February 2024 arXiv papers — page 10

Qiao Han, yong huang, xinling Guo, Yiteng Zhai

Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs) to adversarial examples, which can easily fool DNNs into making incorrect predictions. To mitigate this deficiency, we propose a novel adversarial defense method called "Immunity" (Innovative MoE with MUtual information \& positioN stabilITY) based on a modified Mixture-of-Experts

Yuchen Pan, Junjun Jiang, Kui Jiang, Zhihao Wu

Depression Recognition (DR) poses a considerable challenge, especially in the context of the growing concerns surrounding privacy. Traditional automatic diagnosis of DR technology necessitates the use of facial images, undoubtedly expose the patient identity features and poses privacy risks. In order to mitigate the potential risks associated with the inappr

Wagno Cesar e Silva, Ilya L. Shapiro

We explore the possibility of avoiding cosmological singularity with a bounce solution in the early Universe. The main finding is that simple and well-known semiclassical correction, which describes the mixing of radiation and gravity in the effective action, may provide an analytic solution with a bounce. The solution requires a positive beta function for t

Yi Zeng, Feifei Zhao, Yuxuan Zhao, Dongcheng Zhao

The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument "I think, therefore I am", this paper challenge the idea of a "thinking machine" supported by current AIs since there is no sense of self in them. Current artificial intelligence i

Partha Protim Datta

Safety-critical systems such as automated embedded or industrial systems have a strong dependency on the trustworthiness of data collection. As sensors are the critical component for those systems, it is imperative to address the attack resilience of sensors

H. Gretarsson, H. Fujihara, F. Sato, H. Gotou

The recent synthesis of ruthenium trihalides Ru$X_3$ ($X$ = Cl, Br, I) has enlarged the set of material candidates for Kitaev spin liquid. The realization of Kitaev model necessitates the formation of $J=1/2$ pseudospins in the octahedral crystal field. We use Ru $L_3$-edge resonant inelastic x-ray scattering to investigate the evolution of multiplet structu

Anastasiia Sharipova

We give a proof for $(2n + 1,n)$ and $(2n, n-1)$-periodic Ivrii's conjecture for planar outer billiards. We also give new simple geometric proofs for the 3 and 4-periodic cases for outer and symplectic billiards, and generalize for higher dimensions in case of symplectic billiards.

Tao Li, Kim Hammar, Rolf Stadler, Quanyan Zhu

Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to particular information structures to avoid belief hierarchies.

Roman Cherniha

The Comments are devoted to the paper 'Derivation of lump solutions to a variety of Boussinesq equations with distinct dimensions' (Int J Numer Methods Heat Fluid Flow. 2022;32:3072{3082), in which three new generalizations of the classical Boussinesq equation are suggested that were further investigated in several papers. Here it is shown that the equations

Xiaohan Fei, Chethan Parameshwara, Jiawei Mo, Xiaolong Li

The development of generative models that create 3D content from a text prompt has made considerable strides thanks to the use of the score distillation sampling (SDS) method on pre-trained diffusion models for image generation. However, the SDS method is also the source of several artifacts, such as the Janus problem, the misalignment between the text promp

Ruizhe Kang, Jian-Feng Ge, Yang He, Zhihuai Zhu

BiTeI is a polar semiconductor with strong spin-orbit coupling (SOC) that produces large Rashba spin splitting. Due to its potential utility in spintronics and magnetoelectrics, it is essential to understand how defects impact the spin transport in this material. Using scanning tunneling microscopy and spectroscopy, we image ring-like charging states of sing

Minsung Kim, Abhishek Kumar Singh, Davide Venturelli, John Kaewell

Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood de

Shuan Chen, Yousung Jung

Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted. In this study, we empirically assess the extrapolation capability of state-of-the-art template-free models by meti

Marina Manso Jimeno, Keren Bachi, George Gardner, Yasmin L. Hurd

Functional magnetic resonance imaging techniques benefit from echo-planar imaging's fast image acquisition but are susceptible to inhomogeneities in the main magnetic field, resulting in geometric distortion and signal loss artifacts in the images. Traditional methods leverage a field map or voxel displacement map for distortion correction. However, voxel di

