Search

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. Version accepted by Physical Review D for publication

The fracture characteristics of LuPO4 rare-earth phosphate environmental barrier coating (EBC) material under molten calcium-magnesium aluminosilicate (CMAS) corrosion is quantified. EBCs are crucial for protecting SiC-based ceramic matrix composite components in the hot section of gas turbine engines. Recent research has highlighted the potential of rare-earth phosphates as better EBC materials than third-generation rare-earth silicates for CMAS corrosion resistance. However, the fracture of EBCs under CMAS corrosion during service remains a significant concern. This work investigates the fracture behavior of LuPO4 using mesoscale simulation and experiments. The model uses micrographs taken from fabricated EBC samples for mesoscale fracture simulations. The simulation results are compared with experimental fracture toughness data and validated using statistical tests (p<0.01). The simulation results and experimental observations demonstrate that LuPO4 may exhibit higher fracture resistance than Lu2SiO5 rare-earth silicate under similar CMAS corrosion conditions, offering potential insights for future EBC design and development.

Quantum resources are fundamental for the development of quantum technology. Thus, verifying their presence is crucial for studying their role in various information-processing tasks. Here, we enquire whether can one detect the presence of a quantum resource in some operational task or equivalently does every quantum resource provides an advantage over its free counterpart in some black box scenario where one does not have much information about the devices. We introduce the framework for detecting quantum resources semi-device independently by considering the prepare-and-operation scenario with the restriction on the dimension of the quantum channel connecting the preparation box with the operation box. For any dimension $d$, we show that for any resource theory with less than $d^2$ number of linearly independent free states or free operations, there exist correlations that can detect the presence of a quantum resource. For the particular case when the quantum channel is constrained to transmit only qubits, we explicitly construct witnesses to observe the presence of various quantum resources., Comment: 8 pages, 1 Figure, Comments are welcome!!

The star-critical Ramsey number is a refinement of the concept of a Ramsey number. In this paper, we give equivalent criteria for which the star-critical Ramsey number vanishes. Next, we provide a new general lower bound for multicolor star-critical Ramsey numbers whenever it does not vanish. As an application, we evaluate $r_*(P_k, P_3, P_3)$, where $P_n$ is a path of order $n$. In the process of proving these results, we also show that $r_*(C_5, P_3)=3$, where $C_5$ is a cycle of order $5$., Comment: 18 pages, 6 figures

In addition to standard and non-standard Lagrangians of classical mechanics, we consider, in this work, null Lagrangians that (i) identically satisfy the Euler-Lagrange equation and at the same time can be expressed as (ii) the total derivative of some scalar function. As an addendum to the properties in (i) and (ii) we find that null Lagrangians are also characterized by (iii) vanishing energy functions or Jacobi integrals. By working with higher-order SL(2;R) invariant Schwarzian derivatives introduced recently by Krivonos we demonstrate that these Schwarzians, especially the even-order ones, provide a natural basis to introduce higher-order null Lagrangians in Schwarzian mechanics., Comment: 6 pages

The nonstandard Lagrangian representations of Ricatti and Riccati-type equations that exist in the literature cannot be obtained using Helmholtz solution of the inverse problem. In this work we consider Riccati and higher-order Riccati equations and construct their standard Lagrangian representation by using a simple variant of the Helmholtz theory. We make use of the self-adjoint form of the linear equations corresponding to odd-order equations in the Riccati chain to provide a symmetry-based approach for the solution of inverse problem. Explicit results presented for Lagrangians of the first and third-order Riccati equations show that one cannot Hamiltonize the Riccati family of equations by the traditional method used in classical mechanics., Comment: 12 pages

The strength of the HI signal originating from a distant galaxy at a cosmological distance is several orders of magnitude lower than the foreground and background noise and hence it is difficult to observe this signal at a given radio telescope. However, a few recent studies reported the detection of that signal at the radio band suggests the strength of this signal is somehow magnified. In this analysis, we study the prospects of detecting this signal at different frequency bands of the uGMRT where this signal is supposed to be amplified through the strong gravitational lensing by an isolated neutron star located in a cosmological distance. Our study shows the effects of the lensing parameters on the observables of that amplified signal and discusses its variation with the frequency bands considered here. We present a method to estimate the position and size of an isolated neutron star using the signal-to-noise ratio of that signal supposed to be detected at different frequency bands of the uGMRT. We discuss the scope of multi-messenger astronomy in the era of HI observation where the estimated lensing parameters can be cross-validated using the pulsar detection at the X-ray band from the same location in the sky. Our analysis is equally applicable to any radio telescope with given specifications., Comment: 16 Pages, 4 figures, comments and suggestions are welcome

We introduce Arboretum, the largest publicly accessible dataset designed to advance AI for biodiversity applications. This dataset, curated from the iNaturalist community science platform and vetted by domain experts to ensure accuracy, includes 134.6 million images, surpassing existing datasets in scale by an order of magnitude. The dataset encompasses image-language paired data for a diverse set of species from birds (Aves), spiders/ticks/mites (Arachnida), insects (Insecta), plants (Plantae), fungus/mushrooms (Fungi), snails (Mollusca), and snakes/lizards (Reptilia), making it a valuable resource for multimodal vision-language AI models for biodiversity assessment and agriculture research. Each image is annotated with scientific names, taxonomic details, and common names, enhancing the robustness of AI model training. We showcase the value of Arboretum by releasing a suite of CLIP models trained using a subset of 40 million captioned images. We introduce several new benchmarks for rigorous assessment, report accuracy for zero-shot learning, and evaluations across life stages, rare species, confounding species, and various levels of the taxonomic hierarchy. We anticipate that Arboretum will spur the development of AI models that can enable a variety of digital tools ranging from pest control strategies, crop monitoring, and worldwide biodiversity assessment and environmental conservation. These advancements are critical for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. Arboretum is publicly available, easily accessible, and ready for immediate use. Please see the \href{https://baskargroup.github.io/Arboretum/}{project website} for links to our data, models, and code., Comment: Preprint under review

