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Today's era is the digitized era. Managing such generated big data is an important factor for data scientists. Day by day, it increases the demand for big data storage systems. Different organizations are involved in providing storage-related services. They follow the different architectures or storage models for storing big data. In this survey paper, our target is to highlight such storage architectures which provided by different renowned storage service providers. On an architectural basis, we divide the big data storage systems into five parts, Distributed file systems (DFS), Clustered File Systems (CFS), Cloud Storage, Archive Storage, and Object Storage Systems (OSS). Also, we reveal a detailed architectural view of the big data storage systems provided by the different organizations under these parts., Comment: 32 Pages

In the emergent realm of quantum computing, the Variational Quantum Eigensolver (VQE) stands out as a promising algorithm for solving complex quantum problems, especially in the noisy intermediate-scale quantum (NISQ) era. However, the ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach to ameliorate this challenge by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations. By employing the Qiskit framework, we crafted parameterized quantum circuits using the RY-RZ ansatz and examined their behavior under varying levels of depolarizing noise. Our investigations spanned from determining the expectation values of a Hamiltonian, defined as a tensor product of Z operators, under different noise intensities to extracting the ground state energy. To bridge the observed outcomes under noise with the ideal noise-free scenario, we trained a Feed Forward Neural Network on the error probabilities and their associated expectation values. Remarkably, our model proficiently predicted the VQE outcome under hypothetical noise-free conditions. By juxtaposing the simulation results with real quantum device executions, we unveiled the discrepancies induced by noise and showcased the efficacy of our neural network-based ZNE technique in rectifying them. This integrative approach not only paves the way for enhanced accuracy in VQE computations on NISQ devices but also underlines the immense potential of hybrid quantum-classical paradigms in circumventing the challenges posed by quantum noise. Through this research, we envision a future where quantum algorithms can be reliably executed on noisy devices, bringing us one step closer to realizing the full potential of quantum computing.

Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version

Detection of anomalous situations for complex mission-critical systems holds paramount importance when their service continuity needs to be ensured. A major challenge in detecting anomalies from the operational data arises due to the imbalanced class distribution problem since the anomalies are supposed to be rare events. This paper evaluates a diverse array of machine learning-based anomaly detection algorithms through a comprehensive benchmark study. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical machine learning including various tree-based approaches to deep learning and outlier detection methods. The inclusion of 104 publicly available and a few proprietary industrial systems datasets enhances the diversity of the study, allowing for a more realistic evaluation of algorithm performance and emphasizing the importance of adaptability to real-world scenarios. The paper dispels the deep learning myth, demonstrating that though powerful, deep learning is not a universal solution in this case. We observed that recently proposed tree-based evolutionary algorithms outperform in many scenarios. We noticed that tree-based approaches catch a singleton anomaly in a dataset where deep learning methods fail. On the other hand, classical SVM performs the best on datasets with more than 10% anomalies, implying that such scenarios can be best modeled as a classification problem rather than anomaly detection. To our knowledge, such a study on a large number of state-of-the-art algorithms using diverse data sets, with the objective of guiding researchers and practitioners in making informed algorithmic choices, has not been attempted earlier.

This paper discusses an abstract Kramer sampling theorem for functions within a reproducing kernel Hilbert space (RKHS) of vector valued holomorphic functions. Additionally, we extend the concept of quasi Lagrange-type interpolation for functions within a RKHS of vector valued entire functions. The dependence of having quasi Lagrange-type interpolation on an invariance condition under the generalized backward shift operator has also been discussed. Furthermore, the paper establishes the connection between quasi Lagrange-type interpolation, operator of multiplication by the independent variable, and de Branges spaces of vector valued entire functions., Comment: 15 pages

Generative agents, which implement behaviors using a large language model (LLM) to interpret and evaluate an environment, has demonstrated the capacity to solve complex tasks across many social and technological domains. However, when these agents interact with other agents and humans in presence of social structures such as existing norms, fostering cooperation between them is a fundamental challenge. In this paper, we develop the framework of a 'Normative Module': an architecture designed to enhance cooperation by enabling agents to recognize and adapt to the normative infrastructure of a given environment. We focus on the equilibrium selection aspect of the cooperation problem and inform our agent design based on the existence of classification institutions that implement correlated equilibrium to provide effective resolution of the equilibrium selection problem. Specifically, the normative module enables agents to learn through peer interactions which of multiple candidate institutions in the environment, does a group treat as authoritative. By enabling normative competence in this sense, agents gain ability to coordinate their sanctioning behaviour; coordinated sanctioning behaviour in turn shapes primary behaviour within a social environment, leading to higher average welfare. We design a new environment that supports institutions and evaluate the proposed framework based on two key criteria derived from agent interactions with peers and institutions: (i) the agent's ability to disregard non-authoritative institutions and (ii) the agent's ability to identify authoritative institutions among several options. We show that these capabilities allow the agent to achieve more stable cooperative outcomes compared to baseline agents without the normative module, paving the way for research in a new avenue of designing environments and agents that account for normative infrastructure.