Ruoyu Zhang, Frederik Nathan, Netanel H. Lindner, Mark S. Rudner

Due to photon-assisted transport processes, chiral edge modes induced by periodic driving do not directly mediate quantized transport. Here we show how narrow bandwidth "energy filters" can restore quantization by suppressing photon assisted transport through Floquet sidebands. We derive a Floquet Landauer type equation to describe transport through such an

Jemin Woo, Changsun Ahn

This study proposes a method for qualitatively evaluating and designing human-like driver models for autonomous vehicles. While most existing research on human-likeness has been focused on quantitative evaluation, it is crucial to consider qualitative measures to accurately capture human perception. To this end, we conducted surveys utilizing both video stud

Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari

Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A growing body of work documents how these AI systems often fail to improve services in practice. These failures can often be traced to decisions made during the early stages of AI i

Junehyoung Jeon, Volker Bromm, Boyuan Liu, Steven L. Finkelstein

Observations with the James Webb Space Telescope (JWST) have revealed active galactic nuclei (AGN) powered by supermassive black holes (SMBHs) with estimated masses of $10^7-10^8$ M$_\odot$ at redshifts $z\sim7-9$. Some reside in overmassive systems with higher AGN to stellar mass ratios than locally. Understanding how massive black holes could form so early

Joachim König

We show that a dynamical sequence $(f_n)_{n\in \mathbb{N}}$ of polynomials over a number field whose set of stable primes is of positive density must necessarily have a very restricted, and in particular ``near-solvable" dynamical Galois group. Together with existing heuristics, our results suggest moreover that a polynomial $f$ all of whose iterates are irr

Ziyue Feng, Huangying Zhan, Zheng Chen, Qingan Yan

We present NARUTO, a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning, enabling high-fidelity surface reconstruction. Our approach leverages a multi-resolution hash-grid as the mapping backbone, chosen for its exceptional convergence speed and capacity to capture high-frequency local features.The cent

Xinchun Ma

The HOMFLY polynomial of the $(m,n)$ torus knot $T_{m,n}$ can be extracted from the doubly graded character of the finite-dimensional representation $\mathrm{L}_{\frac{m}{n}}$ of the type $A_{n-1}$ rational Cherednik algebra as observed by Gorsky, Oblomkov, Rasmussen and Shende. It is furthermore conjectured that one can obtain the triply-graded Khovanov-Roz

F. Nisa Bostanci, Ismail Emir Yuksel, Ataberk Olgun, Konstantinos Kanellopoulos

We propose a new RowHammer mitigation mechanism, CoMeT, that prevents RowHammer bitflips with low area, performance, and energy costs in DRAM-based systems at very low RowHammer thresholds. The key idea of CoMeT is to use low-cost and scalable hash-based counters to track DRAM row activations. CoMeT uses the Count-Min Sketch technique that maps each DRAM row

Sina Bahrami, Nicolas Sawaya

Constrained problems are frequently encountered in classical and quantum optimization. Particle conservation, in particular, is commonly imposed when studying energy spectra of chemical and solid state systems. Though particle number-constraining techniques have been developed for fermionic (e.g. molecular electronic structure) Hamiltonians, analogous techni

K. Santhosh Kumar, Dooyong Lee, Shivasheesh Varshney, Bharat Jalan

The mechanism of superconductivity in materials with aborted ferroelectricity and the emergence of a dilute metallic phase in systems like doped-SrTiO$_3$ are outstanding issues in condensed matter physics. This dilute metal has features both similar and different to those found in the normal state of other unconventional superconductors. We have investigate

Rodrigo Santos, João Silva, Luís Gomes, João Rodrigues

To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gerv\'asio PT*, a strong LLaMA~2 7B model was used as a starting point, and its further improvement through additional training was don

