March 2024 arXiv papers — page 105

Zeyu Zhang, Junlin Han, Chenhui Gou, Hongdong Li

Blind image decomposition aims to decompose all components present in an image, typically used to restore a multi-degraded input image. While fully recovering the clean image is appealing, in some scenarios, users might want to retain certain degradations, such as watermarks, for copyright protection. To address this need, we add controllability to the blind

Andreas Bär, Neil Houlsby, Mostafa Dehghani, Manoj Kumar

Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen features are not modified during training. On the other hand, when networks are trained directly on images, data augmentation is a standard recipe

Ronghui Li, YuXiang Zhang, Yachao Zhang, Hongwen Zhang

We propose Lodge, a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture, and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffu

Xiaohan Wang, Yuhui Zhang, Orr Zohar, Serena Yeung-Levy

Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the human cognitive process for long-form video understanding, we emphasize interactive reasoning and planning over the ability to process lengthy visual inputs. We introduce a novel age

Stephanie Fu, Mark Hamilton, Laura Brandt, Axel Feldman

Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime. However, these features often lack the spatial resolution to directly perform dense prediction tasks like segmentation and depth prediction because models aggressively pool informati

Paula Belzig

Symmetries are of fundamental interest in many areas of science. In quantum information theory, if a quantum state is invariant under permutations of its subsystems, it is a well-known and widely used result that its marginal can be approximated by a mixture of tensor powers of a state on a single subsystem. Applications of this quantum de Finetti theorem ra

Barnabás Janzer, Richard Montgomery

The famous tree packing conjecture of Gy\'arf\'as from 1976 says that any sequence of trees $T_1,\ldots,T_n$ such that $|T_i|=i$ for each $i\in [n]$ packs into the complete $n$-vertex graph $K_n$. Packing even just the largest trees in such a sequence has proven difficult, with Bollob\'as drawing attention to this in 1995 by conjecturing that, for each $k$,

Marcos Matabuena, Ciprian M. Crainiceanu

Continuous glucose monitoring (CGM) is a minimally invasive technology that measures blood glucose every few minutes for weeks or months at a time. CGM data are often collected in the free-living environment and is strongly related to sleep, physical activity and meal intake. As the timing of these activities varies substantially within- and between-individu

Arhum Ansari, Pinak Banerjee, Prateksh Dhivakar, Sachin Jain

We study the conformal invariance of inflationary non-Gaussianities associated with scalar fluctuations in a non-Bunch-Davies initial state, known as the $\alpha$-vacuum, in single-field slow-roll inflation. The $\alpha$-vacuum is a one-parameter family of states, including the Bunch-Davies one, that preserves the conformal symmetry of inflationary dynamics

Vishnu Nair, Hanxiu 'Hazel' Zhu, Peize Song, Jizhong Wang

Video games are increasingly accessible to blind and low vision (BLV) players, yet many aspects remain inaccessible. One aspect is the joy players feel when they explore environments and make new discoveries, which is integral to many games. Sighted players experience discovery by surveying environments and identifying unexplored areas. Current accessibility

Lukas Litzba, Eric Kleinherbers, Jürgen König, Ralf Schützhold

We consider a fermionic quantum system exchanging particles with an environment at a fixed temperature and study its reduced evolution by means of a Redfield-I equation with time-dependent (non-Markovian) coefficients. We find that the description can be efficiently reduced to a standard-form Redfield-II equation, however, with a time-dependent effective bat

Anshul Gupta, Samy Tafasca, Arya Farkhondeh, Pierre Vuillecard

Gaze following and social gaze prediction are fundamental tasks providing insights into human communication behaviors, intent, and social interactions. Most previous approaches addressed these tasks separately, either by designing highly specialized social gaze models that do not generalize to other social gaze tasks or by considering social gaze inference a

Juan Maldacena

We review aspects of the Hartle-Hawking no boundary geometry in the context of slow roll inflation. We give an analytic approximation to the geometry and we explain the rationale for the proposal. We also explain why it gives a prediction for the curvature of the universe that is in disagreement with observations and give a quick review of proposed ways to r

