Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of General Allocation Fund, Special Allocation Fund, and Profit-Share Fund on Unemployment

This research attempts to find the impact of the government budget on unemployment in West Java. It is conducted from 2006 until 2017. The data used here is secondary data from Badan Pusat Statistik (BPS). It consists of a general allocation fund (DAU), specific allocation fund (DAK), profit-share of tax and non-tax fund (Taxnon) as independent variables. Meanwhile, we set unemployment as the dependent variable. The method of analysis is Panel Regression with Fixed Effect. Because we firstly face awkward result when analyzing model by including all independent variables, then we try to estimate every single independent variable on the dependent variable. Therefore we have four models to dig the problem. Based on the analysis result, it is found that two independent variables have a negative and significant impact on the dependent variable. Meanwhile, profit-share of tax and non-tax fund (Taxnon) does not show significant impact, but only negative sign. From this finding, we can still say that unemployment can be on the wane due to various budget policies of a nation which directed to the regional development. The bigger the agenda of development, the bigger the decreasing unemployment rate. It is because everybody can be absorbed into employment.
Analyzing Short Term Dynamic Speech Features for Understanding Behavioral Traits of Children with Autism Spectrum Disorder

Computational methodologies have shown promise in advancing diagnostic and intervention research in the domain of Autism Spectrum Disorder (ASD) . Prior works have investigated speech features to assess disorder severity and also to differentiate be-tween children with and without an ASD diagnosis. In this work, we explore short term dynamic functionals of speech features both within and across speakers to understand if local changes in speech provide information toward phenotyping of ASD. We compare the contributions of static and dynamic functionals representing conversational speech toward the clinical diagnosis state. Our results show that predictions obtained from a combination of dynamic and static functionals have comparable or superior performance to the predictions obtained from just static speech functionals. We also analyze the relationship between speech production and ASD diagnosis through correlation analyses between speech functionals and manually-derived behavioral codes related to autism severity. The experimental results support the notion that dynamic speech functionals capture complementary information which can facilitate enriched analysis of clinically-meaningful behavioral inference tasks.
The Torsional and Shear Behavior of Steel Fiber Reinforced RC Members

Abstract Beams and columns are one of the important structural elements of buildings to take up transverse loads such as axial load, bending moment, shear, and torsion. Present work is an experimental investigation on the shear, torsion, and axial load behavior of the structural members like columns and/or beams. The reinforced concrete members with 0, 30, and 60 kg/m3 of steel fibers were tested for torsion, shear and axial loading for this study. The twist angle, the load-deflection behavior, the ultimate shear strength, the torsional moment, and the critical moments were obtained for the loading type and steel fiber ratios. The results show that the increasing steel fiber ratio, increased the torsional moment capacity and decreased the shear strength capacity. On the other hand, increasing the steel fiber content increased the both axial load and moment capacity of RC columns. The shear strength and the torsional moment capacities are defined by the provision of current codes of practice such as ACI318-19, Eurocode-2, British, Australian and Turkish Standards.
Chatbot Designed for Graduate Students’s Guidance Service

The main purpose of this study aimed to develop the chatbot designed for the graduate students’ guidance services, which were divided into two major phases: Phase 1: The development of chatbot designed for the graduate students’ guidance service, and Phase 2: The effects of chatbot’ s implementation for the graduate students’ guidance service. The data were statistically analyzed using mean, standard deviation and content analysis. The development of chatbot designed for graduate students’ guidance services conceptualized with the theory of design and the development of machine learning process together with the theory of flat “dialogflow”-called flat form technology taken from Google. In academic collaborations with graduate students studying at the master's and doctoral levels, as well as the measurement and evaluation-related experts considered the suitability of the chatbot-oriented question and answer structures. Specifically, the educational technology and information technology-related experts’ tryout and evaluation were all implemented for its effectiveness of chatbots. The findings of the study revealed that all the aspects of the applications of chatbot, with its 𝑥 of 4.66 were rated at a very effective level as compared to all the aspects, it showed that the experts’ similar opinions toward the graduate students’ guidance services were mostly found. On the other hands, the graduate students’ satisfactions on their graduate students’ chatbot services, with its 𝑥 of 4.52 were all rated at a very high level as compared to each aspect, it showed that the experts’ similar opinions toward the graduate students’ guidance services were mostly found. The results of the study have shown that the chatbots designed to provide the graduate students’ advices were only one essential element that helped promote its research processaccording to comparing the correlations of thesis progress and the frequency use of chatbots. Also, in terms of designing the structures of questions with its answers, all the aspects of graduates’ instructional management should be extensively manifested for making them more effective.
OPTH_A_247172 1025..1033

