Miracle-dz/UniFAR-FTUs huggingface dataset:
Sistem Seleksi Penerimaan Beasiswa Menggunakan Metode Pendukung Keputusan Simple Additive Weighting

Beasiswa merupakan bantuan yang diberikan kepada siswa/mahasiswa setiap satuan pendidikan bagi para peserta pendidik yang berprestasi atau bagi orang tuanya yang tidak mampu dalam membiayai pendidikan. Dalam pelaksanaannya proses seleksi beasiswa masih dilakukan secara manual dengan cara membandingkan antara satu berkas pendaftar beasiswa dengan pendaftar yang lain. Dengan cara tersebut mengakibatkan proses seleksi menjadi kurang efektif dan efisien ketika banyaknya jumlah pendaftar beasiswa dimana pihak pengambil keputusan mengalami kesulitan dalam menentukan hasil seleksi beasiswa serta serta dalam proses seleksi memerlukan waktu lama. Tujuan dari penelitian ini adalah membantu pihak pengambil keputusan dalam proses penyeleksian penerima beasiswa dengan mudah dan cepat. Metode Simple Additive Weighting ( SAW ) untuk memberikan keputusan terhadap proses seleksi penerima beasiswa, sedangkan dalam pengembangan sistem menggunakan metode waterfall . Dari hasil pengujian akurasi menunjukkan bahwa dari sembilan alternatif terdapat enam alternatif yang sama antara hasil seleksi yang dilakukan secara manual dengan hasil seleksi yang dilakukan dengan sistem.
Tracing diagnosis trajectories over millions of inpatients reveal an unexpected association between schizophrenia and rhabdomyolysis

While it has been technically feasible to create longitudinal representations of individual health at a nationwide scale, the use of these techniques to identify novel disease associations for the risk stratification of patients has had limited success. Here, we created a large-scale US longitudinal disease network of traced readmission patterns (i.e., disease trajectories), merging data from over 10.4 million inpatients from 350 California hospitals through the Healthcare Cost and Utilization Project between 1980 and 2010. We were able to create longitudinal representations of disease progression mapping over 300 common diseases, including the well-known complication of heart failure after acute myocardial infarction. Surprisingly, out of these generated disease trajectories, we discovered an unknown association between schizophrenia, a chronic mental disorder, and rhabdomyolysis, a rare disease of muscle breakdown. It was found that 92 of 3674 patients (2.5%) with schizophrenia were readmitted for rhabdomyolysis (relative risk, 2.21 [1.80–2.71, confidence interval = 0.95] P-value 9.54E-15), which has a general population incidence of 1 in 10,000. We validated this association using independent electronic health records from over 830,000 patients treated over seven years at the University of California, San Francisco (UCSF) medical center. A case review of 29 patients at UCSF who were treated for schizophrenia and who went on to develop rhabdomyolysis demonstrated that the majority of cases (62%) are idiopathic, which suggests a biological connection between these two diseases. Together, these findings demonstrate the power of using public disease registries in combination with electronic medical records to discover novel disease associations. One Sentence Summary Based on the longitudinal health records from millions of California inpatient discharges, we created a temporal network that enabled us to understand statewide patterns of hospital readmissions, which led to the novel finding that hospitalization for schizophrenia is significantly associated with rehospitalization for rhabdomyolysis.
The constant-pressure gas-driven radial displacement and viscous fingering instability in a finite liquid drop in a Hele-Shaw cell

ix CHAPTER 1. GENERAL INTRODUCTION . 1 1.
The geography of climate migration

Abstract In this paper, we investigate the long-term effects of climate change on the mobility of working-age people. We use a world economy model that covers almost all the countries around the world, and distinguishes between rural and urban regions as well as between flooded and unflooded areas. The model is calibrated to match international and internal mobility data by education level for the last 30 years, and is then simulated under climate change variants. We endogenize the size, dyadic, and skill structure of climate migration. When considering moderate climate scenarios, we predict mobility responses in the range of 70–108 million workers over the course of the twenty-first century. Most of these movements are local or inter-regional. South–South international migration responses are smaller, while the South–North migration response is of the “brain drain” type and induces a permanent increase in the number of foreigners in OECD countries in the range of 6–9% only. Changes in the sea level mainly translate into forced local movements. By contrast, inter-regional and international movements are sensitive to temperature-related changes in productivity. Lastly, we show that relaxing international migration restrictions may exacerbate the poverty effect of climate change at origin if policymakers are unable to select/screen individuals in extreme poverty.
RAMBO: Repeated And Merged BloOm Filter for Ultra-fast Multiple Set Membership Testing (MSMT) on Large-Scale Data.