Quantum architecture search (QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS focus on machine learning-based approaches from reinforcement learning, like deep Q-network. While multi-layer perceptron-based deep Q-networks have been applied for QAS, their interpretability remains challenging due to the high number of parameters. In this work, we evaluate the practicality of Kolmogorov-Arnold Networks (KANs) in QAS problems, analyzing their efficiency in the task of quantum state preparation and quantum chemistry. In quantum state preparation, our results show that in a noiseless scenario, the probability of success and the number of optimal quantum circuit configurations to generate the multi-qubit maximally entangled states are $2\times$ to $5\times$ higher than Multi-Layer perceptions (MLPs). Moreover, in noisy scenarios, KAN can achieve a better fidelity in approximating maximally entangled state than MLPs, where the performance of the MLP significantly depends on the choice of activation function. In tackling quantum chemistry problems, we enhance the recently proposed QAS algorithm by integrating Curriculum Reinforcement Learning (CRL) with a KAN structure instead of the traditional MLP. This modification allows us to design a parameterized quantum circuit that contains fewer 2-qubit gates and has a shallower depth, thereby improving the efficiency of finding the ground state of a chemical Hamiltonian. Further investigation reveals that KAN requires a significantly smaller number of learnable parameters compared to MLPs; however, the average time of executing each episode for KAN is higher., Comment: 11 pages and 5 figures, 7 tables. New experiments added and typo removed

In the standard circuit model of quantum computation, the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms, represented as unitary matrices, to bounded depth quantum circuits depends strongly on the set of gates available for the decomposition routine. To investigate this dependence, we explore the design space of discrete quantum gate sets and present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates. The evaluation is conditioned on a set of unitary transformations representing target use cases on the quantum processors. The cost function considers three key factors: (i) the statistical distribution of the decomposed circuits' depth, (ii) the statistical distribution of process fidelities for the approximate decomposition, and (iii) the relative novelty of a gate set compared to other gate sets in terms of the aforementioned properties. The developed software, YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates through this tunable joint cost function. To identify these gate sets, we use the novelty search algorithm, circuit decomposition techniques, and stochastic optimization to implement YAQQ within the Qiskit quantum simulator environment. YAQQ exploits reachability tradeoffs conceptually derived from quantum algorithmic information theory. Our results demonstrate the pragmatic application of identifying gate sets that are advantageous to popularly used quantum gate sets in representing quantum algorithms. Consequently, we demonstrate pragmatic use cases of YAQQ in comparing transversal logical gate sets in quantum error correction codes, designing optimal quantum instruction sets, and compiling to specific quantum processors.

The first star-forming objects which formed at high redshifts during the cosmic dawn (CD) also emitted photons between Lyman-$\alpha$ and Lyman-limit frequencies. These photons are instrumental in coupling the spin temperature of the neutral hydrogen (HI) atoms with the kinetic temperature of the intergalactic medium (IGM). Along with this coupling effect, these photons also impact the kinetic temperature by exchanging energy with the HI atoms. The injected Lyman-$\alpha$ photons in general cool the medium, while the continuum photons heat the medium. While studying this effect in the literature, quasi-static profile around the Lyman-$\alpha$ frequency is assumed. In this paper, we solve the time-dependent coupled dynamics of the photon intensity profile along with the evolution of the thermal state of the IGM and HI spin temperature. It is expected that, during the CD era, the IGM has a mix of continuum photons with 10-20% of injected photons. For this case, we show that the system reaches thermal equilibrium in around 1 Myr, with final temperature in the range 50-100 K. This time scale is comparable to the source lifetime of PopIII stars at high redshifts. One impact of switching off short-lived sources is that it can keep the system heated above the temperature of the quasi-static state. We also show that the quasi-static equilibrium for the continuum photons is only achieved on time scales of 100 Myr at $z\simeq 20$, comparable to the age of the Universe. We also briefly discuss how the Lyman-$\alpha$ induced heating can impact the 21 cm signal from CD., Comment: 18 pages, 6 figures, submitted to ApJ

Neutrino trident production of di-lepton pairs is well recognized as a sensitive probe of both electroweak physics and physics beyond the Standard Model. Although a rare process, it could be significantly boosted by such new physics, and it also allows the electroweak theory to be tested in a new regime. We demonstrate that the forward neutrino physics program at the Large Hadron Collider offers a promising opportunity to measure for the first time, dimuon neutrino tridents with a statistical significance exceeding $5\sigma$. We present predictions for various proposed experiments and outline a specific experimental strategy to identify the signal and mitigate backgrounds, based on "reverse tracking" dimuon pairs in the FASER$\nu$2 detector. We also discuss prospects for constraining beyond Standard Model contributions to neutrino trident rates at high energies., Comment: 19 pages, 11 figures

We present a detailed benchmarking of different treatments of the QCD evolution of unpolarized PDFs at approximate $N^3LO$ ($aN^3LO$) order in the QCD coupling. Namely, the implementations in the public $aN^3LO$ releases of the MSHT and NNPDF global PDF fitters, as well as that of the theoretical FHMRUVV collaboration are compared. This follows the same procedure as in previous benchmarking exercises at lower order, that is by considering the impact of this evolution on a set of simple toy PDFs.

In this paper we report our investigations on visibility with respect to the Kobayashi distance and its applications, with a special focus on planar domains. We prove that totally disconnected subsets of the boundary are removable in the context of visibility. We also show that a domain in $\mathbb{C}^n$ is a local weak visibility domain if and only if it is a weak visibility domain. The above holds also for visibility. Along the way, we prove an intrinsic localization result for the Kobayashi distance. Moreover, we observe some interesting consequences of weak visibility; for example, weak visibility implies compactness of the end topology of the closure of the domain. For planar domains: (i) We provide examples of visibility domains that are not locally Goldilocks at any boundary point. (ii) We provide certain general conditions on planar domains that yield the continuous extension of conformal maps, generalizing the Carath\'{e}odory extension theorem. Our conditions are quite general and assume very little regularity of the boundary. We demonstrate this through examples. (iii) We also provide conditions for the homeomorphic extension of biholomorphic maps up to the boundary. (iv) We prove that a hyperbolic, simply connected domain possesses the visibility property if and only if its boundary is locally connected. This leads us to reformulate the MLC conjecture in terms of visibility. (v) We provide a characterization of visibility for a large class of planar domains including certain uncountably connected domains., Comment: 66 pages. Comments are welcome

