Miracle-dz/UniFAR-FTUs huggingface dataset:
TRIANGULAR SHAPED QMSIW CAVITY BACK SLOT ANTENNA FOR TRI-BAND OPERATION

In this paper, a triangle-shaped Quarter Mode Substrate Integrated Waveguide (QMSIW) cavity back slot antenna is developed using TE110, TE220, and TE310,130 modes for tri-band operation. The QMSIW is obtained from the quadrant part of a square SIW (Substrate Integrated Waveguide) structure. The electric field distributions of resonant modes are studied for the Full Mode SIW, Half Mode SIW, and Quarter Mode SIW cavities through HFSS simulation tool. The generation of the hybrid mode TE310,130 mode is clearly explained with the simulation tool. A rectangular slot is engraved on top layer of the structure along the perfect electric wall, to radiate the EM (Electro Magnetic) wave towards positive Z-direction. Further, a metallized via has been inserted to bring reflection coefficient below −10 dB at the three resonant modes. The developed antenna achieves resonance at 5.2 GHz, 9.88 GHz, and 10.6 GHz frequencies with peak gains of 9 dB, 6.2 dB, and 7.9 dB, respectively. The antenna is designed on a single layer thin substrate which reduces the fabrication complexity.
Improvements of SCNN on lane detection in harsh scenarios

Spatial CNN is capable of capturing spatial relationships of pixels across rows and columns of images for lane detection through message passing layers. We have explored several methods to improve SCNN performance especially in harsh scenarios in terms of accuracy, F1 and training time. (1)We modify the loss function to make background pixels have lower inﬂuence during training. (2)We augment training data by decreasing brightness, adding Gaussain blur and decreasing contrast to existing images. (3)We replace the encoder VGG-16 with ResNet-18 as backbone so that ResNet18 is able to preserve useful low-level information for much deeper layers. (4)We replace Momentum optimizer with SGD+Nesterov momentum optimizer. The ﬁrst three methods achieve higher training lane accuracy but have lower test F1 score than the baseline model. The last method achieves lower training lane accuracy. With ResNet-18 as backbone, the model trains 4 times faster than the baseline model with VGG16. However, the model with ResNet18 shrinks image size and fails to process sufﬁcient information so the F1 score is much lower.
Generation of Realistic Facemasked Faces With GANs

Facial recognition and identification are key aspects of modern technology and are impeded by the advent of facemasks to combat to Covid-19. Large datasets of masked faces are needed in order to improve facial identification with masks. Such datasets may be produces using generative adversarial networks. In this work, realistic pictures of faces with facemasks are generated using a CycleGAN model. A less computationally intensive SimGAN model is also used as a point of comparison. The generated pictures of faces with masks can be used for facial identification and mapped back to the original face with a . 2 % error rate, demonstrating the usefulness of GAN models in face recognition.
ANALYSIS-BASED SEISMIC DESIGN FOR GENERALLY IRREGULAR RC FRAME BUILDINGS ACHIEVING MINIMUM TOTAL REINFORCING STEEL WEIGHT

A new analysis-based seismic design method is presented for highly irregular RC frame buildings for bi-directional ground accelerations. Key Engineering Demand Parameters including inter-storey drifts and material strains limits are limited to target values. This is achieved while simultaneously minimizing total steel volume. This results in economic design solutions experiencing reduced peak base shears and over-turning moments. The methodology relies solely on analysis tools. No knowledge of structural optimization is necessary. The method presented typically requires only a few iterations for convergence. This analysis-redesign approach is therefore quite suitable for practical use.
Design and Fabrication of Flow-based Edge Detection Memristor Crossbar Circuits A Massively Parallel Search using a Human Perception Objective

