May 2023 arXiv papers — page 10

Stephen Sanderson, Charlotte F. Petersen, Debra J. Searles

Fluctuation theorems (FTs) quantify the thermodynamic reversibility of a system, and for deterministic systems they are defined in terms of the dissipation function. However, in a nonequilibrium steady state of deterministic dynamics, the phase space distribution is unknown, making the dissipation function difficult to evaluate without extra information. As

Thanh Xuan Hoang, Daniel Leykam, Yuri Kivshar

We introduce the concept of photonic flatband resonances for the example of an array of high index dielectric particles. We employ the multiple Mie scattering theory and reveal that both short range and long range interactions between the resonators are crucial for the emerging collective resonances and their associated photonic flatbands. By examining both

Ryan Herbst, Ryan Coffee, Nathan Fronk, Kukhee Kim

The LCLS2 Free Electron Laser FEL will generate xray pulses to beamline experiments at up to 1Mhz These experimentals will require new ultrahigh rate UHR detectors that can operate at rates above 100 kHz and generate data throughputs upwards of 1 TBs a data velocity which requires prohibitively large investments in storage infrastructure Machine Learning has

Lu Yin, Gen Li, Meng Fang, Li Shen

Sparse training has received an upsurging interest in machine learning due to its tantalizing saving potential for the entire training process as well as inference. Dynamic sparse training (DST), as a leading sparse training approach, can train deep neural networks at high sparsity from scratch to match the performance of their dense counterparts. However, m

Vasilis Gkatzelis, Mohamad Latifian, Nisarg Shah

We study the problem of designing voting rules that take as input the ordinal preferences of $n$ agents over a set of $m$ alternatives and output a single alternative, aiming to optimize the overall happiness of the agents. The input to the voting rule is each agent's ranking of the alternatives from most to least preferred, yet the agents have more refined

Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide ins

Ricardo Harrilal-Parchment, Diana Pineda, Kemal Akkaya, Abdullah Aydeger

5G brings many improvements to cellular networks in terms of performance, such as lower latency, improved network efficiency, and higher throughput, making it an attractive candidate for many applications. One such domain is industrial applications that may require real-time guarantees to transmit time-critical control messages. Assuming the immense number o

Charles Audet, Jean Bigeon, Romain Couderc, Michael Kokkolaras

This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on sequential stochastic optimization (SSO): the original problem is decomposed into a sequence of subproblems. Each of these subproblems

Mykola Gordovskyy, Philippa K. Browning, Kanya Kusano, Satoshi Inoue

Energetic particle populations in the solar corona and in the heliosphere appear to have different characteristics even when produced in the same solar flare. It is not clear what causes this difference: properties of the acceleration region, the large-scale magnetic field configuration in the flare, or particle transport effects, such as scattering. In this

Zawadi Mdoe, Dinesh Krishnamoorthy, Johannes Jäschke

This paper presents an adaptive horizon multi-stage model-predictive control (MPC) algorithm. It establishes appropriate criteria for recursive feasibility and robust stability using the theory of input-to-state practical stability (ISpS). The proposed algorithm employs parametric nonlinear programming (NLP) sensitivity and terminal ingredients to determine

Robert Hunt, Ze Zhao, Eli Silver, Jinhui Yan

We study the drag on a centimetric sphere in a uniform flow in the presence of a free surface as a function of submergence depth. Through direct force measurements in a custom benchtop recirculating flume, we demonstrate that the drag can significantly exceed the corresponding drag in a single-phase flow and achieves a peak at submergence depths just prior t

Aldo Raeliarijaona, R. E. Cohen

We clarify the nature of hafnia as a proper ferroelectric and show that there is a shallow double well involving a single soft polar mode as in well-known classic ferroelectrics. Using symmetry analysis, density-functional theory (DFT) structural optimizations with and without epitaxial strain, and density functional perturbation theory (DFPT), we examine se

Deepayan Sanyal, Joel Michelson, Yuan Yang, James Ainooson

Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning. One characteristic of such experience is that the learner sees the same object from several different viewpoints. In this paper, we study how learning signals that equate different viewpoints -- e.g.,

