May 2023 arXiv papers — page 49

Dean Dustin, Bertrand Clarke

We give two prediction intervals (PI) for Generalized Linear Models that take model selection uncertainty into account. The first is a straightforward extension of asymptotic normality results and the second includes an extra optimization that improves nominal coverage for small-to--moderate samples. Both PI's are wider than would be obtained without incorpo

Aser Abbas, Brady R. Cox, Khiem T. Tran, Isabella Corey

This paper documents a comprehensive subsurface imaging experiment using stress waves in Newberry, Florida, at a site known for significant spatial variability, karstic voids, and underground anomalies. The experiment utilized advanced sensing technologies, including approximately two kilometers of distributed acoustic sensing (DAS) fiber optic cable, formin

Song Liu, Jiahao Yu, Jack Simons, Mingxuan Yi

Many machine learning problems can be seen as approximating a \textit{target} distribution using a \textit{particle} distribution by minimizing their statistical discrepancy. Wasserstein Gradient Flow can move particles along a path that minimizes the $f$-divergence between the target and particle distributions. To move particles, we need to calculate the co

M. Nizam, K. W. Shin, S. M. Mazza, J. Ott

Low Gain Avalanche Detectors (LGADs) are thin silicon detectors with moderate internal signal amplification and time resolution as good as 17 ps for minimum ionizing particles. However, the current major limiting factor in granularity is due to protection structures preventing breakdown caused by high electric fields at the edge of the segmented implants. Th

Andreas Löhne

For a given polyhedral convex set-valued mapping we define a polyhedral convex cone which we call the natural ordering cone. We show that the solution behavior of a polyhedral convex set optimization problem can be characterized by this cone. Under appropriate assumptions the natural ordering cone is proven to be the smallest ordering cone which makes a poly

Jin Xu, Emilien Dupont, Kaspar Märtens, Tom Rainforth

We introduce Markov Neural Processes (MNPs), a new class of Stochastic Processes (SPs) which are constructed by stacking sequences of neural parameterised Markov transition operators in function space. We prove that these Markov transition operators can preserve the exchangeability and consistency of SPs. Therefore, the proposed iterative construction adds s

Vrushabh Zinage, S P Arjun Ram, Maruthi R. Akella, Efstathios Bakolas

Semi-Global Exponential Stability (SGES) is proved for the combined attitude and position rigid body motion tracking problem, which was previously only known to be asymptotically stable. Dual quaternions are used to jointly represent the rotational and translation tracking error dynamics of the rigid body. A novel nonlinear feedback tracking controller is pr

Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner

A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to traditional global strategies. The "folk wisdom" in the literature is that the focus on local optimization sidesteps the curse of dimensionality; however, little is known concretely

Kıvanç Tatar, Kelsey Cotton, Daniel Bisig

The research in Deep Learning applications in sound and music computing have gathered an interest in the recent years; however, there is still a missing link between these new technologies and on how they can be incorporated into real-world artistic practices. In this work, we explore a well-known Deep Learning architecture called Variational Autoencoders (V

Susheela Sharma, Ji H. Park, Jordan P. Amadio, Mohsen Khadem

In this paper, we present the design, fabrication, and evaluation of a novel flexible, yet structurally strong, Concentric Tube Steerable Drilling Robot (CT-SDR) to improve minimally invasive treatment of spinal tumors. Inspired by concentric tube robots, the proposed two degree-of-freedom (DoF) CT-SDR, for the first time, not only allows a surgeon to intuit

Yifu Cao, Chandan Setty, Laura Fanfarillo, Andreas Kreisel

Several unconventional superconductors show indications of zero-energy excitations in the superconducting state consistent with the existence of a so-called Bogoliubov Fermi surface (BFS). In particular, FeSe doped with S seems to acquire a nonzero density of states at zero energy at low temperatures when doped into the tetragonal phase, consistent with a pr

Marek W. Rupniewski

The calibration of sensors comprising inertial measurement units is crucial for reliable and accurate navigation. Such calibration is usually performed with specialized expensive rotary tables or requires sophisticated signal processing based on iterative minimization of nonlinear functions, which is prone to get stuck at local minima. We propose a novel cal