Peng Rao, Alexander Mook, Johannes Knolle

We study two-dimensional $d$-wave altermagnetic metals taking into account the presence of substrate-induced Rashba spin-orbit coupling. We consider the altermagnet bandstructure using a 2D band Hamiltonian near the $\Gamma$ point under external magnetic field. It is shown that time-reversal-symmetry breaking due to altermagnetism, together with Rashba coupl

Tanumoy Dhar, David Saintillan

The dispersion of a passive colloid immersed in a bath of non-interacting and non-Brownian run-and-tumble microswimmers in two dimensions is analyzed using stochastic simulations and an asymptotic theory, both based on a minimal model of swimmer-colloid collisions characterized solely by frictionless steric interactions. We estimate the effective long-time d

Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen, Neil Sharma

Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault is laborious and time-consuming. To overcome this challenge, various fault localization tools have been developed. These

Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee

Humanoid robots hold great promise in assisting humans in diverse environments and tasks, due to their flexibility and adaptability leveraging human-like morphology. However, research in humanoid robots is often bottlenecked by the costly and fragile hardware setups. To accelerate algorithmic research in humanoid robots, we present a high-dimensional, simula

Nalina Vadakkayil, Massimiliano Esposito, Jan Meibohm

We explore the critical properties of the recently discovered finite-time dynamical phase transition in the non-equilibrium relaxation of Ising magnets after a temperature quench. The transition is characterized by a sudden switch in the relaxation dynamics and it occurs at a sharp critical time. While previous works have focused either on mean-field interac

Xiaofeng Wu, Jia Rao, Wei Chen

The advent of the Transformer architecture has propelled the growth of natural language processing (NLP) models, leading to remarkable achievements in numerous NLP tasks. Yet, the absence of specialized hardware like expansive GPU memory and high-speed interconnects poses challenges for training large-scale models. This makes it daunting for many users to ex

Markus Faulhuber, Irina Shafkulovska, Ilya Zlotnikov

We study Gabor frames with Hermite window functions. Gr\"ochenig and Lyubarskii provided a sufficient density condition for their frame sets, which leads to what we call the "safety region". For rectangular lattices and Hermite windows of order 4 and higher, we enlarge this safety region by providing new points on the boundary of this region. For this purpos

Umberto Straccia, Giovanni Casini

Knowledge Measures (KMs) aim at quantifying the amount of knowledge/information that a knowledge base carries. On the other hand, Belief Change (BC) is the process of changing beliefs (in our case, in terms of contraction, expansion and revision) taking into account a new piece of knowledge, which possibly may be in contradiction with the current belief. We

Xinxin Guo, Lucien Jezequel, Mathieu Padlewski, Hervé Lissek

Nonlinear topology has been much less inquired compared to its linear counterpart. Existing advances have focused on nonlinearities of limited magnitudes and fairly homogeneous types. As such, the realizations have rarely been concerned with the requirements for nonlinearity. Here we explore nonlinear topological protection by determining nonlinear rules and

Quentin Ansel

The partial trace operation is usually considered in composite quantum systems, to reduce the state on a single subsystem. This operation has a key role in the decoherence effect and quantum measurements. However, partial trace operations can be defined in more generic situations. In particular, it can be used to restrict a quantum state (for a single or sev

Raghavendra N. Bhat, Cristian Cobeli, Alexandru Zaharescu

We introduce and study a three-folded linear operator depending on three parameters that has associated a triangular number tilling of the plane. As a result the set of all triples of integers is decomposed in classes of equivalence organized in four towers of two-dimensional triangulations. We provide the full characterization of the represented integers be

Chenguang Wang, Ruoxi Jia, Xin Liu, Dawn Song

Pre-training image representations from the raw text about images enables zero-shot vision transfer to downstream tasks. Through pre-training on millions of samples collected from the internet, multimodal foundation models, such as CLIP, produce state-of-the-art zero-shot results that often reach competitiveness with fully supervised methods without the need

J. Daniel E. Yeo, Christopher E. Thomas, David J. Wilson

Elastic $S-$wave scattering of a charm meson with a light pseudoscalar meson in $J^P =0^+$ is investigated in the flavour $\bar{\mathbf{3}}$, $\mathbf{6}$ and $\overline{\mathbf{15}}$ sectors at the $SU(3)_f$ flavour point using lattice QCD, working on three volumes with $m_{\pi} \approx 700$ MeV. Large bases of interpolating operators are employed to extrac

