March 2024 arXiv papers — page 104

Md Rubel Ahmed, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang

High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype hardware designs rapidly. However, exploring various design space parameters can take much time and effort for hardware engineers to meet specific design specifications. This paper pr

Brett Ehrman, Mathew A. Johnson, Stéphane Lafortune

We study the orbital stability of smooth solitary wave solutions of the Novikov equation, which is a Camassa-Holm type equation with cubic nonlinearities. These solitary waves are shown to exist as a one-parameter family (up to spatial translations) parameterized by their asymptotic endstate, and are encoded as critical points of a particular action function

Hamed Zibaei, Mohammad Saadi Mesgari

Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale optimization problems. Moreover, changing algorithm variables does not influence algorithm convergence much. The PSO algorithm ca

Dingding Cai, Janne Heikkilä, Esa Rahtu

This paper introduces GS-Pose, a unified framework for localizing and estimating the 6D pose of novel objects. GS-Pose begins with a set of posed RGB images of a previously unseen object and builds three distinct representations stored in a database. At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial

Sarah I. Allec, Xiaonan Lu, Daniel R. Cassar, Xuan T. Nguyen

Glasses form the basis of many modern applications and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing is an estimate of a given composition's glass-form

Nicolas Allen Smoot

Congruence families, i.e., $\ell$-adic convergence for well-defined arithmetic subsequences, is a commonplace phenomenon for the coefficients of modular forms. Such families superficially resemble one another, but they often vary substantially in difficulty. Moreover, the critical difficulties associated with a given family will generally manifest themselves

Jafet Belmont, Sara Martino, Janine Illian, Håvard Rue

Modern methods for quantifying and predicting species distribution play a crucial part in biodiversity conservation. Occupancy models are a popular choice for analyzing species occurrence data as they allow to separate the observational error induced by imperfect detection, and the sources of bias affecting the occupancy process. However, the spatial and tem

Simon Allais, Pierre-Alexandre Arlove

In this article, we give an explicit computation of the order spectral selectors of a pair of $C^1$-close Legendrian submanifolds belonging to an orderable isotopy class. The $C^1$-local flatness of the spectral distance and the characterisation of its geodesics are deduced. Another consequence is the $C^1$-local coincidence of spectral and Shelukhin-Chekano

Ciprian Demeter, Ruixiang Zhang

We explore variants of the following open question: Split $[0,1]^2$ into $N^2$ squares with side length $1/N$. Is there a way to select $N$ such squares such that each line intersects only $O(1)$ of them?

Andreas Ziegler, Karl Vetter, Thomas Gossard, Jonas Tebbe

Neuromorphic Computing (NC) and Spiking Neural Networks (SNNs) in particular are often viewed as the next generation of Neural Networks (NNs). NC is a novel bio-inspired paradigm for energy efficient neural computation, often relying on SNNs in which neurons communicate via spikes in a sparse, event-based manner. This communication via spikes can be exploite

Remi A. Chou, Joerg Kliewer

Consider the problem of storing data in a distributed manner over $T$ servers. Specifically, the data needs to (i) be recoverable from any $\tau$ servers, and (ii) remain private from any $z$ colluding servers, where privacy is quantified in terms of mutual information between the data and all the information available at any $z$ colluding servers. For this

Jin Yang, Peijie Qiu, Yichi Zhang, Daniel S. Marcus

Hierarchical transformers have achieved significant success in medical image segmentation due to their large receptive field and capabilities of effectively leveraging global long-range contextual information. Convolutional neural networks (CNNs) can also deliver a large receptive field by using large kernels, enabling them to achieve competitive performance

Patrick L. Combettes, Javier I. Madariaga

We propose stochastic splitting algorithms for solving large-scale composite inclusion problems involving monotone and linear operators. They activate at each iteration blocks of randomly selected resolvents of monotone operators and, unlike existing methods, achieve almost sure convergence of the iterates to a solution without any regularity assumptions or

Max Braun, Noémie Jaquier, Leonel Rozo, Tamim Asfour

We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference capabilities of flow matching methods. By design, RFMP inherits the strengths of flow matching: the ability to encode high-dimensional multimodal distributions, commonly encountered

