Search

This paper presents the multiplicity of neutral and charged hadrons produced in 90 GeV$/c$ proton-carbon interactions from a dataset taken by the NA61/SHINE experiment in 2017. Particle identification via dE/dx was performed for the charged hadrons $\pi^\pm$, $K^\pm$, and $p / \bar{p}$; the neutral hadrons $K^0_S$, $\Lambda$, and $\bar{\Lambda}$ were identified via an invariant mass analysis of their decays to charged hadrons. Double-differential multiplicity results as a function of laboratory momentum and polar angle are presented for each particle species; these results provide vital constraints on the predicted neutrino beam flux for current and future long-baseline neutrino oscillation experiments.

The current Large Hadron Collider (LHC) data show no clear indication of new physics and only incremental improvements are anticipated at the energy frontier in the near future. However, while the focus of the LHC has been on constraining TeV scale physics, new particles could still be hiding below the electroweak scale. In order to obtain sensitivity to a new light boson with couplings to SM fermions, a potentially promising decay channel, for resonances with mass $\gtrsim {\cal O}(10)$ GeV, would be the decay to $b\bar b$ pairs. The measurement of such signatures is challenging due to the trigger requirements at the LHC. In this work, we explore the LHC sensitivity to a light pseudoscalar, or axion-like particle (ALP), in the $b\bar{b}$ final state with an associated photon, using jet substructure techniques, in the mass range between 10 GeV and 100 GeV. We obtain projected exclusions on the ALP-fermion coupling in a region of phase space which has not so far been probed by direct searches. We further discuss the impact that lower trigger thresholds may have on the LHC reach., Comment: 26 pages, 10 figures, 5 tables

We numerically study the nonlinear interactions of high-frequency circularly polarized electromagnetic (EM) waves and low-frequency electron-acoustic (EA) density perturbations driven by the EM wave ponderomotive force in relativistic plasmas with two groups of electrons--the population of relativistic degenerate dense electrons (bulk plasma) and the sparse relativistic nondegenerate (classical) electrons, and immobile singly charged positive ions. By pattern selection, we show that many solitary patterns can be generated and drenched through modulational instability of EM waves at different spatial length scales and that the EM wave radiation spectra emanating from compact astrophysical objects may not settle into stable envelope solitons but into different incoherent states, including the emergence of temporal and spatiotemporal chaos due to collisions and fusions among the patterns with strong EA wave emission. The appearance of these states is confirmed by analyzing the Lyapunov exponent spectra, correlation function, and mutual information. As a result, the redistribution of wave energy from initially excited many solitary patterns at large scales to a few new incoherent patterns with small wavelengths in the system occurs, leading to the onset of turbulence in astrophysical plasmas., Comment: 13 pages, 13 figures

Pilot sequence design over doubly selective channels (DSC) is challenging due to the variations in both the time- and frequency-domains. Against this background, the contribution of this paper is twofold: Firstly, we investigate the optimal sequence design criteria for efficient channel estimation in orthogonal frequency division multiplexing systems under DSC. Secondly, to design pilot sequences that can satisfy the derived criteria, we propose a new metric called oversampled ambiguity function (O-AF), which considers both fractional and integer Doppler frequency shifts. Optimizing the sidelobes of O-AF through a modified iterative twisted approximation (ITROX) algorithm, we develop a new class of pilot sequences called ``oversampled low ambiguity zone (O-LAZ) sequences". Through numerical experiments, we evaluate the efficiency of the proposed O-LAZ sequences over the traditional low ambiguity zone (LAZ) sequences, Zadoff-Chu (ZC) sequences and m-sequences, by comparing their channel estimation performances over DSC.

