October 2023 arXiv papers — page 169

Brian Grajales, Lino Grama

In this paper, we investigate equigeodesics on a compact homogeneous space $M=G/H.$ We introduce a formula for the identification of equigeodesic vectors only relying on the isotropy representation of $M$ and the Lie structure of the Lie algebra of $G$. Applications to $M$-spaces are also discussed.

Noah Lordi, Eugene J. Tsao, Alexander J. Lind, Scott A. Diddams

The fields of precision timekeeping and spectroscopy increasingly rely on optical frequency comb interferometry. However, comb-based measurements are not described by existing quantum theory because they exhibit both large mode mismatch and finite strength local oscillators. To establish this quantum theory, we derive measurement operators for homodyne detec

Nikolai Leonenko, Andriy Olenko, Jayme Vaz

The paper investigates solutions of the fractional hyperbolic diffusion equation in its most general form with two fractional derivatives of distinct orders. The solutions are given as spatial-temporal homogeneous and isotropic random fields and their spherical restrictions are studied. The spectral representations of these fields are derived and the associa

Chen Jiang, Allie Wang, Martin Jagersand

In this paper, we investigate the feasibility of using knowledge graphs to interpret actions and behaviors for robot manipulation control. Equipped with an uncalibrated visual servoing controller, we propose to use robot knowledge graphs to unify behavior trees and geometric constraints, conceptualizing robot manipulation control as semantic events. The robo

Yaser M. Banad, Syed Mohammad Abid Hasan, Sarah S. Sharif, Georgios Veronis

Whispering gallery mode (WGM) resonators are garnering significant attention due to their unique characteristics and remarkable properties. When integrated with optical sensing and processing technology, WGM resonators offer numerous advantages, including compact size, high sensitivity, rapid response, and tunability. This paper comprehensively investigates

Xingjian Han, Benjamin Senderling, Stanley To, Deepak Kumar

The physical plausibility of human motions is vital to various applications in fields including but not limited to graphics, animation, robotics, vision, biomechanics, and sports science. While fully simulating human motions with physics is an extreme challenge, we hypothesize that we can treat this complexity as a black box in a data-driven manner if we foc

Mo Chen, John Clai Owens, Harald Putterman, Max Schäfer

Noise within solid-state systems at low temperatures, where many of the degrees of freedom of the host material are frozen out, can typically be traced back to material defects that support low-energy excitations. These defects can take a wide variety of microscopic forms, and for amorphous materials are broadly described using generic models such as the tun

Francesco Invernici, Anna Bernasconi, Stefano Ceri

The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. Several open-access corpora and literature hubs were established; among them, the COVID-19 Open Research Datase

Johannes Schneider

Neural networks are not learning optimal decision boundaries. We show that decision boundaries are situated in areas of low training data density. They are impacted by few training samples which can easily lead to overfitting. We provide a simple algorithm performing a weighted average of the prediction of a sample and its nearest neighbors' (computed in lat

Qiushi Guo, Iliya Esin, Cheng Li, Chen Chen

Phonon-polaritons are electromagnetic waves resulting from the coherent coupling of photons with optical phonons in polar dielectrics. Due to their exceptional ability to confine electric fields to deep subwavelength scales with low loss, they are uniquely poised to enable a suite of applications beyond the reach of conventional photonics, such as sub-diffra

Chin-Chia Michael Yeh, Xin Dai, Yan Zheng, Junpeng Wang

Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally surpasses its non-MTL counterparts in terms of generalizability. Although MTL has been extensively researched in various domains such as computer vision, natural language processi

I-Chi Chen, Klée Pollock, Yong-Xin Yao, Peter P. Orth

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum compu

Kashu Yamazaki, Taisei Hanyu, Khoa Vo, Thang Pham

Precise 3D environmental mapping is pivotal in robotics. Existing methods often rely on predefined concepts during training or are time-intensive when generating semantic maps. This paper presents Open-Fusion, a groundbreaking approach for real-time open-vocabulary 3D mapping and queryable scene representation using RGB-D data. Open-Fusion harnesses the powe