We evaluate the effectiveness of GPT-4 Turbo in generating educational questions from NCERT textbooks in zero-shot mode. Our study highlights GPT-4 Turbo's ability to generate questions that require higher-order thinking skills, especially at the "understanding" level according to Bloom's Revised Taxonomy. While we find a notable consistency between questions generated by GPT-4 Turbo and those assessed by humans in terms of complexity, there are occasional differences. Our evaluation also uncovers variations in how humans and machines evaluate question quality, with a trend inversely related to Bloom's Revised Taxonomy levels. These findings suggest that while GPT-4 Turbo is a promising tool for educational question generation, its efficacy varies across different cognitive levels, indicating a need for further refinement to fully meet educational standards., Comment: Accepted at Learnersourcing: Student-Generated Content @ Scale 2024

This paper extends the concept of de Branges matrices to any finite $m\times m$ order where $m=2n$. We shall discuss these matrices along with the theory of de Branges spaces of $\mathbb{C}^n$-valued entire functions and their associated functions. A parametrization of these matrices is obtained using the Smirnov maximum principle for matrix valued functions. Additionally, a factorization of matrix valued meromorphic functions is discussed., Comment: 19 Pages

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15\% and 79.79\% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://github.com/AyushRoy2001/FA-Net.

Deep learning and computer vision methods are nowadays predominantly used in the field of ophthalmology. In this paper, we present an attention-aided DenseNet-121 for classifying normal and glaucomatous eyes from fundus images. It involves the convolutional block attention module to highlight relevant spatial and channel features extracted by DenseNet-121. The channel recalibration module further enriches the features by utilizing edge information along with the statistical features of the spatial dimension. For the experiments, two standard datasets, namely RIM-ONE and ACRIMA, have been used. Our method has shown superior results than state-of-the-art models. An ablation study has also been conducted to show the effectiveness of each of the components. The code of the proposed work is available at: https://github.com/Soham2004GitHub/DADGC.

A vast majority of electrical devices have integrated magnetic units, which generate constant magnetic fields with noticeable vibrations. The majority of existing nanogenerators acquire energy through friction/mechanical forces and most of these instances overlook acoustic vibrations and magnetic fields. Magnetic two-dimensional (2D) tellurides present a wide range of possibilities for devising a potential flexible energy harvester. We have synthesized two-dimensional chromium telluride (2D CrTe3) which exhibits ferromagnetic (FM) nature with a Tc of 224 K. The structure exhibits stable high remnant magnetization, making 2D CrTe3 flakes a potential material for harvesting of magneto-acoustic waves at room temperature. A magneto-acoustic nanogenerator (MANG) was fabricated composing of 2D CrTe3 dispersed in a polymer matrix. Basic mechanical stability and sensitivity of the device with change in load conditions were tested. A high surface charge density of 2.919 mC m-2 was obtained for the device. The thermal strain created in the lattice structure was examined using in-situ Raman spectroscopic measurements. The magnetic anisotropy energy (MAE) responsible for long-range FM ordering was calculated with the help of theoretical modelling. The theoretical calculations also showed opening of electronic bandgap which enhances the flexoelectric effects. The MANG can be a potential energy harvester to synergistically tap into the magneto-acoustic vibrations generated from the frequency changes of a vibrating device such as loudspeakers.

Stochastic video generation is particularly challenging when the camera is mounted on a moving platform, as camera motion interacts with observed image pixels, creating complex spatio-temporal dynamics and making the problem partially observable. Existing methods typically address this by focusing on raw pixel-level image reconstruction without explicitly modelling camera motion dynamics. We propose a solution by considering camera motion or action as part of the observed image state, modelling both image and action within a multi-modal learning framework. We introduce three models: Video Generation with Learning Action Prior (VG-LeAP) treats the image-action pair as an augmented state generated from a single latent stochastic process and uses variational inference to learn the image-action latent prior; Causal-LeAP, which establishes a causal relationship between action and the observed image frame at time $t$, learning an action prior conditioned on the observed image states; and RAFI, which integrates the augmented image-action state concept into flow matching with diffusion generative processes, demonstrating that this action-conditioned image generation concept can be extended to other diffusion-based models. We emphasize the importance of multi-modal training in partially observable video generation problems through detailed empirical studies on our new video action dataset, RoAM.

In this investigation, we compute the nuclear matrix elements (NMEs) relevant to the light neutrino-exchange mechanism governing neutrinoless double beta ($0\nu\beta\beta$) decay in $^{136}$Xe. Our method is based on the nonclosure approach within the interacting nuclear shell model framework. This approach considers the genuine effects arising from the excitation energies of two hundred states for each spin-parity of the intermediary nucleus $^{136}$Cs. All computations are performed using the effective shell model Hamiltonian GCN5082. To understand the impact of nuclear structure on $0\nu\beta\beta$ decay, we explore the dependence of the NME on various factors, including the number of intermediate states and their spin-parity characteristics. We identify an optimal closure energy of approximately 3.7 MeV for the $0\nu\beta\beta$ decay of $^{136}$Xe that reproduces the nonclosure NME using the closure approach. The calculated total NME for the light neutrino-exchange $0\nu\beta\beta$ decay of $^{136}$Xe is 2.06 with the CD-Bonn short-range correlation (SRC). These results can be valuable for future experimental investigations into the $0\nu\beta\beta$ decay of $^{136}$Xe., Comment: 10 pages, and 3 figures

Data augmentation is a powerful tool for improving deep learning-based image classifiers for plant stress identification and classification. However, selecting an effective set of augmentations from a large pool of candidates remains a key challenge, particularly in imbalanced and confounding datasets. We propose an approach for automated class-specific data augmentation using a genetic algorithm. We demonstrate the utility of our approach on soybean [Glycine max (L.) Merr] stress classification where symptoms are observed on leaves; a particularly challenging problem due to confounding classes in the dataset. Our approach yields substantial performance, achieving a mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean leaf stress dataset. Our method significantly improves the accuracy of the most challenging classes, with notable enhancements from 83.01% to 88.89% and from 85.71% to 94.05%, respectively. A key observation we make in this study is that high-performing augmentation strategies can be identified in a computationally efficient manner. We fine-tune only the linear layer of the baseline model with different augmentations, thereby reducing the computational burden associated with training classifiers from scratch for each augmentation policy while achieving exceptional performance. This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets. Our findings contribute to the growing body of research in tailored augmentation techniques and their potential impact on disease management strategies, crop yields, and global food security. The proposed approach holds the potential to enhance the accuracy and efficiency of deep learning-based tools for managing plant stresses in agriculture.

