February 2024 arXiv papers — page 20

Eduardo Franco Sotelo Bazán

In this work was researched the problems of classicalization and measurement unifying them in a single problem: the problem of collapse, for this purpose was developed a working program -- the classicalization's program -- in an interpretation of objective collapse that was proposed for this purpose, and which overcomes the limitations of the official interp

Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training toke

Ignacio Soto, Felipe Jimenez, Maria Calderon, Jose E. Naranjo

There are different proposals in the literature on how to protect pedestrians using warning systems to alert drivers of their presence. They can be based on onboard perception systems or wireless communications. The evaluation of these systems has been focused on testing their ability to detect pedestrians. A problem that has received much less attention is

Mingjie Sun, Xinlei Chen, J. Zico Kolter, Zhuang Liu

We observe an empirical phenomenon in Large Language Models (LLMs) -- very few activations exhibit significantly larger values than others (e.g., 100,000 times larger). We call them massive activations. First, we demonstrate the widespread existence of massive activations across various LLMs and characterize their locations. Second, we find their values larg

Remmy Zen, Jan Olle, Luis Colmenarez, Matteo Puviani

The realization of large-scale quantum computers requires not only quantum error correction (QEC) but also fault-tolerant operations to handle errors that propagate into harmful errors. Recently, flag-based protocols have been introduced that use ancillary qubits to flag harmful errors. However, there is no clear recipe for finding a fault-tolerant quantum c

Yunfeng Chen, Yong Liu, Jinhao Xiao, Qunying Wu

This paper investigates a wireless-powered Internet of Things (IoT) network comprising a hybrid access point (HAP) and two devices. The HAP facilitates downlink wireless energy transfer (WET) for device charging and uplink wireless information transfer (WIT) to collect status updates from the devices. To keep the information fresh, concurrent WET and WIT are

Samuel Yen-Chi Chen

Quantum Machine Learning (QML) has surfaced as a pioneering framework addressing sequential control tasks and time-series modeling. It has demonstrated empirical quantum advantages notably within domains such as Reinforcement Learning (RL) and time-series prediction. A significant advancement lies in Quantum Recurrent Neural Networks (QRNNs), specifically ta

Yuxian Gu, Li Dong, Yaru Hao, Qingxiu Dong

This work studies the general principles of improving the learning of language models (LMs), which aims at reducing the necessary training steps for achieving superior performance. Specifically, we present a theory for the optimal learning of LMs. We first propose an objective that optimizes LM learning by maximizing the data compression ratio in an "LM-trai

Marsil Zakour, Partha Pratim Nath, Ludwig Lohmer, Emre Faik Gökçe

Hand-Object Interactions (HOIs) are conditioned on spatial and temporal contexts like surrounding objects, previous actions, and future intents (for example, grasping and handover actions vary greatly based on objects proximity and trajectory obstruction). However, existing datasets for 4D HOI (3D HOI over time) are limited to one subject interacting with on

Eric Hyyppä, Antti Vepsäläinen, Miha Papič, Chun Fai Chan

Improving the speed and fidelity of quantum logic gates is essential to reach quantum advantage with future quantum computers. However, fast logic gates lead to increased leakage errors in superconducting quantum processors based on qubits with low anharmonicity, such as transmons. To reduce leakage errors, we propose and experimentally demonstrate two new a

Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas

We study the problem of learning Single-Index Models under the $L_2^2$ loss in the agnostic model. We give an efficient learning algorithm, achieving a constant factor approximation to the optimal loss, that succeeds under a range of distributions (including log-concave distributions) and a broad class of monotone and Lipschitz link functions. This is the fi

Latifeh PourMohammadBagher, Najmieh Sadat Safarabadi

Gamification has the potential to make significant contributions to financial product delivery, Fintech services, and inclusive growth. The integration of gamification into FinTech applications has shown a positive correlation with the social impact theory. Utilizing gamification in a sustainable and effective manner can be crucial for long-term prospects in

Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal

Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models (LLMs) and retrieval augmented generation (RAG) techniques, their efficacy in very long-term dialogues remains unexplored. To address this research gap, we i

