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In today's digitally driven landscape, robust Information Technology (IT) risk assessment practices are essential for safeguarding systems, digital communication, and data. This paper introduces 'AssessITS', an actionable method designed to provide organizations with comprehensive guidelines for conducting IT and cybersecurity risk assessments. Drawing extensively from NIST 800-30 Rev 1, COBIT 5, and ISO 31000, 'AssessITS' bridges the gap between high-level theoretical standards and practical implementation challenges. The paper outlines a step-by-step methodology that organizations can simply adopt to systematically identify, analyze, and mitigate IT risks. By simplifying complex principles into actionable procedures, this framework equips practitioners with the tools needed to perform risk assessments independently, without too much reliance on external vendors. The guidelines are developed to be straightforward, integrating practical evaluation metrics that allow for the precise quantification of asset values, threat levels, vulnerabilities, and impacts on confidentiality, integrity, and availability. This approach ensures that the risk assessment process is not only comprehensive but also accessible, enabling decision-makers to implement effective risk mitigation strategies customized to their unique operational contexts. 'AssessITS' aims to enable organizations to enhance their IT security strength through practical, actionable guidance based on internationally recognized standards., Comment: 25 pages, 8 figures, 7 tables

We demonstrate terahertz (THz) harmonic generation across the Mott insulator-metal transition in rare-earth nickelates (RNiO$_3$, R = rare-earth atom). The THz harmonic generation is observed in all the three different phases with distinct behaviors: the intensity of harmonics increases upon cooling in both the low-temperature antiferromagnetic (AFM) insulating and high-temperature paramagnetic (PM) metallic phases, while this trend is reversed in the intermediate PM insulating phase. Using single- and two-band Hubbard models, we find different dominant origins of THz harmonics in different phases: strong spin-charge and orbital-charge couplings in the AFM insulating phase, intraband currents from renormalized quasi-particles with frequency-dependent scattering rate in the PM metallic phase, and the reduction of the charge carrier density due to the opening of the Mott gap in the PM insulating phase. Our study offers strategies for efficient THz harmonic generation from Mott and other strongly correlated systems and insights into the fundamental physics of complex materials., Comment: Submitted to Physical Review Letters. 5 pages, 3 figures

Efficient scheduling is crucial for interactive Large Language Model (LLM) applications, where low request completion time directly impacts user engagement. Size-based scheduling algorithms like Shortest Remaining Process Time (SRPT) aim to reduce average request completion time by leveraging known or estimated request sizes and allowing preemption by incoming jobs with shorter service times. However, two main challenges arise when applying size-based scheduling to LLM systems. First, accurately predicting output lengths from prompts is challenging and often resource-intensive, making it impractical for many systems. As a result, the state-of-the-art LLM systems default to first-come, first-served scheduling, which can lead to head-of-line blocking and reduced system efficiency. Second, preemption introduces extra memory overhead to LLM systems as they must maintain intermediate states for unfinished (preempted) requests. In this paper, we propose TRAIL, a method to obtain output predictions from the target LLM itself. After generating each output token, we recycle the embedding of its internal structure as input for a lightweight classifier that predicts the remaining length for each running request. Using these predictions, we propose a prediction-based SRPT variant with limited preemption designed to account for memory overhead in LLM systems. This variant allows preemption early in request execution when memory consumption is low but restricts preemption as requests approach completion to optimize resource utilization. On the theoretical side, we derive a closed-form formula for this SRPT variant in an M/G/1 queue model, which demonstrates its potential value. In our system, we implement this preemption policy alongside our embedding-based prediction method.

Planning contact-rich interactions for multi-finger manipulation is challenging due to the high-dimensionality and hybrid nature of dynamics. Recent advances in data-driven methods have shown promise, but are sensitive to the quality of training data. Combining learning with classical methods like trajectory optimization and search adds additional structure to the problem and domain knowledge in the form of constraints, which can lead to outperforming the data on which models are trained. We present Diffusion-Informed Probabilistic Contact Search (DIPS), which uses an A* search to plan a sequence of contact modes informed by a diffusion model. We train the diffusion model on a dataset of demonstrations consisting of contact modes and trajectories generated by a trajectory optimizer given those modes. In addition, we use a particle filter-inspired method to reason about variability in diffusion sampling arising from model error, estimating likelihoods of trajectories using a learned discriminator. We show that our method outperforms ablations that do not reason about variability and can plan contact sequences that outperform those found in training data across multiple tasks. We evaluate on simulated tabletop card sliding and screwdriver turning tasks, as well as the screwdriver task in hardware to show that our combined learning and planning approach transfers to the real world.

Deep reinforcement learning (RL) policies, although optimal in terms of task rewards, may not align with the personal preferences of human users. To ensure this alignment, a naive solution would be to retrain the agent using a reward function that encodes the user's specific preferences. However, such a reward function is typically not readily available, and as such, retraining the agent from scratch can be prohibitively expensive. We propose a more practical approach - to adapt the already trained policy to user-specific needs with the help of human feedback. To this end, we infer the user's intent through trajectory-level feedback and combine it with the trained task policy via a theoretically grounded dynamic policy fusion approach. As our approach collects human feedback on the very same trajectories used to learn the task policy, it does not require any additional interactions with the environment, making it a zero-shot approach. We empirically demonstrate in a number of environments that our proposed dynamic policy fusion approach consistently achieves the intended task while simultaneously adhering to user-specific needs.

