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Generating accurate code review comments remains a significant challenge due to the inherently diverse and non-unique nature of the task output. Large language models pretrained on both programming and natural language data tend to perform well in code-oriented tasks. However, large-scale pretraining is not always feasible due to its environmental impact and project-specific generalizability issues. In this work, first we fine-tune open-source Large language models (LLM) in parameter-efficient, quantized low-rank (QLoRA) fashion on consumer-grade hardware to improve review comment generation. Recent studies demonstrate the efficacy of augmenting semantic metadata information into prompts to boost performance in other code-related tasks. To explore this in code review activities, we also prompt proprietary, closed-source LLMs augmenting the input code patch with function call graphs and code summaries. Both of our strategies improve the review comment generation performance, with function call graph augmented few-shot prompting on the GPT-3.5 model surpassing the pretrained baseline by around 90% BLEU-4 score on the CodeReviewer dataset. Moreover, few-shot prompted Gemini-1.0 Pro, QLoRA fine-tuned Code Llama and Llama 3.1 models achieve competitive results (ranging from 25% to 83% performance improvement) on this task. An additional human evaluation study further validates our experimental findings, reflecting real-world developers' perceptions of LLM-generated code review comments based on relevant qualitative metrics.

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. This study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization -- carried out by leveraging the pyiron workflow manager over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels. Furthermore, we introduce a data annotation pipeline for generating text prompts based on natural language instructions for the DeepCAD dataset using Mistral and LLaVA-NeXT. The dataset contains $\sim170$K models and $\sim660$K text annotations, from abstract CAD descriptions (e.g., generate two concentric cylinders) to detailed specifications (e.g., draw two circles with center $(x,y)$ and radius $r_{1}$, $r_{2}$, and extrude along the normal by $d$...). Within the Text2CAD framework, we propose an end-to-end transformer-based auto-regressive network to generate parametric CAD models from input texts. We evaluate the performance of our model through a mixture of metrics, including visual quality, parametric precision, and geometrical accuracy. Our proposed framework shows great potential in AI-aided design applications. Our source code and annotations will be publicly available., Comment: Accepted in NeurIPS 2024 (Spotlight)

Exoskeleton robots have become a promising tool in neurorehabilitation, offering effective physical therapy and recovery monitoring. The success of these therapies relies on precise motion control systems. Although computed torque control based on inverse dynamics provides a robust theoretical foundation, its practical application in rehabilitation is limited by its sensitivity to model accuracy, making it less effective when dealing with unpredictable payloads. To overcome these limitations, this study introduces a novel model reference computed torque controller that accounts for parametric uncertainties while optimizing computational efficiency. A dynamic model of a seven-degree-of-freedom human lower limb exoskeleton is developed, incorporating a realistic joint friction model to accurately reflect the physical behavior of the robot. To reduce computational demands, the control system is split into two loops: a slower loop that predicts joint torque requirements based on input trajectories and robot dynamics, and a faster PID loop that corrects trajectory tracking errors. Coriolis and centrifugal forces are excluded from the model due to their minimal impact on system dynamics relative to their computational cost. Experimental results show high accuracy in trajectory tracking, and statistical analyses confirm the controller's robustness and effectiveness in handling parametric uncertainties. This approach presents a promising advancement for improving the stability and performance of exoskeleton-based neurorehabilitation.

Seismogenic zones resist slipping before generating earthquakes. Therefore, slip deficit, also called coupling, is a proxy of interseismic seismogenic zones on plate boundaries. However, when a part of a frictional interface sticks together (locked), its sliding surroundings are braked (coupled), so the coupled zone is an overestimate of the locked zone. Several indicators collectively termed mechanical coupling have been proposed to capture locked zones, but their relationship with true frictional locking is unclear. This study combines the frictional physics that locked and unlocked zones should observe, pointing out that we can estimate frictionally locked segments, known as asperities in fault mechanics, with remarkable simplicity but with high generality. First, we assemble the definitions of locking in various frictional failures and arrive at its unified expression: in any friction law, locking/unlocking is the pre-/post-yield phase of frictional failure, and their interseismic approximation is static-dynamic friction, which imposes either of the stationary (constant slip) or steady (constant stress) state. We then parametrize locked zones as distributed circular asperities on unlocked interfaces, to develop a trans-dimensional slip deficit inversion that incorporates the physical constraints of locking-unlocking (static-dynamic). We apply our method and detect five asperities in the Nankai subduction zone in southwestern Japan. Their spatial distribution indicates the asperity location correlates with seafloor topography. Estimated locked zones are consistent with slip zones of historical megathrust earthquakes while mostly not overlapping with slow-earthquake occurrence zones at depth, supporting a hypothesis that the nests of slow earthquakes are normally in long-wavelength scales coupled but unlocked., Comment: 57 pages, 11 figures