Sisi Zhou

The Heisenberg limit (HL, with estimation error scales as $1/n$) and the standard quantum limit (SQL, $\propto 1/\sqrt{n}$) are two fundamental limits in estimating an unknown parameter in $n$ copies of quantum channels and are achievable with full quantum controls, e.g., quantum error correction (QEC). It is unknown though, whether these limits are still ac

Arsham Farzinnia, Corine Boon

Investigating the optimal nature of social interactions among actors (e.g., people or firms), who seek to achieve certain mutually-agreed objectives, has been the subject of extensive academic research. Using the relational models theory (describing all social interactions as combinations of four basic sociality ingredients: Communal Sharing, Authority Ranki

Grzegorz Chajewski, Dariusz Kaczorowski

We report on the specific heat studies performed on a new generation of CeRh$_2$As$_2$ single crystals. Superior quality of the samples and dedicated experimental protocol allowed us to observe an antiferromagnetic-like behavior in the normal state and to detect the first-order phase transition of magnetic origin within the superconducting state of the compo

Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt

Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption: that the network is trained on a \textit{stationary} data distribution. In settings where this assumption is violated, e.g.\ deep reinforcement learning, learning algorithms become unstable and brittle with respect to

Xinyue Li, Aous Naman, David Taubman

This paper provides a comprehensive study on features and performance of different ways to incorporate neural networks into lifting-based wavelet-like transforms, within the context of fully scalable and accessible image compression. Specifically, we explore different arrangements of lifting steps, as well as various network architectures for learned lifting

Artem Petrov, Hejin Huang, Alfredo Alexander-Katz

According to the universality hypothesis, the phase behavior of different block copolymer melt models having fixed composition depends solely on two parameters: the invariant chain length $\bar{N}$ and the effective interaction parameter $\chi N$. If models behave universally, they can be compared to each other and can predict experiment quantitatively. Here

Jan Rozman, Matej Krajnc, Primož Ziherl

The mechanics of epithelial tissues, which is governed by forces generated in various cell domains, is often investigated using two-dimensional models that account for the apically-positioned actomyosin structures but neglect basolateral mechanics. We employ a more detailed three-dimensional model to study how lateral surface tensions affect the structure an

Yiming Zhou, Wei Dai

We consider minimizing a function consisting of a quadratic term and a proximable term which is possibly nonconvex and nonsmooth. This problem is also known as scaled proximal operator. Despite its simple form, existing methods suffer from slow convergence or high implementation complexity or both. To overcome these limitations, we develop a fast and user-fr

Michael Wiesheu, Theodor Komann, Melina Merkel, Sebastian Schöps

This work features the optimization of a Permanent Magnet Synchronous Motor using 2D nonlinear simulations in an Isogeometric Analysis framework. The rotor and stator designs are optimized for both geometric parameters and surface shapes via modifications of control points. The scaling laws for magnetism are employed to allow for axial and radial scaling, en

Ankur Khapre, Avulu Vinod Kumar, Rajadurai Chandrasekar

In the domain of mechanophotonics, achieving real-time applicability of organic crystals in visible light communication (VLC) technologies necessitates affordable light-emitting diodes (LEDs) as sources of light to run photonic devices through sustainable methods. Here in, we demonstrate an efficient strategy to excite (Z)-3-(3',5'-bis(trifluoromethy

Riccardo Fantoni

We propose a binary nonadditive Asakura-Oosawa-like mixture as an example for the classical limit of a Luttinger liquid. We determine the equation of state and structure of this mixture and discuss the paradoxical situation that one faces when working with a quantum liquid without a ground state. We then propose a new class of one dimensional classical fluid

Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers

We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is facilitated by well-designed state representations, which can surface important features of an environment and hide irrelevant ones. These state representations are typically manually

Peter Weber, Antonia Papandreou-Suppappola

A low-power, compact device for performing measurements in electrical systems with isolated voltage domains is proposed. Isolated measurements are required in numerous applications. For instance, a measurement of the bus voltage for a system with a high supply voltage and lower isolated local voltage level may be needed for system health monitoring and contr