Nicco J Buffolino Alexander F Vu Aana Amin Matthew De Niear Susanna S Park 1 1Department of Ophthalmology & Vision Science, University of California Davis Eye Center, Sacramento, CA, USA; 2University of Nevada-Reno Medical School, Reno, NV, USA Purpose: To determine factors that may affect the repeatability of the foveal avascular zone (FAZ) measurement obtained using optical coherence tomography angiography (OCTA) including instrument type, image segmentation, image quality, and fundus pathology. Patients and Methods: This prospective single-center study enrolled 43 subjects (85 eyes) with retinal vasculopathy, macular edema, optic pathology or normal contralateral eye. The macula was imaged twice using Optovue Angiovue and once using Cirrus Angioplex to obtain 3x3mm OCTA images centered on the fovea. Images were generated by the same operator within 30 mins. The FAZ size for the entire retinal thickness (“overall FAZ”) was measured automatically using the OCTA software. The FAZ size of the superficial and deep retinal vascular plexus layers was measured manually using the enface OCTA images of the segmented layers and Image J analysis. Intraclass correlations coefficient (ICC) was calculated to determine repeatability. Results: For the overall FAZ measurement, repeatability was excellent (ICC 0.953 right eye, 0.938, left eye) using the same machine (intra-instrument) and somewhat lower but still good to excellent (ICC 0.803 right eye, 0.917 left eye) using machines made by different vendors (inter-instrument). For the segmented layers, intra-instrument repeatability of FAZ measurement was excellent (ICC > 0.95) for both plexus layers. Inter-instrument repeatability was good for the superficial plexus layer (ICC 0.86 right eye, 0.88 left eye) but reduced for the deep plexus layer (ICC 0.63 right eye, 0.57 left eye). Suboptimal image quality and presence of retinal vasculopathy and macular edema tended to reduce FAZ repeatability but to a lesser degree. Conclusion: Interand intra-instrument repeatability of the overall FAZ measurement was high using commercial OCTA instruments and only mildly reduced by suboptimal image quality and fundus pathology. For segmented layers, intra-instrument repeatability remained high but inter-instrument repeatability was reduced for the deep plexus layer.
Non-Negative Matrix Factorization Meets Time-Inhomogeneous Markov Chains

Non-negative matrix factorization (NMF) [22] is a popular unsupervised learning approach that allows to obtain part-based representations of non-negative data samples and provide soft-clustering assignments for them. The optimization problem behind NMF is often solved using multiplicative update rules (MUR) that are known to exhibit several ﬂaws related to their convergence and the uniqueness of the obtained solutions. In this paper, we provide a novel theoretical analysis of this optimization procedure by showing its equivalence to a time inhomogeneous Markov chain. This equivalence allows us to (1) derive sufﬁcient conditions required for convergence to a non-negative solution regardless the initialization to take place and (2) to characterize the speed of this convergence. In general, we argue that the established results are negative and lead to an incentive of solving NMF with optimization strategies other than MUR.
OAJSM_A_237997 105..112

