October 2022 arXiv papers — page 50

Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang

Expert decision-makers (DMs) in high-stakes AI-assisted decision-making (AIaDM) settings receive and reconcile recommendations from AI systems before making their final decisions. We identify distinct properties of these settings which are key to developing AIaDM models that effectively benefit team performance. First, DMs incur reconciliation costs from exe

Sourav Pal, Prajakta Sahasrabuddhe

There are several proofs of the classical commutant lifting and intertwining lifting theorems in the literature. In this article, we present analogous proofs to a few $Q$-commuting lifting and $Q$-intertwining lifting theorems. We provide several proofs and show explicit constructions of Ando-type dilations for a pair of $Q$-commuting contractions $(T_1,T_2)

H. J. Hoeijmakers, D. Kitzmann, B. M. Morris, B. Prinoth

Observations of WASP-121 b have suggested an under-abundance of titanium and titanium-oxide from its terminator region. In this study, we aim to determine whether this depletion is global by investigating the day-side emission spectrum. We analyse 8 epochs of high-resolution spectra obtained with ESPRESSO, targeting orbital phases when the day-side is in vie

Sedrick Scott Keh, Rohit K. Bharadwaj, Emmy Liu, Simone Tedeschi

We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences t

Dmitri Pavlov

In the first part of the paper, we prove that the category of diffeological spaces does not admit a model structure transferred via the smooth singular complex functor from simplicial sets, resolving in the negative a conjecture of Christensen and Wu, in contrast to Kihara's model structure on diffeological spaces constructed using a different singular compl

Reza Rastegar

In this note, we present several inequalities in the context of pattern containment, utilizing elementary applications of the Fortuin-Kasteleyn-Ginibre (FKG) inequality and Shearer's lemma.

Anh-Tu Nguyen, Van-Truong Nguyen, Xuan-Thuan Nguyen, Cong-Thanh Vu

Mobile robots have been widely used in various aspects of human life. When a robot moves between different positions in the working area to perform the task, controlling motion to follow a pre-defined path is the primary task of a mobile robot. Furthermore, the robot must remain at its desired speed to cooperate with other agents. This paper presents a devel

Junfei Xiao, Yutong Bai, Alan Yuille, Zongwei Zhou

Vision Transformer (ViT) has become one of the most popular neural architectures due to its great scalability, computational efficiency, and compelling performance in many vision tasks. However, ViT has shown inferior performance to Convolutional Neural Network (CNN) on medical tasks due to its data-hungry nature and the lack of annotated medical data. In th

Gautam Aishwarya, Irfan Alam, Dongbin Li, Sergii Myroshnychenko

We develop an information-theoretic approach to study the Kneser--Poulsen conjecture in discrete geometry. This leads us to a broad question regarding whether R\'enyi entropies of independent sums decrease when one of the summands is contracted by a $1$-Lipschitz map. We answer this question affirmatively in various cases.

Nikiforos Pittaras

In this paper we present a Reinforcement Learning environment that leverages agent cooperation and communication, aimed at detection, learning and ultimately penalizing betrayal patterns that emerge in the behavior of self-interested agents. We provide a description of game rules, along with interesting cases of betrayal and trade-offs that arise. Preliminar

Valentina Pribanić

This article explores the connection between radical isogenies and modular curves. Radical isogenies are formulas designed for the computation of chains of isogenies of fixed small degree $N$, introduced by Castryck, Decru, and Vercauteren at Asiacrypt 2020. One significant advantage of radical isogeny formulas over other formulas with a similar purpose is t

Xuxing Chen, Minhui Huang, Shiqian Ma, Krishnakumar Balasubramanian

Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning, and hyperparameter optimization. Extending single-agent training on bilevel problems to the decentralized setting is a natural generalization, and there has been a flurry of work s

Kan Kitamura

We compare actions on C*-algebras of two constructions of locally compact quantum groups, the bicrossed product and the double crossed product. We give a duality between them as a generalization of Baaj-Skandalis duality. In the case of quantum doubles, this duality also preserves monoidal structures given by twisted tensor products. We also discuss its cons

