May 2023 arXiv papers — page 13

Amna Irshad, Emil Björnson

Traditional point-to-point line-of-sight channels have rank 1, irrespective of the number of antennas and array geometries, due to far-field propagation conditions. By contrast, recent papers in the holographic multiple-input multiple-output (MIMO) literature characterize the maximum channel rank that can be achieved between two continuous array apertures, w

László Tóth, Amin Honarmandi Shandiz, Gábor Gosztolya, Csapó Tamás Gábor

Thanks to the latest deep learning algorithms, silent speech interfaces (SSI) are now able to synthesize intelligible speech from articulatory movement data under certain conditions. However, the resulting models are rather speaker-specific, making a quick switch between users troublesome. Even for the same speaker, these models perform poorly cross-session,

Neil Christian R. Riego, Danny Bell Villarba

The digitalization of news media become a good indicator of progress and signal to more threats. Media disinformation or fake news is one of these threats, and it is necessary to take any action in fighting disinformation. This paper utilizes ground truth-based annotations and TF-IDF as feature extraction for the news articles which is then used as a trainin

Óscar López Pouso, Javier Segura

Four different relations between the Legendre nodes and weights are presented which, unlike the circle and trapezoid theorems for Gauss-Legendre quadrature, hold uniformly in the whole interval $(-1,1)$. These properties are supported by strong asymptotic evidence. The study of these results was originally motivated by the role some of them play in certain f

Jae Won Choi, Girish Chowdhary, Andrew C. Singer, Hari Vishnu

Underwater communication signals typically suffer from distortion due to motion-induced Doppler. Especially in shallow water environments, recovering the signal is challenging due to the time-varying Doppler effects distorting each path differently. However, conventional Doppler estimation algorithms typically model uniform Doppler across all paths and often

Suraj, Shankar Kumar Selvaraja

We demonstrate, for the first time, sputtered PZT as a platform for the development of Si-based photonic devices such as rings, MZI, and electro-optic modulators. We report the optimization of PZT on MgO(002) substrate to obtain highly oriented PZT film oriented towards the (100) plane with a surface roughness of 2 nm. Si gratings were simulated for TE and T

Yunhui Jang, Dongwoo Kim, Sungsoo Ahn

Generating graphs from a target distribution is a significant challenge across many domains, including drug discovery and social network analysis. In this work, we introduce a novel graph generation method leveraging $K^2$-tree representation, originally designed for lossless graph compression. The $K^2$-tree representation {encompasses inherent hierarchy wh

Qisheng Liao, Gus Xia, Zhinuo Wang

In this paper, we propose Calliffusion, a system for generating high-quality Chinese calligraphy using diffusion models. Our model architecture is based on DDPM (Denoising Diffusion Probabilistic Models), and it is capable of generating common characters in five different scripts and mimicking the styles of famous calligraphers. Experiments demonstrate that

Qinglong Tian, Xin Zhang, Jiwei Zhao

We study the domain adaptation problem with label shift in this work. Under the label shift context, the marginal distribution of the label varies across the training and testing datasets, while the conditional distribution of features given the label is the same. Traditional label shift adaptation methods either suffer from large estimation errors or requir

Gayathri Viswanath, Markus Janson, Raffaele Gratton, Vito Squicciarini

Recent observations from B-star Exoplanet Abundance Study (BEAST) have illustrated the existence of sub-stellar companions around very massive stars. In this paper, we present the detection of two lower mass companions to a relatively nearby ($148.7^{+1.5}_{-1.3}$ pc), young ($17^{+3}_{-4}$ Myr), bright (V=$6.632\pm0.006$ mag), $2.58\pm0.06~ M_{\odot}$ B9V s

Martin Gölz, Tanuj Hasija, Michael Muma, Abdelhak M. Zoubir

The identification of the dependent components in multiple data sets is a fundamental problem in many practical applications. The challenge in these applications is that often the data sets are high-dimensional with few observations or available samples and contain latent components with unknown probability distributions. A novel mathematical formulation of

Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian Theis

Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions. Most models for molecule generation rely on the decomposition of molecules into frequently occurring substructures (motifs), from which they generate novel compounds. While motif representations greatly aid in learning molecul

