Miracle-dz/UniFAR-FTUs huggingface dataset:
XLA-NDP: Efficient Scheduling and Code Generation for Deep Learning Model Training on Near-Data Processing Memory

Deep learning (DL) model training must address the memory bottleneck to continue scaling. Processing-in-memory approaches can be a viable solution as they move computations near or into the memory, reducing substantial data movement. However, to deploy applications on such hardware, end-to-end software support is crucial for efficient computation mapping and scheduling as well as extensible code generation, but no consideration has been made for DL training workloads. In this paper, we propose XLA-NDP, a compiler and runtime solution for NDPX, a near-data processing (NDP) architecture integrated with an existing DL training framework. XLA-NDP offloads NDPX kernels and schedules them to overlap with GPU kernels to maximize parallelism based on GPU and NDPX costs, while providing a template-based code generator with low-level optimizations. The experiments showed that XLA-NDP provides up to a 41% speedup (24% on average) over the GPU baseline for four DL model training.
Computational Modeling of Flow Inside a Diseased Carotid Bifurcation

One of the leading causes for death after heart diseases and cancer in all over the world is still stroke. Most strokes happen because an artery that carries blood uphill from the heart to the head is clogged. Most of the time, as with heart attacks, the problem is atherosclerosis, hardening of the arteries, calcified buildup of fatty deposits on the vessel wall. The primary troublemaker is the carotid artery, one on each side of the neck, the main thoroughfare for blood to the brain. Only within the last twenty-five years, though, have researchers been able to put their finger on why the carotid is especially susceptible to atherosclerosis. In this study, the fluid dynamic simulations were done in a diseased carotid bifurcation under the steady flow conditions computationally. Reynolds numbers representing the steady flow were 300, 1020 and 1500 for diastolic, average and systolic peak flow represented by pulsatile flow waveform, respectively. In vivo geometry and boundary conditions were obtained from a patient who has stenosis located at external carotid artery (ECA) and internal carotid artery (ICA) of his common carotid artery (CCA). The location of critical flow fields such as low wall shear stress (WSS), stagnation regions and separation regions were detected near the highly stenosed region and at branching region.
Material-Inherent Noise Sources in Quantum Information Architecture

NISQ is a representative keyword at present as an acronym for “noisy intermediate-scale quantum”, which identifies the current era of quantum information processing (QIP) technologies. QIP science and technologies aim to accomplish unprecedented performance in computation, communications, simulations, and sensing by exploiting the infinite capacity of parallelism, coherence, and entanglement as governing quantum mechanical principles. For the last several decades, quantum computing has reached to the technology readiness level 5, where components are integrated to build mid-sized commercial products. While this is a celebrated and triumphant achievement, we are still a great distance away from quantum-superior, fault-tolerant architecture. To reach this goal, we need to harness technologies that recognize undesirable factors to lower fidelity and induce errors from various sources of noise with controllable correction capabilities. This review surveys noisy processes arising from materials upon which several quantum architectures have been constructed, and it summarizes leading research activities in searching for origins of noise and noise reduction methods to build advanced, large-scale quantum technologies in the near future.
RESEARCHES AND APPLICATIONS OF LOCATING TECHNOLOGY FOR TYPHOON CENTER ON SATELLITE INFRARED CLOUD PICTURE

The methods and technologies for positioning typhoon centers were studied and summarized systematically using the distribution of gray scale and the corresponding characteristics of brightness temperature on satellite infrared cloud imageries.Different positioning methods were designed for typhoon cloud systems with different structures and characteristics.The operational system,which includes seven methods,was established after optimizing and extending the arithmetic and realizing steps of all kinds of methods.Furthermore,the system played an efficient role in the actual operational application.On the whole,the alternation between the operator and computer was the main manipulation mode,and part functions were automatic.It was demonstrated that the system can locate the center of typhoon with any structural characteristics if a appropriated method was chosen.
Iron Line Diagnostics of the Postshock Hot Plasma in Magnetic Cataclysmic Variables Observed with ASCA

The ASCA spectra of ~20 magnetic cataclysmic variables are presented. Owing to the high spectral resolution of the solid-state imaging spectrometer, we successfully resolved the iron Kα emission line into the fluorescent (6.4 keV) and plasma (6.7 and 7.0 keV) components. By comparing the ionization temperature, which is obtained from the intensity ratio of the plasma line components of iron, with the continuum temperature, we have obtained the evidence that the postshock plasma has a temperature distribution. Detailed analysis indicates that the observed temperature distribution is consistent with that expected from the postshock plasma model in the bremsstrahlung cooling domain. In the framework of this postshock plasma model, we have constrained the mass of the white dwarf in totally nine intermediate polars and obtained the iron abundances. The obtained masses are generally consistent with the previous X-ray work. We have found that the iron abundance is generally subsolar, and its distribution probably peaks at 0.2-0.6 solar. We have also shown that the reflection from the white dwarf surface makes significant contribution to the observed fluorescent iron Kα emission line.
A Front-End CMOS Interface Circuit With High Voltage Charge Pump and Oscillator for Capacitive Micromachined Ultrasonic Transducers