Quantum steering is an asymmetric form of quantum nonlocality where one can detect whether a measurement on one system can steer or change another distant system. It is well-known that there are quantum states that are entangled but unsteerable in the standard quantum steering scenario. Consequently, a long-standing open problem in this regard is whether the steerability of every entangled state can be activated in some way. In this work, we consider quantum networks and focus on the swap-steering scenario without inputs and find linear witnesses of network steerability corresponding to any negative partial transpose (NPT) bipartite state and a large class of bipartite states that violate the computable cross-norm (CCN) criterion. Furthermore, by considering that the trusted party can perform tomography of the incoming subsystems, we construct linear inequalities to witness swap-steerability of every bipartite entangled state. Consequently, for every bipartite entangled state one can now observe a form of quantum steering., Comment: 7 pages, 1 figure, Comments are welcome!

We propose a search strategy for MeV-scale feebly interacting massive particle (FIMP) dark matter (DM) at the $e^+e^-$ collider. We argue, detection of a mono-$\gamma$ signal plus missing energy can indicate to an MeV-scale reheating temperature of the Universe, after addressing observed DM abundance and other relevant constraints., Comment: 9 pages, 7 figures, and 1 table

Data annotation, the practice of assigning descriptive labels to raw data, is pivotal in optimizing the performance of machine learning models. However, it is a resource-intensive process susceptible to biases introduced by annotators. The emergence of sophisticated Large Language Models (LLMs) presents a unique opportunity to modernize and streamline this complex procedure. While existing research extensively evaluates the efficacy of LLMs, as annotators, this paper delves into the biases present in LLMs when annotating hate speech data. Our research contributes to understanding biases in four key categories: gender, race, religion, and disability with four LLMs: GPT-3.5, GPT-4o, Llama-3.1 and Gemma-2. Specifically targeting highly vulnerable groups within these categories, we analyze annotator biases. Furthermore, we conduct a comprehensive examination of potential factors contributing to these biases by scrutinizing the annotated data. We introduce our custom hate speech detection dataset, HateBiasNet, to conduct this research. Additionally, we perform the same experiments on the ETHOS (Mollas et al. 2022) dataset also for comparative analysis. This paper serves as a crucial resource, guiding researchers and practitioners in harnessing the potential of LLMs for data annotation, thereby fostering advancements in this critical field., Comment: Accepted at NeurIPS Safe Generative AI Workshop, 2024

We report results from a detailed study of the neutron star X-ray binary, 4U 1608-52 using observations with {\it AstroSat} (LAXPC/SXT) and {\it NICER} during its 2016 and 2020 outbursts. The 0.7--20.0 keV spectra could be well described with the disk blackbody and thermal Comptonization model. The best-fitting inner disk temperature is $\sim$ 1 keV and radius { $\sim$ 22.17$^{+2.57}_{-2.38}$--27.19$^{+2.03}_{-1.85}$} km and no significant evolution was observed in the disk radius after performing flux and time-resolved spectroscopy. We used a multi-Lorentzian approach to fit the power density spectra and obtained broad-band noise variability. We estimated the energy-dependent fractional root mean square and time-lag of the broad-band noise, and these variations are quantitatively modelled as being due to the coherent variation of the disk emission and the coronal heating rate. Thus, the rapid temporal modeling is consistent with the longer term spectral evolution where the inner disk radius does not vary, and instead the variations can be attributed to accretion rate variations which changes the inner disk temperature and the coronal heating rate., Comment: 17 pages, 13 figures, The manuscript has been accepted for publication in The Astrophysical Journal

The INterplanetary Flux ROpe Simulator (INFROS) is an observationally constrained analytical model dedicated for forecasting the strength of the southward component (Bz) of the magnetic field embedded in interplanetary coronal mass ejections (ICMEs). In this work, we validate the model for six ICMEs sequentially observed by two radially-aligned spacecraft positioned at different heliocentric distances. The six selected ICMEs in this study comprise of cases associated with isolated CME evolution as well as those interacting with high-speed streams (HSS) and high-density streams (HDS). For the isolated CMEs, our results show that the model outputs at both the spacecraft are in good agreement with in-situ observations. However, for most of the interacting events, the model correctly captures the CME evolution only at the inner spacecraft. Due to the interaction with HSS and HDS, which in most cases occurred at heliocentric distances beyond the inner spacecraft, the ICME evolution no longer remains self-similar. Consequently, the model underestimates the field strength at the outer spacecraft. Our findings indicate that constraining the INFROS model with inner spacecraft observations significantly enhances the prediction accuracy at the outer spacecraft for the three events undergoing self-similar expansion, achieving a 90 % correlation between observed and predicted Bz profiles. This work also presents a quantitative estimation of the ICME magnetic field enhancement due to interaction which may lead to severe space weather. We conclude that the assumption of self-similar expansion provides a lower limit to the magnetic field strength estimated at any heliocentric distance, based on the remote sensing observations., Comment: 20 pages, 15 figures, Accepted for publication in the Astrophysical Journal Supplement Series

Given any shift-invariant closed subspace $\mathcal{S}$ (aka submodule) of the Hardy space over the unit polydisc $H^2(\mathbb{D}^n)$ (where $n \geq 2$), let $R_{z_j}:=M_{z_j}|_{\mathcal{S}}$, and $E_{z_j}:=P_{\mathcal{S}}\circ ev_{z_j}$, for each $j \in \{1,\ldots,n\}$. Here, $ev_{z_j}$ is the operator evaluating at $0$ in the $z_j$-th variable. In this article, we prove that given any subset $\Lambda \subseteq \{1,\ldots,n\}$, there exists a collection of one-variable inner functions $\{\phi_\lambda (z_\lambda)\}_{\lambda \in \Lambda}$ on $\mathbb{D}^n$, such that \[ \mathcal{S} = \sum_{\lambda \in \Lambda} \phi_\lambda (z_\lambda)H^2(\mathbb{D}^n), \] if and only if the conditions $ (I_{\mathcal{S}}-E_{z_k}E_{z_k}^*)(I_{\mathcal{S}}-R_{z_k}R_{z_k}^*)=0$ for all $k \in \{1,\dots,n\} \setminus \Lambda$, and $(I_{\mathcal{S}}-E_{z_{i}}E_{z_{i}}^*)(I_{\mathcal{S}}-R_{z_{i}}R_{z_{i}}^*)(I_{\mathcal{S}}-E_{z_{j}}E_{z_{j}}^*)(I_{\mathcal{S}}-R_{z_{j}}R_{z_{j}}^*)=0$ for all distinct $i,j \in \Lambda$ are satisfied. Following this, we study R.G. Douglas's question on the commutativity of orthogonal projections onto the corresponding quotient modules., Comment: Preliminary version. Comments are welcome!

