October 2023 arXiv papers — page 179

A R Usha Devi, Sudha, H Akshata Shenoy, H S Karthik

Extending the mathematical framework of Phys. Rev. A 102, 052419 (2020) we construct Lorentz invariant quantities of pure three-qubit states. This method serves as a bridge between the well-known local unitary (LU) invariants viz. concurrences and three-tangle of an arbitrary three-qubit pure state and the Lorentz invariants of its reduced two-qubit systems.

Kalle Koskinen

The generalized mean-field orthoplicial model is a mean-field model on a space of continuous spins on $\mathbb{R}^n$ that are constrained to a scaled $(n-1)$-dimensional $\ell_1$-sphere, equivalently a scaled $(n-1)$-dimensional orthoplex, and interact through a general interaction function. The finite volume Gibbs states of this model correspond to singular

Benjamin Heymann, Alejandro Jofré

We introduce a class of Bayesian bidding games for which we prove that the set of pure Nash equilibria is a (non-empty) sublattice and we give a sufficient condition for uniqueness that is often verified in the context of markets with inelastic demand. We propose a dynamic that converges to the extrema of the equilibrium set and derive a scheme to compute th

Koren Abitbul, Yehuda Dar

Overparameterized autoencoder models often memorize their training data. For image data, memorization is often examined by using the trained autoencoder to recover missing regions in its training images (that were used only in their complete forms in the training). In this paper, we propose an inverse problem perspective for the study of memorization. Given

Alex Kontorovich

These informal notes are based on the author's lecture at the National Academies of Science, Engineering, and Mathematics workshop on "AI to Assist Mathematical Reasoning" in June 2023. The goal is to think through a path by which we might arrive at AI that is useful for the research mathematician.

Seyed Saman Saboksayr, Gonzalo Mateos, Mariano Tepper

We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure from observational data adhering to a linear structural equation model (SEM). Leveraging advances in differentiable, nonconvex characterizations of acyclicity, recent efforts have advocated a continuous constrained optimization paradigm to efficiently explore the space

Lingru Zhou, Yiqi Gao, Manqing Zhang, Peng Wu

In the domain of video surveillance, describing the behavior of each individual within the video is becoming increasingly essential, especially in complex scenarios with multiple individuals present. This is because describing each individual's behavior provides more detailed situational analysis, enabling accurate assessment and response to potential risks,

Peter Krüger, Michiel Balster, Bhargava Ram Niraghatam, Maurice H. M. Janssen

We present the first photoelectron circular dichroism (PECD) measurements of chiral alcohols, and in particular 1-Phenylethanol, using multiphoton ionization at 400 nm. Observed PECD values were rather small at $\sim2$%, but could be reliably extracted using both hemispherical integration and Abel inversion approaches. Experimental uncertainties of $<0.3$% (

Scott C. Mackey, Mark R. Morris, Gabriele Ponti, Konstantina Anastasopoulou

Using deep archival observations from the Chandra X-ray Observatory, we present an analysis of linear X-ray-emitting features located within the southern portion of the Galactic center chimney, and oriented orthogonal to the Galactic plane, centered at coordinates l = 0.08 deg, b = -1.42 deg. The surface brightness and hardness ratio patterns are suggestive

Effie Papageorgiou

This note is concerned with two families of operators related to the fractional Laplacian, the first arising from the Caffarelli-Silvestre extension problem and the second from the fractional heat equation. They both include the Poisson semigroup. We show that on a complete, connected, and non-compact Riemannian manifold of non-negative Ricci curvature, in b

Matteo Novaga, Luciano Sciaraffia

We study the formation of singularities for the curvature flow of networks when the initial data is symmetric with respect to a pair of perpendicular axes and has two triple junctions. We show that, in this case, the set of singular times is finite.