Electric field-induced modulation of the optical properties is crucial for amplitude and phase modulators used in photonic devices. Here, we present a comprehensive study of the band geometry-induced electro-optic effect, specifically focusing on the Fermi surface and disorder-induced contributions. These contributions are crucial for metallic and semimetallic systems and for optical frequencies comparable to or smaller than the scattering rates. We highlight the importance of the quantum metric and metric connection in generating the electro-optic effect in parity-time reversal ($\mathcal{PT}$) symmetric systems such as CuMnAs. Our findings establish the electro-optic effect as a novel tool to probe band geometric effects and open new avenues to design electrically controlled efficient amplitude and phase modulators for photonic applications., Comment: 11 pages, 3 figures

Despite their significant advancements, Multimodal Large Language Models (MLLMs) often generate factually inaccurate information, referred to as hallucination. In this work, we address object hallucinations in MLLMs, where information is generated about an object not present in the input image. We introduce Data-augmented Phrase-level Alignment (DPA), a novel loss which can be applied to instruction-tuned off-the-shelf MLLMs to mitigate hallucinations, while preserving their general vision-language capabilities. To fine-tune MLLMs with DPA, we first generate a set of `hallucinated' and `correct' response pairs through generative data augmentation by selectively altering the ground-truth information of the correct responses at a phrase level. The DPA loss is then used to train MLLMs to reduce the likelihood of hallucinated phrases compared to the correct ones. Our thorough evaluation on various benchmarks confirms the effectiveness of DPA in mitigating hallucination while retaining the out-of-the-box performance of the MLLMs on general tasks. For instance, MLLMs finetuned with DPA, which we refer to as Hallucination Attenuated Language and Vision Assistant (HALVA), improve F1 by up to 13.4% on hallucination visual question-answering and reduce the hallucination rate by up to 4.2% on image description tasks., Comment: 45 pages

Grammatical error correction (GEC) tools, powered by advanced generative artificial intelligence (AI), competently correct linguistic inaccuracies in user input. However, they often fall short in providing essential natural language explanations, which are crucial for learning languages and gaining a deeper understanding of the grammatical rules. There is limited exploration of these tools in low-resource languages such as Bengali. In such languages, grammatical error explanation (GEE) systems should not only correct sentences but also provide explanations for errors. This comprehensive approach can help language learners in their quest for proficiency. Our work introduces a real-world, multi-domain dataset sourced from Bengali speakers of varying proficiency levels and linguistic complexities. This dataset serves as an evaluation benchmark for GEE systems, allowing them to use context information to generate meaningful explanations and high-quality corrections. Various generative pre-trained large language models (LLMs), including GPT-4 Turbo, GPT-3.5 Turbo, Text-davinci-003, Text-babbage-001, Text-curie-001, Text-ada-001, Llama-2-7b, Llama-2-13b, and Llama-2-70b, are assessed against human experts for performance comparison. Our research underscores the limitations in the automatic deployment of current state-of-the-art generative pre-trained LLMs for Bengali GEE. Advocating for human intervention, our findings propose incorporating manual checks to address grammatical errors and improve feedback quality. This approach presents a more suitable strategy to refine the GEC tools in Bengali, emphasizing the educational aspect of language learning., Comment: Accepted at Educational Data Mining 2024

The in-plane anisotropic properties of two-dimensional (2D) group IV monochalcogenides provide an additional degree of freedom which can be used in future optoelectronic devices. Here, it is shown that the third harmonic generation (THG) signal produced by ultrathin tin (II) sulfide (SnS) is in-plane anisotropic with respect to the incident linear polarization of the laser field. We fit the experimental polarization-resolved THG (P-THG) measurements with a nonlinear optics model, which accounts for the orthorhombic crystal structure of 2D SnS. We calculate the relative magnitudes of the \{chi}^(3) tensor components by recording and simultaneously fitting both orthogonal components of the P-THG intensity. Furthermore, we introduce a THG anisotropy ratio, whose calculated values compare the total THG intensity when the excitation linear polarization is along the armchair crystallographic direction with the case when it is along the zigzag direction. Our results provide quantitative information on the anisotropic nature of the THG process in SnS, paving the way to a better understanding of anisotropic nonlinear light-matter interactions, and the development of polarization-sensitive nonlinear optical devices.