H. Abe, S. Abe, V. A. Acciari, I. Agudo

The BL Lac 1ES 2344+514 is known for temporary extreme properties (e.g., a shift of the synchrotron SED peak energy $\nu_{synch,p}$ above 1keV). While those extreme states were so far observed only during high flux levels, additional multi-year observing campaigns are required to achieve a coherent picture. Here, we report the longest investigation of the so

Sascha Klawohn, Gábor Csányi, James P. Darby, James R. Kermode

Gaussian Approximation Potentials are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and sof

Huanchen Zhai, Henrik R. Larsson, Seunghoon Lee, Zhi-Hao Cui

Block2 is an open source framework to implement and perform density matrix renormalization group and matrix product state algorithms. Out-of-the-box it supports the eigenstate, time-dependent, response, and finite-temperature algorithms. In addition, it carries special optimizations for ab initio electronic structure Hamiltonians and implements many quantum

Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng

A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of

Mustafa Altay Karamuftuoglu, Beyza Zeynep Ucpinar, Sasan Razmkhah, Mehdi Kamal

Single Flux Quantum (SFQ) technology represents a groundbreaking advancement in computational efficiency and ultra-high-speed neuromorphic processing. The key features of SFQ technology, particularly data representation, transmission, and processing through SFQ pulses, closely mirror fundamental aspects of biological neural structures. Consequently, SFQ-base

Heda Zhang, Hu Miao, Thomas Z Ward, David G Mandrus

In the presence of external magnetic field, the Kitaev model could either hosts gapped topological anyon or gapless Majorana fermions. In $\alpha$-RuCl$_3$, the gapped and gapless cases are only separated by a thirty-degree rotation of the in-plane magnetic field vector. The presence/absence of the spectral gap is key for understanding the thermal transport

Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng

A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks. However, current research on time series pre-training has mostly focused on models pre-trained solely on data from a single domain, resulting in a l

Neehal Tumma, Mathias Lechner, Noel Loo, Ramin Hasani

Developing autonomous agents that can interact with changing environments is an open challenge in machine learning. Robustness is particularly important in these settings as agents are often fit offline on expert demonstrations but deployed online where they must generalize to the closed feedback loop within the environment. In this work, we explore the appl

Joseph Latessa, Aadi Huria, Deepak Raju

Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where

Chikaha Tsuji, Dai Komukai, Mimo Shirasaka, Hikaru Wada

Our team, TRAIL, consists of AI/ML laboratory members from The University of Tokyo. We leverage our extensive research experience in state-of-the-art machine learning to build general-purpose in-home service robots. We previously participated in two competitions using Human Support Robot (HSR): RoboCup@Home Japan Open 2020 (DSPL) and World Robot Summit 2020,

Alexander Shmakov, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour

Bayesian Optimization (BO), guided by Gaussian process (GP) surrogates, has proven to be an invaluable technique for efficient, high-dimensional, black-box optimization, a critical problem inherent to many applications such as industrial design and scientific computing. Recent contributions have introduced reinforcement learning (RL) to improve the optimizat

Louis-François Bouchard, Mohsen Ben Lazreg, Matthew Toews

This paper proposes a novel hue-like angular parameter to model the structure of deep convolutional neural network (CNN) activation space, referred to as the {\em activation hue}, for the purpose of regularizing models for more effective learning. The activation hue generalizes the notion of color hue angle in standard 3-channel RGB intensity space to $N$-ch

Christopher Boyd, Raymond A. Ryan, Nina Snigireva

We introduce and study the algebraic, analytic and lattice properties of regular homogeneous polynomials and holomorphic functions on complex Banach lattices. We show that the theory of power series with regular terms is closer to the theory of functions of several complex variables than the theory of holomorphic functions on Banach spaces. We extend the con

Manoranjan Dutta

Discrepancies of the $\Lambda {\rm CDM}$ model with small-scale cosmological observations and stringent constraints from direct search experiments cast doubts over typical weak scale cold dark matter candidates e.g. WIMPs. Self-interacting dark matter (SIDM) is a very promising alternative to WIMP, which not only alleviates the small-scale anomalies of the $

