Search

In the standard cosmological framework, neutrinos begin to free-stream after the weak interaction phase ends in the early universe, at a temperature of approximately $T \sim 1 \, {\rm MeV}$. However, the onset of neutrino free-streaming can be delayed if additional interactions occur in the early universe, leaving imprints on both the cosmic microwave background (CMB) angular power spectra and the large-scale structure (LSS) matter power spectra. We present a thorough analysis of early universe neutrino interactions with a fairly generalized parameterization of the interaction rates as a power law in neutrino temperature. In this (6+2) parameter scenario, we constrain the cosmological parameters along with the free-streaming redshift and the sum of the neutrino mass in presence of such interactions, with the help of full shape (FS) galaxy power spectra from BOSS Data Release 12. Our analysis reveals that a combined dataset of FS along with CMB and BAO offers improved constraints on the free-streaming redshift from present data, comparable to the forecast results from future CMB-S4 data. Additionally, we investigate the prospects of future galaxy surveys by forecasting on Euclid mission in combination with Planck and CMB-S4, and find significant improvement on both the free-streaming redshift and the sum of the neutrino mass than the existing constraints as well as than CMB-S4 alone., Comment: 21 pages, 5 sets of figures and 7 tables. Comments are welcome

The transport of particles through channels holds immense significance in physics, chemistry, and biological sciences. For instance, the motion of solutes through biological channels is facilitated by specialized proteins that create water-filled channels and valuable insights can be obtained by studying the transition paths of particles through a channel and gathering statistics on their lifetimes within the channel or their exit probabilities. In a similar vein, we consider a one-dimensional model of channel-facilitated transport where a diffusive particle is subject to attractive interactions with the walls within a limited region of the channel. We study the statistics of conditional and unconditional escape times, in the presence of resetting--an intermittent dynamics that brings the particle back to its initial coordinate randomly. We determine analytically the physical conditions under which such resetting mechanism can become beneficial for faster escape of the particles from the channel thus enhancing the transport. Our theory has been verified with the aid of Brownian dynamics simulations for various interaction strengths and extent. The overall results presented herein highlight the scope of resetting-based strategies to be universally promising for complex transport processes of single or long molecules through biological membranes., Comment: 20 pages, 9 sets of figures

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

Stochastic resetting has recently emerged as an efficient target-searching strategy in various physical and biological systems. The efficiency of this strategy depends on the type of environmental noise, whether it is thermal or telegraphic (active). While the impact of each noise type on a search process has been investigated separately, their combined effects have not been explored. In this work, we explore the effects of stochastic resetting on an active system, namely a self-propelled run-and-tumble particle immersed in a thermal bath. In particular, we assume that the position of the particle is reset at a fixed rate with or without reversing the direction of self-propelled velocity. Using standard renewal techniques, we compute the mean search time of this active particle to a fixed target and investigate the interplay between active and thermal fluctuations. We find that the active search can outperform the Brownian search when the magnitude and flipping rate of self-propelled velocity are large and the strength of environmental noise is small. Notably, we find that the presence of thermal noise in the environment helps reduce the mean first passage time of the run-and-tumble particle compared to the absence of thermal noise. Finally, we observe that reversing the direction of self-propelled velocity while resetting can also reduce the overall search time., Comment: 10 pages, 4 figures

The well-studied red-blue pebble game models the execution of an arbitrary computational DAG by a single processor over a two-level memory hierarchy. We present a natural generalization to a multiprocessor setting where each processor has its own limited fast memory, and all processors share unlimited slow memory. To our knowledge, this is the first thorough study that combines pebbling and DAG scheduling problems, capturing the computation of general workloads on multiple processors with memory constraints and communication costs. Our pebbling model enables us to analyze trade-offs between workload balancing, communication and memory limitations, and it captures real-world factors such as superlinear speedups due to parallelization. Our results include upper and lower bounds on the pebbling cost, an analysis of a greedy pebbling strategy, and an extension of NP-hardness results for specific DAG classes from simpler models. For our main technical contribution, we show two inapproximability results that already hold for the long-standing problem of standard red-blue pebbling: (i) the optimal I/O cost cannot be approximated to any finite factor, and (ii) the optimal total cost (I/O+computation) can only be approximated to a limited constant factor, i.e., it does not allow for a polynomial-time approximation scheme. These results also carry over naturally to our multiprocessor pebbling model.

We study adjacency and Laplacian matrices of Erd\H{o}s-R\'{e}nyi $r$-uniform hypergraphs on $n$ vertices with hyperedge inclusion probability $p$, in the setting where $r$ can vary with $n$ such that $r / n \to c \in [0, 1)$. Adjacency matrices of hypergraphs are contractions of adjacency tensors and their entries exhibit long range correlations. We show that under the Erd\H{o}s-R\'{e}nyi model, the expected empirical spectral distribution of an appropriately normalised hypergraph adjacency matrix converges weakly to the semi-circle law with variance $(1 - c)^2$ as long as $\frac{d_{\avg}}{r^7} \to \infty$, where $d_{\avg} = \binom{n-1}{r-1} p$. In contrast with the Erd\H{o}s-R\'{e}nyi random graph ($r = 2$), two eigenvalues stick out of the bulk of the spectrum. When $r$ is fixed and $d_{\avg} \gg n^{r - 2} \log^4 n$, we uncover an interesting Baik-Ben Arous-P\'{e}ch\'{e} (BBP) phase transition at the value $r = 3$. For $r \in \{2, 3\}$, an appropriately scaled largest (resp. smallest) eigenvalue converges in probability to $2$ (resp. $-2$), the right (resp. left) end point of the support of the standard semi-circle law, and when $r \ge 4$, it converges to $\sqrt{r - 2} + \frac{1}{\sqrt{r - 2}}$ (resp. $-\sqrt{r - 2} - \frac{1}{\sqrt{r - 2}}$). Further, in a Gaussian version of the model we show that an appropriately scaled largest (resp. smallest) eigenvalue converges in distribution to $\frac{c}{2} \zeta + \big[\frac{c^2}{4}\zeta^2 + c(1 - c)\big]^{1/2}$ (resp. $\frac{c}{2} \zeta - \big[\frac{c^2}{4}\zeta^2 + c(1 - c)\big]^{1/2}$), where $\zeta$ is a standard Gaussian. We also establish analogous results for the bulk and edge eigenvalues of the associated Laplacian matrices.

