April 2024 arXiv papers — page 187

Kazi Rafsanjani Amin, Axel M. Eriksson, Mikael Kervinen, Linus Andersson

Detecting quasiparticle tunneling events in superconducting circuits provides information about the population and dynamics of non-equilibrium quasiparticles. Such events can be detected by monitoring changes in the frequency of an offset-charge-sensitive superconducting qubit. This monitoring has so far been performed by Ramsey interferometry assisted by a

Konstantinos Bountrogiannis, Ioannis Papoutsidakis, Anthony Ephremides, Panagiotis Tsakalides

The Age of Incorrect Information (AoII) is studied within the context of remote monitoring a Markov source using variable-length stop-feedback (VLSF) coding. Leveraging recent results on the non-asymptotic channel coding rate, we consider sources with small cardinality, where feedback is non-instantaneous as the transmitted information and feedback message h

Sahiba Arora, Jochen Glück, Lassi Paunonen, Felix L. Schwenninger

In the context of positive infinite-dimensional linear systems, we systematically study $L^p$-admissible control and observation operators with respect to the limit-cases $p=\infty$ and $p=1$, respectively. This requires an in-depth understanding of the order structure on the extrapolation space $X_{-1}$, which we provide. These properties of $X_{-1}$ also e

C. Terry

This is Part 1 in a series of papers about sizes of regular partitions of $3$-uniform hypergraphs. Previous work of the author and Wolf, and independently Chernikov and Towsner, showed that $3$-uniform hypergraphs of small slicewise VC-dimension admit homogeneous partitions. The results of Chernikov and Towsner did not produce explicit bounds, while the work

Yue Wang, Tianfan Fu, Yinlong Xu, Zihan Ma

Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several years to complete, with a high probability of failure during the process. Recently, there has been a burgeoning interest in virtual clinical trials, which simulate real-world scena

Shahina Kunhimon, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan

Single source domain generalization (SDG) holds promise for more reliable and consistent image segmentation across real-world clinical settings particularly in the medical domain, where data privacy and acquisition cost constraints often limit the availability of diverse datasets. Depending solely on visual features hampers the model's capacity to adapt effe

R. Fang, J. Görres, R. J. deBoer, S. Moylan

The $^{15}$N($\alpha,\gamma$)$^{19}$F reaction produces $^{19}$F in asymptotic giant branch (AGB) stars, where the low energy tails of two resonances at $E_{\rm{c.m.}} = 1323 \pm 2$ and $1487 \pm 1.7$ keV are estimated to contribute about $30\%$ of the total reaction rate in these environments. However, recent measurements have shown discrepancies in the ene

Tsuyoshi Idé, Dzung T. Phan, Rudy Raymond

This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generation Blockchain platforms. First, we expand the existing framework for collaborative dictionary learning (CollabDict), which has previously been limited to Gaussian mixture models, by

Kohei Hattori

We estimate ([M/H], [$\alpha$/M]) for 48 million giants and dwarfs in low-dust extinction regions from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 and metal-poor star sample \revise{from} Li et al. The root mean square error of our estimation is 0.0890 dex for [M/H] and 0.0436 dex for [$\alpha$/M], when we evalu

Weixin Liang, Yaohui Zhang, Zhengxuan Wu, Haley Lepp

Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge is accessible, verifiable, and built upon over time. Recently, there has been immense speculation about how many people are using large language models (LLMs) like ChatGPT in the

Qiujiang Jin, Ruichen Jiang, Aryan Mokhtari

In this paper, we explore the non-asymptotic global convergence rates of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method implemented with exact line search. Notably, due to Dixon's equivalence result, our findings are also applicable to other quasi-Newton methods in the convex Broyden class employing exact line search, such as the Davidon-Fletcher-Powell

Deqing Fu, Ruohao Guo, Ghazal Khalighinejad, Ollie Liu

Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs. But do their capabilities change depending on the input modality? In this work, we propose $\textbf{IsoBench}$, a benchmark dataset containing problems from four major areas: math, science, algorithms, and games. Each example is p