In this work, we generalize the algebraic framework for measurement-based quantum computation (MBQC) in one-dimensional symmetry protected topological states recently developed in [Quantum 7, 1215 (2023)], such that in addition to half-odd-integer spins, the integer spin chains can also be incorporated in the framework. The computational order parameter characterizing the efficiency of MBQC is identified, which, for spin-$1$ chains in the Haldane phase, coincides with the conventional string order parameter in condensed matter physics., Comment: 17 pages, 4 figures

In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Generalization. These architectures capture global contextual features and model long-range dependencies, making them promising candidates for improving cross-domain generalization. We conduct a broad study with in-depth analysis and systematically evaluate different variants of these architectures, using extensive pre-training datasets such as ImageNet-1K, ImageNet-21K, JFT-300M, and ImageNet-22K. Additionally, we compare self-supervised and supervised pre-training strategies to assess their impact on FDG performance. Our findings suggest that self-supervised techniques, which focus on reconstructing masked image patches, can better capture the intrinsic structure of images, thereby outperforming their supervised counterparts. Comprehensive evaluations on the Office-Home and PACS datasets demonstrate that adopting advanced architectures pre-trained on larger datasets establishes new benchmarks, achieving average accuracies of 84.46\% and 92.55\%, respectively. Additionally, we observe that certain variants of these advanced models, despite having fewer parameters, outperform larger ResNet models. This highlights the critical role of utilizing sophisticated architectures and diverse pre-training strategies to enhance FDG performance, especially in scenarios with limited computational resources where model efficiency is crucial. Our results indicate that federated learning systems can become more adaptable and efficient by leveraging these advanced methods, offering valuable insights for future research in FDG.

Computationally hard problems based on coding theory, such as the syndrome decoding problem, have been used for constructing secure cryptographic schemes for a long time. Schemes based on these problems are also assumed to be secure against quantum computers. However, these schemes are often considered impractical for real-world deployment due to large key sizes and inefficient computation time. In the recent call for standardization of additional post-quantum digital signatures by the National Institute of Standards and Technology, several code-based candidates have been proposed, including LESS, CROSS, and MEDS. These schemes are designed on the relatively new zero-knowledge framework. Although several works analyze the hardness of these schemes, there is hardly any work that examines the security of these schemes in the presence of physical attacks. In this work, we analyze these signature schemes from the perspective of fault attacks. All these schemes use a similar tree-based construction to compress the signature size. We attack this component of these schemes. Therefore, our attack is applicable to all of these schemes. In this work, we first analyze the LESS signature scheme and devise our attack. Furthermore, we showed how this attack can be extended to the CROSS signature scheme. Our attacks are built on very simple fault assumptions. Our results show that we can recover the entire secret key of LESS and CROSS using as little as a single fault. Finally, we propose various countermeasures to prevent these kinds of attacks and discuss their efficiency and shortcomings., Comment: 35 pages including appendix and bibliography

Proton-proton collisions at energy-frontier facilities produce an intense flux of high-energy light particles, including neutrinos, in the forward direction. At the LHC, these particles are currently being studied with the far-forward experiments FASER/FASER$\nu$ and SND@LHC, while new dedicated experiments have been proposed in the context of a Forward Physics Facility (FPF) operating at the HL-LHC. Here we present a first quantitative exploration of the reach for neutrino, QCD, and BSM physics of far-forward experiments integrated within the proposed Future Circular Collider (FCC) project as part of its proton-proton collision program (FCC-hh) at $\sqrt{s} \simeq 100$ TeV. We find that $10^9$ electron/muon neutrinos and $10^7$ tau neutrinos could be detected, an increase of several orders of magnitude compared to (HL-)LHC yields. We study the impact of neutrino DIS measurements at the FPF@FCC to constrain the unpolarised and spin partonic structure of the nucleon and assess their sensitivity to nuclear dynamics down to $x \sim 10^{-9}$ with neutrinos produced in proton-lead collisions. We demonstrate that the FPF@FCC could measure the neutrino charge radius for $\nu_{e}$ and $\nu_\mu$ and reach down to five times the SM value for $\nu_\tau$. We fingerprint the BSM sensitivity of the FPF@FCC for a variety of models, including dark Higgs bosons, relaxion-type scenarios, quirks, and millicharged particles, finding that these experiments would be able to discover LLPs with masses as large as 50 GeV and couplings as small as $10^{-8}$, and quirks with masses up to 10 TeV. Our study highlights the remarkable opportunities made possible by integrating far-forward experiments into the FCC project, and it provides new motivation for the FPF at the HL-LHC as an essential precedent to optimize the forward physics experiments that will enable the FCC to achieve its full physics potential.