Hiroki Yamamoto, Daichi Yanagisawa, Katsuhiro Nishinari

We investigate a simple network, which has a branching-merging structure, using the totally asymmetric simple exclusion process, considering conflicts at the merging point. For both periodic and open boundary conditions, the system exhibits metastability. Specifically, for open boundary conditions, we observe two types of metastability: hysteresis and a none

Charalampos Daoutis, Elias Kyritsis, Konstantinos Kouroumpatzakis, Andreas Zezas

(abridged) The overwhelming majority of diagnostic tools for galactic activity are focused on active galaxies. Passive or dormant galaxies are often excluded from these diagnostics which usually employ emission line features. In this work, we use infrared and optical colors in order to build an all-inclusive galactic activity diagnostic tool that can discrim

Zhijun Zhang, Xu Zou, Jiahuan Zhou, Sheng Zhong

Analysis of human actions in videos demands understanding complex human dynamics, as well as the interaction between actors and context. However, these interaction relationships usually exhibit large intra-class variations from diverse human poses or object manipulations, and fine-grained inter-class differences between similar actions. Thus the performance

Claudia Danti, Bertram Bitsch, Jingyi Mah

One of the current challenges of planet formation theory is to explain the enrichment of observed exoplanetary atmospheres. Past studies have focused on scenarios where either pebbles or planetesimals were the heavy element enrichment's drivers, we combine here both approaches to understand whether the composition of a planet can constrain its formation path

Konstantinos Pitas, Julyan Arbel

We present a method to improve the calibration of deep ensembles in the small training data regime in the presence of unlabeled data. Our approach is extremely simple to implement: given an unlabeled set, for each unlabeled data point, we simply fit a different randomly selected label with each ensemble member. We provide a theoretical analysis based on a PA

Aniket Banerjee, Urvashi Verma, Satyam Narayan Srivastava, Rana D. Parshad

Classical models of aphid population dynamics are unable to explain multi-peak patterns in field populations. We consider the variable carrying capacity model (VCM), which can generate such complex multi-peak dynamics, but is also demonstrated to show finite-time blow-up behavior via a sign switching structural instability. We build an adaptive behavioral mo

Isaac B. W. Harris, Dirk Englund

Group-IV color centers in diamond (SiV, GeV, SnV) have emerged as leading solid-state spin-photon interfaces for quantum information processing applications. However, these qubits require cryogenic temperatures to achieve high fidelity operation due to interactions with the thermal phonon bath. In this work, we: (i) derive a detailed model of the decoherence

Adel Magra, Aad van der Vaart, Harry van Zanten

We consider a Bayesian approach for the recovery of scalar parameters arising in inverse problems. We consider a general signal-in white noise model where we have access to two independent noisy observations of a function, and of a linear transformation of the function. The linear operator is unknown up to a scalar parameter. We present a Bernstein-von Mises

Vincent Cohen-Addad, David P. Woodruff, Samson Zhou

We consider the classic Euclidean $k$-median and $k$-means objective on data streams, where the goal is to provide a $(1+\varepsilon)$-approximation to the optimal $k$-median or $k$-means solution, while using as little memory as possible. Over the last 20 years, clustering in data streams has received a tremendous amount of attention and has been the test-b

Zhaoyang Wang, Stefan Wesner, Stefan Zellmann

Rendering large adaptive mesh refinement (AMR) data in real-time in virtual reality (VR) environments is a complex challenge that demands sophisticated techniques and tools. The proposed solution harnesses the ExaBrick framework and integrates it as a plugin in COVISE, a robust visualization system equipped with the VR-centric OpenCOVER render module. This s

Wiebe van Breukelen, Animesh Trivedi

Persistent Memory (PM) is non-volatile byte-addressable memory that offers read and write latencies in the order of magnitude smaller than flash storage, such as SSDs. This survey discusses how file systems address the most prominent challenges in the implementation of file systems for Persistent Memory. First, we discuss how the properties of Persistent Mem

Eric Balkanski, Vasilis Gkatzelis, Xizhi Tan, Cherlin Zhu

Aiming to overcome some of the limitations of worst-case analysis, the recently proposed framework of "algorithms with predictions" allows algorithms to be augmented with a (possibly erroneous) machine-learned prediction that they can use as a guide. In this framework, the goal is to obtain improved guarantees when the prediction is correct, which is called

Nicolette Meshkat, Alexey Ovchinnikov, Thomas Scanlon

Structural identifiability concerns the question of which unknown parameters of a model can be recovered from (perfect) input-output data. If all of the parameters of a model can be recovered from data, the model is said to be identifiable. However, in many models, there are parameters that can take on an infinite number of values but yield the same input-ou

