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We present a hybrid continuous variable-discrete variable entanglement swapping protocol using linear optics and homodyne measurements, capable of producing a large number of high-fidelity Bell pairs per signal, with an approximate $0.5$ probability of success. The effectiveness of the protocol is determined by the squeezing strength. To increase the number of Bell pairs, approximately 3 dB of extra squeezing is needed for each additional Bell pair. The protocol also generates single Bell pairs with an approximate $0.75$ probability for squeezing strengths $\lessapprox 15dB$, achievable with current technology., Comment: 5 figures

In this paper, we study the four-point celestial leaf amplitudes of massless scalar and MHV gluon scattering. These leaf amplitudes are non-distributional decompositions of the celestial amplitudes associated with a hyperbolic foliation of the Klein spacetime. Bulk scale invariance imposes constraints on the total conformal weights of the massless scalars or gluons. Using this constraint we show that the four-point leaf amplitudes have a \textit {simple pole singularity at $ z = \bar z $}, where, $ z,\bar z $ are two real independent conformal cross ratios. The distributional nature of the four-point celestial amplitudes is recovered by adding the leaf amplitudes in the timelike and spacelike wedges of the spacetime. We also verify that the MHV gluon leaf amplitudes satisfy a set of differential equations previously obtained for celestial MHV gluon amplitudes by considering the soft gluon theorems and the subleading terms in the OPE expansion between two positive helicity gluons., Comment: 32 pages, 2 figures

In this paper we extend the work in the conference paper 'On the Controllability and Observability of Heterogeneous Networked Systems with distinct node dimensions and inner-coupling matrices' wherein the controllability and observability of a heterogeneous networked system with distinct node dimensions were studied. This paper adds to the conference paper a necessary and sufficient condition for controllability of the networked system. The result demonstrates the dependence of controllability of the network on factors like network topology, inner interactions among nodes and nodal dynamics. The result is formulated by characterizing the left eigenvectors of the network state matrix. Another necessary and sufficient condition for controllability, which is a reformulation of the \textit{Popov-Belevitch-Hautus} controllability condition, a necessary and sufficient condition for observability of the networked system and certain necessary conditions for controllability of the networked system are the other results established in this paper. Variants of these results under certain specific network topologies like path, cycle, star and wheel are also discussed., Comment: Extension of the paper presented in ICCAIRO 2023

Large Language Models for Code (or code LLMs) are increasingly gaining popularity and capabilities, offering a wide array of functionalities such as code completion, code generation, code summarization, test generation, code translation, and more. To leverage code LLMs to their full potential, developers must provide code-specific contextual information to the models. These are typically derived and distilled using program analysis tools. However, there exists a significant gap--these static analysis tools are often language-specific and come with a steep learning curve, making their effective use challenging. These tools are tailored to specific program languages, requiring developers to learn and manage multiple tools to cover various aspects of the their code base. Moreover, the complexity of configuring and integrating these tools into the existing development environments add an additional layer of difficulty. This challenge limits the potential benefits that could be gained from more widespread and effective use of static analysis in conjunction with LLMs. To address this challenge, we present codellm-devkit (hereafter, `CLDK'), an open-source library that significantly simplifies the process of performing program analysis at various levels of granularity for different programming languages to support code LLM use cases. As a Python library, CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs. With this library, developers can effortlessly integrate detailed, code-specific insights that enhance the operational efficiency and effectiveness of LLMs in coding tasks. CLDK is available as an open-source library at https://github.com/IBM/codellm-devkit.

The rapid increase in deepfake technology has raised significant concerns about digital media integrity. Detecting deepfakes is crucial for safeguarding digital media. However, most standard image classifiers fail to distinguish between fake and real faces. Our analysis reveals that this failure is due to the model's inability to explicitly focus on the artefacts typically in deepfakes. We propose an enhanced architecture based on the GenConViT model, which incorporates weighted loss and update augmentation techniques and includes masked eye pretraining. This proposed model improves the F1 score by 1.71% and the accuracy by 4.34% on the Celeb-DF v2 dataset. The source code for our model is available at https://github.com/Monu-Khicher-1/multi-stage-learning

We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, and combines them for use in online MPC. On the popular D4RL benchmark, we show performance that is significantly better than existing model-based offline planning methods using MPC and competitive with state-of-the-art (SOTA) model-based and model-free reinforcement learning methods. We additionally illustrate D-MPC's ability to optimize novel reward functions at run time and adapt to novel dynamics, and highlight its advantages compared to existing diffusion-based planning baselines., Comment: Preprint

Since the onset of LLMs, translating natural language queries to structured SQL commands is assuming increasing. Unlike the previous reviews, this survey provides a comprehensive study of the evolution of LLM-based text-to-SQL systems, from early rule-based models to advanced LLM approaches, and how LLMs impacted this field. We discuss benchmarks, evaluation methods and evaluation metrics. Also, we uniquely study the role of integration of knowledge graphs for better contextual accuracy and schema linking in these systems. The current techniques fall into two categories: in-context learning of corpus and fine-tuning, which then leads to approaches such as zero-shot, few-shot learning from the end, and data augmentation. Finally, we highlight key challenges such as computational efficiency, model robustness, and data privacy with perspectives toward their development and improvements in potential areas for future of LLM-based text-to-SQL system., Comment: 12 pages, 5 figures, 3 tables