We examine the parameter space of the Next to Minimal Supersymmetric Standard Model (NMSSM) with Singlino-dominated neutralino $\widetilde\chi_1^0$ as the lightest supersymmetric particle (LSP). Our study focuses on identifying the regions within this parameter space that produce a thermal relic abundance of $\widetilde\chi_1^0$ smaller than the observed cold dark matter relic density while remaining consistent with constraints from LEP measurements, low-energy experiments, Higgs measurements, LHC data, and dark matter direct detection experiments. We identify the dominant annihilation modes of the LSP neutralino across varying LSP mass ranges $\sim \mathcal{O}(1)-\mathcal{O}(10^{3})~$GeV. Furthermore, we conduct a benchmark study to assess the production rates of triple-boson final states emerging from direct electroweakino pair production at the LHC. Drawing insights from these findings, we perform a detailed collider analysis to explore the future potential of probing the triple-boson final states involving a light Higgs boson at the high-luminosity LHC (HL-LHC)., Comment: 31 pages, 17 figures, 4 tables

Modern Cyber-Physical Systems (CPSs) consist of numerous control units interconnected by communication networks. Each control unit executes multiple safety-critical and non-critical tasks in real-time. Most of the safety-critical tasks are executed with a fixed sampling period to ensure deterministic timing behaviour that helps in its safety and performance analysis. However, adversaries can exploit this deterministic behaviour of safety-critical tasks to launch inference-based-based attacks on them. This paper aims to prevent and minimize the possibility of such timing inference or schedule-based attacks to compromise the control units. This is done by switching between strategically chosen execution rates of the safety-critical control tasks such that their performance remains unhampered. Thereafter, we present a novel schedule vulnerability analysis methodology to switch between valid schedules generated for these multiple periodicities of the control tasks in run time. Utilizing these strategies, we introduce a novel Multi-Rate Attack-Aware Randomized Scheduling (MAARS) framework for preemptive fixed-priority schedulers that minimize the success rate of timing-inference-based attacks on safety-critical real-time systems. To our knowledge, this is the first work to propose a schedule randomization method with attack awareness that preserves both the control and scheduling aspects. The efficacy of the framework in terms of attack prevention is finally evaluated on several automotive benchmarks in a Hardware-in-loop (HiL) environment., Comment: 12 pages including references, Total 10 figures (with 3 having subfigures). This paper was rejected in RTSS 2024 Conference

In this paper, a tunneling method is developed for nonlinear multi-objective optimization problems. The proposed method is free from any kind of priori chosen parameter or ordering information of objective functions. Using this method, global Pareto front can be generated for non-convex problems with more than one local front. An algorithm is developed using some ideas of single objective tunneling method. Convergence of this algorithm is justified under some mild assumptions. In addition to this, some numerical examples are included to justify the theoretical results., Comment: 12 Pages; 2 Figures; 1 Table

We provide a set of theoretical constraints on models in which the Standard Model field content is extended by vector-like fermions and in some cases also by a real scalar singlet. Our approach is based on the study of electroweak vacuum stability, perturbativity of model couplings and gauge couplings unification with the use of renormalization group equations. We show that careful analysis of these issues leads to strong constraints on the parameter space of the considered models. This, in turn, has important implications for phenomenology, as we show using examples of the double Higgs boson production, electroweak precision observables, and the electroweak phase transition., Comment: 22 pages, 12 figures; version accepted for publication in Phys. Rev. D

Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training to achieve precise beamforming. While initial deep learning models that use RGB camera images demonstrated promise in reducing beam training overhead, their performance suffers due to sensitivity to lighting and environmental variations. Due to this sensitivity, Quality of Service (QoS) fluctuates, eventually affecting the stability and dependability of networks in dynamic environments. This emphasizes a critical need for robust solutions. This paper proposes a robust beamforming technique to ensure consistent QoS under varying environmental conditions. An optimization problem has been formulated to maximize users' data rates. To solve the formulated NP-hard optimization problem, we decompose it into two subproblems: the semantic localization problem and the optimal beam selection problem. To solve the semantic localization problem, we propose a novel method that leverages the K-means clustering and YOLOv8 model. To solve the beam selection problem, we propose a novel lightweight hybrid architecture that combines a lightweight transformer with a CNN architecture through a weighted entropy mechanism. This hybrid architecture utilizes multimodal data sources to dynamically predict the optimal beams. A novel metric, Accuracy-Complexity Efficiency (ACE), has been proposed to quantify this. Six testing scenarios have been developed to evaluate the robustness of the proposed model. Finally, the simulation result demonstrates that the proposed model outperforms several state-of-the-art baselines regarding beam prediction accuracy, received power, and ACE in the developed test scenarios.

We propose a novel scenario where thermally under-abundant dark matter (DM) can be revived with a first-order phase transition (FOPT). In the absence of FOPT, thermal DM abundance remains suppressed due to efficient annihilation via mediators. A FOPT brings sharp change to the mass of the mediator at the nucleation temperature such that the final DM relic agrees with observations. We implement this idea in a scenario where DM interacts via a mediator whose initial mass is of same order as DM mass keeping DM annihilation either in allowed or forbidden ballpark. As the mediator mass decreases sharply at the nucleation temperature of the FOPT, the frozen out DM suffers further depletion while keeping final relic within observed limits. DM with sizeable interactions with a light mediator can also give rise to the required self-interactions necessary to solve the small scale structure issues of cold dark matter. While the mechanism is generic to any thermal DM mass, choosing it in the GeV ballpark forces the FOPT to MeV scale which predicts stochastic gravitational waves with nano-Hz frequencies within reach of pulsar timing array (PTA) based experiments like NANOGrav. The existence of light scalar mediator and its mixing with the Higgs keep the scenario verifiable at different particle physics experiments., Comment: 11 pages, 6 captioned figures

Within the first principle calculations, we propose a boron and nitrogen cluster incorporated graphene system for efficient valley polarization. The broken spatial inversion symmetry results in high Berry curvature at K and K' valleys of the hexagonal Brillouin zone in this semiconducting system. The consideration of excitonic quasiparticles within GW approximation along with their scattering processes within many-body Bethe-Salpeter equation gives rise to an optical gap of 1.72 eV with an excitonic binding energy of 0.65 eV. Owing to the negligible intervalley scattering, the electrons in opposite valleys are selectively excited by left- and right-handed circular polarized lights, as evident from the oscillator strength calculations. Therefore, this system can exhibit circular-dichroism valley Hall effect in the presence of the in-plane electric field. Moreover, such excitonic qubits can be exploited for information processing., Comment: 15 pages, 4 figures

Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically distributed (non-IID), typically including both intra-domain and inter-domain heterogeneity. However, recent research is limited to simply using averaged signals as a form of regularization and only focusing on one aspect of these non-IID challenges. Given these limitations, this paper clarifies these two non-IID challenges and attempts to introduce cluster representation to address them from both local and global perspectives. Specifically, we propose a dual-clustered feature contrast-based FL framework with dual focuses. First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity. Then, we facilitate cross-client knowledge sharing by pulling the local representation closer to clusters shared by clients with similar semantics while pushing them away from clusters with dissimilar semantics. Second, since the sizes of local clusters belonging to the same class may differ for each client, we further utilize clustering on the global side and conduct averaging to create a consistent global signal for guiding each local training in a contrastive manner. Experimental results on multiple datasets demonstrate that our proposal achieves comparable or superior performance gain under intra-domain and inter-domain heterogeneity., Comment: This work was accepted by Information Fusion Journal

Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks. However, challenges arise due to vulnerability to adversarial examples (AEs) and the non-independent and identically distributed (non-IID) nature of data distribution among devices, hindering the deployment of adversarially robust and accurate learning models at the edge. While adversarial training (AT) is commonly acknowledged as an effective defense strategy against adversarial attacks in centralized training, we shed light on the adverse effects of directly applying AT in FL that can severely compromise accuracy, especially in non-IID challenges. Given this limitation, this paper proposes FatCC, which incorporates local logit \underline{C}alibration and global feature \underline{C}ontrast into the vanilla federated adversarial training (\underline{FAT}) process from both logit and feature perspectives. This approach can effectively enhance the federated system's robust accuracy (RA) and clean accuracy (CA). First, we propose logit calibration, where the logits are calibrated during local adversarial updates, thereby improving adversarial robustness. Second, FatCC introduces feature contrast, which involves a global alignment term that aligns each local representation with unbiased global features, thus further enhancing robustness and accuracy in federated adversarial environments. Extensive experiments across multiple datasets demonstrate that FatCC achieves comparable or superior performance gains in both CA and RA compared to other baselines.

This technical report delves into an in-depth exploration of adversarial attacks specifically targeted at Deep Neural Networks (DNNs) utilized for image classification. The study also investigates defense mechanisms aimed at bolstering the robustness of machine learning models. The research focuses on comprehending the ramifications of two prominent attack methodologies: the Fast Gradient Sign Method (FGSM) and the Carlini-Wagner (CW) approach. These attacks are examined concerning three pre-trained image classifiers: Resnext50_32x4d, DenseNet-201, and VGG-19, utilizing the Tiny-ImageNet dataset. Furthermore, the study proposes the robustness of defensive distillation as a defense mechanism to counter FGSM and CW attacks. This defense mechanism is evaluated using the CIFAR-10 dataset, where CNN models, specifically resnet101 and Resnext50_32x4d, serve as the teacher and student models, respectively. The proposed defensive distillation model exhibits effectiveness in thwarting attacks such as FGSM. However, it is noted to remain susceptible to more sophisticated techniques like the CW attack. The document presents a meticulous validation of the proposed scheme. It provides detailed and comprehensive results, elucidating the efficacy and limitations of the defense mechanisms employed. Through rigorous experimentation and analysis, the study offers insights into the dynamics of adversarial attacks on DNNs, as well as the effectiveness of defensive strategies in mitigating their impact., Comment: This report pertains to the Capstone Project done by Group 1 of the Fall batch of 2023 students at Praxis Tech School, Kolkata, India. The reports consists of 35 pages and it includes 15 figures and 10 tables. This is the preprint which will be submitted to to an IEEE international conference for review

Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. However, recent studies have shown that FL is vulnerable to adversarial examples (AEs), leading to a significant drop in its performance. Meanwhile, the non-independent and identically distributed (non-IID) challenge of data distribution between edge devices can further degrade the performance of models. Consequently, both AEs and non-IID pose challenges to deploying robust learning models at the edge. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks, which can be termed as federated adversarial training (FAT). Moreover, we address the non-IID challenge by implementing a simple yet effective logits calibration strategy under the FAT framework, which can enhance the robustness of models when subjected to adversarial attacks. Specifically, we employ a direct strategy to adjust the logits output by assigning higher weights to classes with small samples during training. This approach effectively tackles the class imbalance in the training data, with the goal of mitigating biases between local and global models. Experimental results on three dataset benchmarks, MNIST, Fashion-MNIST, and CIFAR-10 show that our strategy achieves competitive results in natural and robust accuracy compared to several baselines., Comment: Accepted by IEEE NOMS 2024

Comprehensive summaries of sessions enable an effective continuity in mental health counseling, facilitating informed therapy planning. Yet, manual summarization presents a significant challenge, diverting experts' attention from the core counseling process. This study evaluates the effectiveness of state-of-the-art Large Language Models (LLMs) in selectively summarizing various components of therapy sessions through aspect-based summarization, aiming to benchmark their performance. We introduce MentalCLOUDS, a counseling-component guided summarization dataset consisting of 191 counseling sessions with summaries focused on three distinct counseling components (aka counseling aspects). Additionally, we assess the capabilities of 11 state-of-the-art LLMs in addressing the task of component-guided summarization in counseling. The generated summaries are evaluated quantitatively using standard summarization metrics and verified qualitatively by mental health professionals. Our findings demonstrate the superior performance of task-specific LLMs such as MentalLlama, Mistral, and MentalBART in terms of standard quantitative metrics such as Rouge-1, Rouge-2, Rouge-L, and BERTScore across all aspects of counseling components. Further, expert evaluation reveals that Mistral supersedes both MentalLlama and MentalBART based on six parameters -- affective attitude, burden, ethicality, coherence, opportunity costs, and perceived effectiveness. However, these models share the same weakness by demonstrating a potential for improvement in the opportunity costs and perceived effectiveness metrics.

