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With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset., Comment: IECON 2024-50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, U.S.A, 2024, pp. 1-6

Let $(x_n)_{n\geq0}$ be a linear recurrence sequence of order $k\geq2$ satisfying $$x_n=a_1x_{n-1}+a_2x_{n-2}+\dots+a_kx_{n-k}$$ for all integers $n\geq k$, where $a_1,\dots,a_k,x_0,\dots, x_{k-1}\in \mathbb{Z},$ with $a_k\neq0$. In 2017, Sanna posed an open question to classify primes $p$ for which the quotient set of $(x_n)_{n\geq0}$ is dense in $\mathbb{Q}_p$. In a recent paper, we showed that if the characteristic polynomial of the recurrence sequence has a root $\pm \alpha$, where $\alpha$ is a Pisot number and if $p$ is a prime such that the characteristic polynomial of the recurrence sequence is irreducible in $\mathbb{Q}_p$, then the quotient set of $(x_n)_{n\geq 0}$ is dense in $\mathbb{Q}_p$. In this article, we answer the problem for certain linear recurrence sequences whose characteristic polynomials are reducible over $\mathbb{Q}$., Comment: To appear at Canadian Mathematical Bulletin. arXiv admin note: text overlap with arXiv:2207.07084

We demonstrate that electroactive thin films incorporating semiconducting polymers and deterministic functionalized gold nanoclusters (ncAu25) lead to integration of the functions of resistive memory device and field-effect transistor (FETs) within a single component (mem-transistor) in a neuromorphic system. Memristor functions originate from ferrocenyl-modified gold nanoclusters (ncAu25-Fc) embedded in polymethyl-methacrylate (PMMA) and devices optimized for maximum 1/0 'flash' memory effect are found to contain 15 wt% ncAu25-Fc. Integrated memristor and neuromorphic functions are obtained by replacing PMMA with poly(3-hexylthiophene) (P3HT) in the active layer, from which transistor effects are derived. Based on the energy band diagrams of ncAu25, PMMA and P3HT, percolation theory is used to explain the memristor 1/0 on/off ratio as a function of ncAu25-Fc concentration. The use of ncAu25-Fc with charge-tunable, ferrocene-modified, ligands is critical toward better cluster-polymer interfaces. Our work shows that nanostructures of polymers and metalorganic frameworks bear strong potential towards neuromorphic devices and the circuital simplification of data storage technology., Comment: The following article has been accepted in Applied Physics Letters. After it is published, it will be found at https://pubs.aip.org/aip/apl with DOI 10.1063/5.0219013 Copyright (2024) D. Singh, P.N. Gunawardene, M.S. Workentin & G. Fanchini. This article is distributed under a CC BY-NC-ND License. https://creativecommons.org/licenses/by-nc-nd/4.0. 15 pages, 5 figures

Wide-Area Linear Optical Polarimeter (WALOP)-South is the first wide-field and survey-capacity polarimeter in the optical wavelengths. On schedule for commissioning in 2024, it will be mounted on the 1 m SAAO telescope in Sutherland Observatory, South Africa to undertake the PASIPHAE sky survey. PASIPHAE program will create the first polarimetric sky map in the optical wavelengths, spanning more than 2000 square degrees of the southern Galactic region. The innovative design of WALOP-South will enable it to measure the linear polarization (Stokes parameters $q$ and $u$), in a single exposure, of all sources in a field of view (FoV) of $35\times35$ arcminutes-squared in the SDSS-r broadband and narrowband filters between 500-750 nm with 0.1 % polarization accuracy. The unique goals of the instrument place very stringent systems engineering goals, including on the performance of the optical, polarimetric, optomechanical, and electronic subsystems. All the subsystems have been designed carefully to meet the overall instrument performance goals. As of May 2024, all the instrument optical and mechanical subsystems have been assembled and are currently getting tested and integrated. The complete testing and characterization of the instrument in the lab is expected to be completed by August 2024. In this paper, we will present (a) the design and development of the entire instrument and its major subsystems, focusing on the opto-mechanical design which has not been reported before, and (b) assembly and integration of the instrument in the lab and early results from lab characterization of the instrument., Comment: 20 pages; presented at 2024 SPIE Astronomical Telescopes + Instrumentation conference