Coronal mass ejection (CME) often produces a soft X-ray (SXR) flare associated with the low-coronal reconnection and a type-II radio burst associated with an interplanetary (IP) CME-shock. SXR flares and type-II bursts outshine the background emission, making them sun-as-a-star observables. Though there exist SXR flare catalogs covering decades of observations, they do not provide the associated type-II luminosity. Besides, since radio burst emission could be beamed, the observed flux dynamic spectrum may vary with line of sight. Using long-term calibrated decameter-hectometric dynamic spectra from the Wind and STEREO spacecraft, we build a catalog of multi-vantage point observations of type-II bursts. Cross-matching with existing catalogs we compile the properties of the associated flare, reconnection, and the CME. Cross-correlation analysis was done between various parameters. Two novel metrics of flare and CME power show a strong correlation revealing a link between particle acceleration strengths in the low-corona and IP space., Comment: Accepted in the Proceedings of IAUS 388

The superposition of complex optical fields in three-dimension (3D) is the basis of several non-trivial wave phenomena. Significant among them are the non-uniform polarization distribution and their topological character, leading to the emergence of transverse spin, transverse momentum, and their dynamics. These aspects are experimentally measured in the nonparaxial focal region of a circularly-polarized Gaussian input beam. A dielectric mirror, kept in the focal region, is axially scanned to obtain the phase and polarization variations in the retro-reflected output beam using an interferometer and spatially-resolved Stokes parameter measurements. The identification of phase and polarization singularities in the beam cross-section and their behaviour as a function of the mirror position enabled us to map and study the phase-polarization variations in the nonparaxial focal region. The lemon-monstar type polarization patterns surrounding the C-point singularity in the output beam are identified and tracked to study the transverse spin dynamics for right- and left- circular polarization of the input beam. Direct measurement of the input beam polarization helicity-independent and helicity-dependent aspects of the transverse and longitudinal spin dynamics in the nonparaxial focal region are the significant findings reported here. The proposed and demonstrated measurement method allows us to investigate the nonparaxial focal region in more detail and has the potential to unravel other intricate optical field effects., Comment: 22 pages including Supporting Information

We construct a novel flux tube entanglement entropy (FTE$^2$), defined as the excess entanglement entropy relative to the vacuum of a region of color flux stretching between a heavy quark-anti-quark pair in pure gauge Yang-Mills theory. We show that FTE$^2$ can be expressed in terms of correlators of Polyakov loops, is manifestly gauge-invariant, and therefore free of the ambiguities in computations of the entanglement entropy in gauge theories related to the choice of the center algebra. Employing the replica trick, we compute FTE$^2$ for $SU(2)$ Yang-Mills theory in (2+1)D and demonstrate that it is finite in the continuum limit. We explore the properties of FTE$^2$ for a half-slab geometry, which allows us to vary the width and location of the slab, and the extent to which the slab cross-cuts the color flux tube. Following the intuition provided by computations of FTE$^2$ in (1+1)D, and in a thin string model, we examine the extent to which our FTE$^2$ results can be interpreted as the sum of an internal color entropy and a vibrational entropy corresponding to the transverse excitations of the string., Comment: 27 pages, 15 figures

Coronal mass ejections (CMEs) are often associated with X-ray (SXR) flares powered by magnetic reconnection in the low-corona, while the CME shocks in the upper corona and interplanetary (IP) space accelerate electrons often producing the type-II radio bursts. The CME and the reconnection event are part of the same energy release process as highlighted by the correlation between reconnection flux ($\phi_{rec}$) that quantifies the strength of the released magnetic free energy during SXR flare, and the CME kinetic energy that drives the IP shocks leading to type-II bursts. Unlike the sun, these physical parameters cannot be directly inferred in stellar observations. Hence, scaling laws between unresolved sun-as-a-star observables, namely SXR luminosity ($L_X$) and type-II luminosity ($L_R$), and the physical properties of the associated dynamical events are crucial. Such scaling laws also provide insights into the interconnections between the particle acceleration processes across low-corona to IP space during solar-stellar 'flare- CME- type-II' events. Using long-term solar data in SXR to radio waveband, we derive a scaling law between two novel power metrics for the flare and CME-associated processes. The metrics of 'flare power' ($P_{flare}=\sqrt{L_X\phi_{rec}}$) and 'CME power' ($P_{CME}= \sqrt{L_R {V_{CME}}^2}$), where $V_{CME}$ is the CME speed, scale as $P_{flare}\propto P_{CME}^{0.76 \pm 0.04}$. Besides, $L_X$ and $\phi_{rec}$ show power-law trends with $P_{CME}$ with indices of 1.12$\pm$0.05 and 0.61$\pm$0.05 respectively. These power-laws help infer the spatially resolved physical parameters, $V_{CME}$ and $\phi_{rec}$, from disk-averaged observables, $L_X$ and $L_R$ during solar-stellar 'flare- CME- type-II' events., Comment: Accepted in A & A Letters

This paper investigates the existence and uniqueness of the mild solutions and the exact null controllability for a class of non-autonomous parabolic evolution systems with nonlocal conditions in Hilbert spaces. We present sufficient conditions for achieving exact null controllability in these systems using the theory of linear evolution systems and the Schauder fixed point theorem. Importantly, our results do not require the compactness or Lipschitz conditions for the function \( g \) in the nonlocal conditions, which are often needed in other studies. We also provide an example to demonstrate the practical application of our results., Comment: 20 pages, 0 figure