Harsh Verma, Sabine Bergler, Narjesossadat Tahaei

We compare three simple and popular approaches for NER: 1) SEQ (sequence-labeling with a linear token classifier) 2) SeqCRF (sequence-labeling with Conditional Random Fields), and 3) SpanPred (span-prediction with boundary token embeddings). We compare the approaches on 4 biomedical NER tasks: GENIA, NCBI-Disease, LivingNER (Spanish), and SocialDisNER (Spani

M. A. Norcia, W. B. Cairncross, K. Barnes, P. Battaglino

Measurement-based quantum error correction relies on the ability to determine the state of a subset of qubits (ancillae) within a processor without revealing or disturbing the state of the remaining qubits. Among neutral-atom based platforms, a scalable, high-fidelity approach to mid-circuit measurement that retains the ancilla qubits in a state suitable for

Sergey N. Pozdnyakov, Michele Ceriotti

Point clouds are versatile representations of 3D objects and have found widespread application in science and engineering. Many successful deep-learning models have been proposed that use them as input. The domain of chemical and materials modeling is especially challenging because exact compliance with physical constraints is highly desirable for a model to

Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang

Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the research on cognitive behaviors of LLMs to explore human-like problem-solving strategies. Along this direction, one representative strategy is self-reflection, which asks an LLM to refine

Arpan Dutta

The Ruled Residue Theorem asserts that given a ruled extension $(K|k,v)$ of valued fields, the residue field extension is also ruled. In this paper we analyse the failure of this theorem when we set $K$ to be algebraic function fields of certain curves of prime degree $p$, provided $p$ is coprime to the residue characteristic and $k$ contains a primitive $p$

Belle II Collaboration, I. Adachi, K. Adamczyk, L. Aggarwal

We present a measurement of the $\tau$-lepton mass using a sample of about 175 million $e^+e^- \to \tau^+\tau^-$ events collected with the Belle II detector at the SuperKEKB $e^+e^-$ collider at a center-of-mass energy of $10.579\,\mathrm{Ge\kern -0.1em V}$. This sample corresponds to an integrated luminosity of $190\,\mathrm{fb^{-1}}$. We use the kinematic

Mohammadreza Izadi, Reza Faieghi

This paper presents a simple method to boost the robustness of quadrotors in trajectory tracking. The presented method features a high-gain disturbance observer (HGDO) that provides disturbance estimates in real-time. The estimates are then used in a trajectory control law to compensate for disturbance effects. We present theoretical convergence results show

Andrew Kresch, Siddharth Mathur

Fix an $I$-adically complete Noetherian ring $A$ and suppose $X$ is a proper $A$-scheme. This article concerns the relationship between the Brauer group of $X$ and that of the various $X_n$ where $X_n$ is the fiber over $A/I^{n+1}$. In particular, we answer a question of Grothendieck by showing that, in positive and mixed characteristic, there are examples o

Wytse van Dijk

We review an explicit approach to obtaining numerical solutions of the Schr\"odinger equation that is conceptionally straightforward and capable of significant accuracy and efficiency. The method and its efficacy are illustrated with several examples. Because of its explicit nature, the algorithm can be readily extended to systems with a higher number of spa

Yubo Jin

In this paper, we calculate the ramified local integrals in the doubling method and present an integral representation of standard $L$-functions for classical groups. We explicitly construct local sections of Eisenstein series such that the local ramified integrals represent certain ramified $L$-factors. As an application, we prove algebraicity of special $L

Ibrahim Ethem Hamamci, Sezgin Er, Omer Faruk Durugol, Gulsade Rabia Cakmak

Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this p

Walid S. Saba

Large language models (LLMs) have achieved a milestone that undenia-bly changed many held beliefs in artificial intelligence (AI). However, there remains many limitations of these LLMs when it comes to true language understanding, limitations that are a byproduct of the under-lying architecture of deep neural networks. Moreover, and due to their subsymbolic

Returaj Burnwal, Anirban Santara, Nirav P. Bhatt, Balaraman Ravindran

Model predictive control (MPC) is a popular approach for trajectory optimization in practical robotics applications. MPC policies can optimize trajectory parameters under kinodynamic and safety constraints and provide guarantees on safety, optimality, generalizability, interpretability, and explainability. However, some behaviors are complex and it is diffic

