April 2023 arXiv papers — page 64

Fabrizio Canfora, Marcela Lagos, Pablo Pais, Aldo Vera

A remarkable exact mapping, valid for low-enough energy scales and close to a sharp boundary distribution of hadronic matter, from the $(3+1)$-dimensional Skyrme model to the sine-Gordon theory in $(1+1)$ dimensions in the attractive regime is explicitly constructed. Besides the intrinsic theoretical interest to be able to describe the prototype of nonintegr

Eugene Vasiliev

We review the recent theoretical and observational developments concerning the interaction of the Large Magellanic Cloud (LMC) with the Milky Way and its neighbourhood. An emerging picture is that the LMC is a fairly massive companion (10-20% of the Milky Way mass) and just passed the pericentre of its orbit, likely for the first time. The gravitational pert

Zoltan Bajnok, Georgios Linardopoulos, István M. Szécsényi, Istvan Vona

We develop a new method to calculate finite size corrections for form factors in two-dimensional integrable quantum field theories. We extract these corrections from the excited state expectation value of bilocal operators in the limit when the operators are far apart. We elaborate the finite size effects explicitly up to the 3rd L\"uscher order and conjectu

Haiying Shan, Muhuo Liu

Let $P(n)$ denote the set of all partitions of $n$, whose elements are nondecreasing sequences of positive integers whose sum is $n$. For ${\bf a}=( n_{1}, n_{2},\ldots, n_{d}) \in P(n)$, let $G({\bf a},v)$ denote the graph obtained from connected graph $G$ appending $d$ paths with lengths $n_{1},n_{2},\ldots,n_{d}$ on vertex $v$ of $G$. We show that the ord

Utkarsh Maurya, Appisetty Krishna Kalyan, Swapnil Bohidar, S. Sivakumar

Glioblastoma brain tumors are highly malignant and often require early detection and accurate segmentation for effective treatment. We are proposing two deep learning models in this paper, namely UNet and Deeplabv3, for the detection and segmentation of glioblastoma brain tumors using preprocessed brain MRI images. The performance evaluation is done for thes

Yukun Song, Carey E. Priebe, Minh Tang

Testing for independence between graphs is a problem that arises naturally in social network analysis and neuroscience. In this paper, we address independence testing for inhomogeneous Erd\H{o}s-R\'{e}nyi random graphs on the same vertex set. We first formulate a notion of pairwise correlations between the edges of these graphs and derive a necessary conditi

Liang Pan, Xinyi Chen, Zhongang Cai, Junzhe Zhang

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relati

A. Posilicano, L. Reginato

Let ${\mathsf D}$ and ${\mathsf H}$ be the self-adjoint, one-dimensional Dirac and Schr\"odinger operators in $L^{2}(\mathbb{R};\mathbb{C}^{2})$ and $L^{2}(\mathbb{R};\mathbb{C})$ respectively. It is well known that, in absence of an external potential, the two operators are related through the equality ${\mathsf D}^2 = ({\mathsf H} + \frac{1}{4}){\mathbb 1}

Chiara Feruglio, Umberto Maio, Roberta Tripodi, Jan Martin Winters

We report the detection of CO(6-5) and CO(7-6) and their underlying continua from the host galaxy of quasar J100758.264+211529.207 (P\=oniu\=a'ena) at z=7.5419, obtained with the NOrthern Extended Millimeter Array (NOEMA). P\=oniu\=a'ena belongs to the HYPerluminous quasars at the Epoch of ReionizatION (HYPERION) sample of 17 $z>6$ quasars selected to be pow

Danilo Maziero, Gregory Azzam, Macarena de La Fuente, Radka Stoyanova

MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3DT1w) and dynamic contrast enhancement (DCE) protoc

Matthias Birkner, Alice Callegaro, Jiří Černý, Nina Gantert

We study a discrete-time branching annihilating random walk (BARW) on the $d$-dimensional lattice. Each particle produces a Poissonian number of offspring with mean $\mu$ which independently move to a uniformly chosen site within a fixed distance $R$ from their parent's position. Whenever a site is occupied by at least two particles, all the particles at tha