Robert A. Wilson

I show how the isomorphism between the Lie groups of types $B_2$ and $C_2$ leads to a faithful action of the Clifford algebra $\mathcal C\ell(3,2)$ on the phase space of 2-dimensional dynamics, and hence to a mapping from Dirac spinors modulo scalars into this same phase space. Extending to the phase space of 3-dimensional dynamics allows one to embed all th

Shuaiyi Huang, De-An Huang, Zhiding Yu, Shiyi Lan

Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object masks which are expensive. We reduce the human annotations to only one point for each object in a video frame during training, and obtain high-quality mask predictions close to ful

David Barral, F. Javier Cardama, Guillermo Díaz, Daniel Faílde

The emerging field of quantum computing has shown it might change how we process information by using the unique principles of quantum mechanics. As researchers continue to push the boundaries of quantum technologies to unprecedented levels, distributed quantum computing raises as an obvious path to explore with the aim of boosting the computational power of

Nicole L. Wallack, Natasha E. Batalha, Lili Alderson, Nicholas Scarsdale

Planets between the sizes of Earth and Neptune are the most common in the Galaxy, bridging the gap between the terrestrial and giant planets in our Solar System. Now that we are firmly in the era of JWST, we can begin to measure, in more detail, the atmospheres of these ubiquitous planets to better understand their evolutionary trajectories. The two planets

Hakam Ghanim

The advancement in technology has made interdisciplinary research more accessible. Particularly the breakthrough in Artificial Intelligence AI has given huge advantages to researchers working in interdisciplinary and multidisciplinary fields. This study investigates the ability of AI models, particularly GPT4 and GPT Data Analyst in creating language maps fo

Yoong S. Phang, Artem V. Talanov

In the quest to unify quantum mechanics with general relativity, the concept of gravitons as quantum carriers of gravitational force stands as a pivotal yet unproven hypothesis. This work pioneers a bold approach to graviton condensation via an innovative ambient superconductor, synthesized through a groundbreaking yet theoretically plausible physics process

Adrian Moldovan, Angel Cataron, Razvan Andonie

In a feedforward network, Transfer Entropy (TE) can be used to measure the influence that one layer has on another by quantifying the information transfer between them during training. According to the Information Bottleneck principle, a neural model's internal representation should compress the input data as much as possible while still retaining sufficient

Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella

While long-context large language models (LLMs) can technically summarize book-length documents (>100K tokens), the length and complexity of the documents have so far prohibited evaluations of input-dependent aspects like faithfulness. In this paper, we conduct the first large-scale human evaluation of faithfulness and content selection on LLM-generated summ

Youcef Remil, Anes Bendimerad, Romain Mathonat, Mehdi Kaytoue

The management of modern IT systems poses unique challenges, necessitating scalability, reliability, and efficiency in handling extensive data streams. Traditional methods, reliant on manual tasks and rule-based approaches, prove inefficient for the substantial data volumes and alerts generated by IT systems. Artificial Intelligence for Operating Systems (AI

Boran Han, Shuai Zhang, Xingjian Shi, Markus Reichstein

In the realm of geospatial analysis, the diversity of remote sensors, encompassing both optical and microwave technologies, offers a wealth of distinct observational capabilities. Recognizing this, we present msGFM, a multisensor geospatial foundation model that effectively unifies data from four key sensor modalities. This integration spans an expansive dat

Fernando Paganini, Andres Ferragut

We consider a spatially distributed demand for electrical vehicle recharging, that must be covered by a fixed set of charging stations. Arriving EVs receive feedback on transport times to each station, and waiting times at congested ones, based on which they make a selfish selection. This selection determines total arrival rates in station queues, which are

Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng

Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However, in tasks involving video instruction-following, providing informative feedback, especially for detecting hallucinations in generated responses, remains a significant challenge. Pre

Jordi Armengol-Estapé, Rodrigo C. O. Rocha, Jackson Woodruff, Pasquale Minervini

The escalating demand to migrate legacy software across different Instruction Set Architectures (ISAs) has driven the development of assembly-to-assembly translators to map between their respective assembly languages. However, the development of these tools requires substantial engineering effort. State-of-the-art approaches use lifting, a technique where so

Alexander Messick

I explore the mathematical transformation that occurs in color coordinate space when physically mixing paints of two different colors. I tested 120 pairs of 16 paint colors and used a linear regression to find the most accurate combination of input parameters, both in RGB space and several other color spaces. I found that the fit with the strongest coefficie