We report the observation of Shapiro steps in a periodically driven Josephson junction between strongly-interacting Fermi superfluids of ultracold atoms. We observe quantized plateaus in the current-potential characteristics, the height and width of which mirror the external drive frequency and the junction nonlinear response. Direct measurements of the current-phase relationship showcase how Shapiro steps arise from the synchronization between the relative phase of the two reservoirs and the external drive. Such mechanism is further supported by the detection of periodic phase-slippage processes, in the form of vortex-antivortex pairs. Our results are corroborated by a circuital model and numerical simulations, overall providing a clear understanding of Shapiro dynamics in atomic Fermi superfluids. Our work demonstrates phase-coherent and synchronization effects in driven strongly-interacting superfluids, opening prospects for studying emergent non-equilibrium dynamics in quantum many-body systems under external drives.

The current-voltage characteristic of a driven superconducting Josephson junction displays discrete steps. This phenomenon, discovered by Sydney Shapiro, forms today's voltage standard. Here, we report the observation of Shapiro steps in a driven Josephson junction in a gas of ultracold atoms. We demonstrate that the steps exhibit universal features, and provide key insight into the microscopic dissipative dynamics that we directly observe in the experiment. Most importantly, the steps are directly connected to phonon emission and soliton nucleation. The experimental results are underpinned by extensive numerical simulations based on classical-field dynamics and represent the transfer of the voltage standard to the realm of ultracold quantum gases., Comment: Update reference

We introduce the notion of a generalized complex (GC) Stein manifold and provide complete characterizations in three fundamental aspects. First, we extend Cartan's Theorem A and B within the framework of GC geometry. Next, we define $L$-plurisubharmonic functions and develop an associated $L^2$ theory. This leads to a characterization of GC Stein manifolds using $L$-plurisubharmonic exhaustion functions. Finally, we establish the existence of a proper GH embedding from any GC Stein manifold into $\mathbb{R}^{2n-2k} \times \mathbb{C}^{2k+1}$, where $2n$ and $k$ denote the dimension and type of the GC Stein manifold, respectively. This provides a characterization of GC Stein manifolds via GH embeddings. Several examples of GC Stein manifolds are given., Comment: 49 pages, comments are welcome

Dark-field X-ray Microscopy (DFXM) is a novel diffraction-based imaging technique that non-destructively maps the local deformation from crystalline defects in bulk materials. While studies have demonstrated that DFXM can spatially map 3D defect geometries, it is still challenging to interpret DFXM images of the high dislocation density systems relevant to macroscopic crystal plasticity. This work develops a scalable forward model to calculate virtual DFXM images for complex discrete dislocation (DD) structures obtained from atomistic simulations. Our new DD-DFXM model integrates a non-singular formulation for calculating the local strain from the DD structures and an efficient geometrical optics algorithm for computing the DFXM image from the strain. We apply the model to complex DD structures obtained from a large-scale molecular dynamics (MD) simulation of compressive loading on a single-crystal silicon. Simulated DFXM images exhibit prominent feature contrast for dislocations between the multiple slip systems, demonstrating the DFXM's potential to resolve features from dislocation multiplication. The integrated DD-DFXM model provides a toolbox for DFXM experimental design and image interpretation in the context of bulk crystal plasticity for the breadth of measurements across shock plasticity and the broader materials science community.

We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (GI) diagnostics. This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments, and it supports multiple question types including yes/no, choice, location, and numerical count. The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification. Our experiments demonstrate the dataset's effectiveness in training models for three selected tasks, showcasing significant applications in medical image analysis and diagnostics. We also present evaluation metrics for each task, highlighting the usability and versatility of our dataset. The dataset and supporting artifacts are available at https://datasets.simula.no/kvasir-vqa., Comment: to be published in VLM4Bio 2024, part of the ACM Multimedia (ACM MM) conference 2024

Localizing sources of troublesome oscillations, particularly forced oscillations (FOs), in power systems has received considerable attention over the last few years. This is driven in part by the massive deployment of phasor measurement units (PMUs) that capture these oscillations when they occur; and in part by the increasing incidents of FOs due to malfunctioning components, wind power fluctuations, and/or cyclic loads. Capitalizing on the frequency divider formula of [1], we develop methods to localize single and multiple oscillatory sources using bus frequency measurements. The method to localize a single oscillation source does not require knowledge of network parameters. However, the method for localizing FOs caused by multiple sources requires this knowledge. We explain the reasoning behind this knowledge difference as well as demonstrate the success of our methods for source localization in multiple test systems., Comment: 5 pages, 5 figures, to appear in the proceedings of IEEE PESGM 2024

