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Colorectal cancer (CRC) is one of the most commonly diagnosed cancers all over the world. It starts as a polyp in the inner lining of the colon. To prevent CRC, early polyp detection is required. Colonosopy is used for the inspection of the colon. Generally, the images taken by the camera placed at the tip of the endoscope are analyzed by the experts manually. Various traditional machine learning models have been used with the rise of machine learning. Recently, deep learning models have shown more effectiveness in polyp detection due to their superiority in generalizing and learning small features. These deep learning models for object detection can be segregated into two different types: single-stage and two-stage. Generally, two stage models have higher accuracy than single stage ones but the single stage models have low inference time. Hence, single stage models are easy to use for quick object detection. YOLO is one of the singlestage models used successfully for polyp detection. It has drawn the attention of researchers because of its lower inference time. The researchers have used Different versions of YOLO so far, and with each newer version, the accuracy of the model is increasing. This paper aims to see the effectiveness of the recently released YOLOv11 to detect polyp. We analyzed the performance for all five models of YOLOv11 (YOLO11n, YOLO11s, YOLO11m, YOLO11l, YOLO11x) with Kvasir dataset for the training and testing. Two different versions of the dataset were used. The first consisted of the original dataset, and the other was created using augmentation techniques. The performance of all the models with these two versions of the dataset have been analysed.

After the first multi-messenger observation of a binary neutron star (BNS) merger powering a short-duration gamma-ray burst (GRB), GW170817-GRB 170817A, remarkable effort is ongoing to unravel the evolution of the collimated, relativistic outflow (or jet) that was launched during the merger and fed the GRB event, imprinting its angular structure onto the follow-up afterglow signal. Current theoretical models, based on relativistic magneto-hydrodynamic (RMHD) simulations, offer detailed insights into the launch and propagation processes that govern jet evolution. Notably, these simulations point out that jet injection parameters, such as luminosity, magnetization, power decay time scale, and launch time relative to merger, play a crucial role. However, the impact of these parameters is typically investigated within simplified jet propagation environments, lacking a direct connection with a realistic BNS merger aftermath. In this work, we present the first suite of 3D RMHD simulations exploring the influence of such parameters on the propagation of magnetized incipient GRB jets injected into magnetized environments directly imported from the outcome of a general-relativistic MHD BNS merger simulation. Our results demonstrate that, alongside the injection parameters, the BNS merger environment has a central role in shaping the overall jet evolution. Specifically, under identical jet parameters, the fate of an incipient jet (whether it successfully breaks out or becomes choked) depends strongly on the properties of such environment. Further quantitative comparison between realistic and simplified environments reveals major differences, emphasizing the importance of incorporating the former for accurate modeling., Comment: 20 pages, 12 figures

In human-robot teams, human situational awareness is the operator's conscious knowledge of the team's states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human-robot collaboration. In human-robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human-robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human-robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human-robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human-robot teams.

Context. The concomitant observation of gravitational wave and electromagnetic signals from a binary neutron star (BNS) merger in 2017 confirmed that these events can produce relativistic jets responsible for short Gamma-Ray Bursts (sGRBs). The complex interaction between the jet and the surrounding post-merger environment shapes the angular structure of the outflow, which is then imprinted in the prompt and afterglow sGRB emission. Aims. The outcome of relativistic (magneto)hydrodynamic simulations of jets piercing through post-merger environments is often used as input to compute afterglow signals to be compared with observations. However, for reliable comparisons, the jet propagation should be followed until nearly ballistic regimes, in which the jet acceleration is essentially over and the angular structure is no longer evolving. This condition is typically reached in 2D simulations, but not in 3D. Our goal is to extend a (specific) jet simulation in 3D up to a nearly ballistic phase, analysing the overall dynamical evolution from the jet breakout. Methods. Our work is based on a previous 3D magnetohydrodynamic jet simulation employing a realistic environment imported from a BNS merger simulation, extended here far beyond the evolution time originally covered. After approximately 3 seconds of the jet evolution on the original spherical grid, we remap the system into a uniform Cartesian grid and reach about 10 seconds without loss of resolution. Results. The specific jet considered here struggles to pierce the dense surroundings, resulting in a rather asymmetrical emerging outflow with relatively low Lorentz factor. The analysis of the energy conversion processes and corresponding acceleration shows that at the end of our simulation 98% of the energy is in kinetic form. Moreover, at that time the angular structure is frozen. We thus obtain suitable inputs for computing the afterglow emission. Our procedure is general and applicable to any jet simulation of the same kind., Comment: 13 pages, 15 figures, matching accepted version on A&A

We introduce several families of entanglement-assisted (EA) Calderbank-Shor-Steane (CSS) codes derived from two distinct classes of low-density parity-check (LDPC) codes. We derive two families of EA quantum QC-LDPC codes, namely, the spatially coupled (SC) and the non-spatially coupled cases. These two families are constructed by tiling permutation matrices of prime and composite orders. We establish several code properties along with conditions for guaranteed girth for the proposed code families. The Tanner graphs of the proposed EA quantum QC-LDPC and EA quantum QC-SC-LDPC codes have girths greater than four, which is required for good error correction performance. Some of the proposed families of codes require only \textit{minimal} Bell pairs to be shared across the quantum transceiver. Furthermore, we construct two families of EA quantum QC-LDPC codes based on a single classical code, with Tanner graphs having girths greater than six, further improving the error correction performance. We evaluate the performance of these codes using both depolarizing and Markovian noise models to assess the random and burst error performance. Using a modified version of the sum-product algorithm over a quaternary alphabet, we show how correlated Pauli errors can be handled within the decoding setup. Simulation results show that nearly an order of improvement in the error correction performance can be achieved with quaternary decoder compared to binary decoder over the depolarizing and Markovian error channels, thereby generalizing the approach of EA quantum QC-LDPC code designs to work with both random and burst quantum error models, useful in practice., Comment: 57 pages, 9 figures. A portion of this work has already been published in 2024 Information Theory Workshop, Shenzhen, China,(https://doi.org/10.1109/ITW61385.2024.10806978)

