Miracle-dz/UniFAR-FTUs huggingface dataset:
Netgraph – Making Searching in Treebanks Easy

Searching in a linguistically annotated treebank is a principal task that requires a sophisticated tool. Netgraph has been designed to perform the searching with maximum comfort and minimum requirements on its users. Although it has been developed primarily for the Prague Dependency Treebank 2.0 (Hajic et al. 2006), it can be used with other treebanks too, both dependency and constituent-structure types, as long as the treebank is transformed to a suitable format. In this paper, we present Netgraph query language and on many examples show how it can be used to search for frequent linguistic phenomena. In section 1 (after this introduction) we extremely briefly describe the Prague Dependency Treebank 2.0, just to make the examples in the subsequent text more understandable. In the next subsection we mention the history of Netgraph and its properties as a tool. In section 2 we offer an introduction to the query language of Netgraph along with the idea of meta-attributes and what they are good for, and present linguistically motivated examples of queries in the Prague Dependency Treebank. Finally, in section 3 we offer some concluding remarks.
The UWA Approach to Modeling Ubiquitous Web Applications

Web applications have already evolved from “static” sites to completely distributed applications; nowadays they are facing a new transformation and are becoming ubiquitous systems that are available anytime, anywhere, and with any media. This new requirement led the UWA Consortium to propose a special purpose design approach to modeling Web applications. This paper introduces the approach and sketches the main design steps.
Clustering Categorical Data Based on Maximal Frequent Itemsets

Clustering categorical data received more attention since recent years, but several aspects of the existing algorithms, such as the interpretabilities of found clusters, the impact of data selection orders, are not well solved. A novel categorical data clustering algorithm called CLUBMIS is proposed in this paper, which can effectively find the interesting clusters. In addition, the clusters can be easily interpreted by the maximal frequent itemsets used in the clustering process. Different from most of the hierarchical clustering algorithm, CLUBMIS clusters datasets based on the summarized information, i.e. maximal frequent itemsets, thus it eliminates the effect of different data selection order.
A Neurolinguistic Theory of Bilingualism

This volume is the outcome of 25 years of research into the neurolinguistic aspects of bilingualism. In addition to reviewing the world literature and providing a state-of-the-art account, including a critical assessment of the bilingual neuroimaging studies, it proposes a set of hypotheses about the representation, organization and processing of two or more languages in one brain. It investigates the impact of the various manners of acquisition and use of each language on the extent of involvement of basic cerebral functional mechanisms. The effects of pathology as a means to understanding the normal functioning of verbal communication processes in the bilingual and multilingual brain are explored and compared with data from neuroimaging studies. In addition to its obvious research benefits, the clinical and social reasons for assessment of bilingual aphasia with a measuring instrument that is linguistically and culturally equivalent in each of a patient’s languages are stressed. The relationship between language and thought in bilinguals is examined in the light of evidence from pathology. The proposed linguistic theory of bilingualism integrates a neurofunctional model (the components of verbal communication and their relationships: implicit linguistic competence, metalinguistic knowledge, pragmatics, and motivation) and a set of hypotheses about language processing (neurofunctional modularity, the activation threshold, the language/cognition distinction, and the direct access hypothesis).
Optimal sensor pairing for TDOA based source localization and tracking in sensor networks

Source localization based on time-difference-of-arrival (TDOA) measurements from spatially separated sensors is an important problem in sensor networks. While extensive research has been performed on algorithm development, limited attention has been paid to sensor geometry design. In this paper, we study the optimal sensor pair geometry for the TDOA based source localization problem. Analytic solutions to the optimal sensor pair geometries, for both static and movable source cases, are derived when there exist no communication constraints. Furthermore, in many applications, sensor platforms such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are movable, and their movements and the communications between sensors are constrained. The problem becomes how to optimize the trajectories for the moving platforms such that optimal source localization and tracking can be achieved. We extend our work to optimal sensor path planning and cast it as a constrained nonlinear optimization problem. The sequential quadratic programming (SQP) method is adopted for a solution. Computer simulations demonstrate good localization performance.
Prevention of Ventricular Arrhythmias With Sarcoplasmic Reticulum Ca2+ ATPase Pump Overexpression in a Porcine Model of Ischemia Reperfusion

