Miracle-dz/UniFAR-FTUs huggingface dataset:
Intermodal spatial attention differs between vision and audition: An event-related potential analysis

Subjects were required to attend to a combination of stimulus modality (vision or audition) and location (left or right). Intermodal attention was measured by comparing event-related potentials (ERPs) to visual and auditory stimuli when the modality was relevant or irrelevant, while intramodal (spatial) attention was measured by comparing ERPs to visual and auditory stimuli presented at relevant and irrelevant spatial locations. Intramodal spatial attention was expressed differently in visual and auditory ERPs. When vision was relevant, spatial attention showed a contralateral enhancement of posterior N1 and P2 components and enhancement of parietal P3. When audition was relevant, spatial attention showed a biphasic fronto-central negativity, starting after around 100 ms. The same effects were also present in ERPs to stimuli that were presented in the irrelevant modality. Thus, spatial attention was not completely modality specific. Intermodal attention effects were also expressed differently in vision and audition. Taken together, the obtained ERP patterns of the present study show that stimulus attributes such as modality and location are processed differently in vision and audition.
EL MARKETING EDUCATIVO, HERRAMIENTA DE APOYO EN LA CALIDAD DEL TALENTO HUMANO EN LAS UNIVERSIDADES

La actual cruzada del gobierno ecuatoriano por mejorar la calidad de la ensenanza universitariaexige el uso de herramientas innovadoras quepermitan identificar no solo las necesidades dedesarrollar proyectos de investigacion sino queademas se vinculen estos proyectos a lasexpectativas del triangulo universidad-empresasestudiantes de lograr convertir los recursosacademicos en productos que se traducen enarticulos, tesis de pregrado, de maestria ydoctorados que tributen a las necesidades de laregion en que se enclava la institucion universitaria.La utilizacion del marketing educativo como herramienta de apoyo a una mejor calidad en laensenanza universitaria sera expuesta en eltranscurso de la presente investigacion.
Phenotypic diversity in siblings with partial androgen insensitivity syndrome

The androgen insensitivity syndrome is a heterogeneous disorder with a wide spectrum of phenotypic abnormalities, ranging from complete female to ambiguous forms that more closely resemble males. The primary abnormality is a defective androgen receptor protein due to a mutation of the androgen receptor gene. This prevents normal androgen action and thus leads to impaired virilisation. A point mutation of the androgen receptor gene affecting two siblings with partial androgen insensitivity syndrome is described. One had cliteromegaly and labial fusion and was raised as a girl, whereas the other sibling had micropenis and penoscrotal hypospadias and was raised as a boy. Both were shown to have the arginine 840 to cysteine mutation. The phenotypic variation in this family is thus dependent on factors other than abnormalities of the androgen receptor gene alone.
Building an HPSG-based Indonesian Resource Grammar (INDRA)

This paper presents the creation and the initial stage development of a broadcoverage Indonesian Resource Grammar (INDRA) within the framework of Head Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) and Minimal Recursion Semantics (MRS) (Copestake et al., 2005). At the present stage, INDRA focuses on verbal constructions and subcategorization since they are fundamental for argument and event structure. Verbs in INDRA were semi-automatically acquired from the English Resource Grammar (ERG) (Flickinger, 2000) via Wordnet Bahasa (Nurril Hirfana Mohamed Noor et al., 2011; Bond et al., 2014). In the future, INDRA will be used in the development process of machine translation. A preliminary evaluation of INDRA on the MRS test-suite shows promising coverage.
Education on Programming with Robots: Examining Students’ Experiences and Views