James McInerney, Nathan Kallus

Uncertainty quantification in deep learning is crucial for safe and reliable decision-making in downstream tasks. Existing methods quantify uncertainty at the last layer or other approximations of the network which may miss some sources of uncertainty in the model. To address this gap, we propose an uncertainty quantification method for large networks based

Jeremy J. Axelrod

Low image contrast is a major limitation in transmission electron microscopy, since samples with low atomic number only weakly phase-modulate the illuminating electron beam, and beam-induced sample damage limits the usable electron dose. The contrast can be increased by converting the electron beam's phase modulation into amplitude modulation using a phase p

Philip J. Morrison

Some ideas relating to a bracket formulation for dissipative systems are considered. The formulation involves a bracket that is analogous to a generalized Poisson bracket, but possesses a symmetric component. Such a bracket is presented for the Navier-Stokes equations.

Nicolas Zapata, Ivan Takmakov, Simon Günzler, Simon Geisert

Josephson junction parametric amplifiers have become essential tools for microwave quantum circuit readout with minimal added noise. Even after improving at an impressive rate in the last decade, they remain vulnerable to magnetic field, which limits their use in many applications such as spin qubits, Andreev and molecular magnet devices, dark matter searche

Haider AL-Shareefy, Michael Butler, Thai Son Hoang

This research paper presents an approach to enhancing the predictive capability of architects in the design and assurance of systems, focusing on systems operating in dynamic and unpredictable environments. By adopting a systems approach, we aim to improve architects' predictive capabilities in designing dependable systems (for example, ML-based systems). An

Nicodemo Mazzaferro, Subarna Sasmal, Pilar Cossio, Glen M. Hocky

Our ability to calculate rates of biochemical processes using molecular dynamics simulations is severely limited by the fact that the time scales for reactions, or changes in conformational state, scale exponentially with the relevant free-energy barriers. In this work, we improve upon a recently proposed rate estimator that allows us to predict transition t

Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng

Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Our daily choices, especially in domains like fashion and retail, are substantially shaped by multi-modal data, such as pictures and textual descriptions. These modalities not only o

Patrick R. Wells, Christopher D. Fassnacht, Simon Birrer, Devon Williams

Time-Delay Cosmography is a technique for measuring $H_0$ with strong gravitational lensing. It requires a correction for line of sight perturbations, and it is necessary to build tools to assess populations of these lines of sight efficiently. We aim demonstrate the techniques necessary to analyze line of sight effects at a population level, and investigate

Diego Bravo, Florencia Cubría, Marcelo Fiori, Gustavo Rama

We examine the capacity of the complementarity spectrum to distinguish non-isomorphic digraphs. We focus on the seven families with exactly three complementarity eigenvalues. Our findings reveal that in some, but not all families, any two non-isomorphic members have different complementarity spectrum. Complementarity eigenvalues outperform traditional eigenv

Alexander Aeppli, Kyungtae Kim, William Warfield, Marianna S. Safronova

We report an optical lattice clock with a total systematic uncertainty of $8.1 \times 10^{-19}$ in fractional frequency units, representing the lowest uncertainty of any clock to date. The clock relies on interrogating the ultra-narrow ${}^1S_0 \rightarrow {}^3P_0$ transition in a dilute ensemble of fermionic strontium atoms trapped in a vertically-oriented,

Yuxuan Li, Sarthak Kumar Maharana, Yunhui Guo

With the increasing prevalence of Machine Learning as a Service (MLaaS) platforms, there is a growing focus on deep neural network (DNN) watermarking techniques. These methods are used to facilitate the verification of ownership for a target DNN model to protect intellectual property. One of the most widely employed watermarking techniques involves embedding

Prabin Bhandari, Abhishek Paudel

Recent advancements in morpheme segmentation primarily emphasize word-level segmentation, often neglecting the contextual relevance within the sentence. In this study, we redefine the morpheme segmentation task as a sequence-to-sequence problem, treating the entire sentence as input rather than isolating individual words. Our findings reveal that the multili

