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This study utilizes mathematical modeling to offer computational insights into biological heat transfer within elastic skin tissues. This heat transfer is driven by a moving heat source that relies on various relaxation times. Time delay parameters were incorporated based on these relaxation times, highlighting the importance of understanding heat transfer mechanisms and thermo-mechanical interactions to ensure the effectiveness of thermal treatment procedures. Given the significant impact of different variables on thermal relaxation times, their effects were analyzed on the distributions of displacement, thermal stress, and temperature in living tissues. By applying advanced mathematical techniques such as Laplace transforms, precise evaluations of these distributions were achieved. The results are presented graphically, with a focus on enhancing treatment outcomes by deepening our understanding of the behavior of living tissues in thermal environments., Comment: Some of the results contain errors due to mistakes in the equations

Physical insight into plasma evolution in the magnetohydrodynamic (MHD) limit can be revealed by decomposing the evolution according to the characteristic modes of the system. In this paper we explore aspects of the eigenenergy decomposition method (EEDM) introduced in an earlier study (Raboonik et al. 2024 , ApJ, 967:80). The EEDM provides an exact decomposition of nonlinear MHD disturbances into their component eigenenergies associated with the slow, Alfv\'en, and fast eigenmodes, together with two zero-frequency eigenmodes. Here we refine the EEDM by presenting globally analytical expressions for the eigenenergies. We also explore the nature of the zero-frequency ``pseudo-advective modes'' in detail. We show that in evolutions with pure advection of magnetic and thermal energy (without propagating waves) a part of the energy is carried by the pseudo advective modes. Exact expressions for the error terms associated with these modes--commonly encountered in numerical simulations--are also introduced. The new EEDM equations provide a robust tool for the exact and unique decomposition of nonlinear disturbances governed by homogeneous quasi-linear partial differential equations, even in the presence of local or global degeneracies.

Precision livestock farming (PLF) aims to improve the health and welfare of livestock animals and farming outcomes through the use of advanced technologies. Computer vision, combined with recent advances in machine learning and deep learning artificial intelligence approaches, offers a possible solution to the PLF ideal of 24/7 livestock monitoring that helps facilitate early detection of animal health and welfare issues. However, a significant number of livestock species are raised in large outdoor habitats that pose technological challenges for computer vision approaches. This review provides a comprehensive overview of computer vision methods and open challenges in outdoor animal monitoring. We include research from both the livestock and wildlife fields in the review because of the similarities in appearance, behaviour, and habitat for many livestock and wildlife. We focus on large terrestrial mammals, such as cattle, horses, deer, goats, sheep, koalas, giraffes, and elephants. We use an image processing pipeline to frame our discussion and highlight the current capabilities and open technical challenges at each stage of the pipeline. The review found a clear trend towards the use of deep learning approaches for animal detection, counting, and multi-species classification. We discuss in detail the applicability of current vision-based methods to PLF contexts and promising directions for future research., Comment: 28 pages, 5 figures, 2 tables

Existing preference optimization methods are mainly designed for directly learning from human feedback with the assumption that paired examples (preferred vs. dis-preferred) are available. In contrast, we propose a method that can leverage unpaired preferred or dis-preferred examples, and works even when only one type of feedback (positive or negative) is available. This flexibility allows us to apply it in scenarios with varying forms of feedback and models, including training generative language models based on human feedback as well as training policies for sequential decision-making problems, where learned (value) functions are available. Our approach builds upon the probabilistic framework introduced in (Dayan and Hinton, 1997), which proposes to use expectation-maximization (EM) to directly optimize the probability of preferred outcomes (as opposed to classic expected reward maximization). To obtain a practical algorithm, we identify and address a key limitation in current EM-based methods: when applied to preference optimization, they solely maximize the likelihood of preferred examples, while neglecting dis-preferred samples. We show how one can extend EM algorithms to explicitly incorporate dis-preferred outcomes, leading to a novel, theoretically grounded, preference optimization algorithm that offers an intuitive and versatile way to learn from both positive and negative feedback.