Bryan C. Daniels, Enrico Borriello

Previous work in Boolean dynamical networks has suggested that the number of components that must be controlled to select an existing attractor is typically set by the number of attractors admitted by the dynamics, with no dependence on the size of the network. Here we study the rare cases of networks that defy this expectation, with attractors that require

Chantal Shaib, Joe Barrow, Alexa F. Siu, Byron C. Wallace

Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace. In practice one may now wish to choose confidently, but with minimal effort, the best performing summarization model when applied to a new domain or purpose. In this work, we empirically investigate the

Christoph Egger, Russell W. F. Lai, Viktoria Ronge, Ivy K. Y. Woo

In a ring-signature-based anonymous cryptocurrency, signers of a transaction are hidden among a set of potential signers, called a ring, whose size is much smaller than the number of all users. The ring-membership relations specified by the sets of transactions thus induce bipartite transaction graphs, whose distribution is in turn induced by the ring sample

Cristian Ramirez-Atencia, David Camacho

Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the

Rachel Xu, Nhu Le, Rebekah Park, Laura Murray

This paper investigates the relationship between social media and eating practices amongst 42 internet users aged 18-26. We conducted an ethnography in the US and India to observe how they navigated eating and health information online. We found that participants portrayed themselves online through a vocabulary we have labeled "the good life": performing hol

Zhiqi Bu, Xinwei Zhang, Mingyi Hong, Sheng Zha

The superior performance of large foundation models relies on the use of massive amounts of high-quality data, which often contain sensitive, private and copyrighted material that requires formal protection. While differential privacy (DP) is a prominent method to gauge the degree of security provided to the models, its application is commonly limited to the

Sarah E. Jones, Timilehin Ayanlade, Benjamin Fallen, Talukder Z. Jubery

Soybean production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, i.e. drought, emerges as a significant risk for soybean production, underscoring the need for advancements in stress monitoring for crop breeding and production. This project combines multi-modal information to identify the most eff

Peter K. F. Kuhfittig

Holding a Morris-Thorne wormhole open requires a violation of the null energy condition, calling for the need for so-called exotic matter near the throat. Many researchers consider exotic matter to be completely unphysical in classical general relativity. It has been shown, however, that the existence of an extra macroscopic dimension can resolve this issue:

Eric Galtier, Hae Ja Lee, Dimitri Khaghani, Nina Boiadjieva

The last decade has shown the great potential that X-ray Free Electron Lasers (FEL) have to study High Energy Density (HED) physics. Experiments at FELs have made significant breakthroughs in Shock Physics and Dynamic Diffraction, Dense Plasma Physics and Warm Dense Matter Science, using techniques such as isochoric heating, inelastic scattering, small angle

Cristian Ramirez-Atencia, Javier Del Ser, David Camacho

Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of in

Shijie Wang, Minsuk Shin, Ray Bai

Estimating the mixing density of a latent mixture model is an important task in signal processing. Nonparametric maximum likelihood estimation is one popular approach to this problem. If the latent variable distribution is assumed to be continuous, then bootstrapping can be used to approximate it. However, traditional bootstrapping requires repeated evaluati

Vilém Zouhar, Shuoyang Ding, Anna Currey, Tatyana Badeka

We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain. We use this dataset to investigate whether machine translation (MT) metrics which are fine-tuned on human-generated MT quality judgements are robust to domain shifts between training and inference. We find that fine-tune

Wajid Ali, Ayaz Akram

This paper presents our approach to accelerate computer architecture simulation by leveraging machine learning techniques. Traditional computer architecture simulations are time-consuming, making it challenging to explore different design choices efficiently. Our proposed model utilizes a combination of application features and micro-architectural features t

Zhongyuan Lyu, Ling Chen, Yuqi Gu

The latent class model is a widely used mixture model for multivariate discrete data. Besides the existence of qualitatively heterogeneous latent classes, real data often exhibit additional quantitative heterogeneity nested within each latent class. The modern latent class analysis also faces extra challenges, including the high-dimensionality, sparsity, and

