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The electromagnetic spectrum in the terahertz frequency region is of significant importance for understanding the formation and evolution of galaxies and stars throughout the history of the universe and the process of planet formation. Within the star forming clouds the constituent atoms and molecules are excited to produce characteristic emission and absorption lines, many of which happen at the terahertz frequencies. Thus, detecting the spectral signatures as unique fingerprints of molecules and atoms require terahertz spectrometers, which need to be operated in a space observatory because of the water vapor absorption in the earth atmosphere. However, current terahertz spectrometers face several challenges that limit their performances and applications, including a low resolution, limited bandwidth, large volume, and complexity. In this paper, we address the last two issues by demonstrating a concept of a compact terahertz spectrometer using metasurface. We start by modelling, designing, and fabricating a metasurface, aiming to optimize its performance within a band from 1.7 to 2.5 THz. Next, we make use of an array of quantum cascade lasers that operate at slightly different frequencies around 2.1 THz to validate the performance of the spectrometer. Finally, we apply the spectrum inversion method to analyse the measured data to confirm a resolution R of at least 273. Our results demonstrated a miniaturized terahertz spectrometer concept successfully.

Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like KNN mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. Deep Learning (DL)-based methods have gained popularity in the semiconductor defect inspection domain because they have been proven robust towards these challenging scenarios. In this research work, we have presented an automated DL-based approach for efficient localization and classification of defects in SEM images. We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on SEM images of semiconductor wafer defects. The use of the proposed CN approach allows improved computational efficiency compared to previously studied DL models. SEMI-CN gets trained to output the center, class, size, and offset of a defect instance. This is different from the approach of most object detection models that use anchors for bounding box prediction. Previous methods predict redundant bounding boxes, most of which are discarded in postprocessing. CN mitigates this by only predicting boxes for likely defect center points. We train SEMI-CN on two datasets and benchmark two ResNet backbones for the framework. Initially, ResNet models pretrained on the COCO dataset undergo training using two datasets separately. Primarily, SEMI-CN shows significant improvement in inference time against previous research works. Finally, transfer learning (using weights of custom SEM dataset) is applied from ADI dataset to AEI dataset and vice-versa, which reduces the required training time for both backbones to reach the best mAP against conventional training method.

With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popularity in semiconductor defect inspection over last few years. In this research work, a new semiconductor defect inspection framework "SEMI-DiffusionInst" is investigated and compared to previous frameworks. To the best of the authors' knowledge, this work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Different feature extractor networks as backbones and data sampling strategies are investigated towards achieving a balanced trade-off between precision and computing efficiency. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes (per class APs). The bounding box and segmentation mAPs achieved by the proposed SEMI-DiffusionInst model are improved by 3.83% and 2.10%, respectively. Among individual defect types, precision on line collapse and thin bridge defects are improved approximately 15\% on detection task for both defect types. It has also been shown that by tuning inference hyperparameters, inference time can be improved significantly without compromising model precision. Finally, certain limitations and future work strategy to overcome them are discussed., Comment: 6 pages, 5 figures, To be published by IEEE in the proceedings of the 2023 ELMAR conference

\textit{Objective:} Diagnosing pain in research and clinical practices still relies on self-report. This study aims to develop an automatic approach that works on resting-state raw EEG data for chronic knee pain prediction. \textit{Method:} A new feature selection algorithm called ``modified Sequential Floating Forward Selection'' (mSFFS) is proposed. The improved feature selection scheme can better avoid local minima and explore alternative search routes. \textit{Results:} The feature selection obtained by mSFFS displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5\%. \textit{Conclusion:} The improved feature selection searches out a compact, effective subset of connectivity features that produces competitive performance on chronic knee pain prediction. \textit{Significance:} We have shown that an automatic approach can be employed to find a compact connectivity feature set that effectively predicts chronic knee pain from EEG. It may shed light on the research of chronic pains and lead to future clinical solutions for diagnosis and treatment., Comment: 9 pages, 4 figures; accepted by IEEE Transactions on Biomedical Engineering