We identify for visco-elasto-plastic (VEP) glassy polymers, physical phenomena during Berkovich nanoindentation, a locally imposed deformation. Live visuals via in situ nanoindentation indicate mainly sink-in during loading, with pile-up after unloading. Scanning Probe Microscopy (SPM) indicates significant volume conserving upflow below the tip, for these high nu, compliant materials, with compliance correlated high geometric fractional contact (including blunt height, h_b), (h_c+h_b)/(h_m+h_b)~0.86-0.95. We adapt the ideal conical indentation framework to VEP Berkovich nanoindentation, to calculate the contact area and visually depict the upflow and the displacement paths, in the material. The combination of SPM and P-h data, indicates a mixed comparison with uniaxial modulus and yield stress, with conventionally defined hardness, H<3*sig_y, and nanoindentation modulus E_N>E. By rationally removing viscoelastic (VE) effects from the loading P-h data, we find instant, zero-time hardness, H_L0>3*sig_y. We apply the power law model to only the recovery onset, to estimate pure elastic recovery. We then deconvolute the VEP nanoindentation into the conventional EP and elastic contributions, isolating the VE component. Constraint-induced sink-in, pile-up and VE recovery of the highly yielded tip-apex region, mirror the converse constrained deformation effects, governing the trends in conventionally defined H_L0 and E_N for glassy polymers., Comment: 92 pages (70 pages including appendices + 22 pages for the appended Supporting information), 38 figures (including 8 in appendices and 18 in Supporting information), 13 Tables (including 0 in appendices and 7 in Supporting Information), Video (Supporting Information 3, Available upon Request)

In this letter, we derive an expression for magnetic dipole transitions that is analogous to the Lyddane-Sachs-Teller relation for dielectric polar lattice vibrations. We thereby define transverse and longitudinal optical frequencies at which paramagnetic resonance and antiresonance occurs, respectively. The relation found here thus permits non-invasive optical analysis of static magnetization properties in paramagnetic materials. Validated through terahertz electron paramagnetic resonance ellipsometry and superconducting quantum interference device measurements on Iron-doped Gallium Nitride, our findings show very good agreement between theory and experiment. We term the excitations associated with the paramagnetic transitions as paramagnetic polaritons which may find use in future photonic applications

Active search formalizes a specialized active learning setting where the goal is to collect members of a rare, valuable class. The state-of-the-art algorithm approximates the optimal Bayesian policy in a budget-aware manner, and has been shown to achieve impressive empirical performance in previous work. However, even this approximate policy has a superlinear computational complexity with respect to the size of the search problem, rendering its application impractical in large spaces or in real-time systems where decisions must be made quickly. We study the amortization of this policy by training a neural network to learn to search. To circumvent the difficulty of learning from scratch, we appeal to imitation learning techniques to mimic the behavior of the expert, expensive-to-compute policy. Our policy network, trained on synthetic data, learns a beneficial search strategy that yields nonmyopic decisions carefully balancing exploration and exploitation. Extensive experiments demonstrate our policy achieves competitive performance at real-world tasks that closely approximates the expert's at a fraction of the cost, while outperforming cheaper baselines.

Given a natural number $n \geq 1$, the odometer semigroup $O_n$, also known as the adding machine or the Baumslag-Solitar monoid with two generators, is a well-known object in group theory. This paper examines the odometer semigroup in relation to representations of bounded linear operators. We focus on noncommutative operators and prove that contractive representations of $O_n$ always admit to nicer representations of $O_n$. We give a complete description of representations of $O_n$ on the Fock space and relate it to the odometer lifting and subrepresentations of $O_n$. Along the way, we also classify Nica covariant representations of $O_n$., Comment: 29 pages. Revised

In this paper, we design quantum circuits for the exponential of scaled $n$-qubit Pauli strings using single-qubit rotation gates, Hadamard gate, and CNOT gates. A key result we derive is that any two Pauli-string operators composed of identity and $X$ gates are permutation similar, and the corresponding permutation matrices are product of CNOT gates, with the $n$-th qubit serving as the control qubit. Consequently, we demonstrate that the proposed circuit model for exponential of any Pauli-string is implementable on low-connected quantum hardware and scalable i.e. quantum circuits for $(n+1)$-qubit systems can be constructed from $n$-qubit circuits by adding additional quantum gates and the extra qubit. We then apply these circuit models to approximate unitary evolution for several classes of Hamiltonians using the Suzuki-Trotter approximation. These Hamiltonians include $2$-sparse block-diagonal Hamiltonians, Ising Hamiltonians, and both time-independent and time-dependent Random Field Heisenberg Hamiltonians and Transverse Magnetic Random Quantum Ising Hamiltonians. Simulations for systems of up to 18 qubits show that the circuit approximation closely matches the exact evolution, with errors comparable to the numerical Trotterization error. Finally, we consider noise models in quantum circuit simulations to account for gate implementation errors in NISQ computers and observe that the noisy simulation closely resembles the noiseless one when gate and idle errors are on the order of $O(10^{-3})$ or smaller., Comment: The title, abstract, and introduction have been revised, and extensive modifications have been applied throughout the paper

A wide variety of engineered and natural systems are modelled as networks of coupled nonlinear oscillators. In nature, the intrinsic frequencies of these oscillators are not constant in time. Here, we probe the effect of such a temporal heterogeneity on coupled oscillator networks, through the lens of the Kuramoto model. To do this, we shuffle repeatedly the intrinsic frequencies among the oscillators at either random or regular time intervals. What emerges is the remarkable effect that frequent shuffling induces earlier onset (i.e., at a lower coupling) of synchrony among the oscillator phases. Our study provides a novel strategy to induce and control synchrony under resource constraints. We demonstrate our results analytically and in experiments with a network of Wien Bridge oscillators with internal frequencies being shuffled in time.