The process of landscaping automotive innovation through patent analysis is crucial for Research and Development teams. It aids in comprehending innovation trends, technological advancements, and the latest technologies from competitors. Traditionally, this process required intensive manual efforts. However, with the advent of Large Language Models (LLMs), it can now be automated, leading to faster and more efficient patent categorization & state-of-the-art of inventive concept extraction. This automation can assist various R\&D teams in extracting relevant information from extensive patent databases. This paper introduces a method based on prompt engineering to extract essential information for landscaping. The information includes the problem addressed by the patent, the technology utilized, and the area of innovation within the vehicle ecosystem (such as safety, Advanced Driver Assistance Systems and more).The result demonstrates the implementation of this method to create a landscape of fuel cell technology using open-source patent data. This approach provides a comprehensive overview of the current state of fuel cell technology, offering valuable insights for future research and development in this field., Comment: 9pages, 4Figures, 1 Flow chart

We present a comprehensive study on enhancing the thermoelectric (TE) performance of bilayer graphene (BLG) through irradiation with arbitrarily polarized light, focusing on $AA$- and $AB$-stacked configurations with zigzag edges. Utilizing a combination of tight-binding theory and density functional theory (DFT), we systematically analyze the impact of light irradiation on electronic and phononic transport properties. Light irradiation alters the electronic hopping parameters, creating an asymmetric transmission function, which significantly increases the Seebeck coefficient, thereby boosting the overall {\it figure of merit} (FOM). For the phononic contribution, DFT calculations reveal that $AB$-stacked BLG exhibits lower lattice thermal conductivity compared to $AA$-stacked, attributed to enhanced anharmonic scattering and phonon group velocity. The combined analysis shows that FOM exceeds unity in both stacking types, with notable improvements near the irradiation-induced gap. Additionally, we explore the dependence of FOM on the system dimensions and temperature, demonstrating that light-irradiated BLG holds great promise for efficient thermoelectric energy conversion and waste heat recovery. Our results show favorable responses over a wide range of irradiation parameters. These findings provide crucial insights into optimizing BLG for advanced TE applications through light-induced modifications., Comment: 12 pages, 12 figures, and supporting material. Comments are welcome

The large-scale deployment of Solidity smart contracts on the Ethereum mainnet has increasingly attracted financially-motivated attackers in recent years. A few now-infamous attacks in Ethereum's history includes DAO attack in 2016 (50 million dollars lost), Parity Wallet hack in 2017 (146 million dollars locked), Beautychain's token BEC in 2018 (900 million dollars market value fell to 0), and NFT gaming blockchain breach in 2022 ($600 million in Ether stolen). This paper presents a comprehensive investigation of the use of large language models (LLMs) and their capabilities in detecting OWASP Top Ten vulnerabilities in Solidity. We introduce a novel, class-balanced, structured, and labeled dataset named VulSmart, which we use to benchmark and compare the performance of open-source LLMs such as CodeLlama, Llama2, CodeT5 and Falcon, alongside closed-source models like GPT-3.5 Turbo and GPT-4o Mini. Our proposed SmartVD framework is rigorously tested against these models through extensive automated and manual evaluations, utilizing BLEU and ROUGE metrics to assess the effectiveness of vulnerability detection in smart contracts. We also explore three distinct prompting strategies-zero-shot, few-shot, and chain-of-thought-to evaluate the multi-class classification and generative capabilities of the SmartVD framework. Our findings reveal that SmartVD outperforms its open-source counterparts and even exceeds the performance of closed-source base models like GPT-3.5 and GPT-4 Mini. After fine-tuning, the closed-source models, GPT-3.5 Turbo and GPT-4o Mini, achieved remarkable performance with 99% accuracy in detecting vulnerabilities, 94% in identifying their types, and 98% in determining severity. Notably, SmartVD performs best with the `chain-of-thought' prompting technique, whereas the fine-tuned closed-source models excel with the `zero-shot' prompting approach.

Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have been shown to generate convincingly human-like text in many settings. However, to what extent do LLM-based simulations \textit{actually} reflect human dialogues? In this work, we answer this question by generating a large-scale dataset of 100,000 paired LLM-LLM and human-LLM dialogues from the WildChat dataset and quantifying how well the LLM simulations align with their human counterparts. Overall, we find relatively low alignment between simulations and human interactions, demonstrating a systematic divergence along the multiple textual properties, including style and content. Further, in comparisons of English, Chinese, and Russian dialogues, we find that models perform similarly. Our results suggest that LLMs generally perform better when the human themself writes in a way that is more similar to the LLM's own style.

Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the embedded cluster expansion (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements.

Spectroscopic identification of distinct nonlinear photocurrents unveils quantum geometric properties of electron wavefunctions and the momentum-space topological structures. This is especially interesting, but still puzzling, for chiral topological semimetals with possibilities of hosting giant quantized circular photogalvanic effect. Here we report a comprehensive terahertz (THz) emission spectroscopic analysis of nonlinear photoconductivity of chiral multifold CoSi at 0.26 ~ 1 eV. We find a large linear shift conductivity (17 {\mu}A/V2), and confirm a giant injection conductivity (167 {\mu}A/V2) as a consequence of strongly interfered non-quantized contributions from the vicinity of multifold nodes with opposite chiralities. The bulk injection current excited by the pump field with a complex wavevector is shown to carry both longitudinal and transverse components. Symmetry analyses further unveil weak nonlocal photon drag effect in addition to the photogalvanic effect. This work not only highlights chiral transition metal monosilicides for mid-infrared photovoltaic applications via various nonlinear optical channels, but also consolidates the THz spectroscopy for quantitative photovoltaic research.