Tigmanshu Bhatnagar, Vikas Upadhyay, Anchal Sharma, P V Madhusudhan Rao

Two-dimensional pin array tactile displays enable access to tactile graphics that are important for the education of students with visual impairments. Due to their prohibitive cost, limited access, and limited research within HCI, the rules to design graphical primitives on these low-resolution tactile displays are unclear. In this paper, eight tactile reade

Sebastián Maldonado, Carla Vairetti, Katherine Jara, Miguel Carrasco

In this paper, we propose a fuzzy adaptive loss function for enhancing deep learning performance in classification tasks. Specifically, we redefine the cross-entropy loss to effectively address class-level noise conditions, including the challenging problem of class imbalance. Our approach introduces aggregation operators, leveraging the power of fuzzy logic

Yifan Yang, Peiyao Xiao, Kaiyi Ji

Federated bilevel optimization (FBO) has shown great potential recently in machine learning and edge computing due to the emerging nested optimization structure in meta-learning, fine-tuning, hyperparameter tuning, etc. However, existing FBO algorithms often involve complicated computations and require multiple sub-loops per iteration, each of which contains

C. Jiang, G. Chen, E. Pallé, F. Murgas

Exoplanet atmospheres are the key to understanding the nature of exoplanets. To this end, transit spectrophotometry provides us opportunities to investigate the physical properties and chemical compositions of exoplanet atmospheres. We aim to detect potential atmospheric signatures in 12 gaseous giant exoplanets using transit spectrophotometry and we try to

Hao Chen, Thomas Barthel

Tensor networks developed in the context of condensed matter physics try to approximate order-$N$ tensors with a reduced number of degrees of freedom that is only polynomial in $N$ and arranged as a network of partially contracted smaller tensors. As we have recently demonstrated in the context of quantum many-body physics, computation costs can be further s

Elisa Conti, Amina Piemontese, Giulio Colavolpe, Armando Vannucci

This paper aims at tackling the problem of signal detection in flat-fading channels. In this context, receivers based on the expectation propagation framework appear to be very promising although presenting some critical issues. We develop a new algorithm based on this framework where, unlike previous works, convergence is achieved after a single forward-bac

Gustavo J. Turiaci, Edward Witten

Generalizing previous results for $N=0$ and $N=1$, we analyze $N=2$ JT supergravity on asymptotically AdS${}_2$ spaces with arbitrary topology and show that this theory of gravity is dual, in a holographic sense, to a certain random matrix ensemble in which supermultiplets of different $R$-charge are statistically independent and each is described by its own

Barry F. Madore, Wendy L. Freedman

We review the theoretical underpinnings, evolutionary status, calibrations and current applications of three bright Population II extragalactic distance indicators: Tip of the Red Giant Branch (TRGB) stars, RR Lyrae variables and J-Branch Asymptotic Giant Branch (JAGB/Carbon) stars. For M_I (TRGB) = -4.05 mag the Hubble constant is determined to be Ho = 69.8

Jay Chan

The discovery of the Standard Model (SM) Higgs boson by ATLAS and CMS at the LHC in 2012 marked a major milestone in particle physics. However, many questions remain unanswered, which has led to an active research program to search for either rare SM phenomena or Beyond Standard Model (BSM) physics that involve the Higgs boson. In this dissertation, I presen

Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan

Web-scale search systems learn an encoder to embed a given query which is then hooked into an approximate nearest neighbor search (ANNS) pipeline to retrieve similar data points. To accurately capture tail queries and data points, learned representations typically are rigid, high-dimensional vectors that are generally used as-is in the entire ANNS pipeline a

Zelalem Gero, Chandan Singh, Hao Cheng, Tristan Naumann

Extracting patient information from unstructured text is a critical task in health decision-support and clinical research. Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning, in contrast to supervised learning which requires much more costly human annotations. However, despite drastic advanc

Harald Garcke, Robert Nürnberg, Quan Zhao

We analyze numerical approximations for axisymmetric two-phase flow in the arbitrary Lagrangian-Eulerian (ALE) framework. We consider a parametric formulation for the evolving fluid interface in terms of a one-dimensional generating curve. For the two-phase Navier-Stokes equations, we introduce both conservative and nonconservative ALE weak formulations in t