We consider the dispersion (van der Waals, vdW) interaction among N parallel elongated objects such as DNA/RNA strands or metallic nanotubes, which are polarizable primarily along the long axis. Within a quasi-one-dimensional model, we prove that the irreducible N -object vdW energy contribution is negative (attractive) for even N and positive (repulsive) for odd N. We confirm these results up to $N=4$ via a 3-dimensional plasma cylinder model. This suggests a preference for even-N clustering of elongated structures in nanoscience and biology. This work could have implications e.g. for nanotube bundle formation and for the clustering of long-chain biomolecules at separations exceeding chemical bond lengths., Comment: 15 pages, 3 figures

Understanding the genetic underpinnings of complex traits and diseases has been greatly advanced by genome-wide association studies (GWAS). However, a significant portion of trait heritability remains unexplained, known as ``missing heritability". Most GWAS loci reside in non-coding regions, posing challenges in understanding their functional impact. Integrating GWAS with functional genomic data, such as expression quantitative trait loci (eQTLs), can bridge this gap. This study introduces a novel approach to discover candidate genes regulated by GWAS signals in both cis and trans. Unlike existing eQTL studies that focus solely on cis-eQTLs or consider cis- and trans-QTLs separately, we utilize adaptive statistical metrics that can reflect both the strong, sparse effects of cis-eQTLs and the weak, dense effects of trans-eQTLs. Consequently, candidate genes regulated by the joint effects can be prioritized. We demonstrate the efficiency of our method through theoretical and numerical analyses and apply it to adipose eQTL data from the METabolic Syndrome in Men (METSIM) study, uncovering genes playing important roles in the regulatory networks influencing cardiometabolic traits. Our findings offer new insights into the genetic regulation of complex traits and present a practical framework for identifying key regulatory genes based on joint eQTL effects.

Optimizing tail latency while efficiently managing computational resources is crucial for delivering high-performance, latency-sensitive services in edge computing. Emerging applications, such as augmented reality, require low-latency computing services with high reliability on user devices, which often have limited computational capabilities. Consequently, these devices depend on nearby edge servers for processing. However, inherent uncertainties in network and computation latencies stemming from variability in wireless networks and fluctuating server loads make service delivery on time challenging. Existing approaches often focus on optimizing median latency but fall short of addressing the specific challenges of tail latency in edge environments, particularly under uncertain network and computational conditions. Although some methods do address tail latency, they typically rely on fixed or excessive redundancy and lack adaptability to dynamic network conditions, often being designed for cloud environments rather than the unique demands of edge computing. In this paper, we introduce SafeTail, a framework that meets both median and tail response time targets, with tail latency defined as latency beyond the 90^th percentile threshold. SafeTail addresses this challenge by selectively replicating services across multiple edge servers to meet target latencies. SafeTail employs a reward-based deep learning framework to learn optimal placement strategies, balancing the need to achieve target latencies with minimizing additional resource usage. Through trace-driven simulations, SafeTail demonstrated near-optimal performance and outperformed most baseline strategies across three diverse services., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

In recent years, there has been a surge of interest in exploring higher-order topology and their semi-metallic counterparts, particularly in the context of Dirac and Weyl semimetals, termed as higher-order Dirac semimetal (HODSM) and higher-order Weyl semimetal (HOWSM). The HODSM phase exhibits hinge Fermi arcs (FAs) with a quantized higher-order topological invariant. Conversely, the HOWSM phase is a hybrid-order topological phase manifesting both surface and hinge FAs as a signature of first- and second-order topology and also possesses both first- and second-order topological invariants. In this work, we investigate a tight binding model for multi-HODSM (mHODSM) hosting multiple hinge FAs having a quantized quadrupolar winding number (QWN) greater than one. Furthermore, we obtain a multi-HOWSM (mHOWSM) phase from the mHODSM by applying an external magnetic field. The mHOWSM phase possesses both the Chern number and the QWN, featuring both surface and multiple hinge FAs. We study the spectral properties of the mHODSM and mHOWSM in different geometries. We also investigate the hinge FA-mediated transport in mHOWSM employing a two-terminal setup., Comment: 10 pages, 7 figures; comments are welcome

Data balancing across multiple modalities/sources appears in various forms in several foundation models (e.g., CLIP and DINO) achieving universal representation learning. We show that this iterative algorithm, usually used to avoid representation collapse, enjoys an unsuspected benefit: reducing the variance of estimators that are functionals of the empirical distribution over these sources. We provide non-asymptotic bounds quantifying this variance reduction effect and relate them to the eigendecays of appropriately defined Markov operators. We explain how various forms of data balancing in contrastive multimodal learning and self-supervised clustering can be interpreted as instances of this variance reduction scheme.

The proliferation of scene text in both structured and unstructured environments presents significant challenges in optical character recognition (OCR), necessitating more efficient and robust text spotting solutions. This paper presents FastTextSpotter, a framework that integrates a Swin Transformer visual backbone with a Transformer Encoder-Decoder architecture, enhanced by a novel, faster self-attention unit, SAC2, to improve processing speeds while maintaining accuracy. FastTextSpotter has been validated across multiple datasets, including ICDAR2015 for regular texts and CTW1500 and TotalText for arbitrary-shaped texts, benchmarking against current state-of-the-art models. Our results indicate that FastTextSpotter not only achieves superior accuracy in detecting and recognizing multilingual scene text (English and Vietnamese) but also improves model efficiency, thereby setting new benchmarks in the field. This study underscores the potential of advanced transformer architectures in improving the adaptability and speed of text spotting applications in diverse real-world settings. The dataset, code, and pre-trained models have been released in our Github., Comment: Accepted in ICPR 2024

In this paper we study infinite horizon nonzero-sum stochastic games for controlled discrete-time Markov chains on a Polish state space with risk-sensitive ergodic cost criterion. Under suitable assumptions we show that the associated ergodic optimality equations admit unique solutions. Finally, the existence of Nash-equilibrium in randomized stationary strategies is established by showing that an appropriate set-valued map has a fixed point.