M. Soheil Shamaee, S. Fathi Hafshejani, Z. Saeidian

In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an $O(\frac{1}{\sqrt{T}})$ convergence rate for the SGD. We conduct a comprehensive implementation to demonstrate the efficiency of the newly proposed step size on the

Andrej Bauer, James E. Hanson

We construct a topos in which the Dedekind reals are countable. The topos arises from a new kind of realizability, which we call parameterized realizability, based on partial combinatory algebras whose application depends on a parameter. Realizers operate uniformly with respect to a given parameter set. Our construction uses a sequence of reals in $[0,1]$, d

Anthony Degleris, Abbas El Gamal, Ram Rajagopal

We consider multi-value expansion planning (MEP), a general bilevel optimization model in which a planner optimizes arbitrary functions of the dispatch outcome in the presence of a partially controllable, competitive electricity market. The MEP problem can be used to jointly plan various grid assets, such as transmission, generation, and battery storage capa

Zhengtian Qiu, Adolfo Ballester-Bolinches

Let $ H $ be a subgroup of a finite group $ G $. We say that $ H $ satisfies the partial $ \Pi $-property in $ G $ if there exists a $G$-chief series $ \varGamma_{G}: 1 =G_{0} < G_{1} < \cdot\cdot\cdot < G_{n}= G $ of $ G $ such that $ | G / G_{i-1} : N_{G/G_{i-1}} (HG_{i-1}/G_{i-1}\cap G_{i}/G_{i-1})| $ is a $ \pi (HG_{i-1}/G_{i-1}\cap G_{i}/G_{i-1}) $-numb

Yijun Yang, Jie He, Pinzhen Chen, Víctor Gutiérrez-Basulto

Several recent papers have investigated the potential of language models as knowledge bases as well as the existence of severe biases when extracting factual knowledge. In this work, we focus on the factual probing performance over unseen prompts from tuning, and using a probabilistic view we show the inherent misalignment between pre-training and downstream

Anil Kumar, Pratik Thakur, Monika Sinha

The massive stars end their lives by supernova explosions leaving central compact objects that may evolve into neutron stars. Initially, after birth, the star remains hot and gradually cools down. We explore the matter and star properties during this initial stage of the compact stars considering the possibility of the appearance of deconfined quark matter i

Ben S. Ashby, Tristan Pryer

In this paper we study the a posteriori bounds for a conforming piecewise linear finite element approximation of the Signorini problem. We prove new rigorous a posteriori estimates of residual type in $L^{p}$, for $p \in (4,\infty)$ in two spatial dimensions. This new analysis treats the positive and negative parts of the discretisation error separately, req

Teng Fei, Srinivas Ravishankar, Zhining Chen, Abhinav Uppal

Visual neural decoding from EEG has improved significantly due to diffusion models that can reconstruct high-quality images from decoded latents. While recent works have focused on relatively complex architectures to achieve good reconstruction performance from EEG, less attention has been paid to the source of this information. We present a unified framewor

Rohit Jena, Pratik Chaudhari, James C. Gee

The paper proposes FireANTs, a multi-scale Adaptive Riemannian Optimization algorithm for dense diffeomorphic image matching. Existing state-of-the-art methods for diffeomorphic image matching are slow due to inefficient implementations and slow convergence due to the ill-conditioned nature of the optimization problem. Deep learning methods offer fast infere

Shuang Song

3D scene modeling techniques serve as the bedrocks in the geospatial engineering and computer science, which drives many applications ranging from automated driving, terrain mapping, navigation, virtual, augmented, mixed, and extended reality (for gaming and movie industry etc.). This dissertation presents a fraction of contributions that advances 3D scene m

Simran Khanuja, Sathyanarayanan Ramamoorthy, Yueqi Song, Graham Neubig

Given the rise of multimedia content, human translators increasingly focus on culturally adapting not only words but also other modalities such as images to convey the same meaning. While several applications stand to benefit from this, machine translation systems remain confined to dealing with language in speech and text. In this work, we take a first step

