October 2023 arXiv papers — page 101

John Revere McHugh

We introduce a new type of equivalence between blocks of finite group algebras called a strong isotypy. A strong isotypy is equivalent to a $p$-permutation equivalence and restricts to an isotypy in the sense of Brou\'{e}. To prove these results we first establish that the group $T_{\mathcal{O}}(B)$ of trivial source $B$-modules, where $B$ is a block of a fi

Raphael Ruschel, A. S. M. Iftekhar, B. S. Manjunath, Suya You

The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme

Jacob Paugh, Zhaoxuan Zhu, Shobhit Gupta, Marcello Canova

Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en route. Existing deterministic and stochastic methods created to solve the eco-driving problem generally suffer from high computational and memory requirements, which makes online imp

Luciano L. Junior

The k-systole of a Riemannian manifold is the infimum of the volume over all homologically non-trivial k-cycles. In this paper we discuss the behavior of the dimension two and co-dimension two systole of the complex projective space for distinguished classes of metrics, namely the homogeneous metrics and the balanced metrics. In particular, we argue that eve

Sourav Chatterjee

We define the spectral gap of a Markov chain on a finite state space as the second-smallest singular value of the generator of the chain, generalizing the usual definition of spectral gap for reversible chains. We then define the relaxation time of the chain as the inverse of this spectral gap, and show that this relaxation time can be characterized, for any

Sinh Van Nguyen, Son Thanh Le, Minh Khai Tran, Le Thanh Sach

Reconstructing and processing the 3D objects are popular activities in the research field of computer graphics, image processing and computer vision. The 3D objects are processed based on the methods like geometric modeling, a branch of applied mathematics and computational geometry, or the machine learning algorithms based on image processing. The computati

Elaheh Jafarigol, William Keely, Tess Hartog, Tom Welborn

This paper is a quantitative analysis of the data collected globally by the World Value Survey. The data is used to study the trajectories of change in individuals' religious beliefs, values, and behaviors in societies. Utilizing random forest, we aim to identify the key factors of religiosity and classify respondents of the survey as religious and non relig

Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen

In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving strong performance. However, since the prompts need to be sampled from a large volume of annotated examples, finding the right prompt may result in high annotation costs. To address thi

S. Isaac Geronimo Anderson, Daniel M. Dunlavy

We extend an existing approach for efficient use of shared mapped memory across Chapel and C++ for graph data stored as 1-D arrays to sparse tensor data stored using a combination of 2-D and 1-D arrays. We describe the specific extensions that provide use of shared mapped memory tensor data for a particular C++ tensor decomposition tool called GentenMPI. We

P. J. Zrelak, Eric C. P. Breard, Josef Dufek

Granular flows are ubiquitous in nature with single flows traversing a wide range of dynamic conditions from initiation to deposition. Many of these flows are responsible for significant hazards and have the ability to generate remotely detectable seismic signals. These signals provide a potential for real-time flow measurements from a safe distance. To full

José Torres Santaella

This paper focuses on the translating solitons of fully nonlinear extrinsic curvature geometric flows in $\mathbb{R}^{n+1}$. We present a generalization of the Spruck-Xiao's and Spruck-Sun's convexity results for $1$-homogeneous convex/concave curvature functions, and further provide several characterizations of the family of grim reaper cylinders under curv

Shiying Li, Caroline Moosmueller

Iterative slice-matching procedures are efficient schemes for transferring a source measure to a target measure, especially in high dimensions. These schemes have been successfully used in applications such as color transfer and shape retrieval, and are guaranteed to converge under regularity assumptions. In this paper, we explore approximation properties re

Stefan Ivkovic

In this paper, we study the dynamics of the adjoint of a weighted composition operator and we give necessary and sufficient conditions for this adjoint operator to be topologically hyper-transitive on the space of Radon measures on a locally compact Hausdorff space. Moreover, we provide sufficient conditions for this operator to be chaotic and we give concre

Kareem A. Wahid, Carlos E. Cardenas, Barbara Marquez, Tucker J. Netherton

Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-contouring algorithms; (2) Auto-contouring models demonstrate commendable performance even with limited medical