Pardis Taghavi, Reza Langari, Gaurav Pandey

This research paper presents an innovative multi-task learning framework that allows concurrent depth estimation and semantic segmentation using a single camera. The proposed approach is based on a shared encoder-decoder architecture, which integrates various techniques to improve the accuracy of the depth estimation and semantic segmentation task without co

Gabriela Jeronimo, Leonardo Lanciano, Pablo Solernó

We present bounds for the geometric degree of the tangent bundle and the tangential variety of a smooth affine algebraic variety $V$ in terms of the geometric degree of $V$. We first analyze the case of curves, showing an explicit relation between these degrees. In addition, for parametric curves, we obtain upper bounds that are linear in the degree of the g

Madhu Kashyap Jagadeesh, Blesson Mathew, K. T. Paul, Gourav Banerjee

We performed the optical spectroscopy of 16 classical Be stars in 11 open clusters older than 100 Myr. Ours is the first spectroscopic study of classical Be stars in open clusters older than 100 Myr. We found that the H alpha emission strength of most of the stars is less than 40 Angstrom, in agreement with previous studies. Our analysis further suggests tha

Alexej Moskovka, Talal Rahman, Jan Valdman, Jon Eivind Vatne

When writing high-performance code for numerical computation in a scripting language like MATLAB, it is crucial to have the operations in a large for-loop vectorized. If not, the code becomes too slow to use, even for a moderately large problem. However, in the process of vectorizing, the code often loses its original structure and becomes less readable. Thi

Athanasios Moschos, Fabian Monrose, Angelos D. Keromytis

We introduce a new class of hardware trojans called interrupt-resilient trojans (IRTs). Our work is motivated by the observation that hardware trojan attacks on CPUs, even under favorable attack scenarios (e.g., an attacker with local system access), are affected by unpredictability due to non-deterministic context switching events. As we confirm experimenta

Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes

Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. This formulation neglects the potential for interactio

Zu-Jian Ying, Wen-Long Wang, Bo-Jian Li

The quantum Rabi model (QRM) is a fundamental model for light-matter interactions. A fascinating feature of the QRM is that it manifests a quantum phase transition which is applicable for critical quantum metrology (CQM). Effective application for CQM needs the exact location of the transition point, however the conventional expression for the transition cou

Lifu Jin, Amedeo Roberto Esposito, Michael Gastpar

Strong data processing inequalities (SDPI) are an important object of study in Information Theory and have been well studied for $f$-divergences. Universal upper and lower bounds have been provided along with several applications, connecting them to impossibility (converse) results, concentration of measure, hypercontractivity, and so on. In this paper, we s

Prasun Roychowdhury, Durvudkhan Suragan, Nurgissa Yessirkegenov

In this paper, we focus on three main objectives related to Hardy-type inequalities on Cartan-Hadamard manifolds. Firstly, we explore critical Hardy-type inequalities that contain logarithmic terms, highlighting their significance. Secondly, we examine the stability of both critical and subcritical cases of the Hardy inequality. Lastly, we establish two weig

R. Loganayagam, Godwin Martin

We present a general conjecture for evaluating the multiple-discontinuity integrals that appear in real-time holography using gravitational Schwinger-Keldysh (grSK) geometry. Our conjecture is valid for arbitrary non-derivative interactions with any number of bulk tree-level exchanges. It is also consistent with a unitary exterior EFT at finite temperature w

Pol van Hoften, Jack Sempliner

We study the Piatetski-Shapiro construction, which takes a totally real field F and a Shimura datum (G,X) and produces a new Shimura datum (H,Y). If F is Galois, then the Galois group Gamma of F acts on (H,Y), and we show that the Gamma-fixed points of the Shimura varieties for (H,Y) recover the Shimura varieties for (G,X) under some hypotheses. For Shimura

Cecilia Ying, Stephen Thomas

In an effort to improve the accuracy of credit lending decisions, many financial intuitions are now using predictions from machine learning models. While such predictions enjoy many advantages, recent research has shown that the predictions have the potential to be biased and unfair towards certain subgroups of the population. To combat this, several techniq

Pramod N. Achar, João Lourenço, Timo Richarz, Simon Riche

We extend the ramified geometric Satake equivalence due to Zhu (for tamely ramified groups) and the third named author (in full generality) from rational coefficients to include modular and integral coefficients.