Oliver Schön, Zhengang Zhong, Sadegh Soudjani

Algorithmic verification of realistic systems to satisfy safety and other temporal requirements has suffered from poor scalability of the employed formal approaches. To design systems with rigorous guarantees, many approaches still rely on exact models of the underlying systems. Since this assumption can rarely be met in practice, models have to be inferred

Yuhang Hu, Yunzhe Wang, Ruibo Liu, Zhou Shen

Integrating Large Language Models (VLMs) and Vision-Language Models (VLMs) with robotic systems enables robots to process and understand complex natural language instructions and visual information. However, a fundamental challenge remains: for robots to fully capitalize on these advancements, they must have a deep understanding of their physical embodiment.

Shirin Shoushtari, Edward P. Chandler, Jialiang Zhang, Manjula Senanayake

Small Angle Neutron Scattering (SANS) is a non-destructive technique utilized to probe the nano- to mesoscale structure of materials by analyzing the scattering pattern of neutrons. Accelerating SANS acquisition for in-situ analysis is essential, but it often reduces the signal-to-noise ratio (SNR), highlighting the need for methods to enhance SNR even with

Adam Rashid, Chung Min Kim, Justin Kerr, Letian Fu

Inventory monitoring in homes, factories, and retail stores relies on maintaining data despite objects being swapped, added, removed, or moved. We introduce Lifelong LERF, a method that allows a mobile robot with minimal compute to jointly optimize a dense language and geometric representation of its surroundings. Lifelong LERF maintains this representation

Xijun Wang, Santiago López-Tapia, Alice Lucas, Xinyi Wu

Generative Adversarial Networks (GANs) have shown great performance on super-resolution problems since they can generate more visually realistic images and video frames. However, these models often introduce side effects into the outputs, such as unexpected artifacts and noises. To reduce these artifacts and enhance the perceptual quality of the results, in

Ge Zhu, Juan-Pablo Caceres, Zhiyao Duan, Nicholas J. Bryan

Diffusion-based audio and music generation models commonly perform generation by constructing an image representation of audio (e.g., a mel-spectrogram) and then convert it to audio using a phase reconstruction model or vocoder. Typical vocoders, however, produce monophonic audio at lower resolutions (e.g., 16-24 kHz), which limits their usefulness. We propo

Dongmin Park, Zhaofang Qian, Guangxing Han, Ser-Nam Lim

Mitigating hallucinations of Large Vision Language Models,(LVLMs) is crucial to enhance their reliability for general-purpose assistants. This paper shows that such hallucinations of LVLMs can be significantly exacerbated by preceding user-system dialogues. To precisely measure this, we first present an evaluation benchmark by extending popular multi-modal b

Jorge A. Zavala, Marco Castellano, Hollis B. Akins, Tom J. L. C. Bakx

The James Webb Space Telescope (JWST) has discovered a surprising population of bright galaxies in the very early universe (<500 Myrs after the Big Bang) that is hard to explain with conventional galaxy formation models and whose physical properties remain to be fully understood. Insight into their internal physics is best captured through nebular lines but,

Graeme E. Addison

We revisit the claimed detection of a new cosmic microwave background (CMB) foreground based on the correlation between low-redshift 2MASS Redshift Survey (2MRS) galaxies and CMB temperature maps from the Planck and WMAP missions. We reproduce the reported measurements but argue that the original analysis significantly underestimated the uncertainties. We cr

Roberto Bruno, Rossana DeMarco, Raffaella D Amicis, Denise Perrone

The problems of heating and acceleration of solar wind particles are of significant and enduring interest in astrophysics. The interactions between waves and particles are crucial in determining the distributions of proton and alpha particles, resulting in non-Maxwellian characteristics including temperature anisotropies and particle beams. These processes c

Paul Waligora, Haseeb Aslam, Osama Zeeshan, Soufiane Belharbi

Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter- and intra-modal relationships between, e.g., visual, textual, physiological, and auditory modalities. This paper proposes an MMER method that relies on a joint multimodal transformer (JMT) for fusion with key-based cross-attention. This framework can