Philip Greengard, Andrew Gelman

Bayesian Improved Surname Geocoding (BISG) is a ubiquitous tool for predicting race and ethnicity using an individual's geolocation and surname. Here we demonstrate that statistical dependence of surname and geolocation within racial/ethnic categories in the United States results in biases for minority subpopulations, and we introduce a raking-based improvem

Luke Snow, Vikram Krishnamurthy

Consider a target being tracked by a cognitive radar network. If the target can intercept noisy radar emissions, how can it detect coordination in the radar network? By 'coordination' we mean that the radar emissions satisfy Pareto optimality with respect to multi-objective optimization over the objective functions of each radar and a constraint on total net

Nicolas Faroß, Moritz Weber

In 2019, Jung-Weber gave an example of a concrete magic unitary $M$, which defines a $C^*$-algebraic model of the quantum permutation group $S_4^+$. We show with the help of a computer that there exist no polynomials up to degree $50$ separating the entries of $M$ from the generators of $C(S_4^+)$. This indicates that the magic unitary $M$ might already defi

Luke Snow, Vikram Krishnamurthy

This paper provides a finite-sample analysis of a passive stochastic gradient Langevin dynamics (PSGLD) algorithm. This algorithm is designed to achieve adaptive inverse reinforcement learning (IRL). Adaptive IRL aims to estimate the cost function of a forward learner performing a stochastic gradient algorithm (e.g., policy gradient reinforcement learning) b

Caden Gobat, Alexander J. van der Horst, David Fitzpatrick

Gamma-ray bursts categorically produce broadband afterglow emission, but in some cases, emission in the optical band is dimmer than expected based on the contemporaneously observed X-ray flux. This phenomenon, aptly dubbed "optical darkness", has been studied extensively in long GRBs (associated with the explosive deaths of massive stars), with possible expl

Xueqian Li, Jianqiao Zheng, Francesco Ferroni, Jhony Kaesemodel Pontes

Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points. The approach utilizes a coordinate neural network to estimate scene flow at runtime, without any training. However, it is up to 100 times slower than current sta

C. Corley-Wiciak, M. H. Zoellner, I. Zaitsev, K. Anand

The lattice strain induced by metallic electrodes can impair the functionality of advanced quantum devices operating with electron or hole spins. Here we investigate the deformation induced by CMOS-manufactured titanium nitride electrodes on the lattice of a buried, 10 nm-thick Si/SiGe Quantum Well by means of nanobeam Scanning X-ray Diffraction Microscopy.

Zhenshan Bing, Aleksandr Mavrichev, Sicong Shen, Xiangtong Yao

Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the significant strides made in the development of reinforcement learning, the practical deployment of this paradigm is hindered by at least two barriers, namely, the engineering of a reward f

Tom Shneer, Jocelyn Ochoa, Alauna C. Wheeler, Isabella C. Reyes

Non-equilibrium transport of particles embedded in a liquid crystal host can, by cooling through a phase transition, be exploited to create a remarkable variety of structures including shells, foams, and gels. Due to the complexity of the multicomponent system and protocol-dependent experimental results, the physical mechanisms behind structure selection rem

S. Hudan, J. E. Johnstone, Rohit Kumar, R. T. deSouza

Measurement of fusion excitation functions for stable nuclei has largely been restricted to nuclei with significant natural abundance. Typically, to investigate neighboring nuclei with low natural abundance has required obtaining isotopically enriched material. This restriction often limits the ability to perform such measurements. We report the measurement

Kai Shen, Zeqian Ju, Xu Tan, Yanqing Liu

Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker identities, prosodies, and styles (e.g., singing). Current large TTS systems usually quantize speech into discrete tokens and use language models to generate these tokens one by one, which suffer from unst

Jiasheng Xu, Tianyi Zhang, Yangqian Wu, Jie Yang

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery.

