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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.

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 such 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 the 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 longer at the vehicle in Session 2 compared to Session 1. Furthermore, participants exhibited intermittent sampling of the vehicle, and they looked 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, 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 perceptions 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

We study the oblique propagation of weakly nonlinear dust-ion-acoustic (DIA) solitary waves (SWs) and shocks in collisional magnetized nonthermal dusty plasmas that are relevant in laboratory and space (Saturn's E-ring) environments. We consider plasmas to be composed of $q$-nonextensive hot electrons, thermal positive ions, and immobile negatively charged dust grains immersed in a static magnetic field and take into account the effects of ion creation (source), and ion loss (sink), ion-neutral and ion-dust collisions, anisotropic ion pressure and dust-charge fluctuations on the evolution of small-amplitude SWs and shocks. The ion-neutral collision enhancement equilibrium dust-charge number is self-consistently determined using Newton's Raphson method. We found that in laboratory dusty plasmas with adiabatic dust-charge variation [i.e., when the dust charging frequency ($\nu_{\rm{ch}}$) is much higher than the dust-plasma oscillation frequency ($\omega_{\rm{pd}}$)], the DIA solitary waves (DIASWs) get damped by the effects of the ion-dust and ion-neutral collisions, whereas the ion creation and ion loss leads to the amplification of solitary waves, and they appear as only compressive types with positive potential. On the other hand, in Saturn's E-ring plasmas, where the collisional and ion creation or ion loss effects are insignificant, the non-adiabaticity of dust-charge variation can give rise to the evolution of either damped DIASWs or DIA shocks, depending on the smallness of the ratios $\nu_{\rm{ch}}/\omega_{\rm{pd}}$ or $\omega_{\rm{pd}}/\nu_{\rm{ch}}$, respectively. Furthermore, two critical values of the nonextensive parameter $q$ exist, below (or above) which, the DIASWs and shocks can appear as rarefactive (or compressive) types. The characteristics of DIASWs and shocks are also analyzed numerically for parameters relevant to the laboratory and Saturn's E-ring plasmas., Comment: Total 19 pages and 21 Figures with some Figure number has subplots

The recent proliferation of personal wireless communication devices is driving the need for multi-band frequency selective components including multiplexers and dual-band filters. This paper presents a simple technique for transforming a single-band bandpass filter (BPF) into a dual-band BPF. A second order (two-pole) single-band bandpass filter was chosen for this research, giving rise to a fourth order (four-pole) dual-band bandpass filter after the proposed filter transformation. Both filters were then implemented using the compact U-shaped microstrip resonator for improved device miniaturization. The proposed work features a centre frequency of 1.4 GHz for the single-band bandpass filter, with a span of 3.4% fractional bandwidth. The dual-band bandpass filter operates at 1.35 and 1.45 GHz. The design implementation employs the commercially available Rogers RT/Duroid 6010LM substrate, having a dissipation factor of 0.0023, dielectric constant of 10.7, diel thickness (h) of 1.27 mm, and top/bottom cladding of 35 microns. The results reported for the theoretical and practical designs show good agreement and improved performance when compared to similar research works in literature. The practical responses of the prototype dual-band BPF indicate a good return loss of better than 18 dB across both bands, and an insertion loss of better than 0.1 dB., Comment: 4 pages, 6 figures, 1 table

This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness and redundancy in the execution for protection. Our approach is at the operation level, is not algorithm-specific, and thus, can be applied for protecting a wide range of algorithms. The countermeasure is empirically evaluated against attacks over operations like modular exponentiation, modular multiplication, polynomial multiplication, and number theoretic transforms. An end-to-end implementation of this countermeasure is demonstrated for RSA-CRT signature algorithm and Kyber Key Generation public key cryptosystems. The countermeasure reduced the power side-channel leakage by two orders of magnitude, to an acceptably secure level in TVLA analysis. For fault injection, the countermeasure reduces the number of faults to 95.4% in average.

Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use., Comment: Corresponding Authors: Rameswar Panda, Ruchir Puri; Equal Contributors: Mayank Mishra, Matt Stallone, Gaoyuan Zhang

Previous studies have shown that eye movement data recorded at 1000 Hz can be used to authenticate individuals. This study explores the effectiveness of eye movement-based biometrics (EMB) by utilizing data from an eye-tracking (ET)-enabled virtual reality (VR) headset (GazeBaseVR) and compares it to the performance using data from a high-end eye tracker (GazeBase) that has been downsampled to 250 Hz. The research also aims to assess the biometric potential of both binocular and monocular eye movement data. GazeBaseVR dataset achieves an equal error rate (EER) of 1.67% and a false rejection rate (FRR) at 10^-4 false acceptance rate (FAR) of 22.73% in a binocular configuration. This study underscores the biometric viability of data obtained from eye-tracking-enabled VR headset., Comment: 9 pages, 6 figures

Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Building upon Gemini's multimodal models, we develop several models within the new Med-Gemini family that inherit core capabilities of Gemini and are optimized for medical use via fine-tuning with 2D and 3D radiology, histopathology, ophthalmology, dermatology and genomic data. Med-Gemini-2D sets a new standard for AI-based chest X-ray (CXR) report generation based on expert evaluation, exceeding previous best results across two separate datasets by an absolute margin of 1% and 12%, where 57% and 96% of AI reports on normal cases, and 43% and 65% on abnormal cases, are evaluated as "equivalent or better" than the original radiologists' reports. We demonstrate the first ever large multimodal model-based report generation for 3D computed tomography (CT) volumes using Med-Gemini-3D, with 53% of AI reports considered clinically acceptable, although additional research is needed to meet expert radiologist reporting quality. Beyond report generation, Med-Gemini-2D surpasses the previous best performance in CXR visual question answering (VQA) and performs well in CXR classification and radiology VQA, exceeding SoTA or baselines on 17 of 20 tasks. In histopathology, ophthalmology, and dermatology image classification, Med-Gemini-2D surpasses baselines across 18 out of 20 tasks and approaches task-specific model performance. Beyond imaging, Med-Gemini-Polygenic outperforms the standard linear polygenic risk score-based approach for disease risk prediction and generalizes to genetically correlated diseases for which it has never been trained. Although further development and evaluation are necessary in the safety-critical medical domain, our results highlight the potential of Med-Gemini across a wide range of medical tasks.

The classical dances from India utilize a set of hand gestures known as Mudras, serving as the foundational elements of its posture vocabulary. Identifying these mudras represents a primary task in digitizing the dance performances. With Kathakali, a dance-drama, as the focus, this work addresses mudra recognition by framing it as a 24-class classification problem and proposes a novel vector-similarity-based approach leveraging pose estimation techniques. This method obviates the need for extensive training or fine-tuning, thus mitigating the issue of limited data availability common in similar AI applications. Achieving an accuracy rate of 92%, our approach demonstrates comparable or superior performance to existing model-training-based methodologies in this domain. Notably, it remains effective even with small datasets comprising just 1 or 5 samples, albeit with a slightly diminished performance. Furthermore, our system supports processing images, videos, and real-time streams, accommodating both hand-cropped and full-body images. As part of this research, we have curated and released a publicly accessible Hasta Mudra dataset, which applies to multiple South Indian art forms including Kathakali. The implementation of the proposed method is also made available as a web application.

The intricate interplay between spin and lattice degrees of freedom in two-dimensional magnetic materials plays a pivotal role in modifying their magnetic characteristics, engendering hybrid quasiparticles, and implementing functional devices. Herein, we present our comprehensive and in-depth investigations on magnetic and lattice excitations of MnPSe3-xSx (x = 0, 0.5, and 1.5) alloys, utilizing temperature- and polarization-dependent Raman scattering. Our experimental results reveal the occurrence of multiple phase transitions, evidenced by notable changes in phonon self-energy and the appearance or splitting of phonon modes. These emergent phases are tied to the development of long and short-range spin-spin correlations, as well as to spin reorientations or magnetic instabilities. Our analysis of two-magnon excitations as a function of temperature and composition showcases their hybridization with phonons whose degree weakens with increasing x. Moreover, the suppression of spin-dependent phonon intensity in chemically most-disordered MnPSe3-xSx (x = 1.5) suggests that chalcogen substitution offers a control knob of tuning spin and phonon dynamics by modulating concurrently superexchange pathways and a degree of trigonal distortions.

We developed an open-source scalar wave transport model to estimate the generalized scattering matrix (S matrix) of a disordered medium in the diffusion regime. Here, the term generalization refers to the incorporation of evanescent wave field modes in addition to propagating modes while estimating the S matrix. For that we used the scalar Kirchhoff-Helmholtz boundary integral formulation together with the Green's function perturbation method to generalize the conventional Fisher-Lee relations to include evanescent modes as well. The estimated S matrix, which satisfies generalized unitarity and reciprocity conditions, is modeled for a 2D disordered waveguide. The generalized transmission matrix contained in the S matrix is used to estimate the optimal phase-conjugate wavefront for focusing onto an evanescent mode. The phenomena of universal transmission value of 2/3 for such an optimal phase conjugate wavefront is also shown in the context of evanescent wave mode focusing through a diffusive disorder. The presented code framework may be of interest to wavefront shaping researchers for visualizing and estimating wave transport properties in general.