This study explores the effectiveness of layer pruning for developing more efficient BERT models tailored to specific downstream tasks in low-resource languages. Our primary objective is to evaluate whether pruned BERT models can maintain high performance while reducing model size and complexity. We experiment with several BERT variants, including MahaBERT-v2 and Google-Muril, applying different pruning strategies and comparing their performance to smaller, scratch-trained models like MahaBERT-Small and MahaBERT-Smaller. We fine-tune these models on Marathi datasets, specifically Short Headlines Classification (SHC), Long Paragraph Classification (LPC) and Long Document Classification (LDC), to assess their classification accuracy. Our findings demonstrate that pruned models, despite having fewer layers, achieve comparable performance to their fully-layered counterparts while consistently outperforming scratch-trained models of similar size. Notably, pruning layers from the middle of the model proves to be the most effective strategy, offering performance competitive with pruning from the top and bottom. However, there is no clear winner, as different pruning strategies perform better in different model and dataset combinations. Additionally, monolingual BERT models outperform multilingual ones in these experiments. This approach, which reduces computational demands, provides a faster and more efficient alternative to training smaller models from scratch, making advanced NLP models more accessible for low-resource languages without compromising classification accuracy., Comment: Accepted at FIRE 2024: 16th meeting of the Forum for Information Retrieval Evaluation

Superconductivity and nontrivial topology are highly sought-after phenomena in quantum materials. While many topological crystalline materials have been found to exhibit superconductivity, their presence in quasicrystals - materials with a unique aperiodic yet ordered structure - has remained largely unexplored. In this work, we report the discovery of superconductivity in a monoclinic approximant to the decagonal quasicrystal Al$_{13}$Os$_{4}$, that exhibits a high superconducting transition temperature and a nontrivial electronic structure. The resistivity, magnetization, specific heat, and $\mu$SR measurements confirm superconductivity with a critical temperature of $\sim5.47$ K. Detailed electronic structure and symmetry analysis reveal nontrivial state with $\mathcal{Z}_{2}=1$ and spin-polarized conducting surface states. Importantly, we identify three-dimensional saddle point van Hove singularities with substantial flat energy dispersion at the Fermi level, which can enhance superconductivity. Our results highlight a rich interplay between superconductivity and nontrivial electronic states in Al$_{13}$Os$_{4}$, demonstrating it as a unique platform for exploring unconventional superconducting states in quasicrystalline materials., Comment: 17 pages, 3 figures

Quarkonia, the bound states of heavy quark-antiquark pairs, are important tools for studying the quark-gluon plasma (QGP). In this study, we examine the behavior of in-medium quarkonium bound states in the QGP by analyzing their spectral functions at two temperatures, $T = 220\,\textrm{MeV}$ and $T = 293\,\textrm{MeV}$. We use physics-motivated information to reconstruct the spectral function from the Euclidean lattice correlator. Near the threshold, the spectral function is estimated through a complex potential, determined non-perturbatively from Wilson line correlators. Our results show that the real part of the potential undergoes color screening above $T_{pc}$, while the imaginary part grows rapidly with increasing distance and temperature. For the ultraviolet (UV) part of the spectral function, we use the perturbative vacuum spectral function, as the temperature effects are suppressed in this region. In the absence of a transport peak in the pseudoscalar channel, we find that this combination effectively describes the pseudoscalar correlator on the lattice, calculated using relativistic quark fields. Our results show that pseudoscalar charmonium ($\eta_c$) experiences significant thermal effects, as indicated by the broadening of the $\eta_c(1S)$ state. In contrast, the $\eta_b(1S)$ state remains intact, with a sharp bound state peak., Comment: contribution to EMMI Workshop: "Aspects of Criticality II", 2-4 July 2024, Wroclaw, Poland

Gastrointestinal (GI) bleeding, a critical indicator of digestive system disorders, re quires efficient and accurate detection methods. This paper presents our solution to the Auto-WCEBleedGen Version V1 Challenge, where we achieved the consolation position. We developed a unified YOLOv8-X model for both detection and classification of bleeding regions in Wireless Capsule Endoscopy (WCE) images. Our approach achieved 96.10% classification accuracy and 76.8% mean Average Precision (mAP) at 0.5 IoU on the val idation dataset. Through careful dataset curation and annotation, we assembled and trained on 6,345 diverse images to ensure robust model performance. Our implementa tion code and trained models are publicly available at https://github.com/pavan98765/Auto-WCEBleedGen., Comment: 11 pages, 4 figures, challenge

In general, the comprehension of any type of complex system depends on the resolution used to look at the phenomena occurring within it. But identifying a priori, for example, the best time frequencies/scales to study a certain system over-time, or the spatial distances at which looking for correlations, symmetries, and fluctuations, is most often non-trivial. Here we describe an unsupervised approach that, starting solely from the data of a system, allows learning the characteristic length-scales of the key events/processes dominating it and the optimal spatiotemporal resolutions to characterize them. We test this approach on time-series data obtained from simulation or experimental trajectories of various example many-body complex systems ranging from the atomic- to the macroscopic-scale and having diverse internal dynamic complexities. Our method automatically performs the analysis of the system's data, analyzing correlations at all relevant inter-particle distances and at all possible inter-frame intervals in which their time-series can be subdivided: namely, at all space-and-time resolutions. The optimal spatiotemporal resolution for studying a certain system thus steps-out as that maximizing information extraction-and-classification from the system's data, which we prove being related to the characteristic spatiotemporal length-scales of the local/collective physical events dominating it. This approach is broadly applicable and can be used to optimize the study of different types of data (static distributions, time-series, or signals). The concept of 'optimal resolution' has general character and provides a robust basis to characterize any type of system based on its data, as well as to guide data analysis in general.

Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popular visual encoders such as ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency caused by stacked self-attention layers. At different operational resolutions, the vision encoder of a VLM can be optimized along two axes: reducing encoding latency and minimizing the number of visual tokens passed to the LLM, thereby lowering overall latency. Based on a comprehensive efficiency analysis of the interplay between image resolution, vision latency, token count, and LLM size, we introduce FastVLM, a model that achieves an optimized trade-off between latency, model size and accuracy. FastVLM incorporates FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images. Unlike previous methods, FastVLM achieves the optimal balance between visual token count and image resolution solely by scaling the input image, eliminating the need for additional token pruning and simplifying the model design. In the LLaVA-1.5 setup, FastVLM achieves 3.2$\times$ improvement in time-to-first-token (TTFT) while maintaining similar performance on VLM benchmarks compared to prior works. Compared to LLaVa-OneVision at the highest resolution (1152$\times$1152), FastVLM achieves comparable performance on key benchmarks like SeedBench and MMMU, using the same 0.5B LLM, but with 85$\times$ faster TTFT and a vision encoder that is 3.4$\times$ smaller.