Dun Yuan, Ekram Hossain, Di Wu, Xue Liu

3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersive and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job sc

Norma Rivano, Nicola Marzari, Thibault Sohier

The electronic and vibrational properties and electron-phonon couplings of one-dimensional materials will be key to many prospective applications in nanotechnology. Dimensionality strongly affects these properties and has to be correctly accounted for in first-principles calculations. Here we develop and implement a formulation of density-functional and dens

Avisek Naug, Antonio Guillen, Ricardo Luna Gutiérrez, Vineet Gundecha

The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation. Given their high energy consumption and exponentially large computational workloads, data centers are prime candidates for optimizing power consumption, especially in areas such

René Mboro

By classical calculation, for a smooth hypersurface $Y\subset \mathbb P^{n+1}_{\mathbb C}$, the product by the hyperplane class is zero on homologically trivial rational cycles i.e. $H_{|Y}\cdot :{\rm CH}_i(Y)_{hom,\mathbb Q}\rightarrow {\rm CH}_{i-1}(Y)_{hom,\mathbb Q}$ is $0$ for any $i$. This note extends that result to some complete intersections.

Misty C. Bentz, Madison Markham, Sara Rosborough, Christopher A. Onken

We describe the results of a new reverberation mapping program focused on the nearby Seyfert galaxy NGC 3227. Photometric and spectroscopic monitoring were carried out from 2022 December to 2023 June with the Las Cumbres Observatory network of telescopes. We detected time delays in several optical broad emission lines, with H$\beta$ having the longest delay

Saaket Agashe, Yue Fan, Anthony Reyna, Xin Eric Wang

Large Language Models (LLMs) have demonstrated emergent common-sense reasoning and Theory of Mind (ToM) capabilities, making them promising candidates for developing coordination agents. This study introduces the LLM-Coordination Benchmark, a novel benchmark for analyzing LLMs in the context of Pure Coordination Settings, where agents must cooperate to maxim

Omar Chehab, Aapo Hyvarinen, Andrej Risteski

Recent research has developed several Monte Carlo methods for estimating the normalization constant (partition function) based on the idea of annealing. This means sampling successively from a path of distributions that interpolate between a tractable "proposal" distribution and the unnormalized "target" distribution. Prominent estimators in this family incl

Yiwen Shao

Multi-channel multi-talker speech recognition presents formidable challenges in the realm of speech processing, marked by issues such as background noise, reverberation, and overlapping speech. Overcoming these complexities requires leveraging contextual cues to separate target speech from a cacophonous mix, enabling accurate recognition. Among these cues, t

Hossein B. Jond, Aykut Yıldız

In a social network, individuals express their opinions on several interdependent topics, and therefore the evolution of their opinions on these topics is also mutually dependent. In this work, we propose a differential game model for the multi-dimensional opinion formation of a social network whose population of agents interacts according to a communication

Chen Dun, Qiutai Pan, Shikai Jin, Ria Stevens

Determining the structure of a protein has been a decades-long open question. A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized. Advances in the transformer neural network architecture -- such as AlphaFold2 -- achieve significant improvements for this problem, by learning from

Zizhao Hu, Mohammad Rostami

Learning new tasks accumulatively without forgetting remains a critical challenge in continual learning. Generative experience replay addresses this challenge by synthesizing pseudo-data points for past learned tasks and later replaying them for concurrent training along with the new tasks' data. Generative replay is the best strategy for continual learning

Canran Wang, Jin Sima, Netanel Raviv

We investigate the problem of encoding data into an $(n, t)$-break-resilient code ($(n, t)$-BRC), i.e., a collections of sequences of length~$n$ from which the original data can be reconstructed even if they are adversarially broken at up to~$t$ arbitrary positions. We establish lower bounds on the redundancy of any $(n, t)$-BRC and present code construction