Maria D. Kazachenko

Solar flares sometimes lead to coronal mass ejections that directly affect the Earth's environment. However, a large fraction of flares, including on solar-type stars, are confined flares. What are the differences in physical properties between confined and eruptive flares? For the first time, we quantify thermodynamic and magnetic properties of hundreds of

Luca Ambrogioni

The behavior of a GP regression depends on the choice of covariance function. Stationary covariance functions are preferred in machine learning applications. However, (non-periodic) stationary covariance functions are always mean reverting and can therefore exhibit pathological behavior when applied to data that does not relax to a fixed global mean value. I

Aman Khullar, Daniel Nkemelu, Cuong V. Nguyen, Michael L. Best

A growing body of work has focused on text classification methods for detecting the increasing amount of hate speech posted online. This progress has been limited to only a select number of highly-resourced languages causing detection systems to either under-perform or not exist in limited data contexts. This is majorly caused by a lack of training data whic

Peter Werner, Alexandre Amice, Tobia Marcucci, Daniela Rus

Many computations in robotics can be dramatically accelerated if the robot configuration space is described as a collection of simple sets. For example, recently developed motion planners rely on a convex decomposition of the free space to design collision-free trajectories using fast convex optimization. In this work, we present an efficient method for appr

Fan Zhang, Daniel Kreuter, Yichen Chen, Sören Dittmer

For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the utilisation of powerful machine learning algorithms without requiring the pooling of data. Healthcare data has many simultaneous challenges which require new methodologies to address,

Stanley F. Dermott, Dan Li, Apostolos A. Christou

The asteroid belt is a unique source of information on some of the most important questions facing solar system science. These questions include the sizes, numbers, types and orbital distributions of the planetesimals that formed the planets, and the identification of those asteroids that are the sources of meteorites and near-Earth asteroids. Answering thes

Christopher Jones, Patrick Gartung

Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the levels of 64 cores (128 threads) with recent AMD EPYC processors, and 128 cores on Ampere Altra Max ARM processors. Over th

Zhe Han, Ping He, Yu Qiu

Cluster exchange groupoids are introduced by King-Qiu as an enhancement of cluster exchange graphs to study stability conditions and quadratic differentials. In this paper, we introduce the exchange groupoid for any finite Coxeter-Dynkin diagram $\Delta$ and show that the fundamental group of which is isomorphic to the corresponding braid group associated wi

Shiyun Wa, Xinai Lu, Minjuan Wang

As Artificial Intelligence (AI) integrates deeper into diverse sectors, the quest for powerful models has intensified. While significant strides have been made in boosting model capabilities and their applicability across domains, a glaring challenge persists: many of these state-of-the-art models remain as black boxes. This opacity not only complicates the

Amartya Mukherjee, Ruikun Zhou, Haocheng Chang, Jun Liu

This paper introduces harmonic control Lyapunov barrier functions (harmonic CLBF) that aid in constrained control problems such as reach-avoid problems. Harmonic CLBFs exploit the maximum principle that harmonic functions satisfy to encode the properties of control Lyapunov barrier functions (CLBFs). As a result, they can be initiated at the start of an expe

Binay Rai, Biswajit Paul, Mohammed Tobrej, Manoj Ghising

We studied the timing and spectral properties of the Be/X-ray pulsar Swift J1626.6-5156 using the \emph{NICER} observations of its 2021 outburst. The most important observation is the positive correlation of the centroid energy of the fundamental cyclotron line with the luminosity. This observation agrees with the usual positive correlation of the centroid e

Jozef Barunik, Lubos Hanus

We propose a novel machine learning approach for probabilistic forecasting of hourly day-ahead electricity prices. In contrast with the recent advances in data-rich probabilistic forecasting, which approximates distributions with few features (such as moments), our method is nonparametric and selects the distribution from all possible empirical distributions

Emil T. Akhmedov, Kirill Gubarev

In this paper we propose generalizations of the Sokolov-Ternov and Unruh effects, and discuss the possibility to measure them on different experiments.