We investigate some previously unexplored (or underexplored) computational aspects of total variation (TV) distance. First, we give a simple deterministic polynomial-time algorithm for checking equivalence between mixtures of product distributions, over arbitrary alphabets. This corresponds to a special case, whereby the TV distance between the two distributions is zero. Second, we prove that unless $\mathsf{NP} \subseteq \mathsf{RP}$, it is impossible to efficiently estimate the TV distance between arbitrary Ising models, even in a bounded-error randomized setting., Comment: 17 pages

Extracting from trajectory data meaningful information to understand complex systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable, if not required, to prevent losing important information. However, to what extent such high-dimensionality is really needed/beneficial often remains not clear. Here we challenge such a fundamental general problem. As first representative cases of a system with internal dynamical complexity, we study atomistic molecular dynamics trajectories of liquid water and ice coexisting in dynamical equilibrium in correspondence of the solid/liquid transition temperature. To attain an intrinsically high-dimensional analysis, we use the Smooth Overlap of Atomic Positions (SOAP) descriptor, obtaining a large dataset containing 2.56e6 576-dimensional SOAP vectors that we analyze in various ways. Surprisingly, our results demonstrate how the time-series data contained in one single SOAP dimension accounting only for <0.001% of the total variance (thus considered negligible and discarded in typical variance-based dimensionality reduction approaches) is as much, and even more descriptive than the entire high-dimensional dataset. Adding more dimensions to the analysis is found detrimental due to "frustrated information" phenomena that are proven general, and are observed also in the analysis of, e.g., experimental trajectories of collective convolutions of active microparticles (used as a second case study). This work explicitly demonstrates how high-dimensional analyses are not necessarily better than low-dimensional ones. These results also demonstrate the importance of considering the quality (relevance) over the quantity of information, especially when dealing with noisy high-dimensional datasets, where the noise of one dimension may have higher statistical weight than the relevant information contained into another.

In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful feature extraction for classification tasks remains crucial. This paper presents Point-GR, a novel deep learning architecture designed explicitly to transform unordered raw point clouds into higher dimensions while preserving local geometric features. It introduces residual-based learning within the network to mitigate the point permutation issues in point cloud data. The proposed Point-GR network significantly reduced the number of network parameters in Classification and Part-Segmentation compared to baseline graph-based networks. Notably, the Point-GR model achieves a state-of-the-art scene segmentation mean IoU of 73.47% on the S3DIS benchmark dataset, showcasing its effectiveness. Furthermore, the model shows competitive results in Classification and Part-Segmentation tasks., Comment: ICPR 2024 G2SP-CV Workshop, Dec 1-5, 2024 Kolkata, India

The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images.

We undertake a reassessment of one of the large angular scale anomalies observed in cosmic microwave background (CMB) temperature signal referred to as Hemispherical Power Asymmetry (HPA). For the present analysis we used \texttt{sevem} cleaned CMB maps from \emph{Planck}'s 2020 final data release (public release 4/PR4). To probe HPA, we employed the local variance estimator (LVE) method with different disc radii ranging from $1^\circ$ to $90^\circ$. It is reaffirmed that HPA is confined to low multipoles or large angular scales of the CMB sky. A dipole like anisotropy was found in the LVE maps with anomalous power for disc radii of $2^\circ$ and upward up to $36^\circ$ at $\gtrsim2\sigma$. In the range $4^\circ$ to $10^\circ$ none of the 600 \texttt{sevem} CMB simulations were found to have a dipole amplitude higher than the data when using LVE method as proposed. Our emphasis here was to revalidate the LVE method in various ways for its optimal usage and probe the hemispherical power asymmetry in the form of a dipole modulation field underlying CMB sky. By and large, our results are in agreement with earlier reported ones with more detailed presentation of explicit and not-so-explicit assumptions involved in the estimation process. The above reported values fall in the reliability range of LVE method after this extensive re-evaluation. We conclude that the hemispherical power asymmetry still remains as a challenge to the standard model., Comment: 30 pages, 10+3 figures, 3 tables, 1 appendix

Optical tweezers have revolutionized particle manipulation at the micro- and nanoscale, playing a critical role in fields such as plasmonics, biophysics, and nanotechnology. While traditional optical trapping methods primarily rely on optical forces to manipulate and organize particles, recent studies suggest that optothermal traps in surfactant solutions can induce unconventional effects such as enhanced trapping stiffness and increased diffusion. Thus, there is a need for further exploration of this system to gain a deeper understanding of the forces involved. This work investigates the behaviour of gold nanoparticles confined in an optothermal trap around a heated anchor particle in a surfactant (CTAC) solution. We observe unexpected radial confinement and synchronized rotational diffusion of particles at micrometre-scale separations from the anchor particle. These dynamics differ from known optical binding and thermophoretic effects, suggesting unexplored forces facilitated by the surfactant environment. This study expands the understanding of optothermal trapping driven by anchor plasmonic particles and introduces new possibilities for nanoparticle assembly, offering insights with potential applications in nanoscale fabrication and materials science., Comment: 24 Pages, 5 Figures

One of the most well-known problems in transportation and logistics is the Capacitated Vehicle Routing Problem (CVRP). It involves optimizing a set of truck routes to service a set of customers, subject to limits on truck capacity, to reduce travel costs. The biggest challenge faced whilst attempting to solve the issue is that the time complexity of the issue grows exponentially with the number of customers and trucks, rendering it virtually intractable to traditional computers and algorithms. In this paper, we propose a method to circumvent this limitation, employing quantum computers to aid classical computers in solving problems faster while reducing complexity. To obtain our results, we employ two algorithms: Hybrid Two Step (H2S) and Hybrid Three Step (H3S). Both algorithms involve two phases: clustering and routing. It has been observed that both algorithms produce promising results, both in terms of solution time and solution cost., Comment: 20 pages, 7 figures and 3 tables