There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis. However, it is difficult to manually generate these annotations, due to the time-consuming nature, and the variability in clinical conventions. Artificial intelligence has been adopted in the field as a potential method to annotate these large datasets, however, a lack of expert annotations or ground truth can inhibit the adoption of these annotations. We recently made a dataset publicly available including annotations and extracted features of up to 104 organs for the National Lung Screening Trial using the TotalSegmentator method. However, the released dataset does not include expert-derived annotations or an assessment of the accuracy of the segmentations, limiting its usefulness. We propose the development of heuristics to assess the quality of the segmentations, providing methods to measure the consistency of the annotations and a comparison of results to the literature. We make our code and related materials publicly available at https://github.com/ImagingDataCommons/CloudSegmentatorResults and interactive tools at https://huggingface.co/spaces/ImagingDataCommons/CloudSegmentatorResults.

The study of effective connectivity (EC) is essential in understanding how the brain integrates and responds to various sensory inputs. Model-driven estimation of EC is a powerful approach that requires estimating global and local parameters of a generative model of neural activity. Insights gathered through this process can be used in various applications, such as studying neurodevelopmental disorders. However, accurately determining EC through generative models remains a significant challenge due to the complexity of brain dynamics and the inherent noise in neural recordings, e.g., in electroencephalography (EEG). Current model-driven methods to study EC are computationally complex and cannot scale to all brain regions as required by whole-brain analyses. To facilitate EC assessment, an inference algorithm must exhibit reliable prediction of parameters in the presence of noise. Further, the relationship between the model parameters and the neural recordings must be learnable. To progress toward these objectives, we benchmarked the performance of a Bi-LSTM model for parameter inference from the Jansen-Rit neural mass model (JR-NMM) simulated EEG under various noise conditions. Additionally, our study explores how the JR-NMM reacts to changes in key biological parameters (i.e., sensitivity analysis) like synaptic gains and time constants, a crucial step in understanding the connection between neural mechanisms and observed brain activity. Our results indicate that we can predict the local JR-NMM parameters from EEG, supporting the feasibility of our deep-learning-based inference approach. In future work, we plan to extend this framework to estimate local and global parameters from real EEG in clinically relevant applications., Comment: Work in progress

Automatic citation generation for sentences in a document or report is paramount for intelligence analysts, cybersecurity, news agencies, and education personnel. In this research, we investigate whether large language models (LLMs) are capable of generating references based on two forms of sentence queries: (a) Direct Queries, LLMs are asked to provide author names of the given research article, and (b) Indirect Queries, LLMs are asked to provide the title of a mentioned article when given a sentence from a different article. To demonstrate where LLM stands in this task, we introduce a large dataset called REASONS comprising abstracts of the 12 most popular domains of scientific research on arXiv. From around 20K research articles, we make the following deductions on public and proprietary LLMs: (a) State-of-the-art, often called anthropomorphic GPT-4 and GPT-3.5, suffers from high pass percentage (PP) to minimize the hallucination rate (HR). When tested with Perplexity.ai (7B), they unexpectedly made more errors; (b) Augmenting relevant metadata lowered the PP and gave the lowest HR; (c) Advance retrieval-augmented generation (RAG) using Mistral demonstrates consistent and robust citation support on indirect queries and matched performance to GPT-3.5 and GPT-4. The HR across all domains and models decreased by an average of 41.93%, and the PP was reduced to 0% in most cases. In terms of generation quality, the average F1 Score and BLEU were 68.09% and 57.51%, respectively; (d) Testing with adversarial samples showed that LLMs, including the Advance RAG Mistral, struggle to understand context, but the extent of this issue was small in Mistral and GPT-4-Preview. Our study contributes valuable insights into the reliability of RAG for automated citation generation tasks., Comment: Work in progress