Rajath Soans, Alexa Gleason, Tosha Shah, Corey Miller

In this paper, we propose a deep learning-based method to segment the skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which can assist in the assessment of drug-induced skeletal abnormalities as a required study in developmental and reproductive toxicology (DART). Our strategy leverages sub-optimal segmentation labels of 22 skull bo

Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka

State-of-the-art speaker recognition systems comprise a speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) back-end. The effectiveness of these components relies on the availability of a large amount of labeled training data. In practice, it is common for domains (e.g., language, channel, demographic) in which a syste

José Correa, Andrés Cristi, Paul Dütting, Mohammad Hajiaghayi

In this work we initiate the study of buy-and-sell prophet inequalities. We start by considering what is arguably the most fundamental setting. In this setting the online algorithm observes a sequence of prices one after the other. At each time step, the online algorithm can decide to buy and pay the current price if it does not hold the item already; or it

J. Korth, D. Gandolfi, J. Šubjak, S. Howard

The TOI-1130 is a known planetary system around a K-dwarf consisting of a gas giant planet, TOI-1130 c, on an 8.4-day orbit, accompanied by an inner Neptune-sized planet, TOI-1130 b, with an orbital period of 4.1 days. We collected precise radial velocity (RV) measurements of TOI-1130 with the HARPS and PFS spectrographs as part of our ongoing RV follow-up p

Zhihui Luo, Xunwu Hu, Meng Wang, Wéi Wú

The newly discovered Ruddlesden-Popper bilayer La$_3$Ni$_2$O$_7$ reaches an remarkable superconducting transition temperature $T_c$ = 80 K under a pressure of above 14 GPa. Here we propose a minimal bilayer two-orbital model of the high-pressure phase of La$_3$Ni$_2$O$_7$. Our model is constructed with the Ni-3d$_{x^2-y^2}$, 3d$_{3z^2-r^2}$ orbitals by using

Nathaniel Dean, Dilip Sarkar

Current algorithms and architecture can create excellent DNN classifier models from example data. In general, larger training datasets result in better model estimations, which improve test performance. Existing methods for predicting generalization performance are based on hold-out test examples. To the best of our knowledge, at present no method exists tha

Jie Bu, Kazi Sajeed Mehrab, Anuj Karpatne

Conditional graph generation tasks involve training a model to generate a graph given a set of input conditions. Many previous studies employ autoregressive models to incrementally generate graph components such as nodes and edges. However, as graphs typically lack a natural ordering among their components, converting a graph into a sequence of tokens is not

Ahmed Sid-Ali, Ioannis Lambadaris, Yiqiang Q. Zhao, Gennady Shaikhet

In this paper, we study an optimal online resource reservation problem in a simple communication network. The network is composed of two compute nodes linked by a local communication link. The system operates in discrete time; at each time slot, the administrator reserves resources for servers before the actual job requests are known. A cost is incurred for

Riccardo Bonalli, Alessandro Rudi

We propose a novel non-parametric learning paradigm for the identification of drift and diffusion coefficients of multi-dimensional non-linear stochastic differential equations, which relies upon discrete-time observations of the state. The key idea essentially consists of fitting a RKHS-based approximation of the corresponding Fokker-Planck equation to such

Jarrod T. Reilly, John Drew Wilson, Simon B. Jäger, Christopher Wilson

We propose a computationally efficient method to derive the unitary evolution that a quantum state is most sensitive to. This allows one to determine the optimal use of an entangled state for quantum sensing, even in complex systems where intuition from canonical squeezing examples breaks down. In this paper we show that the maximal obtainable sensitivity us

Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski, Clare Lyle

A growing body of evidence suggests that neural networks employed in deep reinforcement learning (RL) gradually lose their plasticity, the ability to learn from new data; however, the analysis and mitigation of this phenomenon is hampered by the complex relationship between plasticity, exploration, and performance in RL. This paper introduces plasticity inje

Lucas Chaves Meyles, Pamela E. Harris, Richter Jordaan, Gordon Rojas Kirby

Unit-interval parking functions are subset of parking functions in which cars park at most one spot away from their preferred parking spot. In this paper, we characterize unit-interval parking functions by understanding how they decompose into prime parking functions and count unit-interval parking functions when exactly $k<n$ cars do not park in their prefe