Alain Andres, Lukas Schäfer, Stefano V. Albrecht, Javier Del Ser

One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large numbers of interactions with the environment. Motivated by the recent success of Offline RL and Imitation Learning (IL), we conduct a study to investigate whether agents can lever

Konstantin Makarychev, Liren Shan

We show that the RandomCoordinateCut algorithm gives the optimal competitive ratio for explainable k-medians in l1. The problem of explainable k-medians was introduced by Dasgupta, Frost, Moshkovitz, and Rashtchian in 2020. Several groups of authors independently proposed a simple polynomial-time randomized algorithm for the problem and showed that this algo

Jagannadh Boddapati, Moritz Flaschel, Siddhant Kumar, Laura De Lorenzis

When the elastic properties of structured materials become direction-dependent, the number of their descriptors increases. For example, in two-dimensions, the anisotropic behavior of materials is described by up to 6 independent elastic stiffness parameters, as opposed to only 2 needed for isotropic materials. Such high number of parameters expands the desig

N. Muthusubramanian, P. Duivestein, C. Zachariadis, M. Finkel

We investigate die-level and wafer-scale uniformity of Dolan-bridge and bridgeless Manhattan Josephson junctions, using multiple substrates with and without through-silicon vias (TSVs). Dolan junctions fabricated on planar substrates have the highest yield and lowest room-temperature conductance spread, equivalent to ~100 MHz in transmon frequency. In TSV-in

Oliver Klemp, Bernd Westphal, Stefan Puch

[Context and motivation] The automotive industry is currently undergoing a fundamental transformation towards software defined vehicles. The automotive market of the future demands a higher level of automation, electrification of the power train, and individually configurable comfort functions. [Question/problem] These demands pose a challenge to the automot

Kevin Verhaegh, Bruce Lipschultz, James Harrison, Fabio Federici

This paper shows first quantitative analysis of the detachment processes in the MAST Upgrade Super-X divertor (SXD). We identify an unprecedented impact of plasma-molecular interactions involving molecular ions (likely $D_2^+$), resulting in strong ion sinks (Molecular Activated Recombination - MAR), leading to a reduction of ion target flux. The MAR ion sin

Lina Mezghani, Piotr Bojanowski, Karteek Alahari, Sainbayar Sukhbaatar

The success of transformer models trained with a language modeling objective brings a promising opportunity to the reinforcement learning framework. Decision Transformer is a step towards this direction, showing how to train transformers with a similar next-step prediction objective on offline data. Another important development in this area is the recent em

Sang-Jun Choi, Hai-Peng Sun, Björn Trauzettel

We study the conductance oscillation of an antiferromagnetic layer tunnel junction composed of antiferromagnetic topological insulators (MTIs) such as MnBi$_{2}$Te$_{4}$. In presence of an in-plane magnetic field, we find that the two terminal differential conductance across the junction oscillates as a function of field strength. Notably, the quantum interf

Nicolas Wijsen, Gang Li, Zheyi Ding, David Lario

Particles measured in large gradual solar energetic particle (SEP) events are believed to be predominantly accelerated at shocks driven by coronal mass ejections (CMEs). Ion charge state and composition analyses suggest that the origin of the seed particle population for the mechanisms of particle acceleration at CME-driven shocks is not the bulk solar wind

Michael Quellmalz, Robert Beinert, Gabriele Steidl

Sliced optimal transport reduces optimal transport on multi-dimensional domains to transport on the line. More precisely, sliced optimal transport is the concatenation of the well-known Radon transform and the cumulative density transform, which analytically yields the solutions of the reduced transport problems. Inspired by this concept, we propose two adap

Zhen Bai, Frances Judd, Naomi Polinsky, Elmira Yadollahi

Children growing up in the era of Artificial Intelligence (AI) will be most impacted by the technology across their life span. Participatory Design (PD) is widely adopted by the Interaction Design and Children (IDC) community, which empowers children to bring their interests, needs, and creativity to the design process of future technologies. While PD has dr

Sarah Kleine-Wechelmann, Kim Bastiaanse, Matthias Freundel, Christian Becker-Asano

Laboratories are being increasingly automated. In small laboratories individual processes can be fully automated, but this is usually not economically viable. Nevertheless, individual process steps can be performed by flexible, mobile robots to relieve the laboratory staff. As a contribution to the requirements in a life science laboratory the mobile, dextro