Delineation of tumors and organs-at-risk permits detecting and correcting changes in the patients' anatomy throughout the treatment, making it a core step of adaptive proton therapy (APT). Although AI-based auto-contouring technologies have sped up this process, the time needed to perform the quality assessment (QA) of the generated contours remains a bottleneck, taking clinicians between several minutes up to an hour to complete. This paper introduces a fast contouring workflow suitable for time-critical APT, enabling detection of anatomical changes in shorter time frames and with a lower demand of clinical resources. The proposed AI-infused workflow follows two principles uncovered after reviewing the APT literature and conducting several interviews and an observational study in two radiotherapy centers in the Netherlands. First, enable targeted inspection of the generated contours by leveraging AI uncertainty and clinically-relevant features such as the proximity of the organs-at-risk to the tumor. Second, minimize the number of interactions needed to edit faulty delineations with redundancy-aware editing tools that provide the user a sense of predictability and control. We use a proof of concept that we validated with clinicians to demonstrate how current and upcoming AI capabilities support the workflow and how it would fit into clinical practice.

We report two new radio detections of cataclysmic variables (CVs), and place them in context with radio and X-ray detections of other CVs. We detected QS Vir, a low accretion-rate CV; V2400 Oph, a diskless intermediate polar (IP); and recovered the polar AM Her in the Very Large Array Sky Survey (VLASS) 2-4 GHz radio images. The radio luminosities of these systems are higher than typically expected from coronal emission from stars of similar spectral types, and neither system is expected to produce jets, leaving the origin of the radio emission a puzzle. The radio emission mechanism for these two CVs may be electron-cyclotron maser emission, synchrotron radiation, or a more exotic process. We compile published radio detections of CVs, and X-ray measurements of these CVs, to illustrate their locations in the radio - X-ray luminosity plane, a diagnostic tool often used for X-ray binaries, active galactic nuclei, and radio stars. Several radio-emitting CVs, including these two newly detected CVs, seem to lie near the principal radio/X-ray track followed by black hole X-ray binaries (BHXBs) at low luminosity, suggesting additional complexity in classifying unknown systems using their radio and X-ray luminosities alone.

We compute next-to-next-to-leading order (NNLO) QCD corrections to neutral vector boson production in association with a charm jet at the LHC. This process is studied in the forward kinematics at $\sqrt{s}=13$ TeV, which may provide valuable constraints on the intrinsic charm component of the proton. A comparison is performed between fixed order and NLO predictions matched to a parton shower showing mutual compatibility within the respective uncertainties. NNLO corrections typically lead to a reduction of theoretical uncertainties by a factor of two and the perturbative convergence is further improved through the introduction of a theory-inspired constraint on the transverse momentum of the vector boson plus jet system. A comparison between these predictions with data will require an alignment of a flavour-tagging procedure in theory and experiment that is infrared and collinear safe., Comment: 22 pages, 7 figures. v2 matches the published version

Gravito-inertial asteroseismology saw its birth thanks to high-precision CoRoT and Kepler space photometric light curves. So far, it gave rise to the internal rotation frequency of a few hundred intermediate-mass stars, yet only several tens of these have been weighed, sized, and age-dated with high precision from asteroseismic modelling. We aim to increase the sample of optimal targets for future gravito-inertial asteroseismology by assessing the properties of 15062 newly found Gaia DR3 gravity-mode pulsators. We also wish to investigate if there is any connection between their fundamental parameters and dominant mode on the one hand, and their spectral line broadening measured by Gaia on the other hand. After re-classifying about 22% of the F-type gravity-mode pulsators as B-type according to their effective temperature, we construct histograms of the fundamental parameters and mode properties of the 15062 new Gaia DR3 pulsators. We compare these histograms with those of 63 Kepler bona fide class members. We fit errors-in-variables regression models to couple the effective temperature, luminosity, gravity, and oscillation properties to the two Gaia DR3 parameters capturing spectral line broadening for a fraction of the pulsators. We find that the selected 15062 gravity-mode pulsators have properties fully in line with those of their well-known Kepler analogues, revealing that Gaia has a role to play in asteroseismology. The dominant g-mode frequency is a significant predictor of the spectral line broadening for the class members having this quantity measured. We show that the Gaia vbroad parameter captures the joint effect of time-independent intrinsic and rotational line broadening and time-dependent tangential pulsational broadening. Gaia was not desiged to detect non-radial oscillations, yet its homogeneous data treatment allow us to identify many new gravity-mode pulsators., Comment: 17 pages, 10 figures, 10 tables. Manuscript accepted for publication in Astronomy & Astrophysics. Two datafiles will be made available via CDS; they can already be requested from the main author