Background— Ventricular arrhythmias are life-threatening complications of heart failure and myocardial ischemia. Increased diastolic Ca2+ overload occurring in ischemia leads to afterdepolarizations and aftercontractions that are responsible for cellular electric instability. We inquired whether sarcoplasmic reticulum Ca2+ ATPase pump (SERCA2a) overexpression could reduce ischemic ventricular arrhythmias by modulating Ca2+ overload. Methods and Results— SERCA2a overexpression in pig hearts was achieved by intracoronary gene delivery of adenovirus in the 3 main coronary arteries. Homogeneous distribution of the gene was obtained through the left ventricle. After gene delivery, the left anterior descending coronary artery was occluded for 30 minutes to induce myocardial ischemia followed by reperfusion. We compared this model with a model of permanent coronary artery occlusion. Twenty-four–hour ECG Holter recordings showed that SERCA2a overexpression significantly reduced the number of episodes of ventricular tachycardia after reperfusion, whereas no significant difference was found in the occurrence of sustained or nonsustained ventricular tachycardia and ventricular fibrillation in pigs undergoing permanent occlusion. Conclusions— We show that Ca2+ cycling modulation using SERCA2a overexpression reduces ventricular arrhythmias after ischemia-reperfusion. Strategies that modulate postischemic Ca2+ overload may have clinical promise for the treatment of ventricular arrhythmias.
Fragmentierende Finanzialisierung. Erfolgsentgelte in der deutschen Metall- und Elektroindustrie / Fragmenting Financialization. Profit Sharing in the German Metalworking and Electrical Industry

Zusammenfassung Erfolgsentgelte gewinnen als Entgeltkomponente auch bei Tarifbeschäftigten an Bedeutung und sind die wichtigste Ursache dafür, dass in den letzten Jahren die Spanne zwischen tatsächlichen und tariflichen Entgelten gestiegen ist. Zugleich sind sie entgeltpolitischer Ausdruck der Finanzialisierung der Unternehmenssteuerung bei Großunternehmen. Doch wie verbreitet sind Erfolgsentgelte tatsächlich? Wie werden sie kollektivvertraglich reguliert und wie eng ist der Zusammenhang mit der Finanzialisierung von Unternehmen? Diese Fragen werden mit Blick auf die Metall- und Elektroindustrie analysiert, in der Erfolgsbeteiligungen überdurchschnittlich weit verbreitet sind. Es zeigt sich, dass Erfolgsentgelte zwar zunehmend an finanzwirtschaftliche und wertorientierte Kennziffern gebunden werden, aber nicht das Ergebnis einer umfassenden Finanzialisierungsstrategie der Unternehmen sind. Die Entwicklungsdynamik der Erfolgsentgelte beruht zudem auf einer wachsenden Umverteilung zwischen Kapital und Arbeit, zwischen Unternehmen entlang von Wertschöpfungsketten und zwischen unterschiedlichen Beschäftigtengruppen, und sie trägt damit zur Fragmentierung der Entgeltstrukturen bei. Summary Profit sharing is a wage component of increasing importance and has been one of the main reasons for the upswing of wage drift in recent years. At the same time, profit sharing can be regarded as an expression of the financialization of corporate governance that has become widespread among large companies in Germany. How widespread are profit sharing wages, and how closely are they connected to financial forms of corporate control? This article analyzes the developments of profit sharing in the German metalworking and electrical industry, in which profit-sharing arrangements have been widely adopted. Findings indicate that profit sharing is increasing with the use of financial and valueoriented indicators; however, it is not part of a comprehensive strategy of financialization within companies. At the same time, the dynamics of profit sharing are based on a growing redistribution of income between labor and capital, between enterprises along value chains, and between different categories of employees - all contributing to the rising fragmentation of wage structures.
CBTS: Correlation based transformation strategy for privacy preserving data mining

Mining useful knowledge from corpus of data has become an important application in many fields. Data mining algorithms like clustering, classification work on this data and provide crisp information for analysis. As these data are available through various channels into public domain, privacy for the owners of the data is increasing need. Though privacy can be provided by hiding sensitive data, it will affect the data mining algorithms in knowledge extraction, so an effective mechanism is required to provide privacy to the data and at the same time without affecting the data mining algorithms. Privacy concern is a primary hindrance for quality data analysis. Data mining algorithms on the contrary focus on the mathematical nature than on the private nature of the information. Therefore instead of removing or encrypting sensitive data, we propose transformation strategies that retain the statistical, semantic and heuristic nature of the data while masking the sensitive information. The proposed Correlation Based Transformation Strategy (CBTS) combines Correlation Analysis in tandem with data transformation techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Non Negative Matrix Factorization (NNMF) provides the intended level of privacy preservation and enables data analysis. The outcome of CBTS is evaluated on standard datasets against popular data mining techniques with significant success and Information Entropy is also accounted.
A Handbook for the Study of Mental Health: Labeling and Stigma