Alexandre Barbosa, Hugo Terças, Emmanuel Zambrini Cruzeiro

We propose a non-cryogenic optical quantum memory for noble-gas nuclear spins based on the Atomic Frequency Comb (AFC) protocol. Owing to the hours-long coherence lifetimes of the noble-gas spins and the large bandwidth provided by the AFC independently of the optical depth, we estimate a time-bandwidth product of up to $9.7 \times 10^{15}$ for a realistic e

Michael A. Gerber, Ronald Schroeter, Daniel Johnson, Christian P. Janssen

This paper reports results from a high-fidelity driving simulator study (N=215) about a head-up display (HUD) that conveys a conditional automated vehicle's dynamic "uncertainty" about the current situation while fallback drivers watch entertaining videos. We compared (between-group) three design interventions: display (a bar visualisation of uncertainty clo

Gaoyuan Wang, Jonathan Warrell, Prashant S. Emani, Mark Gerstein

A major challenge in quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencode

Xihan Xiong, Zhipeng Wang, Qin Wang

Staking has emerged as a crucial concept following Ethereum's transition to Proof-of-Stake consensus. The introduction of Liquid Staking Derivatives (LSDs) has effectively addressed the illiquidity issue associated with solo staking, gaining significant market attention. This paper analyzes the LSD market dynamics from the perspectives of both liquidity take

Leon Lang, Davis Foote, Stuart Russell, Anca Dragan

Past analyses of reinforcement learning from human feedback (RLHF) assume that the human evaluators fully observe the environment. What happens when human feedback is based only on partial observations? We formally define two failure cases: deceptive inflation and overjustification. Modeling the human as Boltzmann-rational w.r.t. a belief over trajectories,

Henrique Bursztyn, Miquel Cueca, Rajan Amit Mehta

This paper studies graded manifolds with local coordinates concentrated in non-negative degrees. We provide a canonical description of these objects in terms of classical geometric data and, building on this geometric viewpoint, we prove the Frobenius theorem for distributions in this graded setting.

Raunak Manekar, Elisa Negrini, Minh Pham, Daniel Jacobs

Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for applications involving radiation-sensitive samples. However, most PR methods struggle in low-dose scenarios due to high shot noise. Recent advancements in optical da

Alexander Oberstrass, Jordan DeKraker, Nicola Palomero-Gallagher, Sascha E. A. Muenzing

Understanding the cortical organization of the human brain requires interpretable descriptors for distinct structural and functional imaging data. 3D polarized light imaging (3D-PLI) is an imaging modality for visualizing fiber architecture in postmortem brains with high resolution that also captures the presence of cell bodies, for example, to identify hipp

Sam Estep, Wode Ni, Raven Rothkopf, Joshua Sunshine

Reverse-mode automatic differentiation (autodiff) has been popularized by deep learning, but its ability to compute gradients is also valuable for interactive use cases such as bidirectional computer-aided design, embedded physics simulations, visualizing causal inference, and more. Unfortunately, the web is ill-served by existing autodiff frameworks, which

Remi J. Leano, Aurora Pribram-Jones, David A. Strubbe

Ensemble Density Functional Theory (EDFT) is a generalization of ground-state Density Functional Theory (GS DFT), which is based on an exact formal theory of finite collections of a system's ground and excited states. EDFT in various forms has been shown to improve the accuracy of calculated energy level differences in isolated model systems, atoms, and mole

Carlos A. P. Bengaly, Cássio Pigozzo, Jailson S. Alcaniz

We use a recent Pantheon+SH0ES compilation of Type Ia Supernova distance measurements at low-redshift, i.e., $0.01 \leq z \leq 0.10$, in order to investigate the directional dependency of the deceleration parameter ($q_0$) in different patches ($60^{\circ}$ size) across the sky, as a probe of the statistical isotropy of the Universe. We adopt a cosmographic

T. G. Seidel, A. Bartolo, A. Garnache, M. Giudici

We demonstrate the existence of a multiplicity of co-existing frequency combs in a harmonically mode-locked laser that we link to the splay phases of the Kuramoto model with short range interactions. These splay states are multistable and the laser may wander between them under the influence of stochastic forces. Consequently, the many pulses circulating in

Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin

The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals

CMS Collaboration

The first observation of the decay $\Xi^-_\mathrm{b}$ $\to$ $\psi$(2S)$\Xi^-$ and measurement of the branching ratio of $\Xi^-_\mathrm{b}$ $\to$ $\psi$(2S)$\Xi^-$ to $\Xi^-_\mathrm{b}$ $\to$ J/$\psi$$\Xi^-$ are presented. The J/$\psi$ and $\psi$(2S) mesons are reconstructed using their dimuon decay modes. The results are based on proton-proton colliding beam

Alex J. Feingold, Axel Kleinschmidt, Hermann Nicolai

We propose a novel way to define imaginary root subgroups associated with (timelike) imaginary roots of hyperbolic Kac-Moody algebras. Using in an essential way the theory of unitary irreducible representation of covers of the group SO(2,1), these imaginary root subgroups act on the complex Kac-Moody algebra viewed as a Hilbert space. We illustrate our new v

Adela Frances DePavia, Erasmo Tani, Ali Vakilian

We consider the problem of graph searching with prediction recently introduced by Banerjee et al. (2022). In this problem, an agent, starting at some vertex $r$ has to traverse a (potentially unknown) graph $G$ to find a hidden goal node $g$ while minimizing the total distance travelled. We study a setting in which at any node $v$, the agent receives a noisy

Cameron Churchwell, Max Morrison, Bryan Pardo

A phonetic posteriorgram (PPG) is a time-varying categorical distribution over acoustic units of speech (e.g., phonemes). PPGs are a popular representation in speech generation due to their ability to disentangle pronunciation features from speaker identity, allowing accurate reconstruction of pronunciation (e.g., voice conversion) and coarse-grained pronunc

Luke B. Handley, Erik A. Petigura, Velibor V. Misic, Jack Lubin

Precise Doppler studies of extrasolar planets require fine-grained control of observational cadence, i.e. the timing of and spacing between observations. We present a novel framework for scheduling a set of Doppler campaigns with different cadence requirements at the W. M. Keck Observatory (WMKO). For a set of observing programs and allocated nights on an in

Duarte M. Alves, José Pombal, Nuno M. Guerreiro, Pedro H. Martins

While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task. In this paper, we propose a recipe for tailoring LLMs to multiple tasks present in translation workflows. We perform continued pretraining on a mu

Rong Jiang, Cong Ma

We study nonparametric contextual bandits under batch constraints, where the expected reward for each action is modeled as a smooth function of covariates, and the policy updates are made at the end of each batch of observations. We establish a minimax regret lower bound for this setting and propose a novel batch learning algorithm that achieves the optimal

Manasa Manasa, Mohammad Azam, Tatiana Zajarniuk, Ryszard Diduszko

AeAFe4As4 (Ae = Ca, A = K; 1144) having a transition temperature of 35 K is a stoichiometric family of iron-based superconductors (FBS). Here, we present a detailed study of a high-pressure synthesis of CaKFe4As4 bulks to investigate the impact of these conditions on the superconducting properties of the 1144 family. Additionally, these samples are also prep

Fabian Spaeh, Charalampos E. Tsourakakis

Sequential data naturally arises from user engagement on digital platforms like social media, music streaming services, and web navigation, encapsulating evolving user preferences and behaviors through continuous information streams. A notable unresolved query in stochastic processes is learning mixtures of continuous-time Markov chains (CTMCs). While there

Yanghao Zhang, Tianle Zhang, Ronghui Mu, Xiaowei Huang

Although adversarial training (AT) has proven effective in enhancing the model's robustness, the recently revealed issue of fairness in robustness has not been well addressed, i.e. the robust accuracy varies significantly among different categories. In this paper, instead of uniformly evaluating the model's average class performance, we delve into the issue