Sergei D. Odintsov, Tanmoy Paul

We investigate the inflation and reheating phenomenology in scalar-Einstein-Gauss-Bonnet theory of gravity where a scalar field non-minimally couples with the Gauss-Bonnet (GB) curvature term. Regarding the inflationary phenomenology, we find -- (1) the inflation starts with a quasi de-Sitter phase and has an exit at a finite e-fold, (2) the scalar and tenso

Niklas Müller

Let $(X, \Delta)$ be a klt threefold pair with nef anti-log canonical bundle $-(K_X+\Delta)$. We show that $\kappa(X, -(K_X+\Delta))\geq 0$. To do so, we prove a more general equivariant non-vanishing result for anti-log canonical bundles, which is valid in any dimension.

Lior Bracha, Eitan Shaar, Aviv Shamsian, Ethan Fetaya

Referring Expressions Generation (REG) aims to produce textual descriptions that unambiguously identifies specific objects within a visual scene. Traditionally, this has been achieved through supervised learning methods, which perform well on specific data distributions but often struggle to generalize to new images and concepts. To address this issue, we pr

Si Zuo, Vitor Fortes Rey, Sungho Suh, Stephan Sigg

Human activity recognition (HAR) from on-body sensors is a core functionality in many AI applications: from personal health, through sports and wellness to Industry 4.0. A key problem holding up progress in wearable sensor-based HAR, compared to other ML areas, such as computer vision, is the unavailability of diverse and labeled training data. Particularly,

Ziyu Ni, Linda Wei, Lijian Xu, Simon Yu

Local hemodynamic forces play an important role in determining the functional significance of coronary arterial stenosis and understanding the mechanism of coronary disease progression. Computational fluid dynamics (CFD) have been widely performed to simulate hemodynamics non-invasively from coronary computed tomography angiography (CCTA) images. However, ac

Dipayan Mukherjee, Harkirat Singh Sahota

The conformal correspondence between FLRW universes in the Einstein and Jordan frames allows for an expansion-collapse duality -- an always expanding Einstein frame universe can have a dual Jordan frame description that is contracting forever. The scenario eventually runs into an apparent paradox. When a collapsing universe approaches singularity, the classi

Ashton Palacios, Dinah Bronson, Jon Backman, Karl Warnick

With the proliferation of wideband active services in bands shared with passive receivers for remote sensing and radio astronomy, new methods are needed for deconflicting active and passive users. We have developed a technique for active/passive user coordination that is compatible with essentially any existing wireless communications protocol. The passive u

Siu Man Chan, Massimo Lauria, Jakob Nordström, Marc Vinyals

We consider the pebble game on DAGs with bounded fan-in introduced in [Paterson and Hewitt '70] and the reversible version of this game in [Bennett '89], and study the question of how hard it is to decide exactly or approximately the number of pebbles needed for a given DAG in these games. We prove that the problem of eciding whether $s$~pebbles suffice to r

Qihui Xu, Yingying Peng, Samuel A. Nastase, Martin Chodorow

To what extent can language alone give rise to complex concepts, or is embodied experience essential? Recent advancements in large language models (LLMs) offer fresh perspectives on this question. Although LLMs are trained on restricted modalities, they exhibit human-like performance in diverse psychological tasks. Our study compared representations of 4,442

Akshit Goyal, Avi I. Flamholz, Alexander P. Petroff, Arvind Murugan

Our planet is roughly closed to matter, but open to energy input from the sun. However, to harness this energy, organisms must transform matter from one chemical (redox) state to another. For example, photosynthetic organisms can capture light energy by carrying out a pair of electron donor and acceptor transformations (e.g., water to oxygen, CO$_2$ to organ

Suraj Srinivas, Sebastian Bordt, Hima Lakkaraju

One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause robust models to have rudimentary generative capabilities, including image generation, denois