Our primary objective in this article is to establish H\"ormander type $L^p \rightarrow L^q$ Fourier multiplier theorems in the context of noncompact type Riemannian symmetric spaces $\mathbb{X}$ of arbitrary rank for the range $1 < p \leq 2 \leq q < \infty$. As a consequence of the Fourier multiplier theorem, we also derive a spectral multiplier theorem on $\mathbb{X}$. We then apply this theorem to prove $L^p \rightarrow L^q$ boundedness for functions of the Laplace-Beltrami operator and to obtain embedding theorems and operator estimates for the potentials and heat semigroups. Additionally, we provide mixed-norm versions of the Hausdorff-Young and Paley inequalities. In this context, where the Fourier transform is holomorphic, and its domain consists of various strips, we present two versions of these inequalities and explore their interrelation. Furthermore, our findings and methods are also applicable to harmonic $NA$ groups, also known as Damek-Ricci spaces., Comment: 39 pages. Comments are welcome. arXiv admin note: text overlap with arXiv:2101.08460 by other authors

In this article, we establish three fundamental Fourier inequalities: the Hausdorff-Young inequality, the Paley inequality, and the Hausdorff-Young-Paley inequality for $(l, n)$-type functions on $\mathrm{SL}(2,\mathbb{R})$. Utilizing these inequalities, we demonstrate the $L^p$-$L^q$ boundedness of $(l, n)$-type Fourier multipliers on $\mathrm{SL}(2,\mathbb{R})$. Furthermore, we explore applications related to the $L^p$-$L^q$ estimates of the heat kernel of the Casimir element on $\mathrm{SL}(2,\mathbb{R})$ and address the global well-posedness of certain parabolic and hyperbolic nonlinear equations., Comment: 26 pages. Comments are welcome

Despite the recent broad adoption of Large Language Models (LLMs) across various domains, their potential for enriching information systems in extracting and exploring Linked Data (LD) and Resource Description Framework (RDF) triplestores has not been extensively explored. This paper examines the integration of LLMs within existing systems, emphasising the enhancement of conversational user interfaces (UIs) and their capabilities for data extraction by producing more accurate SPARQL queries without the requirement for model retraining. Typically, conversational UI models necessitate retraining with the introduction of new datasets or updates, limiting their functionality as general-purpose extraction tools. Our approach addresses this limitation by incorporating LLMs into the conversational UI workflow, significantly enhancing their ability to comprehend and process user queries effectively. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems employing conventional chatbots. This integration facilitates a more nuanced and context-aware interaction model, critical for handling the complex query patterns often encountered in RDF datasets and Linked Open Data (LOD) endpoints. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries, indicating a promising direction for future research in this area. This investigation not only underscores the versatility of LLMs in enhancing existing information systems but also sets the stage for further explorations into their potential applications within more specialised domains of web information systems., Comment: This paper has been accepted at the 25th International Web Information Systems Engineering Conference (WISE 2024)

Unravelling the role of charge-lattice interactions on the optoelectronic properties in lead halide perovskites is of great interest due to their unique photophysical properties. While there is broad consensus on the importance of the structural deformations and polar fluctuations on the behavior of charge carriers and quasiparticles, the strongly anharmonic nature of these fluctuations and their strong interactions render theoretical descriptions of lead halides challenging. In this Account, we review our recent efforts to understand how the soft, polar lattice of this class of materials alter their quasiparticle binding energies and fine structure, charge mobilities, and lifetimes of phonons and excess charges. This is reviewed in the context of the effective models we have built, and the novel theoretical methods we have developed to understand bulk crystalline materials, as well as nanostructures, and lower dimensionality lattices. The perspective we detail provides both some design principles for optoelectronic materials and a set of theoretical tools to study them when charge-lattice interactions are important., Comment: 12 pages, 8 figures

Efficient network management in optical backbone networks is crucial for handling continuous traffic growth. In this work, we address the challenges of managing dynamic traffic in C- and C+L-band optical backbone networks while exploring application flexibility, namely the compressibility and delayability metrics. We propose a strategy, named Delay-Aware and Compression-Aware (DACA) provisioning algorithm, which reduces blocking probability, thereby increasing information-carrying capacity of the network compared to baseline strategies.

In Bioinformatics, the applications of flow decomposition in directed acyclic graphs are highlighted in RNA Assembly problem. However, it admits multiple solutions where exactly one solution correctly represents the underlying transcripts. The problem was addressed by Safe and Complete framework~[RECOMB16], which reports all the parts of the solution that are present in every possible solution. Khan et al.~[RECOMB22] first studied flow decomposition in the safe and complete framework. Their algorithm showed superior performance ($\approx20\%$) over the popular heuristic (greedy-width) on sufficiently complex graphs for a unified metric of precision and coverage (F-score). They presented the solution in multiple representations using simple but suboptimal algorithms, which were later optimized by Khan and Tomescu~[ESA22], who also presented an optimal representation. In this paper, we evaluate the practical significance of the optimal algorithms by Khan and Tomescu~[ESA22]. Our work highlights the significance of the theoretically optimal algorithms improving time (up to $60-70\%$) and memory (up to $76-85\%$), and the optimal representations improving output size (up to $135-170\%$) significantly. However, the impact of optimal algorithms was limited due to a large number of extremely short safe paths. We propose heuristics to improve these representations further, resulting in further improvement in time (up to $10\%$) and output size ($10-25\%$). However, in absolute terms, these improvements were limited to a few seconds on real datasets involved due to the smaller size of the graphs. We thus generated large random graphs, to demonstrate the scalability of the above results. The older algorithms [RECOMB22] were not practical on moderately large graphs ($\geq 1M$ nodes), while optimal algorithms [ESA22] were linearly scalable for much larger graphs ($\geq 100M$ nodes).