Nicolas Argibay, Duane D. Johnson, Michael Chandross, Ryan T. Ott

The metallurgy and materials communities have long known and exploited fundamental links between chemical and structural ordering in metallic solids and their mechanical properties. The highest reported strength achievable through the combination of multiple metals (alloying) has rapidly climbed and given rise to new classifications of materials with extraor

Jeffrey M. Epstein

Building high-fidelity quantum computers requires efficient methods for the characterization of gate errors that provide actionable information that may be fed back into engineering efforts. Extraction of realistic error models is also critical to accurate simulation and design of quantum circuits, including those used in quantum error correction. We provide

Yanpeng Sun, Jiahui Chen, Shan Zhang, Xinyu Zhang

In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model. In essence, VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target i

Hanan Gani, Muzammal Naseer, Fahad Khan, Salman Khan

Volumetric medical segmentation is a critical component of 3D medical image analysis that delineates different semantic regions. Deep neural networks have significantly improved volumetric medical segmentation, but they generally require large-scale annotated data to achieve better performance, which can be expensive and prohibitive to obtain. To address thi

Harbir Antil, Rohit Khandelwal

Elliptic reconstruction property, originally introduced by Makridakis and Nochetto for linear parabolic problems, is a well-known tool to derive optimal a posteriori error estimates. No such results are known for nonlinear and nonsmooth problems such as parabolic variational inequalities (VIs). This article establishes the elliptic reconstruction property fo

Yazhou Xing, Yingqing He, Zeyue Tian, Xintao Wang

Video and audio content creation serves as the core technique for the movie industry and professional users. Recently, existing diffusion-based methods tackle video and audio generation separately, which hinders the technique transfer from academia to industry. In this work, we aim at filling the gap, with a carefully designed optimization-based framework fo

Ilyas Fatkhullin, Niao He

This paper revisits the convergence of Stochastic Mirror Descent (SMD) in the contemporary nonconvex optimization setting. Existing results for batch-free nonconvex SMD restrict the choice of the distance generating function (DGF) to be differentiable with Lipschitz continuous gradients, thereby excluding important setups such as Shannon entropy. In this wor

Hari Subramonyam, Divy Thakkar, Andrew Ku, Jürgen Dieber

Generative AI models are increasingly being integrated into human task workflows, enabling the production of expressive content across a wide range of contexts. Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies. This shift requires a deeper understanding of how c

Siddhartha Banerjee, Alankrita Bhatt, Christina Lee Yu

We devise an online learning algorithm -- titled Switching via Monotone Adapted Regret Traces (SMART) -- that adapts to the data and achieves regret that is instance optimal, i.e., simultaneously competitive on every input sequence compared to the performance of the follow-the-leader (FTL) policy and the worst case guarantee of any other input policy. We sho

Bradford A. Barker, Arabi Seshappan, David A. Strubbe

Spin-flip methods applied to excited-state approaches like the Bethe-Salpeter Equation allow access to the excitation energies of open-shell systems, such as molecules and defects in solids. The eigenstates of these solutions, however, are generally not eigenstates of the spin operator $\hat{S}^2$. Even for simple cases where the excitation vector is expecte

Vispi Karkaria, Anthony Goeckner, Rujing Zha, Jie Chen

Laser-directed-energy deposition (DED) offers advantages in additive manufacturing (AM) for creating intricate geometries and material grading. Yet, challenges like material inconsistency and part variability remain, mainly due to its layer-wise fabrication. A key issue is heat accumulation during DED, which affects the material microstructure and properties

Ayana Niwa, Hayate Iso

We introduce AmbigNLG, a novel task designed to tackle the challenge of task ambiguity in instructions for Natural Language Generation (NLG). Ambiguous instructions often impede the performance of Large Language Models (LLMs), especially in complex NLG tasks. To tackle this issue, we propose an ambiguity taxonomy that categorizes different types of instructi

Kunhao Zhong, Evan Saraivanov, James Caputi, Vivian Miranda

We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $\Lambda$CDM, we forecast how well LSST Year 1 data wil