In this short note, we revisit the entanglement wedge representation of AdS$_3$ bulk fields in terms of CFT operator product expansion (OPE) blocks for a general class of blocks. Given a boundary interval and its associated causal diamond, the OPEs involve boundary operators with or without spin, and located either at spacelike or timelike edges of the diamond. Only for a subset of these cases, can the OPE block be dual to a geodesic bulk field. We show that when applied to de Sitter, a suitable combination of Euclidean OPE blocks can represent a dS scalar integrated over the timelike extremal surfaces, which play an important role in defining pseudo-entropy. We also work out some simple higher dimensional examples., Comment: Version 1. 37 pages, 2 figures

We examine bulk viscosity, taking into account trapped neutrinos in baryonic matter, in the context of binary neutron star mergers. Following the merging event, the binary star can yield a remnant compact object with densities up to $5$ nuclear saturation density and temperature upto $50$ MeV resulting in the retention of neutrinos. We employ two relativistic mean field models, NL3 and DDME2, to describe the neutrino-trapped baryonic matter. The dissipation coefficient is determined by evaluating the Modified URCA interaction rate in the dense baryonic medium, and accounting for perturbations caused by density oscillations. We observe the resonant behavior of bulk viscosity as it varies with the temperature of the medium. The bulk viscosity peak remains within the temperature range of $\sim 13-50$ MeV, depending upon the underlying equation of states and lepton fractions. This temperature range corresponds to the relevant domain of binary neutron star mergers. We also note that in presence of neutrinos in the medium the bulk viscosity peak shifts towards higher temperature and the peak value of bulk viscosity also changes. The time scale of viscous dissipation is dictated by the beta-off-equilibrium susceptibilities derived from the nuclear equation of state. The resulting viscous decay time scale ranges from $32-100$ milliseconds, which aligns with the order of magnitude of the post-merger object's survival time in some specific scenarios., Comment: 27 pages, 10 figures

Breast cancer is a major global health concern. Pathologists face challenges in analyzing complex features from pathological images, which is a time-consuming and labor-intensive task. Therefore, efficient computer-based diagnostic tools are needed for early detection and treatment planning. This paper presents a modified version of MultiResU-Net for histopathology image segmentation, which is selected as the backbone for its ability to analyze and segment complex features at multiple scales and ensure effective feature flow via skip connections. The modified version also utilizes the Gaussian distribution-based Attention Module (GdAM) to incorporate histopathology-relevant text information in a Gaussian distribution. The sampled features from the Gaussian text feature-guided distribution highlight specific spatial regions based on prior knowledge. Finally, using the Controlled Dense Residual Block (CDRB) on skip connections of MultiResU-Net, the information is transferred from the encoder layers to the decoder layers in a controlled manner using a scaling parameter derived from the extracted spatial features. We validate our approach on two diverse breast cancer histopathology image datasets: TNBC and MonuSeg, demonstrating superior segmentation performance compared to state-of-the-art methods. The code for our proposed model is available on https://github.com/AyushRoy2001/GRU-Net.

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://github.com/AyushRoy2001/AWGUNET.

The recent IceCube detection of TeV neutrino emission from the nearby active galaxy NGC 1068 suggests that active galactic nuclei (AGN) could make a sizable contribution to the diffuse flux of astrophysical neutrinos. The absence of TeV $\gamma$-rays from NGC 1068 indicates neutrino production in the vicinity of the supermassive black hole, where the high radiation density leads to $\gamma$-ray attenuation. Therefore, any potential neutrino emission from similar sources is not expected to correlate with high-energy $\gamma$-rays. Disk-corona models predict neutrino emission from Seyfert galaxies to correlate with keV X-rays, as they are tracers of coronal activity. Using through-going track events from the Northern Sky recorded by IceCube between 2011 and 2021, we report results from a search for individual and aggregated neutrino signals from 27 additional Seyfert galaxies that are contained in the BAT AGN Spectroscopic Survey (BASS). Besides the generic single power-law, we evaluate the spectra predicted by the disk-corona model. Assuming all sources to be intrinsically similar to NGC 1068, our findings constrain the collective neutrino emission from X-ray bright Seyfert galaxies in the Northern Hemisphere, but, at the same time, show excesses of neutrinos that could be associated with the objects NGC 4151 and CGCG 420-015. These excesses result in a 2.7$\sigma$ significance with respect to background expectations., Comment: 17 pages, 9 figures

Active Galactic Nuclei (AGN) are promising candidate sources of high-energy astrophysical neutrinos since they provide environments rich in matter and photon targets where cosmic ray interactions may lead to the production of gamma rays and neutrinos. We searched for high-energy neutrino emission from AGN using the $\textit{Swift}$-BAT Spectroscopic Survey (BASS) catalog of hard X-ray sources and 12 years of IceCube muon track data. First, upon performing a stacked search, no significant emission was found. Second, we searched for neutrinos from a list of 43 candidate sources and found an excess from the direction of two sources, Seyfert galaxies NGC 1068 and NGC 4151. We observed NGC 1068 at flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $4.02_{-1.52}^{+1.58} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV, with power-law spectral index, $\gamma$ = 3.10$^{+0.26}_{-0.22}$, consistent with previous IceCube results. The observation of a neutrino excess from the direction of NGC 4151 is at a post-trial significance of 2.9$\sigma$. If interpreted as an astrophysical signal, the excess observed from NGC 4151 corresponds to a flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $1.51_{-0.81}^{+0.99} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV and $\gamma$ = 2.83$^{+0.35}_{-0.28}$.

