Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech and Language Processing. Hidden Markov Models

Her sister was called Tatiana. For the first time with such a name the tender pages of a novel, we'll whimsically grace. Alexander Pushkin's novel in verse, Eugene Onegin, serialized in the early 19th century , tells of the young dandy Onegin, his rejection of the love of young Tatiana, his duel with his friend Lenski, and his later regret for both mistakes. But the novel is mainly beloved for its style and structure rather than its plot. Among other interesting structural innovations, the novel is written in a form now known as the One-gin stanza, iambic tetrameter with an unusual rhyme scheme. These elements have caused complications and controversy in its translation into other languages. Many of the translations have been in verse, but Nabokov famously translated it strictly literally into English prose. The issue of its translation and the tension between literal and verse translations have inspired much commentary—see, for example, Hofstadter (1997). In 1913, A. A. Markov asked a less controversial question about Pushkin's text: could we use frequency counts from the text to help compute the probability that the next letter in sequence would be a vowel? In this chapter we introduce a descendant of Markov's model that is a key model for language processing, the hidden Markov model or HMM. The HMM is a sequence model. A sequence model or sequence classifier is a sequence model model whose job is to assign a label or class to each unit in a sequence, thus mapping a sequence of observations to a sequence of labels. An HMM is a probabilistic sequence model: given a sequence of units (words, letters, morphemes, sentences, whatever), they compute a probability distribution over possible sequences of labels and choose the best label sequence. Sequence labeling tasks come up throughout speech and language processing, a fact that isn't too surprising if we consider that language consists of sequences at many representational levels. These include part-of-speech tagging (Chapter 10) named entity tagging (Chapter 20), and speech recognition (Chapter 29) among others. In this chapter we present the mathematics of the HMM, beginning with the Markov chain and then including the main three constituent algorithms: the Viterbi algorithm, the Forward algorithm, and the Baum-Welch or EM algorithm for unsu-pervised (or semi-supervised) learning. In the following chapter we'll see the HMM applied to the task of part-of-speech tagging.
Endogenous anticholinergic substances may exist during acute illness in elderly medical patients.

BACKGROUND The purpose of this study was to determine if serum anticholinergic activity (SACA) arises from endogenous substances produced during illness. METHODS Elderly medical inpatients (N = 612) were screened for anticholinergic medication use in the week prior to the study by interviews of subjects and proxies and review of emergency room, hospital, and nursing home medication administration records. Of 24 subjects without a recent anticholinergic medication history, 15 were recruited and 10 completed the study. Serum samples were obtained on Day 2 of hospital admission. SACA was measured using a radionuclide displacement assay. Medications taken by subjects were assayed for central muscarinic receptor binding at therapeutic concentrations. Results. Eight of the ten subjects had SACA detectable in the serum. No medication used by these subjects had anticholinergic activity at usual therapeutic concentrations. CONCLUSIONS Endogenous anticholinergic substances may exist during acute illness. Characterization of such substances may increase the depth of our understanding of delirium and lead to useful intervention strategies.
Development of a CZT drift ring detector for X and γ ray spectroscopy

CdTe and CZT detectors are considered better choices for high energy γ and X-ray spectroscopy in comparison to Si and HPGe detectors due to their good quantum efficiency and room temperature operation. The performance limitations in CdTe and CZT detectors are mainly associated with poor hole transport and trapping phenomena. Among many techniques that can be used to eliminate the effect of the poor charge transport properties of holes in CdTe and CZT material, the drift ring technique shows promising results. In this work, the performance of a 2.3 mm thick CZT drift ring detector is investigated. Spatially resolved measurements were carried out with an X-ray microbeam (25 and 75 keV) at the Diamond Light Source synchrotron to study the response uniformity and extent of the active area. Higher energy photon irradiation was also carried out at up to 662 keV using different radioisotopes to complement the microbeam data. Different biasing schemes were investigated in terms of biasing the cathode rear electrode (bulk field) and the ring electrodes (lateral fields). The results show that increasing the bulk field with fixed-ratio ring biases and lateral fields with fixed bulk fields increase the active area of the device significantly, which contrasts with previous studies in CdTe, where only an increasing lateral field resulted in an improvement of device performance. This difference is attributed to the larger thickness of the CZT device reported here.
A novel bootstrapping method for positive datasets in cascades of boosted ensembles

