Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Outcome of Cardio-Embolic Stroke Patients Admitted at Referral Neurology Hospital in Bangladesh

Background Stroke is the second leading cause of mortality worldwide; where the majority of stroke is ischemic. Among ischemic stroke, cardio-embolic has both higher severity and mortality. Objective To find out clinical outcomes and determine predictors of mortality related to cardio-embolic stroke. Methodology This prospective cohort study was conducted among patients of acute ischemic stroke of cardiac origin admitted at the National Institute of Neurosciences and Hospital, Bangladesh from 1 st October 2020 to 30 September, 2021. Patients were kept under follow-up to 90 days from discharge. Results A total of 689 ischemic stroke patients were screened, 156 had confirmed Cardio-embolic stroke. So, the frequency of cardio-embolic stroke was 22.64%. Male to female ratio was 1.3:1, mean age of 63 years. Hypertension 119 (76.3%), atrial fibrillation 107 (68.6%), and IHD 40 (25.6%) were most common comorbidities. Interestingly, we found only 23 (14.7%) patients with chronic rheumatic heart diseases. NIH Stroke scale score (median, IQR) during admission was 13 [7- 19]. Overall mortality was 47 (29.9%), among them 30 (19.2%) died within 48 hours of hospital admission while 17 (10.9%) within 90 days of hospital discharge. Modified Rankin score at 90 days was 2 [min 0, max 5] those who survived. Cumulative incidence of recurrent stroke was 9 (7.1%) and incidence of anticoagulant induced hemorrhage were 5 (3.2%) among them. Risk factors associated with mortality (odds ratio, [95% CI], p value) were acute myocardial infarction (1.6 [1.14 - 2.52] , 0.04), raised Troponin (1.89 [1.16-2.99], 0.01), reduced ejection fraction (3.38 [2.17-5.27], <0.001), hypotension (3.12 [2.07 - 4.68], < 0.001), chronic kidney disease (1.82 [1.06 - 3.10], 0.04), raised Creatinine (2.41 [1.52 -3.84], 0.01), raised blood sugar ( 1.82 [1.14 - 2.89], 0.02) , severe stroke (9.45 [3.57 - 25.03] , <0.001), large infarct (53.67 [7.59 - 379.47] , < 0.001), hemorrhagic transformation (4.43 [2.89 - 6.84] , < 0.001) and aspiration pneumonia (1.9 [1.28-2.39], 0.01) . Conclusion Overall frequency, severity, functional disability, and mortality in cardio-embolic stroke are higher. Acute myocardial infarction, severe stroke, presence of hyperglycemia, hypotension, renal impairment, low ejection fraction, large infarct, hemorrhagic transformations, and aspiration pneumonia are both clinically and statistically significantly associated with mortality in cardio-embolic stroke.
Supplementary material for Busy-Quiet Video Disentangling for Video Classification

We train our models in 16 or 64 GPUs (NVIDIA Tesla V100), using Stochastic Gradient Descent (SGD) with momentum 0.9 and cosine learning rate schedule. In order to prevent overfitting, we add a dropout layer before the classification layer of each pathway in the BQN model. Following the experimental settings in [10, 13], the learning rate and weight decay parameters for the classification layers are 5 times of the convolutional layers. Meanwhile, we only apply L2 regularization to the weights in the convolutional and classification layers to avoid overfitting. Hyperparameters for models based on ResNet. For Kinetics400 [3], the initial learning rate, batch size, total epochs, weight decay and dropout ratio are set to 0.08, 512 (8 samples per GPU), 100, 2e-4 and 0.5, respectively. For Something-Something V1 [5], these hyperparameters are set to 0.12, 256, 50, 8e-4 and 0.8, respectively. We use linear warm-up [11] for the first 7 epochs to overcome early optimization difficulty. When fine-tuning the Kinetics models on UCF101 [12] and HMDB51 [9], we freeze all of the batch normalization [8] layers except for the first one to avoid overfitting, following the recipe in [13]. The initial learning rate, batch size, total epochs, weight decay and dropout ratio are set to 0.001, 64 (4 samples per GPU), 10, 1e-4 and 0.8, respectively. Hyperparameters for models based on X3D-M. For Kinetics400, the initial learning rate, batch size, total epochs, weight decay and dropout ratio are set to 0.4, 256 (16 samples per GPU), 256, 5e-5 and 0.5, respectively. For Something-Something V1, the models trained from scratch use the followings hyperparameters: learning rate 0.2, batch size 256, total epochs 100, weight decay 5e-5 and dropout ratio 0.5. When fine-tuning the Kinetics models, the initial learning rate, batch size, total epochs, weight decay and dropout ratio are set to 0.12, 256 (16 samples per GPU), 60, 4e-4 and 0.8, respectively.
The Impact of Health on Labor Market Outcomes: Evidence from a Large-Scale Health Experiment