In machining process, 3D reverse engineering of the mechanical system is an integral, highly important, and yet time consuming step to obtain parametric CAD models from 3D scans. Therefore, deep learning-based Scan-to-CAD modeling can offer designers enormous editability to quickly modify CAD model, being able to parse all its structural compositions and design steps. In this paper, we propose a supervised boundary representation (BRep) detection network BRepDetNet from 3D scans of CC3D and ABC dataset. We have carefully annotated the 50K and 45K scans of both the datasets with appropriate topological relations (e.g., next, mate, previous) between the geometrical primitives (i.e., boundaries, junctions, loops, faces) of their BRep data structures. The proposed solution decomposes the Scan-to-CAD problem in Scan-to-BRep ensuring the right step towards feature-based modeling, and therefore, leveraging other existing BRep-to-CAD modeling methods. Our proposed Scan-to-BRep neural network learns to detect BRep boundaries and junctions by minimizing focal-loss and non-maximal suppression (NMS) during training time. Experimental results show that our BRepDetNet with NMS-Loss achieves impressive results., Comment: 6 pages, 5 figures

Combustion is the primary process in gas turbine engines, where there is a need for efficient air-fuel mixing to enhance performance. High-shear swirl injectors are commonly used to improve fuel atomization and mixing, which are key factors in determining combustion efficiency and emissions. However, under certain conditions, combustors can experience thermoacoustic instability. In this study, a decision tree-based machine learning algorithm is used to classify combustion conditions by analyzing acoustic pressure and high-speed flame imaging from a counter-rotating high-shear swirl injector of a single can combustor fueled by methane. With a constant Reynolds number and varying equivalence ratios, the combustor exhibits both stable and unstable states. Characteristic features are extracted from the data using time series analysis, providing insight into combustion dynamics. The trained supervised machine learning model accurately classifies stable and unstable operations, demonstrating effective prediction of combustion conditions within the studied parameter range.

Backward error (\textit{BE}) analysis emerges as a powerful tool for assessing the backward stability and strong backward stability of iterative algorithms. In this paper, we explore structured \textit{BEs} for a class of three-by-three block saddle point problems (\textit{TBSPPs}), aiming to assess the strong backward stability of iterative algorithms devised to find their solution. Our investigations preserve the inherent matrix structure and sparsity pattern in the corresponding perturbation matrices and derive explicit formulae for the structure \textit{BEs}. Moreover, we provide formulae for the structure-preserving minimal perturbation matrices for which the structured \textit{BE} is attained. Furthermore, using the obtained structured \textit{BEs}, we propose three new termination criteria for the iterative algorithms to solve the \textit{TBSPP.} Numerical experiments are performed to test the strong backward stability of various iterative algorithms, as well as the reliability and effectiveness of the proposed termination criteria.

In this paper, we propose a generalized shift-splitting (GSS) preconditioner, along with its two relaxed variants to solve the double saddle point problem (DSPP). The convergence of the associated GSS iterative method is analyzed, and sufficient conditions for its convergence are established. Spectral analyses are performed to derive sharp bounds for the eigenvalues of the preconditioned matrices. Numerical experiments based on examples arising from the PDE-constrained optimization problems demonstrate the effectiveness and robustness of the proposed preconditioners compared with existing state-of-the-art preconditioners.

Significant research efforts have been dedicated recently to explore the structured backward error (BE) for saddle point problems (SPPs). However, these investigations overlook the inherent sparsity pattern of the coefficient matrix of the SPP. Moreover, the existing techniques are not applicable when the block matrices have Circulant, Toeplitz, or symmetric-Toeplitz structures and do not even provide structure preserving minimal perturbation matrices for which the BE is attained. To overcome these limitations, we investigate the structured BEs of SPPs when the perturbation matrices exploit the sparsity pattern as well as Circulant, Toeplitz, and symmetric-Toeplitz structures. Furthermore, we construct minimal perturbation matrices that preserve the sparsity pattern and the aforementioned structures. Two applications of the obtained results are discussed: (i) deriving structured BEs for the weighted regularized least squares problem and (ii) finding an effective and reliable stopping criterion for numerical algorithms. Finally, numerical experiments validate our findings, showcasing the utility of the obtained structured BEs in assessing the strong stability of numerical algorithms