The progenitor system(s) as well as the explosion mechanism(s) of thermonuclear (Type Ia) supernovae are long-standing issues in astrophysics. Here we present ejecta masses and other physical parameters for 28 recent Type Ia supernovae inferred from multiband photometric and optical spectroscopic data. Our results confirm that the majority of SNe Ia show {\it observable} ejecta masses below the Chandrasekhar-limit (having a mean $M_{\rm ej} \approx 1.1 \pm 0.3$ M$_\odot$), consistent with the predictions of recent sub-M$_{\rm Ch}$ explosion models. They are compatible with models assuming either single- or double-degenerate progenitor configurations. We also recover a sub-sample of supernovae within $1.2 $ M$_\odot$ $< M_{\rm {ej}} < 1.5$ M$_\odot$ that are consistent with near-Chandrasekhar explosions. Taking into account the uncertainties of the inferred ejecta masses, about half of our SNe are compatible with both explosion models. We compare our results with those in previous studies, and discuss the caveats and concerns regarding the applied methodology.

Colonoscopy is the primary method for examination, detection, and removal of polyps. Regular screening helps detect and prevent colorectal cancer at an early curable stage. However, challenges such as variation among the endoscopists' skills, bowel quality preparation, and complex nature of the large intestine which cause large number of polyp miss-rate. These missed polyps can develop into cancer later on, which underscores the importance of improving the detection methods. A computer-aided diagnosis system can support physicians by assisting in detecting overlooked polyps. However, one of the important challenges for developing novel deep learning models for automatic polyp detection and segmentation is the lack of publicly available, multi-center large and diverse datasets. To address this gap, we introduce PolypDB, a large scale publicly available dataset that contains 3934 still polyp images and their corresponding ground truth from real colonoscopy videos to design efficient polyp detection and segmentation architectures. The dataset has been developed and verified by a team of 10 gastroenterologists. PolypDB comprises of images from five modalities: Blue Light Imaging (BLI), Flexible Imaging Color Enhancement (FICE), Linked Color Imaging (LCI), Narrow Band Imaging (NBI), and White Light Imaging (WLI) and three medical centers from Norway, Sweden and Vietnam. Thus, we split the dataset based on modality and medical center for modality-wise and center-wise analysis. We provide a benchmark on each modality using eight popular segmentation methods and six standard benchmark polyp detection methods. Furthermore, we also provide benchmark on center-wise under federated learning settings. Our dataset is public and can be downloaded at \url{https://osf.io/pr7ms/}.

This study presents a novel approach for enhancing American Sign Language (ASL) recognition using Graph Convolutional Networks (GCNs) integrated with successive residual connections. The method leverages the MediaPipe framework to extract key landmarks from each hand gesture, which are then used to construct graph representations. A robust preprocessing pipeline, including translational and scale normalization techniques, ensures consistency across the dataset. The constructed graphs are fed into a GCN-based neural architecture with residual connections to improve network stability. The architecture achieves state-of-the-art results, demonstrating superior generalization capabilities with a validation accuracy of 99.14%., Comment: To be submitted in G2-SP CV 2024. Contains 7 pages, 5 figures

The spin Hall effect is a celebrated phenomenon in spintronics and magnetism that has found numerous applications in digital electronics (memory and logic), but very few in analog electronics. Practically, the only analog application in widespread use is the spin Hall nano-oscillator (SHNO) that delivers a high frequency alternating current or voltage to a load. Here, we report its analogue - a spin Hall nano-antenna (SHNA) that radiates a high frequency electromagnetic wave (alternating electric/magnetic fields) into the surrounding medium. It can also radiate an acoustic wave in an underlying substrate if the nanomagnets are made of a magnetostrictive material. That makes it a dual electromagnetic/acoustic antenna. The SHNA is made of an array of ledged magnetostrictive nanomagnets deposited on a substrate, with a heavy metal nanostrip underlying/overlying the ledges. An alternating charge current passed through the nanostrip generates an alternating spin-orbit torque in the nanomagnets via the spin Hall effect which makes their magnetizations oscillate in time with the frequency of the current, producing confined spin waves (magnons), which radiate electromagnetic waves (photons) in space with the same frequency as the ac current. Despite being much smaller than the radiated wavelength, the SHNA surprisingly does not act as a point source which would radiate isotropically. Instead, there is clear directionality (anisotropy) in the radiation pattern, which is frequency-dependent. This is due to the (frequency-dependent) intrinsic anisotropy in the confined spin wave patterns generated within the nanomagnets, which effectively endows the "point source" with internal anisotropy.