We present a new simulator of Uncrewed Aerial Vehicles (UAVs) that is tailored to the needs of testing cyber-physical security attacks and defenses. Recent investigations into UAV safety have unveiled various attack surfaces and some defense mechanisms. However, due to escalating regulations imposed by aviation authorities on security research on real UAVs, and the substantial costs associated with hardware test-bed configurations, there arises a necessity for a simulator capable of substituting for hardware experiments, and/or narrowing down their scope to the strictly necessary. The study of different attack mechanisms requires specific features in a simulator. We propose a simulation framework based on ROS2, leveraging some of its key advantages, including modularity, replicability, customization, and the utilization of open-source tools such as Gazebo. Our framework has a built-in motion planner, controller, communication models and attack models. We share examples of research use cases that our framework can enable, demonstrating its utility.

We consider a cell-free massive multiple-input multiple-output (CF-mMIMO) system with joint unicast and multi-group multicast transmissions. We derive exact closed-form expressions for the downlink achievable spectral efficiency (SE) of both unicast and multicast users. Based on these expressions, we formulate a joint optimization problem of access point (AP) selection and power control subject to quality of service (QoS) requirements of all unicast and multicast users and per-AP maximum transmit power constraint. The challenging formulated problem is transformed into a tractable form and a novel accelerated projected gradient (APG)-based algorithm is developed to solve the optimization problem. Simulation results show that our joint optimization strategy enhances notably the sum SE (SSE) (up to 58%) compared to baseline schemes, while maintaining low complexity.

This paper addresses the challenge of classifying and assigning programming tasks to experts, a process that typically requires significant effort, time, and cost. To tackle this issue, a novel dataset containing a total of 4,112 programming tasks was created by extracting tasks from various websites. Web scraping techniques were employed to collect this dataset of programming problems systematically. Specific HTML tags were tracked to extract key elements of each issue, including the title, problem description, input-output, examples, problem class, and complexity score. Examples from the dataset are provided in the appendix to illustrate the variety and complexity of tasks included. The dataset's effectiveness has been evaluated and benchmarked using two approaches; the first approach involved fine-tuning the FLAN-T5 small model on the dataset, while the second approach used in-context learning (ICL) with the GPT-4o mini. The performance was assessed using standard metrics: accuracy, recall, precision, and F1-score. The results indicated that in-context learning with GPT-4o-mini outperformed the FLAN-T5 model., Comment: This papaer has been accepted to The 3nd International conference on Machine Learning and Data Engineering (ICMLDE 2024)

The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a diverse range of compact objects, from the most massive black holes at the farthest reaches of the Universe to the lightest white dwarfs in our cosmic backyard, generate a complex and dynamic symphony of gravitational wave signals. Once recorded by gravitational wave detectors, these unique fingerprints have the potential to decipher the birth and growth of cosmic structures over a wide range of scales, from stellar binaries and stellar clusters to galaxies and large-scale structures. The TianQin space-borne gravitational wave mission is scheduled for launch in the 2030s, with an operational lifespan of five years. It will facilitate pivotal insights into the history of our universe. This document presents a concise overview of the detectable sources of TianQin, outlining their characteristics, the challenges they present, and the expected impact of the TianQin observatory on our understanding of them., Comment: TianQin Gravitational Wave Whitepaper, 72 pages, 30 figures

Since the discovery of wireless telegraphy, wireless communication via electromagnetic (EM) signals has become a standard solution to meet the growing demand for information transfer in modern society. To prevent counterfeiting and manipulation by unauthorized individuals and agencies, it is crucial to innovate and enhance security through information encryption. In this paper, we introduce a metasurface that controls amplitude modulation at two different frequencies. Here, focusing on amplitude-frequency modulation for both x- and y- polarizations, we present an encrypted wireless communication protocol that using the chaos algorithm to secure the target data and prevent eavesdroppers from accessing it. The encrypted data is transmitted through the varying amplitudes at two different frequencies for both linear polarizations, achieved using two distinct graphene layers individually controlled by external biasing conditions. The extensive freedom enabled by simultaneous modulation of amplitude, frequency, and polarization allows information to be transmitted across diverse channels, thereby enhancing the security of encrypted information. The simulations demonstrate that encoding data, such as images, and transmitting it through amplitude-frequency modulation for both horizontal and vertical polarizations offer promising opportunities for various applications, including THz communications, anti-counterfeiting, THz data storage, and THz data transmission.