We present a model to describe the concentration-dependent growth of protein filaments. Our model contains two states, a low entropy/high affinity ordered state and a high entropy/low affinity disordered state. Consistent with experiments, our model shows a diffusion-limited linear growth regime at low concentration, followed by a concentration independent plateau at intermediate concentrations, and rapid disordered precipitation at the highest concentrations. We show that growth in the linear and plateau regions is the result of two processes that compete amid the rapid binding and unbinding of non-specific states. The first process is the addition of ordered molecules during the periods where the end of the filament is free of incorrectly bound molecules. The second process is the capture of defects, which occurs when consecutive ordered additions occur on top of incorrectly bound molecules. We show that a key molecular property is the probability that a diffusive collision results in a correctly bound state. Small values of this probability suppress the defect capture growth mode, resulting in a plateau in the growth rate when incorrectly bound molecules become common enough to poison ordered growth. We show that conditions that non-specifically suppress or enhance intermolecular interactions, such as the addition of depletants or osmolytes, have opposite effects on the growth rate in the linear and plateau regimes. In the linear regime stronger interactions promote growth by reducing dissolution events, but in the plateau regime stronger interactions inhibit growth by stabilizing incorrectly bound molecules., Comment: 20 pages, 7 figures

In this paper, we introduce a unified model of early and late dark energy. We call it {\it quintessential early dark energy} model in which early and late dark energy are explained by a single scalar field. In other words two different energy scales are related by a single scalar field potential. To achieve this we introduce the modified steep exponential potential. This potential has a hilltop nature during the early time which consists of a flat region followed by a steep region. This nature of the potential plays a crucial role in achieving early dark energy solution. During recent time, the potential can almost mimic the cosmological constant which can result into late time acceleration. But, at the perturbation level the potential shows significant difference with the $\Lambda$CDM model. We also constrain and compare the models for steep exponential, modified steep exponential, axionlike and power law potentials by using the available background cosmological data from CMB, BAO (including DESI DR1 2024), supernovae (Pantheon$+$, DESY5 and Union3) and Hubble parameter measurements. Even after the presence of required EDE solution in all four potentials we don't get any significant improvement in the value of $H_0$. The maximum improvement we get in the present value of Hubble parameter compared to the standard $\Lambda$CDM model is for the axionlike potential. For other potentials the constraints are similar to the $\Lambda$CDM model. We also see that the data prefers $\Lambda$CDM model over the considered scalar field models at least for the data combinations with Pantheon$+$ and Union3., Comment: 37 pages, 5 tables and 11 figures, more analysis and references added

Measuring the properties of the intergalactic medium (IGM) and sources during the Epoch of Reionization (EoR) is of immense importance. We explore the prospects of probing the IGM and sources through redshifted 21-cm observations of individual ionized bubbles surrounding known luminous sources during the EoR. Accordingly, we simulate HI 21-cm maps, foreground contaminants and system noise which are specific to the uGMRT and SKA1-Low observations. Following the subtraction of the foreground from the total visibility, we employ a visibility-based matched filter technique to optimally combine the desired HI 21-cm signal while minimizing the system noise. Our analysis suggests that these ionized bubbles can be detected with more than $5 \sigma$ significance using approximately $\sim 2000$ and $\sim 3000$ hours of observation time with the uGMRT at redshift $7.1$ and $8.3$ respectively. The SKA1-Low should be able to detect these with more than $8 \sigma$ significance using just $\sim 100$ hrs of observations. Further, we investigate the impact of the foreground subtraction on the detectability and find the signal-to-noise ratio decreases when smaller bandwidth is used. More importantly, we show that the matched filtering method can measure ionized bubble radius and constrain HI-neutral fraction reasonably well, helping deeper insights into source properties and the intergalactic medium., Comment: 26 pages, 14 figures, 1 table

In recent papers, Fuentealba, Henneaux, and Troessaert (FHT) gave definitions for supertranslation invariant Lorentz charges in the ADM Hamiltonian formalism and showed that their definitions match with the Chen, Wang, Yau (CWY) definitions of Lorentz charges at null infinity which are free from ``supertranslation ambiguities''. In this brief note, motivated by the analysis of FHT, we write expressions for the supertranslation invariant Lorentz charges in Beig-Schmidt variables at spacelike and timelike infinity. We present calculations, building upon the work of Comp\`ere, Gralla, and Wei (CGW), to show that our expressions for supertranslation invariant Lorentz charges match the CWY definitions at null infinity., Comment: v1, 15 pages, no figures, for the special issue QG@RRI in Gen. Rel. Grav