In this work, we consider a group of n agents whose interactions can be represented using unsigned or signed structurally balanced graphs or a special case of structurally unbalanced graphs. A Laplacian-based model is proposed to govern the evolution of opinions. The objective of the paper is to analyze the proposed opinion model on the opinion evolution of the agents. Further, we also determine the conditions required to apply the proposed Laplacian-based opinion model. Finally, some numerical results are shown to validate these results., Comment: 8 pages, 5 figures

The competition between the Kondo spin-screening effect and the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction in heavy-fermion systems drives the quantum phase transition between the magnetically ordered and the heavy-Fermi-liquid ground states. Despite intensive investigations of heavy quasiparticles on the Kondo-screened side of the quantum phase transition and of their breakdown at the quantum critical point, studies on the magnetically ordering side are scarce. Using terahertz time-domain spectroscopy, we report a suppression of the Kondo quasiparticle weight in CeCu6-xAux samples on the antiferromagnetic side of the quantum phase transition at temperatures as much as two orders of magnitude above the Neel temperature TN. The suppression results from a quantum frustration effect induced by the temperature-independent RKKY interaction. Hence, our results emphasize that besides critical fluctuations, the RKKY interaction may play an important role in the quantum-critical scenario., Comment: 6 pages, 4 figures

Medical image segmentation is one of the domains where sufficient annotated data is not available. This necessitates the application of low-data frameworks like few-shot learning. Contemporary prototype-based frameworks often do not account for the variation in features within the support and query images, giving rise to a large variance in prototype alignment. In this work, we adopt a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels to learn the semantic segmentation task itself. We use a correlation-based probability score to generate a dynamic prototype for each query pixel from the bag of prototypes obtained from the support feature map. This weighting scheme helps to give a higher weightage to contextually related prototypes. We also propose a quadrant masking strategy in the downstream segmentation task by utilizing prior domain information to discard unwanted false positives. We present extensive experimentations and evaluations on abdominal CT and MR datasets to show that the proposed simple but potent framework performs at par with the state-of-the-art methods., Comment: Accepted to ICPR 2024

Let $M$ be an $n$-dimensional Hadamard manifold of pinched negative curvature $-b^2 \leq K_M \leq -a^2$. The solution of the Dirichlet problem at infinity for $M$ leads to the construction of a family of mutually absolutely continuous probability measures $\{\mu_x\}_{x \in M}$ called the harmonic measures. Fixing a basepoint $o \in M$, the Poisson kernel of $M$ is the function $P : M \times \partial M \to (0, \infty)$ defined by \begin{equation*} P(x, \xi) = \frac{d\mu_x}{d\mu_o}(\xi) \ , \ x \in M, \xi \in \partial M. \end{equation*} We prove the following global upper and lower bounds for the Poisson kernel: \begin{equation*} \frac{1}{C}\: e^{-2K{(o|\xi)}_x}\: e^{a d(x, o)} \le P(x,\xi) \le C\: e^{2K{(x|\xi)}_o}\: e^{-a d(x,o)} \:, \end{equation*} for some positive constants $C \geq 1, K > 0$ depending solely on $a, b$ and $n$. The above estimates may be viewed as a generalization of the well-known formula for the Poisson kernel in terms of Busemann functions for the special case of Gromov hyperbolic harmonic manifolds. These estimates do not follow directly from known estimates on Green's functions or harmonic measures. Instead we use techniques due to Anderson-Schoen for estimating positive harmonic functions in cones. As applications, we obtain quantitative estimates for the convergence $\mu_x \to \delta_{\xi}$ as $x \in M \to \xi \in \partial M$, and for the convergence of harmonic measures on finite spheres to the harmonic measures on the boundary at infinity as the radius of the spheres tends to infinity., Comment: 22 pages, 3 figures

We investigate the low rank matrix completion problem in an online setting with ${M}$ users, ${N}$ items, ${T}$ rounds, and an unknown rank-$r$ reward matrix ${R}\in \mathbb{R}^{{M}\times {N}}$. This problem has been well-studied in the literature and has several applications in practice. In each round, we recommend ${S}$ carefully chosen distinct items to every user and observe noisy rewards. In the regime where ${M},{N} >> {T}$, we propose two distinct computationally efficient algorithms for recommending items to users and analyze them under the benign \emph{hott items} assumption.1) First, for ${S}=1$, under additional incoherence/smoothness assumptions on ${R}$, we propose the phased algorithm \textsc{PhasedClusterElim}. Our algorithm obtains a near-optimal per-user regret of $\tilde{O}({N}{M}^{-1}(\Delta^{-1}+\Delta_{{hott}}^{-2}))$ where $\Delta_{{hott}},\Delta$ are problem-dependent gap parameters with $\Delta_{{hott}} >> \Delta$ almost always. 2) Second, we consider a simplified setting with ${S}=r$ where we make significantly milder assumptions on ${R}$. Here, we introduce another phased algorithm, \textsc{DeterminantElim}, to derive a regret guarantee of $\widetilde{O}({N}{M}^{-1/r}\Delta_{det}^{-1}))$ where $\Delta_{{det}}$ is another problem-dependent gap. Both algorithms crucially use collaboration among users to jointly eliminate sub-optimal items for groups of users successively in phases, but with distinctive and novel approaches., Comment: Appeared at the Forty-first International Conference on Machine Learning, 2024

A harmonic mapping $f=h+\overline{g}$ in $\mathbb{D}$ is $\varphi$-normal if $f^{\#}(z)=\mathcal{O}(|\varphi(z)|), \text{ as } |z|\to 1^-,$ where $f^{\#}(z)={(|h'(z)|+|g'(z)|)}/{(1+|f(z)|^2)}.$ In this paper, we establish several sufficient conditions for harmonic mappings to be $\varphi$-normal. We also extend the five-point theorem of Lappan for $\varphi$-normal harmonic mappings., Comment: 10 pages

Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases. Our study introduces a novel technique integrating momentum within residual blocks for enhanced training dynamics in medical image analysis. We applied our method in two distinct tasks: segmenting liver, lung, & colon data and classifying abdominal pelvic CT and MRI scans. The proposed approach has shown promising results, outperforming state-of-the-art methods on publicly available benchmarking datasets. For instance, in the lung segmentation dataset, our approach yielded significant enhancements over the TransNetR model, including a 5.72% increase in dice score, a 5.04% improvement in mean Intersection over Union (mIoU), an 8.02% improvement in recall, and a 4.42% improvement in precision. Hence, incorporating momentum led to state-of-the-art performance in both segmentation and classification tasks, representing a significant advancement in the field of medical imaging., Comment: 8 pages

