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This paper presents a novel approach to constrain the $\mu$- and y- distortions in the Cosmic Microwave Background (CMB) using the COBE/FIRAS data. The analysis draws from the concept of blackbody radiation inversion (BRI), a mathematical technique typically used to determine the temperature distribution from a radiated power spectrum. We study the deviations from the ideal blackbody spectrum or the spectral distortions by incorporating first a non-zero chemical potential $\mu$ via the Bose-Einstein distribution and then the Compton parameter $y$ while keeping the monopole temperature constant. We infer the results as probability distribution functions on these distortions. Finally, we derive $\mu = (8.913 \pm 0.736) \times 10^{-5}$ and $y = (1.532 \pm 0.092) \times 10^{-5}$ at a $68\%$ confidence interval. The results are consistent with prior values and provide tighter constraints on the CMB spectral distortion and synergies of the primordial Universe., Comment: 16 pages, 4 figures, comments welcome

Distributed Machine Learning (DML) on resource-constrained edge devices holds immense potential for real-world applications. However, achieving fast convergence in DML in these heterogeneous environments remains a significant challenge. Traditional frameworks like Bulk Synchronous Parallel and Asynchronous Stochastic Parallel rely on frequent, small updates that incur substantial communication overhead and hinder convergence speed. Furthermore, these frameworks often employ static dataset sizes, neglecting the heterogeneity of edge devices and potentially leading to straggler nodes that slow down the entire training process. The straggler nodes, i.e., edge devices that take significantly longer to process their assigned data chunk, hinder the overall training speed. To address these limitations, this paper proposes Hermes, a novel probabilistic framework for efficient DML on edge devices. This framework leverages a dynamic threshold based on recent test loss behavior to identify statistically significant improvements in the model's generalization capability, hence transmitting updates only when major improvements are detected, thereby significantly reducing communication overhead. Additionally, Hermes employs dynamic dataset allocation to optimize resource utilization and prevents performance degradation caused by straggler nodes. Our evaluations on a real-world heterogeneous resource-constrained environment demonstrate that Hermes achieves faster convergence compared to state-of-the-art methods, resulting in a remarkable $13.22$x reduction in training time and a $62.1\%$ decrease in communication overhead., Comment: 11 pages, 19 figures, 3 tables; code: https://github.com/DaSH-Lab-CSIS/Hermes

In this study, we present a quantitative and comprehensive analysis of social gaze in people with autism spectrum disorder (ASD). Diverging from traditional first-person camera perspectives based on eye-tracking technologies, this study utilizes a third-person perspective database from the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) interview videos, encompassing ASD participants and neurotypical individuals as a reference group. Employing computational models, we extracted and processed gaze-related features from the videos of both participants and examiners. The experimental samples were divided into three groups based on the presence of social gaze abnormalities and ASD diagnosis. This study quantitatively analyzed four gaze features: gaze engagement, gaze variance, gaze density map, and gaze diversion frequency. Furthermore, we developed a classifier trained on these features to identify gaze abnormalities in ASD participants. Together, we demonstrated the effectiveness of analyzing social gaze in people with ASD in naturalistic settings, showcasing the potential of third-person video perspectives in enhancing ASD diagnosis through gaze analysis.

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). The Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. Here we also highlight Duotang, a web platform that presents genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

Diagnosing autism spectrum disorder (ASD) by identifying abnormal speech patterns from examiner-patient dialogues presents significant challenges due to the subtle and diverse manifestations of speech-related symptoms in affected individuals. This study presents a comprehensive approach to identify distinctive speech patterns through the analysis of examiner-patient dialogues. Utilizing a dataset of recorded dialogues, we extracted 40 speech-related features, categorized into frequency, zero-crossing rate, energy, spectral characteristics, Mel Frequency Cepstral Coefficients (MFCCs), and balance. These features encompass various aspects of speech such as intonation, volume, rhythm, and speech rate, reflecting the complex nature of communicative behaviors in ASD. We employed machine learning for both classification and regression tasks to analyze these speech features. The classification model aimed to differentiate between ASD and non-ASD cases, achieving an accuracy of 87.75%. Regression models were developed to predict speech pattern related variables and a composite score from all variables, facilitating a deeper understanding of the speech dynamics associated with ASD. The effectiveness of machine learning in interpreting intricate speech patterns and the high classification accuracy underscore the potential of computational methods in supporting the diagnostic processes for ASD. This approach not only aids in early detection but also contributes to personalized treatment planning by providing insights into the speech and communication profiles of individuals with ASD.

Diagnosing language disorders associated with autism is a complex challenge, often hampered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and precision. In this study, we explored the application of ChatGPT, a large language model, to overcome these obstacles by enhancing sensitivity and profiling linguistic features for autism diagnosis. This research utilizes ChatGPT natural language processing capabilities to simplify and improve the diagnostic process, focusing on identifying autism related language patterns. Specifically, we compared ChatGPT performance with that of conventional supervised learning models, including BERT, a model acclaimed for its effectiveness in various natural language processing tasks. We showed that ChatGPT substantially outperformed these models, achieving over 10% improvement in both sensitivity and positive predictive value, in a zero shot learning configuration. The findings underscore the model potential as a diagnostic tool, combining accuracy and applicability. We identified ten key features of autism associated language disorders across scenarios. Features such as echolalia, pronoun reversal, and atypical language usage play a critical role in diagnosing ASD and informing tailored treatment plans. Together, our findings advocate for adopting sophisticated AI tools like ChatGPT in clinical settings to assess and diagnose developmental disorders. Our approach promises enhanced diagnostic precision and supports personalized medicine, potentially transforming the evaluation landscape for autism and similar neurological conditions.