Hamiltonian simulation becomes more challenging as the underlying unitary becomes more oscillatory. In such cases, an algorithm with commutator scaling and a weak dependence, such as logarithmic, on the derivatives of the Hamiltonian is desired. We introduce a new time-dependent Hamiltonian simulation algorithm based on the Magnus series expansion that exhibits both features. Importantly, when applied to unbounded Hamiltonian simulation in the interaction picture, we prove that the commutator in the second-order algorithm leads to a surprising fourth-order superconvergence, with an error preconstant independent of the number of spatial grids. This extends the qHOP algorithm [An, Fang, Lin, Quantum 2022] based on first-order Magnus expansion, and the proof of superconvergence is based on semiclassical analysis that is of independent interest.

We study a learning model in which an agent is stationed at each vertex of $\mathbb{T}_{m}$, the rooted tree in which each vertex has $m$ children. At any time-step $t \in \mathbb{N}_{0}$, they are allowed to select one of two available technologies: $B$ and $R$. Let the technology chosen by the agent at vertex $v\in\mathbb{T}_{m}$, at time-step $t$, be $C_{t}(v)$. Let $\{C_{0}(v):v\in\mathbb{T}_{m}\}$ be i.i.d., where $C_{0}(v)=B$ with probability $\pi_{0}$. During epoch $t$, the agent at $v$ performs an experiment that results in success with probability $p_{B}$ if $C_{t}(v)=B$, and with probability $p_{R}$ if $C_{t}(v)=R$. If the children of $v$ are $v_{1},\ldots,v_{m}$, the agent at $v$ updates their technology to $C_{t+1}(v)=B$ if the number of successes among all $v_{i}$ with $C_{t}(v_{i})=B$ exceeds, strictly, the number of successes among all $v_{j}$ with $C_{t}(v_{j})=R$. If these numbers are equal, then the agent at $v$ sets $C_{t+1}(v)=B$ with probability $1/2$. Else, $C_{t+1}(v)=R$. We show that $\{C_{t}(v):v\in\mathbb{T}_{m}\}$ is i.i.d., where $C_{t}(v)=B$ with probability $\pi_{t}$, and $\{\pi_{t}\}_{t \in \mathbb{N}_{0}}$ converges to a fixed point $\pi$ of a function $g_{m}$. For $m \geqslant 3$, there exists a $p(m) \in (0,1)$ such that $g_{m}$ has a unique fixed point, $1/2$, when $p \leqslant p(m)$, and three distinct fixed points, of the form $\alpha$, $1/2$ and $1-\alpha$, when $p > p(m)$. When $m=3$, $p_{B}=1$ and $p_{R} \in [0,1)$, we show that $g_{3}$ has a unique fixed point, $1$, when $p_{R} < \sqrt{3}-1$, two distinct fixed points, one of which is $1$, when $p_{R} = \sqrt{3}-1$, and three distinct fixed points, one of which is $1$, when $p_{R} > \sqrt{3}-1$. When $g_{m}$ has multiple fixed points, we also specify which of these fixed points $\pi$ equals, depending on $\pi_{0}$. For $m=2$, we describe the behaviour of $g_{3}$ for all $p_{B}$ and $p_{R}$.

Network Nonlocality is an advanced study of quantum nonlocality that comprises network structure beyond Bell's theorem. The development of quantum networks has the potential to bring a lot of technological applications in sevaral quantum information processing tasks. Here, we are focusing on how the role of the independence of the measurement choices of the end parties in a network works and can be used to enhance the security in a quantum network. In both three-parties two-sources bilocal network and four-parties three-sources star network scenarios, we are able to show, a practical way to understand the relaxation of the assumptions to enhance a real security protocol if someone wants to breach in a network communications. Theoratically, we have proved that by relaxing the independence of the measurement choices of only one end party we can create a Standard Network Nonlocality(SNN) and more stronger Full Network Nonlocality(FNN) and we can get maximum quantum violation by the classical no-signalling local model. We are able to distinguish between two types of network nonlocality in the sense that the FNN is stronger than SNN, i.e., FNN states all the sources in a network need to distribute nonlocal resources., Comment: 18 pages, 5 figures, 2 tables, latex2e, comments welcome

In the era of generative artificial intelligence (AI), the fusion of large language models (LLMs) offers unprecedented opportunities for innovation in the field of modern education. We embark on an exploration of prompted LLMs within the context of educational and assessment applications to uncover their potential. Through a series of carefully crafted research questions, we investigate the effectiveness of prompt-based techniques in generating open-ended questions from school-level textbooks, assess their efficiency in generating open-ended questions from undergraduate-level technical textbooks, and explore the feasibility of employing a chain-of-thought inspired multi-stage prompting approach for language-agnostic multiple-choice question (MCQ) generation. Additionally, we evaluate the ability of prompted LLMs for language learning, exemplified through a case study in the low-resource Indian language Bengali, to explain Bengali grammatical errors. We also evaluate the potential of prompted LLMs to assess human resource (HR) spoken interview transcripts. By juxtaposing the capabilities of LLMs with those of human experts across various educational tasks and domains, our aim is to shed light on the potential and limitations of LLMs in reshaping educational practices., Comment: Accepted at EDM 2024