An analysis of high-resolution ($R\sim48\,000$) optical spectrum of hot (B1Ibe) post-AGB star LS 4331 (IRAS 17381-1616) is presented. The detailed identification of the observed absorption and emission features in the wavelength range 3700-9200 \r{A} is carried out for the first time. From non-LTE analysis of absorption lines the atmospheric parameters and chemical composition of the star are derived. We estimate $T_{\rm eff}=20~900\pm500$ K, $\log g=2.57\pm0.08$, $V_r=-51.7\pm0.8$ km s$^{-1}$, $\xi_{\rm t}=24\pm4$ km s$^{-1}$ and $v \sin i=30\pm5$ km s$^{-1}$. A abundance analysis for C, N, O, Mg, Al, S and Si reveals that the N and O abundance is close to solar while metal underabundances relative to the solar value (i.e. [Mg/H] = -1.04 dex, [Al/H] = -1.20 dex, [Si/H] = -0.46 dex) are found. We conclude that LS 4331 is a high galactic latitude metal-poor and carbon deficient hot post-AGB star. The underabundance of carbon ([C/H]= -0.64 dex) is similar to that found in other hot post-AGB stars and indicates that the star's AGB phase of evolution was terminated before the third dredge up. From the nebular emission lines the plasma diagnostics are derived. The presence of nebular emission lines in the spectrum of LS 4331 indicates that the photoionization of circumstellar envelope has already started. The nebular parameters and expansion velocity of the nebula is derived. Using the Gaia DR3 distance the absolute luminosity of the star is derived and the star's position on the post-AGB evolutionary tracks suggests that its initial main sequence mass is about 1.2$M_{\odot}$. It is also reported that fast irregular brightness variations with an amplitude of up to 0.3 mag in $V$ band have been found in the star, typical of hot post-AGB objects.

In this article, we study the fractional Br\'{e}zis-Nirenberg type problem on whole domain $\mathbb{R}^N$ associated with the fractional $p$-Laplace operator. To be precise, we want to study the following problem: \begin{equation*} (-\Delta)_{p}^{s}u - \lambda w |u|^{p-2}u= |u|^{p_{s}^{*}-2}u \quad \text{in} ~\mathcal{D}^{s,p}(\mathbb{R}^{N}), \end{equation*} where $s\in (0,1),~p \in (1,\frac{N}{s}), ~p_{s}^{*}= \frac{Np}{N-sp}$ and the operator $(-\Delta)_{p}^{s}$ is the fractional $p$-Laplace operator. The space $\mathcal{D}^{s,p}(\mathbb{R}^{N})$ is the completion of $C_c^\infty(\mathbb{R}^N)$ with respect to the Gaglairdo semi-norm. In this article, we prove the existence of a positive solution to this problem by allowing the Hardy weight $w$ to change its sign., Comment: 28 pages

Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, they have been shown to suffer from a critical limitation pertinent to 'hallucination' in their output. Recent research has focused on investigating and addressing this problem for a variety of tasks such as biography generation, question answering, abstractive summarization, and dialogue generation. However, the crucial aspect pertaining to 'negation' has remained considerably underexplored. Negation is important because it adds depth and nuance to the understanding of language and is also crucial for logical reasoning and inference. In this work, we address the above limitation and particularly focus on studying the impact of negation in LLM hallucinations. Specifically, we study four tasks with negation: 'false premise completion', 'constrained fact generation', 'multiple choice question answering', and 'fact generation'. We show that open-source state-of-the-art LLMs such as LLaMA-2-chat, Vicuna, and Orca-2 hallucinate considerably on all these tasks involving negation which underlines a critical shortcoming of these models. Addressing this problem, we further study numerous strategies to mitigate these hallucinations and demonstrate their impact.

We report the detection of seven new galaxy candidates with redshift $z$ $>$ 11 within the JWST Advanced Deep Extragalactic Survey (JADES) GOODS-S and GOODS-N fields. These new candidates are detected through meticulous analysis of NIRCam photometry in eight filters spanning a wavelength range of 0.8-5.0 $\mu$m. Photometric redshifts of these galaxy candidates are independently measured utilizing spectral energy distribution (SED) fitting techniques using \texttt{EAZY} and \texttt{BAGPIPES} codes, followed by visual scrutiny. Two of these galaxy candidates are located in GOODS-S field, while the remaining five galaxies are located in GOODS-N field. Our analysis reveals that the stellar masses of these galaxies typically range from log $M_{\ast}$/$M_{\odot}$ = 7.75--8.75. Futhermore, these galaxies are typically young with their mass-weighted ages spanning from 80 to 240 Myr. Their specific star formation rates (sSFR), quantified as $\log (\text{sSFR}/\text{Gyr}$), are measured to vary between $\sim 0.95$ to 1.46. These new galaxy candidates offer a robust sample for probing the physical properties of galaxies within the first few hundred Myr of the history of the Universe. We also analyze the relationship between star formation rate (SFR) and stellar mass ($M_\ast$) within our sample. Using linear regression, our analysis yields a slope of $0.71 \pm 0.12$, which we then compare with results from previous studies. Continued investigation through spectroscopic analysis using JWST/NIRSpec is needed to spectroscopically confirm these high-redshift galaxy candidates and investigate further into their physical properties. We plan to follow up on these candidates with future NIRSpec observations., Comment: 12 pages, 10 figures, Submitted to MNRAS

We study transfer learning for estimation in latent variable network models. In our setting, the conditional edge probability matrices given the latent variables are represented by $P$ for the source and $Q$ for the target. We wish to estimate $Q$ given two kinds of data: (1) edge data from a subgraph induced by an $o(1)$ fraction of the nodes of $Q$, and (2) edge data from all of $P$. If the source $P$ has no relation to the target $Q$, the estimation error must be $\Omega(1)$. However, we show that if the latent variables are shared, then vanishing error is possible. We give an efficient algorithm that utilizes the ordering of a suitably defined graph distance. Our algorithm achieves $o(1)$ error and does not assume a parametric form on the source or target networks. Next, for the specific case of Stochastic Block Models we prove a minimax lower bound and show that a simple algorithm achieves this rate. Finally, we empirically demonstrate our algorithm's use on real-world and simulated graph transfer problems.

