families of guided modes. In this work we analyze two-colour spatial solitons, emerging due to $\chi_2$ nonlinear interactions between the modes of non-trivial topology in the fundamental h

Jean-Thibaut Spaniol, Olivia Chitarra, Olivier Pirali, Marie-Aline Martin-Drumel

The methoxy radical, CH$_3$O, has long been studied experimentally and theoretically by spectroscopists because it displays a weak Jahn-Teller effect in its electronic ground state, combined with a strong spin-orbit interaction. In this work, we report an extension of the measurement of the pure rotational spectrum of the radical in its vibrational ground st

Yosef Verbin, Beyhan Pulice, Ali Övgün, Hyat Huang

Inspired by the so-called Palatini formulation of General Relativity and of its modifications and extensions, we consider an analogous formulation of the dynamics of a self-interacting gauge field which is determined by non-linear extension of Maxwell's theory, usually known as nonlinear electrodynamics. In this first order formalism the field strength and t

Xingwei Hu, Xinjie Dai, Aiguo Xiao

To construct positivity-preserving numerical methods, a vast majority of existing works employ transformation techniques such as the Lamperti transformation or logarithmic transformation. However, using these techniques often leads to the transformed stochastic differential equations (SDEs) not meeting the global monotonicity condition, particularly in multi

Mohamed Djilani, Salah Ghamizi, Maxime Cordy

Although adversarial robustness has been extensively studied in white-box settings, recent advances in black-box attacks (including transfer- and query-based approaches) are primarily benchmarked against weak defenses, leaving a significant gap in the evaluation of their effectiveness against more recent and moderate robust models (e.g., those featured in th

Dmitry Chulkov, Ivan Strakhov, Boris Safonov

The Pleiades is the most prominent open star cluster visible from Earth and an important benchmark for simple stellar populations, unified by common origin, age, and distance. Binary stars are its essential ingredient, yet their contribution remains uncertain due to heavy observational biases. A resolved multiplicity survey was conducted for a magnitude-limi

Seok-Jin Kang, Young Rock Kim, Bolun Tong

We categorify a class of quantum groups associated with quivers, possibly with loops, by constructing the corresponding Khovanov-Lauda-Rouquier algebras (KLR) algebras $R$. We prove that the indecomposable projective $R$-modules realize the canonical basis of the negative part $U^-$ of the quantum group. Moreover, for $\Lambda \in P^+$, the cyclotomic KLR al

Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu, Kaize Ding

We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design. We first pre-train a language model using millions of antibody sequence data. Then, we employ the learned representations to guide the training of a diffusion model for joint optimization over both sequence and structure of antibodies.

G. Barca, E. Giovannetti

We analyze the anisotropic Bianchi models, and in particular the Bianchi Type IX known as the Mixmaster universe, where the Misner anisotropic variables obey Deformed Commutation Relations inspired by Quantum Gravity theories. We consider three different deformations, two of which have been able to remove the initial singularity similarly to Loop Quantum Cos

Fengxing Zhu

We consider the $r$-neighbor bootstrap percolation process on the graph with vertex set $V=\{0,1\}^n$ and edges connecting the pairs at Hamming distance $1,2,\dots,k$, where $k\ge 2$. We find asymptotics of the critical probability of percolation for $r=2,3$. In the deterministic setting, we obtain several results for the size of the smallest percolating set

Joris Bekkers

Pressing is a fundamental defensive strategy in football, characterized by applying pressure on the ball owning team to regain possession. Despite its significance, existing metrics for measuring pressing often lack precision or comprehensive consideration of positional data, player movement and speed. This research introduces an innovative framework for qua

Ilmar Gahramanov

This short review is based on the lecture given by the author at the Feza G\"ursey Physics Days School 2024. Here we briefly review analytic solutions (in particular, the Erler-Schnabl solution) of open cubic string field theory for the tachyon vacuum.