Recent advancements in text-to-speech (TTS) synthesis show that large-scale models trained with extensive web data produce highly natural-sounding output. However, such data is scarce for Indian languages due to the lack of high-quality, manually subtitled data on platforms like LibriVox or YouTube. To address this gap, we enhance existing large-scale ASR datasets containing natural conversations collected in low-quality environments to generate high-quality TTS training data. Our pipeline leverages the cross-lingual generalization of denoising and speech enhancement models trained on English and applied to Indian languages. This results in IndicVoices-R (IV-R), the largest multilingual Indian TTS dataset derived from an ASR dataset, with 1,704 hours of high-quality speech from 10,496 speakers across 22 Indian languages. IV-R matches the quality of gold-standard TTS datasets like LJSpeech, LibriTTS, and IndicTTS. We also introduce the IV-R Benchmark, the first to assess zero-shot, few-shot, and many-shot speaker generalization capabilities of TTS models on Indian voices, ensuring diversity in age, gender, and style. We demonstrate that fine-tuning an English pre-trained model on a combined dataset of high-quality IndicTTS and our IV-R dataset results in better zero-shot speaker generalization compared to fine-tuning on the IndicTTS dataset alone. Further, our evaluation reveals limited zero-shot generalization for Indian voices in TTS models trained on prior datasets, which we improve by fine-tuning the model on our data containing diverse set of speakers across language families. We open-source all data and code, releasing the first TTS model for all 22 official Indian languages., Comment: Accepted to NeurIPS 2024 Datasets and Benchmarks track

Robot navigation is an important research field with applications in various domains. However, traditional approaches often prioritize efficiency and obstacle avoidance, neglecting a nuanced understanding of human behavior or intent in shared spaces. With the rise of service robots, there's an increasing emphasis on endowing robots with the capability to navigate and interact in complex real-world environments. Socially aware navigation has recently become a key research area. However, existing work either predicts pedestrian movements or simply emits alert signals to pedestrians, falling short of facilitating genuine interactions between humans and robots. In this paper, we introduce the Hybrid Soft Actor-Critic with Large Language Model (HSAC-LLM), an innovative model designed for socially-aware navigation in robots. This model seamlessly integrates deep reinforcement learning with large language models, enabling it to predict both continuous and discrete actions for navigation. Notably, HSAC-LLM facilitates bidirectional interaction based on natural language with pedestrian models. When a potential collision with pedestrians is detected, the robot can initiate or respond to communications with pedestrians, obtaining and executing subsequent avoidance strategies. Experimental results in 2D simulation, the Gazebo environment, and the real-world environment demonstrate that HSAC-LLM not only efficiently enables interaction with humans but also exhibits superior performance in navigation and obstacle avoidance compared to state-of-the-art DRL algorithms. We believe this innovative paradigm opens up new avenues for effective and socially aware human-robot interactions in dynamic environments. Videos are available at https://hsacllm.github.io/.

Implementing automated unit tests is an important but time consuming activity in software development. Developers dedicate substantial time to writing tests for validating an application and preventing regressions. To support developers in this task, software engineering research over the past few decades has developed many techniques for automating unit test generation. However, despite this effort, usable tools exist for very few programming languages -- mainly Java, C, and C# and, more recently, for Python. Moreover, studies have found that automatically generated tests suffer poor readability and often do not resemble developer-written tests. In this work, we present a rigorous investigation of how large language models (LLMs) can help bridge the gap. We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases. We illustrate how the pipeline can be applied to different programming languages, specifically Java and Python, and to complex software requiring environment mocking. We conducted a through empirical study to assess the quality of the generated tests in terms of coverage, mutation score, and test naturalness -- evaluating them on standard as well as enterprise Java applications and a large Python benchmark. Our results demonstrate that LLM-based test generation, when guided by static analysis, can be competitive with, and even outperform, state-of-the-art test-generation techniques in coverage achieved while also producing considerably more natural test cases that developers find easy to read and understand. We also present the results of a user study, conducted with 161 professional developers, that highlights the naturalness characteristics of the tests generated by our approach.

Invariants are essential for ensuring the security and correctness of Solidity smart contracts, particularly in the context of blockchain's immutability and decentralized execution. This paper introduces InvSol, a novel framework for pre-deployment invariant generation tailored specifically for Solidity smart contracts. Unlike existing solutions, namely InvCon, InvCon+, and Trace2Inv, that rely on post-deployment transaction histories on Ethereum mainnet, InvSol identifies invariants before deployment and offers comprehensive coverage of Solidity language constructs, including loops. Additionally, InvSol incorporates custom templates to effectively prevent critical issues such as reentrancy, out-of-gas errors, and exceptions during invariant generation. We rigorously evaluate InvSol using a benchmark set of smart contracts and compare its performance with state-of-the-art solutions. Our findings reveal that InvSol significantly outperforms these tools, demonstrating its effectiveness in handling new contracts with limited transaction histories. Notably, InvSol achieves a 15% improvement in identifying common vulnerabilities compared to InvCon+ and is able to address certain crucial vulnerabilities using specific invariant templates, better than Trace2Inv.

