July 2023 arXiv papers — page 28

F. Stoppa, S. Bhattacharyya, R. Ruiz de Austri, P. Vreeswijk

Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogues, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely

Guillaume Scamps, Ibrahim Abdurrahman, Matthew Kafker, Aurel Bulgac

New experimental and theoretical results obtained in 2021 made it acutely clear that more than 80 years after the discovery of nuclear fission we do not understand the generation and dynamics of fission fragment (FF) intrinsic spins well, in particular their magnitudes, their spatial orientation, and their correlations. The magnitude and orientation of the p

Emilio J. Gonzalez, Jesus Gonzalez

For a positive integer $n$ and a finite simplicial complex $K$, we describe an algorithmic procedure constructing a maximal discrete gradient field $W(K,n)$ on Abrams' discretized configuration space $\text{DConf}(K,n)$. Computer experimentation shows that the field is generically optimal. We study the field $W(K,n)$ for $n=2$ and $K=\Delta^{m,d}$, the $d$-d

Prajit Sengupta, Anant Mehta, Prashant Singh Rana

Armoured vehicles are specialized and complex pieces of machinery designed to operate in high-stress environments, often in combat or tactical situations. This study proposes a predictive maintenance-based ensemble system that aids in predicting potential maintenance needs based on sensor data collected from these vehicles. The proposed model's architecture

D. D. Yavuz, A. Yadav

We discuss a new approach to simulate quantum algorithms using classical probabilistic bits and circuits. Each qubit (a two-level quantum system) is initially mapped to a vector in an eight dimensional probability space (equivalently, to a classical random variable with eight probabilistic outcomes). The key idea in this mapping is to store both the amplitud

Bangshan Sun, Simon Moser, Alexander Jesacher, Patrick S. Salter

Integrated photonic chips have significant potential in telecommunications, classic computing, quantum systems, and topological photonics. Direct laser writing offers unique capability for creating three-dimensional photonic devices in an optical glass chip with quick prototyping. However, existing laser writing schemes cannot create index-modified structure

Xumei Xi, Yuke Zhao, Quan Liu, Liwen Ouyang

We consider the problem of sequential recommendation, where the current recommendation is made based on past interactions. This recommendation task requires efficient processing of the sequential data and aims to provide recommendations that maximize the long-term reward. To this end, we train a farsighted recommender by using an offline RL algorithm with th

Federica Capellino, Andrea Dubla, Stefan Floerchinger, Eduardo Grossi

A fluid-dynamic approach to charm-quark diffusion in the quark-gluon plasma (QGP) is developed for the first time. Results for integrated yields and momentum distributions of charmed hadrons obtained with a fluid-dynamic description for the dynamics of the QGP coupled to an additional heavy-quark-antiquark current are shown. In addition to the thermodynamic

Xian Teng, Yongsu Ahn, Yu-Ru Lin

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, cau

Yi Li, Timothy Draher, Andrew H. Comstock, Yuzan Xiong

Tunability of coherent coupling between fundamental excitations is an important prerequisite for expanding their functionality in hybrid quantum systems. In hybrid magnonics, the dipolar interaction between magnon and photon usually persists and cannot be switched off. Here, we demonstrate this capability by coupling a superconducting resonator to a layered

Sanaz Karimijafarbigloo, Reza Azad, Dorit Merhof

Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, existing FSS techniques heavily rely on annotated semantic classes, rendering them unsuitable for medical images due to the scarcity of annotations. To address this challenge, multipl

Henrique Ennes, Raphaël Tinarrage

We suggest a new algorithm to estimate representations of compact Lie groups from finite samples of their orbits. Different from other reported techniques, our method allows the retrieval of the precise representation type as a direct sum of irreducible representations. Moreover, the knowledge of the representation type permits the reconstruction of its orbi

Mohammadhossein Mohammadisiahroudi, Ramin Fakhimi, Zeguan Wu, Tamás Terlaky

The use of quantum computing to accelerate complex optimization problems is a burgeoning research field. This paper applies Quantum Linear System Algorithms (QLSAs) to Newton systems within Interior Point Methods (IPMs) to take advantage of quantum speedup in solving Linear Optimization (LO) problems. Due to their inexact nature, QLSAs can be applied only to