With applications in cosmology, infrared astronomy and CMB survey, frequency-division multiplexing (FDM) proved to be a viable readout for transition-edge sensors (TES). We investigate the occurrence of out-of-band resonances (OBR) which could constrain the bandwidth of the FDM readout of TES bolometers. The study includes SPICE modeling of the entire setup including the cryogenic harness, LC filters, Superconducting Quantum Interference Device (SQUID) and room-temperature amplifier. Simulation results show that the long harness (for flight model) could cause multiple reflections that generate repetitive spikes in the spectrum. Peaks of the OBR are mainly due to the parasitic capacitances at the input of SQUID. Implementing a low-pass RC circuit (snubber) at the input of the SQUID dampened the OBR. As a result, the first peak only appears around 20 MHz which is a safe margin for the 1 MHz-3.8 MHz FDM in use in the prototype readout. Using a spectrum analyzer and broadband LNAs,we also measured the OBR for the prototype FDM readout in the lab up to 500 MHz. The measurement was conducted at temperatures of 50 mK and 4 K and for various biasing of the DC SQUID. It turns out that OBR are more intense at 50 mK and are caused by the harness impedance mismatch rather than the SQUID. Simulation codes and supporting materials are available at https://github.com/githubamin/LT-Spice-Simulation-of-FDM-readout., Comment: Keywords: Out of Band Resonances(OBR), FDM, TES, Bolometer, SQUID, Infrared Astronomy. 7 pages, 7 Figures. Accepted for publication in Journal of Low Temperature Physics

Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network. Comparing with conventional machine learning and deep learning methods, we demonstrate the superiority of using VAE for feature extraction, as reflected by the significantly improved classification accuracies, better visualizations and reduced impurity measures in the feature representations. Future work can be directed to gaining an in-depth understanding regarding the spatial patterns that have been learned by the proposed model from a neurological view, as well as improving the interpretability of the proposed model by allowing it to uncover any temporal-related information., Comment: 8 pages, 6 figures

The production of lepton pairs (Drell-Yan process) at the LHC is being measured to high precision, enabling the extraction of distributions that are triply differential in the di-lepton mass and rapidity as well as in the scattering angle described by the leptons. The measurements are performed for a fiducial phase space, defined by cuts on the individual lepton momenta and rapidities. Based on the ATLAS triple-differential Drell-Yan measurement at 8~TeV, we perform a detailed investigation of the phenomenology of this process based on state-of-the-art perturbative predictions in QCD and the electroweak theory. Our results demonstrate the highly non-trivial interplay between measurement variables and fiducial cuts, which leads to forbidden regions at Born level, and induces sensitivity on extra particle emissions from higher perturbative orders. We also investigate the sensitivity of the measurement on parton distributions and electroweak parameters. We derive Standard-Model theory predictions which combine NNLO QCD and NLO EW corrections and include partial N$^3$LO QCD as well as higher-order EW corrections where appropriate. Our results will enable the use of the triple-differential Drell-Yan data in a precise experimental determination of the weak mixing angle., Comment: 49 pages, 22 figures, all data attached as ancillary files, matches published journal version