Chadi Nour, Vera Zeidan

We extend to $\mathrm{C}\times \mathrm{W}^{1,2}$-local minimizers and nonautonomous perturbation function, the necessary optimality conditions derived in [1], via continuous-time approximations, for $\mathrm{W}^{1,2}\times \mathrm{W}^{1,2}$-local minimizers of an optimal control problem governed by a controlled nonconvex sweeping process with autonomous pert

Abdullah Alperen, Metin Aktulga, Pieter Maris, Chao Yang

We examine and compare several iterative methods for solving large-scale eigenvalue problems arising from nuclear structure calculations. In particular, we discuss the possibility of using block Lanczos method, a Chebyshev filtering based subspace iterations and the residual minimization method accelerated by direct inversion of iterative subspace (RMM-DIIS)

Syed Hammad Ahmed, Muhammad Junaid Khan, H. M. Umer Qaisar, Gita Sukthankar

Online video platforms receive hundreds of hours of uploads every minute, making manual content moderation impossible. Unfortunately, the most vulnerable consumers of malicious video content are children from ages 1-5 whose attention is easily captured by bursts of color and sound. Scammers attempting to monetize their content may craft malicious children's

Håvard Bakke Bjerkevik

While decomposition of one-parameter persistence modules behaves nicely, as demonstrated by the algebraic stability theorem, decomposition of multiparameter modules is known to be unstable in a certain precise sense. Until now, it has not been clear that there is any way to get around this and build a meaningful stability theory for multiparameter module dec

Bernard T. Agyeman, Erfan Orouskhani, Mohamed Naouri, Willemijn Appels

Improving the accuracy of soil moisture estimation is required for advancing irrigation scheduling and water conservation efforts. Central to this task are soil hydraulic parameters, which govern moisture dynamics but are rarely known precisely and must therefore be inferred from observational data. In large-scale agricultural fields, estimating the complete

Oluma Ararso Alemu, Hunduma Legesse Geleta

In this paper, we determine and specify the type of curves formed by the vanishing discriminant of some specified family of complex-valued harmonic polynomials with two parameters. We also classify the region formed by curves as bounded and unbounded connected components which in turn used to count the zeros of the complex-valued harmonic polynomials. Our st

Federico Carollo

Central spin systems, in which a {\it central} spin is singled out and interacts nonlocally with several {\it bath} spins, are paradigmatic models for nitrogen-vacancy centers and quantum dots. They show complex emergent dynamics and stationary phenomena which, despite the collective nature of their interaction, are still largely not understood. Here, we der

Xiang Ji, Gen Li

A crucial problem in reinforcement learning is learning the optimal policy. We study this in tabular infinite-horizon discounted Markov decision processes under the online setting. The existing algorithms either fail to achieve regret optimality or have to incur a high memory and computational cost. In addition, existing optimal algorithms all require a long

Yuzhu Huang, Awad Abdelhalim, Anson Stewart, Jinhua Zhao

High-resolution location ("heartbeat") data of transit fleet vehicles is a relatively new data source for many transit agencies. On its surface, the heartbeat data can provide a wealth of information about all operational details of a recorded transit vehicle trip, from its location trajectory to its speed and acceleration profiles. Previous studies have mai

Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin

Modern neural-network-based no-reference image- and video-quality metrics exhibit performance as high as full-reference metrics. These metrics are widely used to improve visual quality in computer vision methods and compare video processing methods. However, these metrics are not stable to traditional adversarial attacks, which can cause incorrect results. O

Sayomi Kamimoto, Jason Hindes, Ira Schwartz

We consider the problem of characterizing the dynamics of interacting swarms after they collide and form a stationary center of mass. Modeling efforts have shown that the collision of near head-on interacting swarms can produce a variety of post-collision dynamics including coherent milling, coherent flocking, and scattering behaviors. In particular, recent