Lorenzo Tentarelli

The paper presents a complete (to the best of the author's knowledge) overview on the existing literature concerning the NLS equation with point-concentrated nonlinearity. Precisely, it mainly covers the following topics: definition of the model, weak and strong local well-posedness, global well-posedness, classification and stability (orbital and asymptotic

Jean-Marie Frère

Glueballs are the most straightforward prediction of QCD, yet while they have likely been produced, none has been unequivocally identified. We pursue a backdoor approach through anomalies, and singularly the $\eta$ and $\eta$' which brings light to this irritating situation. In particular, we advocate to consider the full decay chain $J/\psi \rightarrow X \g

Dhruv Arora, Himanshu Chaudhary, Shibesh Kumar Jas Pacif, G. Mustafa

This manuscript presents a diagnostic analysis of three dark energy models resulting from the parametrization of the deceleration parameter. These models exhibit intriguing features, including late-time acceleration and a cosmological phase transition from early deceleration to late acceleration. The analysis utilizes parametrizations of the deceleration par

Adem Limani

We investigate certain classes of shift invariant subspaces in growth spaces on the unit disc of the complex plane determined by a majorant $w$, which include the classical Korenblum growth spaces. Our main result provides a complete description of shift invariant subspaces generated by Nevanlinna class functions in growth spaces, where we show that they are

László Csató

One of the most popular club football tournaments, the UEFA Champions League, will see a fundamental reform from the 2024/25 season: the traditional group stage will be replaced by one league where each of the 36 teams plays eight matches. To guarantee that the opponents of the clubs are of the same strength in the new design, it is crucial to forecast the p

Konstantin Althaus, Iason Papaioannou, Elisabeth Ullmann

In this paper, we introduce a new algorithm for rare event estimation based on adaptive importance sampling. We consider a smoothed version of the optimal importance sampling density, which is approximated by an ensemble of interacting particles. The particle dynamics is governed by a McKean-Vlasov stochastic differential equation, which was introduced and a

Daniel Lopes

One of the current AI issues depicted in popular culture is the fear of conscious super AIs that try to take control over humanity. And as computational power goes upwards and that turns more and more into a reality, understanding artificial brains might be increasingly important to control and drive AI towards the benefit of our societies. This paper propos

Jordan M. Thomas, Gregory S. Kanter, Prem Kumar

The scalability of quantum networking will benefit from quantum and classical communications coexisting in shared fibers, the main challenge being spontaneous Raman scattering noise. We investigate the coexistence of multi-channel O-band quantum and C-band classical communications. We characterize multiple narrowband entangled photon pair channels across 128

Weihua Xu, Feifei Gao, Yong Zhang, Chengkang Pan

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corr

Siyu Cen, Bangti Jin, Kwancheol Shin, Zhi Zhou

Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject. Calder\'on's method is a relatively recent EIT imaging algorithm that is non-iterative, fast, and capable of reconstructing complex-valued electric impedances. However, due to the regularization vi

Dimitri Colferai, Federico Deganutti, Timothy G Raben, Christophe Royon

We present the full next-to-leading order (NLO) prediction for the jet-gap-jet cross section at the LHC within the BFKL approach. We implement, for the first time, the NLO impact factors in the calculation of the cross section. We provide results for differential cross sections as a function of the difference in rapidity and azimuthal angle betwen the two je

Jinyu Liu, Yinong Zhou, Sebastian Yepez Rodriguez, Matthew A. Delmont

Controlling materials to create and tune topological phases of matter could potentially be used to explore new phases of topological quantum matter and to create novel devices where the carriers are topologically protected. It has been demonstrated that a trivial insulator can be converted into a topological state by modulating the spin-orbit interaction or

Andrea Ferraguti, Dorian Goldfeld, Giacomo Micheli

In this paper we give constructions for infinite sequences of finite non-linear locally recoverable codes $\mathcal C\subseteq \prod\limits^N_{i=1}\mathbb F_{q_i}$ over a product of finite fields arising from basis expansions in algebraic number fields. The codes in our sequences have increasing length and size, constant rate, fixed locality, and minimum dis