Federico M. Zegers, Sean Phillips, Gregory P. Hicks

This work investigates the coverage control problem over a static, compact, and convex workspace and develops a hybrid extension of the continuous-time Lloyd algorithm. Each agent in a multi-agent system (MAS) is equipped with a timer mechanism that generates intermittent measurement and control update events, which may occur asynchronously between agents. B

Nikolaos Nakis, Abdulkadir Celikkanat, Louis Boucherie, Sune Lehmann

Understanding the structure and dynamics of scientific research, i.e., the science of science (SciSci), has become an important area of research in order to address imminent questions including how scholars interact to advance science, how disciplines are related and evolve, and how research impact can be quantified and predicted. Central to the study of Sci

Cristian Ramirez-Atencia, Victor Rodriguez-Fernandez, David Camacho

Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the Mission Planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variab

Indu Panigrahi, Richard Zhu

Most state-of-the-art computer vision models heavily depend on data. However, many datasets exhibit extreme class imbalance which has been shown to negatively impact model performance. Among the training-time and data-generation solutions that have been explored, one subset that leverages existing data is importance sampling. A good deal of this work focuses

Shira Yoffe, Amit Moscovich, Ariel Jaffe

Multimodal datasets, where measurements are obtained from multiple sensors, have become central to many scientific domains. In unsupervised settings, most representation learning methods focus on identifying shared latent structures, such as clusters or continuous processes that appear across modalities. However, some aspects of the data may be observed only

Nectarios C. Papanicolaou, Ivan C. Christov

A linear sixth-order partial differential equation (PDE) of ``parabolic'' type describes the dynamics of thin liquid films beneath surfaces with elastic bending resistance when deflections from the equilibrium film height are small. On a finite domain, the associated sixth-order eigenvalue problem is self-adjoint for the boundary conditions corresponding to

Jakub Przybyło

A locally irregular graph is a graph whose adjacent vertices have distinct degrees. It was conjectured that every connected graph is edge decomposable to $3$ locally irregular subgraphs, unless it belongs to a certain family of exceptions, including graphs of small maximum degrees, which are not decomposable to any number of such subgraphs. Recently Sedlar a

Jamie M. Karthein

As an important set of thermodynamic quantities, knowledge of the equation of state over a broad range of temperatures and chemical potentials in the QCD phase diagram is crucial for our understanding of strongly-interacting matter. There is a good understanding from first-principles results in lattice QCD, perturbative QCD and chiral effective field theory

Mark Sellke

The seminal 1975 work of Brascamp-Lieb-Lebowitz initiated the rigorous study of Ginzberg-Landau random surface models. It was conjectured therein that fluctuations are localized on $\mathbb Z^d$ when $d\geq 3$ for very general potentials, matching the behavior of the Gaussian free field. We confirm this behavior for all even potentials $U:\mathbb R\to\mathbb

Ismail Emir Yuksel, Yahya Can Tugrul, Ataberk Olgun, F. Nisa Bostanci

Processing-using-DRAM (PuD) is an emerging paradigm that leverages the analog operational properties of DRAM circuitry to enable massively parallel in-DRAM computation. PuD has the potential to reduce or eliminate costly data movement between processing elements and main memory. Prior works experimentally demonstrate three-input MAJ (MAJ3) and two-input AND

Hyunsoo Kim, Tristin Metz, Halyna Hodovanets, Daniel Kraft

Most of the half-Heusler RPtBi compounds (R=rare earth) host various surface states due to spin-orbit coupling driven topological band structure. While recent ARPES measurements ubiquitously reported the existence of surface states in RPtBi, their evidence by other experimental techniques remains elusive. Here we report the angle-dependent magnetic field res

Dejan Grubisic, Chris Cummins, Volker Seeker, Hugh Leather

Large language models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often produce repeated samples for low temperatures and incoherent samples for high temperatures. Furthermore, the temperature coefficient has to be tuned for each task, limiting its usab