Shanqing Yu, Meng Zhou, Jintao Zhou, Minghao Zhao

Evolutionary computing, particularly genetic algorithm (GA), is a combinatorial optimization method inspired by natural selection and the transmission of genetic information, which is widely used to identify optimal solutions to complex problems through simulated programming and iteration. Due to its strong adaptability, flexibility, and robustness, GA has s

Yaqing Xy Wang, József Zsolt Bernád

We provide an analytical formula for the volume ratio between bipartite X-states with positive partial transpose and all bipartite X-states. The result applies to arbitrary $m \times n$-bipartite systems and the volume expressions are derived with respect to the Hilbert-Schmidt measure.

Hai-Ming Cao, Bin Li

The molecular dynamics (MD) simulation technique has been widely used in complex systems, but the accessible time scale is limited due to the requirement of small integration timesteps. Here, we propose a novel method, named Exploratory dynamics Sampling with recursion (EdSr), inspired by ordinary differential equation and Taylor expansion formula, which ena

Fangwei Zhong, Kui Wu, Churan Wang, Hao Chen

We introduce UnrealZoo, a collection of over 100 photo-realistic 3D virtual worlds built on Unreal Engine, designed to reflect the complexity and variability of open-world environments. We also provide a rich variety of playable entities, including humans, animals, robots, and vehicles for embodied AI research. We extend UnrealCV with optimized APIs and tool

Evgenii Kruzhkov, Alena Savinykh, Sven Behnke

Robotic applications require a comprehensive understanding of the scene. In recent years, neural fields-based approaches that parameterize the entire environment have become popular. These approaches are promising due to their continuous nature and their ability to learn scene priors. However, the use of neural fields in robotics becomes challenging when dea

Kaleb Debre, Tai Fei, Marius Pesavento

This paper presents a novel sequential estimator for the direction-of-arrival and polynomial coefficients of wideband polynomial-phase signals impinging on a sensor array. Addressing the computational challenges of Maximum-likelihood estimation for this problem, we propose a method leveraging random sampling consensus (RANSAC) applied to the time-frequency s

Zhengdong Li, Frederick Ziyang Hong, C. Patrick Yue

In recent years, Convolutional Neural Networks (CNNs) have been widely adopted in computer vision. Complex CNN architecture running on CPU or GPU has either insufficient throughput or prohibitive power consumption. Hence, there is a need to have dedicated hardware to accelerate the computation workload to solve these limitations. In this paper, we accelerate

Shuai Wang

For the sake of reliability, the kernels of Interactive Theorem Provers (ITPs) are generally kept relatively small. On top of the kernel, additional symbols and inference rules are defined. This paper presents an analysis of how kernel extension reduces the size of proofs and impacts proof checking.

Muhammad Umer, Eleftherios Mastorakis, Dimitris G. Angelakis

Classical optimization is a cornerstone of the success of variational quantum algorithms, which often require determining the derivatives of the cost function relative to variational parameters. The computation of the cost function and its derivatives, coupled with their effective utilization, facilitates faster convergence by enabling smooth navigation thro

Q. Rumman Rahman, Igor Kladarić, Max-Emanuel Kern, Lukáš Lachman

Fock states of the quantum harmonic oscillator are fundamental to quantum sensing and information processing, serving as key resources for exploiting bosonic degrees of freedom. Here, we prepare high Fock states in a high-overtone bulk acoustic wave resonator (HBAR) by coupling it to a superconducting qubit and applying microwave pulses designed using quantu

A. A. Reynoso, G. Usaj, C. A. Balseiro

During the last years there has been much interest, and theoretical discussion, about the possibility to use spin-orbit coupling to control the carriers spins in two-dimensional semiconducting heterostructures. Spin polarization at the sample edges may occur as the response of systems with strong SO-coupling to an external transport current, an effect known

Andrew Warren

We study ``nonlocal diffusion equations'' of the form \[ \partial_{t}\frac{d\rho_{t}}{d\pi}(x)+\int_{X}\left(\frac{d\rho_{t}}{d\pi}(x)-\frac{d\rho_{t}}{d\pi}(y)\right)\eta(x,y)d\pi(y)=0\qquad(\dagger) \] where $X$ is either $\mathbb{R}^{d}$ or $\mathbb{T}^{d}$, $\pi$ is a probability distribution on $X$, and $\eta(x,y)$ is a ``transition kernel'' which may b

