Miracle-dz/UniFAR-FTUs huggingface dataset:
Holographic-Inspired Multibeam Reflectarray With Linear Polarization

In this paper, a novel systematic method based on the holographic theory to design a reflectarray (RA) antenna is presented. According to the holographic technique, one can record features of electromagnetic waves impinging on a specific surface and then, whenever needed, reconstruct each of them. We exploit this property to design a single-feed RA with a multibeam linearly polarized pattern without using any iterative method such as optimization algorithms in the design process. At first, various kinds of interferograms along with related input admittance distributions are introduced according to the polarization and number of radiated beams. Afterward, a simple subwavelength metallic patch as a scalar admittance unit cell is employed to implement the input admittance distribution of the interferograms. Moreover, a fast analytical method, which is fundamentally inspired by the method of moments, has been employed to calculate the input admittance of the utilized element. Finally, as a typical case, an 18 cm $\times $ 18 cm linearly polarized RA with a two-beam radiation pattern has been manufactured and tested at 15 GHz. The sidelobe level, cross-pol, and efficiency of the antenna are found −26 dB, lower than −10 dB, and 47.2%, respectively, which meet the expectations.
Thermal Management of Batteries Using Supercapacitor Hybrid Architecture With Idle Period Insertion Strategy

Thermal analysis and management of batteries have been an important research issue for battery-operated systems, such as electric vehicles and mobile devices. Nowadays, battery packs are designed considering heat dissipation, and external cooling devices, such as a cooling fan, are also widely used to enforce the reliability and extend the lifetime of a battery. However, this type of approaches cannot achieve an immediate temperature drop to avoid a thermal emergency situation. Approaches based on removing the heat from the heat sources via idle period insertion (similar to what is done for silicon devices) would allow faster thermal response; however, it is not obvious how to implement these schemes in the context of batteries. In this paper, we propose the use of a simple parallel battery–supercapacitor hybrid architecture with a dual-mode discharge strategy that can provide immediate temperature management, in which the supercapacitor is used as an energy buffer during the idle periods of the battery. Simulation results show that the proposed method can reduce the battery temperature during charge and discharge while exploiting the advantage of the parallel connection.
A parallel implementation of the covariance matrix adaptation evolution strategy

In many practical optimization problems, the derivatives of the functions to be optimized are unavailable or unreliable. Such optimization problems are solved using derivative-free optimization techniques. One of the state-of-the-art techniques for derivative-free optimization is the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. However, the complexity of CMA-ES algorithm makes it undesirable for tasks where fast optimization is needed. To reduce the execution time of CMA-ES, a parallel implementation is proposed, and its performance is analyzed using the benchmark problems in PythOPT optimization environment.
Laconic Function Evaluation and Applications

We introduce a new cryptographic primitive called laconic function evaluation (LFE). Using LFE, Alice can compress a large circuit f into a small digest. Bob can encrypt some data x under this digest in a way that enables Alice to recover f(x) without learning anything else about Bob's data. For the scheme to be laconic, we require that the size of the digest, the run-time of the encryption algorithm and the size of the ciphertext should all be small, much smaller than the circuit-size of f. We construct an LFE scheme for general circuits under the learning with errors (LWE) assumption, where the above parameters only grow polynomially with the depth but not the size of the circuit. We then use LFE to construct secure 2-party and multi-party computation (2PC, MPC) protocols with novel properties: _We construct a 2-round 2PC protocol between Alice and Bob with respective inputs x_A, x_B in which Alice learns the output f(x_A, x_B) in the second round. This is the first such protocol which is "Bob-optimized", meaning that Alice does all the work while Bob's computation and the total communication of the protocol are smaller than the size of the circuit f or even Alice's input x_A. In contrast, prior solutions based on fully homomorphic encryption are "Alice-optimized". _We construct an MPC protocol, which allows n parties to securely evaluate a function f(x_1,.,x_N) over their respective inputs, where the total amount of computation performed by the parties during the protocol execution is smaller than that of evaluating the function itself! Each party has to individually pre-process the circuit f before the protocol starts and post-process the protocol transcript to recover the output after the protocol ends, and the cost of these steps is larger than the circuit size. However, this gives the first MPC where the computation performed by each party during the actual protocol execution, from the time the first protocol message is sent until the last protocol message is received, is smaller than the circuit size.
A passive RF impedance tuner for 2.4 GHz ISM band applications

This paper presents the design of a passive RF matching network that can be tuned electronically to match a variable load impedance to a fixed source in the 2.4 GHz Industrial, Scientific and Medical (ISM) band. In principle, the design aims at maximum power transfer between the source and the load at any given frequency within the specified range. Theoretical analysis and simulation results have been presented to provide insight into the design of the pi-network circuit. An impedance tuner based on this theory was fabricated on a two-layered FR4 PCB. The fabricated tuner was tested for load impedances up to voltage standing wave ratio (VSWR) ≤ 6, wherein a maximum insertion loss of 4.1 dB was obtained at 2.45 GHz. A maximum insertion loss of 1.7 dB was observed for impedances up to VSWR ≤ 2.4.
AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.

