Miracle-dz/UniFAR-FTUs huggingface dataset:
Beamwidth control of base station antennas employing reflectors and directors

The effects of reflectors and directors on the radiation pattern of a base station antenna are studied. A ±45° linear-polarized cross-dipole with an operating band from 1.7 GHz to 2.7 GHz is designed as an example. The antenna is then encircled by a conducting wall constructed using vertical reflectors to control its horizontal half-power beam-width (HPBW). Subsequently, cross-directors are placed above the antenna, which provides another solution to control the HPBW. A parametric study is conducted, and the findings can serve as design guidelines for the design of wide band base station antennas.
Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy) model, the so-called Improved Profile-Energy (IPro-Energy), and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM), Pro-Energy, and Weather Conditioned Moving Average (WCMA). The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.
On the accuracy of inferring energetic coupling between distant sites in protein families from evolutionary imprints: Illustrations using lattice model

It is suspected that correlated motions among a subset of spatially separated residues drive conformational dynamics not only in multidomain but also in single domain proteins. Sequence and structure‐based methods have been proposed to determine covariation between two sites on a protein. The statistical coupling analysis (SCA) that compares the changes in probability at two sites in a multiple sequence alignment (MSA) and a subset of the MSA has been used to infer the network of residues that encodes allosteric signals in protein families. The structural perturbation method (SPM), that probes the response of a local perturbation at all other sites, has been used to probe the allostery wiring diagram in biological machines and enzymes. To assess the efficacy of the SCA, we used an exactly soluble two dimensional lattice model and performed double‐mutant cycle (DMC) calculations to predict the extent of physical coupling between two sites. The predictions of the SCA and the DMC results show that only residues that are in contact in the native state are accurately identified. In addition, covariations among strongly interacting residues are most easily identified by the SCA. These conclusions are consistent with the DMC experiments on the PDZ family. Good correlation between the SCA and the DMC is only obtained by performing multiple experiments that vary the nature of amino acids at a given site. In contrast, the energetic coupling found in experiments for the PDZ domain are recovered using the SPM. We also predict, using the SPM, several residues that are coupled energetically. Proteins 2009. © 2009 Wiley‐Liss, Inc.
Modeling of radio channels of millimeter waves range

The article deals with the methods of creation of the simulator for the new fifth generation of mobile communication (5G) radio channels. The authors propose the multipath architecture of the simulator, that allows to simulate the attenuation of the millimeter wave signal when the subscriber moves receiving both direct and reflected signals. The attenuation of the signal is modeled depending on the interval and the profile of the communication path and the change in the probability density of the transfer function when the subscribers move and the change of locations and conditions for the reflection of propagating signals.
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.
Flow Equilibria via Online Surge Pricing

We explore issues of dynamic supply and demand in ride sharing services such as Lyft and Uber, where demand fluctuates over time and geographic location. We seek to maximize social welfare which depends on taxicab and passenger locations, passenger valuations for service, and the distances between taxicabs and passengers. Our only means of control is to set surge prices, then taxicabs and passengers maximize their utilities subject to these prices. We study two related models: a continuous passenger-taxicab setting, similar to the Wardrop model, and a discrete passenger-taxicab setting. In the continuous setting, every location is occupied by a set of infinitesimal strategic taxicabs and a set of infinitesimal non-strategic passengers. In the discrete setting every location is occupied by a set of strategic agents, taxicabs and passengers, passengers have differing values for service. We expand the continuous model to a time-dependent setting and study the corresponding online environment. Surge prices are in passenger-taxicab equilibrium if there exists a min cost flow that moves taxicabs about such that (a) every taxicab follows a best response, (b) all strategic passengers at $v$ with value above the surge price $r_v$ for $v$, are served and (c) no strategic passengers with value below $r_v$ are served (non-strategic infinitesimal passengers are always served). This paper computes surge prices such that resulting passenger-taxicab equilibrium maximizes social welfare, and the computation of such surge prices is in poly time. Moreover, it is a dominant strategy for passengers to reveal their true values. We seek to maximize social welfare in the online environment, and derive tight competitive ratio bounds to this end. Our online algorithms make use of the surge prices computed over time and geographic location, inducing successive passenger-taxicab equilibria.
Strongyloides stercoralis seroprevalence in Vietnam