Johannes Anschütz, Arthur-César Le Bras, Lucas Mann

We develop a 6-functor formalism $\mathcal{D}_{[0,\infty)}(-)$ with $\mathbb{Z}_p$-linear coefficients on small v-stacks, and discuss consequences for duality and finiteness for pro-\'etale cohomology of rigid-analytic varieties of general pro-\'etale $\mathbb{Q}_p$-local systems as well as first examples motivated by a potential $p$-adic analog of Fargues-S

Valerio Digiorgio, Urban Senica, Paolo Micheletti, Mattias Beck

The development of photonic integrated components for terahertz has become an active and growing research field. Despite its numerous applications, several challenges are still present in hardware design. We demonstrate an on-chip active wavelength division multiplexer (WDM) operating at THz frequencies. The WDM architecture is based on an inverse design top

Johan Björklund, Georgios Dimitroglou Rizell

We show that any smooth one-dimensional link in the real projective three-plane is the fixed-point locus of a smooth symplectic surface in the complex projective three-plane which is invariant under complex conjugation. The degree of the surface can be taken to be either one or two, depending on the homology class of the link. In other words, there are no ob

Vinith Kumar Lakshmanan, Olivier Lemaire, Antonio Sciarretta

The main goal of Eco-Driving (ED) is to maximize energy efficiency. This study evaluates the energy gains of an ED system for an electric vehicle, obtained from a predictive optimal controller, in a real-world traffic scenario. To this end, a Visual driver Advisory System (VAS) in the form of a personal tablet is used to advise the driver to follow a target

Peng Jin, Hao Li, Li Yuan, Shuicheng Yan

Multimodal representation learning, with contrastive learning, plays an important role in the artificial intelligence domain. As an important subfield, video-language representation learning focuses on learning representations using global semantic interactions between pre-defined video-text pairs. However, to enhance and refine such coarse-grained global in

John H. Selby, Victoria J. Wright, Máté Farkas, Marcin Karczewski

The existence of indistinguishable quantum particles provides an explanation for various physical phenomena we observe in nature. We lay out a path for the study of indistinguishable particles in general probabilistic theories (GPTs) via two frameworks: the traditional GPT framework and the diagrammatic framework of process theories. In the first approach we

Tommaso Matteuzzi, Franco Bagnoli, Michele Baia, Stefano Iubini

We consider finite dynamical networks and define internal reliability according to the synchronization properties of a replicated unit or a set of units. If the states of the replicated units coincide with their prototypes, they are reliable; otherwise, if their states differ, they are anti-reliable. Quantification of reliability with the transversal Lyapuno

Yuan Mi, Pu Ren, Hongteng Xu, Hongsheng Liu

Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep learning models often lack interpretability, fail to obey intrinsic physics, and struggle to cope with the various domains. While geometry-based methods, e.g., graph neural networks (G

Andre Carnevali da Silva

Dimensional Resonance Theory proposes that gravity and fundamental forces can be interpreted as emergent phenomena arising from three-dimensional waves (3D) projected onto lower dimensions. To test the internal consistency of this proposal, we analyze the phi4 kink in (1+1) dimensions, an established topological defect with a well-known oscillation spectrum.

Liangping Ding, Cornelia Lawson, Philip Shapira

Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, the diffusion of GenAI publications acro

Tianxiang Peng, Liang Wang

The wear resistance of magnesium and its alloys is very poor due to their inherent low hardness and poor tribological properties. Improving the wear resistance of magnesium and its alloys plays a significant role in expanding their application range. Various surface treatments have been used to produce protective coatings or layers for solving this problem.