Drift scan observations provide the broad sky coverage and instrumental stability needed to measure the Epoch of Reionization (EoR) 21-cm signal. In such observations, the telescope's pointing center (PC) moves continuously on the sky. The Tracking Tapered Gridded Estimator (TTGE) combines observations from different PC to estimate $P(k_{\perp}, k_{\parallel})$ the 21-cm power spectrum, centered on a tracking center (TC) which remains fixed on the sky. The tapering further restricts the sky response to a small angular region around TC, thereby mitigating wide-field foregrounds. Here we consider $154.2 \, {\rm MHz}$ ($z = 8.2$) Murchison Widefield Array (MWA) drift scan observations. The periodic pattern of flagged channels, present in MWA data, is known to introduce artefacts which pose a challenge for estimating $P(k_{\perp}, k_{\parallel})$. We demonstrate that the TTGE is able to recover $P(k_{\perp}, k_{\parallel})$ without any artefacts, and estimate $P(k)$ within $5 \%$ accuracy over a large $k$-range. We also present preliminary results for a single PC, combining 9 nights of observation $(17 \, {\rm min}$ total). We find that $P(k_{\perp}, k_{\parallel})$ exhibits streaks at a fixed interval of $k_{\parallel}=0.29 \, {\rm Mpc}^{-1}$, which matches $\Delta \nu_{\rm per}=1.28 \, {\rm MHz}$ that is the period of the flagged channels. The streaks are not as pronounced at larger $k_{\parallel}$, and in some cases they do not appear to extend across the entire $k_{\perp}$ range. The rectangular region $0.05 \leq k_{\perp} \leq 0.16 \, {\rm Mpc^{-1}}$ and $0.9 \leq k_{\parallel} \leq 4.6 \, {\rm Mpc^{-1}}$ is found to be relatively free of foreground contamination and artefacts, and we have used this to place the $2\sigma$ upper limit $\Delta^2(k) < (1.85 \times 10^4)^2\, {\rm mK^2}$ on the EoR 21-cm mean squared brightness temperature fluctuations at $k=1 \,{\rm Mpc}^{-1}$., Comment: 15 pages, 11 figures, accepted for publication in PASA

Recent studies have focused on the relationship between charge density wave (CDW) collective electronic ground states and nontrivial topological states. Using angle-resolved photoemission and density functional theory, we establish that YTe$_3$ is a CDW-induced Kramers nodal line (KNL) metal, a newly proposed topological state of matter. YTe$_3$ is a non-magnetic quasi-2D chalcogenide with a CDW wave vector ($q_{\rm cdw}$) of 0.2907c$^*$. Scanning tunneling microscopy and low energy electron diffraction revealed two orthogonal CDW domains, each with a unidirectional CDW and similar YTe$_3$. The effective band structure (EBS) computations, using DFT-calculated folded bands, show excellent agreement with ARPES because a realistic x-ray crystal structure and twin domains are considered in the calculations. The Fermi surface and ARPES intensity plots show weak shadow bands displaced by $q_{\rm cdw}$ from the main bands. These are linked to CDW modulation, as the EBS calculation confirms. Bilayer split main and shadow bands suggest the existence of crossings, according to theory and experiment. DFT bands, including spin-orbit coupling, indicate a nodal line along the $\Sigma$ line from multiple band crossings perpendicular to the KNL. Additionally, doubly degenerate bands are only found along the KNL at all energies, with some bands dispersing through the Fermi level.

We study message identification over a q-ary uniform permutation channel, where the transmitted vector is permuted by a permutation chosen uniformly at random. For discrete memoryless channels(DMCs), the number of identifiable messages grows doubly exponentially. Identification capacity, the maximum second-order exponent, is known to be the same as the Shannon capacity of the DMC. Permutation channels support reliable communication of only polynomially many messages. A simple achievability result shows that message sizes growing as 2^{\epsilon_nn^{q-1}} are identifiable for any \epsilon_n\rightarrow0. We prove two converse results. A ``soft'' converse shows that for any R>0, there is no sequence of identification codes with message size growing as 2^{Rn^{q-1}} with a power-law decay (n^{-\mu}) of the error probability. We also prove a ``strong" converse showing that for any sequence of identification codes with message size 2^{R_n n^{q-1}}, where R_n\rightarrow\infty, the sum of type I and type II error probabilities approaches at least 1 as n\rightarrow\infty. To prove the soft converse, we use a sequence of steps to construct a new identification code with a simpler structure which relates to a set system, and then use a lower bound on the normalized maximum pairwise intersection of a set system. To prove the strong converse, we use results on approximation of distributions. The achievability and converse results are generalized to the case of coding over multiple blocks. We finally study message identification over a q-ary uniform permutation channel in the presence of causal block-wise feedback from the receiver, where the encoder receives an entire n-length received block after the transmission of the block is complete. We show that in the presence of feedback, the maximum number of identifiable messages grows doubly exponentially, and we present a two-phase achievability scheme., Comment: 32 pages. Submitted to IEEE Transactions on Information Theory