Sarthak Kumar Maharana, Baoming Zhang, Yunhui Guo

Real-world vision models in dynamic environments face rapid shifts in domain distributions, leading to decreased recognition performance. Using unlabeled test data, continuous test-time adaptation (CTTA) directly adjusts a pre-trained source discriminative model to these changing domains. A highly effective CTTA method involves applying layer-wise adaptive l

Jatin Abacousnac, Jasna Brujic, David G. Grier

In-line holographic video microscopy records a wealth of information about the microscopic structure and dynamics of colloidal materials. Powerful analytical techniques are available to retrieve that information when the colloidal particles are well-separated. Large assemblies of close-packed particles create holograms that are substantially more challenging

Victoria Ono, Core Francisco Park, Nayantara Mudur, Yueying Ni

Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between dark matter density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical proce

Vasco Costa, João M. Pereira, Joaquim Jorge

Grid space partitioning is a technique to speed up queries to graphics databases. We present a parallel grid construction algorithm which can efficiently construct a structured grid on GPU hardware. Our approach is substantially faster than existing uniform grid construction algorithms, especially on non-homogeneous scenes. Indeed, it can populate a grid in

Beatrice Casey, Joanna C. S. Santos, George Perry

Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of these techniques being developed, it is valuable to see the cur

Nicolas Pichoff, Samuel Marini

This article introduces a method for adjusting macro-particle weights within a particle distribution while preserving statistical and physical properties. The method allows the weights of the new macro-particle distribution to be determined by any continuous function, including uniform. Computational simulations validate the presented approach.

Rajen Kumar, Palash Sarkar, Prashant Kumar Srivastava, Sudhan Majhi

Complete complementary codes (CCCs) are highly valuable in the fields of information security, radar and communication. The spectrally null constrained (SNC) problem arises in radar and modern communication systems due to the reservation or prohibition of specific spectrums from transmission. The literature on SNC-CCCs is somewhat limited in comparison to th

Harshit Joshi, Rama Govindarajan

We show that bodies with two planes of symmetry can display a range of behaviors even without inertia. Any such body supports a conserved quantity in its dynamics, and is either a settler, a drifter or a flutterer, depending only on its shape. At large time, settlers and drifters, respectively, fall vertically and obliquely, while flutterers rotate forever w

S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta

Existing work in scientific machine learning (SciML) has shown that data-driven learning of solution operators can provide a fast approximate alternative to classical numerical partial differential equation (PDE) solvers. Of these, Neural Operators (NOs) have emerged as particularly promising. We observe that several uncertainty quantification (UQ) methods f

Eduardo S. Fraga, Leticia F. Palhares, Cristian Villavicencio

We compute the one-loop QCD correction to the photon-quark-antiquark vertex in an extremely strong magnetic background, i.e., one in which $\sqrt{eB}$ is much larger than all other mass scales. We resort to the lowest-Landau level approximation, and consider on shell fermions. We find that the total magnetic moment is such that the anomalous magnetic moment

Kritkorn Karntikoon, Yiheng Shen, Sreenivas Gollapudi, Kostas Kollias

Solving optimization problems leads to elegant and practical solutions in a wide variety of real-world applications. In many of those real-world applications, some of the information required to specify the relevant optimization problem is noisy, uncertain, and expensive to obtain. In this work, we study how much of that information needs to be queried in or

Saajid Chowdhury, Jesús Pérez-Ríos

Solvation, the result of the complicated interplay between solvent-solute and solvent-internal interactions, is one of the most important chemical processes. Consequently, a complete theoretical understanding of solvation seems a heroic task. However, it is possible to elucidate fundamental solvation mechanisms by looking into simpler systems, such as ion so

Conor M. Artman, Aditya Mate, Ezinne Nwankwo, Aliza Heching

We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experiencing homelessness in New York transition to permanent housing and Leket -- the national food bank of Israel that

Adriano Verdério, Izabele D'Agostin, Mari Sano, Patrícia Massae Kitani

The theory behind the Lights Out game has been developed by several authors. The aim of this work is to present some results related to this game using Linear Algebra. We establish a criterion for the solubility of this game in the case of an $m$ by $n$ grid, which depends on the invertibility of a matrix, and we present the conditions for this to occur, eas