The anions C$_7$N$^-$ and C$_{10}$H$^-$ are the two longest of the linear (C,N)-bearing and (C,H)-bearing chains which have so far been detected in the Interstellar Medium. In order to glean information on their collision-induced rotational state-changing processes, we analyse the general features of new ab initio potentials describing the interaction of both linear anions with H$_2$, one of the most abundant partners in their ISM environment. We employ an artificial neural network fit of the reduced-dimensionality potential energy surface for C$_7$N$^-$...H$_2$ interaction and discuss in detail the spatial features in terms of multipolar radial coefficients. For the C$_{10}$H$^-$...H$_2$ interaction we use the initial grid of two dimensional raw points to generate by quadrature the Legendre expansion directly, further including the long-range terms as discussed in the main text. Quantum scattering calculations are employed to obtain rotationally inelastic cross sections, for collision energies in the range of 10$^{-4}$ to 400 cm$^{-1}$. From them we generate the corresponding inelastic rate coefficients as a function of temperature covering the range from 10 to 50 K. The results for the rate coefficients for the longest cyanopolyyne are compared with the earlier results obtained for the smaller terms of the same series, also in collision with H$_2$. We obtain that the inelastic rate coefficients for the long linear anions are all fairly large compared with the earlier systems. The consequences of such findings on their non-equilibrium rotational populations in interstellar environments are illustrated in our conclusions.

Pulsars are rotating neutron stars that are observed to be slowing down, implying a loss of their kinetic energy. There can be several different physical mechanisms involved in their spin-down process. The properties of fast-rotating pulsars depend on the nature of the neutron star matter, which can also affect the spin-down mechanisms. In this work, we examine three different physical phenomena contributing to the spin-down: magnetic dipole radiation, gravitational mass quadrupole radiation due to the ``mountain" formation, gravitational mass current quadrupole radiation or the r-modes, and calculate the expressions for the braking indices due to all of them. We have also considered jointly the implications of the uncertainties of the equation of the state of neutron star matter and rapid rotation on the braking indices corresponding to the aforementioned processes and their combinations. In all cases, the rapid rotation results in a departure from the standard values in the literature for the braking index when the rotational effects are ignored. If generated with a saturation amplitude within the range of $10^{-4} - 10^{-1}$, the r-mode oscillations dominate the spin-down of millisecond pulsars. We also explore the braking index in the context of millisecond magnetars. This study examines two braking index measurements in the context of newly born millisecond magnetars from two observed short $\gamma$-ray bursts. The measured braking indices for these objects are consistent with our estimation, which allows us to conclude that the spin frequency of the remnants is within the range of $\sim 550-850$ Hz.

We show how to utilize machine learning approaches to improve sliding window algorithms for approximate frequency estimation problems, under the ``algorithms with predictions'' framework. In this dynamic environment, previous learning-augmented algorithms are less effective, since properties in sliding window resolution can differ significantly from the properties of the entire stream. Our focus is on the benefits of predicting and filtering out items with large next arrival times -- that is, there is a large gap until their next appearance -- from the stream, which we show improves the memory-accuracy tradeoffs significantly. We provide theorems that provide insight into how and by how much our technique can improve the sliding window algorithm, as well as experimental results using real-world data sets. Our work demonstrates that predictors can be useful in the challenging sliding window setting.

Materials that possess a porous and defected structure can have a range of useful properties that are sought after, which include their tolerance to nuclear radiation, ability to efficiently store and release isotopes, to immobilize nuclear waste, and to exhibit phase stability even at elevated temperatures. Since nanoscale pores and surface structures can serve as sinks for radiation-induced amorphization, one dimensional (1D) porous nanorods due to their high surface-to-volume ratio have the potential for use as advanced materials in nuclear science applications. In this study, we demonstrate a synthesis and a detailed analysis of microporous 1D octahedral molecular sieves of disodium diniobate hydrate (Na2Nb2O6 H2O) or Sandia Octahedral Molecular Sieves (SOMS). In addition, the stability of these SOMS is evaluated following their exposure to elevated temperatures and neutron irradiation. A surfactant-assisted solvothermal method is used to prepare these SOMS-based nanorods. This relatively low temperature, solution-phase approach can form crystalline nanorods of microporous Na2Nb2O6 H2O. These 1D structures had an average diameter of approximately 50 nm and lengths greater than 1 micrometer. The nanorods adopted a defected microporous phase, which also exhibited a resistance to radiation induced amorphization. The dimensions, phase, and crystallinity of the SOMS-based nanorods after exposure to a high incident flux of neutrons were comparable to those of the as-synthesized products. The radiation tolerance of these microporous SOMS could be useful in the design of materials for nuclear reactors, resilient nuclear fuels, thermally resilient materials, high temperature catalysts, and durable materials for the handling and storage of radioactive waste., Comment: 38 pages, 17 figures

In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. The success of an implementation of this idea is shown using some real data. Then we report several experimental results for critically examining the performance of the derived pricing models., Comment: 24 pages

Nonlinear optics at the nanoscale has emerged as a sought-after platform for sensing and imaging applications. The development of these materials is having an impact on fields that include advanced information technology, signal processing circuits, and cryptography. Lithium tantalate (LiTaO3) is an attractive nonlinear optical material due to its high optical damage threshold (e.g., tolerance to greater than 500 MW per cm^2 from a nanosecond pulsed laser) and broad range of ultraviolet-visible (UV-Vis) transparency relative to many other nonlinear optical materials. Despite many synthetic reports on metal oxides, very little is known about the preparation of uniform, crystalline LiTaO3 nanoparticles (NPs) of a pure phase, as well as details on their mechanism of nucleation and growth. In this article, we introduce a solution-phase method for the preparation of LiTaO3 NPs with tunable dimensions. This solution-phase process results in the formation of crystalline, uniform NPs of LiTaO3 of a pure phase when carried out at 220 C. This method can prepare crystalline LiTaO3 NPs without the need for further heat treatment or the use of an inert atmosphere. Results presented herein also provide insights into the growth mechanism of these NPs. The reaction included the processes of oriented attachment and Ostwald ripening. The results of our study also indicate that the growth of the LiTaO3 NPs was a result of a size focusing effect, which enables the ability to tune their diameters from 200 to 500 nm. The crystalline NPs were optically active towards second harmonic generation. These studies deepen our understanding of the methods by which NPs can be prepared from metal oxides. These studies specifically demonstrate the preparation of optically active LiTaO3 NPs of uniform and controllable dimensions that could be used in a broad range of fundamental studies and applications in nanophotonics., Comment: 36 pages, 13 figures