Label-free biosensors, combined with miniaturized micro-electromechanical sensory platforms, offer an attractive solution for real-time and facile monitoring of biomolecules due to their high sensitivity and selectivity without the need for specifically labeling. Resonators have been acknowledged as an efficient technology for measuring biomolecular binding events including those involving nucleic acid and antibody. Among these, capacitive micromachined ultrasonic transducers (CMUTs) have emerged as a promising candidate for biosensing. However, their usage is often limited by the requirement for high voltage supply and continuous frequency tracking, which can result in significant parasitic effects and measurement errors. In this brief, we present a novel front-end interface circuit for a CMUTs-based biosensor. The circuit, fabricated using TSMC 0.18- $\mu \text{m}$ Bipolar-CMOS-DMOS (BCD) technology, incorporates an on-chip high voltage charge pump and feedback frequency monitoring. The CMUTs array features $20\times20$ circular cells, fabricated using a low-temperature direct bonding technology, with an experimental parallel-resonant frequency of 1.724 MHz and a high quality factor of up to 40.9. To fit the measured electrical characteristics, a five-element equivalent lumped element model is proposed. The high voltage charge pump provides an output voltage of ~20 V, while the feedback oscillator has a ms-level start-up time and a total power dissipation of 3.8 mW. The proposed front-end interface is designed to function as a stand-alone chip for CMUTs-based resonant biodetection.
Epitaxial graphene on 3C-SiC(111) pseudosubstrate: Structural and electronic properties

Structural and electronic properties of epitaxial graphene on 3C-SiC(111) pseudosubstrate epilayers on silicon was investigated in detail by scanning tunneling microscopy (STM), low-energy electron diffraction (LEED), scanning transmission electron microscopy (STEM), and synchrotron angle-resolved photoemission spectroscopy (ARPES). The graphitization process has been observed by distinct features in the atomically resolved STM images and abrupt interface with the number of stacked-graphene layer has been revealed in STEM image. Two different types of carbon atom networks, honeycomb and one sublattice, were atomically resolved by STM. Electronic properties and band structures of the epitaxial graphene are examined with angle-resolved photoemission spectroscopy, showing linear band dispersion K point of the Brillouin zone, with Dirac point about 500 meV below the Fermi level (E-F). These findings are of relevance for various potential applications based on graphene-SiC/Si(111) structures.
Crosstalk between Gut Microbiota and Epigenetic Markers in Obesity Development: Relationship between Ruminococcus, BMI, and MACROD2/SEL1L2 Methylation

Changes in gut microbiota composition and in epigenetic mechanisms have been proposed to play important roles in energy homeostasis, and the onset and development of obesity. However, the crosstalk between epigenetic markers and the gut microbiome in obesity remains unclear. The main objective of this study was to establish a link between the gut microbiota and DNA methylation patterns in subjects with obesity by identifying differentially methylated DNA regions (DMRs) that could be potentially regulated by the gut microbiota. DNA methylation and bacterial DNA sequencing analysis were performed on 342 subjects with a BMI between 18 and 40 kg/m2. DNA methylation analyses identified a total of 2648 DMRs associated with BMI, while ten bacterial genera were associated with BMI. Interestingly, only the abundance of Ruminococcus was associated with one BMI-related DMR, which is located between the MACROD2/SEL1L2 genes. The Ruminococcus abundance negatively correlated with BMI, while the hypermethylated DMR was associated with reduced MACROD2 protein levels in serum. Additionally, the mediation test showed that 19% of the effect of Ruminococcus abundance on BMI is mediated by the methylation of the MACROD2/SEL1L2 DMR. These findings support the hypothesis that a crosstalk between gut microbiota and epigenetic markers may be contributing to obesity development.
Hierarchy in the Forest

Are humans by nature hierarchical or egalitarian? "Hierarchy in the Forest" addresses this question by examining the evolutionary origins of social and political behavior. Christopher Boehm, an anthropologist whose fieldwork has focused on the political arrangements of human and nonhuman primate groups, postulates that egalitarianism is in effect a hierarchy in which the weak combine forces to dominate the strong.The political flexibility of our species is formidable: we can be quite egalitarian, we can be quite despotic. "Hierarchy in the Forest" traces the roots of these contradictory traits in chimpanzee, bonobo, gorilla, and early human societies. Boehm looks at the loose group structures of hunter-gatherers, then at tribal segmentation, and finally at present-day governments to see how these conflicting tendencies are reflected. "Hierarchy in the Forest" claims new territory for biological anthropology and evolutionary biology by extending the domain of these sciences into a crucial aspect of human political and social behavior. This book will be a key document in the study of the evolutionary basis of genuine altruism.
Peripheral and central capillary non-perfusion in diabetic retinopathy: An updated overview