Multiple Set Membership Testing (MSMT) is a well-known problem in a variety of search and query applications. Given a dataset of K different sets and a query q, it aims to find all of the sets containing the query. Trivially, an MSMT instance can be reduced to K membership testing instances, each with the same q, leading to O(K) query time with a simple array of Bloom Filters. We propose a data-structure called RAMBO (Repeated And Merged BloOm Filter) that achieves O(\sqrt{K} log K) query time in expectation with an additional worst-case memory cost factor of O(log K) beyond the array of Bloom Filters. Due to this, RAMBO is a very fast and accurate data-structure. Apart from being embarrassingly parallel, supporting cheap updates for streaming inputs, zero false-negative rate, and low false-positive rate, RAMBO beats the state-of-the-art approaches for genome indexing methods: COBS (Compact bit-sliced signature index), Sequence Bloom Trees (a Bloofi based implementation), HowDeSBT, SSBT, and document indexing methods like BitFunnel. The proposed data-structure is simply a count-min sketch type arrangement of a membership testing utility (Bloom Filter in our case). It indexes k-grams and provides an approximate membership testing based search utility. The simplicity of the algorithm and embarrassingly parallel architecture allows us to index a 170 TB genome dataset in a mere 14 hours on a cluster of 100 nodes while competing methods require weeks.
Virtualization techniques for memory resource exploitation

Cloud infrastructures have become indispensable in our daily lives with the rise of cloud-based services offered by companies like Facebook, Google, Amazon and many others. These cloud infrastructures use a large numbers of servers provisioned with their own computing resources. Each of these servers use a piece of software, called the Hypervisor (``HV''), that allows them to create multiple virtual instances of the server's physical computing resources and abstract them into "Virtual Machines'' (VMs). A VM runs an Operating System, which in turn runs the applications. The VMs within the servers generate varying memory demand behavior. When the demand increases, costly operations such as (virtual) disk accesses and/or VM migrations can occur. As a result, it is necessary to optimize the utilization of the local memory resources within a single computing server. However, pressure on the memory resources can still increase, making it necessary to migrate the VM to a different server with larger memory or add more memory to the same server. At this point, it is important to consider that some of the servers in the cloud infrastructure might have memory resources that they are not using. Considering the possibility to make memory available to the server, new architectures have been introduced that provide hardware support to enable servers to share their memory capacity. This thesis presents multiple contributions to the memory management problem. First, it addresses the problem of optimizing memory resources in a virtualized server through different types of memory abstractions. Two full contributions are presented for managing memory within a single server called SmarTmem and CARLEMM. In this respect, a third contribution is also presented, called CAVMem, that works as the foundation for CARLEMM. Second, this thesis presents two contributions for memory capacity aggregation across multiple servers, offering two mechanisms called GV-Tmem and vMCA, this latter being based on GV-Tmem but with significant enhancements. These mechanisms distribute the server's total memory within a single-server and globally across computing servers using a user-space process with high-level memory management policies.
Clinical Outcomes in Diabetic Patients with Stable Coronary Artery Disease Treated with Second- Generation Drug Eluting Stents (Short and Mid-Term Follow Up)

Buckhound: The increased prevalence of type 2 diabetes mellitus (DM) is an important public health problem. The number of patients with DM worldwide is estimated to rise to 366 million by the year 2030. In general, individuals with DM have more severe and diffuse atherosclerotic disease, which accounts for the significantly higher incidence of cardiovascular events in their native coronary, cerebrovascular, and peripheral arteries. Objective: The aim of this study was to assess the short and mid-term clinical outcomes in type 2 diabetic patients with stable coronary artery disease treated with 2nd generation drug eluting stents. Patients and Methods: This prospective observational study included a total of 100 type 2 DM-CAD patients, attending at Department of Cardiology, Tanta University Hospitals. This study was conducted between January 2018 till January 2019. Assessment of the mid and short-term clinical outcomes in patients was done. Results: Non-significant difference as regards age, sex and family history (p > 0.05) was found. Non-significant difference as regards height, weight, BMI, HTN and smoking (p > 0.05) was found. Significant increase in inverted T wave in Major Adverse Cardiovascular Events (MACE) group; compared to normal group; with significant statistical difference (p = 0.015). Non-significant difference as regards creatinine, ST elevation, ST depression, pathological Q, LBBB and RBBB (p > 0.05) were found. Highly significant decrease in EF in MACE group; compared to normal group; with highly significant statistical difference (p < 0.01) were found. Conclusion: Diabetes mellitus is a condition in which the use of 2nd generation zotarolimus-eluting stents is preferable, in order to reduce the incidence of MACE adverse events.
Communication-Efficient and Byzantine-Robust Distributed Learning With Error Feedback