—We design and fabricate a ﬂow-based circuit for edge detection in images that exploits device-level parallelism in nanoscale memristor crossbars. In our approach, a corpus of human-labeled edges in BSDS500 images is used to learn an edge detection function with ternary values: true, false, and don’t-care. A Boolean crossbar design implementing an approximation of this ternary function using in-memory ﬂow-based computing is then obtained using a massively parallel simulated annealing search executed on GPUs. We demonstrate the success of our approach by fabricating the memristor circuit on a 300nm wafer platform using a custom 65nm CMOS/ReRAM process technology. We demonstrate that our ﬂow-based computing approach is either faster, more energy-efﬁcient or produces fewer incorrect edges than other competing approaches. We show that our design has power and area requirements that are 3.3x and 2.5x lower, respectively, than the previous state-of-the-art.
MT G CA RD GR A DER U SI NG I MA GE CLA SSI F I CA T I ON A ND DET E CT I ON

Given Magic the Gathering’s (MTG) incredibly profitable secondary market based on given cards and their subsequent quality, I was inspired to build a card grader to make accurate prices on individual cards easily available to would-be sellers. To that end, I built a number of object classifiers meant to give users a better prediction as to how much any given card will actually sell for and an object detector to point out flaws and damage on those cards to justify the card grade given with some mixed but promising results
TREE MODEL IN HUMAN RESOURCE MANAGEMENT BASED ON PERSONALITY, COMPETENCE AND SPIRITUALITY TO IMPROVE ORGANIZATIONAL PERFORMANCE

Human Resource Management has a significant influence on the success and development of a company. This research analyses the most important variable in human resources that influences one's success and organizational performance. This research uses literature review methodology by examining journals on the influence of human personality, competency, and spirituality on organizational performance over the past ten years. The scientific analysis uses the concept of learning and developing in the Pumping HR Model. The results show that personality, competency, and spirituality at work are critical and directly affect organizational performance improvement. According to the Pumping HR model, the three variables can be structured like a complete tree by analogy with the roots of stems and fruits or leaves, which describes the variables that must be prioritized hierarchically. Based on this sequence, the root analogy is spirituality, while the stem analogy is competence, and the fruit or leaf analogy is performance. Personality is in a position between the root and stem, which is known as part of the preconscious in Iceberg's theory.
Indianajones.ai: evaluating methods for mapping water management features across the South Indian Neolithic–Iron Age transition

We conﬁgured three convolutional neural networks (ConvNets) to perform seman-1 tic segmentation on high-resolution, multi-temporal satellite Imagery. Our best 2 model correctly classiﬁes seasonal reservoirs, which bear much archaeological 3 signiﬁcance in south Indian prehistory, with a balanced accuracy ( ¯ A ) of 76% on the 4 test set. The deep learning pipeline (including data pre-processing, augmentation, 5 and model training) that we present here may provide insights for archaeologists 6 and geospatial scientists alike who seek to map small features ( ca . meters across) 7 in arid conditions
Exploring Different Deep Learning Architectures on a Large Chest Radiograph Dataset

In this study, multi-class convolutional neural networks are trained to diagnose 14 types of observations from chest radiographs. The radiographs come from a large public chest X-ray dataset known as CheXpert. The three models, VGG-16, ResNet-50, and DenseNet-121 are trained using mini-batch gradient descent, a binary cross entropy loss function, and the adam optimizer. The DenseNet-121 model is trained using both transfer learning and traditional learning. The DenseNet-121 model performed the best out of all three models in every metric, but received low F1-scores. This was a result of class imbalance, which was remedied with an upsampling of underrepresented classes. The DenseNet-121 model was then trained again on the upsampled data, improving in accuracy on the test set and F1-scores.
Comparative Study of the Performance of Two way and One Way DF Relaying with Co-Channel Interference and Channel Estimation Error

— It is known that cooperative communications enhances the bit error rate performance of wireless communication systems. In this letter, we show that cooperative communications also reduce the effect of channel estimation error and co-channel interference. A metric called signal-to-noise gap ratio is used to measure the amount of this reduction in decode and forward two-way relay network (TWRN) and one way relay network (OWRN) compared with the direct link between source and destination. The effect of different parameters on the ability of TWRN and OWRN to reduce the effect of channel estimation error and co-channel interference is studied. It is shown that OWRN achieves more reduction on the effect of channel estimation error and co-channel interference than TWRN. Furthermore, more reduction can be achieved if the number of relays increased.
Pancreatic cancer incidence trends in the United States from 2000-2017: Analysis of Surveillance, Epidemiology and End Results (SEER) database