Understanding the complete light-spin interactions in magnetic systems is the key to manipulating the magnetization using optical means at ultrafast timescales. The selective addressing of spins by terahertz (THz) electromagnetic fields via Zeeman torque is, by far, one of the most successful ultrafast means of controlling magnetic excitations. Here we show that this traditional Zeeman torque on the spins is not sufficient, rather an additional relativistic field-derivative torque is essential to realize the observed magnetization dynamics. We accomplish this by exploring the ultrafast nonlinear magnetization dynamics of rare-earth, Bi-doped iron garnet when excited by two co-propagating THz pulses. By non-thermal optical pump-probe technique, we, first, find the collective exchange resonance mode between rare-earth and transition metal sublattices at 0.48 THz. We further explore the magnetization dynamics via a rather direct and efficient THz time-domain spectroscopic means. We find that the observed nonlinear trace of the magnetic response cannot be mapped to the magnetization precession induced by the Zeeman torque, while the Zeeman torque supplemented by an additional field-derivative torque follows the experimental evidences. This breakthrough not only enhances our comprehension of ultra-relativistic effects but also paves the way for the development of novel technologies harnessing light-induced control over magnetic systems., Comment: 5 figures

Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant challenge to date, mainly due to their nonlinear dynamics and viscoelastic material properties. Conventional control design methods often rely on either complex system modeling or time-intensive manual tuning, both of which require significant amounts of human expertise and thus limit their practicality. In recent works, the data-driven method, Automatic Neural ODE Control (ANODEC) has been successfully used to -- fully automatically and utilizing only input-output data -- design controllers for various nonlinear systems in silico, and without requiring prior model knowledge or extensive manual tuning. In this work, we successfully apply ANODEC to automatically learn to perform agile, non-repetitive reference tracking motion tasks in a real-world SR and within a finite time horizon. To the best of the authors' knowledge, ANODEC achieves, for the first time, performant control of a SR with hysteresis effects from only 30 seconds of input-output data and without any prior model knowledge. We show that for multiple, qualitatively different and even out-of-training-distribution reference signals, a single feedback controller designed by ANODEC outperforms a manually tuned PID baseline consistently. Overall, this contribution not only further strengthens the validity of ANODEC, but it marks an important step towards more practical, easy-to-use SRs that can automatically learn to perform agile motions from minimal experimental interaction time., Comment: Submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

We propose a novel method for user-to-user interference (UUI) mitigation in dynamic time-division duplex multiple-input multiple-output communication systems with multi-antenna users. Specifically, we consider the downlink data transmission in the presence of UUI caused by a user that simultaneously transmits in uplink. Our method introduces an overhead for estimation of the user-to-user channels by transmitting pilots from the uplink user to the downlink users. Each downlink user obtains a channel estimate that is used to design a combining matrix for UUI mitigation. We analytically derive an achievable spectral efficiency for the downlink transmission in the presence of UUI with our mitigation technique. Through numerical simulations, we show that our method can significantly improve the spectral efficiency performance in cases of heavy UUI., Comment: Accepted for presentation at the 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Lucca, Italy, September 10-13, 2024

An unprecedented double-threshold lasing behavior has been observed in a plasmonic random laser composed of Au nanoislands decorated on vertically standing ZnO nanorods, infiltrated with dye-doped polymer matrix. The strong coupling of random laser modes to plasmonic nanocavities results in a dominant absorption of the random laser emission, leading to the first unusual lasing threshold. At higher pump fluences, the nonlinear optical behavior of the Au nanoislands induces a second lasing threshold. Various statistical tools have been employed to analyze the intensity fluctuations of the random laser modes, validating this unique lasing behavior.

Galaxy clusters show large-scale azimuthal X-ray surface brightness fluctuations known as cold fronts. These are overdense (average density jumps $\sim 30\%$ or post-jump density $\sim 130\%$) and have milder discontinuity in pressure. Cold fronts are argued to originate due to sloshing driven by sub-halo passage at close proximity to the cluster center. While this is a viable source of large-scale perturbations, the physical mechanisms that can sustain such density structures (of specific geometry) are not clear. In this work, we explore whether long wavelength thermal instability is an explanation for cold front formation in a cluster core which is perturbed by sub-halos or AGN activity. Using global linear perturbation analysis, we show that internal gravity waves (thermally unstable) can form large-scale three-dimensional spiral structures, akin to observed cold fronts. We explore if the presence of magnetic field (along spherical $\hat{\phi}$) may support such structures (by suppressing small scale Kelvin-Helmholtz modes) or disrupt them (by promoting additional thermal instability). We find that latter happens at shorter wavelengths and only at frequencies above the characteristic buoyancy or Brunt V\"ais\"al\"a frequency ($>N_{\rm BV}$). Our work implies, firstly, that large-scale spirals may be formed and sustained over a long timescale ($>N^{-1}_{\rm BV}$) even in presence of aligned magnetic fields that is otherwise supportive against mixing at the interface. Secondly, short-wavelength (but relatively longer along the field) unstable compressive modes may form within or in the vicinity of such spirals. The instability is an overstable slow wave, and grows in 2D at timescales $\gtrsim 2-3$ times longer than the spiral growth timescale (via thermal instability). Thus we claim that this instability cannot destroy the large scale coherence., Comment: 14 pages, 8 figures in main content and 3 figures in Appendix, to be submitted to MNRAS. Comments are welcome