We develop a communication-efficient distributed learning algorithm that is robust against Byzantine worker machines. We propose and analyze a distributed gradient-descent algorithm that performs a simple thresholding based on gradient norms to mitigate Byzantine failures. We show the (statistical) error-rate of our algorithm matches that of Yin <italic>et al.</italic> (2018), which uses more complicated schemes (coordinate-wise median, trimmed mean). Furthermore, for communication efficiency, we consider a generic class of <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>-approximate compressors from Karimireddi <italic>et al.</italic> (2019) that encompasses sign-based compressors and top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> sparsification. Our algorithm uses compressed gradients and gradient norms for aggregation and Byzantine removal respectively. We establish the statistical error rate for non-convex smooth loss functions. We show that, in certain range of the compression factor <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>, the (order-wise) rate of convergence is not affected by the compression operation. Moreover, we analyze the compressed gradient descent algorithm with error feedback (proposed in Karimireddi <italic>et al.</italic> 2019) in a distributed setting and in the presence of Byzantine worker machines. We show that exploiting error feedback improves the statistical error rate. Finally, we experimentally validate our results and show good performance in convergence for convex (least-square regression) and non-convex (neural network training) problems.
Compact Hairpin-Line Bandpass Filter with Harmonic Suppression by Periodic Grooves

The present paper highlights the design of a third-order double-folded hairpin line Chebyshev bandpass filter centered at 2.5 GHz with improved second harmonic suppression performance applicable for Wireless Local Area Network (WLAN). The conventional hairpin-line filter has been folded twice at the open end arms and accordingly, a size reduction of 19% has been obtained. The skirt characteristics have been improved and the second harmonic has been shifted to higher frequency due to the folding mechanism. However, the harmonic attenuation level remains almost unaffected. Subsequently, square grooves with optimum dimensions and periodicity have been employed at the outer edges of the coupled resonators to achieve phase velocity compensation between even- and odd-modes. As a result, more than 40 dB of harmonic suppression along with size reduction of 25% has been achieved with three periodic square grooves.
Few-Shot Scene Classification With Multi-Attention Deepemd Network in Remote Sensing

Recently, methods of scene classification that are based on deep learning have become increasingly mature in remote sensing. However, training an excellent deep learning model for remote sensing scene classification requires a large number of labeled samples. Therefore, scene classification with insufficient scene images becomes a challenge. The deepEMD network is currently the most popular model for solving these tasks. Although the deepEMD network obtains impressive results on common few-shot baseline datasets, it is insufficient for capturing discriminative feature information about the scene from global and local perspectives. For this reason, an efficient few-shot scene classification scheme in remote sensing is proposed by combining multiple attention mechanisms and the attention-reference mechanism into the deepEMD network in this paper. First, scene features can be extracted by the backbone that incorporates global attention module and local attention module, which enables the backbone to capture discriminative information from both the global level and the local level. Second, the attention-reference mechanism generates the weights of elements in the earth mover’s distance (EMD) formulation, which can effectively alleviate the effects of complex background and intra-class morphological differences. The experimental results on three popular remote sensing benchmark datasets, Aerial Image Dataset (AID), OPTIMAL-31, and UC Merced, illustrate that our proposed scheme obtains state-of-the-art results in few-shot remote sensing scene classification.
Physical activity as a protective factor for successful aging

To divide the types of aging (successful, usual and impaired), as well as factors affecting this process, the protective role of physical activity was discussed in the literature. It was emphasized that physical activity is also a very important protective factor for cognitive aging - mainly executive function and memory. The prefrontal cortex and hippocampus, i.e. the regions of the brain responsible for the control and course of cognitive processes, show susceptibility to stimulation, which is movement exercises, which are prevention of degenerative changes within the brain.
Relationship of Personality Traits and Patient Satisfaction with Fixed Implant Prosthodontic Treatments

1 Assistant Professor, Department of Implant Dentistry, Implant Research Center, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran 2 Associate Professor, Department of Dental Science Research Center, Department of Social Dentistry, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran 3 Assistant Professor, Member of Dental Materials Research Center, Dental Prosthodontics Dept., Faculty of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran 4 Student of Dentistry, Dental Student Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
Effect of Oleosins on the Stability of Oil Bodies in Soymilk