In this work we explore the application of AI to robotic welding. Robotic welding is a widely used technology in many industries, but robots currently do not have the capability to detect welding defects which get introduced due to various reasons in the welding process. We describe how deep-learning methods can be applied to detect weld defects in real-time by recording the welding process with microphones and a camera. Our findings are based on a large database with more than 4000 welding samples we collected which covers different weld types, materials and various defect categories. All deep learning models are trained in an unsupervised fashion because the space of possible defects is large and the defects in our data may contain biases. We demonstrate that a reliable real-time detection of most categories of weld defects is feasible both from audio and video, with improvements achieved by combining both modalities. Specifically, the multi-modal approach achieves an average Area-under-ROC-Curve (AUC) of 0.92 over all eleven defect types in our data. We conclude the paper with an analysis of the results by defect type and a discussion of future work., Comment: 21 pages

The degree of a map between orientable manifolds is a crucial concept in topology, providing deep insights into the structure and properties of the manifolds and the corresponding maps. This concept has been thoroughly investigated, particularly in the realm of simplicial maps between orientable triangulable spaces. In this paper, we concentrate on constructing simplicial degree $d$ self-maps on $n$-spheres. We describe the construction of several such maps, demonstrating that for every $d \in \mathbb{Z} \setminus {0}$, there exists a degree $d$ simplicial map from a triangulated $n$-sphere with $3|d| + n - 1$ vertices to $\mathbb{S}^n_{n+2}$. Further, we prove that, for every $d \in \mathbb{Z} \setminus {0}$, there exists a simplicial map of degree $3 d$ from a triangulated $n$-sphere with $6|d| + n$ vertices, as well as a simplicial map of degree $3d+\frac{d}{|d|}$ from a triangulated $n$-sphere with $6|d|+n+3$ vertices, to $\mathbb{S}^{n}_{n+2}$. Furthermore, we show that for any $|k| \geq 2$ and $n \geq |k|$, a degree $k$ simplicial map exists from a triangulated $n$-sphere $K$ with $|k| + n + 3$ vertices to $\mathbb{S}^n_{n+2}$. We also prove that for $d = 2$ and 3, these constructions produce vertex-minimal degree $d$ self-maps of $n$-spheres. Additionally, for every $n \geq 2$, we construct a degree $n+1$ simplicial map from a triangulated $n$-sphere with $2n + 4$ vertices to $\mathbb{S}^{n}_{n+2}$. We also prove that this construction provides facet minimal degree $n+1$ self-maps of $n$-spheres., Comment: 14 pages, 2 figures

This paper explores key aspects of the theory and applications of conformable fractional order systems. It begins by establishing the existence and uniqueness of the evolution operator for a class of non-autonomous homogeneous systems. Using the Schauder fixed point theorem and the theory of linear evolution systems, we delve into the existence of mild solutions for a class of non-autonomous conformable fractional semi-linear systems. Additionally, the paper addresses the exact null controllability of abstract systems. We present an example to demonstrate the efficiency of the results., Comment: 24 Pages, 0 figures

We establish a Wiener-type integral condition for first-order Sobolev functions defined on a complete, doubling metric measure space supporting a Poincar\'e inequality. It is stronger than the Lebesgue point property, except for a marginal increase in the capacity of the set of non-Lebesgue points.

Alloying stands out as a pivotal technological method employed across various compounds, be they metallic, magnetic, or semiconducting, serving to fine-tune their properties to meet specific requirements. Ternary semiconductors represent a prominent example of such alloys. They offer fine-tuning of electronic bands, the band gap in particular, thus granting the technology of semiconductor heterostructures devices, key elements in current electronics and optoelectronics. In the realm of magnetically ordered systems, akin to electronic bands in solids, spin waves exhibit characteristic dispersion relations, featuring sizeable magnon gaps in many antiferromagnets. The engineering of the magnon gap constitutes a relevant direction in current research on antiferromagnets, aiming to leverage their distinct properties for THz technologies, spintronics, or magnonics. In this study, we showcase the tunability of the magnon gap across the THz spectral range within an alloy comprising representative semiconducting van-der-Waals antiferromagnets FePS$_3$ and NiPS$_3$. These constituents share identical in-plane crystal structures, magnetic unit cells and the direction of the magnetic anisotropy, but differ in the amplitude and sign of the latter. Altogether these attributes result in the wide tunability of the magnon gap in the Fe$_{1-x}$Ni$_x$PS$_3$ alloy in which the magnetic order is imposed by stronger, perpendicular anisotropy of iron., Comment: 6 pages, 1 figure

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants' gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