Self-testing enables the characterization of quantum systems with minimal assumptions on their internal working as such it represents the strongest form of certification for quantum systems. In the existing self-testing literature, self-testing states that are not maximally entangled, but exhibit genuine multipartite nonlocality, have remained an open problem. This is particularly important because, for many-body systems, genuine multipartite nonlocality has been recognized as the strongest form of multipartite quantum correlation. In this work, we present a Cabello-like paradox for scenarios involving an arbitrary number of parties. This paradox is a tool for detecting genuine multipartite nonlocality, allowing for the specific identification and self-testing of states that defy the paradox's limits the most, which turn out to be non-maximally multipartite entangled states. While recent results [\textit{\v{S}upi\'c et al., Nature Physics, 2023}] suggest network self-testing as a means to self-test all quantum states, here we operate within the standard self-testing framework to self-test genuine multipartite non-local and non-maximally entangled states., Comment: 9 pages, 2 figures, comments are welcome

Measurements of $K_S^0$ meson production via its $\pi^{+} \pi^{-}$ decay mode in inelastic $\textit{p+p}$ interactions at incident projectile momenta of 31, 40 and 80 GeV/$c$ ($\sqrt{s_{NN}}=7.7, 8.8$ and $12.3$ GeV, respectively) are presented. The data were recorded by the NA61/SHINE spectrometer at the CERN Super Proton Synchrotron. Double-differential distributions were obtained in transverse momentum and rapidity. The mean multiplicities of $K_S^0$ mesons were determined to be $(5.95 \pm 0.19 (stat) \pm 0.22 (sys)) \times 10^{-2}$ at 31 GeV/$c$, $(7.61 \pm 0.13 (stat) \pm 0.31 (sys)) \times 10^{-2}$ at 40 GeV/$c$ and $(11.58 \pm 0.12 (stat) \pm 0.37 (sys)) \times 10^{-2}$ at 80 GeV/$c$. The results on $K^{0}_{S}$ production are compared with model calculations (Epos1.99, SMASH 2.0 and PHSD) as well as with published data from other experiments., Comment: arXiv admin note: substantial text overlap with arXiv:2106.07535

A cellular automaton (CA)-based modeling approach to simulate wildfire spread, emphasizing its strengths in capturing complex fire dynamics and its integration with geographic information systems (GIS). The model introduces an enhanced CA-based methodology for wildfire prediction, emphasizing interactions between neighboring cells and incorporating major determinants of fire spread, including wind direction, wind speed, and vegetation density, while also accounting for spotting and probabilistic transitions between states in the model to mirror real-world fire behavior., Comment: 12 pages, 7 figures

The certification of quantum nonlocality, which has immense significance in architecting device-independent technologies, confronts severe experimental challenges. Detection loophole, originating from the unavailability of perfect detectors, is one of the major issues amongst them. In the present study we focus on the minimum detection efficiency (MDE) required to detect various forms of genuine nonlocality, originating from the type of causal constraints imposed on the involved parties. In this context, we demonstrate that the MDE needed to manifest the recently suggested $T_2$-type nonlocality deviates significantly from perfection. Additionally, we have computed the MDE necessary to manifest Svetlichny's nonlocality, with state-independent approach markedly reducing the previously established bound. Finally, considering the inevitable existence of noise we demonstrate the robustness of the imperfect detectors to certify $T_2$-type nonlocality., Comment: Comments are welcome!