Aleksandra Hamanowicz, Kirill Tchernyshyov, Julia Roman-Duval, Edward B. Jenkins

The cycling of metals between interstellar gas and dust is a critical aspect of the baryon cycle of galaxies, yet our understanding of this process is limited. This study focuses on understanding dust depletion effects in the low metallicity regime (< 20% Zo) typical of cosmic noon. Using medium-resolution UV spectroscopy from the COS onboard the Hubble Spac

Sridhar Mahadevan

In this paper, we propose GAIA, a generative AI architecture based on category theory. GAIA is based on a hierarchical model where modules are organized as a simplicial complex. Each simplicial complex updates its internal parameters biased on information it receives from its superior simplices and in turn relays updates to its subordinate sub-simplices. Par

Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan

Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) due to their impressive performance on various tasks. However, expensive training as well as inference remains a significant impediment to their widespread applicability. While enforcing sparsity at various levels of the model architecture has found promise in addre

ATLAS Collaboration

Searches for the exclusive decays of the Higgs boson into $D^*\gamma$ and of the $Z$ boson into $D^0\gamma$ and $K^0_s\gamma$ can probe flavour-violating Higgs and $Z$ boson couplings to light quarks. Searches for these decays are performed with a $pp$ collision data sample corresponding to an integrated luminosity of $136.3$ fb$^{-1}$ collected at $\sqrt{s}

Anthuan Ferino-Pérez, Thomas-C. Jagau

We report the first coupled-cluster study of Auger decay in heavy metals. The zinc atom is used as a case study due to its relevance to the Auger emission properties of the $^{67}$Ga radionuclide. Coupled-cluster theory combined with complex basis functions is used to describe the transient nature of the core-ionized zinc atom. We also introduce second-order

Graham Pash, Malik Hassanaly, Shashank Yellapantula

While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulation (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the mod

Sebastian Berns, Vanessa Volz, Laurissa Tokarchuk, Sam Snodgrass

Similarity estimation is essential for many game AI applications, from the procedural generation of distinct assets to automated exploration with game-playing agents. While similarity metrics often substitute human evaluation, their alignment with our judgement is unclear. Consequently, the result of their application can fail human expectations, leading to

Xin An, Gokce Basar, Mikhail Stephanov, Ho-Ung Yee

The state-of-the-art theoretical formalism for a covariant description of non-Gaussian fluctuation dynamics in relativistic fluids is discussed.

Alex Kim, Jia Huang, Rob Monarch, Jerry Kwac

Application developers advertise their Apps by creating product pages with App images, and bidding on search terms. It is then crucial for App images to be highly relevant with the search terms. Solutions to this problem require an image-text matching model to predict the quality of the match between the chosen image and the search terms. In this work, we pr

Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi, Kaushik Roy

Deep Neural Nets (DNNs) have become a pervasive tool for solving many emerging problems. However, they tend to overfit to and memorize the training set. Memorization is of keen interest since it is closely related to several concepts such as generalization, noisy learning, and privacy. To study memorization, Feldman (2019) proposed a formal score, however it

Sergey Y. Kotkovskiy

Based on the analysis of biquaternion quadratic forms of field, it is shown that Maxwell equations arise as a consequence of the principle of conservation of the energy-momentum flow of field in space-time. It turns out that this principle presupposes the existence more general nonlinear field equations. Classical linear Maxwell equations are embedded in a s

René Carmona, Claire Zeng

Recently, a deep-learning algorithm referred to as Deep Galerkin Method (DGM), has gained a lot of attention among those trying to solve numerically Mean Field Games with finite horizon, even if the performance seems to be decreasing significantly with increasing horizon. On the other hand, it has been proven that some specific classes of Mean Field Games en

Vivien Cabannes, Berfin Simsek, Alberto Bietti

This work focuses on the training dynamics of one associative memory module storing outer products of token embeddings. We reduce this problem to the study of a system of particles, which interact according to properties of the data distribution and correlations between embeddings. Through theory and experiments, we provide several insights. In overparameter