Navigating the complex landscape of news articles involves understanding the various actors or entities involved, referred to as news stakeholders. These stakeholders, ranging from policymakers to opposition figures, citizens, and more, play pivotal roles in shaping news narratives. Recognizing their stakeholder types, reflecting their roles, political alignments, social standing, and more, is paramount for a nuanced comprehension of news content. Despite existing works focusing on salient entity extraction, coverage variations, and political affiliations through social media data, the automated detection of stakeholder roles within news content remains an underexplored domain. In this paper, we bridge this gap by introducing an effective approach to classify stakeholder types in news articles. Our method involves transforming the stakeholder classification problem into a natural language inference task, utilizing contextual information from news articles and external knowledge to enhance the accuracy of stakeholder type detection. Moreover, our proposed model showcases efficacy in zero-shot settings, further extending its applicability to diverse news contexts., Comment: Accepted in SIGIR 2024

The main purpose of this paper is to design a local discontinuous Galerkin (LDG) method for the Benjamin-Ono equation. We analyze the stability and error estimates for the semi-discrete LDG scheme. We prove that the scheme is $L^2$-stable and it converges at a rate $\mathcal{O}(h^{k+1/2})$ for general nonlinear flux. Furthermore, we develop a fully discrete LDG scheme using the four-stage fourth order Runge-Kutta method and ensure the devised scheme is strongly stable in case of linear flux using two-step and three-step stability approach under an appropriate time step constraint. Numerical examples are provided to validate the efficiency and accuracy of the method., Comment: Some estimates found incorrect or unclear

We provide supporting details for the search for a 3+1 sterile neutrino using data collected over eleven years at the IceCube Neutrino Observatory. The analysis uses atmospheric muon-flavored neutrinos from 0.5 to 100\, TeV that traverse the Earth to reach the IceCube detector, and finds a best-fit point at $\sin^2(2\theta_{24}) = 0.16$ and $\Delta m^{2}_{41} = 3.5$ eV$^2$ with a goodness-of-fit p-value of 12\% and consistency with the null hypothesis of no oscillations to sterile neutrinos with a p-value of 3.1\%. Several improvements were made over past analyses, which are reviewed in this article, including upgrades to the reconstruction and the study of sources of systematic uncertainty. We provide details of the fit quality and discuss stability tests that split the data for separate samples, comparing results. We find that the fits are consistent between split data sets., Comment: 18 pages, 17 figures, 2 tables. This long-form paper is a companion to the letter "A search for an eV-scale sterile neutrino using improved high-energy {\nu}{\mu} event reconstruction in IceCube."

This Letter presents the result of a 3+1 sterile neutrino search using 10.7 years of IceCube data. We analyze atmospheric muon neutrinos that traverse the Earth with energies ranging from 0.5 to 100 TeV, incorporating significant improvements in modeling neutrino flux and detector response compared to earlier studies. Notably, for the first time, we categorize data into starting and through-going events, distinguishing neutrino interactions with vertices inside or outside the instrumented volume, to improve energy resolution. The best-fit point for a 3+1 model is found to be at $\sin^2(2\theta_{24}) = 0.16$ and $\Delta m^{2}_{41} = 3.5$ eV$^2$, which agrees with previous iterations of this study. The result is consistent with the null hypothesis of no sterile neutrinos with a p-value of 3.1\%., Comment: 9 pages, 3 figures. This letter is supported by the long-form paper "Methods and stability tests associated with the sterile neutrino search using improved high-energy $\nu_\mu$ event reconstruction in IceCube," also appearing on arXiv

In this work, we investigate the effects of H2 on the physical properties of Fe25Pd75. Broadband ferromagnetic resonance (FMR) spectroscopy revealed a significant FMR peak shift induced by H2 absorption for the FCC phased Fe25Pd75. The peak shifted towards higher applied fields, which is contrary to what was previously observed for CoPd alloys. Additionally, we conducted structural and magneto-optical Kerr ellipsometric studies on the Fe25Pd75 film and performed density functional theory calculations to explore the electronic and magnetic properties in both hydrogenated and dehydrogenated states. In the final part of this study, we deposited a Fe25Pd75 layer on top of a microscopic coplanar transmission line and investigated the FMR response of the layer while driven by a microwave current in the coplanar line. We observed a large amplitude FMR response upon hydrogen absorption, as well as desorption rates when cycling between pure N2 and a mixture of 3% H2 + 97% N2.