The usual approach on electrostatic wave decay process for a weak beam-plasma system considers two different wave modes interplaying, the Langmuir and ion-sound mode. In the present paper, a single-mode approach is shown to be feasible for conditions where the respective dispersion relations undergo topological changes. Numerical solutions for the dispersion relation of a beam-plasma system are presented, supporting the modeling of an analytic dispersion relation of a single wave mode. This wave mode is accounted for in the kinetic equations for particles and waves, which rule the evolution of the system. The results are compared against the two-wave mode approach using Langmuir and ion-sound waves, within the context of weak turbulence theory. It is found that the single-mode approach can account for the basic features of particles and waves, since the single mode exhibits both low and high frequency regions, which ultimately play the roles of ion-sound and Langmuir modes, respectively., Comment: Published in Physics of Plasmas

Reconstructing the physical complexity of many-body dynamical systems can be challenging. Starting from the trajectories of their constitutive units (raw data), typical approaches require selecting appropriate descriptors to convert them into time-series, which are then analyzed to extract interpretable information. However, identifying the most effective descriptor is often non-trivial. Here, we report a data-driven approach to compare the efficiency of various descriptors in extracting information from noisy trajectories and translating it into physically relevant insights. As a prototypical system with non-trivial internal complexity, we analyze molecular dynamics trajectories of an atomistic system where ice and water coexist in equilibrium near the solid/liquid transition temperature. We compare general and specific descriptors often used in aqueous systems: number of neighbors, molecular velocities, Smooth Overlap of Atomic Positions (SOAP), Local Environments and Neighbors Shuffling (LENS), Orientational Tetrahedral Order, and distance from the fifth neighbor ($d_5$). Using Onion Clustering -- an efficient unsupervised method for single-point time-series analysis -- we assess the maximum extractable information for each descriptor and rank them via a high-dimensional metric. Our results show that advanced descriptors like SOAP and LENS outperform classical ones due to higher signal-to-noise ratios. Nonetheless, even simple descriptors can rival or exceed advanced ones after local signal denoising. For example, $d_5$, initially among the weakest, becomes the most effective at resolving the system's non-local dynamical complexity after denoising. This work highlights the critical role of noise in information extraction from molecular trajectories and offers a data-driven approach to identify optimal descriptors for systems with characteristic internal complexity., Comment: 19 pages, 5 figures + 3 in supporting information (at the bottom of the manuscript)

Artificial soft matter systems have appeared as important tools to harness mechanical motion for microscale manipulation. Typically, this motion is driven either by the external fields or by mutual interaction between the colloids. In the latter scenario, dynamics arise from non-reciprocal interaction among colloids within a chemical environment. In contrast, we eliminate the need for a chemical environment by utilizing a large area of optical illumination to generate thermal fields. The resulting optothermal interactions introduce non-reciprocity to the system, enabling active motion of the colloidal structure. Our approach involves two types of colloids: passive and thermally active. The thermally active colloids contain absorbing elements that capture energy from the incident optical beam, creating localized thermal fields around them. In a suspension of these colloids, the thermal gradients generated drive nearby particles through attractive thermo-osmotic forces. We investigate the resulting dynamics, which lead to various swimming modes, including active propulsion and chiral motion. We have also experimentally validated certain simulated results. By exploring the interplay between optical forces, thermal effects, and particle interactions, we aim to gain insights into controlling colloidal behavior in non-equilibrium systems. This research has significant implications for directed self-assembly, microfluidic manipulation, and the study of active matter.

In a recent paper, Anupam, Chowdhury, and Sen [arXiv:2308.00038] constructed the non-extremal saddle that reproduces the supersymmetric index of the BMPV black hole with three independent charges in the classical limit. This saddle solution is a finite temperature complex solution saturating the BPS bound. In this paper, we write this solution in a canonical form in terms of harmonic functions on three-dimensional flat base space, thereby showing that it is supersymmetric. We also show that it exhibits the new form of attraction., Comment: 21 pages

In this article, we study the various fundamental groupoid schemes and semi-finite bundles on complex tori and Riemann surfaces. We have computed fundamental groupoid of anisotropic conic, Klein bottle and abelian varieties. We also study the relation among various fundamental groupoid schemes by considering their representations., Comment: 17 pages, Comments are welcome

Recently, general fractional calculus was introduced by Kochubei (2011) and Luchko (2021) as a further generalisation of fractional calculus, where the derivative and integral operator admits arbitrary kernel. Such a formalism will have many applications in physics and engineering, since the kernel is no longer restricted. We first extend the work of Al-Refai and Luchko (2023) on finite interval to arbitrary orders. Followed by, developing an efficient Petrov-Galerkin scheme by introducing Jacobi convolution polynomials as basis functions. A notable property of this basis function, the general fractional derivative of Jacobi convolution polynomial is a shifted Jacobi polynomial. Thus, with a suitable test function it results in diagonal stiffness matrix, hence, the efficiency in implementation. Furthermore, our method is constructed for any arbitrary kernel including that of fractional operator, since, its a special case of general fractional operator.

Submodular optimization has become increasingly prominent in machine learning and fairness has drawn much attention. In this paper, we propose to study the fair $k$-submodular maximization problem and develop a $\frac{1}{3}$-approximation greedy algorithm with a running time of $\mathcal{O}(knB)$. To the best of our knowledge, our work is the first to incorporate fairness in the context of $k$-submodular maximization, and our theoretical guarantee matches the best-known $k$-submodular maximization results without fairness constraints. In addition, we have developed a faster threshold-based algorithm that achieves a $(\frac{1}{3} - \epsilon)$ approximation with $\mathcal{O}(\frac{kn}{\epsilon} \log \frac{B}{\epsilon})$ evaluations of the function $f$. Furthermore, for both algorithms, we provide approximation guarantees when the $k$-submodular function is not accessible but only can be approximately accessed. We have extensively validated our theoretical findings through empirical research and examined the practical implications of fairness. Specifically, we have addressed the question: ``What is the price of fairness?" through case studies on influence maximization with $k$ topics and sensor placement with $k$ types. The experimental results show that the fairness constraints do not significantly undermine the quality of solutions., Comment: 17 pages. To appear in IEEE BigData 2024

Ferroelectric field-effect transistors integrated on 2D semiconducting platforms are extremely relevant for low power electronics. Here, we propose and demonstrate a novel phase-change ferroelectric field effect transistor (PCFE-FET) for steep switching applications. Our gate stack is engineered as a ferroelectric Lanthanum doped hafnium oxide (LHO) proximity coupled with Mott insulator Ti$_x$O$_{2x-1}$(N$_y$) and is integrated onto a 2D MoS$_2$ channel. The interplay of partial polarization switching in the ferroelectric LHO layer and reversible field-tunable metal-insulator transition (MIT) in Ti$_x$O$_{2x-1}$(N$_y$) layer concomitantly triggers polar to non-polar phase transition in the LHO layer between 200 and 220 K. This results in distinctive step-like features in the channel current during DC measurements, and random current fluctuations in high-speed measurements with slim anticlockwise hysteresis. Our devices show subthreshold slopes as steep as 25 mV/dec at 210 K, breaking the Boltzmann limit. Our gate stack is also potentially tunable for operation at temperatures of interest, presenting innovative gate stack engineering approaches for low-power computing solutions.