We survey the history of the capset problem in the context of related results on progression-free sets, discuss recent progress, and mention further directions to explore., Comment: This is a survey paper

We generalize regular unitary dilation and Brehmer's positivity condition to $q$-commuting tuples of contractions., Comment: This is a first draft and will be revised soon

Machine learning (ML) has transformed numerous fields, but understanding its foundational research is crucial for its continued progress. This paper presents an overview of the significant classical ML algorithms and examines the state-of-the-art publications spanning twelve decades through an extensive bibliometric analysis study. We analyzed a dataset of highly cited papers from prominent ML conferences and journals, employing citation and keyword analyses to uncover critical insights. The study further identifies the most influential papers and authors, reveals the evolving collaborative networks within the ML community, and pinpoints prevailing research themes and emerging focus areas. Additionally, we examine the geographic distribution of highly cited publications, highlighting the leading countries in ML research. This study provides a comprehensive overview of the evolution of traditional learning algorithms and their impacts. It discusses challenges and opportunities for future development, focusing on the Global South. The findings from this paper offer valuable insights for both ML experts and the broader research community, enhancing understanding of the field's trajectory and its significant influence on recent advances in learning algorithms.

Floquet quantum circuits are able to realise a wide range of non-equilibrium quantum states, exhibiting quantum chaos, topological order and localisation. In this work, we investigate the stability of operator localisation and emergence of chaos in random Floquet-Clifford circuits subjected to unitary perturbations which drive them away from the Clifford limit. We construct a nearest-neighbour Clifford circuit with a brickwork pattern and study the effect of including disordered non-Clifford gates. The perturbations are uniformly sampled from single-qubit unitaries with probability $p$ on each qubit. We show that the interacting model exhibits strong localisation of operators for $0 \le p < 1$ that is characterised by the fragmentation of operator space into disjoint sectors due to the appearance of wall configurations. Such walls give rise to emergent local integrals of motion for the circuit that we construct exactly. We analytically establish the stability of localisation against generic perturbations and calculate the average length of operator spreading tunable by $p$. Although our circuit is not separable across any bi-partition, we further show that the operator localisation leads to an entanglement bottleneck, where initially unentangled states remain weakly entangled across typical fragment boundaries. Finally, we study the spectral form factor (SFF) to characterise the chaotic properties of the operator fragments and spectral fluctuations as a probe of non-ergodicity. In the $p = 1$ model, the emergence of a fragmentation time scale is found before random matrix theory sets in after which the SFF can be approximated by that of the circular unitary ensemble. Our work provides an explicit description of quantum phases in operator dynamics and circuit ergodicity which can be realised on current NISQ devices., Comment: 21 pages, 14 figures, 2 appendices

In this paper, we compute the partition functions of $\mathcal{N}=1$ SUGRA for different boundary topologies, i.e. sphere and torus, using super-Virasoro TQFT. We use fusion and modular kernels of the super-Liouville theory to compute the necklace-channel conformal block and showcase formalism by proving that the inner product holds for superconformal blocks, defined as states in the Hilbert space. Finally, we compute the out-of-time-order correlator for the torus topology with superconformal primary insertions as matter using the tools of super-Virasoro TQFT and investigate its early-time behaviour., Comment: 48 pages, 4 figures, 1 table

Interpretability provides a toolset for understanding how and why neural networks behave in certain ways. However, there is little unity in the field: most studies employ ad-hoc evaluations and do not share theoretical foundations, making it difficult to measure progress and compare the pros and cons of different techniques. Furthermore, while mechanistic understanding is frequently discussed, the basic causal units underlying these mechanisms are often not explicitly defined. In this paper, we propose a perspective on interpretability research grounded in causal mediation analysis. Specifically, we describe the history and current state of interpretability taxonomized according to the types of causal units (mediators) employed, as well as methods used to search over mediators. We discuss the pros and cons of each mediator, providing insights as to when particular kinds of mediators and search methods are most appropriate depending on the goals of a given study. We argue that this framing yields a more cohesive narrative of the field, as well as actionable insights for future work. Specifically, we recommend a focus on discovering new mediators with better trade-offs between human-interpretability and compute-efficiency, and which can uncover more sophisticated abstractions from neural networks than the primarily linear mediators employed in current work. We also argue for more standardized evaluations that enable principled comparisons across mediator types, such that we can better understand when particular causal units are better suited to particular use cases.

In this paper we present a theoretical analysis of the faster than light communication possibility based on entangled photons. We analyze designs that may be capable to solve the problem of direct information transfer between members of an entangled photon pairs. We consider that experimental verifications can confirm or even refute this. Our hypothesis was that most proofs of the nocommunication theorem are based on a certain set of conditions, and it is possible to provide a broader set of conditions that allow the establishment of entangled states as quantum information channels, without using a classical channel. One basic unit of the proposed design transforms the polarization state of one member of an entangled photon pair into a spatial superposition state. Thus, after the polarization measurement performed on one member, which eliminates the entanglement, the quantum information is maintained in the spatial superposition state of the other member. This can be recovered by a particular measurement based on spatial interference. We have shown that solutions with so-called symmetric functions lead to average results that corresponds to the nocommunication theorem. However, using asymmetric functions the averaged measurement results calculated in a prescribed time window can distinguish the types of measurements performed on the other member of the pair. This can establish a communication code that enables faster-than-light information sharing under specific conditions. There may be also further theoretical consequences: a significant extension of the quantum mechanical nonlocality principle.