Martin W. Licht

We show that the standard partition of unity subordinate to an open cover of a metric space has Lipschitz constant $\max(1,M-1)/\mathcal{L}$, where $\mathcal{L}$ is the Lebesgue number and $M$ is the multiplicity of the cover. If the metric space satisfies the approximate midpoint property, such as length spaces do, then the upper bound improves to $(M-1)/(2

Maryann Rui, Thibaut Horel, Munther Dahleh

Modern data sets, such as those in healthcare and e-commerce, are often derived from many individuals or systems but have insufficient data from each source alone to separately estimate individual, often high-dimensional, model parameters. If there is shared structure among systems however, it may be possible to leverage data from other systems to help estim

Devesh Batra, Salvatore Mercuri, Raad Khraishi

We introduce Longitudinal Predictive Conformal Inference (LPCI), a novel distribution-free conformal prediction algorithm for longitudinal data. Current conformal prediction approaches for time series data predominantly focus on the univariate setting, and thus lack cross-sectional coverage when applied individually to each time series in a longitudinal data

Xin Zhu, Daoguang Yang, Hongyi Pan, Hamid Reza Karimi

The lack of an efficient compression model remains a challenge for the wireless transmission of gearbox data in non-contact gear fault diagnosis problems. In this paper, we present a signal-adaptive asymmetrical autoencoder with a transform domain layer to compress sensor signals. First, a new discrete cosine Stockwell transform (DCST) layer is introduced to

Xiangyu Dong, Xingyi Zhang, Sibo Wang

Graph-level anomaly detection has gained significant attention as it finds applications in various domains, such as cancer diagnosis and enzyme prediction. However, existing methods fail to capture the spectral properties of graph anomalies, resulting in unexplainable framework design and unsatisfying performance. In this paper, we re-investigate the spectra

Sanjeev Kumar, Jitender Singh

In this article, we give an account of some recent irreducibility testing criteria for polynomials having integer coefficients over the field of rational numbers.

Laura B. Hensel, Nutchanon Yongsatianchot, Parisa Torshizi, Elena Minucci

Gestures perform a variety of communicative functions that powerfully influence human face-to-face interaction. How this communicative function is achieved varies greatly between individuals and depends on the role of the speaker and the context of the interaction. Approaches to automatic gesture generation vary not only in the degree to which they rely on d

Kshitijaa Jaglan, Meher Chaitanya, Triansh Sharma, Abhijeeth Singam

The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and opinion formation, we propose a variant o

Vivian Olsiewski Healey, Govind Menon

This work introduces a construction of conformal processes that combines the theory of branching processes with chordal Loewner evolution. The main novelty lies in the choice of driving measure for the Loewner evolution: given a finite genealogical tree $\mathcal{T}$, we choose a driving measure for the Loewner evolution that is supported on a system of part

M. Nakamura, K. Motogi, H. Nakamura, Y. Yonekura

Class II CH3OH masers are used as a convenient tracer of disc-like structures in high-mass star formation. However, more than half of them show a complex distribution in Very Long Baseline Interferometry (VLBI) maps. The origin of such a complex distribution is still unknown. We conducted VLBI monitoring observations to unveil the origin of a complex class I

Andrew Osborne, Chao Yin, Andrew Lucas

We discuss the generic slowing down of quantum dynamics in low energy density states of spatially local Hamiltonians. Beginning with quantum walks of a single particle, we prove that for certain classes of Hamiltonians (deformations of lattice-regularized $H\propto p^{2k}$), the ``butterfly velocity" of particle motion at low energies has an upper bound that

Dongqian Guo, Wencheng Han

Cephalometric landmark detection on lateral skull X-ray images plays a crucial role in the diagnosis of certain dental diseases. Accurate and effective identification of these landmarks presents a significant challenge. Based on extensive data observations and quantitative analyses, we discovered that visual features from different receptive fields affect th

Kartik Ahuja, Amin Mansouri, Yixin Wang

Causal representation learning has emerged as the center of action in causal machine learning research. In particular, multi-domain datasets present a natural opportunity for showcasing the advantages of causal representation learning over standard unsupervised representation learning. While recent works have taken crucial steps towards learning causal repre

R. Thurman-Keup, M. El Baz, V. Scarpine

The Proton Improvement Plan - II (PIP-II) is a new linear accelerator (LINAC) complex being built at Fermilab. It is based on superconducting radiofrequency cavities and will accelerate H- ions to 800 MeV kinetic energy before injection into the existing Booster ring. Measurements of the profile of the beam along the LINAC must be done by non-intercepting me