The JWST has ushered in a new era of exoplanet transit spectroscopy. Among the JWST instruments, the Near-Infrared Spectrograph (NIRSpec) has the most extensive set of configurations for exoplanet time series observations. The NIRSpec Prism and G395H grating represent two extremes in NIRSpec instrument modes, with the Prism spanning a wider spectral range (0.6-5.3 $\mu$m at lower resolution (R$\sim$100) compared to G395H (2.87-5.14 $\mu$m; R$\sim$2700). In this work, we develop a new data reduction framework, JexoPipe, to conduct a homogeneous assessment of the two NIRSpec modes for exoplanet spectroscopy. We use observations of the hot Saturn WASP-39 b obtained as part of the JWST Transiting Exoplanets ERS program to assess the spectral quality and stability between the two instrument modes at different epochs. We explore the noise sources, effect of saturation, and offsets in transmission spectra between the different instrument modes and also between the two G395H NRS detectors. We find an inter-detector offset in G395H of $\sim$ 40-50 ppm, consistent with recent studies. We find evidence for correlated noise in the Prism white light curve. We find the G395H spectrum to be of higher precision compared to the Prism at the same resolution. We also compare the JexoPipe spectra with those reported from other pipelines. Our work underscores the need for robust assessment of instrument performance and identification of optimal practices for JWST data reduction and analyses., Comment: 27 pages, 23 figures

Galactic cosmic ray (GCR) particles have a significant impact on the particle-induced background of X-ray observatories, and their flux exhibits substantial temporal variability, potentially influencing background levels. In this study, we present one-day binned high-energy reject rates derived from the Chandra-ACIS and XMM-Newton EPIC-pn instruments, serving as proxies for GCR particle flux. We systematically analyze the ACIS and EPIC-pn reject rates and compare them with the AMS proton flux. Our analysis initially reveals robust correlations between the AMS proton flux and the ACIS/EPIC-pn reject rates when binned over 27-day intervals. However, a closer examination reveals substantial fluctuations within each 27-day bin, indicating shorter-term variability. Upon daily binning, we observe finer. temporal structures in the datasets, demonstrating the presence of recurrent variations with periods of $\sim$ 25 days and 23 days in ACIS and EPIC-pn reject rates, respectively, spanning the years 2014 to 2018. Notably, during the 2016--2017 period, we additionally detect periodicities of $\sim$13.5 days and 9 days in the ACIS and EPIC-pn reject rates, respectively. Intriguingly, we observe a time lag of $\sim$ 6 days between the AMS proton flux and the ACIS/EPIC-pn reject rates during the second half of 2016. This time lag is not visible before 2016 and aftern2017. The underlying physical mechanisms responsible for this time lag remain a subject of ongoing investigation., Comment: 16 pages, 8 figures, accepted for publication in ApJ

We present a set of controlled hydrodynamical simulations to study the effects of strong galactic outflows on the density and temperature structures, and associated X-ray signatures, of extra-planar and circumgalactic gas. We consider three initial state models, isothermal, isentropic, and rotating cooling-flow, for the hot circumgalactic medium (CGM) into which the outflows are driven. The energy sources are either stellar winds and supernovae, or active galactic nuclei. We consider energy injection rates in the range $10^{40} < \dot{E}_{\rm inj} <10^{44.5}$ erg s$^{-1}$, and compute the time-dependent soft X-ray (0.5-2 keV) surface brightness. For $\dot{E}_{\rm inj} \gtrsim 10^{41} - 10^{42}$ erg s$^{-1}$, with the exact threshold depending on the initial CGM state, the X-ray response is dominated by dense hot gas in the forward shock that eventually fades into the CGM as a sound wave. The shock surrounds an inner hot bubble leading to a radial flattening of the X-ray surface brightness. For lower energy injection rates, the X-ray surface brightness of the initial CGM state is almost unaffected. We present analytic approximations for the outflow shock propagation and the associated X-ray emissions., Comment: Accepted for publication in MNRAS

Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree (BDT) algorithm has been a cornerstone of the high energy physics for analyzing event triggering, particle identification, jet tagging, object reconstruction, event classification, and other related tasks for quite some time. This article presents a comprehensive overview of research conducted by both HEP experimental and phenomenological groups that utilize decision tree algorithms in the context of the Standard Model and Supersymmetry (SUSY). We also summarize the basic concept of machine learning and decision tree algorithm along with the working principle of \texttt{Random Forest}, \texttt{AdaBoost} and two gradient boosting frameworks, such as \texttt{XGBoost}, and \texttt{LightGBM}. Using a case study of electroweakino productions at the high luminosity LHC, we demonstrate how these algorithms lead to improvement in the search sensitivity compared to traditional cut-based methods in both compressed and non-compressed R-parity conserving SUSY scenarios. The effect of different hyperparameters and their optimization, feature importance study using SHapley values are also discussed in detail., Comment: 62 pages, 26 figures, 9 tables

If the graviton possesses a non-zero charge $q_g$, gravitational waves (GW) originating from astrophysical sources would experience an additional time delay due to intergalactic magnetic fields. This would result in a modification of the phase evolution of the observed GW signal similar to the effect induced by a massive graviton. As a result, we can reinterpret the most recent upper limits on the graviton's mass as constraints on the joint mass-charge parameter space, finding $|q_g|/{e} < 3\times 10^{-34}$ where $e$ represents the charge of an electron. Additionally, we illustrate that a charged graviton would introduce a constant phase difference in the gravitational waves detected by two spatially separated GW detectors due to the Aharonov-Bohm effect. Using the non-observation of such a phase difference for the GW event GW190814, we establish a mass-independent constraint $|q_g|/e < 2\times 10^{-26}$. To the best of our knowledge, our results constitute the first-ever bounds on the charge of the graviton. We also discuss various caveats involved in our measurements and prospects for strengthening these bounds with future GW observations., Comment: 9 pages, 3 figures, matches published version in JCAP