Johannes Aspman, Georgios Korpas, Jakub Marecek

There has been a great deal of recent interest in binarized neural networks, especially because of their explainability. At the same time, automatic differentiation algorithms such as backpropagation fail for binarized neural networks, which limits their applicability. By reformulating the problem of training binarized neural networks as a subadditive dual o

Francesco Buccheri, Reinhold Egger, Alessandro De Martino

We consider a smooth interface between a topological nodal-line semimetal and a topologically trivial insulator (e.g., the vacuum) or another semimetal with a nodal ring of different radius. Using a low-energy effective Hamiltonian including only the two crossing bands, we show that these junctions accommodate a two-dimensional zero-energy level and a set of

Leonardo Novicki Neto, Fabio Reway, Yuri Poledna, Maikol Funk Drechsler

Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measu

Yongling Zhao, Dominik Strebel, Dominique Derome, Igor Esau

We introduce a novel methodological advancement by clustering paired near-surface air temperature with the planetary boundary layer height (PBLH) to characterize intra-city clusters for analytics. To illustrate this approach, we analyze three heatwaves (HW): the 2019 HW in Paris, the 2018 HW in Montreal, and the 2017 HW in Zurich. We assess cluster-based cha

Cagla Deniz Bahadir, Benjamin Liechty, David J. Pisapia, Mert R. Sabuncu

Mitotic figure detection in histology images is a hard-to-define, yet clinically significant task, where labels are generated with pathologist interpretations and where there is no ``gold-standard'' independent ground-truth. However, it is well-established that these interpretation based labels are often unreliable, in part, due to differences in expertise l

Priyanka Chakraborty, Adam Foster, Randall Smith, Nancy Brickhouse

In this paper, we present a simple, one-step, self-consistent, and fast resonance scattering model rsapec based on the AtomDB database. This model can be used as an alternative to the commonly used APEC model for fitting such X-ray spectra with optically thick lines. The current model is intended, in general, for verifying the presence of the effect and for

Mahdi Akhi, Nona Ghazizadeh

In this paper, a new approach called HDNA (HTML DNA) is introduced for analyzing and comparing Document Object Model (DOM) trees in order to detect differences in HTML pages. This method assigns an identifier to each HTML page based on its structure, which proves to be particularly useful for detecting variations caused by server-side updates, user interacti

Zhu Wang, Praveen Raj Veluswami, Harsh Mishra, Sathya N. Ravi

Many neural networks deployed in the real world scenarios are trained using cross entropy based loss functions. From the optimization perspective, it is known that the behavior of first order methods such as gradient descent crucially depend on the separability of datasets. In fact, even in the most simplest case of binary classification, the rate of converg

Yuanbo Hou, Siyang Song, Chuang Yu, Wenwu Wang

Most deep learning-based acoustic scene classification (ASC) approaches identify scenes based on acoustic features converted from audio clips containing mixed information entangled by polyphonic audio events (AEs). However, these approaches have difficulties in explaining what cues they use to identify scenes. This paper conducts the first study on disclosin

Motohiro Hirao, Burak Kurkcu, Alireza Ghanbarpour, Masayoshi Tomizuka

Nonlinear stiffness SEAs (NSEAs) inspired by biological muscles offer promise in achieving adaptable stiffness for assistive robots. While assistive robots are often designed and compared based on torque capability and control bandwidth, NSEAs have not been systematically designed in the frequency domain due to their nonlinearity. The describing function, an

Ethan A. Vo, Xiao Wang, Timothy C. Berkelbach

We calculate the band gaps of 12 inorganic semiconductors and insulators composed of atoms from the first three rows of the periodic table using periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD). Our calculations are performed with atom-centered triple-zeta basis sets and up to 64 $k$-points in the Brillouin zon

Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

Behavioural metrics have been shown to be an effective mechanism for constructing representations in reinforcement learning. We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive definite kernels. We leverage this new perspective to define a new metric that is provably equivalent to the recently introduce