Jonathan Campbell, Josefien Kuijper, Mona Merling, Inna Zakharevich

In this paper we introduce a new formalism for $K$-theory, called squares $K$-theory. This formalism allows us to simultaneously generalize the usual three-term relation $[B] = [A] + [C]$ for an exact sequence $A \hookrightarrow B \twoheadrightarrow C$ or for a subtractive sequence $A\hookrightarrow B \leftarrow C$, by defining $K_0$ of a squares category to

Hadil Otay, Khaled Humadi, Gunes Karabulut Kurt

Securing satellite communication networks is imperative in the rapidly evolving landscape of advanced telecommunications, particularly in the context of 6G advancements. This paper establishes a secure low earth orbit (LEO) satellite network paradigm to address the challenges of the evolving 6G era, with a focus on enhancing communication integrity between s

Damien Barbier, Ahmed El Alaoui, Florent Krzakala, Lenka Zdeborová

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically corresponding to non-trivial fixed points of an approximate message-passing algorithm. We enumerate these clusters

Liam G. McDermott, Nazeer A. Mosley

As more neurodivergent students enter college, discussion on neurodiversity in higher education is gaining momentum. It is increasingly imperative that we understand how neurodivergent individuals construct their identities within the context of physics programs. Using hte Critical Disability Physics Identity framework, this study offers a nuanced analysis o

Siyuan Yang, Lu Zhang, Liqian Ma, Yu Liu

Image inpainting aims to fill in the missing pixels with visually coherent and semantically plausible content. Despite the great progress brought from deep generative models, this task still suffers from i. the difficulties in large-scale realistic data collection and costly model training; and ii. the intrinsic limitations in the traditionally user-defined

Sylvain Schmitz, Lia Schütze

A recent breakthrough by K\"unnemann, Mazowiecki, Sch\"utze, Sinclair-Banks, and Wegrzycki (ICALP, 2023) bounds the running time for the coverability problem in $d$-dimensional vector addition systems under unary encoding to $n^{2^{O(d)}}$, improving on Rackoff's $n^{2^{O(d\lg d)}}$ upper bound (Theor. Comput. Sci., 1978), and provides conditional matching l

Guang Yang, Bing-yang Cao

Anisotropic thermal transport plays a key role in both theoretical study and engineering practice of heat transfer, but accurately measuring anisotropic thermal conductivity remains a significant challenge. To address this issue, we propose the three-sensor 2{\omega} method in this study, which is capable of accurately measuring the isotropic or anisotropic

Juan C. Verduzco, Ethan Holbrook, Alejandro Strachan

Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in applications like code generation are well-documented. We explore the ability of the GPT-4 LLM to ameliorate two major challenges

Yoshiki Nakamura

In this note, we give a linear-size translation from formulas of first-order logic into equations of the calculus of relations preserving validity and finite validity. Our translation also gives a linear-size conservative reduction from formulas of first-order logic into formulas of the three-variable fragment of first-order logic.

Limeng Wang, Xuemeng Liu, Yang Li, Duo Chang

In the operation of the integrated energy system (IES), considering further reducing carbon emissions, improving its energy utilization rate, and optimizing and improving the overall operation of IES, an optimal dispatching strategy of integrated energy system considering demand response under the stepped carbon trading mechanism is proposed. Firstly, from t

Nathan Broomhead, David Pauksztello, David Ploog, Jon Woolf

We apply convex geometry (cones, fans) to homological input (abelian categories, hearts of bounded t-structures) to construct a new invariant of an abelian category, its heart fan. This can be viewed as a `universal phase diagram' for Bridgeland stability conditions with the given heart. When the abelian category is the module category of a finite-dimensiona

Ni Dang, Zengjie Zhang, Jizheng Liu, Marion Leibold

Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a finite prediction horizon according to its model. Beyond this, the vehicle should also incorporate the prediction of the trajectory of its nearby vehicles, or target vehicles (TVs) in

Chen Dun, Mirian Hipolito Garcia, Guoqing Zheng, Ahmed Hassan Awadallah

Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind. Due to high computational costs, the current trend is to use prompt instruction tuning to better adjust monolithic, pretrained LLMs for new -- but often i