We present a novel method for efficiently training a face detector using large positive datasets in a cascade of boosted ensembles. We extend the successful Viola-Jones [1] framework which achieved low false acceptance rates through bootstrapping negative samples with the capability to also bootstrap large positive datasets thereby capturing more in-class variation of the target object. We achieve this form of bootstrapping by way of an additional embedded cascade within each layer and term the new structure as the Bootstrapped Dual-Cascaded (BDC) framework. We demonstrate its ability to easily and efficiently train a classifier on large and complex face datasets which exhibit acute in-class variation.
Clinical Efficacy of Human Papillomavirus DNA Detection in Urine from Patients with Various Cervical Lesions

A considerable number of adult Korean women avoid a Pap smear due to fear and discomfort of the pelvic examination. A reliable but noninvasive and comfortable screening method would considerably increase the participation rate. To evaluate the clinical efficacy of urine-based human papillomavirus (HPV) detection by oligonucleotide microarray, the results of HPV test from matched cervical swab specimens were compared. HPV DNA was detected in 70 of 100 cervical samples. HPV 16 was the most prevalent type (38/70), followed by types 18, 58, 52, 33, 35, 31, and 51. HPV DNA was identified in 47 of 90 urine samples. HPV 16 was the most prevalent type (30/45), followed by types 18, 52, 35, 51, 58, 33, and 56. The HPV detection rates of the cervical swabs increased in accordance with the severity of the cytologic and histologic diagnosis. The type specific agreement of HPV DNA tests between cervical swabs and urine was good in HPV 16 (kappa index=0.64 [95% CI: 0.50-0.79]), 18, 52, and 58 and fair in HPV 33 and 35. We propose that a urine HPV test is a valuable adjunctive method for a conventional Pap smear and can be used in population screening for cervical cancer in countries where it is difficult to obtain colposcopic specimens for cultural or religious reasons.
Modeling the branching characteristics and efficiency gains in global multicast trees

We investigate two issues. First, what level of efficiency gain does multicast offer over unicast? Second, how does the shape of multicast trees impact multicast efficiency? We address the first issue by developing a metric to measure multicast efficiency for a number of real and synthetic datasets. We find that group sizes as small as 20 to 40 receivers offer a 60-70% reduction in the number of links traversed compared to separately delivered unicast streams. Addressing the second issue, we have found that almost all multicast trees have similar characteristics in terms of key parameters such as depth, degree frequency and average degree. A final contribution of our work is that we have taken multicast group membership data and multicast path data and compiled datasets which can be used to generate large, realistic multicast trees.
Melatonin Improves The Developmental Competence of Goat Oocytes