Large Language Models (LLMs) have revolutionized the landscape of machine learning, yet current benchmarks often fall short in capturing the diverse behavior of these models in real-world applications. A benchmark's usefulness is determined by its ability to clearly differentiate between models of varying capabilities (separability) and closely align with human preferences. Existing frameworks like Alpaca-Eval 2.0 LC \cite{dubois2024lengthcontrolledalpacaevalsimpleway} and Arena-Hard v0.1 \cite{li2024crowdsourced} are limited by their focus on general-purpose queries and lack of diversity across domains such as law, medicine, and multilingual contexts. In this paper, we address these limitations by introducing a novel data pipeline that curates diverse, domain-specific evaluation sets tailored for LLM-as-a-Judge frameworks. Our approach leverages a combination of manual curation, semi-supervised learning to generate clusters, and stratified sampling to ensure balanced representation across a wide range of domains and languages. The resulting evaluation set, which includes 1573 samples across 14 categories, demonstrates high separability (84\%) across ten top-ranked models, and agreement (84\%) with Chatbot Arena and (0.915) Spearman correlation. The agreement values are 9\% better than Arena Hard and 20\% better than AlpacaEval 2.0 LC, while the Spearman coefficient is 0.7 more than the next best benchmark, showcasing a significant improvement in the usefulness of the benchmark. We further provide an open-source evaluation tool that enables fine-grained analysis of model performance across user-defined categories, offering valuable insights for practitioners. This work contributes to the ongoing effort to enhance the transparency, diversity, and effectiveness of LLM evaluation methodologies., Comment: 14 pages, 8 figures, Under review

In this paper, we report the excitation of coherent density and potential fluctuations induced by magnetohydrodynamic (MHD) activity in the edge plasma region of ADITYA-U Tokamak. When the amplitude of the MHD mode, mainly the m/n = 2/1, increases beyond a threshold value of 0.3-0.4 %, coherent oscillations in the density and potential fluctuations are observed having the same frequency as that of the MHD mode. The mode numbers of these MHD induced density and potential fluctuations are obtained by Langmuir probes placed at different radial, poloidal, and toroidal locations in the edge plasma region. Detailed analyses of these Langmuir probe measurements reveal that the coherent mode in edge potential fluctuation has a mode structure of m/n = 2/1 whereas the edge density fluctuation has an m/n = 1/1 structure. It is further observed that beyond the threshold, the coupled power fraction scales almost linearly with the magnitude of magnetic fluctuations. Furthermore, the rise rates of the coupled power fraction for coherent modes in density and potential fluctuations are also found to be dependent on the growth rate of magnetic fluctuations. The disparate mode structures of the excited modes in density and plasma potential fluctuations suggest that the underlying mechanism for their existence is most likely due to the excitation of the global high-frequency branch of zonal flows occurring through the coupling of even harmonics of potential to the odd harmonics of pressure due to 1/R dependence of the toroidal magnetic field.

An important tool in studying the sub-femtoscale spacetime structure of matter in ultrarelativistic heavy-ion collisions is Hanbury-Brown-Twiss (HBT) intensity interferometry of identical particles in the final state of such collisions. We show here that a variant of an entanglement enabled intensity interferometry ($E^2 I^2$) proposed by Cotler and Wilczek provides a powerful alternative to HBT interferometry in extracting fundamental nonperturbative features of QCD at high energies. In particular, we show that the spatial distributions of color singlet (pomeron) configurations in nuclei can be obtained from exclusive resonant decays of $\rho$-mesons into $\pi^\pm$-pairs in ultrarelativistic ultraperipheral nuclear collisions (UPCs) at RHIC and the LHC. The $E^2 I^2$ framework developed here is quite general. It can be employed to extract information on the spin structure of pomeron couplings as well as enhance the discovery potential for rare odderon configurations from exclusive vector meson decays into few-particle final states both in UPCs and at the Electron-Ion Collider., Comment: 23 pages, 7 Figures

We release Rasa, the first multilingual expressive TTS dataset for any Indian language, which contains 10 hours of neutral speech and 1-3 hours of expressive speech for each of the 6 Ekman emotions covering 3 languages: Assamese, Bengali, & Tamil. Our ablation studies reveal that just 1 hour of neutral and 30 minutes of expressive data can yield a Fair system as indicated by MUSHRA scores. Increasing neutral data to 10 hours, with minimal expressive data, significantly enhances expressiveness. This offers a practical recipe for resource-constrained languages, prioritizing easily obtainable neutral data alongside smaller amounts of expressive data. We show the importance of syllabically balanced data and pooling emotions to enhance expressiveness. We also highlight challenges in generating specific emotions, e.g., fear and surprise., Comment: Accepted at INTERSPEECH 2024. First two authors listed contributed equally

Publicly available TTS datasets for low-resource languages like Hindi and Tamil typically contain 10-20 hours of data, leading to poor vocabulary coverage. This limitation becomes evident in downstream applications where domain-specific vocabulary coupled with frequent code-mixing with English, results in many OOV words. To highlight this problem, we create a benchmark containing OOV words from several real-world applications. Indeed, state-of-the-art Hindi and Tamil TTS systems perform poorly on this OOV benchmark, as indicated by intelligibility tests. To improve the model's OOV performance, we propose a low-effort and economically viable strategy to obtain more training data. Specifically, we propose using volunteers as opposed to high quality voice artists to record words containing character bigrams unseen in the training data. We show that using such inexpensive data, the model's performance improves on OOV words, while not affecting voice quality and in-domain performance., Comment: Accepted at INTERSPEECH 2024