The energy spectra of linearly dispersing gapless spin-3/2 Dirac fermions display birefringence, featuring two effective Fermi velocities, thus breaking the space-time Lorentz symmetry. Here, we consider a non-Hermitian (NH) generalization of this scenario by introducing a masslike anti-Hermitian birefringent Dirac operator to its Hermitian counterpart. The resulting NH operator shows real eigenvalue spectra over an extended NH parameter regime, and a combination of non-spatial and discrete rotational symmetries protects the gapless nature of such quasiparticles. However, at the brink of dynamic mass generation, triggered by Hubbardlike local interactions, the birefringent parameter always vanishes under coarse grain due to Yukawa-type interactions with scalar bosonic order-parameter fluctuations. The resulting quantum critical state is, therefore, described by two decoupled copies of spin-1/2 Dirac fermions with a unique terminal Fermi velocity, which is equal to the bosonic order-parameter velocity, thereby fostering an emergent space-time Lorentz symmetry. Furthermore, depending on the internal algebra between the anti-Hermitian birefringent Dirac operator and the candidate mass order, the system achieves the emergent Yukawa-Lorentz symmetry either by maintaining its non-Hermiticity or by recovering a full Hermiticity. We discuss the resulting quantum critical phenomena and possible microscopic realizations of the proposed scenarios., Comment: 12 Pages, 3 Figures and 1 Table

We implemented symbolic regression techniques to identify suitable analytical functions that map various properties of neutron stars (NSs), obtained by solving the Tolman-Oppenheimer-Volkoff (TOV) equations, to a few key parameters of the equation of state (EoS). These symbolic regression models (SRMs) are then employed to perform Bayesian inference with a comprehensive dataset from nuclear physics experiments and astrophysical observations. The posterior distributions of EoS parameters obtained from Bayesian inference using SRMs closely match those obtained directly from the solutions of TOV equations. Our SRM-based approach is approximately 100 times faster, enabling efficient Bayesian analyses across different combinations of data to explore their sensitivity to various EoS parameters within a reasonably short time.

Training state-of-the-art (SOTA) deep learning models requires a large amount of data. The visual information present in the training data can be misused, which creates a huge privacy concern. One of the prominent solutions for this issue is perceptual encryption, which converts images into an unrecognizable format to protect the sensitive visual information in the training data. This comes at the cost of a significant reduction in the accuracy of the models. Adversarial Visual Information Hiding (AV IH) overcomes this drawback to protect image privacy by attempting to create encrypted images that are unrecognizable to the human eye while keeping relevant features for the target model. In this paper, we introduce the Attack GAN (AGAN ) method, a new Generative Adversarial Network (GAN )-based attack that exposes multiple vulnerabilities in the AV IH method. To show the adaptability, the AGAN is extended to traditional perceptual encryption methods of Learnable encryption (LE) and Encryption-then-Compression (EtC). Extensive experiments were conducted on diverse image datasets and target models to validate the efficacy of our AGAN method. The results show that AGAN can successfully break perceptual encryption methods by reconstructing original images from their AV IH encrypted images. AGAN can be used as a benchmark tool to evaluate the robustness of encryption methods for privacy protection such as AV IH.

Recent developments in Deep Neural Network (DNN) based watermarking techniques have shown remarkable performance. The state-of-the-art DNN-based techniques not only surpass the robustness of classical watermarking techniques but also show their robustness against many image manipulation techniques. In this paper, we performed a detailed security analysis of different DNN-based watermarking techniques. We propose a new class of attack called the Deep Learning-based OVErwriting (DLOVE) attack, which leverages adversarial machine learning and overwrites the original embedded watermark with a targeted watermark in a watermarked image. To the best of our knowledge, this attack is the first of its kind. We have considered scenarios where watermarks are used to devise and formulate an adversarial attack in white box and black box settings. To show adaptability and efficiency, we launch our DLOVE attack analysis on seven different watermarking techniques, HiDDeN, ReDMark, PIMoG, Stegastamp, Aparecium, Distortion Agostic Deep Watermarking and Hiding Images in an Image. All these techniques use different approaches to create imperceptible watermarked images. Our attack analysis on these watermarking techniques with various constraints highlights the vulnerabilities of DNN-based watermarking. Extensive experimental results validate the capabilities of DLOVE. We propose DLOVE as a benchmark security analysis tool to test the robustness of future deep learning-based watermarking techniques.

GitHub, renowned for facilitating collaborative code version control and software production in software teams, expanded its services in 2017 by introducing GitHub Marketplace. This online platform hosts automation tools to assist developers with the production of their GitHub-hosted projects, and it has become a valuable source of information on the tools used in the Open Source Software (OSS) community. In this exploratory study, we introduce GitHub Marketplace as a software marketplace by comprehensively exploring the platform's characteristics, features, and policies and identifying common themes in production automation. Further, we explore popular tools among practitioners and researchers and highlight disparities in the approach to these tools between industry and academia. We adopted the conceptual framework of software app stores from previous studies to examine 8,318 automated production tools (440 Apps and 7,878 Actions) across 32 categories on GitHub Marketplace. We explored and described the policies of this marketplace as a unique platform where developers share production tools for the use of other developers. Furthermore, we systematically mapped 515 research papers published from 2000 to 2021 and compared open-source academic production tools with those available in the marketplace. We found that although some of the automation topics in literature are widely used in practice, they have yet to align with the state of practice for automated production. We discovered that practitioners often use automation tools for tasks like "Continuous Integration" and "Utilities," while researchers tend to focus more on "Code Quality" and "Testing". Our study illuminates the landscape of open-source tools for automation production in industry and research., Comment: Preprint of IST journal paper. arXiv admin note: substantial text overlap with arXiv:2208.00332