Katherine H. Shutta, Denise M. Scholtens, William L. Lowe, Raji Balasubramanian

Network models are powerful tools for gaining new insights from complex biological data. Most lines of investigation in biology involve comparing datasets in the setting where the same predictors are measured across multiple studies or conditions (multi-study data). Consequently, the development of statistical tools for network modeling of multi-study data i

Saharon Shelah, Jonathan L. Verner

We investigate the existence of metric spaces which, for any coloring with a fixed number of colors, contain monochromatic isomorphic copies of a fixed starting space K. In the main theorem we construct such a space of size \(2^{\aleph_0}\) for colorings with \(\aleph_0\) colors and any metric space \(K\) of size \(\aleph_0\). We also give a slightly weaker

Askold Khovanskii

In the paper, we describe all total orders $\succ$ compatible with addition on additive subsemigroup $S$ of finite dimensional spaces over rational numbers. We provide a necessary and sufficient condition under which a finitely generated semigroups $S$ equipped with an order $\succ$ is a well-ordered set. We also present some auxiliary results on orders comp

Bruce Edelman, Ben Farr, Zoheyr Doctor

We introduce the first complete non-parametric model for the astrophysical distribution of the binary black hole (BBH) population. Constructed from basis splines, we use these models to conduct the most comprehensive data-driven investigation of the BBH population to date, simultaneously fitting non-parametric models for the BBH mass ratio, spin magnitude an

Patrick Laferrière, Sofiane Haffouz, David B. Northeast, Philip J. Poole

Single photon emitters are a key component for enabling the practical use of quantum key distribution protocols for secure communications. For long-haul optical networks it is imperative to use photons at wavelengths that are compatible with standard single mode fibers: 1.31 {\mu}m and 1.55 {\mu}m. We demonstrate high purity single photon emission at 1.31 {\

Fangting Zhou, Kejun He, Kunbo Wang, Yanxun Xu

Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest, which has not yet been fully explored. In this article, we develop a novel Bayesian network model for multivariate functional data where the conditional independence and causal structur

P. J. F. Carle, F. Retière, A. Sher, R. Underwood

General Fusion is building the Fusion Demonstration Plant to demonstrate a magnetized target fusion scheme in which a deuterium plasma is heated from 200 eV to 10 keV by piston-driven compression of a liquid-lithium liner. The multilayer coaxial time-of-flight (MCTOF) neutron emission spectrometer is designed to measure the ion temperature near peak compress

A. Bonatto, G. Xia, O. Apsimon, C. Bontoiu

Charged particle acceleration using solid-state nanostructures has attracted attention in recent years as a method of achieving ultra-high-gradient acceleration in the TV/m domain. More concretely, metallic hollow nanostructures could be suitable for particle acceleration through the excitation of wakefields by a laser or a high-intensity charged particle be

S. A. Iqrar, J. Park, M. Afzal, H. J. Sung

The efficient mixing of fluid samples in miniaturized total analysis systems is essential for numerous applications including biological screening assays, chemical extraction, polymerization, cell analysis, and protein folding. Miniaturized microfluidic platforms have recently emerged for microscale fluid mixing with their capabilities of high-throughput sam

Mario Giulianelli

This position paper proposes a conceptual framework for the design of Natural Language Generation (NLG) systems that follow efficient and effective production strategies in order to achieve complex communicative goals. In this general framework, efficiency is characterised as the parsimonious regulation of production and comprehension costs while effectivene

Oleg Konovalov, Valentina Belova, Mehdi Saedi, Irene Groot

We achieved a tripling of the maximum range of perpendicular momentum transfer (q_z) of X-ray scattering from liquid surfaces using a double crystal deflector setup to tilt the incident X-ray beam. This is obtained by using Miller indices of the reflecting crystal atomic planes that are three times higher than usual. We calculate the deviation from the exact

Peter Schaldenbrand, Zhixuan Liu, Jean Oh

We introduce an approach to generating videos based on a series of given language descriptions. Frames of the video are generated sequentially and optimized by guidance from the CLIP image-text encoder; iterating through language descriptions, weighting the current description higher than others. As opposed to optimizing through an image generator model itse