Background DNA methylation is one the epigenetic mechanisms, which is critically involved in gene expression. This phenomenon is mediated by DNA methyl-transferases and is affected by environmental stress, including in vitro maturation (IVM) of oocytes. Melatonin, as an antioxidant, may theoretically be involved in epigenetic regulation via reductions of reactive oxygen species. This study was performed to investigate DNA methylation and the possibility of goat oocyte development after treatment with different concentrations of melatonin. Materials and Methods This experimental study was performed to investigate DNA methylation and the possibility of goat oocyte development after treatment with different concentrations of melatonin. For this purpose, oocytes with granulated cytoplasm were selected and co-cultured with at least two layers of cumulus cells in maturation medium with 10-6M, 10-9M, 10-12M and 0-M (as control group) of melatonin. Nucleus status, glutathione content and devel- opmental competence of the oocytes in each experimental group were assessed. Also, expression of genes associated with DNA methylation, including DNA methyltransferase 1 (DNMT1), DNA methyltransferase 3b (DNMT3b) and DNA methyltransferase 3a (DNMT3a) was evaluated by quantitative real time-polymerase chain reaction (RT-PCR). Results According to our findings, the percentage of oocytes that reached the M-II stage significantly increased in the 10-12 M group (P<0.05). Also, a significant elevation of glutathione content was observed in melatonin-treated oocytes (P<0.05). Analysis of blastocyst formation revealed that developmental competence of the oocytes was higher than the control group (P<0.05). It was observed that melatonin treatment decreased expression levels of DNA meth- yltransferases (DNMTs) and global DNA methylation (P<0.05). In addition, the expression of melatonin receptor1A (MTNR1A) was detected in both cumulus and oocyte by RT-PCR. Conclusion The results suggested that in goat model melatonin affects DNA methylation pattern, leading to an im- provement in the developmental competence of the oocytes.
Politicians, Parties, and Electoral Systems: Brazil in Comparative Perspective

This paper looks at the political consequences and the political origins of the Brazilian electoral system. This system has several unusual features that grant politicians nonpareil autonomy with respect to their parties. These features include a system of proportional representation that uses an open list and a mechanism known as the candidato nato, which allows politicians to get on the ticket despite the opposition of the party leadership. As a result, the electoral system reinforces the individualistic behavior of politicians and has contributed to undermining efforts to build more effective parties. Notwithstanding their frequent laments about the weakness of parties, Brazilian politicians have consistently opted for electoral systems that undermine parties. They have done so because they perceived measures that could strengthen parties as authoritarian, and also in response to their fears that executives would otherwise be able to control them ruthlessly. The extreme party weakness and individualistic patterns of representation that are reinforced by this electoral system have sustained an elitist polity.
On The Projection Operator to A Three-view Cardinality Constrained Set

The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve a cardinality constrained problem, the key challenge is to solve the projection onto the cardinality constraint set, which is NP-hard in general when there exist multiple overlapped cardinality constraints. In this paper, we consider the scenario where the overlapped cardinality constraints satisfy a Three-view Cardinality Structure (TVCS), which reflects the natural restriction in many applications, such as identification of gene regulatory networks and task-worker assignment problem. We cast the projection into a linear programming, and show that for TVCS, the vertex solution of this linear programming is the solution for the original projection problem. We further prove that such solution can be found with the complexity proportional to the number of variables and constraints. We finally use synthetic experiments and two interesting applications in bioinformatics and crowdsourcing to validate the proposed TVCS model and method.
A low-power CMOS neural amplifier with amplitude measurements for spike sorting

Integrated, low-power, low-noise CMOS neural amplifiers have recently grown in importance as large microelectrode arrays have begun to be practical. With an eye to a future where thousands of signals must be transmitted over a limited bandwidth link or be processed in situ, we are developing low-power neural amplifiers with integrated pre-filtering and measurements of the spike signal to facilitate spike-sorting and data reduction prior to transmission to a data-acquisition system. We have fabricated a prototype circuit in a commercially-available 1.5 /spl mu/m, 2-metal, 2-poly CMOS process that occupies approximately 91,000 square /spl mu/m. We report circuit characteristics for a 1.5 V power supply, suitable for single cell battery operation. In one specific configuration, the circuit bandpass filters the incoming signal from 22 Hz to 6.7 kHz while providing a gain of 42.5 dB. With an amplifier power consumption of 0.8 /spl mu/W, the rms input-referred noise is 20.6 /spl mu/V.
How is parenting related to adolescent delinquency? A between- and within-person analysis of the mediating role of self-control, delinquent attitudes, peer delinquency, and time spent in criminogenic settings

