Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Social Media Text Summarization by Learning Sentence Weight Distribution

Recently, encoder-decoder models are widely used in social media text summarization. However, these models sometimes select noise words in irrelevant sentences as part of a summary by error, thus declining the performance. In order to inhibit irrelevant sentences and focus on key information, we propose an effective approach by learning sentence weight distribution. In our model, we build a multi-layer perceptron to predict sentence weights. During training, we use the ROUGE score as an alternative to the estimated sentence weight, and try to minimize the gap between estimated weights and predicted weights. In this way, we encourage our model to focus on the key sentences, which have high relevance with the summary. Experimental results show that our approach outperforms baselines on a large-scale social media corpus.
Complete Genome Sequence of the 2,3-Butanediol-Producing Klebsiella pneumoniae Strain KCTC 2242

ABSTRACT Here we report the full genome sequence of Klebsiella pneumoniae KCTC 2242,consisting of a 5.26-Mb chromosome (57.6% GC%; 5,035 genes [4,923 encoding known proteins, 112 RNA genes]) and a 202-kb plasmid (50.2% GC%; 229 genes [229 encoding known proteins]).
Market Value Evaluation with Ohlson Model: An Empirical Analysis of ATX Indexed Companies in Vienna Stock Exchange

Scholars of finance and accounting propose several models on equity valuation. One of these models, namely Ohlson Model, relates market value with accounting numbers and thereby, offers an opportunity of equity valuation based on accounting data. This paper aims to weight the explanatory power of the Ohlson Model on determination of market value of ATX (Austrian Traded Index) companies. For this purpose, the model parameters are estimated on the basis of past data and its precision is weighted with dataset of validation period. Although the differences between estimated market value and value calculated based on stock price are not statistically significant, the model is limited mainly with the abnormal consequences of the recent financial crisis.
Exploiting resources from closely-related languages for automatic speech recognition in low-resource languages from Malaysia. (Utilisation de ressources dans une langue proche pour la reconnaissance automatique de la parole pour les langues peu dotées de Malaisie)

Languages in Malaysia are dying in an alarming rate. As of today, 15 languages are in danger while two languages are extinct. One of the methods to save languages is by documenting languages, but it is a tedious task when performed manually.Automatic Speech Recognition (ASR) system could be a tool to help speed up the process of documenting speeches from the native speakers. However, building ASR systems for a target language requires a large amount of training data as current state-of-the-art techniques are based on empirical approach. Hence, there are many challenges in building ASR for languages that have limited data available.The main aim of this thesis is to investigate the effects of using data from closely-related languages to build ASR for low-resource languages in Malaysia. Past studies have shown that cross-lingual and multilingual methods could improve performance of low-resource ASR. In this thesis, we try to answer several questions concerning these approaches: How do we know which language is beneficial for our low-resource language? How does the relationship between source and target languages influence speech recognition performance? Is pooling language data an optimal approach for multilingual strategy? Our case study is Iban, an under-resourced language spoken in Borneo island. We study the effects of using data from Malay, a local dominant language which is close to Iban, for developing Iban ASR under different resource constraints. We have proposed several approaches to adapt Malay data to obtain pronunciation and acoustic models for Iban speech.Building a pronunciation dictionary from scratch is time consuming, as one needs to properly define the sound units of each word in a vocabulary. We developed a semi-supervised approach to quickly build a pronunciation dictionary for Iban. It was based on bootstrapping techniques for improving Malay data to match Iban pronunciations.To increase the performance of low-resource acoustic models we explored two acoustic modelling techniques, the Subspace Gaussian Mixture Models (SGMM) and Deep Neural Networks (DNN). We performed cross-lingual strategies using both frameworks for adapting out-of-language data to Iban speech. Results show that using Malay data is beneficial for increasing the performance of Iban ASR. We also tested SGMM and DNN to improve low-resource non-native ASR. We proposed a fine merging strategy for obtaining an optimal multi-accent SGMM. In addition, we developed an accent-specific DNN using native speech data. After applying both methods, we obtained significant improvements in ASR accuracy. From our study, we observe that using SGMM and DNN for cross-lingual strategy is effective when training data is very limited.
H1-Integrity of Carrier Phase Navigation Algorithms using Multiple Reference Receivers

In this paper, a methodology is developed to evaluate carrier phase navigation architectures under reference receiver failure. Different approaches to utilize redundant carrier phase measurements from multiple reference receivers are described. Carrier phase measurements are used to provide high accuracy estimates of a user’s position. But in precision approach applications that involve safety-of-life, such as in autonomous shipboard landing, integrity also plays a critical role. One case where the system integrity becomes at risk is in the situation of GPS reference receiver failure. Integrity risk in these applications is kept at a minimum by equipping the ship (reference station) with redundant receivers. In this paper, we investigate the impact of different methods on accuracy and integrity from several perspectives, including availability performance, cycle resolution capabilities, implementation complexity and computational efficiency.
SNPs in ecological and conservation studies: a test in the Scandinavian wolf population