Jiyue He, Arkady Pertsov, Sanjay Dixit, Katie Walsh

Atrial fibrillation is a heart rhythm disorder that affects tens of millions people worldwide. The most effective treatment is catheter ablation. This involves irreversible heating of abnormal cardiac tissue facilitated by electroanatomical mapping. However, it is difficult to consistently identify the triggers and sources that may initiate or perpetuate atr

François Dahmani, Mahan Mj

Given two automorphisms of a group $G$, one is interested in knowing whether they are conjugate in the automorphism group of $G$, or in the abstract commensurator of $G$, and how these two properties may differ. When $G$ is the fundamental group of a closed orientable surface, we present a uniform finiteness theorem for the class of pseudo-Anosov automorphis

A. Ballester-Bolinches, R. Esteban-Romero, V. Pérez-Calabuig

We show that the number of isomorphism classes of left braces of order 64 with additive group isomorphic to $C_4 \times C_4 \times C_4$ is 1\,515\,429.

Tobias Barthel, Shachar Carmeli, Tomer M. Schlank, Lior Yanovski

We develop a general theory of higher semiadditive Fourier transforms that includes both the classical discrete Fourier transform for finite abelian groups at height $n=0$, as well as a certain duality for the $E_n$-(co)homology of $\pi$-finite spectra, established by Hopkins and Lurie, at heights $n\ge 1$. We use this theory to generalize said duality in th

Johannes Binder, Aleksandra Krystyna Dąbrowska, Mateusz Tokarczyk, Katarzyna Ludwiczak

Hydrogen is an important building block in global strategies towards a future green energy system. To make this transition possible, intense scientific efforts are needed, also in the field of materials science. Two-dimensional crystals, such as hexagonal boron nitride (hBN), are very promising in this regard, as it was demonstrated that micrometer-sized exf

Alexander A. Davydov, Stefano Marcugini, Fernanda Pambianco

We consider the structures of the plane-line and point-line incidence matrices of the projective space $\mathrm{PG}(3,q)$ connected with orbits of planes, points, and lines under the stabilizer group of the twisted cubic. In the literature, lines are partitioned into classes, each of which is a union of line orbits. In this paper, for all $q$, even and odd,

Ziyang Zhang, Plamen Angelov, Eduardo Soares, Nicolas Longepe

Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers. Semantic segmentation plays a key role in mapping the raw hyper-spectral data coming from the satellites into a human understandable form assigning class labels to each pixel. In this paper, we introduce a prototype-bas

Zhenglu Li, Steven G. Louie

We present a theoretical prediction of a phonon-mediated two-gap superconductivity in infinite-layer nickelates Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ by performing $\textit{ab initio}$ $GW$ and $GW$ perturbation theory calculations. Electron $GW$ self-energy effects significantly alter the characters of the two-band Fermi surface and enhance the electron-phonon coupli

Vinay Kumar Verma, Nikhil Mehta, Shijing Si, Ricardo Henao

Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, there exist sparse subnetworks that achieve performance comparable to the original network. Unfortunately, finding these subnetworks involves iterative stages of training and pruning,

Jack Stade

We resolve the complexity of the point-boundary variant of the art gallery problem, showing that it is $\exists\mathbb{R}$-complete, meaning that it is equivalent under polynomial time reductions to deciding whether a system of polynomial equations has a real solution. The art gallery problem asks whether there is a configuration of {\it guards} that togethe

Geyu Liang, Gavin Zhang, Salar Fattahi, Richard Y. Zhang

This paper focuses on the noiseless complete dictionary learning problem, where the goal is to represent a set of given signals as linear combinations of a small number of atoms from a learned dictionary. There are two main challenges faced by theoretical and practical studies of dictionary learning: the lack of theoretical guarantees for practically-used he

Anna T. P. Schauer, Michael Boylan-Kolchin, Katelyn Colston, Omid Sameie

We study how supersonic streaming velocities of baryons relative to dark matter -- a large-scale effect imprinted at recombination and coherent over $\sim 3$ Mpc scales -- affects the formation of dwarf galaxies at $z \gtrsim 5$. We perform cosmological hydrodynamic simulations, including and excluding streaming velocities, in regions centered on halos with

Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin, Alena Pestova

Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers. However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources. To this end

Ekaterina Taktasheva, Tatiana Shavrina, Alena Fenogenova, Denis Shevelev

Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes. However, this fast-growing area lacks standardized evaluation suites for non-English languages, hindering progress outside the Anglo-centric paradigm. To address this line of research, we propose TAPE (Text Attack and Perturbation Evaluation)

Sarath Pattathil, Kaiqing Zhang, Asuman Ozdaglar

Multi-agent interactions are increasingly important in the context of reinforcement learning, and the theoretical foundations of policy gradient methods have attracted surging research interest. We investigate the global convergence of natural policy gradient (NPG) algorithms in multi-agent learning. We first show that vanilla NPG may not have parameter conv

Gili Weiss-Dicker, Amitay Eldar, Yoel Shkolinsky, Tamir Bendory

Single-particle cryo-electron microscopy (cryo-EM) is a leading technology to resolve the structure of molecules. Early in the process, the user detects potential particle images in the raw data. Typically, there are many false detections as a result of high levels of noise and contamination. Currently, removing the false detections requires human interventi

Theodore Wolf

Climate Change is an incredibly complicated problem that humanity faces. When many variables interact with each other, it can be difficult for humans to grasp the causes and effects of the very large-scale problem of climate change. The climate is a dynamical system, where small changes can have considerable and unpredictable repercussions in the long term.

Xingyao Wang, Sha Li, Heng Ji

Large Language Model (LLM) trained on a mixture of text and code has demonstrated impressive capability in translating natural language (NL) into structured code. We observe that semantic structures can be conveniently translated into code and propose Code4Struct to leverage such text-to-structure translation capability to tackle structured prediction tasks.

Alireza Azadbakht, Saeed Reza Kheradpisheh, Ismail Khalfaoui-Hassani, Timothée Masquelier

Deep convolutional neural networks (DCNNs) have become the state-of-the-art (SOTA) approach for many computer vision tasks: image classification, object detection, semantic segmentation, etc. However, most SOTA networks are too large for edge computing. Here, we suggest a simple way to reduce the number of trainable parameters and thus the memory footprint:

Kshitij Gupta

Automated essay scoring is one of the most important problem in Natural Language Processing. It has been explored for a number of years, and it remains partially solved. In addition to its economic and educational usefulness, it presents research problems. Transfer learning has proved to be beneficial in NLP. Data augmentation techniques have also helped bui

Zhenyu Ming, Wei Zhang, Yanwei Xu

For cloud service providers, fine-grained packet loss detection across data centers is crucial in improving their service level and increasing business income. However, the inability to obtain sufficient measurements makes it difficult owing to the fundamental limit that the wide-area network links responsible for communication are not under their management

Guy Revach, Timur Locher, Nir Shlezinger, Ruud J. G. van Sloun

Electrocardiography (ECG) signals play a pivotal role in many healthcare applications, especially in at-home monitoring of vital signs. Wearable technologies, which these applications often depend upon, frequently produce low-quality ECG signals. While several methods exist for ECG denoising to enhance signal quality and aid clinical interpretation, they oft

Tim Schneider, Boris Belousov, Georgia Chalvatzaki, Diego Romeres

Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation is the exploration of the dynamics of the environment when there is continuous contact between the objects being manipulated. This paper proposes a model-based active exploration ap

Ilgiz Murzakhanov, Sarthak Gupta, Spyros Chatzivasileiadis, Vassilis Kekatos

The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options. Such rules enable DERs to respond autonomously in response to time-varying grid loading conditions. The rules comprise affine droop control augmented with

Vibhavasu Pasumarti, Shantanu Desai

We search for a spatial association between radio pulsars and ultra-high energy neutrinos using the publicly available IceCube point source neutrino events catalog. For this purpose we use the unbinned maximum likelihood method to search for a statistically significant excess from each of the pulsars in the ATNF catalog. We do not find any pulsars with detec