Capillary non-perfusion (CNP) is one of the key hallmarks of diabetic retinopathy (DR), which may develop both in the periphery and at the posterior pole. Our perspectives on CNP have extended with the introduction of optical coherence tomography angiography (OCTA) and ultra-widefield imaging, and the clinical consequences of peripheral and macular CNP have been well characterized. Fluorescein angiography (FA) continues to be the gold standard for detecting and measuring CNP, particularly when ultra-widefield imaging is available. OCTA, on the other hand, is a quicker, non-invasive approach that allows for a three-dimensional examination of CNP and may soon be regarded as an useful alternative to FA. In this review, we provide an updated scenario regarding the characteristics, clinical impact, and management of central and peripheral CNP in DR.
P Values for Composite Null Models

Abstract The problem of investigating compatibility of an assumed model with the data is investigated in the situation when the assumed model has unknown parameters. The most frequently used measures of compatibility are p values, based on statistics T for which large values are deemed to indicate incompatibility of the data and the model. When the null model has unknown parameters, p values are not uniquely defined. The proposals for computing a p value in such a situation include the plug-in and similar p values on the frequentist side, and the predictive and posterior predictive p values on the Bayesian side. We propose two alternatives, the conditional predictive p value and the partial posterior predictive p value, and indicate their advantages from both Bayesian and frequentist perspectives.
Cloud Radiative Forcing of the Low-Latitude Tropospheric Circulation: Linear Calculations

Abstract The role of clouds for low-latitude atmospheric circulations is examined in a linearized calculation forced by diabatic heating rates. A comparison of the circulation calculated from total diabatic heating, obtained from reanalysis data, with observed fields determines which aspects of the calculation are realistic and which are not. The role of clouds is quantified by the circulation calculated from atmospheric cloud radiative forcing, which, in turn, has been calculated with the National Center for Atmospheric Research radiative transfer model using cloud properties observed in the International Satellite Cloud Climatology Project. In general, cloud radiative forcing contributes about 20% to the magnitude of low-latitude circulations. It typically reinforces the circulation that is driven by convective latent heating. Cloud radiative forcing tends to have a stronger influence in the lower troposphere than at upper levels. It influences local circulations more than remote ones. In particular, cl.
Simulation-guided regression approach for estimating the size distribution of nanoparticles with dynamic light scattering data

ABSTRACT This article presents a simulation-guided regression approach for estimating the size distribution of nanoparticles from Dynamic Light Scattering (DLS) measurements. The properties and functionalities exhibited by nanoparticles often depend on their sizes, so the precise quantification of the sizes is important for characterizing and monitoring the quality of a nanoparticle synthesis process. The state-of-the-art method used in the size quantification from DLS measurements is the CONTIN, which is based on a computationally ineffective numerical inversion. We propose a new approach that avoids the numerical inversion by reformulating the problem into a regularized regression problem, with the basis functions being generated by a computer simulation of DLS measurements. For many simulation studies and one real data study, our method outperformed the CONTIN in terms of estimation accuracy and computational efficiency.
6F-4 50-kHz Capacitive Micromachined Ultrasonic Transducers for Generating Highly Directional Sound with Parametric Arrays

Capacitive micromachined ultrasonic transducers (CMUTs) were designed, fabricated, and characterized to generate highly directional sound in air with parametric arrays. Finite element modelling was used to design the vacuum-sealed CMUTs to operate at frequencies of approximately 50 kHz and to generate pressures of approximately 139 dB (0 dB = 20 muPa RMS) at the face of the transducer. CMUTs with 40-mum and 60-mum thick membranes were fabricated and characterized. The characterized devices had center frequencies of 46 kHz and 55 kHz and bandwidths of 2.0 kHz and 5.4 kHz, respectively. Driven with a 200-V peak-to-peak excitation voltage, the devices generated up to 115 dB and 107 dB of pressure at 3 m, respectively. The devices were used to produce a narrow (8.7deg) beam of 5 kHz sound, which at 3 m was 58 dB.
Functional Perspectives on Lithic Standardization