The chapter addresses the stigma associated with mental illnesses. It begins by summarizing the development of thinking about labeling and stigma over the past half century within the field of the sociology of mental health. This background leads to our consideration of recent advances in the conceptualization of stigma as we ask: What is stigma? How do different stigmatizing circumstances (mental illness, obesity, facial disfigurement) resemble and differ from one another? We also ask: Why do people stigmatize? In keeping with the Handbook’s emphasis on social class and socioeconomic status (SES), we introduce the concept of “stigma power” and apply this new concept to the questions of “What do people gain by stigmatizing others? ” and “How is stigma power exercised? ” Finally, we consider the steps people take to avoid, mitigate, or overcome stigma, drawing in particular on Thoits’ (Soc Psychol Q 74:6–28, 2011) theoretical statements about this issue. The conceptual growth that has taken place over time opens enormous possibilities for advancing the sociological understanding of mental illness stigma and by doing so helps to illuminate ways in which the effects of stigma might be mitigated.
Multibaseline stereo in the presence of specular reflections

We address the problem of accurate depth estimation using multibaseline stereo in the presence of specular reflections. Specular reflections can cause the intensity and color of corresponding points to change dramatically according to different viewpoints, thus producing severe matching errors for various stereo algorithms. In this paper we propose a new method to deal with this problem by treating specular reflections as occlusions. Our idea is to first detect specular pixels by computing the uncertainty of depth estimates. Then we combine the use of flexible windows and an adaptively selected subset of images to avoid these specular areas in all the multibaseline stereo images. Even though specularities may exist in the reference image, accurate depth is nevertheless estimated for all pixels. Experiments show that our consideration of specular reflections leads to improved stereo results.
A hybrid memory cell using Single-Electron transfer

This paper presents the characterization and design of a Static Random Access Memory (SRAM) cell at nano scale ranges. The proposed SRAM cell incorporates a Single-Electron (SE) turnstile and a Single-Electron Transistor (SET)/MOS circuit in its operation, hence its hybrid nature. Differently from previous cells, the hybrid circuit is utilized to sense (measure) on a voltage-basis the presence of at least an electron as stored in memory, while the turnstile enables the single electron transfer in and out of the storage node. The two memory operations (read and write) are facilitated by utilizing these hybrid circuits; moreover the proposed SRAM cell shows compatibility with MOSFET technology. HSPICE simulation shows that the proposed SRAM cell operates correctly at 45 and 32 nm with good performance in terms of propagation delay, signal integrity, stability and power consumption.
Epilepsy causing pupillary hippus: an unusual semiology

Altered pupillary behavior is commonly present during and following epileptic seizures, but symptomatic pupillary hippus as the main feature of a seizure has not been reported in the modern literature. We present the case of a woman with epileptic seizures consisting of sustained fluctuation of perception of brightness. Bilateral pupillary hippus is the main semiologic feature.This autonomic phenomenon is selective for the pupils and does not involve other autonomic‐mediated responses. An ictal video illustrates this phenomenon. The epileptogenic region, determined by ictal scalp and intracranial electroencephalography (EEG), is localized in the right posterior parietooccipital areas. Pupillary reflexes can be overridden by cortical input; here authors review the literature and discus the physiologic mechanisms underlying this autonomic phenomenon. Fluctuation in perceptual brightness during epileptic seizures may have a basis in ictal pupillary hippus.
The Relationship of Total Atmospheric Ozone to the Sunspot Cycle

For the 27-year period 1933–1959 inclusive, highly significant negative correlation is found between the relative sunspot number and the worldwide average of total atmospheric ozone. Peak correlation with respect to the present-day 10-year sunspot cycle is found at a lag of 1½ to 2 years of the sunspots relative to ozone. The peak correlation of total atmospheric ozone with the mean latitudinal distance from the solar equator of the total area of sunspots is almost identical in magnitude to that with sunspot number. However, the peak correlation with sunspot latitude is found at a lag of less than 6 months of the ozone relative to sunspot latitude, in the opposite direction to that with sunspot number. This phase difference of the negative correlation suggests that atmospheric ozone is much more sensitive to sunspot latitude than to sunspot number.
Ceratopteris richardii (C-fern): a model for investigating adaptive modification of vascular plant cell walls