Federated Learning (FL) has emerged as a privacy-preserving method for training machine learning models in a distributed manner on edge devices. However, on-device models face inherent computational power and memory limitations, potentially resulting in constrained gradient updates. As the model's size increases, the frequency of gradient updates on edge devices decreases, ultimately leading to suboptimal training outcomes during any particular FL round. This limits the feasibility of deploying advanced and large-scale models on edge devices, hindering the potential for performance enhancements. To address this issue, we propose FedRepOpt, a gradient re-parameterized optimizer for FL. The gradient re-parameterized method allows training a simple local model with a similar performance as a complex model by modifying the optimizer's gradients according to a set of model-specific hyperparameters obtained from the complex models. In this work, we focus on VGG-style and Ghost-style models in the FL environment. Extensive experiments demonstrate that models using FedRepOpt obtain a significant boost in performance of 16.7% and 11.4% compared to the RepGhost-style and RepVGG-style networks, while also demonstrating a faster convergence time of 11.7% and 57.4% compared to their complex structure., Comment: Accepted by ACCV 2024

With the advancement of generative artificial intelligence (GenAI) models, their capability to generate content is seeing significant enhancement, leading to widespread applications in the field of data generation and forecasting. Furthermore, GenAI has strong capabilities in data modeling and analysis, which enhances Internet of electric vehicles (IoEV) applications in various aspects. In this paper, we investigate and survey applications of GenAI in the IoEV. Specifically, we categorize GenAI for IoEV into four different layers namely, EV's battery layer, individual electric vehicle (EV) layer, smart grid with EV layer, and security layer. We first introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques., Comment: 25 Pages

Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains. In this work, we explore the potential and limitations of using graph-based synthetic reasoning data as training signals to enhance LLMs' reasoning capabilities. Our extensive experiments, conducted on two established natural language reasoning tasks -- inductive reasoning and spatial reasoning -- demonstrate that supervised fine-tuning (SFT) with synthetic graph-based reasoning data effectively enhances LLMs' reasoning performance without compromising their effectiveness on other standard evaluation benchmarks.

This paper presents a comprehensive evaluation of Urdu Automatic Speech Recognition (ASR) models. We analyze the performance of three ASR model families: Whisper, MMS, and Seamless-M4T using Word Error Rate (WER), along with a detailed examination of the most frequent wrong words and error types including insertions, deletions, and substitutions. Our analysis is conducted using two types of datasets, read speech and conversational speech. Notably, we present the first conversational speech dataset designed for benchmarking Urdu ASR models. We find that seamless-large outperforms other ASR models on the read speech dataset, while whisper-large performs best on the conversational speech dataset. Furthermore, this evaluation highlights the complexities of assessing ASR models for low-resource languages like Urdu using quantitative metrics alone and emphasizes the need for a robust Urdu text normalization system. Our findings contribute valuable insights for developing robust ASR systems for low-resource languages like Urdu.

The thermoelectric characteristics of lead selenium (PbSe) doped with gallium (Ga) are investigated in this study. When the lead sulfide (PbSe) is tuned with appropriate dopants, it exhibits satisfactory ZT values, hence making it a promising thermoelectric material. This study examines the electrical conductivity, Seebeck coefficient, thermal conductivity, and power factor of PbSe, with varying amounts of added Ga. Results indicate that incorporating Ga into PbSe improves its thermoelectric performance, with a maximum ZT value of approximately 1.2 at 873 K for the optimal doping concentration of 0.005 atomic percent. This improvement is attributed to the combined effects of increased electrical conductivity and reduced thermal conductivity. These findings suggest that Ga-doped PbSe is a promising candidate for mid-temperature thermoelectric applications., Comment: 8

The retrieval augmented generation (RAG) system such as Retro has been shown to improve language modeling capabilities and reduce toxicity and hallucinations by retrieving from a database of non-parametric memory containing trillions of entries. We introduce Retro-li that shows retrieval can also help using a small-scale database, but it demands more accurate and better neighbors when searching in a smaller hence sparser non-parametric memory. This can be met by using a proper semantic similarity search. We further propose adding a regularization to the non-parametric memory for the first time: it significantly reduces perplexity when the neighbor search operations are noisy during inference, and it improves generalization when a domain shift occurs. We also show that Retro-li's non-parametric memory can potentially be implemented on analog in-memory computing hardware, exhibiting O(1) search time while causing noise in retrieving neighbors, with minimal (<1%) performance loss. Our code is available at: https://github.com/IBM/Retrieval-Enhanced-Transformer-Little., Comment: Published as a conference paper at European Conference on Artificial Intelligence 2024

Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99\% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol's integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication.