Kangyao Huang, Di Guo, Xinyu Zhang, Xiangyang Ji

It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents. Although we might get anxious under the pressure, it could also be a drive for us to stimulate our potentials to the best in order to keep up with others. Inspired by this, we propose a competitive l

Yujia Yang, Paolo Cattaneo, Arslan S. Raja, Bruce Weaver

Frequency metrology lies at the heart of precision measurement. Optical frequency combs provide a coherent link uniting the microwave and optical domains in the electromagnetic spectrum, with profound implications in timekeeping, sensing and spectroscopy, fundamental physics tests, exoplanet search, and light detection and ranging. Here, we extend this frequ

Arvind Ayyer, James Martin, Lauren Williams

We study a multispecies $t$-PushTASEP system on a finite ring of $n$ sites with site-dependent rates $x_1,\dots,x_n$. Let $\lambda=(\lambda_1,\dots,\lambda_n)$ be a partition whose parts represent the species of the $n$ particles on the ring. We show that for each composition $\eta$ obtained by permuting the parts of $\lambda$, the stationary probability of

Rosana Montes, Liliana Herrera, Emilio Crisol

The application of the Universal Design for Learning framework favors the creation of virtual educational environments for all. It requires developing accessible content, having a usable platform, and the use of flexible didactics and evaluations that promote constant student motivation. The present study aims to design a methodology to evaluate the usabilit

Xiaoyan Xu, Xianye Yu

Let $\{B_t,t\geq0\}$ be a d-dimensional Brownian motion. We prove that the approximation of the higher derivative of renormalized self-intersection local time $$ \int_{0}^{1}\int_{0}^{s}\left(p^{(|k|)}_{d,\epsilon}(B_{s}-B_{r})-E[p^{(|k|)}_{d,\epsilon}(B_{s}-B_{r})]\right)drds, $$ where the multiindex $k=(k_{1},\cdots,k_{d})$, $ p_{d,\epsilon}^{(|k|)}(x_1,x_

Bruno de Melo, Jamiel Sheikh

Performance attribution analysis, defined as the process of explaining the drivers of the excess performance of an investment portfolio against a benchmark, stands as a significant feature of portfolio management and plays a crucial role in the investment decision-making process, particularly within the fund management industry. Rooted in a solid financial a

Wuyang Zhou, Yu-Bang Zheng, Qibin Zhao, Danilo Mandic

A novel tensor decomposition framework, termed Tensor Star (TS) decomposition, is proposed which represents a new type of tensor network decomposition based on tensor contractions. This is achieved by connecting the core tensors in a ring shape, whereby the core tensors act as skip connections between the factor tensors and allow for direct correlation chara

Dena Marie Asta

We prove that kernel density estimation on symmetric spaces of non-compact type, whose L2-risk was bounded above in previous work (Asta,2021), in fact achieves a minimax rate of convergence. With this result, the story for kernel density estimation on all symmetric spaces is completed. The idea in adapting the proof for Euclidean space is to suitably abstrac

Robert I. Booth, Titouan Carette, Cole Comfort

We give generators and relations for the hypergraph props of Gaussian relations and positive affine Lagrangian relations. The former extends Gaussian probabilistic processes by completely-uninformative priors, and the latter extends Gaussian quantum mechanics with infinitely-squeezed states. These presentations are given by adding a generator to the presenta

Michael Brocidiacono, Konstantin I. Popov, Alexander Tropsha

Structure-based virtual screening (SBVS) is a key workflow in computational drug discovery. SBVS models are assessed by measuring the enrichment of known active molecules over decoys in retrospective screens. However, the standard formula for enrichment cannot estimate model performance on very large libraries. Additionally, current screening benchmarks cann

Grigorii Ptitcyn, Nader Engheta

The concept of twistronics and moir\'e physics, which is present in twisted two-dimensional bilayer materials, has recently attracted growing attention in various fields of science and engineering such as condensed matter physics, nanophotonics, polaritonics and excitonics. The twist angle between the two layers has offered an additional degree of control ov