S. G. Zheltoukhov, A. M. Tatarnikov, A. A. Belyakova, E. A. Koksharova

This paper presents a prototype of an infrared photometer, created at SAI of MSU based on the commercial infrared module Gavin-615A. The operating spectral range of the photometer is 3-5 mkm. Investigations of the photometer's detector have shown that its parameters coincide with those stated by the manufacturer. The nonlinearity of the detector does not exc

Xiang Li, Feng Ruan, Huiyuan Wang, Qi Long

Since ChatGPT was introduced in November 2022, embedding (nearly) unnoticeable statistical signals into text generated by large language models (LLMs), also known as watermarking, has been used as a principled approach to provable detection of LLM-generated text from its human-written counterpart. In this paper, we introduce a general and flexible framework

Yoshini Bailung, Neha Shah, Ankhi Roy

Recent measurements from ALICE report that $``$in-jet'' nucleons carry a higher probability of forming a deuteron via coalescence than the nucleons from the underlying event (UE). This study makes use of an event shape classifier to separate the $``$in-jet'' deuterons and the deuterons in the UE produced in high multiplicity proton-proton collisions at $\sqr

Reni Paskaleva, Mykyta Holubakha, Andela Ilic, Saman Motamed

Canonical emotions, such as happy, sad, and fearful, are easy to understand and annotate. However, emotions are often compound, e.g. happily surprised, and can be mapped to the action units (AUs) used for expressing emotions, and trivially to the canonical ones. Intuitively, emotions are continuous as represented by the arousal-valence (AV) model. An interpr

Mingqi Li, Feng Luo

Current soft prompt methods yield limited performance when applied to small-sized models (fewer than a billion parameters). Deep prompt-tuning, which entails prepending parameters in each layer for enhanced efficacy, presents a solution for prompting small-sized models, albeit requiring carefully designed implementation. In this paper, we introduce the Lotte

Tao Hu, Fangzhou Hong, Ziwei Liu

Recent 3D human generative models have achieved remarkable progress by learning 3D-aware GANs from 2D images. However, existing 3D human generative methods model humans in a compact 1D latent space, ignoring the articulated structure and semantics of human body topology. In this paper, we explore more expressive and higher-dimensional latent space for 3D hum

Safwat Ali Khan, Wenyu Wang, Yiran Ren, Bin Zhu

Nearly a decade of research in software engineering has focused on automating mobile app testing to help engineers in overcoming the unique challenges associated with the software platform. Much of this work has come in the form of Automated Input Generation tools (AIG tools) that dynamically explore app screens. However, such tools have repeatedly been demo

Ruikun Hou, Tim Fütterer, Babette Bühler, Efe Bozkir

Classroom observation protocols standardize the assessment of teaching effectiveness and facilitate comprehension of classroom interactions. Whereas these protocols offer teachers specific feedback on their teaching practices, the manual coding by human raters is resource-intensive and often unreliable. This has sparked interest in developing AI-driven, cost

Matias Tueros, Sergio Cabana-Freire, Jaime Álvarez-Muñiz

Atmosphere-skimming showers are initiated by cosmic rays with incoming directions such that the full development of the cascade occurs inside the atmosphere without ever reaching the ground. This new class of showers has been observed in balloon-borne experiments such as ANITA, but a characterisation of their properties is lacking. The interplay between the

Qi Meng, Guang-Juan Wang, Makoto Oka

A comprehensive study of the $S$-wave heavy tetraquark states with identical quarks and antiquarks, specifically $QQ{\bar Q'}\bar Q'$ ($Q, Q'=c,b$), $QQ\bar s\bar s$/$\bar Q\bar Q ss$, and $QQ\bar q\bar q$/$\bar Q\bar Q qq$ ($q=u,d$), are studied in a unified constituent quark model. This model contains the one-gluon exchange and confinement potentials. The

Keisuke Sugiura, Hiroki Matsutani

Point cloud registration serves as a basis for vision and robotic applications including 3D reconstruction and mapping. Despite significant improvements on the quality of results, recent deep learning approaches are computationally expensive and power-hungry, making them difficult to deploy on resource-constrained edge devices. To tackle this problem, in thi

Andrius Tamosiunas, Fernando Cornet-Gomez, Yashar Akrami, Stefano Anselmi

Non-trivial spatial topology of the Universe may give rise to potentially measurable signatures in the cosmic microwave background. We explore different machine learning approaches to classify harmonic-space realizations of the microwave background in the test case of Euclidean $E_1$ topology (the 3-torus) with a cubic fundamental domain of a size scale sign