Luca Resti, Martin Strauss, Matteo Torcoli, Emanuël Habets

Dialogue Enhancement (DE) enables the rebalancing of dialogue and background sounds to fit personal preferences and needs in the context of broadcast audio. When individual audio stems are unavailable from production, Dialogue Separation (DS) can be applied to the final audio mixture to obtain estimates of these stems. This work focuses on Preferred Loudness

Hossein Fatheddin, Sedighe Sajadian

It is plausible that most of the Stars in the Milky Way (MW) Galaxy, like the Sun, consist of planetary systems, instead of a single planet. Out of the estimately discovered 3,950 planet-hosting stars, about 860 of them are known to be multiplanetary systems (as of March, 2023). Gravitational microlensing, which is the magnification in the light of a source

Syed Q. A. Shah, Ather Mahmood, Arun Parthasarathy, Christian Binek

Powder samples have been suggested as a pathway to fabricate isotropic magnetoelectric (ME) materials which effectively only have a pseudoscalar or monopole ME response. We demonstrate that random distribution of ME grains alone does not warrant isotropic ME response because the activation of a non-vanishing ME response requires a ME field cooling protocol w

Katharina V. Hoebel, Andreanne Lemay, John Peter Campbell, Susan Ostmo

Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to train and evaluate disease severity prediction models. However, ordinal categories represent a simplification of an underlying continuous severity spectrum. Using continuous score

Annalisa Caligiuri, Tobias Galla

We study the stationary states of variants of the noisy voter model, subject to fluctuating parameters or external environments. Specifically, we consider scenarios in which the herding-to-noise ratio switches randomly and on different time scales between two values. We show that this can lead to a phase in which polarised and heterogeneous states exist. Sec

Eric Katz, Max Kutler

We make a systematic study of matroidal mixed Eulerian numbers which are certain intersection numbers in the matroid Chow ring generalizing the mixed Eulerian numbers introduced by Postnikov. These numbers are shown to be valuative and obey a log-concavity relation. We establish recursion formulas and use them to relate matroidal mixed Eulerian numbers to th

Jisu Nam, Gyuseong Lee, Sunwoo Kim, Hyeonsu Kim

The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term. While conventional techniques focused on defining hand-designed prior terms, which are difficult to formulate, recent approaches have focused on learning the data term with deep neural networks without explicitly modeling the prior,

ALICE Collaboration

The interaction between $\Lambda$ baryons and kaons/antikaons is a crucial ingredient for the strangeness $S=0$ and $S=-2$ sector of the meson$-$baryon interaction at low energies. In particular, the $\Lambda{\mathrm{\overline{K}}}$ might help in understanding the origin of states such as the $\Xi\mathrm{(1620)}$, whose nature and properties are still under

Haochen Luo, Yi Zhou, Danushka Bollegala

Sense embedding learning methods learn multiple vectors for a given ambiguous word, corresponding to its different word senses. For this purpose, different methods have been proposed in prior work on sense embedding learning that use different sense inventories, sense-tagged corpora and learning methods. However, not all existing sense embeddings cover all s

Bo Yu, Xi Guo, Xun Chen, Xiao-Hua Li

Using gauge/gravity duality, we first study the string breaking and melting of doubly heavy baryon at a finite chemical potential and temperature in this paper. The decay mode $\rm{Q Q q \rightarrow Q q q+Q \bar{q}}$ is investigated with the presence of temperature and chemical potential in this paper. With the increase of temperature and chemical potential,

Nina R Benway, Jonathan L Preston

Because lab accuracy of clinical speech technology systems may be overoptimistic, clinical validation is vital to demonstrate system reproducibility - in this case, the ability of the PERCEPT-R Classifier to predict clinician judgment of American English /r/ during ChainingAI motor-based speech sound disorder intervention. All five participants experienced s

Giovanni Ballarin

This paper proposes a semiparametric sieve approach to estimate impulse response functions of nonlinear time series within a general class of structural autoregressive models. We prove that a two-step procedure can flexibly accommodate nonlinear specifications while avoiding the need to choose fixed parametric forms. Sieve impulse responses are proven to be

Ram Krishna Pandey, Akshit Achara

Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either not available or the annotation is expensive. The objective of this work is to show that by incorporating domain knowledge along with deep learning architectures, we can achieve sim