Haoyang Liu, Aditya Singh, Yijiang Li, Haohan Wang

Enhancing the robustness of deep learning models, particularly in the realm of vision transformers (ViTs), is crucial for their real-world deployment. In this work, we provide a finetuning approach to enhance the robustness of vision transformers inspired by the concept of nullspace from linear algebra. Our investigation centers on whether a vision transform

C. Meskini, C. Sauty, A. Marcowith, N. Vlahakis

Theoretical arguments as well as observations of young stellar objects (YSO) support the presence of a diversified circumstellar environment. A stellar jet is thought to account for most of the stellar spin down and disk wind outflow for the observed high mass loss rate, thus playing a major role in the launching of powerful jets. RY Tau, for instance, is an

Matteo Mariantoni, Noah Gorgichuk

We investigate quantum synchronization phenomena in electrical circuits that incorporate specifically designed nonconservative elements. A dissipative theory of classical and quantized electrical circuits is developed based on the Rayleigh dissipation function. The introduction of this framework enables the formulation of a generalized version of classical P

Fang Liu, Slawomir Skruszewicz, Julian Späthe, Yinyu Zhang

Strong-field ionization can induce electron motion in both the continuum and the valence shell of the parent ion. Here, we explore their interplay by studying laser-induced electron diffraction (LIED) patterns arising from interaction with the potentials of two-hole states of the xenon cation. The quantitative rescattering theory is used to calculate the cor

Yu. G. Ignat'ev

The self-gravitating Higgs field of a scalar charge is studied for the case of an asymmetric scalar doublet containing, along with a canonical and a phantom component. It is shown that in the zero and first approximation of the smallness of the canonical and phantom scalar charges, the gravitational field of the scalar charge is described by the Schwarzschil

Carlo Maccaferri, Riccardo Poletti, Alberto Ruffino, Beniamino Valsesia

Generalizing recent work by Schnabl-Stettinger and Erbin-Firat, we outline a universal algebraic procedure for `adding stubs' to string field theories obeying the BV quantum master equation. We apply our results to classical and quantum closed string field theory as well as to open-closed string field theory. We also clarify several aspects of the integratio

Kareem Shaik, Dali Wang, Weijian Zheng, Qinglei Cao

The understanding of large-scale scientific software poses significant challenges due to its diverse codebase, extensive code length, and target computing architectures. The emergence of generative AI, specifically large language models (LLMs), provides novel pathways for understanding such complex scientific codes. This paper presents S3LLM, an LLM-based fr

Stanislav Filatov, Marcis Auzinsh

We extend Bloch Sphere formalism to pure two qubit systems. Combining insights from Geometric Algebra and analysis of entanglement in different conjugate bases we identify Two Bloch Sphere geometry that is suitable for representing maximally entangled states. It turns out that relative direction of coordinate axes of the two Bloch Spheres may be used to desc

Saram Abbas, Rishad Shafik, Naeem Soomro, Rakesh Heer

Notorious for its 70-80% recurrence rate, Non-muscle-invasive Bladder Cancer (NMIBC) imposes a significant human burden and is one of the costliest cancers to manage. Current tools for predicting NMIBC recurrence rely on scoring systems that often overestimate risk and have poor accuracy. This is where Machine learning (ML)-based techniques have emerged as a

Simon Linke, Tim Ziemer

Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks that visualize a high-dimensional feature space on a low-dimensional map. While SOMs are an excellent tool for data examination and exploration, they inherently cause a loss of detail. Visualizations of the underlying data do not integrate well and, therefore, fail to provide an overall pictu

Martin Kuban, Santiago Rigamonti, Claudia Draxl

Computational materials science produces large quantities of data, both in terms of high-throughput calculations and individual studies. Extracting knowledge from this large and heterogeneous pool of data is challenging due to the wide variety of computational methods and approximations, resulting in significant veracity in the sheer amount of available data

Kevin Zelaya, Matthew Markowitz, Mohammad-Ali Miri

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Programmable photonics provides a flexible platform that can be reconfigured to perform multiple tasks, thereby holding great promise for revolut