A particular class of quantum observable discrimination tasks, which are equivalent to identifying the outcome label associations of unlabeled observables, was introduced in [Physical Review A 109, 052415 (2024)]. In that work, the tasks were investigated within the "single-shot" regime, where a single implementation of the given measurement devices is available. In this work, we explore these tasks for non-binary observables as well as within the multiple-shot scenario, where we have access to finitely many implementations., Comment: 10 pages, 1 figure

The landscape of fake media creation changed with the introduction of Generative Adversarial Networks (GAN s). Fake media creation has been on the rise with the rapid advances in generation technology, leading to new challenges in Detecting fake media. A fundamental characteristic of GAN s is their sensitivity to parameter initialization, known as seeds. Each distinct seed utilized during training leads to the creation of unique model instances, resulting in divergent image outputs despite employing the same architecture. This means that even if we have one GAN architecture, it can produce countless variations of GAN models depending on the seed used. Existing methods for attributing deepfakes work well only if they have seen the specific GAN model during training. If the GAN architectures are retrained with a different seed, these methods struggle to attribute the fakes. This seed dependency issue made it difficult to attribute deepfakes with existing methods. We proposed a generalized deepfake attribution network (GDA-N et) to attribute fake images to their respective GAN architectures, even if they are generated from a retrained version of the GAN architecture with a different seed (cross-seed) or from the fine-tuned version of the existing GAN model. Extensive experiments on cross-seed and fine-tuned data of GAN models show that our method is highly effective compared to existing methods. We have provided the source code to validate our results.

Fault ruptures of regular earthquakes typically grow in a self-similar manner, where the radiated energy is proportional to the seismic moment. Their proportionality factor, termed as scaled energy, has been conventionally described as the ratio of stress drop to stiffness. By analyzing the self-similar circular crack model by Sato and Hirasawa (1973), Matsu'ura (2024) found a correction prefactor for this theoretical representation of the scaled energy, the cubed ratio of the rupture speed to the S-wave speed. The stress drop times the cubed rupture speed is the scaling prefactor of the self-similar seismic moment, and thus, Matsu'ura's solution tells that the seismic moment rate is determined by the scaled energy in the self-similar rupture growth. We rearrange the properties of the self-similar solution from this perspective, apply this Sato-Hirasawa-Matsu'ura relation to a series of seismic events thought to be self-similar, and estimate their source parameters and scaled energies. According to our estimation using the above self-similarity model, seismologically detected low-frequency and very-low-frequency earthquakes have a common scaled energy, while geodetically detectable slow slip events indicate multiple modes with different scaled energies. Meanwhile, for very-low-frequency earthquakes and tectonic tremors, the seismic moments were significantly smaller than expected for self-similar ruptures despite their moments being proportional to cubed duration values. It keeps alive a hitherto less contemplated possibility: the proportionality of the moment to cubed duration may not be a manifestation of the self-similarity for slow earthquake rupture growth., Comment: 21 pages, 1 figure

Recently, neutral atoms have emerged as a promising platform for quantum computing, offering scalability. In this study, we showcase the realization of atomic qubits in atom-molecular Bose-Einstein condensate, belonging to three distinct classes. In the first case, the condensed molecules form a droplet platform with a flat-top configuration, facilitating effective isolation from both external environments and neighbouring molecules. The second atomic qubits have wavefunctions in the ``pulse" form, exhibiting power law behaviour, whereas the third one has ground and excited state wavefunctions in their respective composite forms, $\sech^2{\beta x}$ and $\sech{\beta x}\tanh{\beta x}$. The localization of the qubits depends on the chemical potential, which is governed by the photo association, providing effective control for qubit manipulation. The relevant parameters, such as energy level separation, healing length, and atom numbers, are found to be influenced by the non-linearity and strength of photo associations governing the behaviour of macroscopic qubits and molecular droplets.