Anton Molina, Manu Prakash

Two-component Marangoni contracted droplets can be arranged into arbitrary two-dimensional tiling patterns where they display rich dynamics due to vapor mediated long-range interactions. Recent work has characterized the centered hexagonal honeycomb lattice, showing it to be a highly frustrated system with many metastable states and relaxation occurring over

Yasaman Asgari, Remy Cazabet, Pierre Borgnat

Community structure is a critical feature of real networks, providing insights into nodes' internal organization. Nowadays, with the availability of highly detailed temporal networks such as link streams, studying community structures becomes more complex due to increased data precision and time sensitivity. Despite numerous algorithms developed in the past

József Balogh, Felix Christian Clemen, Adrian Dumitrescu

Let $X$ be an $n$-element point set in the $k$-dimensional unit cube $[0,1]^k$ where $k \geq 2$. According to an old result of Bollob\'as and Meir (1992), there exists a cycle (tour) $x_1, x_2, \ldots, x_n$ through the $n$ points, such that $\left(\sum_{i=1}^n |x_i - x_{i+1}|^k \right)^{1/k} \leq c_k$, where $|x-y|$ is the Euclidean distance between $x$ and

Lorenzo Iorio

When a test particle moves about an oblate spheroid, it is acted upon, among other things, by two standard perturbing accelerations. One, of Newtonian origin, is due to the quadrupole mass moment $J_2$ of the orbited body. The other one, of the order of $\mathcal{O}\left(1/c^2\right)$, is caused by the static, post-Newtonian field arising solely from the mas

Grzegorz Plewa

We consider a massive scalar field with a coordinate-dependent mass in higher-dimensional spacetime. The field satisfies Dirichlet boundary conditions on a brane representing the four-dimensional world. Despite being massive, the theory is scale-invariant. We quantize the theory calculating the zero-point energy. We find the lower bound for the uncertainty p

Jonas Winklmann, Dimitrios Tsevas, Martin Schulz

Neutral atom quantum computers require accurate single atom detection for the preparation and readout of their qubits. This is usually done using fluorescence imaging. The occupancy of an atom site in these images is often somewhat ambiguous due to the stochastic nature of the imaging process. Further, the lack of ground truth makes it difficult to rate the

Luca Zanella, Benedetta Liberatori, Willi Menapace, Fabio Poiesi

We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision. We introduce the novel method AnomalyCLIP, the first to combine Large Language and Vision (LLV) models, such as CLIP, with multiple instance learning for joint video anomaly detection and classification. Our

Lorenzo Iorio

The post-Newtonian orbital effects induced by the mass quadrupole and spin octupole moments of an isolated, oblate spheroid of constant density that is rigidly and uniformly rotating on the motion of a test particle are analytically worked out for an arbitrary orbital configuration and without any preferred orientation of the body's spin axis. The resulting

Isambard Goodbody

We generalise two facts about finite dimensional algebras to finite dimensional differential graded algebras. The first is the Nakayama Lemma and the second is that the simples can detect finite projective dimension. We prove two dual versions which relate to Gorenstein differential graded algebras and Koszul duality respectively. As an application, we prove

Fran Jelenić, Josip Jukić, Martin Tutek, Mate Puljiz

Effective out-of-distribution (OOD) detection is crucial for reliable machine learning models, yet most current methods are limited in practical use due to requirements like access to training data or intervention in training. We present a novel method for detecting OOD data in Transformers based on transformation smoothness between intermediate layers of a

Vipul Upadhyay, M. Tahir Naseem, Özgür E. Müstecaplıoğlu, Rahul Marathe

In this work, we explore the potential of thermodynamics as a tool for identifying the topological phase transition. Specifically, we focus on a one-dimensional Su-Schrieffer-Heeger (SSH) chain sandwiched between two fermionic baths. To investigate distinctive thermodynamic signatures associated with the topological phase, we employ heat flow analysis. Our r

Lishu Zhang, Hui Li, Yanyan Jiang, Zishen Wang

The field of 2D magnetic materials has paved the way for the development of spintronics and nanodevices with new functionalities. Utilizing antiferromagnetic materials, in addition to layered van der Waals (vdW) ferromagnetic materials, has garnered significant interest. In this work, we present a theoretical investigation of the behavior of MnBi$_2$Te$_4$ d