P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus

We present LAISS (Lightcurve Anomaly Identification and Similarity Search), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly ZTF Alert Stream via the ANTARES broker, identifying a manageable $\sim$1-5 candidates per night for expert vetting and coordinating foll

Shashank Pathak

We present an autoformalisation framework for the Lean theorem prover, called GFLean. GFLean uses a high-level grammar writing tool called Grammatical Framework (GF) for parsing and linearisation. GFLean is implemented in Haskell. We explain the functionalities of GFLean, its inner working and discuss its limitations. We also discuss how we can use neural ne

Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani

We study the behavior of optimal ridge regularization and optimal ridge risk for out-of-distribution prediction, where the test distribution deviates arbitrarily from the train distribution. We establish general conditions that determine the sign of the optimal regularization level under covariate and regression shifts. These conditions capture the alignment

Huimin Zeng, Zhenrui Yue, Dong Wang

Existing federated learning (FL) studies usually assume the training label space and test label space are identical. However, in real-world applications, this assumption is too ideal to be true. A new user could come up with queries that involve data from unseen classes, and such open-vocabulary queries would directly defect such FL systems. Therefore, in th

Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping

It is commonplace to produce application-specific models by fine-tuning large pre-trained models using a small bespoke dataset. The widespread availability of foundation model checkpoints on the web poses considerable risks, including the vulnerability to backdoor attacks. In this paper, we unveil a new vulnerability: the privacy backdoor attack. This black-

Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang

This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that necessitates understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. Strategic reasoning is distinguished by its focus on the

Nail Akar, Sennur Ulukus

We study age of information (AoI) in a single-source dual-server status update system for the generate at will (GAW) scenario, consisting of an information source, dual servers, and a monitor. For this system, the method of stochastic hybrid systems (SHS) was used to obtain the mean AoI for the work-conserving ZW (zero wait) policy with out-of-order packet d

Carsten Carstensen, Benedikt Gräßle, Ngoc Tien Tran

The higher-order guaranteed lower eigenvalue bounds of the Laplacian in the recent work by Carstensen, Ern, and Puttkammer [Numer. Math. 149, 2021] require a parameter $C_{\mathrm{st},1}$ that is found $\textit{not}$ robust as the polynomial degree $p$ increases. This is related to the $H^1$ stability bound of the $L^2$ projection onto polynomials of degree

Anatoly G. Baskakov, Ilya A. Krishtal, Natalia B. Uskova

We consider similarity transformations of a perturbed linear operator $A-B$ in a complex Banach space $\mathcal{X}$, where the unperturbed operator $A$ is a generator of a Banach $L_1(\mathbb{R})$-module and the perturbation operator $B$ is a bounded linear operator. The result of the transformation is a simpler operator $A-B_0$. For example, if $A$ is a dif

Nikhil Pinnaparaju, Reshinth Adithyan, Duy Phung, Jonathan Tow

We introduce Stable Code, the first in our new-generation of code language models series, which serves as a general-purpose base code language model targeting code completion, reasoning, math, and other software engineering-based tasks. Additionally, we introduce an instruction variant named Stable Code Instruct that allows conversing with the model in a nat

Tao Hu, Fangzhou Hong, Ziwei Liu

Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However, existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relations are not fully explored. In this paper, we propose a new 4D motion modeling p

Chikai Shang, Rongguang Ye, Jiaqi Jiang, Fangqing Gu

Pareto Set Learning (PSL) is an emerging research area in multi-objective optimization, focusing on training neural networks to learn the mapping from preference vectors to Pareto optimal solutions. However, existing PSL methods are limited to addressing a single Multi-objective Optimization Problem (MOP) at a time. When faced with multiple MOPs, this limita

Ri-Zhao Qiu, Ge Yang, Weijia Zeng, Xiaolong Wang

Scene representations using 3D Gaussian primitives have produced excellent results in modeling the appearance of static and dynamic 3D scenes. Many graphics applications, however, demand the ability to manipulate both the appearance and the physical properties of objects. We introduce Feature Splatting, an approach that unifies physics-based dynamic scene sy