Alejandro Allendes, Gilberto Campaña, Enrique Otárola, Abner J. Salgado

We study the linear elasticity system subject to singular forces. We show existence and uniqueness of solutions in two frameworks: weighted Sobolev spaces, where the weight belongs to the Muckenhoupt class $A_2$; and standard Sobolev spaces where the integrability index is less than $d/(d-1)$; $d$ is the spatial dimension. We propose a standard finite elemen

Christina Chance, Da Yin, Dakuo Wang, Kai-Wei Chang

In this paper, we study whether language models are affected by learned gender stereotypes during the comprehension of stories. Specifically, we investigate how models respond to gender stereotype perturbations through counterfactual data augmentation. Focusing on Question Answering (QA) tasks in fairytales, we modify the FairytaleQA dataset by swapping gend

Cendikiawan Suryaatmadja, Cemile Senem Arabaci, Matthew P. G. Robbins, Joshua Foo

A new approach for operationally studying the effects of spacetime in quantum superpositions of semiclassical states has recently been proposed by some of the authors. This approach was applied to the case of a (2+1)-dimensional Ba\~nados-Teitelboim-Zanelli (BTZ) black hole in a superposition of masses, where it was shown that a two-level system interacting

Krishna Suresh, Aditya Rauniyar, Micah Corah, Sebastian Scherer

Deployment of teams of aerial robots could enable large-scale filming of dynamic groups of people (actors) in complex environments for applications in areas such as team sports and cinematography. Toward this end, methods for submodular maximization via sequential greedy planning can enable scalable optimization of camera views across teams of robots but fac

Paul Ruvolo, Ayush Chakraborty, Rucha Dave, Richard Li

We present a system for creating building-scale, easily navigable 3D maps using mainstream smartphones. In our approach, we formulate the 3D-mapping problem as an instance of Graph SLAM and infer the position of both building landmarks (fiducial markers) and navigable paths through the environment (phone poses). Our results demonstrate the system's ability t

Jiajia Li, Raju Thada Magar, Dong Chen, Feng Lin

Soybeans are a critical source of food, protein and oil, and thus have received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development

Christopher W. Lynn, Qiwei Yu, Rich Pang, William Bialek

Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of $N\sim 100$ neurons. As $N$ increases in new experiments, we enter an undersampled regime where we have to choose which observables should be constrained in the maximum e

Hung Quoc To, Minh Huynh Nguyen, Nghi D. Q. Bui

Code Large Language Models (CodeLLMs) have ushered in a new era in code generation advancements. However, selecting the best code solutions from all possible CodeLLM outputs remains a challenge. Previous methods often overlooked the intricate functional similarities and interactions between solution clusters. We introduce SRank, a novel reranking strategy fo

Xiaoliang Fu, Ken Fong, Qiwen Zheng, Thomas Au

The ISIS buncher system at TRIUMF operates at frequencies of 23MHz, 46MHz, and 4.6MHz. The 23MHz and 46MHz signals drive two buncher cavities, while the 4.6MHz signal drives the 5:1 selector. The previous analog-digital hybrid system has been replaced with a new digital LLRF system due to occasional drifts in the setpoints of the control loops during operati

Jared T. Miller

The space of representations of a surface group into a given simple Lie group is a very active area of research and is particularly relevant to higher Teichm\"uller theory. For a closed surface, classical Teichm\"uller space is a connected component of the moduli space of representations into $PSL(2, \mathbb{R})$ and [Fock:2006] showed that the space of posi

Dongping Zhang, Jason Hartline, Jessica Hullman

Data-driven predictions are often perceived as inaccurate in hindsight due to behavioral responses. In this study, we explore the role of interface design choices in shaping individuals' decision-making processes in response to predictions presented on a shared information display in a strategic setting. We introduce a novel staged experimental design to inv

Siavash Toosi, Adam Peplinski, Philipp Schlatter, Ricardo Vinuesa

High-fidelity simulations are conducted to investigate the turbulent boundary layers around a finite-span NACA0012 wing with a rounded wing-tip geometry at a chord-based Reynolds number of $Re_c=200\,000$ and at various angles of attack up to $10^\circ$. The study aims to discern the differences between the boundary layers on the finite-span wing and those o