Huizi Hao, Kazi Amit Hasan, Hong Qin, Marcos Macedo

ChatGPT has significantly impacted software development practices, providing substantial assistance to developers in a variety of tasks, including coding, testing, and debugging. Despite its widespread adoption, the impact of ChatGPT as an assistant in collaborative coding remains largely unexplored. In this paper, we analyze a dataset of 210 and 370 develop

Sheng-Han Teng, Anna Grünebohm

Functional properties of ferroelectrics and their change with time depend crucially on the defect structure. In particular, point defects and bias fields induced by defect dipoles modify the field hysteresis and play an important role in fatigue and aging. However, a full understanding on how order, agglomeration and strength of defect dipoles affect phase s

Mikko Partanen, Jukka Tulkki

The measurement of the spin angular momentum of circularly polarized light by Beth [Phys. Rev. 50, 115 (1936)] can be explained by using a microscopic torque density. However, the experiment does not resolve the space- and time-dependent evolution of the spin density of light and the wave plate and the covariant form of the microscopic torque density. Here w

Timo Gräßer, Thomas Hahn, Götz S. Uhrig

A recently developed dynamic mean-field theory for disordered spins (spinDMFT) is shown to capture the spin dynamics of nuclear spins very well. The key quantities are the spin autocorrelations. In order to compute the free induction decay (FID), pair correlations are needed in addition. They can be computed on spin clusters of moderate size which are couple

Elham Kashefi, Dominik Leichtle, Luka Music, Harold Ollivier

With the advent of delegated quantum computing as a service, verifying quantum computations is becoming a question of great importance. Existing information theoretically Secure Delegated Quantum Computing (SDQC) protocols require the client to possess the ability to perform either trusted state preparations or measurements. Whether it is possible to verify

Hassan Firouzjahi, Haidar Sheikhahmadi

We consider self-interacting scalar fields with a conformal coupling in the dS background and study the quantum corrections from bubble loop diagrams. Incorporating the perturbative in-in formalism, we calculate the quantum corrections in the vacuum zero point energy and pressure of self-interacting fields with the potential $V \propto \Phi^n $ for even valu

Joshua Clymer, Nick Gabrieli, David Krueger, Thomas Larsen

As AI systems become more advanced, companies and regulators will make difficult decisions about whether it is safe to train and deploy them. To prepare for these decisions, we investigate how developers could make a 'safety case,' which is a structured rationale that AI systems are unlikely to cause a catastrophe. We propose a framework for organizing a saf

Mohamed elShehaby, Aditya Kotha, Ashraf Matrawy

Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly. Therefore, this letter introduces Adaptive Continuous Adversarial Training (ACAT), a method that integrates adversarial training

Ratijit Mitra, Indranil Saha

Recently, centralized receding horizon online multi-robot coverage path planning algorithms have shown remarkable scalability in thoroughly exploring large, complex, unknown workspaces with many robots. In a horizon, the path planning and the path execution interleave, meaning when the path planning occurs for robots with no paths, the robots with outstandin

Marc Lafon, Alexandre Thomas

Combining empirical risk minimization with capacity control is a classical strategy in machine learning when trying to control the generalization gap and avoid overfitting, as the model class capacity gets larger. Yet, in modern deep learning practice, very large over-parameterized models (e.g. neural networks) are optimized to fit perfectly the training dat

Rafael Granero-Belinchón, Martina Magliocca, Alejandro Ortega

In this paper, we prove a couple of new nonlinear functional inequalities of Sobolev type akin to the logarithmic Sobolev inequality. In particular, one of the inequalities reads $$ \int_{\mathbb{S}^1}\arctan\left(\frac{\partial_x u}{u}\right)\partial_xu \,dx\geq \arctan\left(\|u(t)\|_{\dot{W}^{1,1}(\mathbb{S}^1)}\right)\|u(t)\|_{\dot{W}^{1,1}(\mathbb{S}^1)}

Yisheng Zhang, Clara M. Velte

The Karman-Howarth-Monin-Hill (KHMH) equation has been widely applied to scale-by-scale turbulent energy cascade studies in recent years, however, the forms and interpretations are not consistent. The present work generalizes to considering two different spatio-temporal points to reformulate the KHMH equation based on Reynolds decomposition and phase averagi