The demands of ultra-reliable low-latency communication (URLLC) in ``NextG" cellular networks necessitate innovative approaches for efficient resource utilisation. The current literature on 6G O-RAN primarily addresses improved mobile broadband (eMBB) performance or URLLC latency optimisation individually, often neglecting the intricate balance required to optimise both simultaneously under practical constraints. This paper addresses this gap by proposing a DRL-based resource allocation framework integrated with meta-learning to manage eMBB and URLLC services adaptively. Our approach efficiently allocates heterogeneous network resources, aiming to maximise energy efficiency (EE) while minimising URLLC latency, even under varying environmental conditions. We highlight the critical importance of accurately estimating the traffic distribution flow in the multi-connectivity (MC) scenario, as its uncertainty can significantly degrade EE. The proposed framework demonstrates superior adaptability across different path loss models, outperforming traditional methods and paving the way for more resilient and efficient 6G networks., Comment: This manuscript is being submitted for peer review and potential publication in the IEEE Open Journal of the Communications Society

We investigate the effect on cosmological evolution of a strongly coupled quantum field that undergoes renormalization group flow from a UV CFT to an IR CFT. The field theory is defined by perturbation of a holographic CFT by a relevant operator associated with a bulk scalar field that evolves from a local maximum of its potential near the boundary to a local minimum of its potential deep in the bulk. By studying the gravity solutions dual to this theory on $\mathbb{R}^3 \times S^1$, we find that the equation of state parameter $w$ for the field theory has the conformal behavior $w=1/3$ for high and low temperatures, but dips to lower values for intermediate temperatures. Thus, at scales where the field theory has significant scale-dependence, its effect on cosmological evolution is intermediate between matter and radiation. Compared to the unperturbed UV CFT (which acts as radiation), the energy density experiences less dilution during the expansion as a result of the RG flow, and the rate of expansion is greater., Comment: 18 pages, 4 figures

Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains due to variability in soil properties, which can lead to severe wheel slip, compromising navigation efficiency and increasing the risk of entrapment. To address this problem, we introduce SlipNet, a novel approach for predicting wheel slip in segmented regions of diverse terrain surfaces without relying on prior terrain classification. SlipNet employs dynamic terrain segmentation and slip assignment techniques on previously unseen data, enhancing rover navigation capabilities in uncertain environments. We developed a synthetic data generation framework using the high-fidelity Vortex Studio simulator to create realistic datasets that replicate a wide range of deformable terrain conditions for training and evaluation. Extensive simulation results demonstrate that our model, combining DeepLab v3+ with SlipNet, significantly outperforms the state-of-the-art TerrainNet method, achieving lower mean absolute error (MAE) across five distinct terrain samples. These findings highlight the effectiveness of SlipNet in improving rover navigation in challenging terrains.

Rank aggregation combines multiple ranked lists into a consensus ranking. In fields like biomedical data sharing, rankings may be distributed and require privacy. This motivates the need for federated rank aggregation protocols, which support distributed, private, and communication-efficient learning across multiple clients with local data. We present the first known federated rank aggregation methods using Borda scoring and Lehmer codes, focusing on the sample complexity for federated algorithms on Mallows distributions with a known scaling factor $\phi$ and an unknown centroid permutation $\sigma_0$. Federated Borda approach involves local client scoring, nontrivial quantization, and privacy-preserving protocols. We show that for $\phi \in [0,1)$, and arbitrary $\sigma_0$ of length $N$, it suffices for each of the $L$ clients to locally aggregate $\max\{C_1(\phi), C_2(\phi)\frac{1}{L}\log \frac{N}{\delta}\}$ rankings, where $C_1(\phi)$ and $C_2(\phi)$ are constants, quantize the result, and send it to the server who can then recover $\sigma_0$ with probability $\geq 1-\delta$. Communication complexity scales as $NL \log N$. Our results represent the first rigorous analysis of Borda's method in centralized and distributed settings under the Mallows model. Federated Lehmer coding approach creates a local Lehmer code for each client, using a coordinate-majority aggregation approach with specialized quantization methods for efficiency and privacy. We show that for $\phi+\phi^2<1+\phi^N$, and arbitrary $\sigma_0$ of length $N$, it suffices for each of the $L$ clients to locally aggregate $\max\{C_3(\phi), C_4(\phi)\frac{1}{L}\log \frac{N}{\delta}\}$ rankings, where $C_3(\phi)$ and $C_4(\phi)$ are constants. Clients send truncated Lehmer coordinate histograms to the server, which can recover $\sigma_0$ with probability $\geq 1-\delta$. Communication complexity is $\sim O(N\log NL\log L)$.

The recently developed MAB phases, an extension of the MAX phase, have sparked interest in research among scientists because of their better thermo-mechanical properties. In this paper, we have explored four new MAB phases M2AB2 (M=Mo, Ta and A=Ga, Ge) and studied the elastic, electronic, thermal, and optical properties to predict the possible applications. The stability of the new phases has been confirmed by calculating formation energy (Ef), formation enthalpy (H), phonon dispersion curve (PDC), and elastic constant (Cij). The study reveals that M2AB2 (M=Mo, Ta and A=Ga, Ge) exhibit significantly higher elastic constants, elastic moduli, and Vickers hardness values than their counterpart 211 borides. Higher Vickers hardness values of Ta2AB2 (A=Ga, Ge) than Mo2AB2 (A=Ga, Ge) have been explained based on the values of the bond overlap population. The analysis of the density of states and electronic band structure revealed the metallic nature of the borides under examination. The thermodynamic characteristics of M2AB2 (M=Mo, Ta and A=Ga, Ge) under high temperatures (0 to 1000 K) are investigated using the quasi-harmonic Debye model. Critical thermal properties such as melting temperature (Tm), Gruneisen parameter, minimum thermal conductivity (Kmin), Debye temperature, and others are also computed. Compared with 211 MAX phases, the 212 phases exhibit higher values of Debye temperature and Tm, along with a lower value of Kmin. These findings suggest that the studied compounds exhibit superior thermal properties that are suitable for practical applications. The optical characteristics have been examined, and the reflectance spectrum indicates that the materials have the potential to mitigate solar heating across various energy regions., Comment: 37 pages