Raul Borsche, Axel Klar

Starting from a non-local version of the Prigogine-Herman traffic model, we derive a natural hierarchy of kinetic discrete velocity models for traffic flow consisting of systems of quasi-linear hyperbolic equations with relaxation terms. The hyperbolic main part of these models turns out to have several favourable features. In particular, we determine Rieman

Mutsuko Inoguchi, Takashi Hosokawa, Hajime Fukushima, Kei E. I. Tanaka

Young massive clusters (YMCs) are the most massive star clusters forming in nearby galaxies and are thought to be a young analogue to the globular clusters. Understanding the formation process of YMCs leads to looking into very efficient star formation in high-redshift galaxies suggested by recent JWST observations. We investigate possible observational sign

Md Mohsinur Rahman Adnan, Darpan Verma, Chris Sturm, Matthias Schubert

Due to its low symmetry, $\beta$-Ga$_{2}$O$_{3}$ exhibits a strongly anisotropic optical response. As a result, the absorption spectra change with the polarization state of the incoming photons. To understand this phenomenon, here we calculate the complete electromagnetic wave equation solutions as a function of linear polarization angle and photon energy fo

Joo Young Bang, Nimish Pujara

We report an investigation into random-jet-stirred homogeneous turbulence generated in a vertical octagonal prism shaped tank where there are jet arrays on four of the eight vertical faces. We show that the turbulence is homogeneous at all scales in the central region of the tank that spans multiple integral scales in all directions. The jet forcing from fou

Logan Stapleton, Jordan Taylor, Sarah Fox, Tongshuang Wu

Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content which has a risk of harm in many cases, e.g., hate speech. However, prohibited content is not always harmful -- there are instances where generating prohibited content can be benefic

Raymond Feng, Flavio P. Calmon, Hao Wang

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and the standard procedure for handling missing values where first data is imputed, then the imputed data is used for classification -- a procedure referred to as "impute-then-classify" --

Ye Hong, Emanuel Stüdeli, Martin Raubal

Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustainable transport systems. While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context

V. N. Obridko, A. S. Shibalova, D. D. Sokoloff

Traditionally, the solar activity cycle is thought as an interplay of the main dipole component of the solar poloidal magnetic field and the toroidal magnetic field. However, the real picture as presented in the extended solar-cycle models is much more complicated. Here, we develop the concept of the extended solar cycle clarifying what zonal harmonics are r

Jingyuan Selena She, Christopher Potts, Samuel R. Bowman, Atticus Geiger

A number of recent benchmarks seek to assess how well models handle natural language negation. However, these benchmarks lack the controlled example paradigms that would allow us to infer whether a model had learned how negation morphemes semantically scope. To fill these analytical gaps, we present the Scoped Negation NLI (ScoNe-NLI) benchmark, which contai

Kylan Jersey, Ian Harley-Trochimczyk, Yanqi Zhang, Felipe Guzman

The LISA telescopes must exhibit an optical path length stability of $\frac{\mathrm{pm}}{\sqrt{\mathrm{Hz}}}$ in the mHz observation band to meet mission requirements. The optical truss interferometer is a proposed method to aid in the ground testing of the telescopes, as well as a risk-mitigation plan for the flight units. This consists of three Fabry-Perot

Hossein Rezaei, Mohammad Sabokrou

Machine learning models that are overfitted/overtrained are more vulnerable to knowledge leakage, which poses a risk to privacy. Suppose we download or receive a model from a third-party collaborator without knowing its training accuracy. How can we determine if it has been overfitted or overtrained on its training data? It's possible that the model was inte

Aaron Li, Danika Kibby, Jasmine Foo

Tumor recurrence, driven by the evolution of drug resistance is a major barrier to therapeutic success in cancer. Resistance is often caused by genetic alterations such as point mutation, which refers to the modification of a single genomic base pair, or gene amplification, which refers to the duplication of a region of DNA that contains a gene. Here we inve