In this paper, we study the trajectories of massive and massless particles in four dimensional static and spherically symmetric black holes in dRGT massive gravity theory via phase-plane analysis and point out several novel features. In particular, we show the existence of a static sphere, a finite radial distance outside the black holes in these theories, where a massive particle can be at rest, as seen by an asymptotic zero angular momentum observer. Topological arguments show that the stable and unstable static spheres, which come in pairs, have opposite charges. In the presence of angular momentum, we first study the behaviour of massless particles and find the presence of stable and unstable photon spheres in both neutral and charged black holes. Subsequently, we study the motion of massive test particles around these black holes, and find one pair of stable and unstable time-like circular orbits (TCOs), such that the stable and unstable TCO's are disconnected in certain regions. Computing the angular velocity $\Omega_{\text{\tiny CO}}$ of the TCOs, measured by a static observer at rest, shows the unusual nature of its monotonic increase with the radius of TCO, near the location of stable photon sphere. This confirms the existence of Aschenbach effect for spherically symmetric black holes in massive gravity, which was only found to exist in rapidly spinning black holes, with the only other exception being the rare example of gravity coupled to quasi-topological electromagnetism., Comment: 27 pages, 16 figures

Recent advancements in the realizations of superconducting diodes have pushed the diode coefficient $\eta$ towards its theoretical maximum of $\eta=1$. In this work, we describe the construction of logic gates NOT, AND, OR, NAND and NOR using superconducting diodes with $\eta\approx1$ by exploiting their dynamically tunable polarity. We then argue that fundamental theorems suppress $\eta$ in intrinsic superconductors, rendering them likely unsuitable for the proposed devices, and point out that several previous proposals and platforms, remarkably, bypassed this suppression unwittingly. We discuss the realization of the digital logic in one such platform -- Josephson triodes that yielded $\eta\approx1$ -- and argue that phases with spontaneous spatial or magnetic order can overcome some of its drawbacks. Thus, this work provides guiding principles for future platforms and develops the building blocks for superconductors-based digital electronics., Comment: 6+7 pages, 4+1 figures

Phase change materials (PCMs) that exhibit volatile resistive switching are promising for emulating neuronal oscillators. Charge transfer insulators, such as ReNiO3 (where Re represents rare earth metals like Pr, Nd, Sm, Eu...), form a family of PCMs with tunable metal-insulator transition (MIT) temperatures across a broad range. Notably, MIT can be adjusted via chemical doping or strain engineering. EuNiO3, in particular, is an attractive choice for oscillator devices given its bulk transition temperature (TMI) of approximately 190{\deg}C, which is well above room temperature, reducing crosstalk issues while remaining low enough to support energy-efficient applications. We demonstrate a method to stabilize high-quality epitaxial EuNiO3 thin films through scalable reactive RF sputtering and post-annealing, optimizing oxygen partial pressures and annealing temperatures across two substrates. Growth at low or zero oxygen partial pressures resulted in amorphous samples, while higher pressures improved crystallinity but led to the stabilization of Ruddlesden-Popper (RP) phases [An+1BnO3n+1] and associated faults. Post-annealing enhanced crystallinity in all samples, transforming RP phases and faults toward the perovskite phase. We conducted operando XRD and transport measurements on our films, finding the transition temperatures of perovskite phase samples grown on LAO and LSAT to be approximately 300{\deg}C and 250{\deg}C, respectively. We attribute the increase in TMI for LAO samples to in-plane compressive strain (-0.76%), which reduces the Ni-O-Ni bond angles in-plane. Similarly, LSAT samples experience in-plane tensile strain (2.1%), which decreases out-of-plane Ni-O-Ni bond angles, increasing TMI compared to bulk. However, this is counteracted by oxygen vacancies due to lowered formation energies. Films that stabilized in RP phases did not exhibit any transition., Comment: 7 pages, 4 figures, 1 table

Metals owe their unique mechanical properties to how defects emerge and propagate within their crystal structure under stress. However, the mechanisms leading from the early emerging (local) defects to the amplification of dislocations (collective plastic events) are not easy to track. Here, using tensile-stress atomistic simulations of a Copper lattice as a case study, we revisit this classical problem under a new perspective based on local dynamics rather than on purely structural arguments. We use a data-driven approach that allows tracking how local fluctuations emerge and accumulate in the atomic lattice in space and time, anticipating/determining the emergence of local or collective structural defects during deformation. Building solely on the general concepts of local fluctuations and spatiotemporal fluctuation correlations, this approach allows characterizing in a unique way the evolution through the elastic, plastic, and fracture phases, describing metals as complex systems where collective phenomena originate from local dynamical triggering events.

We investigate the influence of inelastic neutrino microphysics in general-relativistic magnetohydrodynamics simulations of a hypermassive neutron star. In particular, we include species/energy groups coupled neutrino-matter interactions, such as inelastic neutrino-electron scattering and electron-positron annihilation kernels, into simulations up to 50 ms. Neutrino-electron inelastic scattering is known to have effective neutrino-matter energy exchange. We show that, with neutrino-electron inelastic scattering, simulations predict 75% higher disc mass with slightly different mass-averaged compositions, and 18% more ejected mass with similar distributions. The enhancement of the mass of the disc and the ejecta results in stronger baryon pollution, leading to less favourable jet launching environments. Furthermore, neutrino luminosities are about 50, 40, and 30% higher for electron neutrino, electron anti-neutrino, and heavy-lepton neutrinos. In contrast, we do not see any significant impacts due to electron-positron annihilation.