Solomon Goldgraber Casspi, Oliver Husser, Guy Revach, Nir Shlezinger

Stochastic control deals with finding an optimal control signal for a dynamical system in a setting with uncertainty, playing a key role in numerous applications. The linear quadratic Gaussian (LQG) is a widely-used setting, where the system dynamics is represented as a linear Gaussian statespace (SS) model, and the objective function is quadratic. For this

Yasmin Moslem, Rejwanul Haque, Andy Way

Research on Machine Translation (MT) has achieved important breakthroughs in several areas. While there is much more to be done in order to build on this success, we believe that the language industry needs better ways to take full advantage of current achievements. Due to a combination of factors, including time, resources, and skills, businesses tend to ap

Roberto Casadio, Wenbin Feng, Ibere Kuntz, Fabio Scardigli

The notions of minimum geometrical length and minimum length scale are discussed with reference to correlation functions obtained from in-in and in-out amplitudes in quantum field theory. Whereas the in-in propagator for metric perturbations does not admit the former, the in-out Feynman propagator shows the emergence of the latter. A connection between the F

Nicolas H. Pavao

We introduce a decomposition of D-dimensional vector amplitudes in terms of building blocks that preserve all partial amplitude relations of the parent gauge-theory. Using this decomposition, we derive a new set of amplitude relations between nonlinear sigma model pions and the pure-scalar sector of Yang-Mills-scalar theory. These new relations indicate an e

Y. Shemerliuk, A. Kuibarov, O. Feia, M. Behnami

Layered semimetallic van der Waals materials TiSe2 has attracted a lot of attention because of interplay of a charge density wave (CDW) state and superconductivity. Its sister compound TiS2, being isovalent to TiSe2 and having the same crystal structure, shows a semiconducting behavior. The natural rises what happens at the transition point in TiSe2-xSx, whi

Wei Han, Hui Chen, Min-Yen Kan, Soujanya Poria

Existing multimodal tasks mostly target at the complete input modality setting, i.e., each modality is either complete or completely missing in both training and test sets. However, the randomly missing situations have still been underexplored. In this paper, we present a novel approach named MM-Align to address the missing-modality inference problem. Concre

A. J. Barclay, A. R. W. McKellar, N. Moazzen-Ahmadi

Weakly-bound CO2-Rg2 trimers are studied by high resolution (0.002 cm-1) infrared spectroscopy in the region of the CO2 nu3 fundamental band (~2350 cm-1), using a tunable OPO to probe a pulsed supersonic slit jet expansion with an effective rotational temperature of about 2 K. CO2-Ar2 spectra have been reported previously, but are extended here to include Rg

Eleftherios-Ermis Tselentis, Ämin Baumeler

It is well-known that if one assumes quantum theory to hold locally, then processes with indefinite causal order and cyclic causal structures become feasible. Here, we study qualitative limitations on causal structures and correlations imposed by local quantum theory. For one, we find a necessary graph theoretic criterion--the "siblings-on-cycles" property--

Dibyakanti Kumar, Vivek Gupta, Soumya Sharma, Shuo Zhang

Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time-consuming and thus limits scale, and the latter often produces naive examples that may lack complex reasoning. This paper

Agustin G. Bonifacio

In a one-commodity economy with single-peaked preferences and individual endowments, we study different ways in which reallocation rules can be strategically distorted by affecting the set of active agents. We introduce and characterize the family of monotonic reallocation rules and show that each rule in this class is withdrawal-proof and endowments-merging

Béranger Seguin

We consider a variant of the ring of components of Hurwitz spaces introduced by Ellenberg, Venkatesh and Westerland. By focusing on Hurwitz spaces classifying covers of the projective line, the resulting ring of components is commutative, which lets us study it from the point of view of algebraic geometry and relate its geometric properties to numerical inva

Natalya S. Kargaltseva, Sergey A. Khaibrakhmanov, Alexander E. Dudorov, Andrey G. Zhilkin