Bu calismada r obotlarla programla egitimi alan ogrencilerin basarilarinin ve goruslerinin belirlenmesi amaclanmistir. Karma arastirma yonteminin kullanildigi calismada yaratici problem cozme testi, robotlar programlamaya yonelik uygulamali performans degerlendirme sinavi ve yari yapilandirilmis gorusme formu ile veri toplanmistir. Yaratici problem cozme becerileri testi OECD tarafindan yapilan PISA 2012 sinavindan alinmistir. Calismaya 9 ortaokul ogrencisi katilmistir. Uygulama surecinde ilk olarak ogrencilerin yaratici problem cozme testini doldurmalari istenmistir. Yaratici problem cozme testi, cevrimici bir ortamda etkilesimli simulasyonlar ve bu simulasyonlara yonelik sorulardan olusmaktadir. Daha sonra ogrencilere bir hafta boyunca robotlarla programlama egitimi verilmistir. Programlama egitimi sonunda egitim ile ilgili performans degerlendirme sinavi yapilmistir. Son olarak ise hazirlanan bir gorusme formu ile ogrencilerin gerceklestirilen etkinlik hakkindaki goruslerine basvurulmustur. Sonuc olarak ogrencilerin uygulamali performans degerlendirme sinav sonuclari incelendiginde almis olduklari egitimin yararli soylenebilir. Yapilan korelasyon testi sonucunda ogrencilerin yaratici problem cozme becerileri ile performans puanlari arasindaki pozitif yonde, anlamli ve orta duzey bir iliski oldugu tespit edilmistir. Gorusme sorularina verilen yanitlar incelendiginde ise ogrencilerin genel olarak olumlu tutuma sahip olduklari, yapilan egitimin guduleyici, eglenceli ve programlama ogrenmelerine katki sagladigi sonucuna ulasilmistir.
Hopf Algebra of Building Sets

The combinatorial Hopf algebra on building sets $BSet$ extends the chromatic Hopf algebra of simple graphs. The image of a building set under canonical morphism to quasi-symmetric functions is the chromatic symmetric function of the corresponding hypergraph. By passing from graphs to building sets, we construct a sequence of symmetric functions associated to a graph. From the generalized Dehn-Sommerville relations for the Hopf algebra $BSet$, we define a class of building sets called eulerian and show that eulerian building sets satisfy Bayer-Billera relations. We show the existence of the $\mathbf{c}\mathbf{d}-$index, the polynomial in two noncommutative variables associated to an eulerian building set. The complete characterization of eulerian building sets is given in terms of combinatorics of intersection posets of antichains of finite sets.
THE APPLICATION OF A GENETIC ALGORITHM FOR A TWO LINK PENDULUM PROGRAMMED STIMULATION

iu-Boris CONONOVICI* TRACT Natural evolution is intrinsically a robust search and optimization m ators taken from natural evolution. This method is fundamentally iterati ions, which is called a population. All the population evolves toward be e genetic algorithm application is a two link pendulum, which tried to be must learn how to swing his legs, the second link of the pendulum, in or ody, the first element. Applied genetic algorithm intends to determine the initial position, on of the second element, and the program by which the movement of lation of the whole system. The two link pendulum is only an example of how can be solved a p a genetic algorithm.
Structural Features for Functional Selectivity at Serotonin Receptors

Dissecting Serotonin Receptors Serotonin receptors are the targets for many widely used drugs prescribed to treat ailments from depression to obesity and migraine headaches (see the Perspective by Palczewski and Kiser). C. Wang et al. (p. 610, published online 21 March) and Wacker et al. (p. 615, published online 21 March) describe crystal structures of two members of the serotonin family of receptors bound to antimigraine medications or to a precursor of the hallucinogenic drug LSD. Subtle differences in the way particular ligands bind to the receptors cause substantial differences in the signals generated by the receptor and the consequent biological responses. The structures reveal how the same ligand can activate one or both of the two main serotonin receptor signaling mechanisms, depending on which particular receptor it binds. Structures of serotonin receptor family members in complex with the fungal alkaloid ergot offer clues for drug designers. [Also see Perspective by Palczewski and Kiser] Drugs active at G protein–coupled receptors (GPCRs) can differentially modulate either canonical or noncanonical signaling pathways via a phenomenon known as functional selectivity or biased signaling. We report biochemical studies showing that the hallucinogen lysergic acid diethylamide, its precursor ergotamine (ERG), and related ergolines display strong functional selectivity for β-arrestin signaling at the 5-HT2B 5-hydroxytryptamine (5-HT) receptor, whereas they are relatively unbiased at the 5-HT1B receptor. To investigate the structural basis for biased signaling, we determined the crystal structure of the human 5-HT2B receptor bound to ERG and compared it with the 5-HT1B/ERG structure. Given the relatively poor understanding of GPCR structure and function to date, insight into different GPCR signaling pathways is important to better understand both adverse and favorable therapeutic activities.
Platinum neurotoxicity pharmacogenetics