ABSTRACT Functional data accumulated over the recent decades confirm that tool use mechanics, working edge maintenance, and hafting are important factors determining stone tool form. Yet such data are rarely considered in studies on lithic standardization, and tool hafting has entered the discussion mostly in the form of untested hypotheses. In this paper, we examine the effects of tool use, resharpening, and hafting on lithic standardization by drawing on recent use-wear data on Paleolithic domestic tools and projectiles. We evaluate morphological constraints posed by different tool use tasks and hafting systems, and the effects of these on blank selection. We conclude that the concept of standardization can be useful in making sense of lithic assemblage patterning, but it needs to be redefined to accommodate functional considerations. We advise shifting the focus from stone tool form to working edge qualities and hafted tool design, which drastically alters the perspective on inter-assemblage variability.
MADGAN:A microbe-disease association prediction model based on generative adversarial networks

Researches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases. In this manuscript, we proposed a novel prediction model named MADGAN to infer potential microbe-disease associations by combining biological information of microbes and diseases with the generative adversarial networks. To our knowledge, it is the first attempt to use the generative adversarial network to complete this important task. In MADGAN, we firstly constructed different features for microbes and diseases based on multiple similarity metrics. And then, we further adopted graph convolution neural network (GCN) to derive different features for microbes and diseases automatically. Finally, we trained MADGAN to identify latent microbe-disease associations by games between the generation network and the decision network. Especially, in order to prevent over-smoothing during the model training process, we introduced the cross-level weight distribution structure to enhance the depth of the network based on the idea of residual network. Moreover, in order to validate the performance of MADGAN, we conducted comprehensive experiments and case studies based on databases of HMDAD and Disbiome respectively, and experimental results demonstrated that MADGAN not only achieved satisfactory prediction performances, but also outperformed existing state-of-the-art prediction models.
Penalty adapting ant algorithm: application to pipe network optimization

A penalty adapting ant algorithm is presented in an attempt to eliminate the dependency of ant algorithms on the penalty parameter used for the solution of constrained optimization problems. The method uses an adapting mechanism for determination of the penalty parameter leading to elimination of the costly process of penalty parameter tuning. The method is devised on the basis of observation that for large penalty parameters, infeasible solutions will have a higher total cost than feasible solutions and vice versa. The method therefore uses the best feasible and infeasible solution costs of the iteration to adaptively adjust the penalty parameter to be used in the next iteration. The pheromone updating procedure of the max–min ant system is also modified to keep ants on and around the boundary of the feasible search space where quality solutions can be found. The sensitivity of the proposed method to the initial value of the penalty parameter is investigated and indicates that the method converges to optimal or near-optimal solutions irrespective of the initial starting value of the penalty parameter. This is significant as it eliminates the need for sensitivity analysis of the method with respect to the penalty factor, thus adding to the computational efficiency of ant algorithms. Furthermore, it is shown that the success rate of the search algorithm in locating an optimal solution is increased when a self-adapting mechanism is used. The presented method is applied to a benchmark pipe network optimization problem in the literature and the results are presented and compared with those of existing algorithms.
An ultra‐wideband high gain antenna with embedded sleeves and hexagonal cavity

Radar detection and imaging systems need ultra‐wideband, high‐gain antennas to improve depth resolution and detection range. This paper proposes an ultra‐wideband high gain differential patch antenna having a peak gain of 12.9 dBi and an average gain of 11.3 dBi in the bandwidth from 7.2 and 26 GHz. Two sleeves are added around the feeding probes to attain ultra‐wideband impedance matching that is explained with an equivalent circuit. A hexagonal cavity with two M‐shaped cutouts improves the gain in the broadside direction by redistributing the current and reshaping the antenna's radiation patterns at different frequencies. The antenna's measured S‐parameter validates the design and confirms it has an ultra‐wide bandwidth. The ultra‐wide bandwidth along with the high gain values make the antenna a promising candidate for high‐resolution radar systems.
Bayesian Estimates of Potential Output and the NAIRU for Taiwan

Previous literature indicates that the NAIRU and potential output are measured with large degree of uncertainty. To account for this large estimate uncertainty, this paper develops the corresponding Bayesian sampling algorithms for Watson's decomposition method and Apel and Jansson's systems approach. We assess our algorithms by conducting a series of Monte Carlo simulation experiments. To illustrate the practical relevance of our algorithms, we also apply them to Taiwan's seasonally unadjusted data and incorporate the idea of seasonal unit root into our Bayesian framework. Simulation and empirical analyses show that our Bayesian sampling algorithms are flexible and do not merely duplicate the maximum likelihood estimates. We find that the maximum likelihood estimate generally understates the parameter variability and puts too little weight on the variance. A Bayesian approach allows for more stochastic variation in the permanent and cyclical components. Our analysis demonstrates that the posterior distribution facilitates assessment of the parameter uncertainty such that a Bayesian approach is rich enough to cope with model specification issues and provides more relevant information for conducting monetary and fiscal policies.