William D. Banks

For an infinite set M of natural numbers, let FS(M) be the set of all nonzero finite sums of distinct numbers in M. An IP set is any set of the form FS(M). Let p_n denote the n-th prime number for each $n \ge 1$. A de Polignac number is any number m such that $p_{n+1}-p_n=m$ for infinitely many n. In this note, we show that every IP set of even natural numbe

Geoff Boeing, Jaehyun Ha

Street networks allow people and goods to move through cities, but they are vulnerable to disasters like floods, earthquakes, and terrorist attacks. Well-planned network design can make a city more resilient and robust to such disruptions, but we still know little about worldwide patterns of vulnerability, or worldwide empirical relationships between specifi

Wenrui Fan, Mohammod N. I. Suvon, Shuo Zhou, Xianyuan Liu

Pathology and anatomy are two essential groups of semantics in medical data. Pathology describes what the diseases are, while anatomy explains where the diseases occur. They describe diseases from different perspectives, providing complementary insights into diseases. Thus, properly understanding these semantics and their relationships can enhance medical vi

Bojana Brkic, Ilija Buric, Maja Buric, Dusko Latas

We study a scalar field on a noncommutative model of spacetime, the fuzzy de Sitter space, which is based on the algebra of the de Sitter group $SO(1,d)$ and its unitary irreducible representations. We solve the Klein-Gordon equation in $d=2,4$ and show, using a specific choice of coordinates and operator ordering, that all commutative field modes can be pro

H. P. Bartling, J. Yun, K. N. Schymik, M. van Riggelen

Spins associated to solid-state colour centers are a promising platform for investigating quantum computation and quantum networks. Recent experiments have demonstrated multi-qubit quantum processors, optical interconnects, and basic quantum error correction protocols. One of the key open challenges towards larger-scale systems is to realize high-fidelity un

Parthapratim Biswas, Devilal Dahal, Stephen R. Elliott

The structural origin of the first sharp diffraction peak (FSDP) in amorphous silica is studied by analyzing chemical and radial ordering of silicon (Si) and oxygen (O) atoms in binary amorphous networks. The study shows that the chemical order involving Si--O and O--O pairs play a major role in the formation of the FSDP in amorphous silica. This is suppleme

Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov

In this paper we analyze the resilience of a network of banks to joint price fluctuations of the external assets in which they have shared exposures, and evaluate the worst-case effects of the possible default contagion. Indeed, when the prices of certain external assets either decrease or increase, all banks exposed to them experience varying degrees of sim

Ziqi Wang, Xander M de Wit, Federico Toschi

Small bubbles in fluids raise to the surface due to Archimede's force. Remarkably, in turbulent flows this process is severely hindered by the presence of vortex filaments, which act as moving potential wells, dynamically trapping light particles and bubbles. Quantifying the statistical weights and roles of vortex filaments in turbulence is, however, still a

Brian Kyanjo, Donna Calhoun, David L. George

This paper presents GeoFlood, a new open-source software package for solving the shallow-water equations (SWE) on a quadtree hierarchy of mapped, logically Cartesian grids managed by the parallel, adaptive library ForestClaw (Calhoun and Burstedde, 2017). The GeoFlood model is validated using standard benchmark tests from Neelz and Pender (2013) as well as t

Haotian Cheng, Yishu Zhou, Freek Ruesink, Margaret Pavlovich

Integrated photonics could bring transformative breakthroughs in computing, networking, imaging, sensing, and quantum information processing, enabled by increasingly sophisticated optical functionalities on a photonic chip. However, wideband optical isolators, which are essential for the robust operation of practically all optical systems, have been challeng

Joseph E. Lawrence, Jonathan R. Mannouch, Jeremy O. Richardson

We develop a multi-state generalisation of the recently proposed mapping approach to surface hopping (MASH) for the simulation of electronically nonadiabatic dynamics. This new approach extends the original MASH method to be able to treat systems with more than two electronic states. It differs from previous approaches in that it is size consistent and rigor