This Paper presents an advanced approach for fine-tuning BiomedCLIP PubMedBERT, a multimodal model, to classify abnormalities in Video Capsule Endoscopy (VCE) frames, aiming to enhance diagnostic efficiency in gastrointestinal healthcare. By integrating the PubMedBERT language model with a Vision Transformer (ViT) to process endoscopic images, our method categorizes images into ten specific classes: angioectasia, bleeding, erosion, erythema, foreign body, lymphangiectasia, polyp, ulcer, worms, and normal. Our workflow incorporates image preprocessing and fine-tunes the BiomedCLIP model to generate high-quality embeddings for both visual and textual inputs, aligning them through similarity scoring for classification. Performance metrics, including classification, accuracy, recall, and F1 score, indicate the models strong ability to accurately identify abnormalities in endoscopic frames, showing promise for practical use in clinical diagnostics., Comment: 11 Pages, 2 Figures, Capsule Vision 2024 Challenge

We present a self-supervised U-Net-based masked autoencoder and noise removal model designed to reconstruct original images. Once adequately trained, this model extracts high-level features, which are then combined with features from the EfficientNet B7 model. These integrated features are subsequently fed into dense layers for classification. Among the approaches of masked input and Gaussian noise removal, we selected the best U-Net reconstruction model. Additionally, we explored various configurations, including EfficientNet with attention, attention fusion of the autoencoder, and classification utilizing U-Net encoder features. The best performance was achieved with EfficientNet B7 combined with U-Net encoder features. We employed the Adam optimizer with a learning rate of 0.0001, achieving a top accuracy of 0.94 on the validation set., Comment: Capsule Vision 2024 Challenge

Reconfigurable holographic surfaces (RHS) are intrinsically amalgamated with reconfigurable intelligent surfaces (RIS), for beneficially ameliorating the signal propagation environment. This potent architecture significantly improves the system performance in non-line-of-sight scenarios at a low power consumption. Briefly, the RHS technology integrates ultra-thin, lightweight antennas onto the transceiver, for creating sharp, high-gain directional beams. We formulate a user sum-rate maximization problem for our RHS-RIS-based hybrid beamformer. Explicitly, we jointly design the digital, holographic, and passive beamformers for maximizing the sum-rate of all user equipment (UE). To tackle the resultant nonconvex optimization problem, we propose an alternating maximization (AM) framework for decoupling and iteratively solving the subproblems involved. Specifically, we employ the zero-forcing criterion for the digital beamformer, leverage fractional programming to determine the radiation amplitudes of the RHS and utilize the Riemannian conjugate gradient algorithm for optimizing the RIS phase shift matrix of the passive beamformer. Our simulation results demonstrate that the proposed RHS-RIS-based hybrid beamformer outperforms its conventional counterpart operating without an RIS in multi-UE scenarios. The sum-rate improvement attained ranges from 8 bps/Hz to 13 bps/Hz for various transmit powers at the base station (BS) and at the UEs, which is significant., Comment: 13 pages, Accepted in IEEE Transactions on Communications

The precision measurement of the tritium $\beta$-decay spectrum performed by the KATRIN experiment provides a unique way to search for general neutrino interactions (GNI). All theoretical allowed GNI terms involving neutrinos are incorporated into a low-energy effective field theory, and can be identified by specific signatures in the measured tritium $\beta$-spectrum. In this paper an effective description of the impact of GNI on the $\beta$-spectrum is formulated and the first constraints on the effective GNI parameters are derived based on the 4 million electrons collected in the second measurement campaign of KATRIN in 2019. In addition, constraints on selected types of interactions are investigated, thereby exploring the potential of KATRIN to search for more specific new physics cases, including a right-handed W boson, a charged Higgs or leptoquarks.

Long short-term memory (LSTM) has emerged as a definitive network for analyzing and inferring time series data. LSTM has the capability to extract spectral features and a mixture of temporal features. Due to this benefit, a similar feature extraction method is explored for the spiking counterparts targeting time-series data. Though LSTMs perform well in their spiking form, they tend to be compute and power intensive. Addressing this issue, this work proposes Multi-Compartment Leaky (MCLeaky) neuron as a viable alternative for efficient processing of time series data. The MCLeaky neuron, derived from the Leaky Integrate and Fire (LIF) neuron model, contains multiple memristive synapses interlinked to form a memory component, which emulates the human brain's Hippocampus region. The proposed MCLeaky neuron based Spiking Neural Network model and its quantized variant were benchmarked against state-of-the-art (SOTA) Spiking LSTMs to perform human stress detection, by comparing compute requirements, latency and real-world performances on unseen data with models derived through Neural Architecture Search (NAS). Results show that networks with MCLeaky activation neuron managed a superior accuracy of 98.8% to detect stress based on Electrodermal Activity (EDA) signals, better than any other investigated models, while using 20% less parameters on average. MCLeaky neuron was also tested for various signals including EDA Wrist and Chest, Temperature, ECG, and combinations of them. Quantized MCLeaky model was also derived and validated to forecast their performance on hardware architectures, which resulted in 91.84% accuracy. The neurons were evaluated for multiple modalities of data towards stress detection, which resulted in energy savings of 25.12x to 39.20x and EDP gains of 52.37x to 81.9x over ANNs, while offering a best accuracy of 98.8% when compared with the rest of the SOTA implementations., Comment: 13 pages, 15 figures. Published to IEEE Transactions on Computer Aided Design

Recent work has suggested that nanoconfined water may exhibit superionic proton transport at lower temperatures and pressures than bulk water. Using first-principles-level simulations, we study the role of nuclear quantum effects in inducing this superionicity in nanoconfined water. We show that nuclear quantum effects increase the ionic conductivity of nanoconfined hexatic water, leading to superionic behaviour at lower temperatures and pressures than previously thought possible. Our work suggests that superionic water may be accessible in graphene nanocapillary experiments., Comment: 8 pages, 3 figures