Kai-Min Chung, Eli Goldin, Matthew Gray

Recent work has introduced the "Quantum-Computation Classical-Communication" (QCCC) (Chung et. al.) setting for cryptography. There has been some evidence that One Way Puzzles (OWPuzz) are the natural central cryptographic primitive for this setting (Khurana and Tomer). For a primitive to be considered central it should have several characteristics. It shoul

Ivan Oleksiyuk, John Andrew Raine, Michael Krämer, Svyatoslav Voloshynovskiy

We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or jet observables, and separates potentially anomalous clusters to construct a signal-enriched region. The spectra of a selected observable (e.g. invariant mass) in these two regions

Mahadevan Ganesh, Stuart C. Hawkins, Darko Volkov

We introduce a new system of surface integral equations for Maxwell's transmission problem in three dimensions. This system has two remarkable features, both of which we prove. First, it is well-posed at all frequencies. Second, the underlying linear operator has a uniformly bounded inverse as the frequency approaches zero, ensuring that there is no low-freq

Alexander Rieder

We consider a novel way of discretizing wave scattering problems using the general formalism of convolution quadrature, but instead of reducing the timestep size ($h$-method), we achieve accuracy by increasing the order of the method ($p$-method). We base this method on discontinuous Galerkin timestepping and use the Z-transform. We show that for a certain c

Arbaz Khan, Felipe Lepe, Jesus Vellojin

This paper studies the family of interior penalty discontinuous Galerkin methods for solving the Herrmann formulation of the linear elasticity eigenvalue problem in heterogeneous media. By employing a weighted Lam\'e coefficient norm within the framework of non-compact operators theory, we prove convergence of both continuous and discrete eigenvalue problems

Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia

In neural network binarization, BinaryConnect (BC) and its variants are considered the standard. These methods apply the sign function in their forward pass and their respective gradients are backpropagated to update the weights. However, the derivative of the sign function is zero whenever defined, which consequently freezes training. Therefore, implementat

Yi Hu, Xiaojuan Tang, Haotong Yang, Muhan Zhang

Despite the impressive performance in a variety of complex tasks, modern large language models (LLMs) still have trouble dealing with some math problems that are simple and intuitive for humans, such as addition. While we can easily learn basic rules of addition and apply them to new problems of any length, LLMs struggle to do the same. Instead, they may rel

Drew Scott, Satyanarayana G. Manyam, Isaac E. Weintraub, David W. Casbeer

Hybrid fuel Unmanned Aerial Vehicles (UAV), through their combination of multiple energy sources, offer several advantages over the standard single fuel source configuration, the primary one being increased range and efficiency. Multiple power or fuel sources also allow the distinct pitfalls of each source to be mitigated while exploiting the advantages with

Paula Magrinya, Pablo Palacios, Pablo Llombart, Rafael Delgado-Buscalioni

The interaction of surfaces in relative motion in wet environments is dominated by lubrication forces, which play a pivotal role in the dynamics of microscopic systems. Here, we develop motile vesicles that exploit lubrication forces to roll on substrates. The activity of the vesicle comes from the confined rotational flow generated by a driven rotating part

Junzhe Chen, Qiao Yang, Senmao Tian, Shunli Zhang

It is critical to deploy complicated neural network models on hardware with limited resources. This paper proposes a novel model quantization method, named the Low-Cost Proxy-Based Adaptive Mixed-Precision Model Quantization (LCPAQ), which contains three key modules. The hardware-aware module is designed by considering the hardware limitations, while an adap

Disha Makhija, Joydeep Ghosh, Yejin Kim

Machine learning methods for estimating heterogeneous treatment effects (HTE) facilitate large-scale personalized decision-making across various domains such as healthcare, policy making, education, and more. Current machine learning approaches for HTE require access to substantial amounts of data per treatment, and the high costs associated with interventio

Ruby Sedgwick, John P. Goertz, Molly M. Stevens, Ruth Misener

With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of expe

Joshua R. Tempelman, Tobias Weidemann, Eric B. Flynn, Kathryn H. Matlack

Clusters of wave-scattering oscillators offer the ability to passively control wave energy in elastic continua. However, designing such clusters to achieve a desired wave energy pattern is a highly nontrivial task. While the forward scattering problem may be readily analyzed, the inverse problem is very challenging as it is ill-posed, high-dimensional, and k