We examined how parenting is directly and indirectly associated with adolescent delinquency. We derived four possible mechanisms from major criminological theories and examined their relative contribution to explaining the relationship between parenting and delinquency: self-control theory (that is, self-control), differential association theory (that is, delinquent attitudes and peer delinquency), and routine activity theory (that is, time spent in criminogenic settings). In addition, we examined how changes in different aspects of parenting during adolescence were directly and indirectly related to changes in delinquency. Results of multilevel structural equation modeling on two waves of panel data on 603 adolescents indicated that parenting was indirectly related to delinquency through self-control, delinquent attitudes, peer delinquency, and time spent in criminogenic settings. However, only when examined together these variables, derived from major criminological theories, almost fully mediate the effects of parenting. Furthermore, changes in parenting during adolescence were indirectly related to changes in delinquency through changes in delinquent attitudes and in peer delinquency.
Decarbonizing the Global Energy System: Implications for Energy Technology and Security

Since the Intergovernmental Panel on Climate Change (IPCC) was formed in 1988, it has engaged a substantial proportion of those individuals with relevant scientific expertise in the process of forming reasonable judgments about the effects of aggregate human activity on the composition of the earth’s atmosphere and about the resulting implications for global climate. It is now widely agreed that in concert with other so-called “greenhouse gases,” carbon dioxide (CO2) released from the burning of fossil fuels for energy is causing the earth’s climate to change. Over the last century, the concentration of CO2 in the atmosphere increased from about 300 to 375 parts per million by volume (ppmv), and global average surface temperature increased by 0.4 to 0.8 C. In the absence of policies designed to substantially reduce global emissions, scenarios developed by the IPCC indicate that CO2 concentrations will reach 550 to 1000 ppmv in 2100 and that global average surface temperature will increase by an additional 1.5 to 6 C (IPCC 2001a).
Position and Velocity Navigation Systems for Unmanned Vehicles

This brief presents a new observer synthesis methodology for a class of kinematic systems with application to the estimation of linear motion quantities of mobile platforms (position and linear velocity), in three dimensions, that: 1) presents globally exponentially stable (GES) error dynamics, which are also input-to-state stable (ISS) with respect to angular quantities; 2) minimizes the L 2 induced norm from a generalized disturbance input to a performance variable; 3) provides a systematic design procedure, based upon robust control theory results, that allows for the use of frequency weights to shape the dynamic response of the observer. A practical application is presented, in the field of ocean robotics, that demonstrates the potential and usefulness of the proposed design methodology and simulation results are included that illustrate the observer achievable performance in the presence of both extreme environmental disturbances and realistic sensors' noise.
A Rudimentary Lexicon and Semantics Help Bootstrap Phoneme Acquisition

Infants spontaneously discover the relevant phonemes of their language without any direct supervision. This acquisition is puzzling because it seems to require the availability of high levels of linguistic structures (lexicon, semantics), that logically suppose the infants having a set of phonemes already. We show how this circularity can be broken by testing, in realsize language corpora, a scenario whereby infants would learn approximate representations at all levels, and then refine them in a mutually constraining way. We start with corpora of spontaneous speech that have been encoded in a varying number of detailed context-dependent allophones. We derive, in an unsupervised way, an approximate lexicon and a rudimentary semantic representation. Despite the fact that all these representations are poor approximations of the ground truth, they help reorganize the fine grained categories into phoneme-like categories with a high degree of accuracy.
Baterías de evaluación neuropsicológica infantiles