Dimerized quantum magnets are exotic crystalline materials where Bose-Einstein condensation of magnetic excitations can happen. However, known dimerized quantum magnets are limited to only a few oxides and halides. Here, we unveil 9 dimerized quantum magnets and 11 conventional antiferromagnets in ternary metal borides MTB$_4$ (M = Sc, Y, La, Ce, Lu, Mg, Ca, Al; T = V, Cr, Mn, Fe, Co, Ni). In this type of structure, 3d transition-metal atoms T are arranged in dimers. Quantum magnetism in these compounds is dominated by strong antiferromagnetic interactions between Cr (both Cr and Mn for M = Mg and Ca) atoms within the structural dimers, with much weaker interactions between the dimers. These systems are proposed to be close to a quantum critical point between a disordered singlet spin-dimer phase, with a spin gap, and the ordered conventional N\'eel antiferromagnetic phase. This new family of dimerized quantum magnets greatly enriches the materials inventory that allows investigations of the spin-gap phase. All the quantum-, conventionally-, and non-magnetic systems identified, together with experimental synthesis methods of a phase suitable for characterization, provide a platform with abundant possibilities to tune the magnetic exchange coupling by doping and study this unconventional type of quantum phase transition. This work opens up new avenues for studying the quantum magnetism of spin dimers in borides and establishes a theoretical workflow for future searches for dimerized quantum magnets in other families or types of materials.

Geometrically frustrated magnetic semiconductor $\textrm{CuMnO}_{2}$ has potential applications as photo-catalyst, in photochemical cells and multi-ferroic devices. Electronic band structure in the antiferromagnetic and ferromagnetic phases of $\textrm{CuMnO}_{2}$ were calculated using first principle density functional theory (DFT) as implemented in VASP. Electronic band structure in the antiferromagnetic state shows indirect band gap ($\sim 0.53$ eV) where as in the ferromagnetic state it shows half-metallic state with 100\% spin polarization. The half-metallic state arises due to \textit{double exchange} mechanism. In the half-metallic state the density of states for the up spin channel shows asymmetric power law behaviour near the Fermi level while the down spin channel shows fully gapped behaviour. The calculated magnetic moment of Mn atom in the ferromagnetic (3.70 $\mu_{B}$) and antiferromagnetic (3.57 $\mu_{B}$) states are consistent with experimental values. Our calculation predicts potential application of $\textrm{CuMnO}_{2}$ in spintronic devices especially in the ferromagnetic state, as a spin injector for spin valves in spintronic devices., Comment: 7 pages 8 figures

We study bounce solutions and associated negative modes in the class of piecewise linear triangular-shaped potentials that may be viewed as approximations of smooth potentials. In these simple potentials, the bounce solution and action can be obtained analytically for a general spacetime dimension $D$. The eigenequations for the fluctuations around the bounce are universal and have the form of a Schr\"odinger-like equation with delta-function potentials. This Schr\"odinger equation is solved exactly for the negative modes whose number is confirmed to be one. The latter result may justify the usefulness of such piecewise linear potentials in the study of false vacuum decay.

The microscopic mechanism of superconductivity in the magic-angle twisted graphene family, including magic-angle twisted trilayer graphene (MATTG), is poorly understood. Properties of MATTG, like Pauli limit violation, suggest unconventional superconductivity. Theoretical studies propose proximal magnetic states in the phase diagram, but direct experimental evidence is lacking. We show direct evidence for an in-plane magnetic order proximal to the superconducting state using two complementary electrical transport measurements. First, we probe the superconducting phase by using statistically significant switching events from superconducting to the dissipative state of MATTG. The system behaves like a network of Josephson junctions due to lattice relaxation-induced moir\'e inhomogeneity in the system. We observe non-monotonic and hysteretic responses in the switching distributions as a function of temperature and in-plane magnetic field. Second, in normal regions doped slightly away from the superconducting regime, we observe hysteresis in magnetoresistance with an in-plane magnetic field; showing evidence for in-plane magnetic order that vanishes $\sim$900 mK. Additionally, we show a broadened Berezinskii-Kosterlitz-Thouless transition due to relaxation-induced moir\'e inhomogeneity. We find superfluid stiffness $J_{\mathrm{s}}$$\sim$0.15 K with strong temperature dependence. Theoretically, the magnetic and superconducting order arising from the magnetic order's fluctuations have been proposed - we show direct evidence for both. Our observation that the hysteretic magnetoresistance is sensitive to the in-plane field may constrain possible intervalley-coherent magnetic orders and the resulting superconductivity that arises from its fluctuations., Comment: 22 pages, 8 figures, and supplementary information

The azimuthal correlation angle, $\Delta\phi$, between the scattered lepton and the leading jet in deep inelastic $e^{\pm}p$ scattering at HERA has been studied using data collected with the ZEUS detector at a centre-of-mass energy of $\sqrt{s} = 318 \;\mathrm{GeV}$, corresponding to an integrated luminosity of $326 \;\mathrm{pb}^{-1}$. A measurement of jet cross sections in the laboratory frame was made in a fiducial region corresponding to photon virtuality $10 \;\mathrm{GeV}^2 < Q^2 < 350 \;\mathrm{GeV}^2$, inelasticity $0.04 < y < 0.7$, outgoing lepton energy $E_e > 10 \;\mathrm{GeV}$, lepton polar angle $140^\circ < \theta_e < 180^\circ$, jet transverse momentum $2.5 \;\mathrm{GeV} < p_\mathrm{T,jet} < 30 \;\mathrm{GeV}$, and jet pseudorapidity $-1.5 < \eta_\mathrm{jet} < 1.8$. Jets were reconstructed using the $k_\mathrm{T}$ algorithm with the radius parameter $R = 1$. The leading jet in an event is defined as the jet that carries the highest $p_\mathrm{T,jet}$. Differential cross sections, $d\sigma/d\Delta\phi$, were measured as a function of the azimuthal correlation angle in various ranges of leading-jet transverse momentum, photon virtuality and jet multiplicity. Perturbative calculations at $\mathcal{O}(\alpha_{s}^2)$ accuracy successfully describe the data within the fiducial region, although a lower level of agreement is observed near $\Delta\phi \rightarrow \pi$ for events with high jet multiplicity, due to limitations of the perturbative approach in describing soft phenomena in QCD. The data are equally well described by Monte Carlo predictions that supplement leading-order matrix elements with parton showering.