Maria Silvina Tomassone, Yangyang Shen, Bill Borghard, Bryant Avila

Catalyst impregnation is one of the most crucial steps for preparing industrial catalysts. The inter-particle variability of the impregnated liquid inside the particles significantly affects the activity and selectivity of the catalyst. Current scale-up practices lead to poor fluid distribution and inhomogeneity in the liquid content. This work aims to under

Mallika Roy, Enric Ventura

In this paper, we explore the behaviour of the fixed subgroups of endomorphisms of free-abelian times free (FATF) groups. We exhibit an algorithm which, given a finitely generated subgroup $\mathcal{H}$ of a FATF group $\mathcal{G}$, decides whether $\mathcal{H}$ is the fixed subgroup of some (finite) family of endomorphisms of $\mathcal{G}$ and, in the affi

Iman Nodozi, Charlie Yan, Mira Khare, Abhishek Halder

We show that the minimum effort control of colloidal self-assembly can be naturally formulated in the order-parameter space as a generalized Schr\"{o}dinger bridge problem -- a class of fixed-horizon stochastic optimal control problems that originated in the works of Erwin Schr\"{o}dinger in the early 1930s. In recent years, this class of problems has seen a

Jin-Peng Liu, Lin Lin

The quantum dense output problem is the process of evaluating time-accumulated observables from time-dependent quantum dynamics using quantum computers. This problem arises frequently in applications such as quantum control and spectroscopic computation. We present a range of algorithms designed to operate on both early and fully fault-tolerant quantum platf

Angela Ramirez, Karik Agarwal, Juraj Juraska, Utkarsh Garg

Dialogue systems need to produce responses that realize multiple types of dialogue acts (DAs) with high semantic fidelity. In the past, natural language generators (NLGs) for dialogue were trained on large parallel corpora that map from a domain-specific DA and its semantic attributes to an output utterance. Recent work shows that pretrained language models

Ryan Kortvelesy

It is often useful to perform integration over learned functions represented by neural networks. However, this integration is usually performed numerically, as analytical integration over learned functions (especially neural networks) is generally viewed as intractable. In this work, we present a method for representing the analytical integral of a learned f

Evgeny Korotyaev, Andrea Mantile, Dmitrii Mokeev

We consider Schr\"{o}dinger equations with linearly energy-depending potentials which are compactly supported on the half-line. We first provide estimates of the number of eigenvalues and resonances for such complex-valued potentials under suitable regularity assumptions. Then, we consider a specific class of energy-dependent Schr\"{o}dinger equations withou

Weizhang Huang, Jinye Shen

A grid-overlay finite difference method is proposed for the numerical approximation of the fractional Laplacian on arbitrary bounded domains. The method uses an unstructured simplicial mesh and an overlay uniform grid for the underlying domain and constructs the approximation based on a uniform-grid finite difference approximation and a data transfer from th

Alexandre Toubiana, Lorenzo Pompili, Alessandra Buonanno, Jonathan R. Gair

The laser-interferometer space antenna (LISA) will be launched in the mid 2030s. It promises to observe the coalescence of massive black-hole (BH) binaries with signal-to-noise ratios (SNRs) reaching thousands. Crucially, it will detect some of these binaries with high SNR both in the inspiral and the merger-ringdown stages. Such signals are ideal for tests

Vaibhav Sharma, Artur Yakimovich

In the past decades, automated high-content microscopy demonstrated its ability to deliver large quantities of image-based data powering the versatility of phenotypic drug screening and systems biology applications. However, as the sizes of image-based datasets grew, it became infeasible for humans to control, avoid and overcome the presence of imaging and s

Joseph Bramante, Nirmal Raj

White dwarfs and neutron stars are far-reaching and multi-faceted laboratories in the hunt for dark matter. We review detection prospects of wave-like, particulate, macroscopic and black hole dark matter that make use of several exceptional properties of compact stars, such as ultra-high densities, deep fermion degeneracies, low temperatures, nucleon superfl

Khaled Abdelaal, Richard Veras

The performance of sparse matrix computation highly depends on the matching of the matrix format with the underlying structure of the data being computed on. Different sparse matrix formats are suitable for different structures of data. Therefore, the first challenge is identifying the matrix structure before the computation to match it with an appropriate d