Nuclear shape and orientation degrees of freedom are incorporated into the calculation of the double-folding nuclear potential within the relativistic mean-field (RMF) formalism. The quadrupole deformations ($\beta_2$), nuclear densities and the effective nucleon-nucleon (NN) interaction potential are obtained using the RMF approach for the Hybrid, NL3$^*$ and NL3 parameterizations. The calculated quadrupole deformations are included in the target densities through the nuclear radius. The deformation and orientation-dependent microscopic nuclear potentials are further employed to obtain fusion barrier characteristics and cross-sections for 12 even-even heavy-ion reactions with doubly magic spherical $^{16}$O and $^{48}$Ca as projectiles along with deformed targets from different mass regions. The results obtained for the relativistic R3Y NN potential are compared with those of the Reid version of the non-relativistic M3Y NN potential as well as with the available experimental data. A decrease in the barrier height and increase in the cross-section is observed upon the inclusion of target quadrupole deformations in the nuclear density distributions at the target orientation angles, $\theta_2\le58^\circ$ for the R3Y NN potential and at $\theta_2\le60^\circ$ for the M3Y NN potential. On comparing the $\theta_2$-integrated cross-section calculated using M3Y and R3Y NN potentials with spherical and deformed densities, one observes that the deformed densities and the relativistic R3Y NN potential obtained for the Hybrid parameter set provide better agreement with the available experimental data for all the considered reactions. Moreover, the modifications in the characteristics of the fusion barrier and hence in the cross-section with the inclusion of nuclear shape degrees of freedom and orientations are found to become more prominent in reactions forming heavier compound nuclei.

This study develops a Convolutional Neural Network (CNN) model for detecting myocardial infarction (MI) from Electrocardiogram (ECG) images. The model, built using the InceptionV3 architecture and optimized through transfer learning, was trained using ECG data obtained from the Ch. Pervaiz Elahi Institute of Cardiology in Pakistan. The dataset includes ECG images representing four different cardiac conditions: myocardial infarction, abnormal heartbeat, history of myocardial infarction, and normal heart activity. The developed model successfully detects MI and other cardiovascular conditions with an accuracy of 93.27%. This study demonstrates that deep learning-based models can provide significant support to clinicians in the early detection and prevention of heart attacks., Comment: 4 pages

We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each concept is expressed. Conveniently, the concepts can be defined as either text queries, e.g., "a dog" and "a turtle," or inspirational images, and the local regions can be selected as any number of vertices on the mesh. We can effectively control the influence of the concepts and mix them together using a novel score distillation approach, referred to as the Blended Score Distillation (BSD). BSD operates on each attention layer of the denoising U-Net of a diffusion model as it extracts and injects the per-objective activations into a unified denoising pipeline from which the deformation gradients are calculated. To localize the expression of these activations, we create a probabilistic Region of Interest (ROI) map on the surface of the mesh, and turn it into 3D-consistent masks that we use to control the expression of these activations. We demonstrate the effectiveness of BSD empirically and show that it can deform various meshes towards multiple objectives. Our project page is at https://threedle.github.io/MeshUp., Comment: Project page: https://threedle.github.io/MeshUp

Parameterizing finger rolling and finger-object contacts in a differentiable manner is important for formulating dexterous manipulation as a trajectory optimization problem. In contrast to previous methods which often assume simplified geometries of the robot and object or do not explicitly model finger rolling, we propose a method to further extend the capabilities of dexterous manipulation by accounting for non-trivial geometries of both the robot and the object. By integrating the object's Signed Distance Field (SDF) with a sampling method, our method estimates contact and rolling-related variables and includes those in a trajectory optimization framework. This formulation naturally allows for the emergence of finger-rolling behaviors, enabling the robot to locally adjust the contact points. Our method is tested in a peg alignment task and a screwdriver turning task, where it outperforms the baselines in terms of achieving desired object configurations and avoiding dropping the object. We also successfully apply our method to a real-world screwdriver turning task, demonstrating its robustness to the sim2real gap.

As connected and autonomous vehicles proliferate, the Controller Area Network (CAN) bus has become the predominant communication standard for in-vehicle networks due to its speed and efficiency. However, the CAN bus lacks basic security measures such as authentication and encryption, making it highly vulnerable to cyberattacks. To ensure in-vehicle security, intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks while remaining lightweight for practical deployment. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. These approaches overlook other exploitable features, making it challenging to adapt to new unseen attack variants and compromising security. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations. The proposed IDS employs a multi-stage approach: an artificial neural network (ANN) in the first stage to detect seen attacks, and a Long Short-Term Memory (LSTM) autoencoder in the second stage to detect new, unseen attacks. To understand and analyze diverse driving behaviors, update the model with the latest attack patterns, and preserve data privacy, we propose a theoretical framework to deploy our IDS in a hierarchical federated learning (H-FL) environment. Experimental results demonstrate that our IDS achieves an F1-score exceeding 0.99 for seen attacks and exceeding 0.95 for novel attacks, with a detection rate of 99.99%. Additionally, the false alarm rate (FAR) is exceptionally low at 0.016%, minimizing false alarms. Despite using DL algorithms known for their effectiveness in identifying sophisticated and zero-day attacks, the IDS remains lightweight, ensuring its feasibility for real-world deployment., Comment: 24 pages