We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple possible modalities. This could emulate a UAV involved in a search-and-rescue operation navigating through an area, observing a stream of aerial images as it goes. The AGL task is associated with two important challenges. Firstly, an agent must deal with a goal specification in one of multiple modalities (e.g., through a natural language description) while the search cues are provided in other modalities (aerial imagery). The second challenge is limited localization time (e.g., limited battery life, urgency) so that the goal must be localized as efficiently as possible, i.e. the agent must effectively leverage its sequentially observed aerial views when searching for the goal. To address these challenges, we propose GOMAA-Geo - a goal modality agnostic active geo-localization agent - for zero-shot generalization between different goal modalities. Our approach combines cross-modality contrastive learning to align representations across modalities with supervised foundation model pretraining and reinforcement learning to obtain highly effective navigation and localization policies. Through extensive evaluations, we show that GOMAA-Geo outperforms alternative learnable approaches and that it generalizes across datasets - e.g., to disaster-hit areas without seeing a single disaster scenario during training - and goal modalities - e.g., to ground-level imagery or textual descriptions, despite only being trained with goals specified as aerial views. Code and models are publicly available at https://github.com/mvrl/GOMAA-Geo/tree/main., Comment: 23 pages, 17 figures

We present the first three-parameter fit to a 3+1 sterile neutrino model using 7.634 years of data from the IceCube Neutrino Observatory on $\nu_\mu+\overline{\nu}_\mu$ charged-current interactions in the energy range 500--9976 GeV. Our analysis is sensitive to the mass-squared splitting between the heaviest and lightest mass state ($\Delta m_{41}^2$), the mixing matrix element connecting muon flavor to the fourth mass state ($|U_{\mu4}|^2$), and the element connecting tau flavor to the fourth mass state ($|U_{\tau4}|^2$). Predicted propagation effects in matter enhance the signature through a resonance as atmospheric neutrinos from the Northern Hemisphere traverse the Earth to the IceCube detector at the South Pole. The remaining sterile neutrino matrix elements are left fixed, with $|U_{e4}|^2= 0$ and $\delta_{14}=0$, as they have a negligible effect, and $\delta_{24}=\pi$ is set to give the most conservative limits. The result is consistent with the no-sterile neutrino hypothesis with a probability of 4.3%. Profiling the likelihood of each parameter yields the 90\% confidence levels: $ 2.4\,\mathrm{eV}^{2} < \Delta m_{41}^2 <9.6\,\mathrm{eV}^{2} $ , $0.0081 < |U_{\mu4}|^2 < 0.10$ , and $|U_{\tau4}|^2< 0.035$, which narrows the allowed parameter-space for $|U_{\tau4}|^2$. However, the primary result of this analysis is the first map of the 3+1 parameter space exploring the interdependence of $\Delta m_{41}^2$, $|U_{\mu4}|^2$, and $|U_{\tau4}|^2$., Comment: 14 pages, 8 figures. Published in PLB

Sharing network nonlocality in an extended quantum network scenario is the new paradigm in the development of quantum theory. In this paper, we investigate the influence of Elegant joint measurement(in short, EJM) bases in an extended bilocal scenario on sharing network nonlocality via sequential measurement. The work essentially based on the newly introduced[Phys. Rev. Lett. 126, 220401(2021)] bilocal inequality with ternary inputs for end parties and EJM as joint measurement bases in $Alice_n-Bob-Charlie_m$ scenario. Here, we are able to capture all simultaneous violation of this inequality for $(n,m)\in \{(2,1),(1,2),(1,1),(2,2)\}$ cases. We further observe the criteria for sharing network nonlocality where we are able to find also the dependence of the sharing on the amount of entanglement of the joint bases. The effect of the nonlinearity in this inequality is also captured in our results with the symmetrical and asymmetrical violation in this extended scenario. The work will generate further the realization of quantum correlations in network scenario., Comment: 7 pages, 4 figures, revtex, comments welcome

The Internet of Things has experienced significant growth and has become an integral part of various industries. This expansion has given rise to the Industrial IoT initiative where industries are utilizing IoT technology to enhance communication and connectivity through innovative solutions such as data analytics and cloud computing. However this widespread adoption of IoT is demanding of algorithms that provide better efficiency for the same training environment without speed being a factor. In this paper we present a novel approach called G Federated Proximity. Building upon the existing FedProx technique our implementation introduces slight modifications to enhance its efficiency and effectiveness. By leveraging FTL our proposed system aims to improve the accuracy of model obtained after the training dataset with the help of normalization techniques such that it performs better on real time devices and heterogeneous networks Our results indicate a significant increase in the throughput of approximately 90% better convergence compared to existing model performance.

Recently, light-matter interaction has been vastly expanded as a control tool for inducing and enhancing many emergent non-equilibrium phenomena. However, conventional schemes for exploring such light-induced phenomena rely on uniform and diffraction-limited free-space optics, which limits the spatial resolution and the efficiency of light-matter interaction. Here, we overcome these challenges using metasurface plasmon polaritons (MPPs) to form a sub-wavelength optical lattice. Specifically, we report a ``nonlocal" pump-probe scheme where MPPs are excited to induce a spatially modulated AC Stark shift for excitons in a monolayer of MoSe$_2$, several microns away from the illumination spot. Remarkably, we identify nearly two orders of magnitude reduction for the required modulation power compared to the free-space optical illumination counterpart. Moreover, we demonstrate a broadening of the excitons' linewidth as a robust signature of MPP-induced periodic sub-diffraction modulation. Our results open new avenues for exploring power-efficient light-induced lattice phenomena below the diffraction limit in active chip-compatible MPP architectures.

This article presents Teledrive, a telepresence robotic system with embodied AI features that empowers an operator to navigate the telerobot in any unknown remote place with minimal human intervention. We conceive Teledrive in the context of democratizing remote care-giving for elderly citizens as well as for isolated patients, affected by contagious diseases. In particular, this paper focuses on the problem of navigating to a rough target area (like bedroom or kitchen) rather than pre-specified point destinations. This ushers in a unique AreaGoal based navigation feature, which has not been explored in depth in the contemporary solutions. Further, we describe an edge computing-based software system built on a WebRTC-based communication framework to realize the aforementioned scheme through an easy-to-use speech-based human-robot interaction. Moreover, to enhance the ease of operation for the remote caregiver, we incorporate a person following feature, whereby a robot follows a person on the move in its premises as directed by the operator. Moreover, the system presented is loosely coupled with specific robot hardware, unlike the existing solutions. We have evaluated the efficacy of the proposed system through baseline experiments, user study, and real-life deployment., Comment: Accepted in Journal of Intelligent Robotic System