Cohen et al. conjectured that for every oriented cycle $C$ there exist an integer $f(C)$ such that every strong $f(C)$-chromatic digraph contains a subdivision of $C$. El Joubbeh confirmed this conjecture for Hamiltonian digraphs. Indeed, he showed that every $3n$-chromatic Hamiltonian digraph contains a subdivision of every oriented cycle of order $n$. In this article, we improve this bound to $2n$. Furthermore, we show that, if $D$ is a digraph containing a Hamiltonian directed path with chromatic number at least $12n-5$, then $D$ contains a subdivision of every oriented cycle of order $n$. Note that a digraph containing a Hamiltonian directed path need not be strongly connected. Thus, our current result provides a deeper understanding of the condition that may be needed to fully solve the conjecture.

Lead-halide perovskite nanocrystals have recently emerged as desirable optical materials for applications such as coherent quantum light emitters and solid-state laser cooling due to their short radiative lifetime and near-unity photoluminescence quantum yield. Here, we investigate the effect of CsPbBr3 nanocrystal size on the radiative lifetime under ambient conditions. High-quality nanocrystals, with monoexponential time-resolved photoluminescence decay behaviors, unveil a non-monotonic trend in radiative lifetime. This non-monotonicity appears to reflect a behavior common among II-VI (CdSe) and perovskites semiconducting nanocrystals. We find that large nanocrystals in the weak quantum confinement regime exhibit long radiative lifetimes due to a thermally accessible population of dim states. Small nanocrystals within the strong quantum confinement regime, surprisingly, also show long radiative lifetimes, due however to a substantial reduction in oscillator strength. Nanocrystals in the intermediate quantum confinement regime displays the shortest radiative lifetime, as their oscillator strength is enhanced relative to particles in the strong confinement regime, but do not have sufficient low-lying dim states like the large particles to counteract this affect. These findings shed light on the impact of nanocrystal size on radiative lifetime and pave the way for tailored optical materials in various optical applications.

We propose an agent architecture that automates parts of the common reinforcement learning experiment workflow, to enable automated mastery of control domains for embodied agents. To do so, it leverages a VLM to perform some of the capabilities normally required of a human experimenter, including the monitoring and analysis of experiment progress, the proposition of new tasks based on past successes and failures of the agent, decomposing tasks into a sequence of subtasks (skills), and retrieval of the skill to execute - enabling our system to build automated curricula for learning. We believe this is one of the first proposals for a system that leverages a VLM throughout the full experiment cycle of reinforcement learning. We provide a first prototype of this system, and examine the feasibility of current models and techniques for the desired level of automation. For this, we use a standard Gemini model, without additional fine-tuning, to provide a curriculum of skills to a language-conditioned Actor-Critic algorithm, in order to steer data collection so as to aid learning new skills. Data collected in this way is shown to be useful for learning and iteratively improving control policies in a robotics domain. Additional examination of the ability of the system to build a growing library of skills, and to judge the progress of the training of those skills, also shows promising results, suggesting that the proposed architecture provides a potential recipe for fully automated mastery of tasks and domains for embodied agents.

In this paper, we present an improved version of the digital signature scheme proposed by Sharafi and Daghigh based on Module-LWE and Module-SIS problems. Our proposed signature scheme has a notably higher security level and smaller decoding failure probability, than the ones in the Sharaf-Daghigh scheme, at the expense of enlarging the module of the underlying basic ring.

We clarify the supersymmetry structure of ADHM instanton linear sigma models, $N=4$ superconformal algebras and superconformal field theories dual to superstrings moving in $AdS_3$ backgrounds in view of some of our recent findings in various investigations. First of all we collect information about the (0, 4) supersymmetries of Witten's original, Ali-Ilahi's complementary and Ali-Salih's complete ADHM instanton linear sigma models. Then we collect information about large, small and middle $N=4$ superconformal algebras and summarize their interrelations with the ADHM instanton linear sigma models. We then argue that the former, that is the supersymmetry of the ADHM instanton linear sigma models, flows to latter, that is the small, middle and large $N=4$ superconformal symmetries, in the infrared limit. Finally we summarize the mapping of these $N=4$ superconformal symmetries onto the superconformal symmetries dual to superstrings moving on $AdS_3\times S^3\times {\mathcal M}^4$ backgrounds with ${\mathcal M}^=K3, T^4$ and $S^3\times S^1$., Comment: LaTeX, 29 Pages