Michael Scholkemper, Michael T. Schaub

Similar to community detection, partitioning the nodes of a network according to their structural roles aims to identify fundamental building blocks of a network. The found partitions can be used, e.g., to simplify descriptions of the network connectivity, to derive reduced order models for dynamical processes unfolding on processes, or as ingredients for va

Onno van Gaans, Anke Kalauch, Mark Roelands

Every atomic JBW-algebra is known to be a direct sum of JBW-algebra factors of type I. Extending Kadison's anti-lattice theorem, we show that each of these factors is a disjointness free anti-lattice. We characterise disjointness, bands, and disjointness preserving bijections with disjointness preserving inverses in direct sums of disjointness free anti-latt

Jiajun Hu, Shijie Shang, Jian Xiao

We introduce a partial positivity notion for algebraic maps via the defect of semismallness. This positivity notion is modeled on $m$-positivity in the analytic setting and $m$-ampleness in the geometric setting. Using this positivity condition for algebraic maps, we establish K\"ahler packages, that is, Hard Lefschetz theorems and Hodge-Riemann bilinear rel

Zeju Li, Konstantinos Kamnitsas, Qi Dou, Chen Qin

This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strate

Sebastien Boyer, Sam Money-Kyrle, Oliver Bent

The accurate prediction of changes in protein stability under multiple amino acid substitutions is essential for realising true in-silico protein re-design. To this purpose, we propose improvements to state-of-the-art Deep learning (DL) protein stability prediction models, enabling first-of-a-kind predictions for variable numbers of amino acid substitutions,

Benjamin A. Miller, Zohair Shafi, Wheeler Ruml, Yevgeniy Vorobeychik

Identifying shortest paths between nodes in a network is an important task in many applications. Recent work has shown that a malicious actor can manipulate a graph to make traffic between two nodes of interest follow their target path. In this paper, we develop a defense against such attacks by modifying the edge weights that users observe. The defender mus

Lei Wu

An important problem in machine learning theory is to understand the approximation and generalization properties of two-layer neural networks in high dimensions. To this end, researchers have introduced the Barron space $\mathcal{B}_s(\Omega)$ and the spectral Barron space $\mathcal{F}_s(\Omega)$, where the index $s\in [0,\infty)$ indicates the smoothness of

Mitchell C. Begelman, Joseph Silk

Large-scale magnetic fields in the nuclear regions of protogalaxies can promote the formation and early growth of supermassive black holes (SMBHs) by direct collapse and magnetically boosted accretion. Turbulence associated with gravitational infall and star formation can drive the rms field strength toward equipartition with the mean gas kinetic energy; thi

Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián

Quantile regression continues to increase in usage, providing a useful alternative to customary mean regression. Primary implementation takes the form of so-called multiple quantile regression, creating a separate regression for each quantile of interest. However, recently, advances have been made in joint quantile regression, supplying a quantile function w

Tobit Klug, Dogukan Atik, Reinhard Heckel

Supervised training of deep neural networks on pairs of clean image and noisy measurement achieves state-of-the-art performance for many image reconstruction tasks, but such training pairs are difficult to collect. Self-supervised methods enable training based on noisy measurements only, without clean images. In this work, we investigate the cost of self-sup

Wei Kou, Xurong Chen

The structure of the proton remains a significant challenge within the field of Quantum Chromodynamics, with the origin of its spin and mass still lacking a satisfactory explanation. In this study, we utilize the gravitational form factor of the proton as the foundation for constructing the configurational entropy of the proton energy system. In our approach

Miao Ye, Chenwei Zhao, Xingsi Xue, Jinqiang Li

The optimal multicast tree problem in the Software-Defined Networking (SDN) multicast routing is an NP-hard combinatorial optimization problem. Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant

Thomas L. Lee, Amos Storkey

For models consisting of a classifier in some representation space, learning online from a non-stationary data stream often necessitates changes in the representation. So, the question arises of what is the best way to adapt the classifier to shifts in representation. Current methods only slowly change the classifier to representation shift, introducing nois

Hongkuan Zhou, Zhenshan Bing, Xiangtong Yao, Xiaojie Su

The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impressive capabilities for addressing tasks in familiar environ