The “Capturing Discriminative Attributes” sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 & 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result).
The Evolution of Content Analysis for Personalized Recommendations at Twitter

We present a broad overview of personalized content recommendations at Twitter, discussing how our approach has evolved over the years, represented by several generations of systems. Historically, content analysis of Tweets has not been a priority, and instead engineering efforts have focused on graph-based recommendation techniques that exploit structural properties of the follow graph and engagement signals from users. These represent "low hanging fruits" that have enabled high-quality recommendations using simple algorithms. As deployed systems have grown in maturity and our understanding of the problem space has become more refined, we have begun to look for other opportunities to further improve recommendation quality. We overview recent investments in content analysis, particularly named-entity recognition techniques built around recurrent neural networks, and discuss how they integrate with existing graph-based capabilities to open up the design space of content recommendation algorithms.
Mitochondrial DNA sequence data support the allocation of Greek mainland chameleons to Chamaeleo africanus

Arnold, E.N. , Burton, J.A. (1978): Reptiles and Amphibians of Britain and Europe. London, Collins & Sons.Bruno, S. , Maugeri, S. (1990): Guida ai serpenti d’ Italia e d’ Europa. Milano, Editoriale Giorgio Mondadori.Carpenter, C.C. , Ferguson, G.W. (1977): Variation and evolution of stereotyped behaviour in reptiles. In: Biologyof theReptilia, Vol. 7, p. 335-554. Gans, C. , Tinkle, D.W. ,Eds, London, New York, San Francisco, AcademicPress.Tomaselli, R. , Balduzzi, A. , Filipello, S. (1973): Carta Bioclimatica d’ Italia. Collana Verde 33, MinisteroAgricoltura eForeste, Roma.
Biocatalytic potential of Streptomyces spp. isolates from rhizosphere of plants and mycorrhizosphere of fungi

Biocatalytic potential of Streptomyces strains isolated from the rhizosphere of plants and from mycorrhizosphere of fungi has been investigated. A total of 118 Streptomyces isolates were selected and functionally screened for 10 different biotechnologically important enzymatic activities: hydrolase (cellulase, cutinase, gelatinase, lipase, protease, polyhydroxyalkanoate (PHA) depolymerase), phenol oxidase and peroxidase (laccase, tyrosinase, and lignin peroxidase), and aminotransferase. Out of 118 tested Streptomyces spp. , 90% showed at least one enzymatic activity. The most abundant were enzymes involved in the biomass degradation, as the production of cutinase, cellulase, and lignin peroxidase were detected in 31%, 40%, and 48% of the isolates, respectively. The improved specific activities of lipase (isolates BV315 and BV100) and tyrosinase (isolates BV87 and BV88) were shown in comparison with the industrially relevant activities of Pseudomonas strains. Plant rhizosphere soils were more prolific source of Streptomyces strains with biocatalytic potential in comparison with mycorrhizosphere soils. Overall, 284 enzyme activities among 118 Streptomyces isolates have been detected. This is the first comprehensive screening of Streptomyces isolates from rhizosphere and mycorrhizosphere soils for novel biocatalysts, showing that specific environmental habitats, such as rhizosphere soils, are “treasure troves” of Streptomyces with biocatalytic potential.
LBFM: Multi-Dimensional Membership Index for Block-Level Data Skipping

Data skipping has been a promising technique to reduce data access in query engines. By maintaining metadata for each block of tuples, a query may skip a block if the metadata indicates that the block does not contain relevant data. Obviously, the key factor is how to build effective metadata by extracting representative features of blocks. In this paper, we propose a multi-dimensional index, Layered Bloom Filter Matrix (LBFM), which adopts a recursively layered framework, and represents the matrix as an ordered hierarchy of hashmap and bitmap to compress space consumption instead of space-consuming bit matrix. Additionally, LBFM supports dimension combination cutting, and optimal indexing strategy could be generated according to it, thus the space efficiency could be further improved. We demonstrate time complexity of LBFM, and theoretically prove that LBFM has lower space consumption than Bloom Filter Matrix algorithm. We proto- typed our index technique on Spark SQL. Our experiments on TPC-H and a real-world workload show that LBFM gains significant improvement in aspect of query response time over traditional methods.
Effects of Physiotherapy and Occupational Therapy in A Case with Winchester Syndrome