We investigate the phenomenon of magnomechanically induced grating (MMIG) within a cavity magnomechanical system, comprising magnons (spins in a ferromagnet, such as yttrium iron garnet), cavity microwave photons, and phonons [\textit{J. Li, S.-Y. Zhu, and G. S. Agarwal, Phys. Rev. Lett. \textbf{121}, 203601 (2018)}]. By applying an external standing wave control, we observe modifications in the transmission profile of a probe light beam, signifying the presence of MMIG. Through numerical analysis, we explore the diffraction intensities of the probe field, examining the impact of interactions between cavity magnons, magnon-phonon interactions, standing wave field strength, and interaction length. MMIG systems leverage the unique properties of magnons, and collective spin excitations with attributes like long coherence times and spin-wave propagation. These distinctive features can be harnessed in MMIG systems for innovative applications in information storage, retrieval, and quantum memories, offering various orders of diffraction grating., Comment: 12 pages, 8 figures

Deep neural networks (DNNs) have made significant strides in tackling challenging tasks in wireless systems, especially when an accurate wireless model is not available. However, when available data is limited, traditional DNNs often yield subpar results due to underfitting. At the same time, large language models (LLMs) exemplified by GPT-3, have remarkably showcased their capabilities across a broad range of natural language processing tasks. But whether and how LLMs can benefit challenging non-language tasks in wireless systems is unexplored. In this work, we propose to leverage the in-context learning ability (a.k.a. prompting) of LLMs to solve wireless tasks in the low data regime without any training or fine-tuning, unlike DNNs which require training. We further demonstrate that the performance of LLMs varies significantly when employed with different prompt templates. To solve this issue, we employ the latest LLM calibration methods. Our results reveal that using LLMs via ICL methods generally outperforms traditional DNNs on the symbol demodulation task and yields highly confident predictions when coupled with calibration techniques., Comment: Accepted at IEEE GLOBECOM 2024

In this article, we investigate the K\"ahler immersions of special Asymptotically Locally Euclidean (ALE) K\"ahler metrics into complex space forms. We provide a relation between K\"ahler immersions problem of these metrics and the sign of their mass. Our result shows that these metrics with positive mass do not admit a K\"ahler immersion into complex Euclidean space. Moreover, we also obtain that these metrics with any sign of mass cannot be K\"ahler immersed into complex hyperbolic space., Comment: arXiv admin note: text overlap with arXiv:1712.04298 by other authors

District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH network topology and allow for local anomaly detection without disclosing sensitive information, such as substation locations. The approach leverages a multi-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial neighborhood creation. Moreover, it introduces a merging technique that reduces noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD) and modified z-scores to flag anomalous substations. The results reveal that SHEDAD outperforms traditional clustering methods, achieving significantly lower intra-cluster variance and distance. Additionally, SHEDAD effectively isolates and identifies two distinct categories of anomalies: supply temperatures and substation performance. We identified 30 anomalous substations and reached a sensitivity of approximately 65\% and specificity of approximately 97\%. By focusing on this subset of poor-performing substations in the network, SHEDAD enables more targeted and effective maintenance interventions, which can reduce energy usage while optimizing network performance., Comment: 12 pages, 5 figures, FMEC2024

This study explores heat and turbulent modulation in three-dimensional multiphase Rayleigh-B\'enard convection using direct numerical simulations. Two immiscible fluids with identical reference density undergo systematic variations in dispersed-phase volume fractions, $0.0 \leq \Upphi \leq 0.5$, and ratios of dynamic viscosity, $\lambda_{\mu}$, and thermal diffusivity, $\lambda_{\alpha}$, within the range $[0.1-10]$. The Rayleigh, Prandtl, Weber, and Froude numbers are held constant at $10^8$, $4$, $6000$, and $1$, respectively. Initially, when both fluids share the same properties, a 10\% Nusselt number increase is observed at the highest volume fractions. In this case, despite a reduction in turbulent kinetic energy, droplets enhance energy transfer to smaller scales, smaller than those of single-phase flow, promoting local mixing. By varying viscosity ratios, while maintaining a constant Rayleigh number based on the average mixture properties, the global heat transfer rises by approximately 25\% at $\Upphi=0.2$ and $\lambda_{\mu}=10$. This is attributed to increased small-scale mixing and turbulence in the less viscous carrier phase. In addition, a dispersed phase with higher thermal diffusivity results in a 50\% reduction in the Nusselt number compared to the single-phase counterpart, owing to faster heat conduction and reduced droplet presence near walls. The study also addresses droplet-size distributions, confirming two distinct ranges dominated by coalescence and breakup with different scaling laws., Comment: Accepted on the Journal of Fluid Mechanics