Tsukasa Ishibashi, Hiroaki Karuo

We generalize the quantum duality map $\mathbb{I}_{\mathcal{A}}$ of Allegretti--Kim [AK17] for punctured closed surfaces to general marked surfaces. When the marked surface has no interior marked points, we investigate its compatibility with the quantum duality map $\mathbb{I}_{\mathcal{X}}$ on the dual side based on the quantum bracelets basis [Thu14, MQ23]

Ali Fayzi, Mohammad Fayzi, Kourosh Dadashtabar Ahmadi

In contemporary times, people rely heavily on the internet and search engines to obtain information, either directly or indirectly. However, the information accessible to users constitutes merely 4% of the overall information present on the internet, which is commonly known as the surface web. The remaining information that eludes search engines is called th

Zi-Yang Lin, Jian-Bo Cheng, Bo-Lin Huang, Shi-Lin Zhu

A partial width formula is proposed using the analytical extension of the wave function in momentum space. The distinction of the Riemann sheets is explained from the perspective of the Schrodinger equation. The analytical form in coordinate space and the partial width are derived subsequently. Then a coupled-channel analysis is performed to investigate the

A. G. Rudnitskiy, M. A. Shchurov, S. V. Chernov, T. A. Syachina

This paper examines options for orbit configurations for a space interferometer. In contrast to previously presented concepts for space very long baseline interferometry, we propose a combination of regular and retrograde near-Earth circular orbits in order to achieve a faster filling of $(u,v)$ coverage. With the rapid relative motion of the telescopes, it

Pablo A. Prieto-Díaz, Santiago E. Ibáñez, Marcos Vera

This paper investigates the fluid dynamics of mixing in the tanks of small-scale vanadium redox flow batteries. These systems use two redox pairs dissolved in separate electrolytes to convert electrical energy into chemical energy, a process that can be reversed in an efficient way to restore the initial electrical energy with negligible chemical losses. Aft

Tibin M Thomas, Pallab Sinha Mahapatra

Vapor condensation is extensively used in applications that demand the exchange of a substantial amount of heat energy or the vapor-liquid phase conversion. In conventional condensers, the condensate removal from a subcooled surface is caused by gravity force. This restricts the use of such condensers in space applications or in horizontal orientations. The

Yifu Zhang, Hongru Li, Tao Yang, Rui Tao

Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are available for new tissues/organs. Transfer l

Jiaxin Bai, Xin Liu, Weiqi Wang, Chen Luo

Querying knowledge graphs (KGs) using deep learning approaches can naturally leverage the reasoning and generalization ability to learn to infer better answers. Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs. However, in the real world, we also need to make logical inferences about events, states, and activities

Yifu Zhang, Hongru Li, Shimeng Shi, Youqi Li

Transfer learning leverages knowledge from other domains and has been successful in many applications. Transfer learning methods rely on the overall similarity of the source and target domains. However, in some cases, it is impossible to provide an overall similar source domain, and only some source domains with similar local features can be provided. Can tr

Noam Elata, Bahjat Kawar, Tomer Michaeli, Michael Elad

Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising steps. Despite their good performance, diffusion models are computationally expensive, requiring many neural function evaluations (NFEs). In this work, we propose an anytime diffusion

Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer

Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when synthesizing novel views of time-evolving 3D scenes. However, the common reliance on backward deformation fields makes reanimation of the captured object poses challenging. Moreover, the state of the art dynamic models are often limited by low visual fidelity, long reconstruction t

Laura Cabayol-Garcia, Jonás Chaves-Montero, Andreu Font-Ribera, Christian Pedersen

The Lyman-$\alpha$ forest offers a unique avenue for studying the distribution of matter in the high redshift universe and extracting precise constraints on the nature of dark matter, neutrino masses, and other $\Lambda$CDM extensions. However, interpreting this observable requires accurate modelling of the thermal and ionisation state of the intergalactic m

Yanwen Li, Luyang Luo, Huangjing Lin, Pheng-Ann Heng

Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to be delineated precisely from the medical images which are often of low resolutions. On