Xianyue Peng, Hang Gao, Gengyue Han, Hao Wang

Urban traffic congestion is a critical predicament that plagues modern road networks. To alleviate this issue and enhance traffic efficiency, traffic signal control and vehicle routing have proven to be effective measures. In this paper, we propose a joint optimization approach for traffic signal control and vehicle routing in signalized road networks. The o

Claire-Alice Hébert, Joshua E. Meyers, My H. Do, Patricia R. Burchat

High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear -- correlated distortions of images of distant galaxies due to weak gravitational lensing caused by the large-scale mass distribution in the Universe. For un

Isaac Legred, Bubakar O. Sy-Garcia, Katerina Chatziioannou, Reed Essick

Relations between neutron star properties that do not depend on the nuclear equation of state offer insights on neutron star physics and have practical applications in data analysis. Such relations are obtained by fitting to a range of phenomenological or nuclear physics equation of state models, each of which may have varying degrees of accuracy. In this st

Kentaro Nojima-Schmunk, David Turzak, Kevin Kim, Andrew Vu

Lighter-than-air vehicles or blimps, are an evolving platform in robotics with several beneficial properties such as energy efficiency, collision resistance, and ability to work in close proximity to human users. While existing blimp designs have mainly used propeller-based propulsion, we focus our attention to an alternate locomotion method, flapping wings.

Vernon Barger, Kaoru Hagiwara, Ya-Juan Zheng

The total cross section of the process $\mu^- \mu^+ \to \nu_\mu \bar{\nu}_\mu t \bar{t} H$ has strong dependence on the CP phase $\xi$ of the top Yukawa coupling, where the ratio of $\xi=\pi$ and $\xi = 0$ (SM) grows to 670 at $\sqrt{s}$ = 30 TeV, 3400 at 100 TeV. We study the cause of the strong energy dependence and identify its origin as the $(E/m_W^{})^2

Charity S. Jacobs, Lynnette Hui Xian Ng, Kathleen M. Carley

The cross-strait relationship between China and Taiwan is marked by increasing hostility around potential reunification. We analyze an unattributed bot network and how repeater bots engaged in an influence campaign against Taiwan following US House Speaker Nancy Pelosi's visit to Taiwan in 2022. We examine the message amplification tactics employed by four k

Keshav Agrawal, Susan Athey, Ayush Kanodia, Emil Palikot

As online educational technology products have become increasingly prevalent, rich evidence indicates that learners often find it challenging to establish regular learning habits and complete their programs. Concurrently, online products geared towards entertainment and social interactions are sometimes so effective in increasing user engagement and creating

S. Fatigoni, P. A. R. Ade, Z. Ahmed, M. Amiri

Time-Division Multiplexing is the readout architecture of choice for many ground and space experiments, as it is a very mature technology with proven outstanding low-frequency noise stability, which represents a central challenge in multiplexing. Once fully populated, each of the two BICEP Array high frequency receivers, observing at 150GHz and 220/270GHz, w

Ziang Yan, Abhishek S. Maniyar, Ludovic van Waerbeke

The cosmic infrared background (CIB) is the accumulated infrared (IR) radiation mainly from interstellar dust heated up by early stars. In this work, we measure the cross-correlation between galaxies from the unWISE catalog and the CIB maps from the Planck satellite to simultaneously constrain the cosmic star formation rate (SFR), dust spectral energy distri

Blair W. Lebert, Subin Kim, Beom Hyun Kim, Sae Hwan Chun

A comparative resonant inelastic x-ray scattering (RIXS) study of three well-known Kitaev materials is presented: $\alpha$-Li$_2$IrO$_3$, Na$_2$IrO$_3$, and $\alpha$-RuCl$_3$. Despite similar low-energy physics, these materials show distinct electronic properties, such as the large difference in the size of the charge gap. The RIXS spectra of the spin-orbit

Aritra Bhattacharya, Arun Ram

In this paper we use the double affine Hecke algebra to compute the Macdonald polynomial products $E_\ell P_m$ and $P_\ell P_m$ for type $SL_2$ and type $GL_2$ Macdonald polynomials. Our method follows the ideas of Martha Yip but executes a compression to reduce the sum from $2\cdot 3^{\ell-1}$ signed terms to $2\ell$ positive terms. We show that our rule fo