Fatemeh Tavakkoli, Pouria Sarhadi, Benoit Clement, Wasif Naeem

Deep Reinforcement Learning (DRL) techniques have received significant attention in control and decision-making algorithms. Most applications involve complex decision-making systems, justified by the algorithms' computational power and cost. While model-based versions are emerging, model-free DRL approaches are intriguing for their independence from models,

Suresh Bolusani, Mathieu Besançon, Ksenia Bestuzheva, Antonia Chmiela

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements

Satvik Venkatesh, Arthur Benilov, Philip Coleman, Frederic Roskam

There have been significant advances in deep learning for music demixing in recent years. However, there has been little attention given to how these neural networks can be adapted for real-time low-latency applications, which could be helpful for hearing aids, remixing audio streams and live shows. In this paper, we investigate the various challenges involv

Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva

Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To help address this question, we introduce RAVEL (Resolving Attribute-Value Entanglements in Language Models), a dataset that enables tightly controlled, quantitative comparisons be

Patrick Pynadath, Riddhiman Bhattacharya, Arun Hariharan, Ruqi Zhang

Discrete distributions, particularly in high-dimensional deep models, are often highly multimodal due to inherent discontinuities. While gradient-based discrete sampling has proven effective, it is susceptible to becoming trapped in local modes due to the gradient information. To tackle this challenge, we propose an automatic cyclical scheduling, designed fo

Ion Victor Gosea, Luisa Peterson, Pawan Goyal, Jens Bremer

In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This is a non-intrusive, data-driven method for learning dynamical systems from time-domain data. The application in our study is carbon dioxide methanation, an important reaction withi

Elspeth K. H. Lee

The calculation of internal atmospheric (longwave) fluxes is a key component of any model of exoplanet atmospheres that requires radiative-transfer (RT) calculations. For atmospheres containing a strong scattering component such as cloud particles, most 1D multiple-scattering RT methods typically involve numerically expensive matrix inversions. This computat

William W. Symes

Adaptive Waveform Inversion (AWI) applied to transient transmitted wave data can yield estimates of index of refraction (or wave velocity) similar to those obtained by travel time inversion. The AWI objective function measures normalized mean-square dispersion about zero time of a family of filters, one filter for each source-reeciver pair, designed to match

Negar Heidari, Alexandros Iosifidis

Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process. By harnessing the power of machine learning-based methods, CAD designers can optimize their workflows, save time and effort while making

Logan E. Beaver

This letter addresses the constraint compatibility problem of control barrier functions (CBFs), which occurs when a safety-critical CBF requires a system to apply more control effort than it is capable of generating. This inevitably leads to a safety violation, which transitions the system to an unsafe (and possibly dangerous) trajectory. We resolve the cons

Nicolas Heurtel

Linear optical circuits can be used to manipulate the quantum states of photons as they pass through components including beam splitters and phase shifters. Those photonic states possess a particularly high level of expressiveness, as they reside within the bosonic Fock space, an infinite-dimensional Hilbert space. However, in the domain of linear optical qu

Kaspar Rufibach, Jan Beyersmann, Tim Friede, Claudia Schmoor

The SAVVY project aims to improve the analyses of adverse events (AEs) in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). This paper summarizes key features and conclusions from the various SAVVY papers. Through theoretical investigations using simulations and in an empiric

Juan Wang, Hongguang Liang, Yan Li, Chuan-Hsun Li

We investigate the expansion dynamics of spin-orbit-coupled Bose-Einstein condensates subjected to a synthetic magnetic field, after their release from an external harmonic trap. Our findings reveal that the condensate experiences a spin-dependent rotation and separation due to the rigid-like rotational velocity field, which leads to a spin density deflectio

Divya Garikapati, Sneha Sudhir Shetiya

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of Artificial Intelligence (AI) and learning algorithms, propelling vehicles into realms of unprecedented autonomy. This paper provides a comprehensive explo