The ability to manipulate electrons with the intense laser pulse enables an unprecedented control over the electronic motion on its intrinsic timescale. Present work explores the desired control of photocurrent generation in monolayer graphene on ultrafast timescale. The origin of photocurrent is attributed to the asymmetric residual electronic population in the conduction band after the end of the laser pulse, which also facilitates valley polarization. Present study offers a comprehensive analysis of the differences between these two observables, namely photocurrent and valley polarization. It is found that the corotating circularly polarized $\omega-2\omega$ laser pulses allow the generation of photocurrent but no valley polarization, whereas counterrotating circularly polarized $\omega-2\omega$ laser pulses yield significant valley polarization without any photocurrent in graphene. Different laser parameters, such as subcycle phase, wavelength, and intensity provide different knobs to control the generation of the photocurrent. In addition, threefold increase in the photocurrent's amplitude can be achieved by altering electronic properties of graphene via strain engineering. Our findings reveal intriguing underlying mechanisms into the interplay between the symmetries of the graphene's electronic structure and the driving laser pulses, shedding light on the potential for harnessing graphene's properties for novel applications in ultrafast photonics, optoelectronic devices, and quantum technologies., Comment: 19 pages, 6 figures

Random non-reciprocal interactions between a large number of conserved densities are shown to enhance the stability of the system towards pattern formation. The enhanced stability is an exact result when the number of species approaches infinity and is confirmed numerically by simulations of the multi-species non-reciprocal Cahn-Hilliard model. Furthermore, the diversity in dynamical patterns increases with increasing number of components and novel steady states such as pulsating or spatiotemporally chaotic condensates are observed. Our results may help to unravel the mechanisms by which living systems self-organise via metabolism.

We study the dynamics of the interacting models between the Gauss-Bonnet (GB) coupled scalar field and the dark matter fluid in a homogeneous and isotropic background. A key feature of GB coupling models is the varying speed of gravitational waves (GWs). We utilize recent constraints on the GW speed and conduct our analysis in two primary scenarios: model-dependent and model-independent. In the model-dependent scenario, where determining the GW speed requires a specific GB coupling functional form, we choose an exponential GB coupling. We adopt a dynamical system analysis to obtain the necessary constraints on the model parameters that describe different phases of the universe and produce a stable late-time accelerating solution following the GW constraint, and find that to satisfy all these constraints, fine-tuning of the free parameters involved in the models is often needed. In the model-independent scenario, the GW speed is fixed to one, and we construct the autonomous system to identify the late-time stable accelerating critical points. Furthermore, we adopt a Bayesian inference method using late-time observational data sets, including 31 data points from cosmic chronometer data (Hubble data) and 1701 data points from Pantheon+ and find that all the observational constraints can be satisfied without fine-tuning. In addition, we also utilize simulated binned Roman and LSST data to study the evolution of the universe in the model-independent scenario. We find that the model shows significant deviation at higher redshifts from $\Lambda$CDM and fits the current data much better than $\Lambda$CDM within the error bars., Comment: 31 pages, 21 figures, and 3 tables

Understanding the abundance pattern of metal-poor stars and the production of heavy elements through various nucleosynthesis processes offers crucial insights into the chemical evolution of the Milky Way, revealing primary sites and major sources of rapid neutron-capture process ($r$-process) material in the Universe. In this fifth data release from the $R$-Process Alliance, we present the detailed chemical abundances of 41 faint (down to V = 15.8) and extremely metal-poor (down to [Fe/H] = -3.3) halo stars selected from the R-Process Alliance (RPA). We obtained high-resolution spectra for these objects with the HORuS spectrograph on the Gran Telescopio Canarias. We measure the abundances of light, alpha, Fe-peak, and neutron-capture elements. We report the discovery of five CEMP, one limited-$r$, three $r$-I, and four $r$-II stars, and six Mg-poor stars. We also identify one star of a possible globular cluster origin at an extremely low metallicity at [Fe/H] = -3.0. This adds to the growing evidence of a lower limit metallicity floor for globular cluster abundances. We use the abundances of Fe-peak elements and the alpha-elements to investigate the contributions from different nucleosynthesis channels in the progenitor supernovae. We find the distribution of [Mg/Eu] as a function of [Fe/H] to have different enrichment levels, indicating different possible pathways and sites of their production. We also reveal differences in the trends of the neutron-capture element abundances of Sr, Ba, and Eu of various $r$-I and $r$-II stars from the RPA data releases, which provide constraints on their nucleosynthesis sites and subsequent evolution., Comment: 34 pages, 16 figures, 7 tables; Accepted for publication in The Astrophysical Journal Supplement

This paper introduces an innovative graphical analysis tool for investigating the dynamics of Memristor Cellular Nonlinear Networks (M-CNNs) featuring 2nd-order processing elements, known as M-CNN cells. In the era of specialized hardware catering to the demands of intelligent autonomous systems, the integration of memristors within Cellular Nonlinear Networks (CNNs) has emerged as a promising paradigm due to their exceptional characteristics. However, the standard Dynamic Route Map (DRM) analysis, applicable to 1st-order systems, fails to address the intricacies of 2nd-order M-CNN cell dynamics, as well the 2nd-order DRM (DRM2) exhibits limitations on the graphical illustration of local dynamical properties of the M-CNN cells, e.g. state derivative's magnitude. To address this limitation, we propose a novel integration of M-CNN cell vector field into the cell's phase portrait, enhancing the analysis efficacy and enabling efficient M-CNN cell design. A comprehensive exploration of M-CNN cell dynamics is presented, showcasing the utility of the proposed graphical tool for various scenarios, including bistable and monostable behavior, and demonstrating its superior ability to reveal subtle variations in cell behavior. Through this work, we offer a refined perspective on the analysis and design of M-CNNs, paving the way for advanced applications in edge computing and specialized hardware., Comment: Presented at the 18th IEEE International Workshop on Cellular Nanoscale Networks and their Applications (CNNA'23) and the 8th Memristor and Memristive Symposium