SUMMARY Strongyloidiasis is a neglected tropical disease caused by the roundworm Strongyloides stercoralis affecting 30–100 million people worldwide. Many Southeast-Asian countries report a high prevalence of S. stercoralis infection, but there are little data from Vietnam. Here, we evaluated the seroprevalence of S. stercoralis related to geography, sex and age in Vietnam through serological testing of anonymized sera. Sera (n = 1710, 1340 adults and 270 children) from an anonymized age-stratified serum bank from four regions in Vietnam between 2012 and 2013 were tested using a commercial Strongyloides ratti immunoglobulin G ELISA. Seroreactivity was found in 29·1% (390/1340) of adults and 5·5% (15/270) of children. Male adults were more frequently seroreactive than females (33·3% vs. 24·9%, P = 0·001). The rural central highlands had the highest seroprevalence (42·4% of adults). Seroreactivity in the other regions was 29·9% (Hue) and 26·0% and 18·2% in the large urban centres of Hanoi and Ho Chi Minh City, respectively. We conclude that seroprevalence of S. stercoralis was high in the Vietnamese adult population, especially in rural areas.
Structure and hydrodynamics of a DNA G-quadruplex with a cytosine bulge

Abstract The identification of four-stranded G-quadruplexes (G4s) has highlighted the fact that DNA has additional spatial organisations at its disposal other than double-stranded helices. Recently, it became clear that the formation of G4s is not limited to the traditional G3+NL1G3+NL2G3+NL3G3+ sequence motif. Instead, the G3 triplets can be interrupted by deoxythymidylate (DNA) or uridylate (RNA) where the base forms a bulge that loops out from the G-quadruplex core. Here, we report the first high-resolution X-ray structure of a unique unimolecular DNA G4 with a cytosine bulge. The G4 forms a dimer that is stacked via its 5′-tetrads. Analytical ultracentrifugation, static light scattering and small angle X-ray scattering confirmed that the G4 adapts a predominantly dimeric structure in solution. We provide a comprehensive comparison of previously published G4 structures containing bulges and report a special γ torsion angle range preferentially populated by the G4 core guanylates adjacent to bulges. Since the penalty for introducing bulges appears to be negligible, it should be possible to functionalize G4s by introducing artificial or modified nucleotides at such positions. The presence of the bulge alters the surface of the DNA, providing an opportunity to develop drugs that can specifically target individual G4s.
Ultra Broad Band Neural Activity Portends Seizure Onset in a Rat Model of Epilepsy

Epilepsy affects over 70 million people worldwide and 30% of patients' seizures cannot be controlled with medications, motivating the development of alternative therapies such as electrical stimulation. Current stimulation strategies attempt to stop seizures after they start, but none aim to prevent seizures altogether. Preventing seizures requires knowing when the brain is entering a preictal state (i.e., approaching seizure onset). Here we show that such preictal activity can be detected by an informative neural signal that progressively and monotonically changes as the brain approaches a seizure event. Specifically, we use local field potentials (LFP) from a rat model of epilepsy to develop an innovative measure of signal novelty relative to nonseizure activity, that shows the presence of progressive neural dynamics in an ultra broad band (4 Hz - 5 kHz). The measure is extracted from functional connectivity features computed from the LFPs which are used as an input to a one-class Support Vector Machine (SVM). The SVM outputs a scalar signal which quantifies how novel the current activity looks relative to baseline (non-seizure) activity and shows a progression towards seizure onset minutes ahead of time. The use of ultra broad band multivariate features into the SVM results in a novelty signal that has a significantly higher slope in the progression to seizure onset when compared to using power in conventional frequency bands (4 – 500 Hz) on individual channels as input features to the SVM. Functional connectivity in conjunction with the SVM is a strategy that generates a new measurement of novelty that can be used by closed-loop systems for seizure forecasting and prevention.
On the Automatic Classification of Reading Disorders

In this paper, we present an automatic classification approa ch to identify reading disorders in children. This identification is b ased on a standardized test. In the original setup the test is performed by a human su pervisor who measures the reading duration and notes down all reading errors of the child at the same time. In this manner we recorded tests of 38 children who were suspected to have reading disorders. The data was confronted to an auto m tic system which employs speech recognition to identify the reading errors. In a subsequent classification experiment — based on the speech recognizer’s outpu t and the duration of the test — 94.7 % of the children could be classified correct ly.
Towards miniaturized pH sensor based on carbon nanotubes assembled by DEP on titanium electrodes?