Interacting and understanding with text heavy visual content with multiple images is a major challenge for traditional vision models. This paper is on enhancing vision models' capability to comprehend or understand and learn from images containing a huge amount of textual information from the likes of textbooks and research papers which contain multiple images like graphs, etc and tables in them with different types of axes and scales. The approach involves dataset preprocessing, fine tuning which is by using instructional oriented data and evaluation. We also built a visual chat application integrating CLIP for image encoding and a model from the Massive Text Embedding Benchmark which is developed to consider both textual and visual inputs. An accuracy of 96.71% was obtained. The aim of the project is to increase and also enhance the advance vision models' capabilities in understanding complex visual textual data interconnected data, contributing to multimodal AI., Comment: 5 pages, 4 figures (including 1 graph)

Higher-order quantum theory deals with causal quantum processes, described by quantum combs, and test procedures, described by quantum testers, "measuring" these processes. In this work, we show that "jointly non-implementable" or incompatible quantum testers perform better in distinguishability tasks than their compatible counterparts. To demonstrate our finding, we consider a specific two-party game based on distinguishing quantum combs. We show that the player does better at winning the game when they have exclusive access to incompatible testers over compatible ones. Moreover, we show that, using the resource theoretic measure convex weight, any general quantum resource present in testers is resourceful in quantum comb exclusion tasks. These investigations generalise, respectively, an earlier finding that incompatibility of quantum observables to be a bona fide resource in quantum state distinguishability tasks and another finding that any resource present in observables result in improved performance in state exclusion or antidistinguishability tasks., Comment: 16 pages and 2 figures. Information regarding notation has been made more cohesive. Parts of the text rewritten for improved informational as well as notational clarity. Scientific content remains unchanged

Topological insulators (TIs) with symmetry-protected surface states, offer exciting opportunities for next-generation photonic and optoelectronic device applications. The heterojunctions of TIs and semiconductors (e.g. Si, Ge) have been observed to excellent photo-responsive characteristics. However, the realization of high-frequency operations in these heterojunctions can be hindered by unwanted 1/f (or Flicker) noise and phase noise. Therefore, an in-depth understanding of 1/f noise figures becomes paramount for the effective utilization of such materials.Here we report optoelectronic response and 1/f noise characteristics of a p-n diode fabricated using topological insulator, Bi2Se3 and silicon for potential photo-detector. Through meticulous temperature-dependent current-voltage (I-V) and capacitance-voltage (C-V) measurements, we ascertain crucial parameters like barrier height, ideality factor, and reverse saturation current of the photodetector. The low-frequency 1/f conductance noise spectra suggest a significant presence of trap states influencing the optoelectronic transport properties. The forward noise characteristics exhibit typical 1/f features, having a uni-slope across four decades of frequency, suggesting a homogeneous distribution of barrier height. The spectral and photocurrent-dependent responses show the power law behavior of noise level on photon flux. The hybrid heterojunction demonstrates excellent photo-response and reasonably low noise level, promising signatures for the room-temperature visible photodetector applications., Comment: 9 pages, 6 figures, research article

Volkov-Pankratov states are nontopological massive bound states which generally arise across the smooth interface between two adjacent regions of a two-band semimetal, over which a gap parameter changes sign smoothly. In this work, we show that these modes can be engineered even for a generic smooth interface without any sign inversion. We consider threefold and twofold topological semimetals in which two adjacent regions are illuminated by light with different phases. We show that the interface can exhibit an asymmetric Rosen-Morse potential well for a certain parameter regime even without any sign change of the gap term. Such a quantum well can host a number of Volkov-Pankratov states. We also note that even in a two-band two-dimensional semimetal like graphene, the Volkov-Pankratov states can emerge if one induces a momentum shift rather than opening a gap. Finally, we discuss the transport signatures over those interfacial quantum wells. We note that although the Ramsauer-Townsend effect appears over the symmetric-type P\"oschl-Teller potential well, this effect is absent over an asymmetric Rosen-Morse potential well. We reveal that a transition from a unit transmission to a unit reflection can be achieved by just controlling light parameters in a periodically driven graphene. We observe that the unit reflection phenomenon is direction sensitive; i.e., only incoming electrons from one particular side (left or right) can be perfectly reflected back without any transmission., Comment: 10 pages, 8 figures

We present de Sitter Teukolsky waves -- linearised quadrupolar gravitational waves in the transverse-traceless gauge in de Sitter spacetime. In the cosmological constant $\Lambda$ going to zero limit, our solutions match to Teukolsky solutions. For non-zero $\Lambda$, we compare our solutions to the wider literature, where different authors have constructed linearised gravitational perturbations in de Sitter spacetime with varied motivations. For de Sitter Teukolsky waves, we compute the energy flux across future timelike infinity $\mathcal{I}^{+}$ and show that it is manifestly positive., Comment: 45 pages, 2 figures