When humans perform insertion tasks such as inserting a cup into a cupboard, routing a cable, or key insertion, they wiggle the object and observe the process through tactile and proprioceptive feedback. While recent advances in tactile sensors have resulted in tactile-based approaches, there has not been a generalized formulation based on wiggling similar to human behavior. Thus, we propose an extremum-seeking control law that can insert four keys into four types of locks without control parameter tuning despite significant variation in lock type. The resulting model-free formulation wiggles the end effector pose to maximize insertion depth while minimizing strain as measured by a GelSight Mini tactile sensor that grasps a key. The algorithm achieves a 71\% success rate over 120 randomly initialized trials with uncertainty in both translation and orientation. Over 240 deterministically initialized trials, where only one translation or rotation parameter is perturbed, 84\% of trials succeeded. Given tactile feedback at 13 Hz, the mean insertion time for these groups of trials are 262 and 147 seconds respectively., Comment: 7 pages, 5 figures, 3 tables

The behavior of many complex systems, from nanostructured materials to animal colonies, is governed by local transitions that, while involving a restricted number of interacting units, may generate collective cascade phenomena. Tracking such local events and understanding how they emerge and propagate throughout these systems represent often a challenge. Common strategies monitor specific parameters, tailored ad hoc to describe certain systems, over time. However, such approaches typically require prior knowledge of the underpinning physics and are poorly transferable to different systems. Here we present LEAP, a general, transferable, agnostic analysis approach that can reveal precious information on the physics of a variety of complex dynamical systems simply starting from the trajectory of their constitutive units. Built on a bivariate combination of two abstract descriptors, LENS and {\tau}SOAP, the LEAP analysis allows (i) detecting the emergence of local fluctuations in simulation or experimentally-acquired trajectories of any type of multicomponent system, (ii) classifying fluctuations into categories, and (iii) correlating them in space and time. We demonstrate how LEAP, just building on the abstract concepts of local fluctuations and their spatiotemporal correlation, efficiently reveals precious insights on the emergence and propagation of local and collective phenomena in a variety of complex dynamical systems ranging from the atomic- to the microscopic-scale. Given its abstract character, we expect that LEAP will offer an important tool to understand and predict the behavior of systems whose physics is unknown a priori, as well as to revisit a variety of known complex physical phenomena under a new perspective.

This paper evaluates and compares the performance of model-free and model-based reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using PID as a reference point. The comparison focuses on their ability to handle varying flight dynamics and wind disturbances in a simulated environment. Our results show that the Temporal Difference Model Predictive Control agent outperforms both the PID controller and other model-free reinforcement learning methods in terms of tracking accuracy and robustness over different reference difficulties, particularly in nonlinear flight regimes. Furthermore, we introduce actuation fluctuation as a key metric to assess energy efficiency and actuator wear, and we test two different approaches from the literature: action variation penalty and conditioning for action policy smoothness. We also evaluate all control methods when subject to stochastic turbulence and gusts separately, so as to measure their effects on tracking performance, observe their limitations and outline their implications on the Markov decision process formalism., Comment: Published at ICINCO 2024

The anisotropic light-matter interactions in 2D materials have garnered significant attention for their potential to develop futuristic polarization-based optoelectronic devices, such as photodetectors and photo-actuators. In this study, we investigate the polarization-dependent interactions in ferroelectric 3R alpha-In2Se3 using Angle-Resolved Polarized Raman Spectroscopy (ARPRS) with different excitation lasers. Our experimental findings supported by complementary Density Functional Theory calculations demonstrate that the light-matter interactions depend not only on the crystallographic orientation but also on the excitation energy. Scanning transmission electron microscopy (STEM) confirms the highly anisotropic 3R crystal structure of alpha-In2Se3. This anisotropy in crystal structure facilitates significant optical anisotropy, driven by a complex interplay of electron-photon and electron-phonon interactions, which is reflected in the complex nature of the Raman tensor elements. These anisotropy interactions extend to the materials electrical response under light illumination. Remarkably, the anisotropic photo-response can be tuned by both polarization and wavelength of the incident light, making In2Se3 a promising material for advanced polarization-sensitive photodetection applications., Comment: 19 pages, 6 figures, supporting information

Tumors in the brain result from abnormal cell growth within the brain tissue, arising from various types of brain cells. When left undiagnosed, they lead to severe neurological deficits such as cognitive impairment, motor dysfunction, and sensory loss. As the tumor grows, it causes an increase in intracranial pressure, potentially leading to life-threatening complications such as brain herniation. Therefore, early detection and treatment are necessary to manage the complications caused by such tumors to slow down their growth. Numerous works involving deep learning (DL) and artificial intelligence (AI) are being carried out to assist physicians in early diagnosis by utilizing the scans obtained through Magnetic Resonance Imaging (MRI). Our research proposes DL frameworks for localizing, segmenting, and classifying the grade of these gliomas from MRI images to solve this critical issue. In our localization framework, we enhance the LinkNet framework with a VGG19- inspired encoder architecture for improved multimodal tumor feature extraction, along with spatial and graph attention mechanisms to refine feature focus and inter-feature relationships. Following this, we integrated the SeResNet101 CNN model as the encoder backbone into the LinkNet framework for tumor segmentation, which achieved an IoU Score of 96%. To classify the segmented tumors, we combined the SeResNet152 feature extractor with an Adaptive Boosting classifier, which yielded an accuracy of 98.53%. Our proposed models demonstrated promising results, with the potential to advance medical AI by enabling early diagnosis and providing more accurate treatment options for patients., Comment: 36 pages, 27 figures, 5 tables

Directional motion is commonly observed in various living active systems, such as bacterial colonies moving through confined environments. In these systems, the dynamics arise from the collective effects of mutual interactions between individual elements, as well as their interactions with obstacles or boundaries. In this study, we turn our focus to an artificial system and experimentally investigate the emergence of directional revolution in dimer and trimer structures composed of colloidal particles in ring-shaped optical illumination. In this case, the movement of these colloidal structures is exclusively facilitated by optothermal interactions without any direct mechanical force applied from the external optical field. Depending on the optical absorption properties of the colloidal particles, these optothermal interactions can exhibit both attractive and repulsive characteristics. The attractive interactions provide the necessary driving force that propels the motion, while the repulsive interactions serve to control the structural parameters of the system. The arrangement and interaction of the colloidal particles within these dimer and trimer structures fuel the controlled, directional revolution, with the optical gradient force acting as a confining factor, guiding the movement along a specific path. Notably, the dynamics of these systems can be tuned by altering the intensity of the optical field. This study can be useful as a model for understanding insights into biological systems where group dynamics and environmental interactions are key to coordinated movement.