J. Browne, K. A. Chipps, K. Schmidt, H. Schatz

The rate of the final step in the astrophysical $\alpha$p-process, the $^{34}$Ar($\alpha$,\textit{p})$^{37}$K reaction, suffers from large uncertainties due to lack of experimental data, despite having a considerable impact on the observable light curves of x-ray bursts and the composition of the ashes of hydrogen and helium burning on accreting neutron star

Mariana Pinto, Inês Dutra, Joaquim Fonseca

Autonomous driving has become one of the most popular research topics within Artificial Intelligence. An autonomous vehicle is understood as a system that combines perception, decision-making, planning, and control. All of those tasks require that the vehicle collects surrounding data in order to make a good decision and action. In particular, the overtaking

Yufeng Zhang, Fengzhuo Zhang, Zhuoran Yang, Zhaoran Wang

In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language models? (b) What is a proper performance metric for ICL and what is the error rate? (c) How does the transformer architecture enable ICL? To answer these questions, we adopt a Bayesian

Anni Chen, Bhuwan Dhingra

Since the introduction of the SemEval 2020 Task 11 (Martino et al., 2020a), several approaches have been proposed in the literature for classifying propaganda based on the rhetorical techniques used to influence readers. These methods, however, classify one span at a time, ignoring dependencies from the labels of other spans within the same context. In this

A. B. Albidah, V. Fedun, A. A. Aldhafeeri, I. Ballai

Through their lifetime sunspots undergo a change in their area and shape and, as they decay, they fragment into smaller structures. Here, for the first time we analyze the spatial structure of magnetohydrodynamic (MHD) slow body and fast surface modes in observed umbrae as their cross-sectional shape changes. The Proper Orthogonal Decomposition (POD) and Dyn

Ethan T. Neil, Jacob W. Sitison

Model averaging is a useful and robust method for dealing with model uncertainty in statistical analysis. Often, it is useful to consider data subset selection at the same time, in which model selection criteria are used to compare models across different subsets of the data. Two different criteria have been proposed in the literature for how the data subset

Jonathan Mei, Alexander Moreno, Luke Walters

Second order stochastic optimizers allow parameter update step size and direction to adapt to loss curvature, but have traditionally required too much memory and compute for deep learning. Recently, Shampoo [Gupta et al., 2018] introduced a Kronecker factored preconditioner to reduce these requirements: it is used for large deep models [Anil et al., 2020] an

Salahaldeen Rababa, Asma Yamin, Shuxia Lu, Ashraf Obaidat

Currently, many researchers and analysts are working toward medical diagnosis enhancement for various diseases. Heart disease is one of the common diseases that can be considered a significant cause of mortality worldwide. Early detection of heart disease significantly helps in reducing the risk of heart failure. Consequently, the Centers for Disease Control

Matan Eilat

Suppose that there exists a discrete subset $X$ of a complete, connected, $n$-dimensional Riemannian manifold $M$ such that the Riemannian distances between points of $X$ correspond to the Euclidean distances of a net in $\mathbb{R}^{n}$. What can then be derived about the geometry of $M$? In arXiv:2004.08621 it was shown that if $n=2$ then $M$ is isometric

Davide Carbone, Mengjian Hua, Simon Coste, Eric Vanden-Eijnden

Energy-based models (EBMs) are generative models inspired by statistical physics with a wide range of applications in unsupervised learning. Their performance is best measured by the cross-entropy (CE) of the model distribution relative to the data distribution. Using the CE as the objective for training is however challenging because the computation of its

Dinor Nagar, Nikita Vladimirov, Christian T. Farrar, Or Perlman

Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of

Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen

This paper reveals a new appeal of the recently emerged large-kernel Convolutional Neural Networks (ConvNets): as the teacher in Knowledge Distillation (KD) for small-kernel ConvNets. While Transformers have led state-of-the-art (SOTA) performance in various fields with ever-larger models and labeled data, small-kernel ConvNets are considered more suitable f

Yu. M. Andreev, D. Banerjee, B. Banto Oberhauser, J. Bernhard

In this study, we present the measurement of the intrinsic hadronic contamination at the CERN SPS H4 beamline configured to transport electrons and positrons at 100 GeV/c momentum. The analysis was performed using data collected by the NA64-$e$ experiment in 2022. Our study is based on calorimetric measurements, exploiting the different interaction mechanism