Shreya Vardhan, Annie Y. Wei, Yijian Zou

We probe the multipartite entanglement structure of the vacuum state of a CFT in 1+1 dimensions, using recovery operations that attempt to reconstruct the density matrix in some region from its reduced density matrices on smaller subregions. We use an explicit recovery channel known as the twirled Petz map, and study distance measures such as the fidelity, r

Hooman Vaseli, Ang Nan Gu, S. Neda Ahmadi Amiri, Michael Y. Tsang

Aortic stenosis (AS) is a common heart valve disease that requires accurate and timely diagnosis for appropriate treatment. Most current automatic AS severity detection methods rely on black-box models with a low level of trustworthiness, which hinders clinical adoption. To address this issue, we propose ProtoASNet, a prototypical network that directly detec

M. J. Gullans, M. Caranti, A. R. Mills, J. R. Petta

As quantum devices make steady progress towards intermediate scale and fault-tolerant quantum computing, it is essential to develop rigorous and efficient measurement protocols that account for known sources of noise. Most existing quantum characterization protocols such as gate set tomography and randomized benchmarking assume the noise acting on the qubits

Mitchell Jubeir, Daniel W van Wyk

We characterize ultragraph Leavitt path algebras that are Rickart, locally Rickart, graded Rickart, and graded Rickart *-rings. We also characterize ultragraph Leavitt path algebras that are Baer, locally Baer, graded Baer, Baer *-rings, and combinations of these. These characterizations build on and generalize the work of Hazrat and Vas on Leavitt path alge

Mayee F. Chen, Nicholas Roberts, Kush Bhatia, Jue Wang

The quality of training data impacts the performance of pre-trained large language models (LMs). Given a fixed budget of tokens, we study how to best select data that leads to good downstream model performance across tasks. We develop a new framework based on a simple hypothesis: just as humans acquire interdependent skills in a deliberate order, language mo

Davide Gaiotto, Gregory W. Moore, Andrew Neitzke, Fei Yan

We explain the physical origin of a curious property of algebras $\mathcal{A}_\mathfrak{q}$ which encode the rotation-equivariant fusion ring of half-BPS line defects in four-dimensional $\mathcal{N}=2$ supersymmetric quantum field theories. These algebras are a quantization of the algebras of holomorphic functions on the three-dimensional Coulomb branch of

Zhengdi Sun, Yunqin Zheng

A quantum field theory with a finite abelian symmetry $G$ may be equipped with a non-invertible duality defect associated with gauging $G$. For certain $G$, duality defects admit an alternative construction where one starts with invertible symmetries with certain 't Hooft anomaly, and gauging a non-anomalous subgroup. This special type of duality defects are

Stefan H. Sack, Daniel J. Egger

Quantum computers are increasing in size and quality, but are still very noisy. Error mitigation extends the size of the quantum circuits that noisy devices can meaningfully execute. However, state-of-the-art error mitigation methods are hard to implement and the limited qubit connectivity in superconducting qubit devices restricts most applications to the h

Benedict von Harling, Oleksii Matsedonskyi, Geraldine Servant

Electroweak Baryogenesis (EWBG) paired with the Composite Higgs (CH) scenario provides a well-motivated and testable framework for addressing the questions of the origin of the matter-antimatter asymmetry and the naturalness of the electroweak scale. The appeal of both concepts however experiences increasing pressure from the experimental side, as no conclus

Matthew Sullivan

The $[[7,1,3]]$ Steane code and $[[23,1,7]]$ quantum Golay code have been identified as good candidates for fault-tolerant quantum computing via code concatenation. These two codes have transversal implementations of all Clifford gates, but require some other scheme for fault-tolerant $T$ gates. Using magic states, Clifford operations, and measurements is on

Martin Ringbauer, Marcel Hinsche, Thomas Feldker, Paul K. Faehrmann

Quantum computers are now on the brink of outperforming their classical counterparts. One way to demonstrate the advantage of quantum computation is through quantum random sampling performed on quantum computing devices. However, existing tools for verifying that a quantum device indeed performed the classically intractable sampling task are either impractic

Samantha C. Berek, Marta Reina-Campos, Gwendolyn Eadie, Alison Sills

The presence or absence of star clusters in galaxies, and the properties of star cluster populations compared to their host galaxy properties, are important observables for validating models of cluster formation, galaxy formation, and galaxy assembly. In this work, we apply a Bayesian approach to fit two models to data from surveys of young clusters in star