Amirkeivan Mohtashami, Matteo Pagliardini, Martin Jaggi

Scaling language models to larger and deeper sizes has led to significant boosts in performance. Even though the size of these models limits their application in compute-constrained environments, the race to continually develop ever larger and deeper foundational models is underway. At the same time -- regardless of the model size -- task-specific techniques

Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, Pedram Zaree

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows. This paper surveys research in the emerging interdisciplinary field of adversarial attacks on LLMs, a subfield of trustworthy ML, combining the perspectives of Na

Benyamin Ghojogh, Milad Amir Toutounchian

Density estimation, which estimates the distribution of data, is an important category of probabilistic machine learning. A family of density estimators is mixture models, such as Gaussian Mixture Model (GMM) by expectation maximization. Another family of density estimators is the generative models which generate data from input latent variables. One of the

Chunyi Li, Shengxuan Liu

Some questions are posted at the end of Chapter 16 of Huybrechts' book 'Lectures on K3 Surfaces', concerning the bounded derived category of a K3 surface $D^b(S)$. Let $E$ be a spherical object in $D^b(S)$. The first question asks if there always exists a non-zero object $F$ satisfying RHom$(E,F)=0$. Further, let $E$ be a spherical bundle. The second questio

Bahareh Medghalchi, Andreas Vogel

Automated event detection has emerged as one of the fundamental practices to monitor the behavior of technical systems by means of sensor data. In the automotive industry, these methods are in high demand for tracing events in time series data. For assessing the active vehicle safety systems, a diverse range of driving scenarios is conducted. These scenarios

William Connacher, Jeremy Orosco, Oliver Schmidt, James Friend

It is now known that capillary waves driven upon a fluid interface by high frequency ($>1$~MHz) ultrasound exhibit capillary wave turbulence: the appearance of waves with phase and wavelength far removed from the excitation signal that drives them. An important step towards understanding atomization phenomena driven in this system, these capillary waves may

Bhavya Giri Goswami, Manan Tayal, Karthik Rajgopal, Pushpak Jagtap

Autonomy advances have enabled robots in diverse environments and close human interaction, necessitating controllers with formal safety guarantees. This paper introduces an experimental platform designed for the validation and demonstration of a novel class of Control Barrier Functions (CBFs) tailored for Unmanned Ground Vehicles (UGVs) to proactively preven

Alexander Bernal

Quantum tomography has become an indispensable tool in order to compute the density matrix $\rho$ of quantum systems in Physics. Recently, it has further gained importance as a basic step to test entanglement and violation of Bell inequalities in High-Energy Particle Physics. In this work, we present the theoretical framework for reconstructing the helicity

Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

How to reduce compute and memory requirements of neural networks (NNs) without sacrificing performance? Many recent works use sparse Mixtures of Experts (MoEs) to build resource-efficient large language models (LMs). Here we introduce several novel perspectives on MoEs, presenting a general framework that unifies various methods to approximate two-layer NNs

Marco Romito, Francesco Triggiano

The signature is a fundamental object that describes paths (that is, continuous functions from an interval to a Euclidean space). Likewise, the expected signature provides a statistical description of the law of stochastic processes. We propose a feature extraction model for time series built upon the expected signature. This is computed through a Gaussian p

Yu Sun, Zihui Wu, Yifan Chen, Berthy T. Feng

Estimating high-quality images while also quantifying their uncertainty are two desired features in an image reconstruction algorithm for solving ill-posed inverse problems. In this paper, we propose plug-and-play Monte Carlo (PMC) as a principled framework for characterizing the space of possible solutions to a general inverse problem. PMC is able to incorp

Satoru Hayami

We numerically investigate the stability of the skyrmion crystals in a centrosymmetric lattice structure by focusing on the role of magnetic frustration arising from the multi-sublattice degree of freedom. By analyzing an effective three-sublattice spin model with the antiferromagnetic exchange interaction between different sublattices and the momentum-resol

Diego Gomez, Michael Bowling, Marlos C. Machado

The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging. The Laplacian representation is a promising approach to address these problems by inducing informative state encoding and intrinsic rewards for temporally-extended ac