Plant cell walls are essential for most aspects of plant growth, development, and survival, including cell division, expansive cell growth, cell-cell communication, biomechanical properties, and stress responses. Therefore, characterizing cell wall diversity contributes to our overall understanding of plant evolution and development. Recent biochemical analyses, concomitantly with whole genome sequencing of plants located at pivotal points in plant phylogeny, have helped distinguish between homologous characters and those which might be more derived. Most plant lineages now have at least one fully sequenced representative and although genome sequences for fern species are in progress they are not yet available for this group. Ferns offer key advantages for the study of developmental processes leading to vascularisation and complex organs as well as the specific differences between diploid sporophyte tissues and haploid gametophyte tissues and the interplay between them. Ceratopteris richardii has been well investigated building a body of knowledge which combined with the genomic and biochemical information available for other plants will progress our understanding of wall diversity and its impact on evolution and development.
Creative Subversions: A Politics beyond Representation in the UK

This statement was written by students from the University for Strategic Optimism in December 2010, during the national student protests that unfolded as a response to the cuts to education and welfare and the unjust restructuring of the UK economy. The article voices the solidarity not only among students but also among all people(s) in Britain who will be negatively affected by the austerity measures of the British government.
Deep neural network-based statistical parametric speech synthesis system using improved time-frequency trajectory excitation model

This paper proposes a deep neural network (DNN)-based statistical parametric speech synthesis system using an improved time-frequency trajectory excitation (ITFTE) model. The ITFTE model, which efﬁciently reduces the parametric redundancy of a TFTE model, improved the perceptual quality of the vocoding process and the estimation accuracy of the training process. However, there remain problems related to training ITFTE parameters in a hidden Markov model (HMM) framework, such as inefﬁciency of representing cross-dimensional correlations between ITFTE parameters, over-smoothed outputs caused by statistical averaging, and an over-ﬁtted model due to a decision tree-based state clustering paradigm. To alleviate these limitations, a centralized DNN replaces the decision trees of the HMM training process. Analysis of trainability con-ﬁrms that the DNN training process improves the model accuracy, which results in improved perceptual quality of synthesized speech. Objective and subjective test results also verify that the proposed system performs better than the conventional HMM-based system.
Diverse Power Iteration Embeddings: Theory and Practice

Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE). DPIE shows almost the same effectiveness of spectral embeddings and yet is three order of magnitude faster than spectral embeddings computed from eigen-decomposition. Our DPIE is unique in that (1) it finds linearly independent embeddings and thus shows diverse aspects of dataset; (2) the proposed regularized DPIE is effective if we need many embeddings; (3) we show how to efficiently orthogonalize DPIE if one needs; and (4) Diverse Power Iteration Value (DPIV) provides the importance of each DPIE like an eigen value. Such various aspects of DPIE and DPIV ensure that our algorithm is easy to apply to various applications, and we also show the effectiveness and efficiency of DPIE on clustering, anomaly detection, and feature selection as our case studies.
Automatic Induction of Romanization Systems from Bilingual Corpora

In this article we present a novel corpus-based method for inducing romanization systems for languages through a bilingual alignment of transliteration word pairs. First, the word pairs are aligned using a nonparametric Bayesian approach, and then for each grapheme sequence to be romanized, a particular romanization is selected according to a userspecified criterion. As far as we are aware, this paper is the only one to describe a method for automatically deriving complete romanization systems. Unlike existing human-derived romanization systems, the proposed method is able to discover induced romanization systems tailored for specific purposes, for example, for use in data mining, or efficient user input methods. Our experiments study the romanization of four totally different languages: Russian, Japanese, Hindi and Myanmar. The first two languages already have standard romanization systems in regular use, Hindi has a large number of diverse systems, and Myanmar has no standard system for romanization. We compare our induced romanization system to existing systems for Russian and Japanese. We find that the systems so induced are almost identical to Russian, and 69% identical to Japanese. We applied our approach to the task of transliteration mining, and used Levenshtein distance as the romanization selection criterion. Our experiments show that our induced romanization system was able to match the performance of the human created system for Russian, and offer substantially improved mining performance for Japanese. We provide an analysis of the mechanism our approach uses to improve mining performance, and also analyse the differences in characteristics between the induced system for Japanese and the official Japanese Nihon-shiki system. In order to investigate the limits of our approach, we studied the romanization of Myanmar, a low-resource language with a large vocabulary of graphemes. We estimate the approximate corpus size required to effectively romanize the most frequency k graphemes in the language for all values of k up to 1800. key words: romanization, transliteration, input method