The critical point of strongly interacting matter is searched for at the CERN SPS by the NA61/SHINE experiment in central $^{40}$Ar +$^{45}$Sc collisions at 13$A$, 19$A$, 30$A$, 40$A$, and 75$A$ GeV/$c$. The dependence of the second-order scaled factorial moments of proton multiplicity distributions on the number of subdivisions in transverse momentum space is measured. The intermittency analysis uses statistically independent data sets for every subdivision in transverse and cumulative-transverse momentum variables. The results obtained do not indicate the searched intermittent pattern. An upper limit on the fraction of correlated protons and the intermittency index is obtained based on a comparison with the Power-law Model., Comment: 38 pages, 20 figures

Recently several reports showing ultra-high electrostrain (> 1 %) have appeared in Pb-free piezoceramics. However, there is lack of clarity on the nature of the ultrahigh strain. Here, we demonsrate that the ultrahigh strain is a consequence of bending of the disc. We show that the propensity for bending arises from the difference in the response magnitude of the grains at the positive and negative surfaces of the piezoceramic when the field is applied., Comment: 8 pages 4 figures

This paper presents the energy dependence of multiplicity and net-electric charge fluctuations in p+p interactions at beam momenta 20, 31, 40, 80, and 158 GeV/c. Results are corrected for the experimental biases and quantified with the use of cumulants and factorial cumulants. Cumulant ratios are an essential tool in the search for the critical point of strongly interacting matter in heavy ion collisions. Measurements performed in p+p interactions provide a vital baseline estimation in these studies. The measured signals are compared with the string hadronic models EPOS1.99 and FTFP-BERT., Comment: 28 pages, 10 figures. The version to be published in Eur. Phys. J. C

Strong interactions preserve an approximate isospin symmetry between up ($u$) and down ($d$) quarks, part of the more general flavor symmetry. In the case of $K$ meson production, if this isospin symmetry were exact, it would result in equal numbers of charged ($K^+$ and $K^-$) and neutral ($K^0$ and $\overline K^{\,0}$) mesons in the final state. Here, we report results on the relative abundance of charged over neutral $K$ meson production in argon and scandium nuclei collisions at a center-of-mass energy of 11.9 GeV per nucleon pair. We find that the production of $K^+$ and $K^-$ mesons at mid-rapidity is $(18.4\pm 6.1)\%$ higher than that of the neutral $K$ mesons. Although with large uncertainties, earlier data on nucleus-nucleus collisions in the collision center-of-mass energy range $2.6 < \sqrt{s_{NN}} < 200$~\GeV are consistent with the present result. Using well-established models for hadron production, we demonstrate that known isospin-symmetry breaking effects and the initial nuclei containing more neutrons than protons lead only to a small (few percent) deviation of the charged-to-neutral kaon ratio from unity at high energies. Thus, they cannot explain the measurements. The significance of the flavor-symmetry violation beyond the known effects is 4.7$\sigma$ when the compilation of world data with uncertainties quoted by the experiments is used. New systematic, high-precision measurements and theoretical efforts are needed to establish the origin of the observed large isospin-symmetry breaking., Comment: The paper includes updates based on the final results of $K^+$ and $K^-$ yields published in Eur. Phys. J.C 84 (2024) 4, 416

Self-testing is a method to certify quantum states and measurements in a device-independent way. The device-independent certification of quantum properties is purely based on input-output measurement statistics of the involved devices with minimal knowledge about their internal workings. Bipartite pure entangled states can be self-tested, but, in the case of multipartite pure entangled states, the answer is not so straightforward. Nevertheless, \v{S}upi\'{c} et al. recently introduced a novel self-testing method for any pure entangled quantum state, which leverages network assistance and relies on bipartite entangled measurements. Hence, their scheme loses the true device-independent flavor of self-testing. In this regard, we provide a self-testing scheme for genuine multipartite pure entangle states in the true sense by employing a generalized Hardy-type non-local argument. Our scheme involves only local operations and classical communications and does not depend on bipartite entangled measurements and is free from any network assistance. In addition, we provide the device-independent bound of the maximum probability of success for generalized Hardy-type nonlocality argument., Comment: 7 pages, one figure, comments are welcome