Vinh Tran, Kishalay De, Lynne Hillenbrand

Large-amplitude accretion outbursts in young stars are expected to play a central role in proto-stellar assembly. Outburst identification historically has taken place using optical techniques, but recent, systematic infrared searches are enabling their discovery in heavily dust-obscured regions of the Galactic plane. Here, we present the discovery of WTP$\,$

Saibal De, Reese E. Jones, Hemanth Kolla

Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional primary solution fields of a model with stochastic input parameters. However, due to

Hagen Soltau, Izhak Shafran, Alex Ottenwess, Joseph R. JR Duffy

We propose a Perceiver-based sequence classifier to detect abnormalities in speech reflective of several neurological disorders. We combine this classifier with a Universal Speech Model (USM) that is trained (unsupervised) on 12 million hours of diverse audio recordings. Our model compresses long sequences into a small set of class-specific latent representa

Dwayne Woods

This article explores the intricate dynamics of the Nile Basin dispute, a complex conflict involving Egypt, Ethiopia, and Sudan. Our central argument is that we can gain unique insights into this dispute by employing the principles of game theory and the Steinhaus cake-cutting problem - a mathematical model of fair division. These theoretical frameworks offe

Jiaying Wu, Jiafeng Guo, Bryan Hooi

It is commonly perceived that fake news and real news exhibit distinct writing styles, such as the use of sensationalist versus objective language. However, we emphasize that style-related features can also be exploited for style-based attacks. Notably, the advent of powerful Large Language Models (LLMs) has empowered malicious actors to mimic the style of t

Howard Baer, Vernon Barger, Xerxes Tata, Kairui Zhang

In natural supersymmetric models defined by no worse than a part in thirty electroweak fine-tuning, winos and binos are generically expected to be much heavier than higgsinos. Moreover, the splitting between the higgsinos is expected to be small, so that the visible decay products of the heavier higgsinos are soft, rendering the higgsinos quasi-invisible at

Uğur Aydın, Naci Saldi

In this paper, we investigate the robustness of stationary mean-field equilibria in the presence of model uncertainties, specifically focusing on infinite-horizon discounted cost functions. To achieve this, we initially establish convergence conditions for value iteration-based algorithms in mean-field games. Subsequently, utilizing these results, we demonst

Mouhcine Assouli, Badr Missaoui

This paper introduces Deep Policy Iteration (DPI), a novel approach that integrates the strengths of Neural Networks with the stability and convergence advantages of Policy Iteration (PI) to address high-dimensional stochastic Mean Field Games (MFG). DPI overcomes the limitations of PI, which is constrained by the curse of dimensionality to low-dimensional p

Paul Duetting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu

We investigate auction mechanisms for AI-generated content, focusing on applications like ad creative generation. In our model, agents' preferences over stochastically generated content are encoded as large language models (LLMs). We propose an auction format that operates on a token-by-token basis, and allows LLM agents to influence content creation through

E. Polisensky, B. Das, W. Peters, M. E. Shultz

Although the majority of hot magnetic stars have extremely stable, $\sim$kG strength surface magnetic fields with simple topologies, a subset undergo small-scale explosions due to centrifugal breakout (CBO). The resulting small-scale flares are typically below the sensitivity of current magentospheric diagnostics and do not generate detectable transient sign

L. Di Lucchio, G. Modanese

By collecting and expanding several numerical recipes developed in previous work, we implement an object-oriented Python code, based on the networkX library, for the realization of the configuration model and Newman rewiring. The software can be applied to any kind of network and "target" correlations, but it is tested with focus on scale-free networks and a

Jens Hornbostel, Herman Rohrbach, Marcus Zibrowius

We study equivariant Hermitian K-theory for representations of symplectic groups, especially $\mathrm{SL}_2$. The results are used to establish an Atiyah-Segal completion theorem for Hermitian $K$-theory and symplectic groups.