This paper introduces Adaptive Mixture Importance Sampling (AMIS) as a novel approach for optimizing key performance indicators (KPIs) in large-scale recommender systems, such as online ad auctions. Traditional importance sampling (IS) methods face challenges in dynamic environments, particularly in navigating through complexities of multi-modal landscapes and avoiding entrapment in local optima for the optimization task. Instead of updating importance weights and mixing samples across iterations, as in canonical adaptive IS and multiple IS, our AMIS framework leverages a mixture distribution as the proposal distribution and dynamically adjusts both the mixture parameters and their mixing rates at each iteration, thereby enhancing search diversity and efficiency. Through extensive offline simulations, we demonstrate that AMIS significantly outperforms simple Gaussian Importance Sampling (GIS), particularly in noisy environments. Moreover, our approach is validated in real-world scenarios through online A/B experiments on a major search engine, where AMIS consistently identifies optimal tuning points that are more likely to be adopted as mainstream configurations. These findings indicate that AMIS enhances convergence in noisy environments, leading to more accurate and reliable decision-making in the context of importance sampling off-policy estimators., Comment: Accepted at the CONSEQUENCES '24 workshop, co-located with ACM RecSys '24

Clustering artworks based on style has many potential real-world applications like art recommendations, style-based search and retrieval, and the study of artistic style evolution in an artwork corpus. However, clustering artworks based on style is largely an unaddressed problem. A few present methods for clustering artworks principally rely on generic image feature representations derived from deep neural networks and do not specifically deal with the artistic style. In this paper, we introduce and deliberate over the notion of style-based clustering of visual artworks. Our main objective is to explore neural feature representations and architectures that can be used for style-based clustering and observe their impact and effectiveness. We develop different methods and assess their relative efficacy for style-based clustering through qualitative and quantitative analysis by applying them to four artwork corpora and four curated synthetically styled datasets. Our analysis provides some key novel insights on architectures, feature representations, and evaluation methods suitable for style-based clustering., Comment: 29 pages

Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation -- continuously providing new items within a single session in a contextually coherent manner -- has been an emerging topic in current literature. User feedback -- a positive or negative response to the item presented -- is used to drive content recommendations by learning user preferences. We extend this idea to session-based recommendation to provide context-coherent music recommendations by modelling negative user feedback, i.e., skips, in the loss function. We propose a sequence-aware contrastive sub-task to structure item embeddings in session-based music recommendation, such that true next-positive items (ignoring skipped items) are structured closer in the session embedding space, while skipped tracks are structured farther away from all items in the session. This directly affects item rankings using a K-nearest-neighbors search for next-item recommendations, while also promoting the rank of the true next item. Experiments incorporating this task into SoTA methods for sequential item recommendation show consistent performance gains in terms of next-item hit rate, item ranking, and skip down-ranking on three music recommendation datasets, strongly benefiting from the increasing presence of user feedback., Comment: To-appear at 18th ACM Conference on Recommendation Systems

We conduct systematic experiments to investigate the dynamics of liquid jet breakup and the resulting droplet size distribution, emphasizing the influence of liquid jet velocity and needle exit diameter. We precisely control jet formation using a pressurized water tank equipped with needles of different sizes. Our study quantifies breakup dynamics through dimensionless parameters such as the liquid Weber number and the needle exit area ratio. Our key findings identify three distinct breakup regimes, such as dripping, Rayleigh, and wind-induced, each dictated by the interplay of surface tension and aerodynamic forces for various combinations of liquid jet velocity and needle exit diameter. We construct a regime map to delineate different breakup behaviours in the We - Ar space. It is observed that lower jet velocities produce narrow probability density functions for jet breakup length due to stable jets, whereas higher velocities result in broader distributions. Increasing jet velocity extends breakup lengths for moderate flow rates due to enhanced stability in the Rayleigh regime, but higher velocities induce instability, leading to shorter breakup lengths. Additionally, we analyze the effects of the needle exit area ratio and liquid Weber number on droplet size distribution, highlighting the transition from mono-modal to bi-modal distribution under varying conditions., Comment: 15 pages, 12 figures, Physics of Fluids

Autonomous agent applications powered by large language models (LLMs) have recently risen to prominence as effective tools for addressing complex real-world tasks. At their core, agentic workflows rely on LLMs to plan and execute the use of tools and external Application Programming Interfaces (APIs) in sequence to arrive at the answer to a user's request. Various benchmarks and leaderboards have emerged to evaluate an LLM's capabilities for tool and API use; however, most of these evaluations only track single or multiple isolated API calling capabilities. In this paper, we present NESTFUL, a benchmark to evaluate LLMs on nested sequences of API calls, i.e., sequences where the output of one API call is passed as input to a subsequent call. NESTFUL has a total of 300 human annotated samples divided into two types - executable and non-executable. The executable samples are curated manually by crawling Rapid-APIs whereas the non-executable samples are hand picked by human annotators from data synthetically generated using an LLM. We evaluate state-of-the-art LLMs with function calling abilities on NESTFUL. Our results show that most models do not perform well on nested APIs in NESTFUL as compared to their performance on the simpler problem settings available in existing benchmarks.

Cardiac arrest remains a leading cause of death worldwide, necessitating proactive measures for early detection and intervention. This project aims to develop and assess predictive models for the timely identification of cardiac arrest incidents, utilizing a comprehensive dataset of clinical parameters and patient histories. Employing machine learning (ML) algorithms like XGBoost, Gradient Boosting, and Naive Bayes, alongside a deep learning (DL) approach with Recurrent Neural Networks (RNNs), we aim to enhance early detection capabilities. Rigorous experimentation and validation revealed the superior performance of the RNN model, which effectively captures complex temporal dependencies within the data. Our findings highlight the efficacy of these models in accurately predicting cardiac arrest likelihood, emphasizing the potential for improved patient care through early risk stratification and personalized interventions. By leveraging advanced analytics, healthcare providers can proactively mitigate cardiac arrest risk, optimize resource allocation, and improve patient outcomes. This research highlights the transformative potential of machine learning and deep learning techniques in managing cardiovascular risk and advances the field of predictive healthcare analytics.