Awildo Gutierrez, Elijah Leake, Caelyn Rivas-Sobie, Jordy Lopez Garcia

We investigate six operations on chemical reaction networks, all of which have been proven to preserve important dynamical properties, namely, the capacity for nondegenerate multistationarity (multiple steady states) and periodic orbits. Both multistationarity and periodic orbits are properties that are known to be precluded when the deficiency (a nonnegativ

Anjalie Field, Amanda Coston, Nupoor Gandhi, Alexandra Chouldechova

Although much literature has established the presence of demographic bias in natural language processing (NLP) models, most work relies on curated bias metrics that may not be reflective of real-world applications. At the same time, practitioners are increasingly using algorithmic tools in high-stakes settings, with particular recent interest in NLP. In this

Eileen T. Meyer, Aamil Shaik, Yanbo Tang, Nancy Reid

Unexpectedly strong X-ray emission from extragalactic radio jets on kiloparsec scales has been one of the major discoveries of Chandra, the only X-ray observatory capable of sub-arcsecond-scale imaging. The origin of this X-ray emission, which appears as a second spectral component from that of the radio emission, has been debated for over two decades. The m

Brandon Theodorou, Lucas Glass, Cao Xiao, Jimeng Sun

Despite many efforts to address the disparities, the underrepresentation of gender, racial, and ethnic minorities in clinical trials remains a problem and undermines the efficacy of treatments on minorities. This paper focuses on the trial site selection task and proposes FRAMM, a deep reinforcement learning framework for fair trial site selection. We focus

Xiang Li, Chung-Ching Lin, Yinpeng Chen, Zicheng Liu

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training. We propose an adversarial masked contrastive painting (AMCP) process, which creates a contrast between the original image and a painted image in which a masked area is painted using off-the-shelf generative models. During the painting process, inpainting and

Mahdiyeh Abbasi, Shusen Liao, Yunxuan Zhu, Douglas Natelson

Tunneling metal-insulator-metal (MIM) junctions can exhibit an open-circuit photovoltage (OCPV) response under illumination that may be useful for photodetection. One mechanism for photovoltage generation is hot carrier tunneling, in which photoexcited carriers generate a net photocurrent that must be balanced by a drift current in the open-circuit configura

Xiaofeng Liu, Helen A. Shih, Fangxu Xing, Emiliano Santarnecchi

Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain. Yet, the static DL model is likely to perform poorly in a continually evolving environment, requiring appropriate model updates. In an incremental learning setting, we would expect that well-train

Sneha Mohanty, Milan Müller, Christian Schindelhauer

We introduce a solution for a specific case of Indoor Localization which involves a directed signal, a reflected signal from the wall and the time difference between them. This solution includes robust localization with a given wall, finding the right wall from a group of walls, obtaining the reflecting wall from measurements, using averaging techniques for

Shang-Min Tsai, Julianne I. Moses, Diana Powell, Elspeth K. H. Lee

JWST has recently detected the first robust photochemical product on an exoplanet: sulfur dioxide (SO$_2$) on WASP-39b (Rustamkulov et al. 2023; Alderson et al. 2023; Tsai et al. 2023b). The data from the NIRISS instrument also reveal signs of partial coverage of clouds (Feinstein et al. 2023). Most of the previous studies have focused on interpreting spectr

Yujia Bao, Theofanis Karaletsos

We introduce Contextual Vision Transformers (ContextViT), a method designed to generate robust image representations for datasets experiencing shifts in latent factors across various groups. Derived from the concept of in-context learning, ContextViT incorporates an additional context token to encapsulate group-specific information. This integration allows t

Konstantin Klemm, Erik Andreas Martens

Transport networks are crucial for the functioning of natural and technological systems. We study a mathematical model of vascular network adaptation, where the network structure dynamically adjusts to changes in blood flow and pressure. The model is based on local feedback mechanisms that occur on different time scales in the mammalian vasculature. The cost