Single nucleotide polymorphisms (SNPs) have the potential to become the genetic marker of choice in studies of the ecology and conservation of natural populations because of their capacity to access variability across the genome. In this study, we provide one of the first demonstrations of SNP discovery in a wild population in order to address typical issues of importance in ecology and conservation in the recolonized Scandinavian and neighbouring Finnish wolf Canis lupus populations. Using end sequence from BAC (bacterial artificial chromosome) clones specific for dogs, we designed assays for 24 SNP loci, 20 sites of which had previously been shown to be polymorphic in domestic dogs and four sites were newly identified as polymorphic in wolves. Of the 24 assayed loci, 22 SNPs were found to be variable within the Scandinavian population and, importantly, these were able to distinguish individual wolves from one another (unbiased probability of identity of 4.33 × 10−8), providing equivalent results to that derived from 12 variable microsatellites genotyped in the same population. An assignment test shows differentiation between the Scandinavian and neighbouring Finnish wolf populations, although not all known immigrants are accurately identified. An exploration of the misclassification rates in the identification of relationships shows that neither 22 SNP nor 20 microsatellite loci are able to discriminate across single order relationships. Despite the remaining obstacle of SNP discovery in nonmodel organisms, the use of SNPs in ecological and conservation studies is encouraged by the advent of large scale screening methods. Furthermore, the ability to amplify extremely small fragments makes SNPs of particular use for population monitoring, where faecal and other noninvasive samples are routinely used.
Sensory-Motor Systems Lab ETH

Highly immersive environments for sports simulation can help elucidate if and how athletes perform under high pressure situations. We used a rowing simulator with a CAVE setup to test the influence of virtual competitors on 10 experienced rowers. All participants were using the simulator for the first time. The objective was to assess the degree of presence by quantifying how the actions of the virtual competitors triggered behavioral changes in the experienced rowers. The participants completed a virtual 2000 m race with two competing boats, one being behind and one ahead of the participant. For two trials, each boat would come closer to the participant without overtaking, resulting in four experimental conditions. The behavior of the participants was assessed with biomechanical variables, questionnaires, and an interview after the race. Behavioral changes were detected with statistically significant differences in the extracted variables of oar angles, timing variables, velocities, and work. The results for biomechanical variables indicate individual response patterns depending on perception of competitors and self-confidence. Self-reporting indicated a high degree of presence for most participants. Overall, the experimental paradigm worked but was compromised by perceptive and subjective factors. In future, the setup will be used to investigate rowing performance further with a focus on motor learning and training of pressure situations. DOI: https://doi.org/10.1162/PRES_a_00004 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-43953 Originally published at: Wellner, M; Sigrist, R; Riener, R (2010). Virtual competitors influence rowers. Presence, 19(4):313-330. DOI: https://doi.org/10.1162/PRES_a_00004 Mathias Wellner* Roland Sigrist Sensory-Motor Systems Lab ETH Zurich
Inactivity-induced oxidative stress: A central role in age-related sarcopenia?

Abstract Ageing causes a progressive decline in skeletal muscle mass that may lead to decreased strength and functionality. The term sarcopenia is especially used to characterise this geriatric syndrome. Numerous conditions and behaviours are considered to accelerate the progression of sarcopenia such as chronic diseases, malnutrition and physical inactivity. As people in modern countries are more and more sedentary, the impact of physical inactivity on the prevalence of sarcopenia might be more and more important in the future. In this review, we discuss how reactive oxygen species (ROS) could mediate the effects of lifelong inactivity in the onset and progression of age-related sarcopenia. Although the cellular mechanisms responsible for muscle ROS production are not necessarily the same, both inactivity and ageing are indeed known to increase basal ROS concentrations in skeletal muscle. New data and literature review are provided showing that chronic ROS overproduction induced by physical inactivity may exacerbate the activation of some redox-sensitive signalling pathways involved in age-related sarcopenia. We also address the scientific evidences implicating the role of ROS overproduction in the precocious failure of aged muscles to activate intracellular signalling responses to contractions.
Normative data for female adolescents with eating disorders on the Children's Depression Inventory.

OBJECTIVE Given the importance of assessing depressive symptoms and suicidal ideation in adolescents with eating disorders (EDs), the aim was to provide normative data on the Children's Depression Inventory (CDI) for female adolescents presenting for treatment of an ED. METHOD The data source was the Helping to Outline Paediatric Eating Disorders (HOPE) Project registry (N = 1000), a prospective, ongoing registry study comprising consecutive pediatric tertiary ED referrals. Females (N = 256; 12-17 years) with DSM-5 EDs completed the CDI at intake. RESULTS Results on the CDI revealed a pattern of increasing depressive scores with age and higher scores among patients with anorexic spectrum disorders. The prevalence of suicidal ideation was high and had the same pattern as CDI scores. The mean score on the CDI in the sample was higher than community samples and clinical samples of adolescents with post-traumatic stress disorder, obsessive compulsive disorder, and other clinical disorders. DISCUSSION Females adolescents with EDs are at high-risk of depression and suicidal ideation. These data provide information about variation in CDI scores to guide clinicians in interpretation of scores.
Piecewise Fuzzy Model Construction and Controller Design based on Piecewise Lyapunov Function