Emma Willett, Andrea Miglio, J. Ted Mackereth, Cristina Chiappini

The radial metallicity distribution of the Milky Way's disc is an important observational constraint for models of the formation and evolution of our Galaxy. It informs our understanding of the chemical enrichment of the Galactic disc and the dynamical processes therein, particularly radial migration. We investigate how the metallicity changes with guiding r

Diego Sotillo-Ramos, Maria Bergemann, Jennifer K. S. Friske, Annalisa Pillepich

Recent observational studies have uncovered a small number of very metal-poor stars with cold kinematics in the Galactic disc and bulge. However, their origins remain enigmatic. We select a total of 138 Milky Way (MW) analogs from the TNG50 cosmological simulation based on their $z=0$ properties: disky morphology, stellar mass, and local environment. In orde

C. Yamila Yaryura, Mario G. Abadi, Stefan Gottlöber, Noam I. Libeskind

We study the properties of associations of dwarf galaxies and their dependence on the environment. Associations of dwarf galaxies are extended systems composed exclusively of dwarf galaxies, considering as dwarf galaxies those galaxies less massive than $M_{\star, \rm max} = 10^{9.0}$ ${\rm M}_{\odot}\,h^{-1}$. We identify these particular systems using a se

Antón Makarov, Márcio M. Taddei, Eneko Osaba, Giacomo Franceschetto

Satellite image acquisition scheduling is a problem that is omnipresent in the earth observation field; its goal is to find the optimal subset of images to be taken during a given orbit pass under a set of constraints. This problem, which can be modeled via combinatorial optimization, has been dealt with many times by the artificial intelligence and operatio

Reuven Balkin, Javi Serra, Konstantin Springmann, Stefan Stelzl

We study how light scalar fields can change the stellar landscape by triggering a new phase of nuclear matter. Scalars coupled to nucleons can develop a non-trivial expectation value at finite baryon density. This sourcing of a scalar reduces the nucleon mass and provides an additional energy density and pressure source. Under generic conditions, a new groun

B. King, T. Heinzl, T. G. Blackburn

By combining an adiabatic approach based on a `locally monochromatic' approximation with a local Hilbert transform, it is demonstrated how vacuum birefringence in the strong field regime can be calculated using a rate approach suitable for Monte Carlo simulation codes. Results for the flipping of the photon's polarisation (helicity) are benchmarked with eval

Stefano Antonini, Martin Sasieta, Brian Swingle

We construct entangled microstates of a pair of holographic CFTs whose dual semiclassical description includes big bang-big crunch AdS cosmologies in spaces without boundaries. The cosmology is supported by inhomogeneous heavy matter and it partially purifies the bulk entanglement of two disconnected auxiliary AdS spacetimes. We show that the island formula

Michael Winer, Brian Swingle

The spectral form factor (SFF) is an important diagnostic of energy level repulsion in random matrix theory (RMT) and quantum chaos. The short-time behavior of the SFF as it approaches the RMT result acts as a diagnostic of the ergodicity of the system as it approaches the thermal state. In this work we observe that for systems without time-reversal symmetry

Sarah E. I. Bosman, Javier Álvarez-Márquez, Luis Colina, Fabian Walter

We present a JWST/MRS spectrum of the quasar J1120+0641 at z=7.0848, the first spectroscopic observation of a reionisation-era quasar in the rest-frame infrared ($0.6<\lambda<3.4\mu$m). In the context of the mysterious fast assembly of the first supermassive black holes at z>7, our observations enable for the first time the detection of hot torus dust, the H

Martin Hoferichter, Bastian Kubis, Marvin Zanke

We study the transition form factors (TFFs) of axial-vector mesons in the context of currently available experimental data, including new constraints from $e^+ e^- \to f_1(1285) \pi^+ \pi^-$ that imply stringent limits on the high-energy behavior and, for the first time, allow us to provide an unambiguous determination of the couplings corresponding to the t

William E. Harris, Marta Reina-Campos

JWST imaging of the rich galaxy cluster Abell 2744 at $z=0.308$ has been used by the UNCOVER team (Bezanson et al. 2022) to construct mosaic images in the NIRCAM filters. The exceptionally deep images in the ($F115W$, $F150W$, $F200W$) bands reveal a large population of unresolved pointlike sources across the field, the vast majority of which are globular cl