Noor Ul Ain, Rodrigo Hernangómez, Alexandros Palaios, Martin Kasparick

Reliable wireless communications play an important role in the automotive industry as it helps to enhance current use cases and enable new ones such as connected autonomous driving, platooning, cooperative maneuvering, teleoperated driving, and smart navigation. These and other use cases often rely on specific quality of service (QoS) levels for communicatio

Sai Swetha Venkata Kolluru, Nicolas Besse, Rahul Pandit

Spectral methods yield numerical solutions of the Galerkin-truncated versions of nonlinear partial differential equations involved especially in fluid dynamics. In the presence of discontinuities, such as shocks, spectral approximations develop Gibbs oscillations near the discontinuity. This causes the numerical solution to deviate quickly from the true solu

Roghayeh Maleki, Andriaherimanana Sarobidy Razafimahatratra

Let $p$ be a prime and $q = p^k$. A subset $\mathcal{F} \subset \operatorname{\Gamma L}_{2}(q)$ is intersecting if any two semilinear transformations in $\mathcal{F}$ agree on some non-zero vector in $\mathbb{F}_q^2$. We show that any intersecting set of $\operatorname{\Gamma L}_{2}(q)$ is of size at most that of a stabilizer of a non-zero vector, and we cha

Luis Itza Vazquez-Salazar, Silvan Käser, Markus Meuwly

Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles, Deep Evidential Regression (DER), and Gaussian Mixture Models (GMM) - were applied to the H-transfer reaction between ${\it syn-}$Criegee and vinyl hydroxyperoxide. The results i

Jia-Bao Ji, Zhaoheng Guo, Taran Driver, Cynthia S. Trevisan

Attosecond photoemission or photoionization delays are a unique probe of the structure and the electronic dynamics of matter. However, spectral congestion and spatial delocalization of valence electron wave functions set fundamental limits to the complexity of systems that can be studied and the information that can be retrieved, respectively. Using attoseco

Fabien Le Floc'h

We present closed analytical approximations for the pricing of basket options, also applicable to Asian options with discrete averaging under the Black-Scholes model with time-dependent parameters. The formulae are obtained by using a stochastic Taylor expansion around a log-normal proxy model and are found to be highly accurate for Asian options in practice

Rohit Kumar Mishra, Chandni Thakkar

In this paper, a restricted transverse ray transform acting on vector and symmetric $m$-tensor fields is studied. We developed inversion algorithms using restricted transverse ray transform data to recover symmetric $m$-tensor fields in $\mathbb{R}^3$ and vector fields in $\mathbb{R}^n$. We restrict the transverse ray transform to all lines going through a f

Tamara Czinczoll, Christoph Hönes, Maximilian Schall, Gerard de Melo

While (large) language models have significantly improved over the last years, they still struggle to sensibly process long sequences found, e.g., in books, due to the quadratic scaling of the underlying attention mechanism. To address this, we propose NextLevelBERT, a Masked Language Model operating not on tokens, but on higher-level semantic representation

Cale Rankin, Ting-Kam Leonard Wong

We modify the JKO scheme, which is a time discretization of Wasserstein gradient flows, by replacing the Wasserstein distance with more general transport costs on manifolds. We show when the cost function has a mixed Hessian which defines a Riemannian metric, our modified JKO scheme converges under suitable conditions to the corresponding Riemannian Fokker--

Huiyu Xiong, Lanxiao Wang, Heqian Qiu, Taijin Zhao

To address the problem of catastrophic forgetting due to the invisibility of old categories in sequential input, existing work based on relatively simple categorization tasks has made some progress. In contrast, video captioning is a more complex task in multimodal scenario, which has not been explored in the field of incremental learning. After identifying

Ashwin Prasad Shivarpatna Venkatesh, Samkutty Sabu, Amir M. Mir, Sofia Reis

The application of Large Language Models (LLMs) in software engineering, particularly in static analysis tasks, represents a paradigm shift in the field. In this paper, we investigate the role that current LLMs can play in improving callgraph analysis and type inference for Python programs. Using the PyCG, HeaderGen, and TypeEvalPy micro-benchmarks, we evalu