In this paper, the pH response of a chemiresistor based on single walled carbon nanotubes (SWNT) was studied. SWNT functionalized with carboxylic groups (COOH-SWNT) were assembled by dielectrophoresis (DEP) between titanium electrodes. Current-voltage I (V) characteristics show that the device conductance decreases as the solution pH increases. DEP enabled to fabricate micronic size structures with low amounts of carbon nanotubes by comparison with SWNT dense films. These structures yielded responses to pH variations.
A Visual Distance for WordNet

Measuring the distance between concepts is an important field of study of Natural Language Processing, as it can be used to improve tasks related to the interpretation of those same concepts. WordNet, which includes a wide variety of concepts associated with words (i.e., synsets), is often used as a source for computing those distances. In this paper, we explore a distance for WordNet synsets based on visual features, instead of lexical ones. For this purpose, we extract the graphic features generated within a deep convolutional neural networks trained with ImageNet and use those features to generate a representative of each synset. Based on those representatives, we define a distance measure of synsets, which complements the traditional lexical distances. Finally, we propose some experiments to evaluate its performance and compare it with the current state-of-the-art.
Effect of Matrix Attachment Regions on Resveratrol Production in Tobacco with Transgene of Stilbene Synthase from Parthenocissus henryana

Matrix attachment regions (MARs) were used to increase resveratrol production in stilbene synthase (STS) gene-transformed tobacco (Nicotiana tabacum L.) in this study. MARs are DNA sequences that bind to the cell’s pertinacious nuclear matrix to form DNA loop domains. A range of effects of MARs sequence on mean expression level and variation in expression of transgenes has been reported usually using β-glucuronidase (GUS) gene as reporter gene. The present study investigated the effects of MARs sequence from yeast on transgene expression of STS for the first time. The results of in vitro binding assay showed that the MARs sequence could specifically bind to the matrix of tobacco prepared by lihium diiodosalicylate (LIS) procedure. The stilbene synthase is the key enzyme in metabolic pathway of resveratrol biosynthesis and a cDNA for STS was cloned from Parthenocissus henryana (Hemsl.) Diels et Gilg by RTPCR method with primers designed according to the conserved sequence of STS gene in grapevine. The cloned sequence showed high nucleotide identity (93.8%) with the STS gene of Vitis vinifera cv. Optima, and the predicted protein sequence also had Cys activity core and IPNSAGAIAGN motifs which were specific for stilbene synthases. With or without flanking MARs from yeast, the STS gene under control of CaMV35S promoter with Ω enhancer has been transferred into tobacco. Northern blot and HPLC analysis of the leaf extracts of transgenic tobacco showed the resveratrol had been produced in STS genetransformed plants, while there was no resveratrol found in the untransformed tobacco. In stably transformed strains, the content of resveratrol in the plants with STS gene flanking by MARs averaged about 2fold higher than those lacking MARs. The results proved that flanking STS by MARs was one of the effective way to increase resveratrol production in transgenic plants, and this work could also play basic roles on the STS gene transformed vegetables or fruits with strong resistance to disease and benefits to human health.
Identification of a Bioactive Compound from Myrcianthes cysplatensis

Abstract Myrcianthes cisplatensis (Cambess.) O. Berg (Myrtaceae) grows freely in Uruguay especially in the banks of rivers and streams. It is locally known as guayabo colorado and it's fruits are edible and used for the preparation of marmalades. In this work we present the results of the bioguided isolation and structural elucidation of the main active compound as well as it's antibacterial activity. Through repeated chromatography a pure compound could be obtained. The compound was studied by different spectroscopic techniques and could be unambiguously identified as α-methyl-1-(2′, 4′, 6,-trimethoxyphenyl)-1-propanone. When assaying for antistaphylococcal activity, it showed MICs of 62.5 μg/mL for the sensible strain (ATC 6538p) and 250 μg/mL for the multirresistant ones (ATCC 43300 and ATC 700699). This shows that the bioguided fractionation is appropriate even when not very active compounds are isolated
Detecting Unusual Temporal Patterns in Fisheries Time Series Data