While economists have posited that health investments increase earnings, isolating the causal effect of health is challenging due to reverse causality and unobserved heterogeneity. We examine the labor market effects of a randomized controlled trial, the Multiple Risk Factor Intervention Trial (MRFIT), which monitored nearly 13,000 men for over six years. We find that this intervention, which provided a bundle of treatments to reduce coronary heart disease mortality, increased earnings and family income. We find few differences in estimated gains by baseline health and occupation characteristics. (JEL I12, J24, J31)
Improving Teaching Quality and Problem Solving Ability Through Contextual Teaching and Learning in Differential Equations: a Lesson Study Approach

Differential equations is a branch of mathematics which is closely related to mathematical modeling that arises in real-world problems. Problem solving ability is an essential component to solve contextual problem of differential equations properly. The purposes of this study are to describe contextual teaching and learning (CTL) model in differential equations course, to improve lecturerV· abilities in implementing CTL, and to improve VWXGHQWV· problem solving ability in differential equations. The study was conducted in the fifth semester of 2015/2016 academic year with 34 students of mathematics education Universitas Muhammadiyah Surakarta as participants. The CTL model was applied by lesson study approach which involved three stages namely plan, do, and see in each cycle. This research was conducted in four cycles. The study results found that discovery-based CTL could be applied in differential equations course. The lecturer abilities to design discovery-based contextual learning plan, to present real-world problem in learning process, to design learning strategy and assessment instruments of problem solving improved significantly. Problem solving ability of students also improved during teaching and learning process.
Characteristic and recognition of ultrasonic TOFD signal and image for planar defect

Considering several typical planar defects that were common flaws in thick aluminum weld, the characteristics of Ultrasonic TOFD (time of flight diffraction) was studied. Ultrasonic TOFD A-scan signal and B, D-scan image were analyzed and interpreted. Linearization was introduced in processing B and D-scan image. The results show that planar defect can be effectively recognized, located and sized by the characteristics of the received signal and image. Linearization effectively improves time resolution of the received images, which makes the characteristics of the defect more markedly and leads to more accurate quantitative measurement.
Information Discovery in E-commerce

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Monitoring dust particle orientation with measurements of sunlight dichroic extinction

Alignment of irregularly shaped dust aerosols leading to linear dichroism has been reported in atmospheric layers. The present study intents to quantify the excess linear polarization of direct solar radiation propagating through atmospheric layers, when these contain oriented dust particles. In order to record the linear polarization, we have used the Solar Polarimeter (SolPol). SolPol is an instrument that measures the polarization of direct solar irradiance at 550nm. It is installed on an astronomical tracker in order target the solar disk. Using the measurements, the Stokes parameters are retrieved (I, Q/I, U/I and V/I) with an accuracy of ~1% and precision of 1 ppm. Collocated measurements of a sun-photometer (Aerosol Robotic Network; AERONET) and lidar are used to quantify the Aerosol Optical Depth (AOD) and identify the vertical distribution of dust layers, respectively. We will present indications of dust particle orientation recorded at the PANGEA station in the island of Antikythera, Greece, and at Nicosia, Cyprus during the preparatory phase for the ASKOS campaign in July 2021. The relation of the linear polarization of the solar irradiance to other optical properties of the dust layer is investigated.
Current Thought of Plant Cell Wall: Structure, Biosynthesis, and Future Application