Matilda Lundblad Martin Hägglund 2,3 Christoffer Thomeé 2 Eric Hamrin Senorski 4 Jan Ekstrand Jón Karlsson Markus Waldén 1Department of Orthopedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 2Football Research Group, Linköping University, Linköping, Sweden; 3Division of Physiotherapy, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; 4Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 5Division of Community Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden Background: There is limited epidemiological information on injury rates and injury mechanisms for lateral collateral ligament (LCL) and posterior cruciate ligament (PCL) injuries in male professional soccer. In addition, time trends and lay-off times for these injuries have not yet been determined. Aim: To determine injury rates and circumstances of LCL and PCL injuries over 17 seasons in men’s professional soccer. Methods: A prospective cohort study, in which 68 professional European soccer teams were followed over 17 consecutive seasons (2001/2002 to 2017/2018). The teams’ medical staff recorded player exposure and time-loss injuries. Lay-off time was reported as the median and the first and third quartile. Injury rate was defined as the number of injuries per 1000 playerhours. Results: One hundred and twenty-eight LCL and 28 PCL injuries occurred during 2,554,686 h of exposure (rate 0.05 and 0.01/1000 h, respectively). The median lay-off time for LCL injuries was 15 (Q1=7, Q3=32) days, while it was 31 days for PCL injuries (Q1=15, Q3=74). The match injury rate for LCL injuries was 11 times higher than the training injury rate (0.21 vs 0.02/1000 h, rate ratio [RR] 10.5, 95% CI 7.3 to 15.1 p<0.001) and the match injury rate for PCL injuries was 20 times higher than the training injury rate (0.056 vs 0.003/1000 h, RR 20.1, 95% CI 8.2 to 49.6, p<0.001). LCL injuries saw a significant annual decrease of approximately 3.5% (p=0.006). In total, 58% (63/108) of all LCL injuries and 54% (14/26) of all PCL injuries were related to contact mechanism. Conclusion: This study with prospectively registered data on LCL and PCL injuries in men’s professional soccer shows that the median lay-off from soccer for LCL and PCL injuries is approximately 2 and 4 weeks respectively. These rare knee ligament injuries typically occur during matches and are associated with a contact injury mechanism.
Influence of Roasting Treatments on Oil Quality Properties of Two Sesame Seed Varieties

The objective of this study was to evaluate the effect of roasting at 160°C for 5, 10 and 15 minutes on physicochemical properties, lipid oxidation, fatty acids profile, rancimat value, conjugated dienes and conjugated triene content of Giza 32 and Shandaweel 3 sesame seeds varieties. Results showed that all studied samples contained moisture (3.60 5.98%), crude protein (22.92 24.31%), oil (51.68 55.26%), fiber (7.12 8.14%), ash (3.92 4.73%), and carbohydrates (9.63 12.01%); in raw and roasted sesame seed varieties (RSS). Data revealed that acid value, peroxide value, TBA, conjugated diene and conjugated triene, increased gradually by roasting treatments of seeds, while the iodine value was slightly decreased by roasting. A little changes in the saturated and unsaturated fatty acids were found during roasting. Palmitic acid was the predominant saturated fatty acids followed by stearic acid, while oleic and linoleic acids recorded the highest levels among unsaturated fatty acids in both sesame oils. Data also revealed that the thermal stability (Rancimat value) of sesame oils was enhancement by roasting treatments.
Escaping Saddle Points with Inequality Constraints via Noisy Sticky Projected Gradient Descent

We give the ﬁrst analysis of convergence of a ﬁrst-order method in smooth non-convex optimization under a bounded number of inequality constraints. Our algorithm uses ˜ O ( 1 ε 2 + k √ ε ) gradient evaluations for k linear inequality constraints and converges to a relaxed deﬁnition of an (cid:15) -second- order stationary point (for which the gradient and the Hessian are evaluated with respect to active constraints).
On Chemistry , Synthesis And Various Biological Activities Of Substituted Azetidinones

Heteroaromatic compounds have attracted considerable attention in the design of biologically active molecules and advanced organic materials. The chemistry of β-lactams has taken an prestigious place in organic and medicinal chemistry so the review on recent methods in the synthesis of substituted Azetidinone derivatives rendered as a lead molecule for designing potential bioactive agents and it accompanying additional various synthetic information and its orientations would encompass great deal of help to researchers, chemists and pharmacologists to make it the best, most productive, economical and medicinal important compounds which will be expected to show potent pharmacological activities. In future it would be useful to design different new drugs to bring in the market by using rapid, operationally simple, efficient and green procedure. This has led to the discovery of a wide variety of compounds that are of high interest from the point of view antibacterial, antiinflammatory, antihyperlipidemic, CNS activity, anticancer, antimicrobial, pesticidal, cytotoxic, antidiabetic, antitumor, antifungal, antitubercular activities.
Convex Programs for Global Optimization of Convolutional Neural Networks in Polynomial-Time