Zhuangyu Liu, Jinfeng Liu, Shunyi Zhao, Xiaoli Luan

The importance of accurate soil moisture data for the development of modern closed-loop irrigation systems cannot be overstated. Due to the diversity of soil, it is difficult to obtain an accurate model for agro-hydrological system. In this study, soil moisture estimation in 1D agro-hydrological systems with model mismatch is the focus. To address the proble

Aditya Sant, Bhaskar D. Rao

Detection for one-bit massive MIMO systems presents several challenges especially for higher order constellations. Recent advances in both model-based analysis and deep learning frameworks have resulted in several robust one-bit detector designs. Our work builds on the current state-of-the-art gradient descent (GD)-based detector. We introduce two novel cont

Baifeng Shi, Siyu Gai, Trevor Darrell, Xin Wang

Transfer learning involves adapting a pre-trained model to novel downstream tasks. However, we observe that current transfer learning methods often fail to focus on task-relevant features. In this work, we explore refocusing model attention for transfer learning. We introduce Top-Down Attention Steering (TOAST), a novel transfer learning algorithm that keeps

Yuan Yang, Siheng Xiong, Ali Payani, Ehsan Shareghi

Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature. This paper introduces LogicLLaMA, a LLaMA-7B model fine-tuned for NL-FOL translation using LoRA on a single GPU. LogicLLaMA is capable of directly translating natural language into FOL rules, which outperforms G

J. P. Carlomagno, S. A. Ferraris, D. Gómez Dumm, A. G. Grunfeld

We study the $T {-} {\mu}$ phase diagram of quark matter under the influence of a strong uniform magnetic field in the framework of a nonlocal extension of the Polyakov Nambu Jona Lasinio model (PNJL). The existence of a critical end point (CEP) is found for the whole considered range of the magnetic field (up to 1 $GeV^{2}$). We analyze the location of this

Hossein Shahbeik, Alireza Shafizadeh, Mohammad Hossein Nadian, Dorsa Jeddi

Co-pyrolysis of biomass feedstocks with polymeric wastes is a promising strategy for improving the quantity and quality parameters of the resulting liquid fuel. Numerous experimental measurements are typically conducted to find the optimal operating conditions. However, performing co-pyrolysis experiments is highly challenging due to the need for costly and

Henrik A. Kjeldsberg, Joakim Sundnes, Kristian Valen-Sendstad

Computational fluid dynamics (CFD) in combination with patient-specific medical images has been used to correlate flow phenotypes with disease initiation, progression and outcome, in search of a prospective clinical tool. A large number of CFD software packages are available, but are typically based on rigid domains and low-order finite volume methods, and a

Hao Wang, Shivchander Sudalairaj, John Henning, Kristjan Greenewald

Existing private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synt

Emma Whelan, Aisling Murphy, Ilaria Pascucci

Spectro-astrometry is applied to echelle spectra of the young intermediate mass star T CrA. The aim is to better understand the origin of the [O I] and [S II] emission from T CrA and further explore the usefulness of spectro-astrometry to the search for a reliable tracer of MHD disk winds. The analysis reveals a small-scale curved jet in an east-west directi

David Qiu, David Rim, Shaojin Ding, Oleg Rybakov

With the rapid increase in the size of neural networks, model compression has become an important area of research. Quantization is an effective technique at decreasing the model size, memory access, and compute load of large models. Despite recent advances in quantization aware training (QAT) technique, most papers present evaluations that are focused on co

Anna-Lisa Vollmer, Daniel Leidner, Michael Beetz, Britta Wrede

Humans have developed the capability to teach relevant aspects of new or adapted tasks to a social peer with very few task demonstrations by making use of scaffolding strategies that leverage prior knowledge and importantly prior joint experience to yield a joint understanding and a joint execution of the required steps to solve the task. This process has be

Pedro Silva, Bhawna Juneja, Shloka Desai, Ashudeep Singh

As the use of online platforms continues to grow across all demographics, users often express a desire to feel represented in the content. To improve representation in search results and recommendations, we introduce end-to-end diversification, ensuring that diverse content flows throughout the various stages of these systems, from retrieval to ranking. We d

Claire Barale, Michael Rovatsos, Nehal Bhuta

In this paper, we introduce an end-to-end pipeline for retrieving, processing, and extracting targeted information from legal cases. We investigate an under-studied legal domain with a case study on refugee law in Canada. Searching case law for past similar cases is a key part of legal work for both lawyers and judges, the potential end-users of our prototyp