The measurement of the $^7$Be($d$,$^3$He)$^6$Li* transfer cross section at 5 MeV/u is carried out. The population of the 2.186 MeV excited state of $^6$Li in this reaction channel is observed for the first time. The experimental angular distributions have been analyzed in the finite range DWBA and coupled-channel frameworks. The effect of the $^7$Be($d$,$^3$He)$^6$Li reaction on both the $^6$Li and $^7$Li abundances are investigated at the relevant big-bang nucleosynthesis energies. The excitation function is calculated by TALYS and normalized to the experimental data. The $S$ factor of the ($d$,$^3$He) channel from the present work is about 50$\%$ lower than existing data at nearby energies. At big-bang energies, the $S$ factor is about three orders of magnitude smaller than that of the ($d,p$) channel. The ($d$,$^3$He) reaction rate is found to have a less than 0.1$\%$ effect on the $^{6,7}$Li abundances., Comment: 7 pages, 5 figures

In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an improvement of $>8\%$ relevance, $>7\%$ engagement, and $>5\%$ ads CTR in Pinterest's production search system. The main contributors to these gains are improved content understanding, better multi-task learning, and real-time serving. We enrich our entity representations using diverse text derived from image captions from a generative LLM, historical engagement, and user-curated boards. Our multitask learning setup produces a single search query embedding in the same space as pin and product embeddings and compatible with pre-existing pin and product embeddings. We show the value of each feature through ablation studies, and show the effectiveness of a unified model compared to standalone counterparts. Finally, we share how these embeddings have been deployed across the Pinterest search stack, from retrieval to ranking, scaling to serve $300k$ requests per second at low latency. Our implementation of this work is available at https://github.com/pinterest/atg-research/tree/main/omnisearchsage., Comment: 8 pages, 5 figures, to be published as an oral paper in TheWebConf Industry Track 2024

The relativistic degrees of freedom ($N_{\rm eff}$) is one of the crucial cosmological parameters. The precise measurement of $N_{\rm eff}$ at the time of cosmic microwave background formation, by Planck 2018 can be used to understand the new fundamental interactions, in particular involving light mediators. Presence of any new particle with sufficient energy density and sizeable interactions with Standard Model particles at the temperature around $\sim$ MeV can significantly alter the neutrino decoupling and hence $N_{\rm eff}$. Thus the bound on $N_{\rm eff}$ can place stringent constraints on various beyond Standard Model paradigms involving light particles. $U(1)_X$ models are among such scenarios and are widely studied in several aspects. In this work, we consider several popular $U(1)_X$ models with light $Z'$ boson like $U(1)_{B-L}$, $U(1)_{B - 3L_i}$, $U(1)_{B_i - 3 L_j}$, $U(1)_{L_i - L_j}$; $i,j =1,2,3$ being the flavour indices and study their impact on $N_{\rm eff}$. We also examine the constraints from ground based experiments like Xenon1T, Borexino, trident, etc. Our analysis shows that for light mass $M_{Z'} \lesssim \mathcal{O} (\rm{MeV})$ the $N_{\rm eff}$ provides the most stringent constraints on the $Z'$ mass and coupling, far exceeding the existing constraints from other experiments., Comment: Accepted in PRD

Demonstrating contextual correlations in quantum theory through the violation of a non-contextuality inequality necessarily needs some ``contexts" and thus assumes some compatibility relations between the measurements. As a result, any self-testing protocol based on the maximal violation of such inequality is not free from such assumptions. In this work, we propose temporal inequalities derived from non-contextuality inequalities for multi-qubit systems without assuming any compatibility relations among the measurements. We demonstrate that the new inequalities can be maximally violated via a sequential measurement scenario. Moreover, using the maximal violation of these temporal inequalities we are able to certify multi-qubit graph states and the measurements., Comment: 18 pages, 1 figure. Comments are welcome

As a next-generation wireless technology, the in-band full-duplex (IBFD) transmission enables simultaneous transmission and reception of signals over the same frequency, thereby doubling spectral efficiency. Further, a continuous up-scaling of wireless network carrier frequencies arising from ever-increasing data traffic is driving research on integrated sensing and communications (ISAC) systems. In this context, we study the co-design of common waveforms, precoders, and filters for an IBFD multi-user (MU) multiple-input multiple-output (MIMO) communications with a distributed MIMO radar. This paper, along with companion papers (Part I and II), proposes a comprehensive MRMC framework that addresses all these challenges. In the companion papers, we developed signal processing and joint design algorithms for this distributed system. In this paper, we tackle multi-target detection, localization, and tracking. This co-design problem that includes practical MU-MIMO constraints on power and quality-of-service is highly non-convex. We propose a low-complexity procedure based on Barzilai-Borwein gradient algorithm to obtain the design parameters and mixed-integer linear program for distributed target localization. Numerical experiments demonstrate the feasibility and accuracy of multi-target sensing of the distributed FD ISAC system. Finally, we localize and track multiple targets by adapting the joint probabilistic data association and extended Kalman filter for this system., Comment: 29 pages, 8 figures, 1 table