Cisplatin, carboplatin, and oxaliplatin anticancer drugs are commonly used to treat lung, colorectal, ovarian, breast, head and neck, and genitourinary cancers. However, the efficacy of platinum-based drugs is often compromised because of the substantial risk for severe toxicities, including neurotoxicity. Neurotoxicity can result in both acute and chronic debilitation. Moreover, colorectal cancer patients treated with oxaliplatin discontinue therapy more often because of peripheral neuropathy than tumor progression, potentially compromising patient benefit. Numerous methods to prevent neurotoxicity have thus far proven unsuccessful. To circumvent this life-altering side effect while taking advantage of the antitumor activities of the platinum agents, efforts to identify mechanism-based biomarkers are under way. In this review, we detail findings from the current literature for genetic markers associated with neurotoxicity induced by single-agent and combination platinum chemotherapy. These data have the potential for broad clinical implications if mechanistic associations lead to the development of toxicity modulators to minimize the noxious sequelae of platinum chemotherapy. [Mol Cancer Ther 2009;8(1):10–6]
Transcriptional response of lignin-degrading enzymes to 17α-ethinyloestradiol in two white rots

Fungal, ligninolytic enzymes have attracted a great attention for their bioremediation capabilities. A deficient knowledge of regulation of enzyme production, however, hinders the use of ligninolytic fungi in bioremediation applications. In this work, a transcriptional analyses of laccase and manganese peroxidase (MnP) production by two white rots was combined with determination of pI of the enzymes and the evaluation of 17α‐ethinyloestradiol (EE2) degradation to study regulation mechanisms used by fungi during EE2 degradation. In the cultures of Trametes versicolor the addition of EE2 caused an increase in laccase activity with a maximum of 34.2 ± 6.7 U g−1 of dry mycelia that was observed after 2 days of cultivation. It corresponded to a 4.9 times higher transcription levels of a laccase‐encoding gene (lacB) that were detected in the cultures at the same time. Simultaneously, pI values of the fungal laccases were altered in response to the EE2 treatment. Like T. versicolor, Irpex lacteus was also able to remove 10 mg l−1 EE2 within 3 days of cultivation. While an increase to I. lacteus MnP activity and MnP gene transcription levels was observed at the later phase of the cultivation. It suggests another metabolic role of MnP but EE2 degradation.
Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.
Efficient Convolutional Neural Network with Binary Quantization Layer

In this paper we introduce a novel method for segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by a large margin.
Software-Defined Network Controlled Switching between Millimeter Wave and Terahertz Small Cells

—Small cells are a cost-effective way to reliably expand network coverage and provide signiﬁcantly increased capacity for end users. The ultra-high bandwidth available at millimeter (mmWave) and Terahertz (THz) frequencies can effectively realize short-range wireless access links in small cells enabling potential uses cases such as driver-less cars, data backhauling and ultra-high-deﬁnition infotainment services. This paper describes a new software deﬁned network (SDN) framework for vehicles equipped with transceivers capable of dynamically switching between THz and mmWave bands. We present a novel SDN controlled admission policy that preferentially handoffs between the mmWave and THz small cells, accommodates asymmetric uplink/downlink trafﬁc, performs error recovery and handles distinct link states that arise due to motion along practical vehicular paths. We then analytically derive the resulting capacity of such a small cell network by accounting for the channel characteristics unique to both these spectrum bands, relative distance and the contact times between a given transceiver pair. We then formulate the optimal procedure for scheduling multiple vehicles at a given infrastructure tower, with regards to practical road congestion scenarios. The search for the optimal schedule is shown to be a NP-hard problem. Hence, we design a computationally-feasible polynomial-time scheduling algorithm that runs at the SDN controller and compare its performance against the optimal procedure and random access. Additionally, we present a simulation-based case study for the use case of data center backhauling in Boston city to showcase the beneﬁts of our approach.
Effect of Plasma Uric Acid on Antioxidant Capacity, Oxidative Stress, and Insulin Sensitivity in Obese Subjects