Roberto de A. Capistrano-Filho, Boumediène Chentouf, Victor H. Gonzalez Martinez, Juan Ricardo Muñoz

The boundary stabilization problem of the Boussinesq KdV-KdV type system is investigated in this paper. An appropriate boundary feedback law consisting of a linear combination of a damping mechanism and a delay term is designed. Then, first, considering time-varying delay feedback together with a smallness restriction on the length of the spatial domain and

Moriah Elkin, Gregg Musiker, Kayla Wright

We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several Gr(3,n) cluster variables that may be written as degree two or degree three polynomials in terms of Pl\"ucker coordinates, and give generating functions for their image

Pilar Páez-Guillán, Salvatore Siciliano, David A. Towers

In this paper we continue the study of the subalgebra lattice of a Leibniz algebra. In particular, we find out that solvable Leibniz algebras with an upper semi-modular lattice are either almost-abelian or have an abelian ideal spanned by the elements with square zero. We also study Leibniz algebras in which every subalgebra is a weak quasi-ideal, as well as

Zirui Liu, Zhimeng Jiang, Shaochen Zhong, Kaixiong Zhou

Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detection in social networks. However, the trained GNNs still make errors and these errors may cause serious negative impact on society. \textit{Model editing}, which corrects the model

M. D. Voisei

In this note new properties for the Gossez example are presented in regard to its representability, closedness, and maximal monotonicity with respect to the two dual systems it naturally inhabits.

Luiz L. Lopes, Victor B. T. Alves, César O. V. Flores, German Lugones

We investigate possible traces of the nuclear symmetry energy slope ($L$) in the gravitational wave emission of neutron stars. For fixed stellar mass values, we examine how the slope influences the stellar radius, compactness, the tidal deformability, the frequency of the quadrupole fundamental fluid mode, and the damping time of the mode due to the gravitat

Songyang Zhang, Tianhang Yu, Brian Choi, Feng Ouyang

Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless systems. Generally, radiomaps are constructed from radio strength measurements by deployed sensors and user devices. Howe

Xin Liu, Daniel McDuff, Geza Kovacs, Isaac Galatzer-Levy

Large language models (LLMs) can capture rich representations of concepts that are useful for real-world tasks. However, language alone is limited. While existing LLMs excel at text-based inferences, health applications require that models be grounded in numerical data (e.g., vital signs, laboratory values in clinical domains; steps, movement in the wellness

James Weaver, Patrick B. Ryan, Victoria Y. Strauss, Marc A. Suchard

Outcome phenotype measurement error is rarely corrected in comparative effect estimation studies in observational pharmacoepidemiology. Quantitative bias analysis (QBA) is a misclassification correction method that algebraically adjusts person counts in exposure-outcome contingency tables to reflect the magnitude of misclassification. The extent QBA minimize

Po-han Li, Sravan Kumar Ankireddy, Ruihan Zhao, Hossein Nourkhiz Mahjoub

Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node due to limited communication bandwidth. A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based t

Benedict Bauer, Stefan Gerhold

We characterize all real matrix semigroups satisfying a mild boundedness assumption, without assuming continuity. Besides the continuous solutions of the semigroup functional equation, we give a description of solutions arising from non-measurable solutions of Cauchy's functional equation. To do so, we discuss the primary decomposition and the Jordan-Chevall

Alex Akins, Mark Hofstadter, Bryan Butler, A. James Friedson

We present observations of Uranus in northern spring with the VLA from 0.7 cm to 5 cm. These observations reveal details in thermal emission from Uranus' north pole at 10s of bars, including a dark collar near 80N and a bright spot at the polar center. The bright central spot resembles observations of polar emission on Saturn and Neptune at shallower pressur

Wanyun Cui, Xingran Chen

Previous research has demonstrated that natural language explanations provide valuable inductive biases that guide models, thereby improving the generalization ability and data efficiency. In this paper, we undertake a systematic examination of the effectiveness of these explanations. Remarkably, we find that corrupted explanations with diminished inductive

Luc Jaulin

This paper proposes a minimal contractor and a minimal separator for an area delimited by an hyperbola of the plane. The task is facilitated using actions induced by the hyperoctahedral group of symmetries. An application related to the localization of an object using a TDoA (Time Differential of Arrival) technique is proposed.