Saddam Hussain, Anirban Chatterjee, Kaushik Bhattacharya

This work explores the dynamical stability of cosmological models where dark matter and dark energy can non-minimally couple to spacetime (scalar) curvature. Two different scenarios are presented here. In the initial case, only dark matter sector is coupled to curvature in the presence of a quintessence scalar field. In the second case both dark matter and t

Sadaf Salehkalaibar, Buu Phan, Jun Chen, Wei Yu

We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we consider two different perception loss functions (PLFs). The first, PLF-JD, considers the joint distribution (JD) of all the video frames u

Jeff Maki, Fei Zhou

Conformal dynamics can appear in quantum gases when the interactions are fine tuned to be scale symmetric. One well-known example of such a system is a three-dimensional Fermi gas at a Feshbach resonance. In this letter, we illustrate how conformal dynamics can also emerge in the infrared limit in one-dimensional harmonically trapped Fermi gases, even though

ALICE Collaboration

The cross section for coherent photonuclear production of J/$\psi$ is presented as a function of the electromagnetic dissociation (EMD) of Pb. The measurement is performed with the ALICE detector in ultra-peripheral Pb-Pb collisions at a centre-of-mass energy per nucleon pair of $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV. Cross sections are presented in five differen

Emanuele Zangrando, Steffen Schotthöfer, Gianluca Ceruti, Jonas Kusch

Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during training and inference phases. Given its easy implementation and flexibility, one promising approach is layer factorization, which reshapes weight tensors into a matrix format and parameterizes them as the product of

Olivier Bernardi, Éric Fusy, Shizhe Liang

We define some Schnyder-type combinatorial structures on a class of planar triangulations of the pentagon which are closely related to 5-connected triangulations. The combinatorial structures have three incarnations defined in terms of orientations, corner-labelings, and woods respectively. The wood incarnation consists in 5 spanning trees crossing each othe

X. E. Wang

In a talk given in 2013, S. Hawking conjectured that the event horizon of black holes does not exist and suggested redefining black holes as bound states of the gravitational field. Inspired by this idea, we investigated the coupling of the Bardeen action and a complex scalar field model. Numerically, we obtained a class of boson stars solutions with the mag

Lucio La Cava, Luca Maria Aiello, Andrea Tagarelli

The migration of Twitter users to Mastodon following Elon Musk's acquisition presents a unique opportunity to study collective behavior and gain insights into the drivers of coordinated behavior in online media. We analyzed the social network and the public conversations of about 75,000 migrated users and observed that the temporal trace of their migrations

A. Biteri-Uribarren, P. Alsina-Bolívar, C. Munuera-Javaloy, R. Puebla

The detection of individual molecules and their dynamics has been a long-standing challenge in the field of nanotechnology. In this work, we present a method that utilizes a nitrogen vacancy (NV) center and a dangling-bond on the diamond surface to measure the coupling between two electronic targets tagged on a macromolecule. To achieve this, we design a mul

Ismaïl Razack

Let $X$ be a compact, oriented, second countable pseudomanifold. We show that $HH^\ast_\bullet(\widetilde N^\ast_\bullet(X;\mathbb{Q}))$, the Hochschild cohomology of the blown-up intersection cochain complex of $X$, is well defined and endowed with a Batalin-Vilkovisky algebra structure. Furthermore, we prove that it is a topological invariant. More general

Antonio Barletta

The definitions of temporal instability and of spatial instability in a flow system are comparatively surveyed. The simple model of one-dimensional Burgers' flow is taken as the scenario where such different conceptions of instability are described. The temporal analysis of instability stems from Lyapunov's theory, while the spatial analysis of instability i

Shohreh Janjan, Fardin Kheirandish

In this paper, we find the quantum propagator for a general time-dependent quadratic Hamiltonian. The method is based on the properties of the propagator and the fact that the quantum propagator fulfills two independent partial differential equations originating from Heisenberg equations for positions and momenta. As an application of the method, we find the

Sung Hwan Mun, Hye-jin Shim, Hemlata Tak, Xin Wang

This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well as target speakers' spoofed inputs should be addressed. Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and cou