Ahmet Seven, İbrahim Ünal

Motivated by the recent work of R. Casals on binary invariants for matrix mutation, we study the matrix congruence relation on quasi-Cartan matrices. We obtain a classification and determine normal forms modulo 4. We also establish their mutation invariance properties. In particular, we obtain new mutation invariants, which include the one obtained by R. Cas

Chenyue Liu, Ali Mostafavi

Understanding the fundamental characteristics that shape the inherent flood risk disposition of urban areas is critical for integrated urban design strategies for flood risk reduction. Flood risk disposition specifies an inherent and event-independent magnitude of property flood risk and measures the extent to which urban areas are susceptible to property da

Anni Li, Andres S. Chavez Armijos, Christos G. Cassandras

We derive time and energy-optimal policies for a Connected Autonomous Vehicle (CAV) to execute lane change maneuvers in mixed traffic, i.e., in the presence of both CAVs and Human Driven Vehicles (HDVs). These policies are also shown to be robust with respect to the unpredictable behavior of HDVs by exploiting CAV cooperation which can eliminate or greatly r

Miao Zhang, Rumi Chunara

Performance disparities of image recognition across demographic groups are known to exist in deep learning-based models, due to imbalanced group representations or spurious correlation between group and target labels. Previous work has addressed such challenges without relying on expensive group labels, typically by upweighting high-loss samples or balancing

Louis Lortie, Steven Dahdah, James Richard Forbes

This paper presents a data-driven method to identify an asymptotically stable Koopman system from noisy data. In particular, the proposed approach combines approximations of the system's forward- and backward-in-time dynamics to reduce bias caused by noisy data while enforcing asymptotic stability. A Koopman model of an inherently asymptotically stable s

Yining Jiao, Amy Oldenburg, Yinghan Xu, Srikamal Soundararajan

Optical coherence tomography (OCT) is a popular modality in ophthalmology and is also used intravascularly. Our interest in this work is OCT in the context of airway abnormalities in infants and children where the high resolution of OCT and the fact that it is radiation-free is important. The goal of airway OCT is to provide accurate estimates of airway geom

Zili Wang, Sean B. Andersson, Roberto Tron

Deep learning methods have been widely used in robotic applications, making learning-enabled control design for complex nonlinear systems a promising direction. Although deep reinforcement learning methods have demonstrated impressive empirical performance, they lack the stability guarantees that are important in safety-critical situations. One way to provid

Joshua S. Bloom

Even at modest amplification, the optical depth to gravitational lensing through the Galaxy is $<10^{-5}$. However, the large apparent isotropic-equivalent energy of GRB 221009A coupled with a path through low Galactic latitude suggests that the conditional probability that this particular GRB was lensed is greater than the very low a priori expectation. Wit

Steven Abel, Michael Spannowsky, Simon Williams

We delve into the use of photonic quantum computing to simulate quantum mechanics and extend its application towards quantum field theory. We develop and prove a method that leverages this form of Continuous-Variable Quantum Computing (CVQC) to reproduce the time evolution of quantum-mechanical states under arbitrary Hamiltonians, and we demonstrate the meth

Raghavendra Addanki, Siddharth Bhandari

Average Treatment Effect (ATE) estimation is a well-studied problem in causal inference. However, it does not necessarily capture the heterogeneity in the data, and several approaches have been proposed to tackle the issue, including estimating the Quantile Treatment Effects. In the finite population setting containing $n$ individuals, with treatment and con

Volkan Kumtepeli, Holger Hesse, Thomas Morstyn, Seyyed Mostafa Nosratabadi

Dispatch of a grid energy storage system for arbitrage is typically formulated into a rolling-horizon optimization problem that includes a battery aging model within the cost function. Quantifying degradation as a depreciation cost in the objective can increase overall profits by extending lifetime. However, depreciation is just a proxy metric for battery ag

Arthur Douillard, Qixuan Feng, Andrei A. Rusu, Adhiguna Kuncoro

Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodating ML approaches that require high bandwidth communication between devices working in parallel. In this work, we propose a co-designed modular architecture and training approach f