Ilia Belov, Alexander Berezhnoy, Dmitri Melikhov

We present the first theoretical calculation of nonfactorizable charm-quark loop contributions to the $B_s\to \gamma l^+l^-$ amplitude. We calculate the relevant form factors, $H_{A,V}^{\rm NF}(k'^2,k^2)$, and provide convenient parametrizations of our results in the form of fit functions of two variables, $k'^2$ and $k^2$, applicable in the region below had

Zhexin Zhao

The quantum interaction between free electrons and photons is fundamental to free-electron based light sources and free-electron quantum optics applications. A large coupling between free electrons and photons is generally desired. In this manuscript, I obtain the upper bound for the quantum coupling between free electrons and photons. The upper bound has a

Dan Haramati, Tal Daniel, Aviv Tamar

Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, however, domains with more than a few objects are difficult for RL agents due to the curse of dimensionality, especially when learning from

Jiming Ren, Haris Miller, Karen M. Feigh, Samuel Coogan

This paper presents an incremental replanning algorithm, dubbed LTL-D*, for temporal-logic-based task planning in a dynamically changing environment. Unexpected changes in the environment may lead to failures in satisfying a task specification in the form of a Linear Temporal Logic (LTL). In this study, the considered failures are categorized into two classe

Bushra F. Alsaqer, Alaa F. Alsaqer, Amna Asif

The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such

Yue Sun, Chao Chen, Yuesheng Xu, Sihong Xie

Ensuring both accuracy and robustness in time series prediction is critical to many applications, ranging from urban planning to pandemic management. With sufficient training data where all spatiotemporal patterns are well-represented, existing deep-learning models can make reasonably accurate predictions. However, existing methods fail when the training dat

Hsing-Huan Chung, Shravan Chaudhari, Yoav Wald, Xing Han

In real-world graph data, distribution shifts can manifest in various ways, such as the emergence of new categories and changes in the relative proportions of existing categories. It is often important to detect nodes of novel categories under such distribution shifts for safety or insight discovery purposes. We introduce a new approach, Recall-Constrained O

Deng-Shan Wang, Cheng Zhu, Xiaodong Zhu

The good Boussinesq equation has several modified versions such as the modified Boussinesq equation, Mikhailov-Lenells equation and Hirota-Satsuma equation. This work builds the full relations among these equations by Miura transformation and invertible linear transformations and draws a pyramid diagram to demonstrate such relations. The direct and inverse s

Uliana Yakhnevych, Marlo Kunzner, Leonard M. Verhoff, Julius Ratzenberger

Conductive ferroelectric domain walls (DWs) represent a promising topical system for the development of nanoelectronic components and devices. DWs show very different properties as compared to their bulk counterparts. Of central interest here is the domain wall current (DWC) of charged DWs in 5mol\% Mg-doped lithium niobate single crystals; in contrast to fo

Jing Gao, Weijun Zhang, Zhitao Zhang

In this paper, we study the following fully nonlinear elliptic equations \begin{equation*} \left\{\begin{array}{rl} \left(S_{k}(D^{2}u)\right)^{\frac1k}=\lambda f(-u) & in\quad\Omega \\ u=0 & on\quad \partial\Omega\\ \end{array} \right. \end{equation*} and coupled systems \begin{equation*} \left\{\begin{array}{rl} (S_{k}(D^{2}u))^\frac1k=\lambda g(-u,-v) & i

Michael Garrett

This study examines the hypothesis that the rapid development of Artificial Intelligence (AI), culminating in the emergence of Artificial Superintelligence (ASI), could act as a "Great Filter" that is responsible for the scarcity of advanced technological civilisations in the universe. It is proposed that such a filter emerges before these civilisations can

Pratishtha Abrol, Pahulpreet Singh, Indranil Chakrabarty

A tripartite state is said to be a potential resource for secret sharing if the state imposes restrictions on the teleportation fidelity of the bipartite dealer--reconstructor and dealer--assistant channels in addition of being useful for the state reconstruction. Given a secret shareable state in a pure three-qubit system, we are able to characterize the se