Ayhan Can Erdur, Daniel Scholz, Josef A. Buchner, Stephanie E. Combs

Brain metastases (BMs) are the most frequently occurring brain tumors. The treatment of patients having multiple BMs with stereo tactic radiosurgery necessitates accurate localization of the metastases. Neural networks can assist in this time-consuming and costly task that is typically performed by human experts. Particularly challenging is the detection of

Shihao Zou, Xianying Huang, Xudong Shen

Emotion Recognition in Conversation (ERC) plays an important role in driving the development of human-machine interaction. Emotions can exist in multiple modalities, and multimodal ERC mainly faces two problems: (1) the noise problem in the cross-modal information fusion process, and (2) the prediction problem of less sample emotion labels that are semantica

CTA Consortium

Approximately one hundred sources of very-high-energy (VHE) gamma rays are known in the Milky Way. A survey of the entire Galactic Plane in the energy range from a few tens of GeV to a few hundred TeV has been proposed as a Key Science Project for the upcoming Cherenkov Telescope Array Observatory (CTAO). This article presents the status of the studies towar

Dora Hermes, Tal Pal Attia, Sándor Beniczky, Jorge Bosch-Bayard

Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Sharing data enriched with standard terms can facilitate data exploration, from case studies to mega-analyses. The machine readability of such electrophysiological event annotations is essential for performing analyses efficiently acro

Anton Ayzenberg, Maxim Beketov, German Magai

In this paper we study the nerves of two types of coverings of a sphere $S^{d-1}$: (1) coverings by open hemispheres; (2) antipodal coverings by closed hemispheres. In the first case, nerve theorem implies that the nerve is homotopy equivalent to $S^{d-1}$. In the second case, we prove that the nerve is homotopy equivalent to a wedge of $(2d-2)$-dimensional

Rutger Campbell, Jim Geelen, Matthew E. Kroeker

Melchior's inequality implies that the average line-length in a simple, rank-$3$, real-representable matroid is less than $3$. A similar result holds for complex-representable matroids, using Hirzebruch's inequality, but with a weaker bound of $4$. We show that the average plane-size in a simple, rank-$4$, complex-representable matroid is bounded above by an

F. Barbaro, L. Canton, Y. Lashko, L. Zangrando

The production of 47Sc, a promising radioisotope for targeted radionuclide therapy, by means of hospital-cyclotron reactions is investigated. Two possible routes are considered: the proton-induced reaction on enriched 50Ti targets and the deuteron-induced reaction on enriched 49Ti targets. The cross-sections of the reactions are calculated using the TALYS co

Hao Wang, Chunhua Zhu, Helei Liu, Sufen Guo

Neutrino losses play a crucial role in the evolution of massive stars. We study the neutrino luminosity of stars ranging from 20 to 90 M_{\odot} from Zero Age Main Sequence (ZAMS) to Fe Core Collapse (FeCC) with different rotation and metallicity in a neutrino Hertzsprung-Russell diagram. In our simulations, we consider {\omega}/{\omega}crit = 0 and 0.7 to r

Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang

GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can introduce temperature-based controllability for exploration and exploitation. We propose \textit{Logit-scaling GFlowNets} (Logit-GFN), a novel architectural design that greatly accelerates

Ya-Ping Li, Yi-Xian Chen, Douglas N. C. Lin

We perform a series of 3D simulations to study the accretion of giant planet embedded in protoplanetary discs (PPDs) over gap-opening timescales. We find that the accretion mass flux mainly comes from the intermediate latitude above the disc midplane. The circumplanetary disc (CPD) for a super-thermal planet is rotation-supported up to $\sim$20-30\% of the p

Dong Chen, Kaihang Pan, Guoming Wang, Yueting Zhuang

Recent years have seen a surge of interest in anomaly detection for tackling industrial defect detection, event detection, etc. However, existing unsupervised anomaly detectors, particularly those for the vision modality, face significant challenges due to redundant information and sparse latent space. Conversely, the language modality performs well due to i