Jie Zhou, Moheb Sheikhi, Ashok Dheenan, Haris Abbasi

In this work, we report the fabrication and characterizations of a monocrystalline GaAs/$\beta$-Ga$_2$O$_3$ p-n heterojunction by employing semiconductor grafting technology. The heterojunction was created by lifting off and transfer printing a p-type GaAs single crystal nanomembrane to an Al$_2$O$_3$-coated n-type$\beta$-Ga$_2$O$_3$ epitaxial substrate. The

Colin S. Gordon, Sergey Matskevich

Interactive proof assistants are computer programs carefully constructed to check a human-designed proof of a mathematical claim with high confidence in the implementation. However, this only validates truth of a formal claim, which may have been mistranslated from a claim made in natural language. This is especially problematic when using proof assistants t

Kumar Vijay Mishra, M. Ashok Kumar, Ting-Kam Leonard Wong

Information geometry is a study of statistical manifolds, that is, spaces of probability distributions from a geometric perspective. Its classical information-theoretic applications relate to statistical concepts such as Fisher information, sufficient statistics, and efficient estimators. Today, information geometry has emerged as an interdisciplinary field

Suyash C. Vishnoi, Michele D. Simoni

The present work investigates surrogate model-based optimization for real-time curbside traffic management operations. An optimization problem is formulated to minimize the congestion on roadway segments caused by vehicles stopping on the segment (e.g., ride-hailing or delivery operations) and implemented in a model predictive control framework. A hybrid sim

Johan Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro

In value-based deep reinforcement learning with replay memories, the batch size parameter specifies how many transitions to sample for each gradient update. Although critical to the learning process, this value is typically not adjusted when proposing new algorithms. In this work we present a broad empirical study that suggests {\em reducing} the batch size

Francesco Fanelli, Eduard Feireisl

We consider a scaled Navier--Stokes--Fourier system describing the motion of a compressible, heat-conducting, viscous fluid driven by inhomogeneous boundary temperature distribution together with the gravitational force of a massive object placed outside the fluid. We identify the limit system in the low Mach/low Froude number regime for the ill prepared ini

Chris Timberlake, Elliot Simcox, Hendrik Ulbricht

We report feedback cooling of translational and librational degrees of freedom of a levitated micromagnet cylinder, utilizing a piezoelectric actuator to apply linear feedback to high-Q mechanical modes. The normal modes are measured with a superconducting pick-up coil coupled to a DC SQUID, and phase information is fed back to the piezoelectric actuator to

Alejandro Parada-Mayorga, Landon Butler, Alejandro Ribeiro

In this paper we discuss the results recently published in~[1] about algebraic signal models (ASMs) based on non commutative algebras and their use in convolutional neural networks. Relying on the general tools from algebraic signal processing (ASP), we study the filtering and stability properties of non commutative convolutional filters. We show how non com

Yao Dou, Philippe Laban, Claire Gardent, Wei Xu

In this tutorial, we focus on text-to-text generation, a class of natural language generation (NLG) tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria (e.g., readability or linguistic styles), while largely retaining the original meaning and the length of the text. This includes many

Ram Band, Philippe Charron

We prove upper and lower bounds for the number of zeroes of linear combinations of Schr\"odinger eigenfunctions on metric (quantum) graphs. These bounds are distinct from both the interval and manifolds. We complement these bounds by giving non-trivial examples for the lower bound as well as sharp examples for the upper bound. In particular, we show that eve

Yas Al-Hadeethi, Chandraman Patil, Wafa Said Bait Haridh, Moustafa Ahmed

Recent research and development in the mid-infrared (IR) wavelength range (2-20 um) for a variety of applications, such as trace gas monitoring, thermal imaging, and free space communications have shown tremendous and fascinating progress. MXenes, which mainly refer to two-dimensional (2D) transition-metal carbides, nitrides, and carbonitrides, have drawn a

Jonas Eidesen

We show that the collection of regular Borel measures on a second-countable locally compact Hausdorff space has the structure of a sheaf. With this we give an alternate description of the pullback of a regular Borel measure along a local homeomorphism. We are able to use these tools to give a description of the KMS-weights for the gauge-action on the graph C