Rüdiger Brecht, Alex Bihlo

In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here, we ask the question of how these numerical forecasts would have turned out, if machine learning based sol

Steven A. Frank

When a biological system robustly corrects component-level errors, the direct pressure on component performance declines. Components may become less reliable, maintain more genetic variability, or drift neutrally in design, creating the basis for new forms of organismal complexity. This article links the protection-decay dynamic to other aspects of robust an

Sainyam Galhotra, Yue Gong, Raul Castro Fernandez

Data is a central component of machine learning and causal inference tasks. The availability of large amounts of data from sources such as open data repositories, data lakes and data marketplaces creates an opportunity to augment data and boost those tasks' performance. However, augmentation techniques rely on a user manually discovering and shortlisting use

Oleksandr Fedoruk, Konrad Klimaszewski, Aleksander Ogonowski, Rafał Możdżonek

The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In a classical approach image transformations i.e. rotations, cropping and brightness changes are used. In this work, a StyleGAN2-ADA

Jianwei Huang, Chandan Setty, Liangzi Deng, Jing-Yang You

Emergent phases often appear when the electronic kinetic energy is comparable to the Coulomb interactions. One approach to seek material systems as hosts of such emergent phases is to realize localization of electronic wavefunctions due to the geometric frustration inherent in the crystal structure, resulting in flat electronic bands. Recently, such efforts

Guobin Wang, Da Sheng, Yunfan Yang, Hui Li

Silicon carbide (SiC) is an important semiconductor material for fabricating power electronic devices that exhibit higher switch frequency, lower energy loss and substantial reduction both in size and weight in comparison with its Si-based counterparts1-4. Currently, most devices, such as metal-oxide-semiconductor field effect transistors, which are core dev

Alexandre Agossah, Frédérique Krupa, Matthieu Perreira Da Silva, Patrick Le Callet

In the past years, AI has seen many advances in the field of NLP. This has led to the emergence of LLMs, such as the now famous GPT-3.5, which revolutionise the way humans can access or generate content. Current studies on LLM-based generative tools are mainly interested in the performance of such tools in generating relevant content (code, text or image). H

Mary Barker, Benjamin Standaert, Ben Wormleighton

The McKay correspondence has had much success in studying resolutions of 3-fold quotient singularities through a wide range of tools coming from geometry, combinatorics, and representation theory. We develop a computational perspective in this setting primarily realised through a web application to explore mutations of quivers with potential and crepant tria

M. Geyer, V. Venkatraman Krishnan, P. C. C. Freire, M. Kramer

PSR J1933$-$6211 is a 3.5-ms pulsar in a 12.8-d orbit with a white dwarf (WD). Its high proper motion and low dispersion measure result in such significant interstellar scintillation that high signal-to-noise detections require long observing durations or fortuitous timing. We turn to the sensitive MeerKAT telescope and, combined with historic Parkes data, l

Chunyan Wang, Dong Zhang, Liyan Zhang, Jinhui Tang

Thanks to the advantages of the friendly annotations and the satisfactory performance, Weakly-Supervised Semantic Segmentation (WSSS) approaches have been extensively studied. Recently, the single-stage WSSS was awakened to alleviate problems of the expensive computational costs and the complicated training procedures in multi-stage WSSS. However, results of

Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu

The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information. Furthermore, s

Lei Li, Jing Chen, Bozhong Tian, Ningyu Zhang

Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP). In contrast, k-Nearest-Neighbor (kNN) classifiers, as the lazy learning paradigm, tend to mitigate over-fitting and isolated noise. In this paper, we revisit kNN classifiers for augmenting the PLM

Alan M Lewis, Paolo Lazzaroni, Mariana Rossi

We present a local and transferable machine learning approach capable of predicting the real-space density response of both molecules and periodic systems to external homogeneous electric fields. The new method, SALTER, builds on the Symmetry-Adapted Gaussian Process Regression SALTED framework. SALTER requires only a small, but necessary, modification to th

Carlos L. Benavides-Riveros

Wigner functions are broadly used to probe non-classical effects in the macroscopic world. Here we develop an orbital-free functional framework to compute the 1-body Wigner quasi-probability for both fermionic and bosonic systems. Since the key variable is a quasi-density, this theory is particularly well suited to circumvent the problem of finding the Pauli

M. Rudelson

Let $A$ be an $n \times n$ random matrix with independent identically distributed non-constant subgaussian entries. Then for any $k \le c \sqrt{n}$, \[ \text{rank}(A) \ge n-k \] with probability at least $1-\exp(-c'kn)$.