The aim of this study was to describe the effect of oleosins on the stability of oil bodies in the production of soymilk. We investigated the protein content, average particle diameter and SDS-PAGE analysis of oil bodies at different stages of the soymilk manufacturing process. N-terminal sequencing analysis showed that 11-kDa and 9-kDa fragments, which were not digested by papain, were present at the hydrophilic C-terminal domains of the 24-kDa oleosin and did not undergo decomposition. In terms of colloidal stability, oil bodies from soybeans had the highest floating rate. The binding of oleosin and soymilk proteins was shown to play a role in colloidal stability, especially in solutions with less than 2.5 mg/mL protein. Using transmission electron microscopy, it was found that the surface of the oil bodies was coated in proteins, increasing their stability. was determined and the effect of oleosins on colloidal stability was evaluated.
Stream-Based Active Learning with Multiple Kernels

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this paper we introduce a new research problem, named stream-based active multiple kernel learning (AMKL), where a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary in many real-world applications since acquiring a true label is costly or time-consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret $\mathcal{O}(\sqrt{T})$ as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label-requests.
Simulation and measurement of particle trajectory in an electrostatic precipitator with multiple wire electrodes

The purpose of this study is to show the validity of the simulation result for particle charge and trajectory in an ESP with multiple wire electrodes.The ESP in this study has wires-and-plates configuration composed of three high-voltage application wire electrodes placed between grounded plate electrodes with a gap of 15 mm. The potential, the electric field intensity and the negative ion density were calculated by coupled analysis of Poison’s and current- continuity equations. The fundamental equation of flow field considering the ionic gas flow was a k-ω turbulence flow model. The charge of the particles was calculated from the differential equations of the electric and diffusion charging equations, taking into account the temporal and spatial electric field and ion density. The charged particle trajectory in the ESP was analyzed by an equation of motion considering fluid drag force and Coulomb’s force. In the measurement of the charged particle trajectory, oil mist as the tracer particle was mixed into the gas flow, and analyzed by a particle image velocimetry (PIV) using the software.As a result, the analysis value of the current density distribution on the surface of the grounded plate electrode agreed with the experimental value. The analysis results of the particle trajectory showed that the particle velocity increased, and moved toward the grounded plate electrode as the discharge power increased. The x and y components of particle velocity measured using PIV almost agreed with the simulated result. These results indicated the validity of the simulation result for charged particle trajectory, which was calculated by fitting the analyzed corona current to the experimental value, in the gas flow considering the ionic flow in an ESP with multiple wire electrodes.
A Comprehensive Benchmark of Neural Networks for System Identification

This paper compares a wide variety of neural network architectures applied in the context of black-box modeling for robotics and control. We compare six different architectural concepts and four activation functions, with over three hundred different models. Those models were applied to three robotics datasets to show the differences in performance between the architectures along with their limitations.
Paléoimagerie appliquée aux Homo sapiens de Qafzeh (Paléolithique moyen, Levant sud). Variabilité normale et pathologique

Au Levant, le Paleolithique moyen (environ 200-50 Ka B.P.) correspond a une periode cruciale pour la comprehension des mecanismes evolutifs et la dispersion des Homo sapiens hors d’Afrique. Le site de Qafzeh (Basse Galilee), au croisement des trois grandes aires geographiques de l’ancien monde, comprend des niveaux archeologiques chronologiquement situes au cœur de cette periode (92 ± 5 ka BP). Il est exceptionnel en raison a la fois d’une riche documentation anthropologique comprenant des individus de toutes les classes d’âges (du perinatal a l’adulte) et de la presence de pratiques funeraires uniques (sepulture double, depot funeraire). Si de nombreuses etudes ont deja ete publiees sur cette documentation fossile, les progres technologiques recents de la Paleoimagerie permettent d’extraire des donnees inedites. Ainsi grâce a des outils 2D (radiographies, coupes CT) et 3D (reconstructions virtuelles a partir de donnees photogrammetriques ou de [micro]CT-scan) des analyses sont realisees sur la tete osseuse de trois individus (Qafzeh 6, 9 et 25). Deux types d’analyses sont menes : une etude morphometrique de structures internes (i.e. labyrinthe osseux) et une etude paleopathologique. L’examen de l’oreille interne devoile ainsi une diversite inattendue pour la population presente a Qafzeh ; par ailleurs des rapprochements au sein des echantillons de comparaison, entre quelques individus classiquement consideres comme archaiques et d’autres modernes, se degagent. L’etude paleopathologique quant a elle revele sur Qafzeh 9 la presence de troubles de la croissance osseuse et dentaire, qui font echo aux anomalies du developpement precedemment decrites sur des fossiles immatures du site. Ces donnees nous permettent de mieux apprehender la variabilite individuelle normale et pathologique documentee a Qafzeh et de replacer ces chasseurs-cueilleurs nomades dans le contexte plus large des groupes locaux et eurasiatiques associes au contexte archeologique Mousterien.