Daniel Abraham, Mark Nishimura, Xiaozhi Cao, Congyu Liao

MRI data is acquired in Fourier space/k-space. Data acquisition is typically performed on a Cartesian grid in this space to enable the use of a fast Fourier transform algorithm to achieve fast and efficient reconstruction. However, it has been shown that for multiple applications, non-Cartesian data acquisition can improve the performance of MR imaging by pr

Chengguang Xu, Hieu T. Nguyen, Christopher Amato, Lawson L. S. Wong

Navigating in unseen environments is crucial for mobile robots. Enhancing them with the ability to follow instructions in natural language will further improve navigation efficiency in unseen cases. However, state-of-the-art (SOTA) vision-and-language navigation (VLN) methods are mainly evaluated in simulation, neglecting the complex and noisy real world. Di

Selim Gatti, Mario V. Wüthrich

In general insurance, claims are often lower-truncated and right-censored because insurance contracts may involve deductibles and maximal covers. Most classical statistical models are not (directly) suited to model lower-truncated and right-censored claims. A surprisingly flexible family of distributions that can cope with lower-truncated and right-censored

Yiqin Zhao, Ashkan Ganj, Tian Guo

Physical environment understanding is vital in delivering immersive and interactive mobile augmented reality (AR) user experiences. Recently, we have witnessed a transition in the design of environment understanding systems, from visual data focused to centering on the concept of spatial context, including user, device, and environment information. Even thou

John O. Dabiri

The world's oceans are in constant motion, transporting the sun's heat from the equator to the poles, bringing marine life fresh supplies of oxygen and nutrients, and sequestering nearly half of our carbon dioxide emissions since the Industrial Revolution. Within this dynamic aquatic milieu exists another type of motion: the perpetual teeming of trillions of

Yuqing Qiu, Elizabeth D. White, Edwin M. Munro, Suriyanarayanan Vaikuntanathan

Cell polarization relies on long-range cortical flows, which are driven by active stresses and resisted by the cytoskeletal network. While the general mechanisms that contribute to cortical flows are known, a quantitative understanding of the factors that tune flow speeds has remained lacking. Here, we combine physical simulation, representation learning, an

Parvin Malekzadeh, Ming Hou, Konstantinos N. Plataniotis

Sample efficiency is central to developing practical reinforcement learning (RL) for complex and large-scale decision-making problems. The ability to transfer and generalize knowledge gained from previous experiences to downstream tasks can significantly improve sample efficiency. Recent research indicates that successor feature (SF) RL algorithms enable kno

Daye Nam, Andrew Macvean, Brad Myers, Bogdan Vasilescu

Almost no modern software system is written from scratch, and developers are required to effectively learn to use third-party libraries or software services. Thus, many practitioners and researchers have looked for ways to create effective documentation that supports developers' learning. However, few efforts have focused on how people actually use the docum

Sergei Drozdov

Given a Euclidean simplex of dimension $n\geqslant 2$ let its radii of inscribed and circumscribed spheres be $r$ and $R$, and the distance between the centers of the inscribed and circumscribed spheres be $d.$ Then, $(R-nr)(R+(n-2)r) \geqslant d^2.$

Jianer Chen, Qin Huang, Iyad Kanj, Qian Li

We present streaming algorithms for the graph $k$-matching problem in both the insert-only and dynamic models. Our algorithms, with space complexity matching the best upper bounds, have optimal or near-optimal update time, significantly improving on previous results. More specifically, for the insert-only streaming model, we present a one-pass algorithm with

Zhaoshen Zhai

We construct a system of 33 essential simple closed curves that are pairwise non-homotopic and intersect at most once on the oriented, closed surface of genus 3. Moreover, we show that our construction is saturated, in the sense that it is not properly contained in any other such system of curves.

José Tito Mendonça, Fernando Haas

We extend de concept of Compton scattering to the case of plasmons. This concept was originally applied to electrons in vacuum. Here, we consider electrons in a plasma, and study the scattering properties of photon-plasmon interactions. We show that a number $n$ of plasmons with frequency $ \omega \simeq \omega_p$ is scattered by an electron, for an incident

Mazen M. Alhwaimel, Zhenbo Qin

For a line bundle $L$ on a smooth projective surface $X$ and nonnegative integers $k_1, \ldots, k_N$, Okounkov \cite{Oko} introduced the reduced generating series $\big \langle {\rm ch}_{k_1}^{L} \cdots {\rm ch}_{k_N}^{L} \big \rangle'$ for the intersection numbers among the Chern characters of the tautological bundles over the Hilbert schemes of points on $