Jason Holmes, Lian Zhang, Yuzhen Ding, Hongying Feng

Purpose: To introduce the concept of using large language models (LLMs) to re-label structure names in accordance with the American Association of Physicists in Medicine (AAPM) Task Group (TG)-263 standard, and to establish a benchmark for future studies to reference. Methods and Materials: The Generative Pre-trained Transformer (GPT)-4 application programmi

Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad

Concurrent estimation and control of robotic systems remains an ongoing challenge, where controllers rely on data extracted from states/parameters riddled with uncertainties and noises. Framework suitability hinges on task complexity and computational constraints, demanding a balance between computational efficiency and mission-critical accuracy. This study

Ken C. L. Wong, Hongzhi Wang, Tanveer Syeda-Mahmood

Due to the computational complexity of 3D medical image segmentation, training with downsampled images is a common remedy for out-of-memory errors in deep learning. Nevertheless, as standard spatial convolution is sensitive to variations in image resolution, the accuracy of a convolutional neural network trained with downsampled images can be suboptimal when

Tommaso Cortopassi

In this note, we prove a $L^p$ version of the well known stability estimate for regular Lagrangian flows derived by Gianluca Crippa and Camillo De Lellis in \cite{crippa2008estimates}. As far as we know, the only estimate of this kind readily available in the literature is for the case $p=1$. With minor modifications to the proof in \cite{crippa2008estimates

Yingcheng Liu, Neerav Karani, Neel Dey, S. Mazdak Abulnaga

The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effective semi-supervised learning method for improving placenta segmentation in fetal EPI MRI time series. We employ consistency regularization loss that promotes consistency under sp

Kathleen Emerson, Sophie Morel

We prove that the definitions of a $d$-dimensional pseudocharacter (or pseudorepresentation) given by Chenevier and V. Lafforgue agree over any ring. We also compare the scheme of Lafforgue's $G$-pseudocharacters of a group with its $G$-character variety.

Stanislav Kublanovsky

We find necessary and sufficient conditions for the finite separability of finitely generated commutative rings. Namely, we prove that every such ring is a finite extension of its torsion ideal $I_k$ where $k$ is square-free, and $I_k$ is a subdirect product of a finite ring and finitely many zero divisor-free rings of prime characteristic each of which is a

Damaris Schindler, Rajula Srivastava, Niclas Technau

Let $\mathcal{M}\subset \mathbb{R}^n$ be a compact and sufficiently smooth manifold of dimension $d$. Suppose $\mathcal{M}$ is nowhere completely flat. Let $N_{\mathcal{M}}(\delta,Q)$ denote the number of rational vectors $\mathbf{a}/q$ within a distance of $\delta/q$ from $\mathcal{M}$ so that $q \in [Q,2Q)$. We develop a novel method to analyse $N_{\mathca

Aidan Z. H. Yang, Ricardo Brancas, Pedro Esteves, Sofia Aparicio

Data analysts use SQL queries to access and manipulate data on their databases. However, these queries are often challenging to write, and small mistakes can lead to unexpected data output. Recent work has explored several ways to automatically synthesize queries based on a user-provided specification. One promising technique called text-to-SQL consists of t

Arjun Karuvally, J. Eliot B. Moss

In resource limited computing systems, sequence prediction models must operate under tight constraints. Various models are available that cater to prediction under these conditions that in some way focus on reducing the cost of implementation. These resource constrained sequence prediction models, in practice, exhibit a fundamental tradeoff between the cost

Valentin Haberl, Piotr Szewczak, Lyubomyr Zdomskyy

Using combinatorial covering properties, we show that there is no concentrated set of reals of size $\omega_2$ in the Miller model. The main result refutes a conjecture of Bartoszy\'{n}ski and Halbeisen. We also prove that there are no $\gamma$-set of reals of size $\omega_2$ in the Miller model.