Aoi Ito, Shota Horiguchi

Large-scale pretrained models using self-supervised learning have reportedly improved the performance of speech anti-spoofing. However, the attacker side may also make use of such models. Also, since it is very expensive to train such models from scratch, pretrained models on the Internet are often used, but the attacker and defender may possibly use the sam

Robin Herlan, Alessandra Mastrobuono-Battisti, Nadine Neumayer

The Sagittarius dwarf spheroidal (Sgr dSph) galaxy is currently being accreted and disrupted by the tidal field of the Milky Way. Recent observations have shown that the central region of the dwarf hosts at least three different stellar populations, ranging from old and metal-poor over intermediate metal-rich to young metal-rich. While the intermediate-age m

Wanyun Cui, Xingran Chen

Recent years have witnessed the emergence of textual commonsense knowledge bases, aimed at providing more nuanced and context-rich knowledge. The integration of external commonsense into language models has been shown to be a key enabler in advancing the state-of-the-art for a wide range of NLP tasks. However, incorporating textual commonsense descriptions i

Everton S. Medeiros, Ulrike Feudel

Utilizing a paradigmatic model for the motion of interacting self-propelled particles, we demonstrate that local accelerations at the level of individual particles can drive transitions between different collective dynamics, leading to a control process. We find that the ability to trigger such transitions is hierarchically distributed among the particles an

Qian Huang, Eric Zelikman, Sarah Li Chen, Yuhuai Wu

Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM). However, lexical symbol meanings can also be determined and even redefined by their structural role in a long context. In this paper, we ask: is it possible for a language model to be performant without \emph{any} fixed token embeddin

Qasim Ajao, Olukotun Oludamilare, Lanre Sadeeq

The rising popularity of mobile payments can be attributed to the widespread use of smartphones and their applications. Despite its potential to simplify our lives, its adoption in African countries has been limited. This paper aims to enhance our understanding of the critical factors that influence the acceptance of mobile payments in Nigeria by exploring t

Denis Polly

We give a classification of rotational cmc surfaces in non-Euclidean space forms in terms of explicit parametrizations using Jacobi elliptic functions. Our method hinges on a Lie sphere geometric description of rotational linear Weingarten surfaces and can thus be applied to a more general class of surfaces. As another application of this framework, we give

Marc Levine

We give an algebraic construction of orientation data for the Hilbert scheme of 0-dimensional subschemes of a smooth projective threefold endowed with a choice of square root of the canonical sheaf.

Christian Wiedemann, Henrik M. Bette, Matthias Wächter, Jan A. Freund

In the last few years, the dynamical characterization of the power output of a wind turbine by means of a Langevin equation has been well established. For this approach, temporally highly resolved measurements of wind speed and power output are used to obtain the drift and diffusion coefficients of the energy conversion process. These coefficients fully dete

Defne Ege Ozan, Luca Magri

We model acoustic dynamics in space and time from synthetic sensor data. The tasks are (i) to predict and extrapolate the spatiotemporal dynamics, and (ii) reconstruct the acoustic state from partial observations. To achieve this, we develop acoustic neural networks that learn from sensor data, whilst being constrained by prior knowledge on acoustic and wave

R. K. Cochrane, R. Kondapally, P. N. Best, J. Sabater

We present a detailed study of the cosmic star formation history over $90$ per cent of cosmic time ($0\lesssim z\lesssim4$), using deep, radio continuum observations that probe star formation activity independent of dust. The Low Frequency Array Two Metre Sky Survey has imaged three well-studied extragalactic fields, Elais-N1, Bo\"otes and the Lockman Hole,

Przemysław Gordinowicz, Paweł Twardowski

We restate Thomassen's theorem of 3-extendability, an extension of the famous planar 5-choosability theorem, in terms of graph polynomials. This yields an Alon--Tarsi equivalent of 3-extendability.