We provide practical simulation methods for scalar field theories on a quantum computer that yield improved asymptotics as well as concrete gate estimates for the simulation and physical qubit estimates using the surface code. We achieve these improvements through two optimizations. First, we consider a different approach for estimating the elements of the S-matrix. This approach is appropriate in general for 1+1D and for certain low-energy elastic collisions in higher dimensions. Second, we implement our approach using a series of different fault-tolerant simulation algorithms for Hamiltonians formulated both in the field occupation basis and field amplitude basis. Our algorithms are based on either second-order Trotterization or qubitization. The cost of Trotterization in occupation basis scales as $\widetilde{O}(\lambda N^7 |\Omega|^3/(M^{5/2} \epsilon^{3/2})$ where $\lambda$ is the coupling strength, $N$ is the occupation cutoff $|\Omega|$ is the volume of the spatial lattice, $M$ is the mass of the particles and $\epsilon$ is the uncertainty in the energy calculation used for the $S$-matrix determination. Qubitization in the field basis scales as $\widetilde{O}(|\Omega|^2 (k^2 \Lambda +kM^2)/\epsilon)$ where $k$ is the cutoff in the field and $\Lambda$ is a scaled coupling constant. We find in both cases that the bounds suggest physically meaningful simulations can be performed using on the order of $4\times 10^6$ physical qubits and $10^{12}$ $T$-gates which corresponds to roughly one day on a superconducting quantum computer with surface code and a cycle time of 100 ns, placing simulation of scalar field theory within striking distance of the gate counts for the best available chemistry simulation results., Comment: main text, 50 pages, supplementary 64 pages

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning, but faces challenges with heterogeneous data distributions across clients. This paper introduces FedSat, a novel FL approach designed to tackle various forms of data heterogeneity simultaneously. FedSat employs a cost-sensitive loss function and a prioritized class-based weighted aggregation scheme to address label skewness, missing classes, and quantity skewness across clients. While the proposed cost-sensitive loss function enhances model performance on minority classes, the prioritized class-based weighted aggregation scheme ensures client contributions are weighted based on both statistical significance and performance on critical classes. Extensive experiments across diverse data-heterogeneity settings demonstrate that FedSat significantly outperforms state-of-the-art baselines, with an average improvement of 1.8% over the second-best method and 19.87% over the weakest-performing baseline. The approach also demonstrates faster convergence compared to existing methods. These results highlight FedSat's effectiveness in addressing the challenges of heterogeneous federated learning and its potential for real-world applications.

The skew symmetric Laplace uniform distribution SSLUD({\mu}) is introduced in Lohot, R. K. and Dixit, V. U. (2024) using the skewing mechanism of Azzalini (1985). Here we derive the most powerful (MP) test for symmetry of the SSLUD({\mu}). Since the form of the test statistic is complicated and it is difficult to obtain its exact distribution, critical values and the power of MP test are obtained using simulation. Further, we construct a confidence interval (CI) for parameter {\mu} assuming asymptotic normality and empirical distribution of the maximum likelihood estimator of {\mu}. These two methods are compared based on the average length and coverage probability of the CI. Finally, the CI of the parameter {\mu} is constructed using data on the transformed daily percentage change in the price of NIFTY 50, an Indian stock market index given in Lohot, R. K. and Dixit, V. U. (2024)., Comment: 21 pages, 7 tables

This study addresses the challenge of social bipedal navigation in a dynamic, human-crowded environment, a research area largely underexplored in legged robot navigation. We present a zonotope-based framework that couples prediction and motion planning for a bipedal ego-agent to account for bidirectional influence with the surrounding pedestrians. This framework incorporates a Social Zonotope Network (SZN), a neural network that predicts future pedestrian reachable sets and plans future socially acceptable reachable set for the ego-agent. SZN generates the reachable sets as zonotopes for efficient reachability-based planning, collision checking, and online uncertainty parameterization. Locomotion-specific losses are added to the SZN training process to adhere to the dynamic limits of the bipedal robot that are not explicitly present in the human crowds data set. These loss functions enable the SZN to generate locomotion paths that are more dynamically feasible for improved tracking. SZN is integrated with a Model Predictive Controller (SZN-MPC) for footstep planning for our bipedal robot Digit. SZN-MPC solves for collision-free trajectory by optimizing through SZN's gradients. and Our results demonstrate the framework's effectiveness in producing a socially acceptable path, with consistent locomotion velocity, and optimality. The SZN-MPC framework is validated with extensive simulations and hardware experiments., Comment: 19 pages, 19 figures. arXiv admin note: text overlap with arXiv:2403.16485, arXiv:2310.09969

In their work [9], Feng Luo and Richard Stong introduced the concept of the average edge order, denoted as $\mu_0(K)$. They demonstrated that if $\mu_0(K)\leq \frac{9}{2}$ for a closed $3$-manifold $K$, then $K$ must be a sphere. Building upon this foundation, Makoto Tamura extended similar results to $3$-manifolds with non-empty boundaries in [10, 11]. In our present study, we extend these findings to normal $3$-pseudomanifolds. Specifically, we establish that for a normal $3$-pseudomanifold $K$ with singularities, $\mu_0(K)\geq\frac{30}{7}$. Moreover, equality holds if and only if $K$ is a one-vertex suspension of $\mathbb{RP}^2$ with seven vertices. Furthermore, we establish that when $\frac{30}{7}\leq\mu_0(K)\leq\frac{9}{2}$, the $3$-pseudomanifold $K$ can be derived from some boundary complexes of $4$-simplices by a sequence of possible operations, including connected sums, bistellar $1$-moves, edge contractions, edge expansions, vertex folding, and edge folding. In the end, we discuss some normal $3$-pseudomanifolds exhibiting higher average edge orders., Comment: 10 pages