Artificial Intelligence (AI) is increasingly acknowledged as a vital component for the advancement and competitiveness of modern organizations, including small and medium enterprises (SMEs). However, the adoption of AI technologies in SMEs faces significant barriers, primarily related to cost, lack of technical skills, and employee acceptance. This study proposes a comprehensive, phased framework designed to facilitate the effective adoption of AI in SMEs by systematically addressing these barriers. The framework begins with raising awareness and securing commitment from leadership, followed by the adoption of low-cost, general-purpose AI tools to build technical competence and foster a positive attitude towards AI. As familiarity with AI technologies increases, the framework advocates for the integration of task-specific AI tools to enhance efficiency and productivity. Subsequently, it guides organizations towards the in-house development of generative AI tools, providing greater customization and control. Finally, the framework addresses the development of discriminative AI models to meet highly specific and precision-oriented tasks. By providing a structured and incremental approach, this framework ensures that SMEs can navigate the complexities of AI integration effectively, driving innovation, efficiency, and competitive advantage. This study contributes to the field by offering a practical, prescriptive framework tailored to the unique needs of SMEs, facilitating the successful adoption of AI technologies and positioning these organizations for sustained growth in a competitive landscape.

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

Superconducting QUantum Interference Devices (SQUIDs) are extremely sensitive magnetic flux sensors which render them useful in a wide array of instrumentation. SQUIDs are often paired with other detectors as a readout mechanism to obtain quantitative insight. SQUIDs have impacted many fields but much less addressed is its impact on the field of fundamental physics, particularly in the search for dark matter. Dark matter is believed to make up around 27% of all mass-energy content of the universe and will provide critical insight into understanding large-scale structures of the universe. Axions and WIMPs are the prominent two dark matter candidates whose search has been fueled by the usage of SQUID read-outs.

The advent of Ultra-Reliable Low Latency Communication (URLLC) alongside the emergence of Open RAN (ORAN) architectures presents unprecedented challenges and opportunities in Radio Resource Management (RRM) for next-generation communication systems. This paper presents a comprehensive trade-off analysis of Deep Reinforcement Learning (DRL) approaches designed to enhance URLLC performance within ORAN's flexible and dynamic framework. By investigating various DRL strategies for optimising RRM parameters, we explore the intricate balance between reliability, latency, and the newfound adaptability afforded by ORAN principles. Through extensive simulation results, our study compares the efficacy of different DRL models in achieving URLLC objectives in an ORAN context, highlighting the potential of DRL to navigate the complexities introduced by ORAN. The proposed study provides valuable insights into the practical implementation of DRL-based RRM solutions in ORAN-enabled wireless networks. It sheds light on the benefits and challenges of integrating DRL and ORAN for URLLC enhancements. Our findings contribute to the ongoing discourse on advancements in URLLC and ORAN, offering a roadmap for future research to pursue efficient, reliable, and flexible communication systems., Comment: The manuscript is currently being reviewed by IEEE Communications Magazine

We present a computational study of the pairwise interactions between defects in the recently introduced non-reciprocal Cahn-Hilliard model. The evolution of a defect pair exhibits dependence upon their corresponding topological charges, initial separation, and the non-reciprocity coupling constant $\alpha$. We find that the stability of isolated topologically neutral targets significantly affects the pairwise defect interactions. At large separations, defect interactions are negligible and a defect pair is stable. When positioned in relatively close proximity, a pair of oppositely charged spirals or targets merge to form a single target. At low $\alpha$, like-charged spirals form rotating bound pairs, which are however torn apart by spontaneously formed targets at high $\alpha$. Similar preference for charged or neutral solutions is also seen for a spiral target pair where the spiral dominates at low $\alpha$, but concedes to the target at large $\alpha$. Our work sheds light on the complex phenomenology of non-reciprocal active matter systems when their collective dynamics involves topological defects.

Accurate assessment of myocardial tissue stiffness is pivotal for the diagnosis and prognosis of heart diseases. Left ventricular diastolic stiffness ($\beta$) obtained from the end-diastolic pressure-volume relationship (EDPVR) has conventionally been utilized as a representative metric of myocardial stiffness. The EDPVR can be employed to estimate the intrinsic stiffness of myocardial tissues through image-based in-silico inverse optimization. However, whether $\beta$, as an organ-level metric, accurately represents the tissue-level myocardial tissue stiffness in healthy and diseased myocardium remains elusive. We developed a modeling-based approach utilizing a two-parameter material model for the myocardium (denoted by $a_f$ and $b_f$) in image-based in-silico biventricular heart models to generate EDPVRs for different material parameters. Our results indicated a variable relationship between $\beta$ and the material parameters depending on the range of the parameters. Interestingly, $\beta$ showed a very low sensitivity to $a_f$, once averaged across several LV geometries, and even a negative correlation with $a_f$ for small values of $a_f$. These findings call for a critical assessment of the reliability and confoundedness of EDPVR-derived metrics to represent tissue-level myocardial stiffness. Our results also underscore the necessity to explore image-based in-silico frameworks, promising to provide a high-fidelity and potentially non-invasive assessment of myocardial stiffness., Comment: 4 pages, 5 figures, accepted in the IEEE EMBC 2024 conference

We present analytic results for three-loop fermionic corrections to the heavy-light form factors in perturbative quantum chromodynamics. Specifically, we present all light quark contributions and contributions from two heavy quark loops. We use the method of differential equations to compute all relevant three-loop master integrals. The results for all these contributions are expressed in terms of harmonic polylogarithms and generalized harmonic polylogarithms., Comment: 34 pages

In the rapidly evolving landscape of 5G and beyond, cloud-native Open Radio Access Networks (O-RAN) present a paradigm shift towards intelligent, flexible, and sustainable network operations. This study addresses the intricate challenge of energy efficient (EE) resource allocation that services both enhanced Mobile Broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users. We propose a novel distributed learning framework leveraging on-policy and off-policy transfer learning strategies within a deep reinforcement learning (DRL)--based model to facilitate online resource allocation decisions under different channel conditions. The simulation results explain the efficacy of the proposed method, which rapidly adapts to dynamic network states, thereby achieving a green resource allocation.