Oxidative stress is purported to be involved in the pathogenesis of obesity-associated insulin resistance. We evaluated whether alterations in levels of circulating uric acid (UA), a systemic antioxidant, affects the following: 1) systemic (plasma and saliva) nonenzymatic antioxidant capacity (NEAC); 2) markers of systemic (urinary 8-iso-prostaglandin-F2α) and muscle (carbonylated protein content) oxidative stress; and 3) whole-body insulin sensitivity (percentage increase in glucose uptake during a hyperinsulinemic-euglycemic clamp procedure). Thirty-one obese subjects (BMI 37.1 ± 0.7 kg/m2) with either high serum UA (HUA; 7.1 ± 0.4 mg/dL; n = 15) or normal serum UA (NUA; 4.5 ± 0.2 mg/dL; n = 16) levels were studied; 13 subjects with HUA levels were studied again after reduction of serum UA levels to 0 by infusing a recombinant urate oxidase. HUA subjects had 20–90% greater NEAC, but lower insulin sensitivity (40%) and levels of markers of oxidative stress (30%) than subjects in the NUA group (all P < 0.05). Acute UA reduction caused a 45–95% decrease in NEAC and a 25–40% increase in levels of systemic and muscle markers of oxidative stress (all P < 0.05), but did not affect insulin sensitivity (from 168 ± 25% to 156 ± 17%, P = NS). These results demonstrate that circulating UA is a major antioxidant and might help protect against free-radical oxidative damage. However, oxidative stress is not a major determinant of insulin action in vivo.
Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

Objectives Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. Methods The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. Results Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). Conclusions The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.
The Difference between the Amati and Ghirlanda Relations

We point out that the beaming correction commonly inferred from the achromatic breaks in the afterglow light curve is biased in situations where the isotropic equivalent energy is affected by factors other than the spread in opening angles. In particular, it underestimates the beaming factor of sources observed off-axis. Here we show that both the slopes and scatters in the Amati and Ghirlanda relations, and the difference between them, are quantitatively consistent with a recently proposed model in which the Eiso-νpeak relation, as originally derived by Amati et al., is due to viewing angle effects. The quantitative difference between them confirms the relations between opening angle and break time suggested by Frail et al.
Indexing confusion networks for morph-based spoken document retrieval

In this paper, we investigate methods for improving the performance of morph-based spoken document retrieval in Finnish by extracting relevant index terms from confusion networks. Our approach uses morpheme-like subword units ("morphs") for recognition and indexing. This alleviates the problem of out-of-vocabulary words, especially with inflectional languages like Finnish. Confusion networks offer a convenient representation of alternative recognition candidates by aligning mutually exclusive terms and by giving the posterior probability of each term. The rank of the competing terms and their posterior probability is used to estimate term frequency for indexing. Comparing against 1-best recognizer transcripts, we show that retrieval effectiveness is significantly improved. Finally, the effect of pruning in recognition is analyzed, showing that when recognition speed is increased, the reduction in retrieval performance due to the increase in the 1-best error rate can be compensated by using confusion networks.
Ranking Dairy Sires by a Linear Programming Dairy Farm Model

Abstract Dairy sires were ranked for overall merit by an average daughter's contribution to farm net profit. Biological characteristics of sires and economic factors of a dairy farm were linked by linear programming. Availability and constraints of resources were in the model. Average daughter's returns over variable costs attributable to sire proofs for several traits was the measure of sire's net merit. The index of total economic merit for sires was the amount of change of net profit by milking progeny of different sires. Ranking considered sire's contribution to milk yield and feed intake of daughters caused by variations of proofs for milk, fat percent, and size, sire's nonreturn rate, veal calf sales of the offspring, and labor costs of slow versus fast milking daughters. Size of quota for daily milk shipments, cow housing capacity, labor for milking, and milk tank capacity were critical in determining ranking and forced greater emphasis on traits other than milk yield. Correlations between sire ranking on returns over variable costs and sire proofs were highest for milk and significant for fat percent, milking speed, size, and nonreturn rate. These traits had high standard partial correlations with and explained most of the variation of the index of total economic merit. This method aims at maximizing farm profits and may be applied to rank dairy sires on a national basis or to select sires for specific dairy farm operations. Production dollar index ratings on the same sires were closely correlated (−.87 to −.96) with profit index ranking but are potentially misleading if constraints exist that limit maximum milk output per farm.
Incumbent and Challenger Campaign Spending Effects in Proportional Electoral Systems

Positive effects of campaign spending on electoral outcomes have been found in several comparative, multiparty contexts, but very few of these systems use proportional representation. The few studies examining spending effects in multiparty elections have found that incumbent spending is no less effective than challenger spending, contrary to the vast bulk of empirical literature drawn from single-member district contexts. This study reexamines incumbent—challenger differences in spending effects under the single transferable vote. Examining the Irish general elections of 2002, the authors find a positive and statistically significant relationship between spending and votes. Candidates that spend more win more votes, and outspending one’s rivals means winning more of the vote share. Spending more also directly increases a candidate’s chance of winning a seat. Finally, incumbent spending is considerably less effective than spending by challengers from other parties but no less effective than spending by challengers from a candidate’s own party.