O. Cristi Stoica, Iulian D. Toader

This paper explains why spacetime singularities do not constitute a breakdown of physical laws, and points out that the difference between the metrics at singularities and those outside of singularities is factual, rather than nomological.

Dilimulati Aierken, Michael Bachmann

Systematic microcanonical inflection-point analysis of precise numerical results obtained in extensive generalized-ensemble Monte Carlo simulations reveals a bifurcation of the coil-globule transition line for polymers with a bending stiffness exceeding a threshold value. The region, enclosed by the toroidal and random-coil phases, is dominated by structures

George Wynne

Performing inference in Bayesian models requires sampling algorithms to draw samples from the posterior. This becomes prohibitively expensive as the size of data sets increase. Constructing approximations to the posterior which are cheap to evaluate is a popular approach to circumvent this issue. This begs the question of what is an appropriate space to perf

Tamanna Jain

We compute the scattering angle $\chi$ for hyperboliclike encounters in massless Scalar-Tensor (ST) theories up to third post-Newtonian (PN) order for the conservative part of the dynamics. To calculate the gauge-invariant scattering angle as a function of energy and orbital angular momentum, we use the approach of Effective-One-Body formalism as introduced

Fan Xu, Yong-Feng Huang, Jin-Jun Geng

Searching for afterglows not associated with any gamma-ray bursts (GRBs) is a longstanding goal of transient surveys. These surveys provide the very chance of discovering the so-called orphan afterglows. Recently, a promising orphan afterglow candidate, AT2021any, was found by the Zwicky Transient Facility. Here we perform multi-wavelength fitting of AT2021a

Stephen M. Gordon, Jonathan R. McDaniel, Kevin W. King, Vernon J. Lawhern

There exist very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make an accurate i

Darshan C. Ganji, Saad Ashfaq, Ehsan Saboori, Sudhakar Sah

A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices. Quantization methods such as Learned Step Size Quantization can achieve model accuracy that is comparable to full-precision floating-point baselines even with sub-byte quantization. Howe

Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong

Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structura

James Koch, WoongJo Choi, Ethan King, David Garcia

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity simulations of t

Yue Song, Benrong Mu

Recently, a conjecture has been proposed, which indicates a correlation between super-entropy black holes and the thermodynamic instability \cite{Cong:2019bud}. W.Cong et al. suggested that the $C_{V}$ (specific heat capacity at constant volume) and $C_{P}$ (specific heat capacity at constant pressure) of a super-entropy black hole could not be greater than

Bavo D. C. Campo, Katrien Antonio

Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. We commonly rely on methods such as the random effects approach (Campo and Antonio, 2023) to incorporate these covariates in a predictive

Bachir Bekka

Given a group $\Gamma,$ its Bohr compactification $\operatorname{Bohr}(\Gamma)$ and its profinite completion $\operatorname{Prof}(\Gamma)$ are compact groups naturally associated to $\Gamma$; moreover, $\operatorname{Prof}(\Gamma)$ can be identified with the quotient of $\operatorname{Bohr}(\Gamma)$ by its connected component $\operatorname{Bohr}(\Gamma)_0.$

Liyao Lyu, Huan Lei

We introduce a machine-learning-based coarse-grained molecular dynamics (CGMD) model that faithfully retains the many-body nature of the inter-molecular dissipative interactions. Unlike common empirical CG models, the present model is constructed based on the Mori-Zwanzig formalism and naturally inherits the heterogeneous state-dependent memory term rather t

Abhishek Goudar, Timothy D. Barfoot, Angela P. Schoellig

Range-only (RO) localization involves determining the position of a mobile robot by measuring the distance to specific anchors. RO localization is challenging since the measurements are low-dimensional and a single range sensor does not have enough information to estimate the full pose of the robot. As such, range sensors are typically coupled with other sen