Giulia Cerini, Nico Cappelluti, Massimiliano Galeazzi, Eugenio Ursino

The Universe has evolved from an initial diffuse, uniform gas to a complex structure that includes both voids and high-density galaxy clusters connected by gaseous filaments, known as the Cosmic Web, and traced by 3D surveys of galaxies. The filamentary structure contains a significant fraction of the baryonic matter and is predicted to be mostly in the form

I. de Medeiros Varzielas, M. Levy, J. T. Penedo, S. T. Petcov

We analyse the possibility of describing quark masses, mixing and CP violation in $S'_4$ modular flavour models without flavons. We focus on the case where the closeness of the modulus to the point of residual $\mathbb{Z}^{ST}_3$ symmetry (the cusp) plays a role in generating quark mass hierarchies and discuss the role modular form normalisations play in suc

Pol Massana, David L. Nidever, Knut Olsen

In this paper, we analyse the metallicity structure of the Magellanic Clouds using parameters derived from the Gaia DR3 low-resolution XP spectra, astrometry and photometry. We find that the qualitative behavior of the radial metallicity gradients in the LMC and SMC are quite similar, with both of them having a metallicity plateau at intermediate radii and a

Yuber F. Perez-Gonzalez

Kerr black holes radiate neutrinos in an asymmetric pattern, preferentially in the lower hemisphere relative to the black hole's rotation axis, while antineutrinos are predominantly produced in the upper hemisphere. Leveraging this asymmetric emission, we explore the potential of high-energy, $E_\nu \gtrsim 1$ TeV, neutrino and antineutrino detection to reve

Diego Royo, Talha Sultan, Adolfo Muñoz, Khadijeh Masumnia-Bisheh

Non-line-of-sight (NLOS) imaging methods are capable of reconstructing complex scenes that are not visible to an observer using indirect illumination. However, they assume only third-bounce illumination, so they are currently limited to single-corner configurations, and present limited visibility when imaging surfaces at certain orientations. To reason about

Qian Du, Xin-Ran Ma, Su-Peng Kou

In the paper, we study the non-Hermitian system under dissipation and give the effective 2*2 Hamiltonian in the k-space by reducing the N*N Hamiltonian in the real space for them. It is discovered that the energy band shows an imaginary line gap. To describe these phenomena, we propose the theory of "non-Hermitian tearing", in which the tearability we define

Marco Bonici, Federico Bianchini, Jaime Ruiz-Zapatero

We present Capse.jl, a novel neural network-based emulator designed for rapid and accurate prediction of Cosmic Microwave Background (CMB) temperature, polarization, and lensing angular power spectra. The emulator computes predictions in just a few microseconds with emulation errors below $0.1\sigma$ for all the scales relevant for the upcoming CMB-S4 survey

Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev, Daniil Shlenskii

Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems. One of the research directions aimed at improv

M. T. Valdivia-Mena, J. E. Pineda, D. M. Segura-Cox, P. Caselli

The infall of gas from outside natal cores has proven to feed protostars after the main accretion phase (Class 0). This changes our view of star formation to a picture that includes asymmetric accretion (streamers), and a larger role of the environment. However, the connection between streamers and the filaments that prevail in star-forming regions is unknow

Pierre Fernandez, Antoine Chaffin, Karim Tit, Vivien Chappelier

The task of discerning between generated and natural texts is increasingly challenging. In this context, watermarking emerges as a promising technique for ascribing generated text to a specific model. It alters the sampling generation process so as to leave an invisible trace in the generated output, facilitating later detection. This research consolidates w

Rajeev Yasarla, Hong Cai, Jisoo Jeong, Yunxiao Shi

We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation. MAMo can augment and improve any single-image depth estimation networks into video depth estimation models, enabling them to take advantage of the temporal information to predict more accurate depth. In MAMo, we augment model with memory which aids the depth predic

Xubo Liu, Zhongkai Zhu, Haohe Liu, Yi Yuan

Despite breakthroughs in audio generation models, their capabilities are often confined to domain-specific conditions such as speech transcriptions and audio captions. However, real-world audio creation aims to generate harmonious audio containing various elements such as speech, music, and sound effects with controllable conditions, which is challenging to