H. N. Long, L. T. Hue

Based on the Peccei-Quinn (PQ) assignment, the PQ charge operator in the 3-3-1 model with Cosmological Inflation is constructed in terms of diagonal generators $T_3$ and $T_8$ of the $SU(3)_L$ subgroup. The formula shows that difference of PQ charges of up and down quarks is 2, i.e., $\De Q_A =2$, while for electric charge, are assumed to be equal, while the

Hiraku Abe, Haozhi Zeng

The Peterson variety (which we denote by $Y$) is a subvariety of the flag variety, introduced by Dale Peterson to describe the quantum cohomology rings of all the partial flag varieties. Motivated by the mirror symmetry for partial flag varieties, Rietsch studied the totally nonnegative part $Y_{\ge0}$ and its cell decomposition. Based on the structure of th

Hiraku Abe, Haozhi Zeng

The Peterson variety is a remarkable variety introduced by Dale Peterson to describe the quantum cohomology rings of all the partial flag varieties. The rational cohomology ring of the Peterson variety is known to be isomorphic to that of a particular toric orbifold which naturally arises from the given root system. In this paper, we show that it is not an a

Abhijit Biswas, David I. Ketcheson, Steven Roberts, Benjamin Seibold

Explicit Runge--Kutta (RK) methods are susceptible to a reduction in the observed order of convergence when applied to initial-boundary value problem with time-dependent boundary conditions. We study conditions on explicit RK methods that guarantee high-order convergence for linear problems; we refer to these conditions as weak stage order conditions. We pro

Sirui Wu, Nicola R. Napolitano, Crescenzo Tortora, Rodrigo von Marttens

The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. Total "central" masses can be inferred via galaxy dynamics or with gravitational lensing, but these methods have limitations. We propose a novel approach, based on Random Forest, to make predictions on the total and dark matter conten

Hao Shi, Chengshan Pang, Jiaming Zhang, Kailun Yang

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety. While previous studies have limitations in using only depth or height information, we find both depth and height matter and they are in fact compleme

Alexander Venus, Erik Leitinger, Stefan Tertinek, Florian Meyer

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent's position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the

Zakaria Ouaras

In this paper, we present an algebro-geometric construction of the Hitchin connection in the parabolic setting for a fixed determinant line bundle. Our strategy is based on Hecke modifications, where we provide a decomposition formula for the parabolic determinant line bundle and the canonical line bundle of the moduli space of parabolic bundles. As a specia

Mojtaba A. Farahani, M. R. McCormick, Ramy Harik, Thorsten Wuest

Manufacturing is gathering extensive amounts of diverse data, thanks to the growing number of sensors and rapid advances in sensing technologies. Among the various data types available in SMS settings, time-series data plays a pivotal role. Hence, TSC emerges is crucial in this domain. The objective of this study is to fill this gap by providing a rigorous e

Zarko Sakotic, Alexander Ware, Michelle Povinelli, Daniel Wasserman

Reducing device volume is one of the key requirements for advanced nanophotonic technologies, however this demand is often at odds with designing highly absorbing elements which usually require sizeable thicknesses, such as for detector and sensor applications. Here we theoretically explore the thickness limitations of perfectly absorbing resonant systems an

Maura B. Paterson, Douglas R. Stinson

(Strong) circular external difference families (which we denote as CEDFs and SCEDFs) can be used to construct nonmalleable threshold schemes. They are a variation of (strong) external difference families, which have been extensively studied in recent years. We provide a variety of constructions for CEDFs based on graceful labellings ($\alpha$-valuations) of

Leonardo Videla, Mauricio Tejo, Cristóbal Quiñinao, Pablo A. Marquet

We study the large-time behavior of an ensemble of entities obeying replicator-like stochastic dynamics with mean-field interactions as a model for a primordial ecology. We prove the propagation-of-chaos property and establish conditions for the strong persistence of the $N$-replicator system and the existence of invariant distributions for a class of associ

Gunhee Cho, Guofang Wei, Guang Yang

We provide a probabilistic proof of the fundamental gap estimate for Schr\"odinger operators in convex domains on the sphere, which extends the probabilistic proof of F. Gong, H. Li, and D. Luo for the Euclidean case. Our results further generalize the results achieved for the Laplacian by S. Seto, L. Wang, and G. Wei, as well as by C. He, G. Wei, and Qi S.