The escalating number of pending cases is a growing concern world-wide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI) tools in the processing of legal documents. Adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly enhance processing capabilities. With the aim of achieving this objective, we put forward a set of diverse attributes for criminal case proceedings. We use a state-of-the-art sequence labeling framework to automatically extract attributes from the legal documents. Moreover, we demonstrate the efficacy of the extracted attributes in a downstream task, namely legal judgment prediction., Comment: Presented in Mining and Learning in the Legal Domain (MLLD) workshop 2023

This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts that cause high bandwidth consumption, more latency, and experience with bad quality of services (QoS). In particular, these challenges hinder applications like intelligent transportation systems (ITS), metaverse, mixed reality, and the Internet of Everything, where real-time and efficient data transmission is paramount. Therefore, to reduce communication overhead and maintain the QoS of emerging applications such as metaverse, ITS, and digital twin creation, this work proposes a novel semantic communication framework. First, an intelligent semantic transmitter is designed to capture the meaningful information (e.g., the rode-side image in ITS) by designing a domain-specific Mobile Segment Anything Model (MSAM)-based mechanism to reduce the potential communication traffic while QoS remains intact. Second, the concept of generative AI is introduced for building the SemCom to reconstruct and denoise the received semantic data frame at the receiver end. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintain a superior quality reconstruction under different signal-to-noise (SNR) channel conditions. Finally, we have tested and evaluated the proposed semantic communication (SemCom) framework with the real-world 6G scenario of ITS; in particular, the base station equipped with an RGB camera and a mmWave phased array. Experimental results demonstrate the efficacy of the proposed SemCom framework by achieving high-quality reconstruction across various SNR channel conditions, resulting in 93.45% data reduction in communication.

Recent discovery of superconductivity at a transition temperature of $73$K in the doped layered compound Ba$_{2}$CuO$_{3+x}$ for $x\sim 0.2$ has generated a lot of interest. Experiments in this alternately stacked oxygen octahedral and chain layered structure reveal that a compression of the octahedra causes the Cu- {$d_{z^{2}}$} orbital to lie above the Cu- {$d_{x^2-y^2}$} orbital unlike in the well-known cuprate superconducting materials. Our first-principle calculations and low-energy Hamiltonian studies on the $x$ = 0.25 system reveal that this energy ordering results in the formation of $d_{z^2}$ dominated electron pockets. The strong nesting in the Fermi pockets leads to an AFM spin fluctuation mediated $d_{xy}$ wave superconducting state dominated by pairing among the $d_{z^{2}}$ orbitals. This is in contrast to the cuprate superconductors (e.g., YBCO) where both electron and hole pockets exist and the superconducting state with B$_{1g}$ symmetry is formed by the $d_{x^2-y^2}$ orbital electrons. Unlike the earlier reports, we find that inter-layer hybridization has an important contribution to the low-energy band structure and formation of the unconventional superconducting state., Comment: 13 pages, 12 figures

Two-dimensional (2D) noncentrosymmetric systems offer potential opportunities for exploiting the valley degrees of freedom for advanced information processing, owing to non-zero Berry curvature. However, such valley polarization in 2D materials is crucially governed by the intervalley excitonic scattering in momentum space due to reduced electronic degrees of freedom and consequent enhanced electronic correlation. Here, we study the valley excitonic properties of two 2D noncentrosymmetric complementary structures, namely, BC$_6$N and B$_3$C$_2$N$_3$ using first principles-based GW calculations combined with the Bethe-Salpeter equation (BSE), that brings the many-body interactions among the quasiparticles. The \textbf{k}-resolved oscillator strength of their first bright exciton indicates their ability to exhibit valley polarization under the irradiation of circularly polarized light of different chiralities. Both the systems show significant singlet excitonic binding energies of 0.74 eV and 1.31 eV, respectively. Higher stability of dark triplet excitons as compared to the singlet one can lead to higher quantum efficiency in both the systems. The combination of large excitonic binding energies and the valley polarization ability with minimal intervalley scattering make them promising candidates for applications in advanced optical devices and information storage technologies., Comment: 18 pages, 6 figures