En este artículo nos centramos en la evaluación neuropsicológica infantil. En la evaluación neuropsicológica infantil, existen acercamientos que tienen un carácter híbrido, por el hecho de servirse de métodos tanto cuantitativos como cualitativos. Se consideran como ingredientes esenciales de la exploración neuropsicológica de niños en edad escolar tres elementos: historia clínica o anamnesis, observación y aplicación de pruebas. No obstante, en este capítulo, nos centraremos exclusivamente en el estudio de algunas baterías neuropsicológicas, tradicionalmente aplicadas en el ámbito de la evaluación neuropsicológica infantil. Las baterías de evaluación neuropsicológica de niños en edad preescolar y escolar, en algunos casos, son versiones especialmente desarrolladas de las versiones ya existentes para la población adulta, no obstante, y aunque no son muchas, actualmente disponemos de diversas baterías generales de evaluación neuropsicológica específicamente dirigidas a la población infantil, un ejemplo es la batería Luria-Inicial. Son diversas las baterías que podemos citar que asumen el modelo de Luria y que están comercializadas, por ejemplo, la batería NEPSY. No obstante, a pesar de la diversidad de instrumentos existentes en el contexto de habla hispana, no son suficientes para desarrollar datos normativos para la diversidad cultural mexicana. Palabras clave: Evaluación neuropsicológica infantil; Batería Luria-Inicial; Plasticidad y perfiles neuropsicológicos. ABSTRACT
Assessment of Intervention Effects on In Vivo Peer Interactions in Adolescents with Autism Spectrum Disorders

ASSESSMENT OF INTERVENTION EFFECTS ON IN VIVO PEER INTERACTIONS IN ADOLESCENTS WITH AUTISM SPECTRUM DISORDERS Bridget K. Dolan, B.A. Marquette University, 2013 To date, there are no known published studies that have assessed the effectiveness of social skills intervention for adolescents with Asperger’s Syndrome (AS) or high functioning autism (HFA) via an observational method. Specifically, previous studies have relied on self-report measures from parents, adolescents, or teachers about the improvement of social skills. This study aimed to evaluate the effectiveness of a randomized controlled trial of a social skills intervention program, the Program for the Education and Enrichment of Relational Skills (PEERS), by coding a digitally recorded, in vivo, social interaction between adolescent participants with ASD and a typically developing adolescent. Forty-five participants were randomly assigned to either an experimental group or waitlist control group. Adolescent participants engaged in a 10minute peer interaction with an unfamiliar, typically developing, gender-matched adolescent at preand posttreatment in order to evaluate the effectiveness of PEERS in teaching key social skills. Data were coded using the Contextual Assessment of Social Skills (CASS: Ratto, Turner-Brown, Rupp, Mesibov, & Penn, 2010). Although results showed that participants demonstrated a clear learning of concepts taught in PEERS, behavior effects in the in vivo social interaction were not significant; however, these findings add to the minimal research in this area.
Discretization Algorithm that Uses Class-Attribute Interdependence Maximization

Most of the existing machine learning algorithms are able to extract knowledge from databases that store discrete attributes (features). If the attributes are continuous, the algorithms can be integrated with a discretization algorithm that transforms them into discrete attributes. The paper describes an algorithm, called CAIM (class-attribute interdependence maximization), for discretization of continuous attributes that is designed to work with supervised learning algorithms. The algorithm maximizes the class-attribute interdependence and, at the same time, generates possibly minimal number of discrete intervals. Its big advantage is that it does not require the user to pre-define the number of intervals, in contrast to many existing discretization algorithms. The CAIM algorithm and five other stateof-the-art discretization algorithms were tested on well-known machine learning datasets consisting of continuous and mixed-mode attributes. The tests show that the proposed algorithm generates discrete attributes with, almost always, the highest classattribute interdependency when compared with other algorithms, and at the same time it always generates the lowest number of intervals. The discretized datasets were used in conjunction with the CLIP4 machine learning algorithm. The accuracy of the rules generated by the CLIP4 shows that the proposed algorithm significantly improves classification performance; it also performs best in comparison with other five discretization algorithms. The CAIM algorithm’s speed is comparable to the simplest unsupervised algorithms and outperforms other supervised discretization algorithms.
Mining Latent Features from Reviews and Ratings for Item Recommendation

In this paper, we propose a probabilistic model based on collaborative filtering and extended topic model for item recommendation. It allows us to extract the item features or user preferences which are represented with meaningful phrases. We develop efficient inference algorithms using Gibbs-EM sampling for posterior inference of our model. We evaluate the model on Amazon review dataset and the experiment results show that our model outperforms state-of-the-art methods on the task of recommendation.