Background: Recent incidence trends of pancreatic cancers were reviewed by demographics and histologic type to observe any new findings. Methods: Data was used from the Surveillance, Epidemiology, and End Results (SEER) registry 18 (2000-2017) and it underwent temporal trend analysis. Pancreatic cancer incidence rates were reported based on histological subtype and demographics. Results: The incidence rate of white males increased significantly during 2000-2017 (annual percent change (APC) = 3.5%) compared to previously reported APCs. The incidence of white females grew from an APC of 1.29% to 2.9%. Rates among black ethnicity increased with an APC of 4.2%. Rates among Hispanics and other ethnicities also showed increment. The rates for ductal adenocarcinoma showed a positive trend in all races, with the APC ≥ 6 % for females and APC ≥ 6.5 % for males. The rates of non-secretory endocrine tumors showed a decline in both genders of all five races in recent years after showing an initial positive trend till 2010. Rates for pancreatic adenocarcinoma continued to rise in all ethnicities from 2000-2017. Interestingly, there was a rise in carcinoid type pancreatic neuroendocrine tumors (PNETs) in all ethnicities. Cumulatively, males had a higher incidence than females; male to female Incidence Risk Ratio (IRRs) was 1.32. The IRR was > 1 for age groups ≥ 35 years. The male to female IRRs was less than 1 for cystic adenocarcinoma, secretory endocrine, and solid pseudopapillary carcinomas (IRR = 0.5, 0.9, and 0.2 respectively, confidence intervals 0.4–0.6 and 0.9-1.3, 0.2–0.3, respectively). Conclusion: Pancreatic cancer incidence continued to rise in the years 2000-2017. However, incidence differed by demographics and histologic type. Interestingly, recent years discerned a rise in PNETs (carcinoid type) which has not been reported previously.
INTEGRATION OF COMPLEX EVENT PROCESSING INTO MULTI-AGENT SYSTEMS: TWO USE CASES FOR DISTRIBUTED SOFTWARE DEVELOPMENT SUPPORT

Complex event processing (CEP) systems are typically utilised for smart environments to cope with large numbers of components and events without simultaneously increasing latencies for user interaction. Although CEP systems can simplify the development of components with CEP queries, it remains challenging to debug such complex distributed systems. Using CEP introduces an additional software development paradigm which can make it harder for developers to obtain an overview of system components or to pinpoint errors. We introduce an approach to seamlessly integrate CEP into an existing publish/subscribe middleware for smart environments, leveraging agent-based developing techniques to maintain the scalability and latency characteristics of the existing system. Our approach envisions a system in which it is irrelevant whether a feature was implemented through an agent or through a CEP query. The paper illustrates our system’s architecture, describes its prototypical implementation, and exemplarily shows its applicability based on two real-world evaluations: first, a sensor network for air quality measurements; and second, an omnidirectional walking-in-place recognition system for virtual-reality applications.
Groups generated by involutions, numberings of posets, and central measures

1. Definitions. An infinite countable ordered set {P,≻, ∅} with minimal element ∅ and no maximal elements is called a locally finite poset if all its principal ideals are finite. A monotone numbering of P (or a part of P ) is an injective map φ : N → P from the set of positive integers to P satisfying the following conditions: if φ(n) ≻ φ(m), then n > m, with φ(0) = ∅. The distributive lattice ΓP of all finite ideals of a locally finite poset {P,≻} forms an N-graded graph (the Hasse diagram of the lattice). A monotone numbering of P can be identified in a natural way with a maximal path in the lattice ΓP . The set TP of all monotone numberings of P , that is, the space of infinite paths in the graph ΓP can be endowed with the natural structure of a Borel and topological space. In the terminology related to the Young graph, the poset P is the set of Z+-finite ideals, that is, Young diagrams, and monotone numberings are Young tableaux. Let P be a finite (|P | < n ∈ N) or locally finite (n = ∞) poset. For each i < n, we define an involution σi acting correctly on the space of numberings TP = {φ} of P :
A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification

- Heart disease is the most common reason for human mortality that causes almost one-third of deaths throughout the world. Detecting the disease early increases the chances of survival of the patient and there are several ways a sign of heart disease can be detected early. This research proposes to convert cleansed and normalized heart sound into visual mel scale spectrograms and then using visual domain transfer learning approaches to automatically extract features and categorize between heart sounds. Some of the previous studies found that the spectrogram of various types of heart sounds is visually distinguishable to human eyes, which motivated this study to experiment on visual domain classification approaches for automated heart sound classification. It will use convolution neural network-based architectures i.e. ResNet, MobileNetV2, etc as the automated feature extractors from spectrograms. These well-accepted models in the image domain showed to learn generalized feature representations of cardiac sounds collected from different environments with varying amplitude and noise levels. Model evaluation criteria used were categorical accuracy, precision, recall, and AUROC as the chosen dataset is unbalanced. The proposed approach has been implemented on datasets A and B of the PASCAL heart sound collection and resulted in ~ 90% categorical accuracy and AUROC of ~0.97 for both sets.
Reconstructing QCD spectral functions with Gaussian processes

We reconstruct ghost and gluon spectral functions in 2+1 flavor QCD with Gaussian process regression. This framework allows us to largely suppress spurious oscillations and other common reconstruction artifacts by specifying generic magnitude and length scale parameters in the kernel function. The Euclidean propagator data are taken from lattice simulations with domain wall fermions at the physical point. For the infrared and ultraviolet extensions of the lattice propagators as well as the low-frequency asymptotics of the ghost spectral function, we utilize results from functional computations in Yang-Mills theory and QCD. This further reduces the systematic error significantly. Our numerical results are compared against a direct real-time functional computation of the ghost and an earlier reconstruction of the gluon in Yang-Mills theory. The systematic approach presented in this work offers a promising route towards unveiling real-time properties of QCD.
Quantum computing for classical problems: variational quantum eigensolver for activated processes

The theory of stochastic processes impacts both physical and social sciences. At the molecular scale, stochastic dynamics is ubiquitous because of thermal fluctuations. The Fokker–Plank–Smoluchowski equation models the time evolution of the probability density of selected degrees of freedom in the diffusive regime and it is, therefore, a workhorse of physical chemistry. In this paper we report on the development and implementation of a variational quantum eigensolver to solve the Fokker–Planck–Smoluchowski eigenvalue problem. We show that such an algorithm, typically adopted to address quantum chemistry problems, can be effectively applied to classical systems, paving the way to new applications of quantum computers. We compute the conformational transition rate in a linear chain of rotors with nearest-neighbour interactions. We provide a method to encode the probability distribution for a given conformation of the chain on a quantum computer and assess its scalability in terms of operations. A performance analysis on noisy quantum emulators and quantum devices (IBMQ Santiago) is provided for a small chain which shows results in good agreement with the classical benchmark without any further addition of error mitigation techniques.
Evolution of a Mode of Oscillation within Turbulent Accretion Disks

We investigate the effects of subsonic turbulence on a normal mode of oscillation (a possible origin of the high-frequency quasi-periodic oscillations (HFQPOs) within some black hole accretion disks). We consider perturbations of a time-dependent background (steady-state disk plus turbulence), obtaining an oscillator equation with stochastic damping, (mildly) nonlinear restoring, and stochastic driving forces. The (long-term) mean values of our turbulent functions vanish. In particular, turbulence does not damp the oscillation modes, so “turbulent viscosity” is not operative. However, the frequency components of the turbulent driving force near that of the mode can produce significant changes in the amplitude of the mode. Even with an additional (phenomenological constant) source of damping, this leads to an eventual “blowout” (onset of effects of nonlinearity) if the turbulence is sufficiently strong or the damping constant is sufficiently small. The infrequent large increases in the energy of the mode could be related to the observed low duty cycles of the HFQPOs. The width of the peak in the power spectral density (PSD) is proportional to the amount of nonlinearity. A comparison with observed continuum PSDs indicates the conditions required for the visibility of the mode.
A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision

Background and purpose: Automatic segmentation of anatomical structures is critical for many medical applications. However, the results are not always clinically acceptable and require tedious manual revision. Here, we present a novel concept called artificial intelligence–assisted contour revision (AIACR) and demonstrate its feasibility. Materials and Methods: The proposed clinical workflow of AIACR is as follows: given an initial contour that requires a clinician’s revision, the clinician indicates where a large revision is needed, and a trained deep learning (DL) model takes this input to update the contour. This process repeats until a clinically acceptable contour is achieved. The DL model is designed to minimize the clinician’s input at each iteration and to minimize the number of iterations needed to reach acceptance. In this proof-of-concept study, we demonstrated the concept on 2D axial images of three head-and-neck cancer datasets, with the clinician’s input at each iteration being one mouse click on the desired location of the contour segment. The performance of the model is quantified with Dice Similarity Coefficient (DSC) and 95 percentile of Hausdorff Distance (HD95). Results: The average DSC/HD95 (mm) of the auto-generated initial contours were 0.82/4.3, 0.73/5.6 and 0.67/11.4 for three datasets, which were improved to 0.91/2.1, 0.86/2.4 and 0.86/4.7 with three mouse clicks, respectively. Each DL-based contour update requires ~20 ms. Conclusion: We proposed a novel AIACR concept that uses DL models to assist clinicians in revising contours in an efficient and effective way, and we demonstrated its feasibility by using 2D axial CT images from three head-and-neck cancer datasets.
Open clusters with proper motions fully separatedfrom the field stars using Gaia DR2

ABSTRACT The study of open star clusters makes us understand a lot about the composition and construction of the Milky Way Galaxy. Thanks to the Gaia DR2 database that helps us to get the genetic members of star clusters using their proper motions and parallaxes, and estimating their physical properties in a very accurate way. This study aims to detect the reasons that make clusters (within 2 kpc) have proper motions completely separated from the background field stars. We studied a large sample of open stellar clusters taken from Dias’s catalogue and drew the vector point diagrams using the astrometric data of Gaia DR2. By visual inspection, we specified 108 objects as separated clusters and 377 ones as melted clusters. We studied their mean parameters in the four galactic quadrants of the Milky Way Galaxy.
Magnetic properties of the nanocrystalline DyMnO3

We report on the X-ray and neutron diffraction and magnetic measurements of the nanosamples of DyMnO3 annealed at temperatures of 800 °C, 850 °C and 900 °C. The diffraction data indicate that all the samples crystallize in an orthorhombic crystal structure (space group Pnma). The crystal structure parameters change slightly with preparation. All the samples are antiferromagnets at low temperatures. The Mn magnetic moments order near 40 K, while those of Dy below 8.4 K. The macroscopic magnetic and neutron diffraction data indicate a small difference of properties between the DyMnO3 samples synthesized at different temperatures. The observed broadening of magnetic peaks connected with the Dy sublattice suggests a cluster-like character of magnetic ordering.
Pixyz: a Python library for developing deep generative models

With the recent rapid progress in the study of deep generative models (DGMs), there is a need for a framework that can implement them in a simple and generic way. In this research, we focus on two features of DGMs: (1) deep neural networks are encapsulated by probability distributions, and (2) models are designed and learned based on an objective function. Taking these features into account, we propose a new Python library to implement DGMs called Pixyz. This library adopts a step-by-step implementation method with three APIs, which allows us to implement various DGMs more concisely and intuitively. In addition, the library introduces memoization to reduce the cost of duplicate computations in DGMs to speed up the computation. We demonstrate experimentally that this library is faster than existing probabilistic programming languages in training DGMs. GRAPHICAL ABSTRACT