Plant cell walls are unique, complex, diverse and dynamic structures that change throughout the process of cell division, growth and differentiation. Plant cell walls are not only a single key determinant of overall plant form, but they also play an important role in sustaining human societies. Evidence of how cell walls are crucial for plant structure is that without cell walls, plants would be elastic piles of protoplasm, more like slime moulds than stately trees. Unlike animals, in which specialized skeletal system provides physical support, the strength, flexibility, texture and overall shape of higher plants depend on the cumulative properties of walls. Moreover, plants are composed of approximately 35-40 cell types that are distinguished from each other by the chemistry and organization of their walls. In human societies, cell walls directly affect the quality of most plant-based products, including paper and pulping, textile, food quality and texture, dietary fibre, malting and brewing and bioethanol production (Cosgrove, 2005; Farrokhi et al., 2006). The chemical structure of most wall components has been intensively studied and defined in detail, however, the enzymes responsible for cell wall biosynthesis and also its restructure remain poorly understood. However, currently, there has been real progress towards understanding the cell wall biosynthesis, using various emerging approaches, such as genetic and biochemical approaches (Farrokhi et al., 2006). This review will elaborate on recent advances in our knowledge of the structure cell walls of flowering plants, particularly the structure of primary cell walls. Moreover, this review will also explain the current knowledge towards understanding primary wall biosynthesis and also the future application of cell wall study in biotechnology.
Demystifying the Adversarial Robustness of Random Transformation Defenses

Neural networks’ lack of robustness against attacks raises concerns in security-sensitive settings such as autonomous vehicles. While many coun-termeasures may look promising, only a few with-stand rigorous evaluation. Defenses using random transformations (RT) have shown impressive results, particularly BaRT (Raff et al., 2019) on ImageNet. However, this type of defense has not been rigorously evaluated, leaving its robustness properties poorly understood. Their stochastic properties make evaluation more challenging and render many proposed attacks on deterministic models inapplicable. First, we show that the BPDA attack (Athalye et al., 2018a) used in BaRT’s evaluation is ineffective and likely over-estimates its robustness. We then attempt to con-struct the strongest possible RT defense through the informed selection of transformations and Bayesian optimization for tuning their parameters. Furthermore, we create the strongest possible attack to evaluate our RT defense. Our new attack vastly outperforms the baseline, reducing the accuracy by 83% compared to the 19% re-duction by the commonly used EoT attack ( 4 . 3 × improvement). Our result indicates that the RT defense on Imagenette dataset (a ten-class subset of ImageNet) is not robust against adversarial examples. Extending the study further, we use our new attack to adversarially train RT defense (called AdvRT), resulting in a large robustness gain. Code is available at https://github.com/wagner-group/demystify-random-transform.
Modeling and Predicting Blood Flow Characteristics through Double Stenosed Artery from CFD simulation using Deep Learning Models

Establishing patient-specific finite element analysis (FEA) models for computational fluid dynamics (CFD) of double stenosed artery models involves time and effort, restricting physicians' ability to respond quickly in time-critical medical applications. Such issues might be addressed by training deep learning (DL) models to learn and predict blood flow characteristics using a dataset generated by CFD simulations of simplified double stenosed artery models with different configurations. When blood flow patterns are compared through an actual double stenosed artery model, derived from IVUS imaging, it is revealed that the sinusoidal approximation of stenosed neck geometry, which has been widely used in previous research works, fails to effectively represent the effects of a real constriction. As a result, a novel geometric representation of the constricted neck is proposed which, in terms of a generalized simplified model, outperforms the former assumption. The sequential change in artery lumen diameter and flow parameters along the length of the vessel presented opportunities for the use of LSTM and GRU DL models. However, with the small dataset of short lengths of doubly constricted blood arteries, the basic neural network model outperforms the specialized RNNs for most flow properties. LSTM, on the other hand, performs better for predicting flow properties with large fluctuations, such as varying blood pressure over the length of the vessels. Despite having good overall accuracies in training and testing across all the properties for the vessels in the dataset, the GRU model underperforms for an individual vessel flow prediction in all cases. The results also point to the need of individually optimized hyperparameters for each property in any model rather than aiming to achieve overall good performance across all outputs with a single set of hyperparameters.
Poisson-Boltzmann Theory of Bionanosystems