Sivakumar Vishnuvardhan Mambakkam, Stephanie Law

The study of van der Waals (vdW) materials has seen increased interest in recent years, due to the wide range of uses for these materials because of their unique mechanical, electronic, and optical properties. This area has recently expanded further into studying the behavior of vdW nanomaterials, as decreasing dimensions open up opportunities to interact wi

Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang

Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive to small changes in their environment. This presents a serious concern for the real world deployment of MARL algorithms, where the testing environment may slightly differ from the

Ilya Pavlyukevich, Andrey Pilipenko

In this paper we study Markov chains with the state space given by the coordinate axes of $\mathbb R^m$, $m \geq 2$, whose step sizes on each positive half-axis are distributed according to a centered probability distribution with variance $v_i^2 \in (0, \infty)$, $i = 1,\ldots, m$. Under very mild assumptions on the jumps sizes on the negative half-axes, we

Giselle Gonzalez Garcia, Christian Weilbach

The recent advent of powerful Large-Language Models (LLM) provides a new conversational form of inquiry into historical memory (or, training data, in this case). We show that by augmenting such LLMs with vector embeddings from highly specialized academic sources, a conversational methodology can be made accessible to historians and other researchers in the H

Antônio H. Ribeiro, Dave Zachariah, Francis Bach, Thomas B. Schön

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the best solution when the training data were corrupted by the worst-case attacks. Linear models are among the sim

Sharan Subramanian, Leilani H. Gilpin

The application of Artificial Intelligence in the medical market brings up increasing concerns but aids in more timely diagnosis of silent progressing diseases like Diabetic Retinopathy. In order to diagnose Diabetic Retinopathy (DR), ophthalmologists use color fundus images, or pictures of the back of the retina, to identify small distinct features through

Kévin Le Balc'h, Jérémy Martin

In this article, we prove the (uniform) global exponential stabilization of the cubic defocusing Schr\"odinger equation on the torus d-dimensional torus, for d=1, 2 or 3, with a linear damping localized in a subset of the torus satisfying some geometrical assumptions. In particular, this answers an open question of Dehman, G\'erard, Lebeau from 2006. Our app

Byunghyun Lee, Anindya Bijoy Das, David J. Love, Christopher G. Brinton

Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an algorithm for designing constant modulus waveforms for DFRC systems. Particularly, we jointly optimize the correlation properties and the spatial beam pattern. For communication,

Cheol Jun Cho, Abdelrahman Mohamed, Shang-Wen Li, Alan W Black

Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing. Yet, the discovered units often remain in phonetic space and the units beyond phonemes are largely underexplored. Here, we demonstrate that a syllabic organization emerges in learning sentence-level representation of speech. In part

Marcus Edwards

IBM has developed a quantum assembly (QASM) language particular to gate model quantum computing since 2017 [CBSG17]. Version 3.0 which adds timing, pulse control, and gate modifiers is currently undergoing finalization in 2023 [CJA+21]. In a similar vein, Pakin of Los Alamos National Laboratory published a quantum macro assembler (QMASM) for D-Wave quantum a

Geovanni Cortes-Rangel, Luis A. Zapata, Pedro R. Rivera-Ortiz, Megan Reiter

We present a study of six dusty and gaseous pillars (containing the HH 1004 and HH 1010 objects) and globules (that contain the HH 666, HH 900, HH 1006, and HH 1066 objects) localized in the Carina nebula using sensitive and high angular resolution ($\sim$0.3$''$) Atacama Large Millimeter/Sub-millimeter Array (ALMA) observations. This is a more extensive stu

Jackson Taylor, Scott Ransom, Prajwal V. Padmanabh

Pulsar timing is a powerful tool that, by accounting for every rotation of a pulsar, precisely measures the spin frequency, spin frequency derivatives, astrometric position, binary parameters when applicable, properties of the ISM, and potentially general relativistic effects. Typically, this process demands fairly stringent scheduling requirements for monit