Sheetal Sharma, Ramni Gupta

Local multiplicity fluctuations are an useful tool to understand the dynamics of the particle production and the phase-space changes from quarks to hadrons in ultrarelativistic heavy-ion collisions. The study of scaling behavior of multiplicity fluctuations in geometrical configurations in multiparticle production can be performed using the factorial moments

Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann

Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, and interpret this data at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To enable the development of the

Dominik Kreiss, Thomas Augustin

Researchers in psychology characterize decision-making as a process of eliminating options. While statistical modelling typically focuses on the eventual choice, we analyze consideration sets describing, for each survey participant, all options between which the respondent is pondering. Using a German pre-election poll as a prototypical example, we give a pr

Daniel Deniz, Cornelia Fermuller, Eduardo Ros, Manuel Rodriguez-Alvarez

Neuromorphic visual sensors are artificial retinas that output sequences of asynchronous events when brightness changes occur in the scene. These sensors offer many advantages including very high temporal resolution, no motion blur and smart data compression ideal for real-time processing. In this study, we introduce an event-based dataset on fine-grained ma

Hong Lu, Chuan Li, Yinheng Li, Jie Zhao

The drug development process necessitates that pharmacologists undertake various tasks, such as reviewing literature, formulating hypotheses, designing experiments, and interpreting results. Each stage requires accessing and querying vast amounts of information. In this abstract, we introduce a Large Language Model (LLM)-based Natural Language Interface desi

Thao Nguyen, Yuheng Li, Utkarsh Ojha, Yong Jae Lee

Text-conditioned image editing has emerged as a powerful tool for editing images. However, in many situations, language can be ambiguous and ineffective in describing specific image edits. When faced with such challenges, visual prompts can be a more informative and intuitive way to convey ideas. We present a method for image editing via visual prompting. Gi

Nicholas Anto-Sztrikacs, Brett Min, Marlon Brenes, Dvira Segal

We extend and benchmark the recently-developed Effective-Hamiltonian (EFFH) method [PRX Quantum $\bf{4}$, 020307 (2023)] as an approximation to the equilibrium state ("mean-force Gibbs state") of a quantum system at strong coupling to a thermal bath. The EFFH method is an approximate framework. Through a combination of the reaction-coordinate mapping, a pola

Beiqi Zhang, Liming Fu, Peng Liang, Jiaxin Yu

Code review is widely known as one of the best practices for software quality assurance in software development. In a typical code review process, reviewers check the code committed by developers to ensure the quality of the code, during which reviewers and developers would communicate with each other in review comments to exchange necessary information. As

B. -L. Najera-Santos, R. Rousseau, K. Gerashchenko, H. Patange

Owing to their strong dipole moment and long coherence times, superconducting qubits have demonstrated remarkable success in hybrid quantum circuits. However, most qubit architectures are limited to the GHz frequency range, severely constraining the class of systems they can interact with. The fluxonium qubit, on the other hand, can be biased to very low fre

Suzanne Gildert, Geordie Rose

Machines with human-level intelligence should be able to do most economically valuable work. This aligns a major economic incentive with the scientific grand challenge of building a human-like mind. Here we describe our approach to building and testing such a system. Our approach comprises a physical humanoid robotic system; a software based control system f

Hamid Saleem, Zain H. Saleem

Physical mechanism for the creation of solar spicules is proposed with three stages of their life cycle. It is assumed that at stage-I, the density hump is formed locally in the xy-plane in lower chromosphere in the presence of temperature gradients of electrons and ions along z-axis (the vertical direction). In this region, the density structure of quasi-ne

Anwesha Bhattacharyya, Yaqun Wang, Joel Vaughan, Vijayan N. Nair

The perceived advantage of machine learning (ML) models is that they are flexible and can incorporate a large number of features. However, many of these are typically correlated or dependent, and incorporating all of them can hinder model stability and generalizability. In fact, it is desirable to do some form of feature screening and incorporate only the re

Lucy Xiaoyang Shi, Archit Sharma, Tony Z. Zhao, Chelsea Finn

While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC). Waypoints can help address this problem by reducing the horizon of the learning problem for BC, and thus, the errors compounded over time. However, waypoint labeling is underspeci

Joseph Peetz, Scott E. Smart, Spyros Tserkis, Prineha Narang

Simulating physical systems on quantum devices is one of the most promising applications of quantum technology. Current quantum approaches to simulating open quantum systems are still practically challenging on NISQ-era devices, because they typically require ancilla qubits and extensive controlled sequences. In this work, we propose a hybrid quantum-classic

Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei

This paper presents a case study on the design, administration, post-processing, and evaluation of surveys on large language models (LLMs). It comprises two components: (1) A statistical method for eliciting beliefs encoded in LLMs. We introduce statistical measures and evaluation metrics that quantify the probability of an LLM "making a choice", the associa

Jean-François Aujol, Luca Calatroni, Charles Dossal, Hippolyte Labarrière

We consider a combined restarting and adaptive backtracking strategy for the popular Fast Iterative Shrinking-Thresholding Algorithm frequently employed for accelerating the convergence speed of large-scale structured convex optimization problems. Several variants of FISTA enjoy a provable linear convergence rate for the function values $F(x_n)$ of the form

Zhiyu Cao, Zihan Chen, Prerna Mishra, Hamed Amini

Financial contagion has been widely recognized as a fundamental risk to the financial system. Particularly potent is price-mediated contagion, wherein forced liquidations by firms depress asset prices and propagate financial stress, enabling crises to proliferate across a broad spectrum of seemingly unrelated entities. Price impacts are currently modeled via

Ananya Mukherjee, Abhijit Kumar Saha

In a pure inverse seesaw framework, achieving a substantial lepton asymmetry that can be converted into the observed baryon asymmetry of the Universe is extremely challenging. The difficulty arises primarily due to two reasons, (i) partial cancellation of the lepton asymmetries associated with the components of a pseudo-Dirac pair, and (ii) strong wash out c

Andrés Carnero Bravo

We give a decomposition of the suspension of a polyhedral join in terms of the polyhedral smash product of the suspension of the family of pairs, and study some cases in which the formula can be desuspended, particularly for polyhedral joins over independence complexes of graphs. We also give some lower bounds for the connectivity of polyhedral joins. We use

F. Carnesecchi, S. Strazzi, A. Alici, R. Arcidiacono

This paper describes the new concept of the double-LGAD. The goal is to increase the charge at the input of the electronics, keeping a time resolution equal or better than a standard (single) LGAD; this has been realized by adding the charges of two coupled LGADs while still using a single front-end electronics. The study here reported has been done starting

Jérôme Buzzi, Sylvain Crovisier, Yuri Lima

We construct symbolic dynamics for three dimensional flows with positive speed. More precisely, for each $\chi>0$, we code a set of full measure for every invariant probability measure which is $\chi$-hyperbolic. These include all ergodic measures with entropy bigger than $\chi$ as well as all hyperbolic periodic orbits of saddle-type with Lyapunov exponent

Yiannis Kiouvrekis, Ioannis S. Stamatiou

In this article, we construct a numerical method for a stochastic version of the Susceptible Infected Susceptible (SIS) epidemic model, expressed by a suitable stochastic differential equation (SDE), by using the semi-discrete method to a suitable transformed process. We prove the strong convergence of the proposed method, with order $1,$ and examine its sta

Daniel Hernández-Hernández, Joshué Helí Ricalde-Guerrero

In this paper we obtain results for the existence and uniqueness of solutions to coupled Forward-Backward Stochastic Differential Equations (FBSDEs) with jumps defined on a random environment. This environment corresponds to a measured-valued process, similar to the one found in Conditional McKean-Vlasov Differential Equations and Mean-Field Games with Commo

Amir Moslemi, Navid Khaledi

The challenge of removing cancerous cells lies in the limitation of organ at risk, which restricts the ability to increase the radiation dose adequately for enhancing treatment effectiveness. This survey provides a comprehensive overview of the 4{\pi} planning technique for radiation therapy. In radiation therapy, the gantry of Medical linear accelerators ca

Qisong Yang, Thiago D. Simão, Nils Jansen, Simon H. Tindemans

Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them in the real world. However, the real-world target task might be unknown prior to deployment. Reward-free RL trains an agent without the reward to adapt quickly once the reward is r

Hao Li, Daowen Qiu, Le Luo, Mateus Paulo

Simon's problem is one of the most important problems demonstrating the power of quantum algorithms, as it greatly inspired the proposal of Shor's algorithm. The generalized Simon's problem is a natural extension of Simon's problem, and also a special hidden subgroup problem. In this paper, we present two key contributions. Firstly, we characterize the struc