Peter Kocsis, Julien Philip, Kalyan Sunkavalli, Matthias Nießner

We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations, we propose to condition the generation on shading and normal maps. We

Pierre-Loup Beauregard

Gentrification is associated with rapid demographic changes within inner-city neighborhoods. While many fear that gentrification drives low-income people from their homes and communities, there is limited evidence of the consequences of these changes. I use Canadian administrative tax files to track the movements of incumbent workers and their income traject

Chenghong Bian, Yulin Shao, Haotian Wu, Emre Ozfatura

We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal, called DeepJSCC-AF, or leverages neural networks to extract relevant features from its received signal, called DeepJSCC-PF (Process-and-Forward). We consider both half- and full-

Noureddine Toumi, Roland Malhamé, Jérôme Le Ny

This paper investigates the social optimum for a dynamic linear quadratic collective choice problem where a group of agents choose among multiple alternatives or destinations. The agents' common objective is to minimize the average cost of the entire population. A naive approach to finding a social optimum for this problem involves solving a number of linear

Imogen Camp, Ben Gripaios, Khoi Le Nguyen Nguyen

We show how methods of number theory can be used to study anomalies in gauge quantum field theories in spacetime dimension two. To wit, the anomaly cancellation conditions for the abelian part of the local anomaly admit solutions if and only if they admit solutions in the reals and in the $p$-adics for every prime $p$ and we use this to build an algorithm to

Declan McNamara, Jackson Loper, Jeffrey Regier

For training an encoder network to perform amortized variational inference, the Kullback-Leibler (KL) divergence from the exact posterior to its approximation, known as the inclusive or forward KL, is an increasingly popular choice of variational objective due to the mass-covering property of its minimizer. However, minimizing this objective is challenging.

Max E. Lee, Shy Genel, Benjamin D. Wandelt, Benjamin Zhang

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally challenging to sample these high dimensional parameter spaces with simulations, particularly for halos in the high-mass end of

Chandrodoy Chattopadhyay, Josh Ott, Thomas Schaefer, Vladimir V. Skokov

We present simulations of stochastic fluid dynamics in the vicinity of a critical endpoint belonging to the universality class of the Ising model. This study is motivated by the challenge of modeling the dynamics of critical fluctuations near a conjectured critical endpoint in the phase diagram of Quantum Chromodynamics (QCD). We focus on the interaction of

Zhongjie Huang, Bo Wang, Ellis Ye Yuan

We present a differential representation for holographic four-point correlators. In this representation, the correlators are given by acting differential operators on certain seed functions. The number of these functions is much smaller than what is normally seen in known examples of holographic correlators, and all of them have simple Mellin amplitudes. Thi

Swagat S. Mishra

These lecture notes provide a pedagogical introduction to some aspects of the inflationary cosmology, including the background scalar field dynamics, generation of primordial seed perturbations via quantum fluctuations during inflation, and the process of reheating after inflation in the single-field inflationary paradigm.

D. Giataganas, A. Kehagias, A. Riotto

We study the physics of photon rings in a wide range of axisymmetric black holes admitting a separable Hamilton-Jacobi equation for the geodesics. Utilizing the Killing-Yano tensor, we derive the Penrose limit of the black holes, which describes the physics near the photon ring. The obtained plane wave geometry is directly linked to the frequency matrix of t

Godwin Martin, Shivam K. Sharma

We initiate the study of an open EFT for finite-temperature holographic systems with interacting fermions. In particular, we do this for Yukawa interactions in the bulk using the real-time formalism (grSK geometry). From the bulk perspective, this study corresponds to Yukawa scattering against a black hole, incorporating the effects of Hawking radiation. We

Numan Saeed, Muhammad Ridzuan, Fadillah Adamsyah Maani, Hussain Alasmawi

Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage. This knowledge enables the formulation of effective treatment plans that lead to improved patient outcomes. In the past few years, deep learning models have provided a feasible sol

Antti Rantala, Thorsten Naab, Natalia Lahén

Observations and high-resolution hydrodynamical simulations indicate that massive star clusters assemble hierarchically from sub-clusters with a universal power-law cluster mass function. We study the consequences of such assembly for the formation of intermediate-mass black holes (IMBHs) at low metallicities ($Z=0.01\;Z_\mathrm{\odot}$) with our updated N-b