Sebastian Paeckel, Thomas Köhler, Salvatore R. Manmana, Benjamin Lenz

We present matrix-product state (MPS) based band Lanczos method as solver for quantum cluster methods such as the variational cluster approximation. While a na\"ive implementation of MPS as cluster solver would barely improve its range of applicability, we show that our approach makes it possible to treat cluster geometries well beyond the reach of exact dia

Jiajie Kong, Robert Lund

This paper reviews and compares popular methods, some old and some very recent, that produce time series having Poisson marginal distributions. The paper begins by narrating ways where time series with Poisson marginal distributions can be produced. Modeling nonstationary series with covariates motivates consideration of methods where the Poisson parameter d

Alex Nathan, Dimosthenis Kaponis, Saul Lustgarten

This paper addresses the issue of blockchain protocol risks, a foundational category of risks affecting Distributed Ledger Technology (DLT) which underpins digital assets, smart contracts, and decentralised applications. It presents a comprehensive risk management framework developed in collaboration with financial institutions, blockchain development teams

Ngoc Anh Phan, Yangyang Wang

Mixed mode oscillations (MMOs) are complex oscillatory behaviors of multiple-timescale dynamical systems in which there is an alternation of large-amplitude and small-amplitude oscillations. It is well known that MMOs in two-timescale systems can arise either from a canard mechanism associated with folded node singularities or a delayed Andronov-Hopf bifurca

Hanul Hwang, Suhas S. Jain

A phase-field method for unstructured grids that is accurate, conservative, and robust is proposed in this work. The proposed method also results in bounded transport of volume fraction, and the interface thickness adapts automatically to local grid size. In addition to this, we present a novel formulation for two-phase flows on collocated grids that is prov

Dragan Prekrat, Dragana Ranković, Neli Kristina Todorović-Vasović, Samuel Kováčik

In this contribution, we summarize our recent studies of the phase structure of the Grosse-Wulkenhaar model and its connection to renormalizability. Its action contains a special term that couples the field to the curvature of the noncommutative background space. We first analyze the numerically obtained phase diagram of the model and its three phases: the o

Roman Pasechnik, Marek Taševský

In this review, we present the current status of phenomenological research on constraining the multi-dimensional proton (and nucleus) structure at high energies through studies of the so-called gluon Wigner distributions. We provide a brief pedagogical introduction into the corresponding theoretical definitions and modelling of exclusive and diffractive scat

Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro

Neural tangent kernels (NTKs) provide a theoretical regime to analyze the learning and generalization behavior of over-parametrized neural networks. For a supervised learning task, the association between the eigenvectors of the NTK kernel and given data (a concept referred to as alignment in this paper) can govern the rate of convergence of gradient descent

Zhaoqi Chen, Ralph Etienne-Cummings

A neuromorphic SLAM system shows potential for more efficient implementation than its traditional counterpart. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, vector cells, and place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells. Th

Cheol Jun Cho, Abdelrahman Mohamed, Alan W Black, Gopala K. Anumanchipalli

Self-Supervised Learning (SSL) based models of speech have shown remarkable performance on a range of downstream tasks. These state-of-the-art models have remained blackboxes, but many recent studies have begun "probing" models like HuBERT, to correlate their internal representations to different aspects of speech. In this paper, we show "inference of articu

Chun Shen, Björn Schenke, Wenbin Zhao

This work presents the first Bayesian inference study of the (3+1)D dynamics of relativistic heavy-ion collisions and Quark-Gluon Plasma (QGP) viscosities using an event-by-event (3+1)D hydrodynamics + hadronic transport theoretical framework and data from the Relativistic Heavy Ion Collider (RHIC) Beam Energy Scan program. Robust constraints on initial stat

Hamidreza Moradi, Fatemeh Mehradnia

The X-ray diffractometer in the laboratory is a crucial instrument for analyzing materials in science. It can be used on almost any crystal material, and if the machine parameters are appropriately controlled, it can offer a lot of information about the samples characteristics. Nevertheless, the data obtained from these machines are complicated by an aberrat

Borja Sierra Miranda

Cyclic proof theory studies proofs where cycles are allowed. This is useful for developing proof theory for logics with fixpoint operators: cycles can be used to represent the unfolding of a fixpoint. However, this cyclic character is not unique to such explicit fixpoints. For example, modal logics whose frames have a Noetherian (conversely wellfounded) cond