Songül ATASAVun uySAL, İpek ALEMDAROĞLu, Öznur yILMAZ, Hülya KAyIHAn, Ayşe KARADuMAn 1 PT PhD, Hacettepe University Faculty of Health Sciences, Department of Occupational Therapy, songula@hacettepe.edu. tr (corresponding author) 2 PT PhD, Bezmialem University, Faculty of Health Sciences, Department of Physical Therapy and Rehabilitation, ipekalemdaroglu@ windowslive.com 3 PT PhD Prof, Hacettepe University Faculty of Health Sciences, Department of Physical Therapy and Rehabilitation, oznurtunca@ yahoo.com, aykaradu@hacettepe.edu. tr 4 PT PhD Prof, Hacettepe University Faculty of Health Sciences, Department of Occupational Therapy, hkayihan@hacettepe.edu. tr
Random Forests for Evaluating Pedagogy and Informing Personalized Learning

Random forests are presented as an analytics foundation for educational data mining tasks. The focus is on course- and program-level analytics including evaluating pedagogical approaches and interventions and identifying and characterizing at-risk students. As part of this development, the concept of individualized treatment effects (ITE) is introduced as a method to provide personalized feedback to students. The ITE quantifies the effectiveness of intervention and/or instructional regimes for a particular student based on institutional student information and performance data. The proposed random forest framework and methods are illustrated on a study of the efficacy of a supplemental, weekly, one-unit problem-solving session in a large enrollment, bottleneck introductory statistics course. The analytics tools are used to identify factors for student success, characterize students benefitting from the supplemental instruction section, develop an objective criterion to, at the beginning of the semester, identify and advise these students into that section, and suggest intervention initiatives for at-risk groups in the course.
Health information needs and seeking behaviors among korean mothers of young children in the United States

Previous studies have noted mothers’ roles as active health information seekers, health managers, and caregivers for their family members. However, little is known about health information needs and seeking behaviors among Korean mothers of young children living in the United States. Using paper‐based surveys, this study found that vaccination, child dental health, parenting, nutrition and diets, and physical exercise were ranked as the five most important topics by the Korean mothers of young children aged 0 to 10 years old in this sample. Furthermore, this study revealed that mothers of boys evaluated the importance of the following two topics significantly higher than those of girls: circumcision (t=3.16, p < .01) and breastfeeding (t=2.47, p < .05). Also, age of child was an important factor influencing mothers’ information needs in three health topics: child dental health (F=5.292, p < .01), physical exercise (F=7.107, p < .01), and child skin care (F=5.640, p < .01). Implications for developing health information services are discussed.
Determination of Serum 25-Hydroxyvitamin D_3 by LC/MS/MS and Its Monthly Variation in Sapporo Indoor Workers

25-Hydroxyvitamin D_3 (25(OH)D_3) as the metabolite of vitamin D, is connected with various of diseases, and important to people with limited sunshine. Thus, the investigation of serum 25-hydroxyvitamin D and its variation in these people is necessary. In this study, a simple, precise, and accurate method for serum 25(OH)D_3 determination by LC/MS/MS was developed. Serum samples were obtained monthly for one year from 11 male and 11 female indoor workers in Sapporo, Japan, and the overall 25(OH)D_3 concentration was 12.9 ± 4.7 ng/mL. The 25(OH)D_3 in females was significantly lower than that in males (14.0 ± 5.0 vs . 11.9 ± 4.3 ng/mL). The serum 25(OH)D_3 concentration in males and females were both strongly correlated to UV-B radiation ( r ^2 = 0.8477 and 0.7384, respectively), with a two-month's lag. Also the monthly change in 25(OH)D_3 in males was more significant than that in females.
Autoencoder-based network anomaly detection

Anomaly detection is critical given the raft of cyber attacks in the wireless communications these days. It is thus a challenging task to determine network anomaly more accurately. In this paper, we propose an Autoencoder-based network anomaly detection method. Autoencoder is able to capture the non-linear correlations between features so as to increase the detection accuracy. We also apply the Convolutional Autoencoder (CAE) here to perform the dimensionality reduction. As the Convolutional Autoencoder has a smaller number of parameters, it requires less training time compared to the conventional Autoencoder. By evaluating on NSL-KDD dataset, CAE-based network anomaly detection method outperforms other detection methods.
Application of stained blood cells for the differentiation of arteries and nerves in cadavers