The collapse of the magnetic rotating protostellar cloud with mass of $10\,M_{\odot}$ is numerically studied. The initial ratios of the thermal, magnetic, and rotational energies of the cloud to the modulus of its gravitational energy are 0.3, 0.2 and 0.01, respectively. The emphasis is on the evolution and properties of the quasi-magnetostatic primary disk

Miguel Flores R., Luis J. Corral, Celia R. Fierro-Santillán, Silvana G. Navarro

In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine learning and deep learning algorithms in order to establish a reliable way to fit a stellar model using two methods: the c

Michael A. Klatt, Günter Last, Norbert Henze

We introduce a rigorous and sensitive significance test for hyperuniformity that yields reliable results even from a single sample. Our approach is based on a detailed analysis of the empirical Fourier transform of a stationary point process in $\mathbb{R}^d$. For large system sizes, we derive the asymptotic covariances and establish a multivariate central l

Mrunal Jadhav, Matthew Guzdial

There has been significant research interest in Procedural Level Generation via Machine Learning (PLGML), applying ML techniques to automated level generation. One recent trend is in the direction of learning representations for level design via embeddings, such as tile embeddings. Tile Embeddings are continuous vector representations of game levels unifying

Pedro Abdalla, Afonso S. Bandeira, Martin Kassabov, Victor Souza

The Kuramoto model is fundamental to the study of synchronization. It consists of a collection of oscillators with interactions given by a network, which we identify respectively with vertices and edges of a graph. In this paper, we show that a graph with sufficient expansion must be globally synchronizing, meaning that a homogeneous Kuramoto model of identi

Yulei Niu, Long Chen, Chang Zhou, Hanwang Zhang

Knowledge distillation (KD) is essentially a process of transferring a teacher model's behavior, e.g., network response, to a student model. The network response serves as additional supervision to formulate the machine domain, which uses the data collected from the human domain as a transfer set. Traditional KD methods hold an underlying assumption that the

Ankur Sikarwar, Arkil Patel, Navin Goyal

Humans can reason compositionally whilst grounding language utterances to the real world. Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whether neural models exhibit similar capabilities. In this work, we present a simple transformer-based model that outperforms specialized architectures on ReaSCAN and a modified vers

Hualie Jiang, Rui Xu, Wenjie Jiang

Stereo-matching is a fundamental problem in computer vision. Despite recent progress by deep learning, improving the robustness is ineluctable when deploying stereo-matching models to real-world applications. Different from the common practices, i.e., developing an elaborate model to achieve robustness, we argue that collecting multiple available datasets fo

Khondhaker Al Momin, H M Imran Kays, Arif Mohaimin Sadri

Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during hurricane Laura compounded by the COVID-19 pandemic. Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron

Benjamin Brück, Yuri Santos Rego, Robin J. Sroka

Let $\mathbb{K}$ be a number field with ring of integers $\mathfrak{O}$ and let $\mathcal{G}$ be a Chevalley group scheme not of type $\mathtt{E}_8$, $\mathtt{F}_4$ or $\mathtt{G}_2$. We use the theory of Tits buildings and a result of T\'oth on Steinberg modules to prove that $H^{\operatorname{vcd}}(\mathcal{G}(\mathfrak{O}); \mathbb{Q}) = 0$ if $\mathfrak{

Wibson W. G. Silva, Adrielson de A. Dias, Alexandre R. Rodrigues, Francisco E. Chávez

We study the magnetic interactions experimentally in nanostructures of nanowire arrays. The intensity value obtained from the interactions provides information about its magnetic behavior. We observed two types of experimental magnetic behavior, i. e., demagnetized and magnetized. Our approach represents the first experimental way to analyze the magnetic beh

Chenxi Lola Deng, Enzo Tartaglione

NeRFs have revolutionized the world of per-scene radiance field reconstruction because of their intrinsic compactness. One of the main limitations of NeRFs is their slow rendering speed, both at training and inference time. Recent research focuses on the optimization of an explicit voxel grid (EVG) that represents the scene, which can be paired with neural n