Daniel Lozano-Gómez, Yasir Iqbal, Matthias Vojta

Classical spin-liquids are paramagnetic phases which feature nontrivial patterns of spin correlations within their ground-state manifold whose degeneracy scales with system size. Often they harbor fractionalized excitations, and their low-energy fluctuations are described by emergent gauge theories. In this work, we discuss a model composed of chiral three-b

Christopher M. Langlett, Joaquin F. Rodriguez-Nieva

Our current understanding of quantum chaos in many-body quantum systems hinges on the random matrix theory(RMT) behavior of eigenstates and their energy level statistics. Although RMT has been remarkably successful in describing `coarse' features of many-body quantum Hamiltonians in chaotic regimes, such as the Wigner-Dyson level spacing statistics or the vo

Yifan Hong, Jinkang Guo, Andrew Lucas

Passive error correction protects logical information forever in the thermodynamic limit by updating the system based only on local information and few-body interactions. A paradigmatic example is the classical two-dimensional Ising model: a Metropolis-style Gibbs sampler retains the sign of the initial magnetization (a logical bit) for thermodynamically lon

Alberto Nardin, Daniele De Bernardis, Rifat Onur Umucalilar, Leonardo Mazza

We study the quantum dynamics in response to time-dependent external potentials of the edge modes of a small fractional quantum Hall fluid composed of few particles on a lattice in a bosonic Laughlin-like state at filling {\nu} = 1/2. We show that the nonlinear chiral Luttinger liquid theory provides a quantitatively accurate description even for the small l

Anastasia Gvozdenko, Søren S. Larsen, Michael A. Beasley, Ivan Cabrera-Ziri

The chemical composition of globular clusters (GCs) across the Local Group provides information on chemical abundance trends. The host galaxy, Sextans A, is a low-surface-brightness dwarf irregular galaxy located on the edge of the Local Group.We derive the dynamical mass of the GC together with the mass-to-light ratio and the abundances of the ${\alpha}$ (M

Antonios Alexos, Yu-Dai Tsai, Ian Domingo, Maryam Pishgar

Creating controlled methods to simulate neurodegeneration in artificial intelligence (AI) is crucial for applications that emulate brain function decline and cognitive disorders. We use IQ tests performed by Large Language Models (LLMs) and, more specifically, the LLaMA 2 to introduce the concept of ``neural erosion." This deliberate erosion involves ablatin

Carys Herbert, Dirk Froebrich, Siegfried Vanaverbeke, Aleks Scholz

We present spot properties on 32 periodic young stellar objects in IC 5070. Long term, $\sim$5 yr, light curves in the $V$, $R$, and $I$-bands are obtained through the HOYS (Hunting Outbursting Young Stars) citizen science project. These are dissected into six months long slices, with 3 months oversampling, to measure 234 sets of amplitudes in all filters. W

Christoph Bartsch, Taro V. Brown, Karol Kampf, Umut Oktem

Recently, Arkani-Hamed et al. proposed the existence of zeros in scattering amplitudes in certain quantum field theories including the cubic adjoint scalar theory Tr($\phi^3$), the $SU(N)$ non-linear sigma model (NLSM) and Yang-Mills (YM) theory. These hidden zeros are special kinematic points where the amplitude vanishes and factorizes into a product of low

Shunsuke Baba, Keiichi Wada, Takuma Izumi, Yuki Kudoh

Recent submillimeter observations have revealed signs of pc-scale molecular inflow and atomic outflow in the nearest Seyfert 2 galaxy, the Circinus galaxy. To verify the gas kinematics suggested by these observations, we performed molecular and atomic line transfer calculations based on a physics-based 3D radiation-hydrodynamic model, which has been compared

Zhou Jiang, Zhenxin Zhu, Pengfei Li, Huan-ang Gao

Autonomous vehicles are gradually entering city roads today, with the help of high-definition maps (HDMaps). However, the reliance on HDMaps prevents autonomous vehicles from stepping into regions without this expensive digital infrastructure. This fact drives many researchers to study online HDMap generation algorithms, but the performance of these algorith