Michael Oberaigner, Manuel Ederer, Sandeep Kumar Chaluvadi, Pasquale Orgiani

Knowledge of atomic-level details of structure, chemistry, and electronic states is paramount for a comprehensive understanding of emergent properties at oxide interfaces. We utilise a novel methodology based on atomic-scale electron energy loss spectroscopy (EELS) to spatially map the electronic states tied to the formation of a two-dimensional electron gas

Ogan Özsoy, Gianmassimo Tasinato

We propose a cosmological dark matter production mechanism in the form of a longitudinal massive vector boson. We build upon the work of Graham et.al. including non-minimal couplings of the massive vector with gravity, developing a well motivated set-up from an effective field theory perspective. We carefully track the dynamics of vector field in passing fro

Ana Dodik, Oded Stein, Vincent Sitzmann, Justin Solomon

We propose a variational technique to optimize for generalized barycentric coordinates that offers additional control compared to existing models. Prior work represents barycentric coordinates using meshes or closed-form formulae, in practice limiting the choice of objective function. In contrast, we directly parameterize the continuous function that maps an

Wenhan Yang, Jingdong Gao, Baharan Mirzasoleiman

Contrastive Language-Image Pre-training (CLIP) on large image-caption datasets has achieved remarkable success in zero-shot classification and enabled transferability to new domains. However, CLIP is extremely more vulnerable to targeted data poisoning and backdoor attacks, compared to supervised learning. Perhaps surprisingly, poisoning 0.0001% of CLIP pre-

Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz, Pierre Comon

Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multim

Philipp Schaer, Johann Schaible, Leyla Jael Castro

Meta-evaluation studies of system performances in controlled offline evaluation campaigns, like TREC and CLEF, show a need for innovation in evaluating IR-systems. The field of academic search is no exception to this. This might be related to the fact that relevance in academic search is multilayered and therefore the aspect of user-centric evaluation is bec

S. V. Kalenskii, E. A. Mikheeva

The results of a spectral survey of the region of massive star formation DR21OH in the 4-mm wavelength range are presented. Sixty-nine molecules and their isotopologues have been detected, ranging from simple diatomic or triatomic species such as SO, SiO and CCH, to complex organic molecules such as CH$_3$OCHO or CH$_3$OCH$_3$. The obtained results qualitati

Jack Griffiths, Ana Flávia Suzana, Longlong Wu, Samuel D. Marks

Material functionality can be strongly determined by structure extending only over nanoscale distances. The pair distribution function presents an opportunity to shift structural studies beyond idealized crystal models and investigate structure over varying length scales. Applying this method with ultrafast time resolution has the potential to similarly disr

Awais Khan, Khalid Mahmood Malik

The Automatic Speaker Verification (ASV) system is vulnerable to fraudulent activities using audio deepfakes, also known as logical-access voice spoofing attacks. These deepfakes pose a concerning threat to voice biometrics due to recent advancements in generative AI and speech synthesis technologies. While several deep learning models for speech synthesis d

Albero Bocchinfuso, Daniela Calvetti, Erkki Somersalo

We consider inverse problems estimating distributed parameters from indirect noisy observations through discretization of continuum models described by partial differential or integral equations. It is well understood that the errors arising from the discretization can be detrimental for ill-posed inverse problems, as discretization error behaves as correlat

M. Ayyash, X. Xu, M. Mariantoni

We propose a fast scheme to generate Schr\"odinger cat states in a superconducting resonator using a continuously driven qubit without resorting to the dispersive regime, two-photon drives, or engineered two-photon dissipation. We provide analysis for when the qubit is on and off resonance from the drive. We extend our analysis to account for a third level i

Rocco Caprio, Adam M. Johansen

Markov chain Monte Carlo (MCMC) algorithms are based on the construction of a Markov chain with transition probabilities leaving invariant a probability distribution of interest. In this work, we look at these transition probabilities as functions of their invariant distributions, and we develop a notion of derivative in the invariant distribution of a MCMC

Xabier Oianguren-Asua, Albert Solé, Carlos F. Destefani, Xavier Oriols

In this chapter we offer an introduction to weak values from a three-fold perspective: first, outlining the protocols that enable their experimental determination; next, deriving their correlates in the quantum formalism and, finally, discussing their ontological significance according to different quantum theories or interpretations. We argue that weak valu