Bakhyt Aitzhanova, Ualbai Umirbaev

We prove that every derivation and every locally nilpotent derivation of the subalgebra $K[x^n, x^{n-1}y,\ldots,xy^{n-1}, y^n]$, where $n\geq 2$, of the polynomial algebra $K[x,y]$ in two variables over a field $K$ of characteristic zero is induced by a derivation and a locally nilpotent derivation of $K[x,y]$, respectively. Moreover, we prove that every aut

Ahmad Jafar Arifi, Ho-Meoyng Choi, Chueng-Ryong Ji, Yongseok Oh

The issue of resulting in the same physical observables with different current components, in particular from the minus current, has been challenging in the light-front quark model (LFQM) even for the computation of the two-point functions such as meson decay constants. At the level of one-body current matrix element computation, we show the uniqueness of ps

Dennis Bonatsos, K. E. Karakatsanis, Andriana Martinou, T. J. Mertzimekis

Shape coexistence in even-even nuclei is observed when the ground state band of a nucleus is accompanied by another K=0 band at similar energy but with radically different structure. We attempt to predict regions of shape coexistence throughout the nuclear chart using the parameter-free proxy-SU(3) symmetry and standard covariant density functional theory. W

Kajal Eybpoosh, Mansoor Rezghi, Abbas Heydari

Spectral Clustering (SC) is widely used for clustering data on a nonlinear manifold. SC aims to cluster data by considering the preservation of the local neighborhood structure on the manifold data. This paper extends Spectral Clustering to Local and Global Structure Preservation Based Spectral Clustering (LGPSC) that incorporates both global structure and l

Fenglin Liu, Bang Yang, Chenyu You, Xian Wu

Language models (LMs), including large language models (such as ChatGPT), have the potential to assist clinicians in generating various clinical notes. However, LMs are prone to produce ``hallucinations'', i.e., generated content that is not aligned with facts and knowledge. In this paper, we propose the Re$^3$Writer method with retrieval-augmented generatio

Boel Nelson, Elena Pagnin, Aslan Askarov

Transport layer data leaks metadata unintentionally -- such as who communicates with whom. While tools for strong transport layer privacy exist, they have adoption obstacles, including performance overheads incompatible with mobile devices. We posit that by changing the objective of metadata privacy for $\textit{all traffic}$, we can open up a new design spa

Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen

The task of query rewrite aims to convert an in-context query to its fully-specified version where ellipsis and coreference are completed and referred-back according to the history context. Although much progress has been made, less efforts have been paid to real scenario conversations that involve drawing information from more than one modalities. In this p

Andres F. Duque, Myriam Lizotte, Guy Wolf, Kevin R. Moon

Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI

El Hadji S. Diop, Valérie Burdin, V. B. Surya Prasath

We propose here a proof of existence of a minimizer of a segmentation functional based on a priori information on target shapes, and formulated with level sets. The existence of a minimizer is very important, because it guarantees the convergence of any numerical methods (either gradient descents techniques and variants, or PDE resolutions) used to solve the

Jiyue He, Kuk Jin Jang, Katie Walsh, Jackson Liang

Left atrial voltage maps are routinely acquired during electroanatomic mapping in patients undergoing catheter ablation for atrial fibrillation. For patients, who have prior catheter ablation when they are in sinus rhythm, the voltage map can be used to identify low voltage areas using a threshold of 0.2 - 0.45 mV. However, such a voltage threshold for maps

Allan P. Fordy, Qing Huang

In this paper we revisit the subject of stationary flows of Lax hierarchies of a coupled KdV class. We explain the main ideas in the standard KdV case and then consider the dispersive water waves (DWW) case, with respectively 2 and 3 Hamiltonian representations. Each Hamiltonian representation gives us a different form of stationary flow. Comparing these, we

Samuel Belkadi, Lifeng Han, Yuping Wu, Goran Nenadic

The practice of fine-tuning Pre-trained Language Models (PLMs) from general or domain-specific data to a specific task with limited resources, has gained popularity within the field of natural language processing (NLP). In this work, we re-visit this assumption and carry out an investigation in clinical NLP, specifically Named Entity Recognition on drugs and