AbstractLong-term sampling of fisheries data is an important source of information for making inferences about the temporal dynamics of populations that support ecologically and economically important fisheries. For example, time series of catch-per-effort data are often examined for the presence of long-term trends. However, it is also of interest to know whether certain sampled locations are exhibiting temporal patterns that deviate from the overall pattern exhibited across all sampled locations. Patterns at these “unusual” sites may be the result of site-specific abiotic (e.g., habitat) or biotic (e.g., the presence of an invasive species) factors that cause these sites to respond differently to natural or anthropogenic drivers of population dynamics or to management actions. We present a Bayesian model selection approach that allows for detection of unique sites—locations that display temporal patterns with documentable inconsistencies relative to the overall global average temporal pattern. We applie.
Mathematical Analysis on Out-of-Sample Extensions

Let [Formula: see text] be a data set in [Formula: see text], where [Formula: see text] is the training set and [Formula: see text] is the test one. Many unsupervised learning algorithms based on kernel methods have been developed to provide dimensionality reduction (DR) embedding for a given training set [Formula: see text] ([Formula: see text]) that maps the high-dimensional data [Formula: see text] to its low-dimensional feature representation [Formula: see text]. However, these algorithms do not straightforwardly produce DR of the test set [Formula: see text]. An out-of-sample extension method provides DR of [Formula: see text] using an extension of the existent embedding [Formula: see text], instead of re-computing the DR embedding for the whole set [Formula: see text]. Among various out-of-sample DR extension methods, those based on Nyström approximation are very attractive. Many papers have developed such out-of-extension algorithms and shown their validity by numerical experiments. However, the mathematical theory for the DR extension still need further consideration. Utilizing the reproducing kernel Hilbert space (RKHS) theory, this paper develops a preliminary mathematical analysis on the out-of-sample DR extension operators. It treats an out-of-sample DR extension operator as an extension of the identity on the RKHS defined on [Formula: see text]. Then the Nyström-type DR extension turns out to be an orthogonal projection. In the paper, we also present the conditions for the exact DR extension and give the estimate for the error of the extension.
Directionality of cross-linguistic influence: Which referring choices do bilingual Ukrainian–English children make?

Aims and research questions: This paper presents a new study addressing the issue of cross-linguistic influence in acquisition of referring expressions. The main research question is how to predict directionality of this influence in a dual language development. Methodology: The method is an elicited production task. We consider the phenomenon of direct object referring choices, i.e. noun, pronoun and null element, in a ‘null-object’–‘overt-object’ language pair (Ukrainian and English). Data and Analysis: Participants of the experiment are 4–6-year-old Ukrainian–English bilinguals (N20) and Ukrainian monolinguals (N21). The data are analyzed in the statistical program R, utilizing the R-library function lme4. The results are presented as odds ratios (ORs) of each direct object type. Findings: Our data reveal that while there is no significant difference in Ukrainian object types in most of the age groups, there is a considerable amount of null object usage in English at the ages of four to five. Originality: The innovative nature of this study lies in: (i) the consideration of a licit object omission at a later stage of language development (from 4 to 6 years of age); (ii) the examination of an under-investigated language combination (i.e. English and Ukrainian); and (iii) the innovative approach to linguistic data analysis (e.g. comparing OR values). Implications: Our findings suggest that the directionality of influence in dual language acquisition depends on the developmental stage, language-specific means of syntax–pragmatics interaction, and extra-linguistic input-related factors. At the early stages of development, the null-object language is likely to influence the overt-object language, especially under conditions of limited exposure to the latter.
Quantitative Analysis of Four Catechins from Green Tea Extract in Human Plasma Using Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry for Pharmacokinetic Studies

Green tea is consumed as a beverage worldwide and has beneficial effects, such as a lower risk of cardiovascular disease and cancer. A quantitative analysis of the beneficial components in plasma is important for understanding the potential health benefits of green tea. Four catechins—epigallocatechin-3-gallate (EGCG), epicatechin-3-gallate (ECG), epigallocatechin (EGC), and epicatechin (EC)—which account for the majority of the components of green tea, were analyzed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). In this study, a validated method was optimized to obtain the blood concentrations after the one-time ingestion of 630 mg green tea extract with digoxin and then after the ingestion of 630 mg green tea repeatedly for 15 days. The calibration curve, including the LLOQ, was constructed over 1–500 ng/mL for EGCG, ECG, and EGC and 0.1–50 ng/mL for EC. The method for inter- and intra-validation was applied, acceptable for both accuracy and precision. We successfully developed an appropriate UPLC-MS/MS method for human plasma with good reproducibility and sensitivity. Thus, this method could be applied for future preclinical and clinical studies on EGCG, ECG, EGC, and EC.