Laplace distribution is popular in the field of economics and finance. Still, data sets often show a lack of symmetry and a tendency of being bounded from either side of their support. In view of this, we introduce a new family of skew distribution using the skewing mechanism of Azzalini (1985), namely, skew-symmetric-Laplace-uniform distribution (SSLUD). Here uniform distribution is used not only to introduce skewness in Laplace distribution but also to restrict distribution support on one side of the real line. This paper provides a comprehensive description of the essential distributional properties of SSLUD. Estimators of the parameter are obtained using the method of moments and the method of maximum likelihood. The finite sample and asymptotic properties of these estimators are studied using simulation. It is observed that the maximum likelihood estimator is better than the moment estimator through a simulation study. Finally, an application of SSLUD to real-life data on the daily percentage change in the price of NIFTY 50, an Indian stock market index, is presented.

In recent work, we demonstrated a double-copy relation between inclusive gluon radiation in shock wave collisions of ultrarelativistic nuclei and inclusive graviton radiation in trans-Planckian gravitational shock wave collisions. We compute here the corresponding gravitational shock wave propagators in general relativity and demonstrate that they too obey a double copy relation to gluon shock wave propagators computed previously. These results provide key input in a renormalization group approach towards computing the high frequency radiation spectrum in close black hole encounters., Comment: 16 pages, revised version to appear in Physical Review D

We present TORAX, a new, open-source, differentiable tokamak core transport simulator implemented in Python using the JAX framework. TORAX solves the coupled equations for ion heat transport, electron heat transport, particle transport, and current diffusion, incorporating modular physics-based and ML models. JAX's just-in-time compilation ensures fast runtimes, while its automatic differentiation capability enables gradient-based optimization workflows and simplifies the use of Jacobian-based PDE solvers. Coupling to ML-surrogates of physics models is greatly facilitated by JAX's intrinsic support for neural network development and inference. TORAX is verified against the established RAPTOR code, demonstrating agreement in simulated plasma profiles. TORAX provides a powerful and versatile tool for accelerating research in tokamak scenario modeling, pulse design, and control., Comment: 16 pages, 7 figures

Data driven models for automated diagnosis in radiology suffer from insufficient and imbalanced datasets due to low representation of pathology in a population and the cost of expert annotations. Datasets can be bolstered through data augmentation. However, even when utilizing a full suite of transformations during model training, typical data augmentations do not address variations in human anatomy. An alternative direction is to synthesize data using generative models, which can potentially craft datasets with specific attributes. While this holds promise, commonly used generative models such as Generative Adversarial Networks may inadvertently produce anatomically inaccurate features. On the other hand, diffusion models, which offer greater stability, tend to memorize training data, raising concerns about privacy and generative diversity. Alternatively, inpainting has the potential to augment data through directly inserting pathology in medical images. However, this approach introduces a new challenge: accurately merging the generated pathological features with the surrounding anatomical context. While inpainting is a well established method for addressing simple lesions, its application to pathologies that involve complex structural changes remains relatively unexplored. We propose an efficient method for inpainting pathological features onto healthy anatomy in MRI through voxelwise noise scheduling in a latent diffusion model. We evaluate the method's ability to insert disc herniation and central canal stenosis in lumbar spine sagittal T2 MRI, and it achieves superior Frechet Inception Distance compared to state-of-the-art methods.

The recent observation of the muon $(g-2)_{\mu}$ anomaly continues to challenge the explanations provided by the Standard Model. However, this anomaly can potentially find reconciliation within the framework of two-Higgs doublet models, provided that the pseudoscalar mass remains low. The introduction of additional fermionic components, such as a generation of vector-like quarks, not only broadens the acceptable parameter range for elucidating the anomaly but also presents an opportunity to circumvent conflicts with constraints from B-decays and heavy Higgs searches. We demonstrate the efficacy of fitting the anomaly in the muon magnetic moment within these models, assuming that vector-like quarks do not undergo mixing with Standard Model quarks. With interactions following a type-X pattern for standard model quarks and a type-II pattern for vector-like quarks, results in models designated as type-XII2HDMVLQ. Additionally, we have explored double Higgs production within this model and observed when both the heavy Higgs and VLQ contribute the double Higgs production cross section significantly enhanced., Comment: 21 pages, multiple figures, comments are welcome

We study the properties of massive fields extrapolated to the blowup of spatial infinity ($\hat{i}^0$), extending the program initiated in arXiv:2207.06406. In the free theory, we find an explicit representation of boundary two-point functions and boundary to bulk two-point functions, and also present an HKLL-type reconstruction formula for local bulk operators in terms of smeared boundary operators. We study interacting Wightman correlators and find that, generically, interacting massive fields decay slower than free fields as one approaches $\hat{i}^0$. We propose that meaningful correlators at $\hat{i}^0$ can be obtained through an LSZ-like prescription that isolates the on-shell part of bulk Wightman correlators before extrapolating them to $\hat{i}^0$. We show that a natural basis for operators at $\hat{i}^0$, defined via this prescription, is given by the average of "in" and "out" operators defined at $i^-$ and $i^+$ respectively. Therefore, correlators at $\hat{i}^0$ and cross correlators between $\hat{i}^0$, $i^-$ and $i^+$ can be represented within the class of asymptotic observables studied by Caron-Huot et al. in arXiv:2308.02125. We present several sample calculations., Comment: 54 pages