Eric Heisler, Siddharth Saurav, Aadesh Deshmukh, Sandip Mazumder

Heterogeneous computing environments combining CPU and GPU resources provide a great boost to large-scale scientific computing applications. Code generation utilities that partition the work into CPU and GPU tasks while considering data movement costs allow researchers to more quickly and easily develop high-performance solutions, and make these resources ac

Isaac E. Weintraub, Alexander Von Moll, David W. Casbeer, Satyanarayana G. Manyam

This paper considers an M-pursuer N-evader scenario involving virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evaders. However, upon reaching the virtual target, the pursuers must decide which evader to capture. It is assumed that there are more pursuers than

Eric Heisler, Cheng-Hau Yang, Aadesh Deshmukh, Baskar Ganapathysubramanian

We present a high-level domain-specific language (DSL) interface to drive an adaptive incomplete $k$-d tree-based framework for finite element (FEM) solutions to PDEs. This DSL provides three key advances: (a) it abstracts out the complexity of implementing non-trivial FEM formulations, (b) it simplifies deploying these formulations on arbitrarily complicate

A. Poro, F. Ahangarani Farahani, E. Jahangiri, A. Sarostad

We refined the ephemeris of seven transiting exoplanets HAT-P-6b, HAT-P-12b, HAT-P-18b, HAT-P-22b, HAT-P-32b, HAT-P-33b, and HAT-P-52b. We observed 11 transits from eight observatories in different filters for HAT-P-6b and HAT-P-32b. Also, the Exoplanet Transit Database (ETD) observations for each of the seven exoplanets were analyzed, and the light curves o

Arik Avagyan

In the standard homodyne configuration, an unknown optical state is combined with a local oscillator (LO) on a beam splitter (BS). Good quadrature measurements require a high-amplitude LO and two high-efficiency photodiodes whose signals are subtracted and normalized. By changing the LO phase, it is then possible to infer the optical state in the mode matchi

Phat Do, Matt Coler, Jelske Dijkstra, Esther Klabbers

We train a MOS prediction model based on wav2vec 2.0 using the open-access data sets BVCC and SOMOS. Our test with neural TTS data in the low-resource language (LRL) West Frisian shows that pre-training on BVCC before fine-tuning on SOMOS leads to the best accuracy for both fine-tuned and zero-shot prediction. Further fine-tuning experiments show that using

Yuchen Zhuang, Yue Yu, Lingkai Kong, Xiang Chen

Learning from noisy labels is a challenge that arises in many real-world applications where training data can contain incorrect or corrupted labels. When fine-tuning language models with noisy labels, models can easily overfit the label noise, leading to decreased performance. Most existing methods for learning from noisy labels use static input features for

Roman Pogodin, Jonathan Cornford, Arna Ghosh, Gauthier Gidel

A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the choice of distance for synaptic changes - i.e. the geometry of synaptic plasticity. Gradient descent assumes

Matan Grauer, Ehud Behar

A large fraction of the baryons at low redshift are undetected, and likely reside in the tenuous, hot intergalactic medium (IGM). One way to probe the missing baryons is through their absorption of bright sources. The anomalous absorption excess in the X-ray afterglows of $\gamma$-ray bursts (GRBs) has been suggested to result from the missing baryons. In or

Wojciech Bruzda, Grzegorz Rajchel-Mieldzioć, Karol Życzkowski

We analyze the set of real and complex Hadamard matrices with additional symmetry constrains. In particular, we link the problem of existence of maximally entangled multipartite states of $2k$ subsystems with $d$ levels each to the set of complex Hadamard matrices of order $N=d^k$. To this end, we investigate possible subsets of such matrices which are, dual

Angel Beshirov, Suzan Hadzhieva, Ivan Koychev, Milena Dobreva

Search in collections of digitised historical documents is hindered by a two-prong problem, orthographic variety and optical character recognition (OCR) mistakes. We present a new search engine for historical documents, DuoSearch, which uses ElasticSearch and machine learning methods based on deep neural networks to offer a solution to this problem. It was t

Tri Nguyen, Shahana Ibrahim, Xiao Fu

The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i.e., $\textit{deep constrained clustering}$ (DCC) -- has proven effective for incorporating weak supervision into massive data clustering: Less than 1% of pair similarity annotations can often substantially enhance the clustering accuracy. However, bey