We propose a scheme aimed at achieving spatio-spectral control over spontaneous emission within a four-level atom-light coupling system interacting with optical vortices carrying orbital angular momentum (OAM). The atom comprises a ground level and two excited states coupled with two laser fields, forming a V subsystem where the upper states exclusively decay to a common fourth state via two channels. By investigating various initial states of the atom and considering the presence or absence of quantum interference in spontaneous emission channels, we analyze how the characteristics of the OAM-carrying vortex beam imprint onto the emission spectrum. The interplay between the optical vortex and the quantum system, including its environment modes, induces a wide variety of spatio-spectral behaviour, including two-dimensional spectral-peak narrowing, spectralpeak enhancement, spectral-peak suppression, and spontaneous emission reduction or quenching in the spatial azimuthal plane. Our findings shed light on the dynamics of atom-vortex beam light interactions and offer insights into the manipulation of emission properties at the quantum level.

Marginal emissions rates -- the sensitivity of carbon emissions to electricity demand -- are important for evaluating the impact of emissions mitigation measures. Like locational marginal prices, locational marginal emissions rates (LMEs) can vary geographically, even between nearby locations, and may be coupled across time periods because of, for example, storage and ramping constraints. This temporal coupling makes computing LMEs computationally expensive for large electricity networks with high storage and renewable penetrations. Recent work demonstrates that decentralized algorithms can mitigate this problem by decoupling timesteps during differentiation. Unfortunately, we show these potential speedups are negated by the sparse structure inherent in power systems problems. We address these limitations by introducing a parallel, reverse-mode decentralized differentiation scheme that never explicitly instantiates the solution map Jacobian. We show both theoretically and empirically that parallelization is necessary to achieve non-trivial speedups when computing grid emissions sensitivities. Numerical results on a 500 node system indicate that our method can achieve greater than 10x speedups over centralized and serial decentralized approaches.

We prove two conjectures proposed by Chabert and Halberstadt concerning P\'olya groups of $S_4$-fields and $D_4$-fields. More generally, the latter will be proved for $D_n$-fields with $n \geq 4$ an even integer. Further, generalizing a result of Zantema, we also prove that the pre-P\'olya group of a non-Galois field of a prime degree, e.g. an $A_5$-field, coincides with its P\'olya group.

In the Internet of Things (IoT) networks, the Routing Protocol forLow-power and Lossy Networks (RPL) is a widely adopted standard due toits efficiency in managing resource-constrained and energy-limited nodes.However, persistent challenges such as high energy consumption, unstablelinks, and suboptimal routing continue to hinder network performance,affecting both the longevity of the network and the reliability of datatransmission. This paper proposes an enhanced RPL routing mechanismby integrating the Tabu Search optimization algorithm to address theseissues. The proposed approach focuses on optimizing the parent and childselection process in the RPL protocol, leveraging a composite cost func-tion that incorporates key parameters including Residual Energy, Trans-mission Energy, Distance to Sink, Hop Count, Expected TransmissionCount (ETX), and Link Stability Rate. Through extensive simulations,we demonstrate that our method significantly improves link stability, re-duces energy consumption, and enhances the packet delivery ratio, leadingto a more efficient and longer-lasting IoT network. The findings suggestthat Tabu Search can effectively balance the trade-offs inherent in IoTrouting, providing a practical solution for improving the overall perfor-mance of RPL-based networks., Comment: 20 Pages

We report the first experimental determination of the interspecies $s$-wave scattering length\,($a_{34}$) between the $2\,^3S_1\,(F=3/2,m_F=3/2)$ state of $^3$He$^*$ and the $2\,^3S_1\,(m_J=1)$ state of $^4$He$^*$. We determine $a_{34}$ by inducing oscillations in a trapped Bose-Einstein condensate of $^4$He$^*$ and measuring the damping rate of these oscillations due to the presence of $^3$He$^*$ atoms. The deduced value of $a_{34}=29\pm3$\,nm is in good agreement with theoretical predictions. The knowledge of this scattering length is important for many fundamental experiments between these helium isotopes.

Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI from reaching widespread adoption in routine clinical and research exams. Accelerated parameter mapping techniques leverage parallel imaging, signal modelling and deep learning to offer more practical quantitative MRI acquisitions. However, the achievable acceleration and the quality of maps are often limited. Joint MAPLE is a recent state-of-the-art multi-parametric and scan-specific parameter mapping technique with promising performance at high acceleration rates. It synergistically combines parallel imaging, model-based and machine learning approaches for joint mapping of T1, T2*, proton density and the field inhomogeneity. However, Joint MAPLE suffers from prohibitively long reconstruction time to estimate the maps from a multi-echo, multi-flip angle (MEMFA) dataset at high resolution in a scan-specific manner. In this work, we propose a faster version of Joint MAPLE which retains the mapping performance of the original version. Coil compression, random slice selection, parameter-specific learning rates and transfer learning are synergistically combined in the proposed framework. It speeds-up the reconstruction time up to 700 times than the original version and processes a whole-brain MEMFA dataset in 21 minutes on average, which originally requires ~260 hours for Joint MAPLE. The mapping performance of the proposed framework is ~2-fold better than the standard and the state-of-the-art evaluated reconstruction techniques on average in terms of the root mean squared error.

Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing manipulation planners are restricted to specific systems and often depend on specialized algorithms for using demonstration. Therefore, we introduce a flexible motion planner tailored to dexterous and whole-body manipulation tasks. Our planner creates readily usable demonstrations for reinforcement learning algorithms, eliminating the need for additional training pipeline complexities. With this approach, we can efficiently learn policies for complex manipulation tasks, where traditional reinforcement learning alone only makes little progress. Furthermore, we demonstrate that learned policies are transferable to real robotic systems for solving complex dexterous manipulation tasks.

We construct various novel and elementary examples of dynamics with metric attractors that have intermingled basins. A main ingredient is the introduction of random walks along orbits of a given dynamical system. We develop theory for it and use it in particular to provide examples of thick metric attractors with intermingled basins., Comment: 24 pages

When designing agents for operation in uncertain environments, designers need tools to automatically reason about what agents ought to do, how that conflicts with what is actually happening, and how a policy might be modified to remove the conflict. These obligations include ethical and social obligations, permissions and prohibitions, which constrain how the agent achieves its mission and executes its policy. We propose a new deontic logic, Expected Act Utilitarian deontic logic, for enabling this reasoning at design time: for specifying and verifying the agent's strategic obligations, then modifying its policy from a reference policy to meet those obligations. Unlike approaches that work at the reward level, working at the logical level increases the transparency of the trade-offs. We introduce two algorithms: one for model-checking whether an RL agent has the right strategic obligations, and one for modifying a reference decision policy to make it meet obligations expressed in our logic. We illustrate our algorithms on DAC-MDPs which accurately abstract neural decision policies, and on toy gridworld environments.

Integrated super-resolution sensing and communication (ISSAC) has emerged as a promising technology to achieve extremely high precision sensing for those key parameters, such as the angles of the sensing targets. In this paper, we propose an efficient channel estimation scheme enabled by ISSAC for millimeter wave (mmWave) and TeraHertz (THz) systems with a hybrid analog/digital beamforming architecture, where both the pilot overhead and the cost of radio frequency (RF) chains are significantly reduced. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) can estimate channel parameters accurately without requiring dedicate a priori known pilots. In particular, the proposed method consists of two stages. First, the angles of the multi-path channel components are estimated in a pilot-free manner during the transmission of data symbols. Second, the multi-path channel coefficients are estimated with very few pilots. Compared to conventional channel estimation schemes that rely solely on channel training, our approach requires the estimation of much fewer parameters in the second stage. Furthermore, with channel multi-path angles obtained, the beamforming gain can be achieved when pilots are sent to estimate the channel path gains. To comprehensively investigate the performance of the proposed scheme, we consider both the basic line-of-sight (LoS) channels and more general multi-path channels. We compare the performance of the minimum mean square error (MMSE) of channel estimation and the resulting beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training. It is demonstrated that our proposed method significantly outperforms the benchmarking scheme. Simulation results are presented to validate our theoretical findings., Comment: 13 pages, 8 figures