A. Tichai, K. Kapás, T. Miyagi, M. A. Werner

The recently proposed combination of the valence-space in-medium similarity renormalization group (VS-IMSRG) with the density matrix renormalization group (DMRG) offers a scalable and flexible many-body approach for strongly correlated open-shell nuclei. We use the VS-DMRG to investigate the low-lying spectroscopy of N=50 isotones, which are characteristic f

Xiao Chen, Silas Hoffman, James N. Fry, Hai-Ping Cheng

We simulate the coherence of two coupled electron spins interacting with a bath of nuclei using the generalized cluster correlation expansion (gCCE) method. An exchange interaction between the electrons facilitates a family of entangling gates that can be spoiled by nuclear-induced dephasing. Consequently, we study the dephasing of the coherent two-electron

Alec Dektor

We introduce new methods for integrating nonlinear differential equations on low-rank manifolds. These methods rely on interpolatory projections onto the tangent space, enabling low-rank time integration of vector fields that can be evaluated entry-wise. A key advantage of our approach is that it does not require the vector field to exhibit low-rank structur

Yangfan Shi, Feng Long, Gregory J. Herczeg, Daniel Harsono

High-angular resolution imaging by ALMA has revealed the near-universality and diversity of substructures in protoplanetary disks. However, disks around M-type pre-main-sequence stars are still poorly sampled, despite the prevalence of M-dwarfs in the galaxy. Here we present high-resolution (~50 mas, 8 au) ALMA Band 6 observations of six disks around mid-M s

Apostolos I. Rikos, Jiaqi Hu, Themistoklis Charalambous, Karl Henrik Johannson

We present DMaC, a novel distributed, finite-time algorithm that guarantees max-consensus in directed networks with unreliable communication links experiencing packet drops. Unlike existing methods, DMaC ensures all nodes compute the exact maximum state under arbitrary packet loss patterns. It incorporates a fully distributed termination mechanism, enabling

Alexander Unnervik, Hatef Otroshi Shahreza, Anjith George, Sébastien Marcel

Backdoor attacks allow an attacker to embed a specific vulnerability in a machine learning algorithm, activated when an attacker-chosen pattern is presented, causing a specific misprediction. The need to identify backdoors in biometric scenarios has led us to propose a novel technique with different trade-offs. In this paper we propose to use model pairs on

Soham Ghosh

The Casas-Alvero conjecture predicts that every univariate polynomial over an algebraically closed field of characteristic zero sharing a common factor with each of its Hasse-Schmidt derivatives is a power of a linear polynomial. The conjecture for polynomials of a fixed degree is equivalent to the projective variety of such polynomials being one-dimensional

Son Nguyen, Pavlo Pylyavskyy

Elements of Lusztig's dual canonical bases are Schur-positive when evaluated on (generalized) Jacobi-Trudi matrices. This deep property was proved by Rhoades and Skandera, relying on a result of Haiman, and ultimately on the (proof of) Kazhdan-Lusztig conjecture. For a particularly tractable part of the dual canonical basis - called Temperley-Lieb immanants

Andrew Eells, Brandon Dave, Pascal Hitzler, Cogan Shimizu

The previously introduced Modular Ontology Modeling methodology (MOMo) attempts to mimic the human analogical process by using modular patterns to assemble more complex concepts. To support this, MOMo organizes organizes ontology design patterns into design libraries, which are programmatically queryable, to support accelerated ontology development, for both

Asaf Ferber, Liam Hardiman

We are presented with a graph, $G$, on $n$ vertices with $m$ edges whose edge set is unknown. Our goal is to learn the edges of $G$ with as few queries to an oracle as possible. When we submit a set $S$ of vertices to the oracle, it tells us whether or not $S$ induces at least one edge in $G$. This so-called OR-query model has been well studied, with Angluin

Andrew Ellis, Ran Spiegler

A representative researcher has repeated opportunities for empirical research. To process findings, she must impose an "identifying assumption." She conducts research when the assumption is sufficiently plausible (taking into account both current beliefs and the quality of the opportunity), and updates beliefs as if the assumption were perfectly valid. We st