En-Ze Li, Yi-Yang Liu, Ming-Xin Dong, Dong-Sheng Ding

Efficient and tunable qubit unidirectional routers and spin-wave diodes play an important role in both classical and quantum information processing domains. Here, we reveal that multi-level neutral cold atoms can mediate both dissipative and coherent couplings. Interestingly, we investigate and practically implement this paradigm in experiments, successfully

Natalia Grigoriadou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou

In this paper, we present our team's submissions for SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The participants were asked to perform binary classification to identify cases of fluent overgeneration hallucinations. Our experimentation included fine-tuning a pre-trained model on hallucination

Drew Horton, Tom Logan, Daphne Skipper, Emily Speakman

The notion of the $x$-minute city is again popular in urban planning, but the practical implications of developing walkable neighborhoods have not been rigorously explored. What is the scale of the challenge that cities needing to retrofit face? Where should new stores or amenities be located? For 500 cities in the United States, we explored how many additio

E. Albuquerque, M. Araújo, A. Benaglia, A. Boletti

The CMS detector will be upgraded for the HL-LHC to include a MIP Timing Detector (MTD). The MTD will consist of barrel and endcap timing layers, BTL and ETL respectively, providing precision timing of charged particles. The BTL sensors are based on LYSO:Ce scintillation crystals coupled to SiPMs with TOFHIR2 ASICs for the front-end readout. A resolution of

Felipe Parodi, Jordan Matelsky, Alejandra Regla-Vargas, Elizabeth Foglia

Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making; thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the desig

Yi Xu

With the implementation of personal data privacy regulations, the field of machine learning (ML) faces the challenge of the "right to be forgotten". Machine unlearning has emerged to address this issue, aiming to delete data and reduce its impact on models according to user requests. Despite the widespread interest in machine unlearning, comprehensive survey

Quanyan Zhu

Cyber resilience is a complementary concept to cybersecurity, focusing on the preparation, response, and recovery from cyber threats that are challenging to prevent. Organizations increasingly face such threats in an evolving cyber threat landscape. Understanding and establishing foundations for cyber resilience provide a quantitative and systematic approach

Chen Yang, Junzhuo Li, Xinyao Niu, Xinrun Du

Uncovering early-stage metrics that reflect final model performance is one core principle for large-scale pretraining. The existing scaling law demonstrates the power-law correlation between pretraining loss and training flops, which serves as an important indicator of the current training state for large language models. However, this principle only focuses

Siddhant Jain, Daniel Watson, Eric Tabellion, Aleksander Hołyński

We present VIDIM, a generative model for video interpolation, which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data, VIDIM uses cascaded diffusion models to first generate the target video at low resolution, and then generate the high-resolution video conditioned on the low-res

Tristan Le Roy-Deloison, Edwin Peter Lobo, Jef Pauwels, Stefano Pironio

Photon losses are the main obstacle to fully photonic implementations of device-independent quantum key distribution (DIQKD). Motivated by recent work showing that routed Bell scenarios offer increased robustness to detection inefficiencies for the certification of long-range quantum correlations, we investigate DIQKD protocols based on a routed setup. In th

Slava G. Turyshev

We explore interstellar light transmission facilitated by gravitational lensing, focusing on axially-symmetric lensing configurations where the transmitter, lens, and receiver are nearly aligned. Positioning an optical transmitter in the lens's focal region, we investigate the caustic formed by a diffraction-limited annular beam of light emitted by the trans

Qi Zhang, Yi Zhou, Ashley Prater-Bennette, Lixin Shen

Distributionally robust optimization (DRO) is a powerful framework for training robust models against data distribution shifts. This paper focuses on constrained DRO, which has an explicit characterization of the robustness level. Existing studies on constrained DRO mostly focus on convex loss function, and exclude the practical and challenging case with non

Yu-Hang Ji, Chuan He, Si-Tong Yan, Jun-Jie Jiang

The systematic error induced by the quadratic Zeeman effect is non-negligible in atom interferometers and must be precisely evaluated. We theoretically analyze the phase shift induced by the Zeeman effect, and use a hyperfine ground state exchange (HGSE) method to evaluate the systematic error in the long-baseline $^{85}$Rb-$^{87}$Rb dual-species atom interf