Naeem Md Sami, Mia Naeini

Cascading failures pose a significant threat to power grids and have garnered considerable research interest in the power system domain. The inherent uncertainty and severe impact associated with cascading failures have raised concerns, prompting the development of various techniques to study these complex phenomena. In recent years, advancements in monitori

Luke Finnerty, Tobias Schofield, Ben Sappey, Jerry W. Xuan

We present Keck/KPIC high-resolution ($R\sim35,000$) $K$-band thermal emission spectroscopy of the ultra-hot Jupiter WASP-33b. The use of KPIC's single-mode fibers greatly improves both blaze and line-spread stabilities relative to slit spectrographs, enhancing the cross-correlation detection strength. We retrieve the dayside emission spectrum with a nested

Luiz L. Lopes

I investigate the use of the SU(3) Clebsch-Gordan coefficients in light of the relations of completeness and closure. I show that in the case of $\alpha_V = F/(F+D)~\neq$ 1, there is an additional interaction: the exchange of a $\rho$ meson between a $\Lambda$ and a $\Sigma^0$ hyperon that only affects the symmetric coupling. I then calculate these additiona

Izhar Neder, Chaviva Sirote, Meital Geva, Yoav Lahini

We experimentally and theoretically study the dynamics of a one-dimensional array of pendula with a mild spatial gradient in their self-frequency and where neighboring pendula are connected with weak and alternating coupling. We map their dynamics to the topological Su-Schrieffer-Heeger (SSH) model of charged quantum particles on a lattice with alternating h

Michael Antesberger, Marco Túlio Quintino, Philip Walther, Lee A. Rozema

The field of indefinite causal order (ICO) has seen a recent surge in interest. Much of this research has focused on the quantum SWITCH, wherein multiple parties act in a superposition of different orders in a manner transcending the quantum circuit model. This results in a new resource for quantum protocols, and is exciting for its relation to issues in fou

V. A. Khodel, J. W. Clark, M. V. Zverev

Landau's quasiparticle formalism is generalized to describe a wide class of strongly correlated Fermi systems, in addition to conventional Fermi liquids. This class includes (i) so-called marginal exemplars and (ii) systems that harbor interaction-driven flat bands, in both of which manifestations of non-Fermi-liquid behavior are well documented. Specificall

Maleknaz Nayebi, Konstantin Kuznetsov, Andreas Zeller, Guenther Ruhe

Evolving software with an increasing number of features is harder to understand and thus harder to use. Software release planning has been concerned with planning these additions. Moreover, software of increasing size takes more effort to be maintained. In the domain of mobile apps, too much functionality can easily impact usability, maintainability, and res

Srinjoy Ganguly, Sai Nandan Morapakula, Luis Miguel Pozo Coronado

Sentiment classification is one the best use case of classical natural language processing (NLP) where we can witness its power in various daily life domains such as banking, business and marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradu

Durgesh Nandini, Ute Schmid

There have been remarkable breakthroughs in Machine Learning and Artificial Intelligence, notably in the areas of Natural Language Processing and Deep Learning. Additionally, hate speech detection in dialogues has been gaining popularity among Natural Language Processing researchers with the increased use of social media. However, as evidenced by the recent

Zhipeng Lu, Gulzhan Aldan, Danielle Levin, Matthew F. Campbell

The goal of ultrathin lightweight photophoretic flyers, or light-flyers for short, is to levitate continuously in Earth's upper atmosphere using only sunlight for propulsive power. We previously reported light-flyers that levitated by utilizing differences in thermal accommodation coefficient (TAC) between the top and bottom of a thin film, made possible by

Megh Vipul Doshi, Michael Hagenow, Robert Radwin, Michael Gleicher

Handheld kinesthetic haptic interfaces can provide greater mobility and richer tactile information as compared to traditional grounded devices. In this paper, we introduce a new handheld haptic interface which takes input using bidirectional coupled finger flexion. We present the device design motivation and design details and experimentally evaluate its per