János Barát, Zoltán L. Blázsik

In an orientation $O$ of the graph $G$, an arc $e$ is deletable if and only if $O-e$ is strongly connected. For a $3$-edge-connected graph $G$, the Frank number is the minimum $k$ for which $G$ admits $k$ strongly connected orientations such that for every edge $e$ of $G$ the corresponding arc is deletable in at least one of the $k$ orientations. H\"orsch an

Yasaman Omid, Zohre Mashayekh Bakhsh, Farbod Kayhan, Yi Ma

This paper examines the uplink transmission of a single-antenna handsheld user to a cluster of satellites, with a focus on utilizing the inter-satellite links to enable cooperative signal detection. Two cases are studied: one with full CSI and the other with partial CSI between satellites. The two cases are compared in terms of capacity, overhead, and bit er

Zhi-Wei Liu, Jun-Xu Lu, Ming-Zhu Liu, Li-Sheng Geng

The spins of the pentaquark states $P_c(4440)$ and $P_c(4457)$ play a decisive role in unraveling their nature, but remain undetermined experimentally. Assuming that they are $\Sigma_c\bar{D}^{*}$ bound states, we demonstrate how one can determine their spins by measuring the $\Sigma_c^+\bar{D}^{(*)0}$ correlation functions. We show that one can use the $\Si

Igor Balla

Determining the maximum number of unit vectors in $\mathbb{R}^r$ with no pairwise inner product exceeding $\alpha$ is a fundamental problem in geometry and coding theory. In 1955, Rankin resolved this problem for all $\alpha \leq 0$ and in this paper, we show that the maximum is $(2+o(1))r$ for all $0 \leq \alpha \ll r^{-2/3}$, answering a question of Bukh a

Celestine Ugwu, Modesta Ezema, Uchenna Ome, Lizzy Ofusori

The COVID19 pandemic has helped amplify the importance of Cyber Hygiene. As the reliance on the Internet and IT services increased during the pandemic. This in turn has introduced a new wave of criminal activities such as cybercrimes. With the emergent of COVID19 which lead to increase in cyberattacks incidents, the pattern and sophistication, there is an ur

You-Wei Benson Chen, Keng Hao Ooi, Daniel Spector

In this paper we introduce capacitary analogues of the Hardy-Littlewood maximal function, \begin{align*} \mathcal{M}_C(f)(x):= \sup_{r>0} \frac{1}{C(B(x,r))} \int_{B(x,r)} |f|\;dC, \end{align*} for $C=$ the Hausdorff content or a Riesz capacity. For these maximal functions, we prove a strong-type $(p,p)$ bound for $1<p \leq+\infty$ on the capacitary integrat

Naoki Yoshinaga

Accurate neural models are much less efficient than non-neural models and are useless for processing billions of social media posts or handling user queries in real time with a limited budget. This study revisits the fastest pattern-based NLP methods to make them as accurate as possible, thus yielding a strikingly simple yet surprisingly accurate morphologic

Kazuki Irie, Anand Gopalakrishnan, Jürgen Schmidhuber

Real-time recurrent learning (RTRL) for sequence-processing recurrent neural networks (RNNs) offers certain conceptual advantages over backpropagation through time (BPTT). RTRL requires neither caching past activations nor truncating context, and enables online learning. However, RTRL's time and space complexity make it impractical. To overcome this problem,

Guillaume Huguet, Alexander Tong, Edward De Brouwer, Yanlei Zhang

Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for

Alberto Facchini, Marino Gran, Mara Pompili

We link the recent theory of $L$-algebras to previous notions of Universal Algebra and Categorical Algebra concerning subtractive varieties, commutators, multiplicative lattices, and their spectra. We show that the category of $L$-algebras is subtractive and normal in the sense of Zurab Janelidze, but neither the category of $L$-algebras nor that of pre-$L$-

Junpeng Wang, Mengke Ge, Bo Ding, Qi Xu

With the widespread use of deep neural networks(DNNs) in intelligent systems, DNN accelerators with high performance and energy efficiency are greatly demanded. As one of the feasible processing-in-memory(PIM) architectures, 3D-stacked-DRAM-based PIM(DRAM-PIM) architecture enables large-capacity memory and low-cost memory access, which is a promising solutio