The structure and function of BNS (bionanosystems) such as macromolecules, viruses and ribosomes are strongly affected by electrostatic interactions. Yet their supra-million atom size makes them difficult to simulate via a straightforward PoissonBoltzmann (PB) approach. Here we explore a multiscale approach that results in a coarse-grained PB equation that follows rigorously from the all-atom PB equation. The derivation of the coarse-grained equation follows from an ansatz on the dependence of the electrical potential in two distinct ways, i.e. one reflecting atomic-scale variations and the other capturing nanometer-scale features. With this ansatz and a series expansion of the potential in a length-scale ratio, the coarse-grained PB equation is obtained. This multiscale methodology and an efficient computational methodology provide a way to efficiently simulate BNS electrostatics with atomic-scale resolution for the first time, avoiding the need for excessive supercomputer resources. The coarse-grained PB equation contains a tensorial dielectric constant that mediates the channeling of the electric field along macromolecules in an aqueous medium. The multiscale approach and novel salinity connections to the PB equation presented here should enhance the accuracy and wider applicability of PB modeling.
A Capsule Robot Attitude Transformation Perception Method based on Intestinal Fold Features

: As the world medical level of ascension, the traditional endoscopy has can't satisfy people's needs, using tiny robots enter human body bowel check is becoming a new way of examination. Active robot has become a research hotspot due to its controllable attitude. In this paper, an attitude localization method based on the features of intestinal folds is proposed, and the contour of intestinal tract is obtained by light compensation and adaptive threshold segmentation. The contour HU moment is used for coarse matching, and the shape context is used for fine matching to obtain the matching relationship of contour point pairs. The constraint equation is constructed from the contour centroid and the minimum enclose rectangular centroid angle to limit the search range of point pairs. Finally, the attitude transformation matrix of the robot is solved by using the optimal matching relation and multi-view geometric constraints.
Pollination and Breeding system of Ranunuclus japonicus Thunb. in Japan

Penelitian tentang penyerbukan dan sistem reproduksi Ranunculus japonicus telah dilakukan di kebun botani Universitas Osaka City, Katano, Osaka, Japan. Hasil penelitian menunjukkan bahwa putik R. japonicus lebih dahulu masak daripada benang sarinya (protogyny). Pada tumbuhan ini terjadi ketidakcocokan silang sendiri (self-incompatible). Tumbuhan ini tidak mengalami penyerbukan sendiri (selfing) dan juga tidak membentuk biji tanpa pembuahan (agamospermy), tetapi melakukan penyerbukan silang (out-crossing) dan membutuhkan perantara (pollinator) untuk pembentukan bijinya.
Resistance to Peanut Stripe Virus in Transgenic Peanuts (Arachis hypogaea L.) Carrying PStV cp Gene Was Stabile up to Seven Generations of Selfing

Plant Molecular Biology Laboratory, Department of Agronomy and Horticulture, Faculty of Agriculture, Bogor Agricultural Univesity (IPB-Bogor). Balai Besar Bioteknologi dan Sumberdaya Genetik Pertanian, Bogor, Jl Tentara Pelajar, Bogor Department of Plant Protection, Faculty of Agriculture, Bogor Agricultural Univesity (IPB-Bogor) *Recent Address: Department of Agronomy, Faculty of Agriculture, University of Lampung, Jl.Sumantri Brojonegoro No.1 Bandarlampung 35145 E-mail: hapsorodwi@yahoo.com
Cache Ensemble Analysis for Understanding Algorithmic Memory Performance

—We present an approach to studying ensembles of cache simulations which vary in cache features (such as size, associativity, block replacement policy, etc.) or qualities of the program (such as choice of algorithm, data storage layouts, etc.). Through the qualities of variation between their members, these ensembles can reﬂect the computational structure of programs and expose their performance characteristics, leading to better understanding of code and performance improvements that can be valuable in conserving scarce computing resources. We include several case studies looking at various cache performance scenarios, including some surprising performance bottlenecks in common programming operations, demonstrating the usefulness of our approach.
Duplex Conversation in Outbound Agent System