We study training of Convolutional Neural Networks (CNNs) with ReLU activations and introduce exact convex optimization formulations with a polynomial complexity with respect to the number of data samples, the number of neurons and data dimension. Particularly, we develop a convex analytic framework utilizing semi-inﬁnite duality to obtain equivalent convex optimization problems for two-layer CNNs, where convex problems are regularized by the sum of (cid:96) 2 norms of variables.
Cardiac’s Sounds Signals Analysis

Fandi Tadj eddine, S. M. Debbal , L. Hamza Cherif, F. Meziani 1 Genie Biomedical Laboratory (GBM), Faculty of Technology, University Aboubekr Belkaid Tlemcen BP 119, Algeria. *Corresponding Author: S.M. Debbal. Genie Biomedical Laboratory (GBM), Faculty of Technology, University Aboubekr Belkaid Tlemcen BP 119, Algeria. Received date: May 21, 2021; Accepted date: June 03, 2021; Published date: June 17, 2021 Citation: Fandi T. eddine, S. M. Debbal, L. Hamza Cherif, F. Meziani (2021) Cardiac’s Sounds Signals Analysis. J, Heart and Vasculature. 1(4); DOI: 10.31579/JHV-2021/019 Copyright: © 2021, S. M. Debbal, This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bottom-Up Fabrication of Ultrathin 2D Zr Metal–Organic Framework Nanosheets through a Facile Continuous Microdroplet Flow Reaction

Direct synthesis of the ultrathin and discrete 2D metal–organic framework (MOF) nanosheets is extremely challenging. Herein, we present the first facile continuous bottom-up strategy for preparing ultrathin (∼3 nm) 2D MOF nanosheets with high crystallinity comprising assemblies of a few layers. Unlike conventional solvothermal synthetic methods for 2D MOFs, the weak interlayer interaction in the vertical direction of the 2D materials is restricted under microdroplet flow reaction conditions. The 2D MOF nanosheets with a large lateral area and a few layers thick were directly synthesized by suppressing the lamellar stacking of the nanosheets under the dynamic growth conditions. The 2D MOF nanosheets were characterized by scanning and transmission electron microscopy, atomic force microscopy, X-ray photoelectron spectroscopy, powder X-ray diffraction, infrared spectroscopy, thermogravimetric analysis, gas adsorption, and light scattering techniques, which were supported by density functional theory (DFT) ca.
SNICAR-AD v3: A Community Tool for Modeling Spectral Snow Albedo

Abstract. The Snow, Ice, and Aerosol Radiative (SNICAR) model has been used in various capacities over the last 15 years to model the spectral albedo of snow with light-absorbing constituents (LAC). Recent studies have extended the model to include an adding-doubling two-stream solver and representations of non-spherical ice particles, carbon dioxide snow, snow algae, and new types of mineral dust, volcanic ash, and brown carbon. New options also exist for ice refractive indices and solar zenith angle-dependent surface spectral irradiances used to derive broadband albedo. The model spectral range was also extended deeper into the ultraviolet for studies of extraterrestrial and high-altitude cryospheric surfaces. Until now, however, these improvements and capabilities have not been merged into a unified code base. Here, we document the formulation and evaluation of the publicly-available SNICAR-ADv3 source code, web-based model, and accompanying library of particle optical properties. The use of non-spherical ice grains, which scatter less strongly into the forward direction, reduce the simulated albedo perturbations from LAC by ~9–31 %, depending on which of the three available non-spherical shapes are applied. The model compares very well against measurements of snow albedo from seven studies, though key properties affecting snow albedo are not fully constrained with measurements, including ice effective grain size of the top sub-millimeter of the snowpack, mixing state of LAC with respect to ice grains, and site-specific LAC optical properties. The new default ice refractive indices produce extremely high pure snow albedo (> 0.99) in the blue and ultraviolet part of the spectrum, with such values measured so far only in Antarctica. More work is needed particularly in the representation of snow algae, including experimental verification of how different pigment expressions and algal cell concentrations affect snow albedo. Representations and measurements of the influence of liquid water on spectral snow albedo are also needed.
Scaling parallel Gaussian process optimization with adaptive batching and resparsiﬁcation