Qinbo Chen

This paper studies a perturbation problem given by the equation: \begin{equation*} H(x, d_xu_\lambda, \lambda u_\lambda(x))+\lambda V(x,\lambda)=c \quad \text{in $M$}, \end{equation*} where $M$ is a closed manifold and $\lambda>0$ is a perturbation parameter. The Hamiltonian $H(x,p,u):T^*M\times \mathbb{R}\to \mathbb{R}$ satisfies certain convexity, superlin

Feimin Huang, Guiqin Qiu, Yi Wang, Xiaozhou Yang

We investigate the large time behavior of solutions to the two-dimensional viscous Burgers equation $u_t+uu_x+uu_y=\Delta u$, toward a non-self-similar rarefaction wave of inviscid Burgers equation with two initial constant states, seperated by a curve $y=\varphi(x)$, and prove that the above 2D non-self-similar rarefaction wave is time-asymptotically stable

Shlomo Kashani

We present QuantumLLMInstruct (QLMMI), an innovative dataset featuring over 500,000 meticulously curated instruction-following problem-solution pairs designed specifically for quantum computing - the largest and most comprehensive dataset of its kind. Originating from over 90 primary seed domains and encompassing hundreds of subdomains autonomously generated

Ivan Sinev, Felix Ulrich Richter, Ivan Toftul, Nikita Glebov

Chiral metasurfaces provide invaluable tools capable of controlling structured light required for biosensing, photochemistry, holography, and quantum photonics. Here we suggest and realize a universal strategy for controlling the chiral response of resonant metasurfaces via the interplay of meta-atom geometry and lattice arrangements within all five possible

Chongxiao Li, Di Huang, Pengwei Jin, Tianyun Ma

Customized processors are attractive solutions for vast domain-specific applications due to their high energy efficiency. However, designing a processor in traditional flows is time-consuming and expensive. To address this, researchers have explored methods including the use of agile development tools like Chisel or SpinalHDL, high-level synthesis (HLS) from

Matan Ben-Tov, Mahmood Sharif

Dense embedding-based text retrieval$\unicode{x2013}$retrieval of relevant passages from corpora via deep learning encodings$\unicode{x2013}$has emerged as a powerful method attaining state-of-the-art search results and popularizing Retrieval Augmented Generation (RAG). Still, like other search methods, embedding-based retrieval may be susceptible to search-

Li Guo, Hongyu Xiang, Bin Zhang

This paper offers a Hopf algebraic interpretation of a functional equation of multiple zeta functions, motivated by the classical symmetry of the Riemann zeta function. Starting from the extended shuffle algebra that encodes multiple zeta values (MZVs) at integer arguments, we show that its subalgebra corresponding to nonpositive arguments carries a natural

Jannik Henze, Julia Lademann, Sebastian Becker-Genschow, André Bresges

The integration of artificial intelligence (AI) into education presents new opportunities for supporting learning processes. This study investigates the impact of AI-assisted versus traditional Excel-based data analysis on both learning outcomes and emotional-motivational responses in a physics education context. A custom GPT-based chatbot, ExperiMentor, was

J. A. Gracey

We renormalize Quantum Chromodynamics (QCD) when gauge fixed in the nonlinear Curci-Ferrari gauge to four loops in the modified minimal subtraction (MSbar) scheme. We reproduce the four loop QCD MSbar beta-function from the Slavnov-Taylor identity for this gauge which relates the coupling constant renormalization to the gluon, Faddeev-Popov ghost and gauge p

Ingvar Ziemann

We prove a Bernstein inequality for vector-valued self-normalized martingales. We first give an alternative perspective of the corresponding sub-Gaussian bound due to Abbasi-Yadkori et al. via a PAC-Bayesian argument with Gaussian priors. By instantiating this argument to priors drawn uniformly over well-chosen ellipsoids, we obtain a Bernstein bound.

Sagar Gautam, Manil T. Mohan

In this work, we consider the following 2D stochastic convective Brinkman-Forchheimer (SCBF) equations in a bounded smooth domain $\mathcal{O}$: \begin{align*} \mathrm{d}\boldsymbol{u}+\left[-\mu \Delta\boldsymbol{u}+(\boldsymbol{u}\cdot\nabla)\boldsymbol{u}+\alpha\boldsymbol{u}+\beta|\boldsymbol{u}|^{r-1}\boldsymbol{u}+\nabla p\right]\mathrm{d}t=\sqrt{\math