The sunflower equation describes the motion of the tip of a plant due to the auxin transportation under the influence of gravity. This work proposes the fractional-order generalization to this delay differential equation. The equation contains two fractional orders and infinitely many equilibrium points. The problem is important because the coefficients in the linearized equation near the equilibrium points are delay-dependent. We provide a detailed stability analysis of each equilibrium point using linearized stability. We find the boundary of the stable region by setting the purely imaginary value to the characteristic root. This gives the conditions for the existence of the critical values of the delay at which the stability properties change. We observed the following bifurcation phenomena: stable for all the delay values, a single stable region in the delayed interval, and a stability switch. We also observed a multi-scroll chaotic attractor for some values of the parameters., Comment: 15 pages, 26 figures

With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based analysis. However, incomplete or missing metadata prevents effective automation. We therefore propose a deep-learning method for classification of prostate cancer scanning sequences based on a combination of image data and DICOM metadata. We demonstrate superior results compared to metadata or image data alone, and make our code publicly available at https://github.com/deepakri201/DICOMScanClassification.

This manuscript deals with the stability and bifurcation analysis of the equation $D^{2\alpha}x(t)+c D^{\alpha}x(t)=a x(t)+b x(t-\tau)$, where $0<\alpha<1$ and $\tau>0$. We sketch the boundaries of various stability regions in the parameter plane under different conditions on $\alpha$ and $b$. First, we provide the stability analysis of this equation with $\tau=0$. Change in the stability of the delayed counterpart is possible only when the characteristic roots cross the imaginary axis. This leads to various delay-independent as well as delay-dependent stability results. The stability regions are bifurcated on the basis of the following behaviors with respect to the delay $\tau$ viz. stable region for all $\tau>0$, unstable region, single stable region, stability switch, and instability switch., Comment: 55 pages, 100 figures

Deep learning has shown great promise in the ability to automatically annotate organs in magnetic resonance imaging (MRI) scans, for example, of the brain. However, despite advancements in the field, the ability to accurately segment abdominal organs remains difficult across MR. In part, this may be explained by the much greater variability in image appearance and severely limited availability of training labels. The inherent nature of computed tomography (CT) scans makes it easier to annotate, resulting in a larger availability of expert annotations for the latter. We leverage a modality-agnostic domain randomization approach, utilizing CT label maps to generate synthetic images on-the-fly during training, further used to train a U-Net segmentation network for abdominal organs segmentation. Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data. Our method results in Dice scores of 0.90 (0.08) and 0.91 (0.08) for the right and left kidney respectively, compared to a pretrained nnU-Net model yielding 0.87 (0.20) and 0.91 (0.03). We will make our code publicly available., Comment: 13 pages

We examine five machine learning-based architectures to estimate the droplet size distributions obtained using digital inline holography. The architectures, namely, U-Net, R2 U-Net, Attention U-Net, V-Net, and Residual U-Net are trained using synthetic holographic images. Our assessment focuses on evaluating the training, validation, and prediction performance of these architectures. We found that U-Net and R2 U-Net to be the most proficient, displaying consistent performance trends and achieving the highest Intersection Over Union (IOU) scores compared to the other three architectures. We employ additional training using experimental holographic images for the two top-performing architectures to validate their efficacy further. Subsequently, they are employed to segment an experimental dataset illustrating the bag breakup phenomenon, facilitating the extraction of size distribution. The extracted size distribution from U-Net and R2 U-Net segmentation is then compared with the analytical model proposed by \cite{jackiw2022prediction} by employing the gamma and log-normal distributions. Our findings indicate that the gamma distribution provides a more accurate prediction of the multi-modal size distribution than the log-normal distribution owing to its long exponential tail. The present study offers valuable insights into the effectiveness of machine learning architectures in estimating particle/droplet sizes, highlighting their practical application in real-world experimental scenarios., Comment: 36 pages, 11 figures, International Journal of Multiphase Flow