The demonstration of the functional arteries to first year medical students in the dissection room is very important, but after cadaver fixation with formalin and other preserving materials the arteries will be empty and the fixative material will take the place of blood. Thus, the arteries will be colorless and students will find it hard to differentiate between arteries and nerves. The aim of this study was to stain arteries and differentiate them from nerves in cadavers by means of an easy method, the use of stained blood cells. Whole blood cells (packed cells) were initially treated with a 1% glutaraldehyde solution, washed with PBS and then stained with safranin. 10 ml of stained blood cells was injected into the internal iliac artery of the cadaver. The stained blood cells injected into the internal iliac artery differentiated the internal pudendal arteries from the pudendal nerve and the inferior rectal artery from the inferior rectal nerve. The preparation of stained blood cells and their application could be used by all medical and dental students to facilitate learning of the functional arteries.
a-Stratified Computerized Adaptive Testing in the Presence of Calibration Error

a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated item parameter estimates as if they are the true population parameter values. Consequently, capitalization on chance may occur. In this article, we examined the performance of the AST method under more realistic conditions where item parameter estimates instead of true parameter values are used in the CAT. Its performance was compared against that of the MFI method when the latter is used in conjunction with Sympson–Hetter or randomesque exposure control. Results indicate that the MFI method, even when combined with exposure control, is susceptible to capitalization on chance. This is particularly true when the calibration sample size is small. On the other hand, AST is more robust to capitalization on chance. Consistent with previous investigations using true item parameter values, AST yields much more balanced item pool usage, with a small loss in the precision of latent trait estimates. The loss is negligible when the test is as long as 40 items.
Cross-language projection of multilayer semantic annotation in the NewsReader Wikinews Italian Corpus (WItaC)

English. In this paper we present the annotation of events, entities, relations and coreference chains performed on Italian translations of English annotated texts. As manual annotation is a very expensive and time-consuming task, we devised a crosslingual projection procedure based on the manual alignment of annotated elements. Italiano. In questo articolo descriviamo l’annotazione degli eventi, delle entità, delle relazioni e delle catene di coreferenza realizzata su traduzioni in italiano di testi inglesi già annotati. Essendo l’annotazione manuale un compito molto dispendioso, abbiamo ideato una procedura di proiezione interlinguale basata sull’allineamento degli elementi annotati.
The role of meta-learners in the adaptive selection of classifiers

The use of machine learning techniques able to classify source code components in defective or not received a lot of attention by the research community in the last decades. Previous studies indicated that no machine learning classifier is capable of providing the best accuracy in any context, highlighting interesting complementarity among them. For these reasons ensemble methods, that combines several classifier models, have been proposed. Among these, it was proposed ASCI (Adaptive Selection of Classifiers in bug predIction), an adaptive method able to dynamically select among a set of machine learning classifiers the one that better predicts the bug proneness of a class based on its characteristics. In summary, ASCI experiments each classifier on the training set and then use a meta-learner (e.g., Random Forest) to select the most suitable classifier to use for each test set instance. In this work, we conduct an empirical investigation on 21 open source software systems with the aim of analyzing the performance of several classifiers used as meta-learner in combination with ASCI. The results show that the selection of the meta-learner has not strong influence in the results achieved by ASCI in the context of within-project bug prediction. Indeed, the use of lightweight classifiers such as Naive Bayes or Logistic Regression is suggested.
Evaluation of some soil test methods in acid soils for available phosphorus for pea of Senapati district of Manipur (India)

In an field experiment conducted on pea (Pisum sativum L.) during rabi season of 2013–14 in acid soil of Senapati district, Manipur, India phosphorus was given in the form of single super phosphate and levels of P2O5 kg ha-1 (0, 40 and 60) had significant influence on total dry matter yield, phosphorus concentration in plants and phosphorus uptake by plant. Six extractants were tried viz. Bray 1, Bray 2, Mehlich 1, Mehlich 3, Troug and Olsen to assess the available phosphorus status of the acid soils. The suitability of these extractants was in the descending order: Bray 1> Troug> Bray 2> Mehlich 3> Olsen (pH-8.5)> Mehlich 1. Bray's P 1 extractable phosphorus showed the highest and positive correlations with dry matter yield (control) phosphorus content (control), phosphorus uptake (control), Bray's percent yield and uptake. Therefore this extractant may be used as an index of available phosphorus for pea (Arkel) grown on acid soils of Senapati district, Manipur, the critical level being 14 ppm (mg/kg). The critical limit of phosphorus concentration in plant at 40 days of planting was 0.42% according to the graphical procedure of Cate and Nelson (1965) using a scattered diagram.
Language Model

The long short-term memory (LSTM) language model (LM) has been widely investigated for automatic speech recognition (ASR) and natural language processing (NLP). Although excellent performance is obtained for large vocabulary tasks, tremendous memory consumption prohibits the use of LSTM LMs in low-resource devices. The memory consumption mainly comes from the word embedding layer. In this paper, a novel binarized LSTM LM is proposed to address the problem. Words are encoded into binary vectors and other LSTM parameters are further binarized to achieve high memory compression. This is the first effort to investigate binary LSTMs for large vocabulary language modeling. Experiments on both English and Chinese LM and ASR tasks showed that binarization can achieve a compression ratio of 11.3 without any loss of LM and ASR performance and a compression ratio of 31.6 with acceptable minor performance degradation.