Past research relates design creativity to 'divergent thinking,' i.e., how well the concept space is explored during the early phase of design. Researchers have argued that generating several concepts would increase the chances of producing better design solutions. 'Variety' is one of the parameters by which one can quantify the breadth of a concept space explored by the designers. It is useful to assess variety at the conceptual design stage because, at this stage, designers have the freedom to explore different solution principles so as to satisfy a design problem with substantially novel concepts. This article elaborates on and critically examines the existing variety metrics from the engineering design literature, discussing their limitations. A new distance-based variety metric is proposed, along with a prescriptive framework to support the assessment process. This framework uses the SAPPhIRE model of causality as a knowledge representation scheme to measure the real-valued distance between two design concepts. The proposed framework is implemented in a software tool called 'VariAnT.' Furthermore, the tool's application is demonstrated through an illustrative example.

The identification of complex prebiotic molecules using millimeter and submillimeter telescopes allows us to understand how the basic building blocks of life are formed in the universe. In the interstellar medium (ISM), ethylene glycol ((CH$_{2}$OH)$_{2}$) is the simplest sugar alcohol molecule, and it is the reduced alcohol of the simplest sugar-like molecule, glycolaldehyde (CH$_{2}$OHCHO). We present the first detection of the rotational emission lines of $aGg^{\prime}$ conformer of ethylene glycol ((CH$_{2}$OH)$_{2}$) towards the hot molecular core G358.93$-$0.03 MM1 using the Atacama Large Millimeter/Submillimeter Array (ALMA). The estimated column density of $aGg^{\prime}$-(CH$_{2}$OH)$_{2}$ towards the G358.93$-$0.03 MM1 is (4.5$\pm$0.1)$\times$10$^{16}$ cm$^{-2}$ with an excitation temperature of 155$\pm$35 K. The abundance of $aGg^{\prime}$-(CH$_{2}$OH)$_{2}$ with respect to H$_{2}$ is (1.4$\pm$0.5)$\times$10$^{-8}$. Similarly, the abundances of $aGg^{\prime}$-(CH$_{2}$OH)$_{2}$ with respect to CH$_{2}$OHCHO and CH$_{3}$OH are 3.1$\pm$0.5 and (6.1$\pm$0.3)$\times$10$^{-3}$. We compare the estimated abundance of $aGg^{\prime}$-(CH$_{2}$OH)$_{2}$ with the existing three-phase warm-up chemical model abundance of (CH$_{2}$OH)$_{2}$, and we notice the observed abundance and modelled abundance are nearly similar. We discuss the possible formation pathways of $aGg^{\prime}$-(CH$_{2}$OH)$_{2}$ towards the hot molecular cores, and we find that $aGg^{\prime}$-(CH$_{2}$OH)$_{2}$ is probably created via the recombination of two CH$_{2}$OH radicals on the grain surface of G358.93$-$0.03 MM1., Comment: Published in Monthly Notices of the Royal Astronomical Society (MNRAS)

The generation of $S_z=1$ triplet Cooper pairs has been predicted theoretically in superconducting-ferromagnetic hybrid heterostructures in the presence of spin-orbit coupling [F. S. Bergeret and I. V. Tokatly, Phys. Rev. B 89, 134517 (2014) and Jacobsen et al., Sci. Rep. 6, 23926 (2016)]. In this study, we experimentally investigate vertical Josephson junctions where the weak link is formed from a ferromagnetic layer with perpendicular magnetic anisotropy sandwiched by two non-magnetic layers with weak or strong spin-orbit coupling. We find that the decay of the Josephson coupling is reduced in the latter case, possibly indicating the presence of $S_z=1$ spin-triplet correlations. We speculate that the canted magnetization required for these correlations is provided by the interaction of magnetization with Meissner effect in the superconducting layers.

Reionization is one of the least understood processes in the evolution history of the Universe, mostly because of the numerous astrophysical processes occurring simultaneously about which we do not have a very clear idea so far. In this article, we use the Gaussian Process Regression (GPR) method to learn the reionization history and infer the astrophysical parameters. We reconstruct the UV luminosity density function using the HFF and early JWST data. From the reconstructed history of reionization, the global differential brightness temperature fluctuation during this epoch has been computed. We perform MCMC analysis of the global 21-cm signal using the instrumental specifications of SARAS, in combination with Lyman-$\alpha$ ionization fraction data, Planck optical depth measurements and UV luminosity data. Our analysis reveals that GPR can help infer the astrophysical parameters in a model-agnostic way than conventional methods. Additionally, we analyze the 21-cm power spectrum using the reconstructed history of reionization and demonstrate how the future 21-cm mission SKA, in combination with Planck and Lyman-$\alpha$ forest data, improves the bounds on the reionization astrophysical parameters by doing a joint MCMC analysis for the astrophysical parameters plus 6 cosmological parameters for $\Lambda$CDM model. The results make the GPR-based reconstruction technique a robust learning process and the inferences on the astrophysical parameters obtained therefrom are quite reliable that can be used for future analysis.

Open quantum dynamics can be categorized in several ways, including according to their divisibility. For any quantum channel acting for any finite time period, we propose measures of P-indivisibility and CP-indivisibility, where P and CP stand for positivity and complete positivity respectively. Subsequently, we also propose two quantities to measure the resourcefulness - with respect to an arbitrary quantum resource - of any quantum channel within any finite time interval. Moreover, we find a bridge between these two classes of metrics, viz. those quantifying divisibilities of quantum channels and those gauging their resourcefulness, by identifying two separate relations between elements of one class with those of the other. Lastly, we verify the two relations using quantum non-Markovianity as a channel resource., Comment: 7 pages, 3 figures