The study of angular correlations of heavy-flavor particles in hadronic collisions can provide crucial insight into the heavy quark production, showering, and hadronization processes. The comparison with model predictions allows us to discriminate among different approaches for heavy quark production and hadronization, as well as different treatments of the underlying event employed by the models to reproduce correlation observables. In ultra-relativistic heavy-ion collisions, where a deconfined state of matter, the quark--gluon plasma (QGP), is created, heavy-flavor correlations can shed light on the modification of the heavy quark fragmentation due to the interaction between charm and beauty quarks with the QGP constituents, as well as characterize their energy loss processes while traversing the medium. Insight into the possible emergence of collective-like mechanisms in smaller systems, resembling those observed in heavy-ion collisions, can also be obtained by performing correlation studies in high-multiplicity proton--proton and proton--nucleus collisions. In this review, the most recent and relevant measurements of heavy-flavor correlations performed in all collision systems at the LHC and RHIC will be presented, and the new understandings that they provide will be discussed.

In a recent paper, Bilu et al. studied a conjecture of Marques and Lengyel on the $p$-adic valuation of the Tribonacci sequence. In this article, we do a similar study on the $p$-adic valuation of third order linear recurrence sequences by considering a generalisation of the conjecture of Marques and Lengyel for third order linear recurrence sequences. We obtain analogues of the results of Bilu et al. for certain third order linear recurrence sequences. Also, as a consequence of the conjecture, we prove that the solution set of the Diophantine equation given by $x_n=m!$ in positive integers $n,m$ is finite for certain third order linear recurrence sequences $(x_n)$., Comment: 12 pages

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency., Comment: Accepted in the IEEE Power and Energy Society General Meeting, 2024

Purpose - Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly impact the surgeon's perception and control of the tool-to-tissue interaction forces. Methods - We present a situational-aware, force control technique aimed at regulating interaction forces during robot-assisted skullbase drilling. The contextual interaction information derived from the digital twin environment is used to enhance sensory perception and suppress undesired high forces. Results - To validate our approach, we conducted initial feasibility experiments involving a medical and two engineering students. The experiment focused on further drilling around critical structures following cortical mastoidectomy. The experiment results demonstrate that robotic assistance coupled with our proposed control scheme effectively limited undesired interaction forces when compared to robotic assistance without the proposed force control. Conclusions - The proposed force control techniques show promise in significantly reducing undesired interaction forces during robot-assisted skullbase surgery. These findings contribute to the ongoing efforts to enhance surgical precision and safety in complex procedures involving the lateral skull base., Comment: *These authors contributed equally to this work

Skull base surgery is a demanding field in which surgeons operate in and around the skull while avoiding critical anatomical structures including nerves and vasculature. While image-guided surgical navigation is the prevailing standard, limitation still exists requiring personalized planning and recognizing the irreplaceable role of a skilled surgeon. This paper presents a collaboratively controlled robotic system tailored for assisted drilling in skull base surgery. Our central hypothesis posits that this collaborative system, enriched with haptic assistive modes to enforce virtual fixtures, holds the potential to significantly enhance surgical safety, streamline efficiency, and alleviate the physical demands on the surgeon. The paper describes the intricate system development work required to enable these virtual fixtures through haptic assistive modes. To validate our system's performance and effectiveness, we conducted initial feasibility experiments involving a medical student and two experienced surgeons. The experiment focused on drilling around critical structures following cortical mastoidectomy, utilizing dental stone phantom and cadaveric models. Our experimental results demonstrate that our proposed haptic feedback mechanism enhances the safety of drilling around critical structures compared to systems lacking haptic assistance. With the aid of our system, surgeons were able to safely skeletonize the critical structures without breaching any critical structure even under obstructed view of the surgical site., Comment: *These authors contributed equally