Here, we investigate the thermoelectric properties of the marcasite-type compounds MSb$_2$ (M = Ta, Nb) in the temperature range of 310-730 K. These compounds were synthesized by a solid-state reaction followed by the spark plasma sintering process. The Rietveld refinement method confirms the monoclinic phase with space group C2/m for both compounds. The observed values of Seebeck coefficients exhibit non-monotonic behaviour in the studied temperature range, with the maximum magnitude of -14.4 and -22.7 $\mu$V K$^{-1}$ for TaSb$_2$ and NbSb$_2$, respectively at ~444 K. The negative sign of S in the full temperature window signifies the n-type behaviour of these compounds. Both electrical and thermal conductivities show an increasing trend with temperature. The experimentally observed thermoelectric properties are understood through the first-principles DFT and Boltzmann transport equation. A pseudogap in the density of states around the Fermi level characterizes the semimetallic behaviour of these compounds. The multi-band electron and hole pockets were found to be mainly responsible for the temperature dependence of transport properties. The experimental power factors are found to be ~0.09 and ~0.42 mW m$^{-1}$ K$^{-2}$ at 310 K for TaSb2 and NbSb2, respectively. From the DFT-based calculations, the maximum possible power factors for p-type conduction are predicted as ~1.14 and ~1.74 mW m$^{-1}$ K$^{-2}$, while these values are found to be ~1.16 and ~1.80 mW m$^{-1}$ K$^{-2}$ for n-type TaSb$_2$ and NbSb$_2$, respectively at 300 K with the corresponding doping concentrations. The present study suggests that the combined DFT and Boltzmann transport theory are found to be reasonably good at explaining the experimental transport properties, and moderate power factors are predicted.

The complex interaction dynamics of intense femtosecond (fs) pulses and their picosecond (ps)-long leading edge with nanostructured solids occur at both the nanometer and the femtosecond scales, making them extremely difficult to measure directly. Here, we present pump-probe-based measurements that capture the ultrafast evolution of relativistically intense laser-driven grating plasma on fs time and nanometer spatial scales. We measure the transient reflectivity and spectrum of the scattered or diffracted UV-probe pulses from the grating structures with 100s of fs resolution. Our measurements capture the initial onset of the solid-to-plasma transition and the subsequent grating plasma expansion, a few ps before the peak of the intense fs pulse. We measure the instantaneous position of the electron critical surface, its velocity, and its acceleration, which are very crucial for fundamental understanding and applications in ion/electron acceleration and high harmonic generation, while also providing valuable benchmarks for simulations. Particle-in-cell (PIC) simulations corroborate the observations well offering further insight into this process.

We present a Wideband Tapered Gridded Estimator (TGE), which incorporates baseline migration and variation of the primary beam pattern for neutral hydrogen (${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$) 21-cm intensity mapping (IM) with large frequency bandwidth radio-interferometric observations. Here we have analysed $394-494 \, {\rm MHz}$ $(z = 1.9 - 2.6)$ uGMRT data to estimate the Multi-frequency Angular Power Spectrum (MAPS) $C_\ell(\Delta\nu)$ from which we have removed the foregrounds using the polynomial fitting (PF) and Gaussian Process Regression (GPR) methods developed in our earlier work. Using the residual $C_\ell(\Delta\nu)$ to estimate the mean squared 21-cm brightness temperature fluctuation $\Delta^2(k)$, we find that this is consistent with $0 \pm 2 \sigma$ in several $k$ bins. The resulting $2\sigma$ upper limit $\Delta^2(k) < (4.68)^2 \, \rm{mK^2}$ at $k=0.219\,\rm{Mpc^{-1}}$ is nearly $15$ times tighter than earlier limits obtained from a smaller bandwidth ($24.4 \, {\rm MHz}$) of the same data. The $2\sigma$ upper limit $[\Omega_{{\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}} b_{{\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}}] < 1.01 \times 10^{-2}$ is within an order of magnitude of the value expected from independent estimates of the ${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$ mass density $\Omega_{{\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}}$ and the ${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$ bias $b_{{\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}}$. The techniques used here can be applied to other telescopes and frequencies, including $\sim 150 \, {\rm MHz}$ Epoch of Reionization observations., Comment: Accepted for publication in MNRAS

This article is a continuation of "Quantum symmetry in multigraphs (part I)". In this article, we give an explicit construction of a non-Bichon type co-action on a multigraph that is, it preserves quantum symmetry of (V,E) in our sense but not always in Bichon's sense. This construction itself is motivated from automorphisms of quantum graphs.