This work addresses resource allocation challenges in multi-cell wireless systems catering to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) users. We present a distributed learning framework tailored to O-RAN network architectures. Leveraging a Thompson sampling-based Deep Reinforcement Learning (DRL) algorithm, our approach provides real-time resource allocation decisions, aligning with evolving network structures. The proposed approach facilitates online decision-making for resource allocation by deploying trained execution agents at Near-Real Time Radio Access Network Intelligent Controllers (Near-RT RICs) located at network edges. Simulation results demonstrate the algorithm's effectiveness in meeting Quality of Service (QoS) requirements for both eMBB and URLLC users, offering insights into optimising resource utilisation in dynamic wireless environments.

We report the properties of molecular beam epitaxy deposited AlBN thin films on a recently developed epitaxial nitride metal electrode Nb2N. While a control AlN thin film exhibits standard capacitive behavior, distinct ferroelectric switching is observed in the AlBN films with increasing Boron mole fraction. The measured remnant polarization Pr of 15 uC/cm2 and coercive field Ec of 1.45 MV/cm in these films are smaller than those recently reported on films deposited by sputtering, due to incomplete wake-up, limited by current leakage. Because AlBN preserves the ultrawide energy bandgap of AlN compared to other nitride hi-K dielectrics and ferroelectrics, and it can be epitaxially integrated with GaN and AlN semiconductors, its development will enable several opportunities for unique electronic, photonic, and memory devices., Comment: DOI: 10.1063/5.0181217

We use path integral Monte Carlo to study the energetics of excitons in layered, hybrid organic-inorganic perovskites in order to elucidate the relative contributions of dielectric confinement and electron-phonon coupling. While the dielectric mismatch between polar perovskite layers and non-polar ligand layers significantly increases the exciton binding energy relative to their three dimensional bulk crystal counterparts, formation of exciton polarons attenuates this effect. Dielectric confinement is well described by a fractional dimension scaling law as a function of layer thickness. The contribution from polaron formation is found to be a non-monotonic function of the lead halide layer thickness, which is clarified by a general variational theory. Accounting for both of these effects provides a description of exciton binding energies in good agreement with experimental measurements. By studying isolated layers and stacked layered crystals of various thicknesses, with ligands of varying polarity, we provide a systematic understanding of the excitonic behavior of this class of materials and how to engineer their photophysics.

In this study, we explore the accelerated expansion of the universe within the framework of modified $f(Q)$ gravity. The investigation focus on the role of bulk viscosity in understanding the universe's accelerated expansion. Specifically, a bulk viscous matter-dominated cosmological model is considered, with the bulk viscosity coefficient expressed as $\zeta= \zeta_0 \rho H^{-1} + \zeta_1 H $. We consider the power law $f(Q)$ function $f(Q)=\alpha Q^n $, where $\alpha$ and $n$ are arbitrary constants and derive the analytical solutions for the field equations corresponding to a flat FLRW metric. Subsequently, we used the combined Cosmic Chronometers (CC)+Pantheon+SH0ES sample to estimate the free parameters of the obtained analytic solution. We conduct Bayesian statistical analysis to estimate the posterior probability by employing the likelihood function and the MCMC random sampling technique, along with the AIC and BIC statistical assessment criteria. In addition, we explore the evolutionary behavior of significant cosmological parameters. The effective equation of state (EOS) parameter predicts the accelerating behavior of the cosmic expansion phase. Further, by the statefinder and $Om(z)$ diagnostic test, we found that our viscous model favors quintessence-type behavior and can successfully describe the late-time scenario., Comment: General Relativity and Gravitation published version

In-memory computing (IMC) has gained significant attention recently as it attempts to reduce the impact of memory bottlenecks. Numerous schemes for digital IMC are presented in the literature, focusing on logic operations. Often, an application's description has data dependencies that must be resolved. Contemporary IMC architectures perform read followed by write operations for this purpose, which results in performance and energy penalties. To solve this fundamental problem, this paper presents in-memory mirroring (IMM). IMM eliminates the need for read and write-back steps, thus avoiding energy and performance penalties. Instead, we perform data movement within memory, involving row-wise and column-wise data transfers. Additionally, the IMM scheme enables parallel cloning of entire row (word) with a complexity of $\mathcal{O}(1)$. Moreover, our analysis of the energy consumption of the proposed technique using resistive random-access memory crossbar and experimentally validated JART VCM v1b model. The IMM increases energy efficiency and shows 2$\times$ performance improvement compared to conventional data movement methods., Comment: Accepted in IFIP/IEEE VLSI-SoC 2024

Terahertz (THz) magnonics represent the notion of mathematical algebraic operations of magnons such as addition and subtraction in THz regime which is an emergent dissipationless ultrafast alternative to existing data processing technologies. Spin waves on antiferromagnets with a twist in spin order host such magnons in THz regime, which possess advantage of higher processing speeds, additional polarization degree of freedom and longer propagation lengths compared to that of gigahertz magnons in ferromagnets. While interaction among THz magnons is the crux of algebra operations, it requires magnetic orders with closely spaced magnon modes for easier experimental realization of their interactions. Herein, rich wealth of magnons spanning a narrow energy range of 0.4 to 10 meV is unraveled in Co4Ta2O9 using magneto-THz spectroscopy. Rare multitude of ten excitation modes, either of magnons or hybrid magnon-phonon modes is presented. Among other attributes, spin lattice interaction suggests a correlation among spin and local lattice distortion, magnetostriction, and magnetic exchange interaction signifying a THz magnetoelectric effect. This unification of structural, magnetic and dielectric facets, and their magnetic field control in a narrow spectrum unwinds the mechanism underneath the system's complexity while the manifestation of multitude of spin excitation modes is a potential source to design multiple channels in spin-wave computing based devices.