Accurate segmentation of the Left Ventricle (LV) holds substantial importance due to its implications in disease detection, regional analysis, and the development of complex models for cardiac surgical planning. CMR is a golden standard for diagnosis of serveral cardiac diseases. LV in CMR comprises of three distinct sections: Basal, Mid-Ventricle, and Apical. This research focuses on the precise segmentation of the LV from Cardiac MRI (CMR) scans, joining with the capabilities of Machine Learning (ML). The central challenge in this research revolves around the absence of a set of parameters applicable to all three types of LV slices. Parameters optimized for basal slices often fall short when applied to mid-ventricular and apical slices, and vice versa. To handle this issue, a new method is proposed to enhance LV segmentation. The proposed method involves using distinct sets of parameters for each type of slice, resulting in a two-phase segmentation approach. The initial phase categorizes images into three groups based on the type of LV slice, while the second phase aims to segment CMR images using parameters derived from the preceding phase. A publicly available dataset (Automated Cardiac Diagnosis Challenge (ACDC)) is used. 10-Fold Cross Validation is used and it achieved a mean score of 0.9228. Comprehensive testing indicates that the best parameter set for a particular type of slice does not perform adequately for the other slice types. All results show that the proposed approach fills a critical void in parameter standardization through a two-phase segmentation model for the LV, aiming to not only improve the accuracy of cardiac image analysis but also contribute advancements to the field of LV segmentation.

Analysis of matrix-variate data is becoming increasingly common in the literature, particularly in the field of clustering and classification. It is well-known that real data, including real matrix-variate data, often exhibit high levels of asymmetry. To address this issue, one common approach is to introduce a tail or skewness parameter to a symmetric distribution. In this regard, we introduced here a new distribution called the matrix-variate skew t distribution (MVST), which provides flexibility in terms of heavy tail and skewness. We then conduct a thorough investigation of various characterizations and probabilistic properties of the MVST distribution. We also explore extensions of this distribution to a finite mixture model. To estimate the parameters of the MVST distribution, we develop an efficient EM-type algorithm that computes maximum likelihood (ML) estimates of the model parameters. To validate the effectiveness and usefulness of the developed models and associated methods, we perform empirical experiments using simulated data as well as three real data examples. Our results demonstrate the efficacy of the developed approach in handling asymmetric matrix-variate data.

We propose a variational inference approach to sample from the posterior distribution for solving inverse problems. From a pre-trained diffusion model, our approach trains a conditional flow model to minimize the divergence between the proposal variational distribution and the posterior distribution implicitly defined through the diffusion model. Once trained, the flow model is capable of sampling from the posterior distribution with a single NFE, amortized with respect to the measurement. The proposed method paves a new path for distilling a diffusion prior for efficient posterior sampling. We show that our method is applicable to standard signals in Euclidean space, as well as signals on manifold.

In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12\% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area., Comment: Accepted to the 6th NLP for Conversational AI Workshop at ACL 2024(NLP4ConvAI)

Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model Predictive Control (MPC) capable of handling temperature constraints and heating-power saturation while delivering enhanced precision, overshoot control, and settling times compared to state-of-the-art methods. We employ a Non-linear Auto-Regressive with Exogenous inputs (NARX) model to define a linear control-oriented model at each operating point. Using a high-fidelity simulator, several simulation studies have been conducted to evaluate the proposed method's robustness and performance under parametric uncertainty, indicating overshoot and average steady-state error less than $2^\circ \mathrm{C}$ and $0.7^\circ \mathrm{C}$ ($7^\circ \mathrm{C}$ and $2^\circ \mathrm{C}$) for the nominal (worst-case) scenario. Finally, we applied the proposed method to a lab-scale thermoforming platform, resulting in a close response to the simulation analysis with overshoot and average steady-state error metrics less than $5.3^\circ \mathrm{C}$ and $1^\circ \mathrm{C}$, respectively.

Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves., Comment: 19 pages, 3 figures

First time in six decades, uncountable infinite exact solutions of FitzHugh-Nagumo model with diffusion have been found. FitzHugh-Nagumo model is a nonlinear dynamical system applicable to neurosciences, chemical kinetics, cell division, population dynamics, electronics, epidemiology, cardiac physiology and pattern formation. Non-classical symmetry analysis has been carried out to find invariant solutions. In many ways this finding makes the corpus of asymptotics and numerics obsolete. Non singular, physically stable and meaningful solutions could now be found without distorting the actual model or introducing forced conditions.