Mohammad Sadil Khan, Elona Dupont, Sk Aziz Ali, Kseniya Cherenkova

Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD-SIGNet, an end-to-end trainable and auto-regressive architecture to recover the design history of a CAD model represented

Deja Workman, Christopher L. Dancy

In this paper we hope to identify how the typical or standard artificial intelligence development process encourages or facilitates the creation of racialized technologies. We begin by understanding Sylvia Wynter's definition of the biocentric Man genre and its exclusion of Blackness from humanness. We follow this with outlining what we consider to be the ty

Robert Friedman, Phillip Griffiths

An I-surface $S$ is an algebraic surface of general type with $K_S^2 = 1$ and $p_g(S) = 2$. Recent research has centered on trying to give an explicit description of the KSBA compactification of the moduli space of these surfaces. The possible normal Gorenstein examples have been enumerated by work of Franciosi-Pardini-Rollenske. The goal of this paper is to

Abdeslem Lyaghfouri

We investigate a class of n-dimensional free boundary elliptic problems which includes the dam problem, the aluminum problem, and the lubrication problem. We establish that the free boundary in this class is a porous set, which implies its Hausdorff dimension being less than $n$, which in turn leads to its Lebesgue measure being zero. Our proof relies on the

Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre

Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) o

Pavle Stipsic, Marko Vojinovic

We study the details of the explicit and spontaneous symmetry breaking of the constrained 3BF action representing the Standard Model coupled to Einstein-Cartan gravity. First we discuss how each particular constraint breaks the original symmetry of the topological 3BF action. Then we investigate the spontaneous symmetry breaking and the Higgs mechanism for t

Panagiota Fatourou, Nikos Giachoudis, George Mallis

In this paper, we study the question whether techniques employed, in a conventional system, by state-of-the-art concurrent algorithms to avoid contended hot spots are still efficient for recoverable computing in settings with Non-Volatile Memory (NVM). We focus on concurrent FIFO queues that have two end-points, head and tail, which are highly contended. We

J. A. Mendez-Bermudez, R. Aguilar-Sanchez, J. M. Sigarreta, E. D. Leonel

The Riemann-Liouville fractional standard map (RL-fSM) is a two-dimensional nonlinear map with memory given in action-angle variables $(I,\theta)$. The RL-fSM is parameterized by $K$ and $\alpha\in(1,2]$ which control the strength of nonlinearity and the fractional order of the Riemann-Liouville derivative, respectively. In this work, we present a scaling st

Yunpeng Huang, Yaonan Gu, Jingwei Xu, Zhihong Zhu

As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, disparity, adversarial vulnerability, and inconsistency. Ensuring the reliability and responsibility of FMs is crucial for the sustainable development of the AI ecosystem. In this concise

J. Miguel Calderón

Let A be an abelian group, not necessarily finite. The main objective of this paper is to provide two constructions for a fibered A-biset functor. The first is the lower plus construction, and the other is the upper plus construction. These constructions coincide with the lower plus and upper plus constructions for biset functors (see [2]) when the fiber is

Tapas Singha, Pierre Sens

We study a model of contraction-based cell motility inside a microchannel to investigate the regulation of cell polarization and motion by the mechanical resistance of the environment. A positive feedback between the asymmetry of the acto-myosin cortex density and cell motion gives rise to a spontaneous symmetry breaking beyond a threshold contractility that

Davide Cugini, Francesco Ghisoni, Angela Rosy Morgillo, Francesco Scala

This work introduces a novel NISQ-friendly procedure for estimating spectral gaps in quantum systems. By leveraging Adiabatic Thermalization, we are able to create the Spectral Gap Superposition state, a newly defined quantum state exhibiting observable fluctuations in time that allow for the accurate estimation of any energy gap. Our method is tested by est

Javier Gonzalez-Conde, Zachary Morrell, Marc Vuffray, Tameem Albash

Demonstrations of quantum advantage for certain sampling problems have generated considerable excitement for quantum computing and have further spurred the development of circuit-model quantum computers, which represent quantum programs as a sequence of quantum gates acting on a finite number of qubits. Amongst this excitement, analog quantum computation has