We developed a 2D Finite-Difference Time-Domain (FDTD) method for modeling a space-time modulated guidestar targeting wavefront shaping applications in disordered media. Space-time modulation in general (a particular example being the acousto-optic effect) is used here as a guidestar for the transverse confinement of light around the tagged region surrounded by disorder. Together with the guidestar, the iterative optical phase conjugation (IOPC) method is used to overcome the diffusion of light due to multiple scattering. A phase sensitive lock-in detection technique is utilized to estimate the steady-state amplitude and phase of the modulated wavefronts emerging from the guidestar region continuously operating in the Raman-Nath regime. As the IOPC scheme naturally converges to the maximally transmitting eigenchannel profile, one could use the position of the guidestar within the disorder to channelize the maximal transmission through the tagged region. The associated code developed in MATLAB is provided as an open source (MIT license) package. The code package is referred by the acronym STAR-FDTD where STAR stands for Space-Time modulated Acousto-optic guidestaR., Comment: Accepted in Journal of Physics : Photonics, Focus Issue on Foundational Skills and Tools for Building Wavefront Shaping Systems, Original submission on Nov 30, 2023 and revised submission on Jul 31, 2024

This paper presents IITP-VDLand, a comprehensive dataset of Decentraland parcels sourced from diverse platforms. Unlike existing datasets which have limited attributes and records, IITP-VDLand offers a rich array of attributes, encompassing parcel characteristics, trading history, past activities, transactions, and social media interactions. Alongside, we introduce a key attribute in the dataset, namely Rarity score, which measures the uniqueness of each parcel within the virtual world. Addressing the significant challenge posed by the dispersed nature of this data across various sources, we employ a systematic approach, utilizing both available APIs and custom scripts, to gather it. Subsequently, we meticulously curate and organize the information into four distinct segments: (1) Characteristics Data-Fragment, (2) OpenSea Trading History Data-Fragment, (3) Ethereum Activity Transactions Data-Fragment, and (4) Social Media Data-Fragment. We envisage that this dataset would serve as a robust resource for training machine- and deep-learning models specifically designed to address real-world challenges within the domain of Decentraland parcels. The performance benchmarking of more than 20 state-of-the-art price prediction models on our dataset yields promising results, achieving a maximum R2 score of 0.8251 and an accuracy of 74.23% in case of Extra Trees Regressor and Classifier. The key findings reveal that the ensemble models performs better than both deep learning and linear models for our dataset. We observe a significant impact of coordinates, geographical proximity, rarity score, and few other economic indicators on the prediction of parcel prices.

Two-dimensional (2D) layered materials, whether in bulk form or reduced to just a single layer, have potential applications in spintronics and capacity for advanced quantum phenomena. A prerequisite for harnessing these opportunities lies in gaining a comprehensive understanding of the spin behavior in 2D materials. The low dimensionality motivates an understanding of the spin correlations over a wide length scale, from local to long range order. In this context, we focus on the magnetism in bulk \MPSe ~and \MPS, 2D layered van der Waals antiferromagnetic semiconductors. These materials have similar honeycomb Mn layers and magnetic ordering temperatures, but distinct spin orientations and exchange interactions. We utilize neutron scattering to gain deeper insights into the local magnetic structures and spin correlations in the paramagnetic and ordered phases by systematically investigating a MnPS$_{x}$Se$_{3-x}$ ($x$ = 0, 1, 1.5, 2, 3) series of powder samples using total neutron scattering measurements. By employing magnetic pair distribution function (mPDF) analysis, we unraveled the short-range magnetic correlations in these materials and explored how the non-magnetic anion S/Se mixing impacts the magnetic correlations. The results reveal that the magnetism can be gradually tuned through alteration of the non-magnetic S/Se content, which tunes the atomic structure. The change in magnetic structure is also accompanied by a control of the magnetic correlation length within the 2D honeycomb layers. Complimentary inelastic neutron scattering measurements allowed a quantification of the change in the magnetic exchange interactions for the series and further highlighted the gradual evolution of spin interactions in the series MnPS$_{x}$Se$_{3-x}$.