Matteo Ciotola, Giovanni Poggi, Giuseppe Scarpa

In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images. Given the lack of ground truth data, most deep learning-based methods carry out supervised training in a reduced-resolution domain. However, models trained on downsized images tend to perform poorly on high-resolution target images. For this reason, sever

Francesco Saverio Bucci, Matteo Cristofaro, Pier Luigi Giardino

With the appearance of SARS-CoV-2, two epidemics have spread: one for health and one for information. The virus has generated an unprecedented infodemic, contributing to the establishment of a climate of great uncertainty. Massive and redundant information has also been an effective vector of propaganda, on a global scale, by state and non-state actors. A re

Sergio A. Ortega, Miguel A. Martin-Delgado

Szegedy's quantum walk is an algorithm for quantizing a general Markov chain. It has plenty of applications such as many variants of optimizations. In order to check its properties in an error-free environment, it is important to have a classical simulator. However, the current simulation algorithms require a great deal of memory due to the particular formul

Pawel Miera, Hubert Szolc, Tomasz Kryjak

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a~key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (accordin

Anastasia-Dimitra Lipitakis, George Kousiouris, Mara Nikolaidou, Cleopatra Bardaki

Experimental data can aid in gaining insights about a system operation, as well as determining critical aspects of a modelling or simulation process. In this paper, we analyze the data acquired from an extensive experimentation process in a serverless Function as a Service system (based on the open source Apache Openwhisk) that has been deployed across 3 ava

Wendy Ccoya, Edson Pinto

This study is main goal is to provide a comparative comparison of libraries using machine learning methods. Experts in natural language processing (NLP) are becoming more and more interested in sentiment analysis (SA) of text changes. The objective of employing NLP text analysis techniques is to recognize and categorize feelings related to twitter users utte

Nikitas Stamatopoulos, William J. Zeng

Pricing financial derivatives on quantum computers typically includes quantum arithmetic components which contribute heavily to the quantum resources required by the corresponding circuits. In this manuscript, we introduce a method based on Quantum Signal Processing (QSP) to encode financial derivative payoffs directly into quantum amplitudes, alleviating th

Nicole Sobski, Sarah C. Millholland

Compact systems of multiple close-in super-Earths/sub-Neptunes ("compact multis") are a ubiquitous outcome of planet formation. It was recently discovered that the outer edges of compact multis are located at smaller orbital periods than expected from geometric and detection biases alone, suggesting some truncation or transition in the outer architectures. H

Juan Giraldo, José Ossorio, Norha M. Villegas, Gabriel Tamura

Quantum computing has the potential to surpass the capabilities of current classical computers when solving complex problems. Combinatorial optimization has emerged as one of the key target areas for quantum computers as problems found in this field play a critical role in many different industrial application sectors (e.g., enhancing manufacturing operation

Marco Capaldo, Antonio Di Crescenzo, Franco Pellerey

Given a random variable $X$ and considered a family of its possible distortions, we define two new measures of distance between $X$ and each its distortion. For these distance measures, which are extensions of the Gini's mean difference, conditions are determined for the existence of a minimum, or a maximum, within specific families of distortions, generaliz

Leo Touzo, Pierre Le Doussal, Gregory Schehr

Recently, we introduced the active Dyson Brownian motion model (DBM), in which $N$ run-and-tumble particles interact via a logarithmic repulsive potential in the presence of a harmonic well. We found that in a broad range of parameters the density of particles converges at large $N$ to the Wigner semi-circle law, as in the passive case. In this paper, we pro

Christopher Malon

When faced with a large number of product reviews, it is not clear that a human can remember all of them and weight opinions representatively to write a good reference summary. We propose an automatic metric to test the prevalence of the opinions that a summary expresses, based on counting the number of reviews that are consistent with each statement in the

Shengren Hou, Edgar Mauricio Salazar Duque, Peter Palensky, Pedro P. Vergara

The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capabilities of deep neural networks (DNNs), deep reinforcement learning (DRL) algorithms can learn good-quality c

Zexu Pan, Marvin Borsdorf, Siqi Cai, Tanja Schultz

Humans possess the remarkable ability to selectively attend to a single speaker amidst competing voices and background noise, known as selective auditory attention. Recent studies in auditory neuroscience indicate a strong correlation between the attended speech signal and the corresponding brain's elicited neuronal activities, which the latter can be measur