Wentao Zhang, Yujun Huang, Tong Zhang, Qingsong Zou

Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge. To address the catastrophic f

J. -B. Melin, G. W. Pratt

We measured the average Compton profile of 461 clusters detected jointly by the South Pole Telescope (SPT) and Planck. The number of clusters included in this analysis is about one order of magnitude larger than in previous analyses. We propose an innovative method developed in Fourier space to combine optimally the Planck and SPT-SZ data, allowing us to per

Suraj S. Hegde, Toni Ehmcke, Tobias Meng

One of the most important practical hallmarks of topological matter is the presence of topologically protected, exponentially localised edge states at interfaces of regions characterised by unequal topological invariants. Here, we show that even when driven far from their equilibrium ground state, Chern insulators can inherit topological edge features from t

Feihu Liu, Guoce Xin, Suting Ye, Jingjing Yin

Given relative prime positive integers $A=(a_1, a_2, ..., a_n)$, the Frobenius number $g(A)$ is the largest integer not representable as a linear combination of the $a_i$'s with nonnegative integer coefficients. We find the ``Stable" property introduced for the square sequence $A=(a,a+1,a+2^2,\dots, a+k^2)$ naturally extends for $A(a)=(a,ha+dB)=(a,ha+d,ha+b_

Rita Mastroianni, Ugo Locatelli

We introduce a quasi-periodic restricted Hamiltonian to describe the secular motion of a small-mass planet in a multi-planetary system. In particular, we refer to the motion of $\upsilon$-And $b$ which is the innermost planet among those discovered in the extrasolar system orbiting around the $\upsilon$-Andromedae A star. We preassign the orbits of the Super

Sebastian Horvat, Iulian D. Toader

This entry reviews Rudolf Carnap's philosophical views on the quantum mechanics of his time. It also offers some thoughts on how Carnap might have reacted to some recent developments in the foundations of quantum mechanics.

Maxime Bouchereau, Philippe Chartier, Mohammed Lemou, Florian Méhats

Ordinary Differential Equations are generally too complex to be solved analytically. Approximations thereof can be obtained by general purpose numerical methods. However, even though accurate schemes have been developed, they remain computationally expensive: In this paper, we resort to the theory of modified equations in order to obtain ''on the fly'' cheap

Jaan Einasto, Gert Hütsi, Lauri-Juhan Liivamägi, Changbom Park

We quantify the evolution of matter and galaxy clustering in cosmological hydrodynamical simulations via correlation and bias functions of matter and galaxies. We use simulations TNG100 and TNG300 with epochs from $z=5$ to $z=0$. We calculate spatial correlation functions of galaxies, $\xi(r)$, for simulated galaxies and dark matter (DM) particles to charact

Quentin Berger, Loïc Béthencourt, Camille Tardif

In this article, we consider additive functionals $\zeta_t = \int_0^t f(X_s)\mathrm{d} s$ of a c\`adl\`ag Markov process $(X_t)_{t\geq 0}$ on $\mathbb{R}$. Under some general conditions on the process $(X_t)_{t\geq 0}$ and on the function $f$, we show that the persistence probabilities verify $\mathbb{P}(\zeta_s < z \text{ for all } s\leq t ) \sim \mathcal{V

Jodi Dianetti

We consider a class of non-cooperative N-player non-zero-sum stochastic differential games with singular controls, in which each player can affect a linear stochastic differential equation in order to minimize a cost functional which is quadratic in the state and linear in the control. We call these games linear-quadratic-singular stochastic differential gam

E. Acampora, R. Ambrosino, A. Castaldo, R. Iervolino

One of the main challenges concerning next generation tokamaks (such as DEMO) will be the development of a heat and power exhaust system able to withstand the large loads expected in the divertor region. A dedicated Divertor Tokamak Test (DTT) facility has been proposed in the EUROfusion Roadmap, with the aim of testing unconventional solutions, such as adva