Aims. We present the validation results for arrival times and geomagnetic impact of Coronal Mass Ejections (CMEs), using the cone and spheromak CME models implemented in EUropean Heliospheric FORecasting Information Asset (EUHFORIA). Validating numerical models is crucial in ensuring their accuracy and performance with respect to real data. Methods. We compare CME plasma and magnetic field signatures, measured in situ by satellites at the L1 point, with the simulation output of EUHFORIA. The validation of this model was carried out by using two datasets in order to ensure a comprehensive evaluation. The first dataset focuses on 16 CMEs that arrived at the Earth, offering specific insights into the model's accuracy in predicting arrival time and geomagnetic impact. Meanwhile, the second dataset encompasses all CMEs observed over eight months within Solar Cycle 24, regardless of whether they arrived at Earth, covering periods of both solar minimum and maximum activity. This second dataset enables a more comprehensive evaluation of the model's predictive precision in term of CME arrivals and misses. Results. Our results show that EUHFORIA provides good estimates in terms of arrival times, with root mean square errors (RMSE) values of 9 hours. Regarding the number of correctly predicted ICME arrivals and misses, we find a 75% probability of detection in a 12 hours time window and 100% probability of detection in a 24 hours time window. The geomagnetic impact forecasts, measured by the $K_p$ index, provide different degrees of accuracy, ranging from 31% to 69%. These results validate the use of cone and spheromak CMEs for real-time space weather forecasting.

Large language models can be prompted to produce text. They can also be prompted to produce "explanations" of their output. But these are not really explanations, because they do not accurately reflect the mechanical process underlying the prediction. The illusion that they reflect the reasoning process can result in significant harms. These "explanations" can be valuable, but for promoting critical thinking rather than for understanding the model. I propose a recontextualisation of these "explanations", using the term "exoplanations" to draw attention to their exogenous nature. I discuss some implications for design and technology, such as the inclusion of appropriate guardrails and responses when models are prompted to generate explanations., Comment: In Proceedings of the ACM CHI 2024 Workshop on Human-Centered Explainable AI (HCXAI 2024)

Galactic PeVatrons are sources that can accelerate cosmic rays to PeV energies. The high-energy cosmic rays are expected to interact with the surrounding ambient material or radiation, resulting in the production of gamma rays and neutrinos. To optimize for the detection of such associated production of gamma rays and neutrinos for a given source morphology and spectrum, a multi-messenger analysis that combines gamma rays and neutrinos is required. In this study, we use the Multi-Mission Maximum Likelihood framework (3ML) with IceCube Maximum Likelihood Analysis software (i3mla) and HAWC Accelerated Likelihood (HAL) to search for a correlation between 22 known gamma-ray sources from the third HAWC gamma-ray catalog and 14 years of IceCube track-like data. No significant neutrino emission from the direction of the HAWC sources was found. We report the best-fit gamma-ray model and 90% CL neutrino flux limit from the 22 sources. From the neutrino flux limit, we conclude that the gamma-ray emission from five of the sources can not be produced purely from hadronic interactions. We report the limit for the fraction of gamma rays produced by hadronic interactions for these five sources.

We propose an anisotropy quantifier of the HI 21-cm signal traditionally used to clock the astrophysics of the reionization era as a post-reionization dark energy diagnostic. We find that the anisotropy probe can be measured at SNR $\sim 10$ in both auto-correlation and in cross-correlation with the Ly-$\alpha$ forest over a wide $z$ and $k-$range. We propose to use the BAO signature on the anisotropy signal to measure $( H(z), D_A(z))$. Subsequently, we put constraints on a dark energy model involving a negative cosmological constant on top of a quintessence scalar field and find that such a model is consistent with futuristic observations., Comment: Submitted

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food.

Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challenges of manufacturing high-fidelity and scalable quantum processors. Quantum gate set tomography (QGST) is a critical method for characterizing quantum processors and understanding their operational capabilities and limitations. This paper introduces ML4QGST as a novel approach to QGST by integrating machine learning techniques, specifically utilizing a transformer neural network model. Adapting the transformer model for QGST addresses the computational complexity of modeling quantum systems. Advanced training strategies, including data grouping and curriculum learning, are employed to enhance model performance, demonstrating significant congruence with ground-truth values. We benchmark this training pipeline on the constructed learning model, to successfully perform QGST for $3$ gates on a $1$ qubit system with over-rotation error and depolarizing noise estimation with comparable accuracy to pyGSTi. This research marks a pioneering step in applying deep neural networks to the complex problem of quantum gate set tomography, showcasing the potential of machine learning to tackle nonlinear tomography challenges in quantum computing., Comment: 14 pages