Ana María Botero, Kiumars Kaveh, Christopher Manon

We define equivariant Chern classes of a toric vector bundle over a proper toric scheme over a DVR. We provide a combinatorial description of them in terms of piecewise polynomial functions on the polyhedral complex associated to the toric scheme, which factorize through to an extended Bruhat--Tits building. We further motivate this definition from an arithm

Rüdiger Kürsten

Clustering is one of the mayor collective phenomena observed in active matter. We study the overdamped motion of interacting active Brownian particles in two dimensions. An instability in the pair correlation function causes the onset of clustering. This clustering mechanism depends mainly on the self-propulsion properties of the active particles and details

Tobias Boltz, Jose L. Martinez, Connie Xu, Kathryn R. L. Baker

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into

Dominic Guri, Moonyoung Lee, Oliver Kroemer, George Kantor

This paper presents a modular, reconfigurable robot platform for robot manipulation in agriculture. While robot manipulation promises great advancements in automating challenging, complex tasks that are currently best left to humans, it is also an expensive capital investment for researchers and users because it demands significantly varying robot configurat

Diego A. Riva, Carolina A. Evangelista, Paul F. Puleston, Luis Corsiglia

This work presents an algorithm for determining the parameters of a nonlinear dynamic model of the respiratory system in patients undergoing assisted ventilation. Using the pressure and flow signals measured at the mouth, the model's quadratic pressure-volume characteristic is fit to this data in each respiratory cycle by appropriate estimates of the model p

Jialu Bao, Emanuele D'Osualdo, Azadeh Farzan

We present Bluebell, a program logic for reasoning about probabilistic programs where unary and relational styles of reasoning come together to create new reasoning tools. Unary-style reasoning is very expressive and is powered by foundational mechanisms to reason about probabilistic behaviour like independence and conditioning. The relational style of reaso

Alia Gilbert, Sachit Krishnan, R. Brent Gillespie

A human operator using a manual control interface has ready access to their own command signal, both by efference copy and proprioception. In contrast, a human supervisor typically relies on visual information alone. We propose supplying a supervisor with a copy of the operators command signal, hypothesizing improved performance, especially when that copy is

Gabriele Grillo, Dario D. Monticelli, Fabio Punzo

We consider the porous medium equation (PME) on complete noncompact manifolds $M$ of nonnegative Ricci curvature. We require nonparabolicity of the manifold and construct a natural space $X$ of functions, strictly larger than $L^1$, in which the Green function on $M$ appears as a weight, such that the PME admits a solution in the weak dual (i.e. potential) s

Skyler Wang

Recent work in CHI and CSCW has devoted increasing attention to how the design of network hospitality platforms shapes user experiences and relational outcomes. In this article, I interrogate how different risk factors emerge based on the type of exchanges these platforms facilitate. To do so, I juxtapose two prominent network hospitality platforms: one faci

David F. Anderson, Ayman Badawi, Jim Coykendall

Let $R$ be a commutative ring with $1 \neq 0$. A proper ideal $I$ of $R$ is a {\it square-difference factor absorbing ideal} (sdf-absorbing ideal) of $R$ if whenever $a^2 - b^2 \in I$ for $0 \neq a, b \in R$, then $a + b \in I$ or $a - b \in I$. In this paper, we introduce and investigate sdf-absorbing ideals.

Satvik Singh, Mizanur Rahaman, Nilanjana Datta

Consider an open quantum system with (discrete-time) Markovian dynamics. Our task is to store information in the system in such a way that it can be retrieved perfectly, even after the system is left to evolve for an arbitrarily long time. We show that this is impossible for classical (resp. quantum) information precisely when the dynamics is mixing (resp. a

Niloofar Yousefi, Mainuddin Shaik, Nitin Agarwal

This study aims to investigate the comprehensive characterization of information content in multimedia (videos), particularly on YouTube. The research presents a multi-method framework for characterizing multimedia content by clustering signals from various modalities, such as audio, video, and text. With a focus on South China Sea videos as a case study, th