Quentin Berger, Loïc Béthencourt

We revisit here a famous result by Sparre Andersen on persistence probabilities $\mathbf{P}(S_k>0 \;\forall\, 0\leq k\leq n)$ for symmetric random walks $(S_n)_{n\geq 0}$. We give a short proof of this result when considering sums of random variables that are only assumed exchangeable and sign-invariant. We then apply this result to the study of persistence

Bogdan Tanygin, Simone Melchionna

Langevin and Brownian simulations play a prominent role in computational research, and state of the art integration algorithms provide trajectories with different stability ranges and accuracy in reproducing statistical averages. The practical usability of integrators is an important aspect to allow choosing large time steps while ensuring numerical stabilit

Lars Vogt, Marcel Konrad, Manuel Prinz

Knowledge graphs and ontologies provide promising technical solutions for implementing the FAIR Principles for Findable, Accessible, Interoperable, and Reusable data and metadata. However, they also come with their own challenges. Nine such challenges are discussed and associated with the criterion of cognitive interoperability and specific FAIREr principles

Jianwei Xu

Phase is a basic ingredient for quantum states since quantum mechanics uses complex numbers to describe quantum states. In this letter, we introduce a rigorous framework to quantify the phase of quantum states. To do so, we regard phase as a quantum resource, and specify the free states and free operations. We determine the conditions a phase measure should

Gerardo L. Rossini, George I. Japaridze

An unusual metallic phase is proven to develop in the one dimensional ionic Hubbard model, at half-filling and zero magnetization, at intermediate electron-electron repulsion $U$ when second neighbors hopping is allowed and tuned close to a topological Lifshitz transition (connected with a change of the Fermi surface in the non-interacting system). The metal

Tobias Pfandzelter, David Bermbach

Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. The lack of standardized benchmarks for such systems, however, hinders their development and deployment, as different approaches cannot be compared quantitatively. Existing cloud data benchmarks are inadequate for

Tao Zhu, Wen Zhao, Jian-Ming Yan, Yuan-Zhu Wang

Gravitational wave (GW) observations provide sensitive tests of parity and Lorentz symmetries of gravity. Any violation of these fundamental symmetries induces possible deviations in the GW propagations. Through a systematic parametrization for characterizing possible derivations from GW propagations in general relativity, we construct the modified GW wavefo

Edgardo Solano-Carrillo, Angel Bueno Rodriguez, Borja Carrillo-Perez, Yannik Steiniger

Generative adversarial networks (GANs) are successfully used for image synthesis but are known to face instability during training. In contrast, probabilistic diffusion models (DMs) are stable and generate high-quality images, at the cost of an expensive sampling procedure. In this paper, we introduce a simple method to allow GANs to stably converge to their

Henrik Glavind Clausen, Salahuddin Abdul Rahman, Özkan Karabacak, Rafal Wisniewski

In variational quantum algorithms (VQAs), the most common objective is to find the minimum energy eigenstate of a given energy Hamiltonian. In this paper, we consider the general problem of finding a sufficient control Hamiltonian structure that, under a given feedback control law, ensures convergence to the minimum energy eigenstate of a given energy functi

Jin Sun

In this paper, we study curvature estimates for nodal sets of harmonic functions in the plane. We prove that at any point $p$, the curvature of each nodal curve of any harmonic function $u$ is upper bounded by $$\left|{\kappa(u)(p)}\right|\leq \frac{4(n+1)}{nr}\cos n\alpha_0,$$ where $u$ has only $n$ nodal curves in $B_r(p)$ intersecting at $p$, and $\alpha_

Santiago Moroni-García, Arturo Camacho-Guardian

The exchange of collective modes has been demonstrated to be a powerful tool for inducing superconductivity and superfluidity in various condensed matter and atomic systems. In this article, we study the mediated interactions of collective excitations in an ultracold gas of hard-core bosons. We show that the induced interaction supports two-body states with

Imene Zemzemi, Arnaud Beck, Arnd Specka

With the currently available laser powers, it is possible to reach the blowout regime in the Laser WakeField Acceleration (LWFA) where the electrons are completely expelled off-axis behind the laser pulse. This regime is particularly interesting thanks to its linear focusing forces and to its accelerating forces that are independent of the transverse coordin