Donald Salisbury

The conventional Rosenfeld-Bergmann-Dirac constrained Hamiltonian algorithm applied to Einstein-Yang-Mills theory is shown to be equivalent to a local gauge theoretic extension of Cartan's invariant integral approach to classical mechanics. In addition, the Hamiltonian generators of Legendre-projectable spacetime diffeomorphism and gauge symmetries are deriv

Johannes Gäßler, Günter Quast, Daniel Savoiu, Cedric Verstege

Fitting models to measured data is one of the standard tasks in the natural sciences, typically addressed early on in physics education in the context of laboratory courses, in which statistical methods play a central role in analysing and interpreting experimental results. The increased emphasis placed on such methods in modern school curricula, together wi

Andi Zhang, Damon Wischik

An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD. But this approach has been found to fail, in deep learning settings. In this paper, we list some falsehoods that machine learning researchers believe about density-based OOD detection

P B Jones

The growth of a longitudinal or quasi-longitudinal Langmuir mode in the outward-moving beam of ions and protons that forms the open sector of an ion-proton pulsar magnetosphere radiates as an analogue of an end-fed high-impedance horizontal straight-wire antenna an integral number of half-waves in length. The radiation has, broadly, the energy flux, linear p

Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks

We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of s

Ali Bajravani

We use results of M. Aprodu and E. Sernesi to extend a result by Fulton--Harris--Lazarsfeld in Brill--Noether theory of line bundles %and, as well, a result by Aprod-Sernesi in theory of Secant Loci, to Brill--Noether loci of stable bundles inside the moduli space of rank two stable vector bundles on a smooth projective algebraic curve. As a consequence; if

Zhipeng Xie, Yahe Li

This paper proposes a novel prompt-based finetuning method (called DLM-SCS) for few-shot text classification by utilizing the discriminative language model ELECTRA that is pretrained to distinguish whether a token is original or generated. The underlying idea is that the prompt instantiated with the true label should have higher semantic consistency score th

Jiheng Duan, Minghui Li, Hou Ian

Modern cryptographic protocols rely on sophisticated hash functions to generate quasi-unique numbers that serve as signatures for user authentication and other security verifications. The security could be compromised by finding texts hash-mappable to identical numbers, forming so-called collision attack. Seeding a disturbance vector in the hash mapping to o

Karl Friston, Lancelot Da Costa, Dalton A. R. Sakthivadivel, Conor Heins

This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time. The main results are a method or principle of least action that can be used to emulate the behaviour of particles in open exchange with their external milieu. Particles are defin

Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ranging from temperature scaling to different Bayesian treatments of neural networks, have been proposed t

Tian Gu, Yi Han, Rui Duan

Transfer learning aims to improve the performance of a target model by leveraging data from related source populations, which is known to be especially helpful in cases with insufficient target data. In this paper, we study the problem of how to train a high-dimensional ridge regression model using limited target data and existing regression models trained i

Zixuan Xu, Banghuai Li, Ye Yuan, Anhong Dang

Recently significant progress has been made in pedestrian detection, but it remains challenging to achieve high performance in occluded and crowded scenes. It could be attributed mostly to the widely used representation of pedestrians, i.e., 2D axis-aligned bounding box, which just describes the approximate location and size of the object. Bounding box model

Francois Richard

After the long-standing mismatch between ALR and AFBb measurements for predicting the Higgs boson mass, the recent and very precise result on Mw confirms the existence of a crisis in the sector of electroweak measurements. In this note, it is shown that ALR and Mw can be simply reconciled by assuming a common value for the T parameter. Consequently, the AFBb

Andreas Georgis, Panagiotis Mermigkas, Petros Maragos

Traditional monocular Visual Simultaneous Localization and Mapping (vSLAM) systems can be divided into three categories: those that use features, those that rely on the image itself, and hybrid models. In the case of feature-based methods, new research has evolved to incorporate more information from their environment using geometric primitives beyond points

Zhuoxu Huang, Zhiyou Zhao, Banghuai Li, Jungong Han

Transformer with its underlying attention mechanism and the ability to capture long-range dependencies makes it become a natural choice for unordered point cloud data. However, separated local regions from the general sampling architecture corrupt the structural information of the instances, and the inherent relationships between adjacent local regions lack