Gaussian processes (GP) are one of the most successful frameworks to model uncertainty. However, classical GP optimization (e.g., GP-UCB) suffers from major scalability limitations. Selecting each query point has a computational cost at least quadratic in the number of dimensions and iterations, which quickly becomes unfeasible. Moreover, to reduce experimental time costs queries must be selected in batches (e.g., using GP-BUCB) and evaluated in parallel. In this paper, we introduce BBKB (Batch Budgeted Kernel Bandit), the ﬁrst sparse GP-UCB approximation that provably runs in constant per-step amortized time and achieves the same regret as GP-UCB. Moreover, it is capable of batching queries, while greatly reducing the computational complexity compared to GP-BUCB. This is obtained by adaptively delaying costly updates to the sparse approximation of the GP, in combination with a novel bound for the evolution of the posterior variances that allows us to accurately choose when a resparsiﬁcation is necessary. Moreover, the same bound can be used to decide how much to delay feedback, allowing us to choose larger batches compared to GP-BUCB. Finally, we show experimentally that BBKB is much faster than existing methods.
The Impact of Lockdown on eBC Mass Concentration over a Semi-arid Region in Southern India: Ground Observation and Model Simulations

In the present study, we focused on the impact of lockdown on black carbon (eBC) mass concentrations and their associated radiative implications from 01st March to 30th June 2020, over a semi-arid station, i.e., in the district of Anantapur in Southern India. The mean eBC mass concentration was observed before lockdown (01st–24th March 2020) and during the lockdown (25th March–30th June 2020) period and was about 1.74 ± 0.36 and 1.11 ± 0.14 µg m–3, respectively. The sharp decrease (~35%) of eBC mass concentration observed during the lockdown (LD) period as compared with before lockdown (BLD) period, was mainly due to the reduction of anthropogenic activities and meteorology. Furthermore, during the entire LD period, the net composite forcing at the top of the atmosphere (TOA) and at the surface (SUR) varied from –4.52 to –6.19 Wm–2 and –22.91 to –29.35 Wm–2, respectively, whereas the net forcing in the atmosphere (ATM) varied from 17.27 to 23.16 Wm–2 . Interestingly, the amount of energy trapped in the atmosphere due to eBC is 11.19 Wm–2 before LD and 8.56 Wm–2 during LD. It is concluded that eBC contributes almost 43–50% to the composite forcing. As a result, the eBC atmospheric heating rate decreased significantly (25%) when compared to before lockdown days to lockdown days. © The Author(s).
Coresets for the DP-Means Clustering Problem

We present coresets, a technique for approximately solving clustering problems on a small subset of the data, for the DP-Means clustering problem. DP-Means is a variant of K-Means where the number of clusters is not ﬁxed but inferred from the data. We ﬁrst show the existence of coresets of size O ( d d k ∗ log n/(cid:15) d ) where k ∗ is the optimal number of centers and then propose a practical coreset construction algorithm that can be used to solve large instances of DP-Means clustering. We empirically demonstrate that coresets signiﬁcantly outperform naive uniform subsampling and observe substantial speedups in runtime while achieving a low approximation error compared to solving the full instance.
A homotopy approach for nonconvex disjunctive programs in machine learning

We study a class of non-convex problems that can be formulated as a disjunctive program whose feasible region is represented as a union of convex polytopes. We introduce a novel homotopy-type optimization framework for this class of non-convex problems. As an example of such problems, we mainly focus in this paper on semi-supervised SVM (S 3 VM) and develop a homotopy-type S 3 VM algorithm. Numerical experiments on S 3 VM demonstrate the advantage of our framework over other non-convex optimization methods such as CCCP.