Simran Chourasia, Aleksandr Svetogorov, Akashdeep Kamra, Wolfgang Belzig

Recently introduced magnetic materials called altermagnets (AM) feature zero net magnetization but a momentum dependent magnetic exchange field, which can have intriguing implications when combined with superconductivity. In our work, we use the quasiclassical framework to study the effects of such a material on a conventional superconductor (S) in an AM/S b

Amedeo Bertuzzi, Davide Ferrari, Antonio Manzalini, Michele Amoretti

Academic and industrial sectors have been engaged in a fierce competition to develop quantum technologies, fueled by the explosive advancements in quantum hardware. While universal quantum computers have been shown to support up to hundreds of qubits, the scale of quantum annealers has reached three orders of magnitude (i.e., thousands of qubits). Therefore,

Aidan Curtis, George Matheos, Nishad Gothoskar, Vikash Mansinghka

Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems. However, the typical TAMP problem formulation assumes full observability and deterministic action effects. These assumptions limit the ability of the planner to gather information and make decisions that

Javier Mancilla, André Sequeira, Tomas Tagliani, Francisco Llaneza

Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast datasets can be exploited. Nonetheless, classical models struggle once data is scarce and skewed. Quantum feature spaces are projected to find

Gergely Bodó, Markus Riedle

In this work, we present a comprehensive theory of stochastic integration with respect to arbitrary cylindrical L\'evy processes in Hilbert spaces. Since cylindrical L\'evy processes do not enjoy a semi-martingale decomposition, our approach relies on an alternative approach to stochastic integration by decoupled tangent sequences. The space of deterministic

Haoyue Tang, Tian Xie, Aosong Feng, Hanyu Wang

Solving image inverse problems (e.g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image). By using the input image as guidance, we can leverage a pretrained diffusion generative model to solve a wide range of image inverse tasks without task specific model fin

Florian Kluger, Eric Brachmann, Michael Ying Yang, Bodo Rosenhahn

Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such as cuboids or cylinders. Inferring these primitives is important for attaining high-level, abstract scene descriptions. Previous approaches for primitive-based abstraction estimate

Jason Hartline, Aleck Johnsen, Anant Shah

This paper takes a game theoretic approach to the design and analysis of online algorithms and illustrates the approach on the finite-horizon ski-rental problem. This approach allows beyond worst-case analysis of online algorithms. First, we define "subgame optimality" which is stronger than worst case optimality in that it requires the algorithm to take adv

Darin Tsui, Amirali Aghazadeh

Protein language models leverage evolutionary information to perform state-of-the-art 3D structure and zero-shot variant prediction. Yet, extracting and explaining all the mutational interactions that govern model predictions remains difficult as it requires querying the entire amino acid space for $n$ sites using $20^n$ sequences, which is computationally e

Matthieu Cadiot, Jean-Philippe Lessard, Jean-Christophe Nave

In this paper, we present a methodology for establishing constructive proofs of existence of smooth, stationary, non-radial localized patterns in the planar Swift-Hohenberg equation. Specifically, given an approximate solution $u_0$, we construct an approximate inverse for the linearization around $u_0$, enabling the development of a Newton-Kantorovich appro

Pietro Brighi, Andreas Nunnenkamp

We study the dynamics of a Bose-Hubbard model coupled to an engineered environment which in the non-interacting limit is described by the celebrated Hatano-Nelson model. At strong interactions, two bosons occupying the same site form a so-called repulsively bound pair, or doublon. Using tensor-network simulations, we clearly identify a distinct doublon light

David Futer, Jessica S. Purcell, Saul Schleimer

This paper employs knot invariants and results from hyperbolic geometry to develop a practical procedure for checking the cosmetic surgery conjecture on any given one-cusped manifold. This procedure has been used to establish the following computational results. First, we verify that all knots up to 19 crossings, and all one-cusped 3-manifolds in the SnapPy

Jiarui Li, Ye Yuan, Zehua Zhang

We proposed an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for domain-specific and time-sensitive queries related to private knowledge-bases. Our system integrates RAG pipeline with upstream datasets processing and downstream performance evaluation. Addressing

J. M. Aldaz, A. Caldera

We characterize the geometrically doubling condition of a metric space in terms of the uniform $L^1$-boundedness of superaveraging operators, where uniform refers to the existence of bounds independent of the measure being considered.