Luís Felipe P. Cattelan, Danilo Silva

This paper addresses the problem of selective classification for deep neural networks, where a model is allowed to abstain from low-confidence predictions to avoid potential errors. We focus on so-called post-hoc methods, which replace the confidence estimator of a given classifier without modifying or retraining it, thus being practically appealing. Conside

Alireza Shafizadeh, Hossein Shahbeik, Shahin Rafiee, Aysooda Moradi

Hydrothermal carbonization (HTC) is a process that converts biomass into versatile hydrochar without the need for prior drying. The physicochemical properties of hydrochar are influenced by biomass properties and processing parameters, making it challenging to optimize for specific applications through trial-and-error experiments. To save time and money, mac

Antonio Valerio Miceli-Barone, Fazl Barez, Ioannis Konstas, Shay B. Cohen

Large Language Models (LLMs) have successfully been applied to code generation tasks, raising the question of how well these models understand programming. Typical programming languages have invariances and equivariances in their semantics that human programmers intuitively understand and exploit, such as the (near) invariance to the renaming of identifiers.

Emanuil S. Yanev, Thomas P. Darlington, Sophia A. Ladyzhets, Matthew C. Strasbourg

Localized states in two-dimensional (2D) transition metal dichalcogenides (TMDCs) have been the subject of intense study, driven by potential applications in quantum information science. Despite the rapidly growing knowledge surrounding these emitters, their microscopic nature is still not fully understood, limiting their production and application. Motivate

Devvrat Tiwari, Subhashish Banerjee

Recently, the out-of-time-ordered correlator (OTOC) has gained much attention as an indicator of quantum chaos. In the semi-classical limit, its exponential growth rate resembles the classical Lyapunov exponent. The quantum-classical correspondence has been supported for the one-body chaotic systems as well as realistic systems with interactions, as in the D

Olga Kozachek, Alexey Bobtsov, Nikolay Nikolaev

The paper proposes an adaptive observer of the state vector of a nonlinear time varying system based on measurements of the output variable. The problem is solved under the assumption that the control matrix (vector) and the nonlinear component of the equation of state of the system contain unknown constant parameters. When developing an adaptive observer, t

B. Q. Wang, S. R. Wu

In present work, the evolution of scalar field and electromagnetic field under the background of the regularized dilatonic black bounces spacetimes are investigated, we obtain an obvious echoes signal which appropriately reports the properties of regularized dilatonic black bounces spacetimes and disclose the physical reasons behind such phenomena. By studyi

Eric Kleinherbers, Hendrik Mannel, Jens Kerski, Martin Geller

The use of single electron spins in quantum dots as qubits requires detailed knowledge about the processes involved in their initialization and operation as well as their relaxation and decoherence. In optical schemes for such spin qubits, spin-flip Raman as well as Auger processes play an important role, in addition to environment-induced spin relaxation. I

Lucas Torroba Hennigen, Yoon Kim

Masked language models (MLM) do not explicitly define a distribution over language, i.e., they are not language models per se. However, recent work has implicitly treated them as such for the purposes of generation and scoring. This paper studies methods for deriving explicit joint distributions from MLMs, focusing on distributions over two tokens, which mak

Konstantinos F. Dialektopoulos, Purba Mukherjee, Jackson Levi Said, Jurgen Mifsud

Neural networks have shown great promise in providing a data-first approach to exploring new physics. In this work, we use the full implementation of late time cosmological data to reconstruct a number of scalar-tensor cosmological models within the context of neural network systems. In this pipeline, we incorporate covariances in the data in the neural netw

Konstantinos F. Dialektopoulos, Purba Mukherjee, Jackson Levi Said, Jurgen Mifsud

In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in Artificial Neural Networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results wi

Konstantina Christakopoulou, Alberto Lalama, Cj Adams, Iris Qu

Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation. Their potential for deeper user understanding and improved personalized user experience on recommendation platforms is, however, largely untapped. This paper aims to address this gap. Recommender systems today capture users' interests through enc

Veronika Baumann, Časlav Brukner

The Wigner's friend experiment is a thought experiment in which a so-called superobserver (Wigner) observes another observer (the friend) who has performed a quantum measurement on a physical system. In this setup Wigner treats the friend, the system and potentially other degrees of freedom involved in the friend's measurement as one joint quantum system. In