Intelligent outbound is a popular way to contact customers. The traditional outbound agents communicate with users in a simplex way. The user and the agent cannot speak at the same time, and the user cannot actively interrupt the conversation while the agent is playing audio generated by TTS. The traditional solution is based on the output of the VAD module, once the user voice is detected, the agent will immediately stop talking. However, the user sometimes expresses the short answer at will, not to interrupt the agent, and it will cause the agent to be frequently interrupted. In addition, when users say named entity nouns(numbers, locations, company names, etc), their speech speed is slow and the pause time between words is longer, and they may be interrupted by the agent unreasonably. We pro-pose a method to identify user’s interruption requests and discontinuous expressions by analyzing the semantic information of the user’s utterance. As a result, ﬂuency of the dialogue is improved.
HEAVY METAL POLLUTION WITHIN SEDIMENT

Heavy metal pollution in Semarang City and Demak Municipal are increasing as the increase of industrial activity at both location. This research aimed to study the difference of heavy metal concentration in sediment, and to study the level of heavy metal pollution within mangrove area of Semarang City and Demak Municipal coastal areas. This research was conducted through sampling and laboratory analysis. At each location, 3 stations was occupied with 3 sampling points at each stations. Sampling was conducted for sediment within mangrove area at both location, while laboratory analysis was conducted in Laboratory of Analytical Chemistry, Chemistry Major, Faculty of Science and Mathematics Diponegoro University. Heavy metals in sediment observed in this research was Co, Cu, Cr, Pb, Zn, Ni, Fe, Mn and Mg. The result showed that heavy metal concentration in sediment on both location was quiet high. Sequentially, the highest to lowest concentration of heavy metal was Fe > Zn > Mn > Mg > Ni > Cu > Cr > Co > Pb at Semarang City and Fe > Mn > Mg > Zn > Ni > Pb > Cu > Co > Cr at Demak Municipal. Heavy metal concentration of sediment for Co, Cu, Cr, Zn and Ni are significantly different between Semarang City and Demak Municipal area. Heavy metal in sediment for Zn, Ni, Fe and Mn at both location had exceeded the recommended limit by ANZECC/ARMCANZ and ISQG.
ISLAMIC HIGHER EDUCATION AND RELIGIOUS TRANSFORMATION OF 7+( 086/,0 &20081,7<·6 6855281',1*

The emergence and the development of PTKI (Perguruan Tinggi Keagamaan Islam/Islamic Higher Education) in Indonesia are much influenced by social, political, ideology and religious condition of the FRPPXQLW\·V VXUURXQGLQJ 6LQFH LWV HPHUJHQFH 37. , LV DOZD\V SUHSDULQJ DQG IL[LQJ DOO WKH WKLQJV in order to EH DEOH WR DQVZHU WKH VRFLHW\·V QHHGV Hereby, this study aims to investigate the contribution of IAIN (Institut Agama Islam Negeri/State Islamic Institute) Surakarta to the development of Muslim FRPPXQLW\·V UHOLJLRVLW\ ZLWKLQ LWV VXUURXQGLQJV DQG LWV YDULRXV VXSSRUWHG DQG LQKLELWHG IDFWRUV %DVHG on the qualitative method, the result of this study revealed that IAIN Surakarta had real contribution to the development of the Muslim religiosity in Pucangan, Kartasura. The emerging of religious education institutions, arising from various religious activities, and raising people's awareness on the importance of religious education can be used as indicators of the VXFFHVVIXOQHVV RI ,$,1 6XUDNDUWD·V DV DQ DJHQW RI Islamization, as well as an agent of social change, especially in the transformation process of religious values to its surrounding community.
The Research on π-equivalent Circuit of LTCC Capacitor

The π-equivalent circuit model of laminated MIM LTCC capacitor is deduced in this paper, and the parameters of the equivalent circuit are extracted in detail. Laminated 2-layers capacitor is utilized to verify the model. It can be seen the frequency responses between π-equivalent circuit and 3D HFSS model get a good agreement up to the fundamental self-resonance frequency (SRF). It can be observed that RF parasitic effect is more obvious when increasing frequency up to its secondary resonant frequency, so more accurate equivalent circuit needs to be extracted.