Trapped human detection in search and rescue (SAR) scenarios poses a significant challenge in pervasive computing. This study addresses this issue by leveraging machine learning techniques, given their high accuracy. However, accurate identification of trapped individuals is hindered by the curse of dimensionality and noisy data. Particularly in non-line-of-sight (NLOS) situations during catastrophic events, the curse of dimensionality may lead to blind spots due to noise and uncorrelated values in detections. This research focuses on harmonizing information through wireless communication and identifying individuals in NLOS scenarios using ultra-wideband (UWB) radar signals. Employing independent component analysis (ICA) for feature extraction, the study evaluates classification performance using ensemble algorithms on both static and dynamic datasets. The experimental results demonstrate categorization accuracies of 88.37% for static data and 87.20% for dynamic data, highlighting the effectiveness of the proposed approach. Finally, this work can help scientists and engineers make instant decisions during SAR operations., Comment: 20 pages,4 figures, 1 algorithm and 3 tables

The diversity and heterogeneity of biomarkers has made the development of general methods for single-step quantification of analytes difficult. For individual biomarkers, electrochemical methods that detect a conformational change in an affinity binder upon analyte binding have shown promise. However, because the conformational change must operate within a nanometer-scale working distance, an entirely new sensor, with a unique conformational change, must be developed for each analyte. Here, we demonstrate a modular electrochemical biosensor, built from DNA origami, which is easily adapted to diverse molecules by merely replacing its analyte binding domains. Instead of relying on a unique nanometer-scale movement of a single redox reporter, all sensor variants rely on the same 100-nanometer scale conformational change, which brings dozens of reporters close enough to a gold electrode surface that a signal can be measured via square wave voltammetry, a standard electrochemical technique. To validate our sensor's mechanism, we used single-stranded DNA as an analyte, and optimized the number of redox reporters and various linker lengths. Adaptation of the sensor to streptavidin and PDGF-BB analytes was achieved by simply adding biotin or anti-PDGF aptamers to appropriate DNA linkers. Geometrically-optimized streptavidin sensors exhibited signal gain and limit of detection markedly better than comparable reagentless electrochemical sensors. After use, the same sensors could be regenerated under mild conditions: performance was largely maintained over four cycles of DNA strand displacement and rehybridization. By leveraging the modularity of DNA nanostructures, our work provides a straightforward route to the single-step quantification of arbitrary nucleic acids and proteins., Comment: 14 pages in main, 6 figures; 16 pages in supplementary information, 8 figures, 6 tables

This study explores the aerothermal behaviour of a rigid mechanically deployable aeroshell developed at Imperial College London for high payload atmospheric entry missions. The multiphysics CFD software STAR-CCM+ is used to perform a Conjugate Heat Transfer analysis on the aeroshell's faceted geometry. Results are presented for four different geometry models tested in air at Mach 5 with angles of attack 0{\deg}, 5{\deg} and 10{\deg}. The predicted surface heat transfer reveals areas of elevated heat loads at the ribs between facets and at the aeroshell shoulder, due to local boundary layer thinning. The increase in heat transfer at the ribs depends on the sharpness of the rib: more rounded shapes result in lower heat fluxes. Comparison with high-speed wind tunnel tests shows good agreement with experimental data. Stanton number and temperature profiles agree within 8% and 2%, respectively. The discrepancies between experiments and simulations are largest at the sharp ribs of the aeroshell. The sources of error can be associated with three-dimensional effects neglected in the heat flux derivations from temperature measurements as well as experimental uncertainties., Comment: Conference paper presented at HiSST: 2nd International Coneference on High-Speed Vehicle Science Technology (Bruges, Belgium 2022)

In ferroelectric (FE) materials, characteristics of a domain wall (DW), such as charge neutrality, width, polarization, speed, favorability, etc. strongly depend on the orientation of the domains. As an emerging FE material, Hafnium Zirconium Oxide (HZO) based lateral DWs have been widely studied both with theory and experiments. But DW of Head-to-Head/ Tail-to-Tail (H-H/T-T) type is yet to be reported in HZO based FEs. In this work, we analyze T-T DW in freestanding HZO and Metal-HZO-Metal structures with vacancy-free non-stoichiometric oxygen termination using the first principles calculation. Our analysis indicates that stable T-T DW forms in this type of structure, but H-H DW is energetically unfavorable. This signifies the possibility of T-T DW formation locally near vacancy-free region of HZO thin films. It is widely known to the FE research community that FE HZO unit cell has unique ferroelectric-dielectric (FE-DE) segmentation and different microscopic configurations of both upward and downward polarized domains. Considering different combinations of the domains, we find that cross pattern of DE and FE segments in two neighboring domains along the polarization direction (where DE segment of one domain faces the FE segment of the other domain) ensures the stability of the T-T DW with minimum energy. Here, atomic configuration in the T-T DW resembles $pbcn$ phase and the DW is half unit cell thick. Our calculation indicates that polarization bound charges are being compensated by ionic charges near the DW and surface/interface. In this work, we also investigate domain kinetics to provide a more comprehensive understanding of this type of DW.