Avrim Blum, Kavya Ravichandran

We give nearly-tight upper and lower bounds for the improving multi-armed bandits problem. An instance of this problem has $k$ arms, each of whose reward function is a concave and increasing function of the number of times that arm has been pulled so far. We show that for any randomized online algorithm, there exists an instance on which it must suffer at le

Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo, Sayak Paul

One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. In this paper, we offer a comprehensive investigation of this limitation, while also developing datasets and methods that support algorithmic solutions to improve

Sondre Wold, Petter Mæhlum, Oddbjørn Hove

Existing methods for complexity estimation are typically developed for entire documents. This limitation in scope makes them inapplicable for shorter pieces of text, such as health assessment tools. These typically consist of lists of independent sentences, all of which are too short for existing methods to apply. The choice of wording in these assessment to

Mohamed-Amine Lahmeri, Walid R. Ghanem, Christina Bonfert, Robert Schober

In this paper, we investigate joint 3-dimensional (3D) trajectory planning and resource allocation for rotary-wing unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) sensing. To support emerging real-time SAR applications and enable live mission control, we incorporate real-time communication with a ground station (GS). The UAV's main mission is th

Weijun Zhang, Zhitao Zhang

In this paper, we are concerned with the monotonic and symmetric properties of convex solutions to fully nonlinear elliptic systems. We mainly discuss Monge-Amp\`ere type systems for instance, considering \begin{equation*} \det(D^2u^i)=f^i(x,{\bf u},\nabla u^i), \ 1\leq i\leq m, \end{equation*} over bounded domains of various cases, including the bounded smo

Yuhui Li, Qiusen Wei, Guoheng Huang, Xiaochen Yuan

Medical landmark detection is crucial in various medical imaging modalities and procedures. Although deep learning-based methods have achieve promising performance, they are mostly designed for specific anatomical regions or tasks. In this work, we propose a universal model for multi-domain landmark detection by leveraging transformer architecture and develo

Vladimir Parfenyev, Mark Blumenau, Ilia Nikitin

Obtaining system parameters and reconstructing the full flow state from limited velocity observations using conventional fluid dynamics solvers can be prohibitively expensive. Here we employ machine learning algorithms to overcome the challenge. As an example, we consider a moderately turbulent fluid flow, excited by a stationary force and described by a two

Fengtao Zhou, Yingxue Xu, Yanfen Cui, Shenyan Zhang

Gastric cancer (GC) is a prevalent malignancy worldwide, ranking as the fifth most common cancer with over 1 million new cases and 700 thousand deaths in 2020. Locally advanced gastric cancer (LAGC) accounts for approximately two-thirds of GC diagnoses, and neoadjuvant chemotherapy (NACT) has emerged as the standard treatment for LAGC. However, the effective

Molei Liu, Xinyi Wang, Chuan Hong

With the increasing availability of electronic health records (EHR) linked with biobank data for translational research, a critical step in realizing its potential is to accurately classify phenotypes for patients. Existing approaches to achieve this goal are based on error-prone EHR surrogate outcomes, assisted and validated by a small set of labels obtaine

Mark Whitmeyer

A natural way of quantifying the ``amount of information'' in decision problems yields a globally concave value for information. Another (in contrast, adversarial) way almost never does.

Griffin Adams

The rapid adoption of Electronic Health Records (EHRs) has been instrumental in streamlining administrative tasks, increasing transparency, and enabling continuity of care across providers. An unintended consequence of the increased documentation burden, however, has been reduced face-time with patients and, concomitantly, a dramatic rise in clinician burnou

Qiang Hu, Zhenyu Yi, Ying Zhou, Fan Huang

We propose MonoBox, an innovative box-supervised segmentation method constrained by monotonicity to liberate its training from the user-unfriendly box-tightness assumption. In contrast to conventional box-supervised segmentation, where the box edges must precisely touch the target boundaries, MonoBox leverages imprecisely-annotated boxes to achieve robust pi

Alexander V. Khrabrov, David. J. Smith, Igor D. Kaganovich

A physical and numerical model of the Townsend discharge in molecular hydrogen and deuterium has been developed to meet the needs of designing a plasma-based switching device for power grid application. The model allows to predict the low-pressure branch of the Paschen curve for applied voltage in the range of several hundred kiloVolts. In the regime of inte