Rayleigh Lei, Abel Rodriguez

Latent factor models are widely used in the social and behavioral science as scaling tools to map discrete multivariate outcomes into low dimensional, continuous scales. In political science, dynamic versions of classical factor models have been widely used to study the evolution of justices' preferences in multi-judge courts. In this paper, we discuss a new

David Jiang, Dongxu Liu, Efi Efrati, Nhung Nguyen

Objective: Use of a vascular punch to produce circular heel and toe arteriotomies for femoral endarterectomy with patch angioplasty is a novel technique. This study investigated the plausibility of this approach and the mechanical advantages of the technique using finite element models. Methods: The patient underwent a standard femoral endarterectomy. Prior

Zhenyu Zhu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly) zero-training error under the lazy training regime. For this purpose, we unify three interrelated concepts of ov

Brendan T. Reed, F. J. Fattoyev, C. J. Horowitz, J. Piekarewicz

The recently published CREX results suggest a rather peculiar picture for the density dependence of the symmetry energy. Whereas PREX favors a large neutron skin thickness in $^{208}$Pb, thereby suggesting a stiff equation of state, CREX suggests instead a much softer equation of state. This discrepancy has caused a large spur in the theoretical community si

Kevin Ma, Daniele Grandi, Christopher McComb, Kosa Goucher-Lambert

Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating

Ana Nikolikj, Michal Pluháček, Carola Doerr, Peter Korošec

Leave-one-problem-out (LOPO) performance prediction requires machine learning (ML) models to extrapolate algorithms' performance from a set of training problems to a previously unseen problem. LOPO is a very challenging task even for state-of-the-art approaches. Models that work well in the easier leave-one-instance-out scenario often fail to generalize well

Yanli Zhou, Reuben Feinman, Brenden M. Lake

Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do. Here, we study these distinctively human abilities across

Ankita Agarwal, Tanvi Banerjee, William L. Romine, Krishnaprasad Thirunarayan

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar

Michel J. G. Weber

Let $X=\{X_j , j\ge 1\}$ be a sequence of independent, square integrable variables taking values in a common lattice $\mathcal L(v_{ 0},D )= \{v_{ k}=v_{ 0}+D k , k\in \Z\}$. Let $S_n=X_1+\ldots +X_n$, $a_n= {\mathbb E\,} S_n$, and $\s_n^2={\rm Var}(S_n)\to \infty$ with $n$. Assume that for each $j$, $\t_{X_j} =\sum_{k\in \Z}{\mathbb P}\{X_j=v_k\}\wedge{\mat

Tony C. Dorlas, Baptiste Savoie

In this paper, we revisit the Dobrushin uniqueness theorem for Gibbs measures of lattice systems of interacting particles at thermal equilibrium. In a nutshell, Dobrushin's uniqueness theorem provides a practical way to derive sufficient conditions on the inverse temperature and/or model parameters assuring uniqueness of Gibbs measures by reducing the unique

Hao Liu, Pieter Abbeel

Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands posed by the self-attention mechanism and the large feedforward network in Transformers limit their ability to handle long sequences, thereby creating chall

Nasser Heydari, Kazuo Muroi

In this article, we study some of quadratic equations and their solutions found in the Susa Mathematical Texts (\textbf{SMT}). We show that the Susa scribes used this group of equations in different problems and took a standard approach, known as completing the square, to find solutions

Lee Tae Young

We find all irreducible hypergeometric sheaves whose geometric monodromy group is finite, almost quasisimple and has the projective special linear group $PSL_n(q)$ with $n\geq 3$ as a composition factor. We use the classification of semisimple elements with specific spectra in irreducible Weil representations to prove that if an irreducible hypergeometric sh

Bryant Avila, Pedro Augusto, Manuel Zimmer, Matteo Serafino

Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward

Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin

Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discr

Nahid Alam, Steven Kolawole, Simardeep Sethi, Nishant Bansali

Vision Transformers (ViTs) have demonstrated state-of-the-art performance on many Computer Vision Tasks. Unfortunately, deploying these large-scale ViTs is resource-consuming and impossible for many mobile devices. While most in the community are building for larger and larger ViTs, we ask a completely opposite question: How small can a ViT be within the tra