Michael J. Wilensky, Miguel F. Morales, Bryna J. Hazelton, Pyxie L. Star

We present deep upper limits from the 2014 Murchison Widefield Array (MWA) Phase I observing season, with a particular emphasis on identifying the spectral fingerprints of extremely faint radio frequency interference (RFI) contamination in the 21~cm power spectra (PS). After meticulous RFI excision involving a combination of the \textsc{SSINS} RFI flagger an

B. Folsom, E. Laface

An algorithm is demonstrated that performs first-principles tracking of relativistic charged-particles. A covariant approach is used which relies on retarded vector potentials for trajectory integration instead of performing electromagnetic field calculations. When accounting for retardation effects, the peak vector potential and corresponding Lorentz force

Juan Gutiérrez, Christian Valqui

A conjecture attributed to Smith states that every pair of longest cycles in a $k$-connected graph intersect each other in at least $k$ vertices. In this paper, we show that every pair of longest cycles in a~$k$-connected graph on $n$ vertices intersect each other in at least~$\min\{n,8k-n-16\}$ vertices, which confirms Smith's conjecture when $k\geq (n+16)/

Zeljko Kraljevic, Anthony Shek, Joshua Au Yeung, Ewart Jonathan Sheldon

Protecting patient privacy in healthcare records is a top priority, and redaction is a commonly used method for obscuring directly identifiable information in text. Rule-based methods have been widely used, but their precision is often low causing over-redaction of text and frequently not being adaptable enough for non-standardised or unconventional structur

Jiawen Xu, Claas Grohnfeldt, Odej Kao

In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining. However, practical deep classifiers often misidentify these samples, leading to erroneous predictions. Such misclassifications can degrade model performance. Techniques like open set recognition offer a means to detect these

Erika M. Holmbeck, Jeff J. Andrews

While it is now known that double neutron star binary systems (DNSs) are copious producers of heavy elements, there remains much speculation about whether they are the sole or even principal site of rapid neutron-capture (r-process) nucleosynthesis, one of the primary ways in which heavy elements are produced. The occurrence rates, delay times, and galactic

Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah, Fatima Tuz Zohora

Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of novel AI applications, but this will also require the development of appropriate hardware accelerators, partic

Ben Ohayon, Andreas Abeln, Silvia Bara, Thomas Elias Cocolios

We propose an experiment to measure the nuclear charge radii of light elements with up to 20~times higher accuracy. These are essential both for understanding nuclear physics at low energies, and for experimental and theoretical applications in simple atomic systems. Such comparisons advance the understanding of bound-state quantum electrodynamics and are us

M. Pöntinen, M. Granvik, A. A. Nucita, L. Conversi

Up to 150000 asteroids will be visible in the images of the ESA Euclid space telescope, and the instruments of Euclid offer multiband visual to near-infrared photometry and slitless spectra of these objects. Most asteroids will appear as streaks in the images. Due to the large number of images and asteroids, automated detection methods are needed. A non-mach

CMS Collaboration

The identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10 GeV. One provides a continuous variable as an alternative to a cut-based identification selection and o

Ji Zhang, Xu Luo, Lianli Gao, Difan Zou

Deep neural networks often fail to adapt representations to novel tasks under distribution shifts, especially when only a few examples are available. This paper identifies a core obstacle behind this failure: channel bias, where networks develop a rigid emphasis on feature dimensions that were discriminative for the source task, but this emphasis is misalign

Tianlai Chen, Madeleine Dumas, Rio Watson, Sophia Vincoff

Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. Th

Hoang-Dung Bui

Multi-robot Motion Planning (MRMP) is an active research field which has gained attention over the years. MRMP has significant roles to improve the efficiency and reliability of multi-robot system in a wide range of applications from delivery robots to collaborative assembly lines. This survey provides an overview of MRMP taxonomy, state-of-the-art algorithm