Ziteng Sun, Uri Mendlovic, Yaniv Leviathan, Asaf Aharoni

Speculative decoding is an effective method for lossless acceleration of large language models during inference. It uses a fast model to draft a block of tokens which are then verified in parallel by the target model, and provides a guarantee that the output is distributed identically to a sample from the target model. In prior works, draft verification is p

Rishabh Bhardwaj, Akshay Yelleshpur Srikant

Conformally soft operators and their associated soft theorems on the celestial sphere encode the low energy behaviour of bulk scattering amplitudes. They lead to an infinite dimensional symmetry algebra of the celestial CFT at tree-level. In this paper, we introduce new operators in the celestial CFT in order to extend the definition of conformally soft curr

Ivan Di Terlizzi, Marco Baiesi, Felix Ritort

We derive, in more general conditions, a recently introduced variance sum rule (VSR) [I. Di Terlizzi et al., 2024 Science 383 971] involving variances of displacement and force impulse for overdamped Langevin systems in a nonequilibrium steady state (NESS). This formula allows visualising the effect of nonequilibrium as a deviation of the sum of variances fr

Guanxing Fu, Paul P. Hager, Ulrich Horst

We consider both $N$-player and mean-field games of optimal portfolio liquidation in which the players are not allowed to change the direction of trading. Players with an initially short position of stocks are only allowed to buy while players with an initially long position are only allowed to sell the stock. Under suitable conditions on the model parameter

Garazi Retegui, Jaione Etxeberria, María Dolores Ugarte

Rare cancers affect millions of people worldwide each year. However, estimating incidence or mortality rates associated with rare cancers presents important difficulties and poses new statistical methodological challenges. In this paper, we expand the collection of multivariate spatio-temporal models by introducing adaptable shared spatio-temporal components

Praveen Kumar, Thomas Dent

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave (GW) signals requires a model of the source population distribution. In previous searches using the PyCBC framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template ma

Vanessa Aisyahsari Hanschke, Dylan Rees, Merve Alanyali, David Hopkinson

Researchers urge technology practitioners such as data scientists to consider the impacts and ethical implications of algorithmic decisions. However, unlike programming, statistics, and data management, discussion of ethical implications is rarely included in standard data science training. To begin to address this gap, we designed and tested a toolbox calle

J. M. Aldaz

We explore boundedness properties in the context of metric measure spaces, of some natural operators of convolution type whose study is suggested by certain transformations used in computer vision.

Berk Cicek, Arda Sarp Yenicesu, Cankut Bora Tuncer, Kutay Demiray

This paper introduces H-MaP, a hybrid sequential manipulation planner that addresses complex tasks requiring both sequential actions and dynamic contact mode switches. Our approach reduces configuration space dimensionality by decoupling object trajectory planning from manipulation planning through object-based waypoint generation, informed contact sampling,

Vladimir Sovljanski, Mario Paolone

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model different frequency regions of the Li-ion cell's impedance spectrum. We derive a noise covariance matrix

Ozge Mercanoglu Sincan, Richard Bowden

Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos. In this paper, we introduce a lightweight, modular SLT framework, Spotter+GPT, that leverages the power of Large Language Models (LLMs) and avoids heavy end-to-end training. Spotter+GPT breaks down the SLT task into two distinct st

Andrew Wentzel, Lauren Levine, Vipul Dhariwal, Zarah Fatemi

We present a visual computing framework for analyzing moral rhetoric on social media around controversial topics. Using Moral Foundation Theory, we propose a methodology for deconstructing and visualizing the \textit{when}, \textit{where}, and \textit{who} behind each of these moral dimensions as expressed in microblog data. We characterize the design of thi

Hao Cui, Taha Yasseri

Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation. However, th

Laetitia Laguzet, Gabriel Turinici

Motivated by a heat radiative transport equation, we consider a particle undergoing collisions in a space-time domain and propose a method to sample its escape time, space and direction from the domain. The first step of the procedure is an estimation of how many elementary collisions is safe to take before chances of exiting the domain are too high; then th