Alexey Bobtsov, Vladimir Virobyev, Nikolay Nikolaev, Anton Pyrkin

The article is devoted to the problem of synthesis of observers of state variables for linear stationary objects operating under conditions of noise or disturbances in the measurement channel. The paper considers a fully observable linear stationary system with known parameters. It is assumed that the state variables are not measured, and the measured output

Yue Zhang

The QCD axion needs not be an exact pseudoscalar for solving the strong CP problem. Its imperfectness can play a profound role cosmologically. We propose effective operators, where the Peccei-Quinn field linearly couples to Standard Model particles, provide a dynamical solution to the domain wall problem that prevails in post-inflationary axion models with d

Koji Mukai, Magaretha L. Pretorius

Recent advances in time-domain astronomy have led to fresh observational insights into intermediate polars, a subtype of magnetic cataclysmic variables generally accreting via a partial accretion disc. These new discoveries include detections of superhumps, low states, and outbursts. However, these studies have largely relied on relative photometry. Here we

Tim Browning, Igor Shparlinski

The question of whether or not a given integral polynomial takes infinitely many square-free values has only been addressed unconditionally for polynomials of degree at most 3. We address this question, on average, for polynomials of arbitrary degree.

Iwo Bialynicki-Birula, Zofia Bialynicka-Birula

We derive new solutions of the Schr\"odinger equation which describe the motion of particles in the Penning trap. These solutions are direct counterparts of classical orbits. They are obtained by injection of classical trajectories into the wave functions of stationary solutions.

Ron Belmont, Jasmine Brewer, Quinn Brodsky, Paul Caucal

sPHENIX is a next-generation detector experiment at the Relativistic Heavy Ion Collider, designed for a broad set of jet and heavy-flavor probes of the Quark-Gluon Plasma created in heavy ion collisions. In anticipation of the commissioning and first data-taking of the detector in 2023, a RIKEN-BNL Research Center (RBRC) workshop was organized to collect the

Harsh Sharma, Hongliang Mu, Patrick Buchfink, Rudy Geelen

This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical symplectic model reduction approaches employ linear symplectic subspaces for representing the high-dimensional system states in a reduced-dimensional coordinate system. While these approximations

Bader Abu Radi, Orna Kupferman

A nondeterministic automaton is semantically deterministic (SD) if different nondeterministic choices in the automaton lead to equivalent states. Semantic determinism is interesting as it is a natural relaxation of determinism, and as some applications of deterministic automata in formal methods can actually use automata with some level of nondeterminism, ti

Dhruv Nandakumar, Devin Quinn, Elijah Soba, Eunyoung Kim

In today's interconnected digital landscape, the proliferation of malware poses a significant threat to the security and stability of computer networks and systems worldwide. As the complexity of malicious tactics, techniques, and procedures (TTPs) continuously grows to evade detection, so does the need for advanced methods capable of capturing and character

Trung Chau

We study the algebraic sets of pairs of matrices defined by the vanishing of the anti-diagonal as well as the cross-diagonal of their commutator matrix. We prove that, over a field of prime characterisitic, the coordinate ring of the latter is always $F$-regular and, with exactly one exception, so is that of the former, thus proving a conjecture of Kadyrsizo

Yue Wu, Shrimai Prabhumoye, So Yeon Min, Yonatan Bisk

Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach,

Kursat Sozer, Alexis Virelizier

Given a crossed module $\chi$, we introduce Hopf $\chi$-(co)algebras which generalize Hopf algebras and Hopf group-(co)algebras. We interpret them as Hopf algebras in some symmetric monoidal category. We prove that their categories of representations are monoidal and $\chi$-graded (meaning that both objects and morphisms have degrees which are related via $\

Gerd Kortemeyer, Christine Kortemeyer, Wolfgang Bauer

At large institutions of higher education, students frequently have a choice whether to attend the introductory physics sequence asynchronously online, on-site in a traditional lecture-setting, or in a reformed studio setting. In this study, we investigate how these different settings are correlated with measures of self-efficacy, interest in physics, and su