We demonstrate that Cherenkov radiation can be manipulated in terms of operation frequency, bandwidth, and efficiency by simultaneously controlling the properties of drifting electrons and the photonic states supported by their surrounding media. We analytically show that the radiation rate strongly depends on the momentum of the excited photonic state, in terms of magnitude, frequency dispersion, and its variation versus the properties of the drifting carriers. This approach is applied to design and realize miniaturized, broadband, tunable, and efficient terahertz and far-infrared sources by manipulating and boosting the coupling between drifting electrons and engineered hyperbolic modes in graphene-based nanostructures. The broadband, dispersive, and confined nature of hyperbolic modes relax momentum matching issues, avoid using electron beams, and drastically enhance the radiation rate - allowing that over 90% of drifting electrons emit photons. Our findings open a new paradigm for the development of solid-state terahertz and infrared sources., Comment: 4 figures

To understand and harness the physical mechanisms of ferroelectric Hafnium Zirconium Oxide (HZO)-based devices, there is a need for clear understanding of domain interactions, their dynamics, negative capacitance effects, and other multi-domain characteristics. These crucial attributes depend on the coupling between neighboring domains quantified by the gradient energy coefficient (g). Furthermore, HZO has unique orientation-dependent lateral multidomain configurations. To develop an in-depth understanding of multi-domain effects, there is a need for thorough analysis of g. In this work, the energetics of multidomain configurations and domain growth mechanism corresponding to lateral domain walls of HZO are analyzed and gradient energy coefficients are quantified using first-principles Density Functional Theory calculations. These results indicate that one lateral direction exhibits the following characteristics: i) DW is ultra-sharp and domain growth occurs unit-cell-by-unit-cell, ii) the value of g is negative and in the order of $10^{-12} Vm^{3}C^{-1}$, and iii) g reduces (increases) with compressive (tensile) strain. In contrast, in the other lateral direction, the following attributes are observed: i) DW is gradual and domain growth occurs in quanta of half-unit-cell, ii) g is positive and in the order of $10^{-10} Vm^{3}C^{-1}$, and iii) g increases (reduces) with compressive (tensile) strain.

In this paper, a new design has been proposed for the realization of high-frequency memcapacitor emulators built with three OTAs. This paper also proposes the application of memcapacitor as an amplitude modulator. Furthermore, applications of memcapacitor as a filter, Oscillator point attractor, and periodic doubler are also shown. The proposed circuits can be configured in both incremental and decremental topology. The proposed circuits and their application claim that the circuit is much simpler in design and can be utilized in both topologies. The performance of all the proposed circuits has been verified on Cadence Virtuoso Spectre using standard CMOS 180nm. Furthermore, post-layout simulations and their comparison have been carried out., Comment: 40 Pages, 25 figures, 6 Tables. arXiv admin note: substantial text overlap with arXiv:2205.06221

This paper proposes a new design for realizing grounded and floating meminductor emulators built with two operational transconductance amplifiers (OTAs) and two second-generation current conveyors. The proposed grounded and floating emulators claim that the circuits are much simpler in design and can be utilized in incremental and decremental topologies. The proposed circuits' performance has been verified with Cadence Virtuoso Spectre using standard CMOS 180nm technology. Furthermore, the layout of the proposed circuits has been designed, and post-layout simulations have been performed. The non-ideal and Monte Carlo analyses have been carried out in detail. This paper also proposes the application of a meminductor as an Amplitude Modulator (AM). Moreover, the experimental results are presented to verify the theoretical and simulation analyses of proposed meminductor emulator circuits., Comment: 48 Pages, 22 Figures

We investigate lateral recoil forces exerted on nanoparticles located near plasmonic platforms with in-plane nonreciprocal response. To this purpose, we first develop a comprehensive theoretical framework based on the Lorentz force within the Rayleigh approximation combined with nonreciprocal Green's functions and then derive approximate analytical expressions to model lateral recoil forces, demonstrating their explicit dependence on the dispersion relation of the system and unveiling the mechanisms that govern them. In particular, a dominant lateral recoil force component appears due to the momentum imbalance of nonreciprocal surface plasmons supported by the platform. This force can be orders of magnitude larger than other recoil force components, acts only along or against the direction of the external bias, and is quasi-independent of the direction, polarization, and wavelength of the incident plane wave. Lateral recoil forces are explored using drift-biased graphene metasurfaces, a platform that is also proposed to sort nanoparticles as a function of their size. Nonreciprocal plasmonic systems may enable new venues to trap, bind, and manipulate nanoparticles and to alleviate some of the challenges of conventional optical tweezers., Comment: 39 pages - 6 figures - 3 tables