This paper presents piecewise fuzzy model construction divided by arbitrary affine hyper-planes and controller design based on the so-called piecewise Lyapunov function. In our previous papers, we proposed the switching fuzzy model construction method with arbitrary dividing hyper- planes. However, we assumed that the dividing planes contain the origin, that is, linear hyper-planes in the papers. In this paper, we propose piecewise fuzzy model construction method with arbitrary dividing hyper-planes which are permitted not to pass through the origin. We can obtain sectors which can cover nonlinear dynamics more tightly than ordinary method. By utilizing continuity matrices which are constructed from coefficients of dividing hyper-planes, we construct piecewise fuzzy model which is continuous on boundaries of divided regions caused by dividing planes. Moreover, we derive controller design conditions based on the so-called piecewise Lyapunov function in terms of LMIs.
Measurement of Aluminium in Clinical Samples

Measurement of aluminium in clinical samples is important for the protection of patients with chronic renal failure, and for investigations of dementia. The types of specimen that need to be analysed include body fluids and tissues, dialysis fluids, water and foods and the concentrations that may be found can range from around 1 microgram/L to several mg/L. Although techniques must afford exceptional sensitivity they will also be susceptible to the effects of external contamination and it is imperative that those involved with the analysis, from sample collection through to the determination, ensure that precautions are taken to prevent undue addition of the metal. Of the methods that may be used ETAAS is clearly the most appropriate. Great care is required to achieve reliable performance but with experienced personnel very good results can be obtained. Regular reviews of recent analytical developments are presented in the Atomic Spectrometry Updates series and these are recommended to those who may wish to look for further information.
Levels and Patterns of Material Deprivation in Ireland: After the ‘Celtic Tiger’

In this paper we use the first full wave of the Irish component of the EU Statistics on Income and Living Conditions survey to evaluate conflicting interpretations of levels and patterns of material deprivation in Ireland after the Celtic Tiger. Radical critics of Irish economic policies have seen the Irish case as a particularly good illustration of the tendency for globalization to be accompanied by widespread economic vulnerability and marginalization. Such arguments, however, have focused unduly on relative income poverty measures. Here, employing a multidimensional perspective that encompasses not only income but also a range of deprivation dimensions, we adopt a tiered approach to the analysis of economic vulnerability and multiple deprivation. Our analysis identifies one fifth of the population as being economically vulnerable. A sub-group constituting one half of this economically vulnerable cluster is identified as ? consistently poor? Finally, seven per cent of the population are identified as maximally deprived in that they exhibit high risks of deprivation across a range of life-style deprivation dimensions. Both the levels and depth of material deprivation are a good deal more modest than suggested by radical critics of the recent Irish experience.
Federated Aggregated Search

This thesis investigates the problem of creating an 'aggregated search engine' with federated verticals. The core component for a good performance of the system is a vertical representation created by sampling each vertical. However, due to the heterogeneous nature of the documents, many times this sample is not descriptive enough. With the help of Wikipedia, we experiment with three techniques found in the literature aimed to enrich the vertical representation: a) using only Wikipedia articles as representation; b) using a combination of Wikipedia articles and the vertical sample; and c) expanding the contents of each sampled document. We discovered how by applying LDA to model the hidden topics of each vertical it is possible to identify Wikipedia articles with the same theme coverage. Then, by using only those articles for representation of some particular verticals, the selection task is improved. Finally, we experimented with the modeled topics together with Wikipedia categories and query categorization to boost the score of the verticals that could be associated with the query string. Although in this case our results are inconclusive, the numbers suggest that the approach could lead to a better vertical selection.
Hierarchical Techniques to Improve Hybrid Point Cloud Registration

Reconstructing 3D objects by gathering information from multiple spatial viewpoints is a fundamental problem in a variety of applications ranging from heritage reconstruction to industrial image processing. A central issue is known as the ”point set registration or matching” problem. where the two sets being considered are to be rigidly aligned. This is a complex problem with a huge search space that suffers from high computational costs or requires expensive and bulky hardware to be added to the scanning system. To address these issues, a hybrid hardware-software approach was presented in (Pribanić et al., 2016) allowing for fast software registration by using commonly available (smartphone) sensors. In this paper we present hierarchical techniques to improve the performance of this algorithm. Additionally, we compare the performance of our algorithm against other approaches. Experimental results using real data show how the algorithm presented greatly improves the time of the previous algorithm and perform best over all studied algorithms.
Routine intraoperative angiography during aneurysm surgery.

OBJECT The routine use of intraoperative angiography as an aid in the surgical treatment of aneurysms is uncommon. The advantages of the ability to visualize residual aneurysm or unintended occlusion of parent vessels intraoperatively must be weighed against the complications associated with repeated angiography and prolonged vascular access. The authors reviewed the results of their routine use of intraoperative angiography to determine its safety and efficacy. METHODS Prospectively gathered data from all aneurysm cases treated surgically between January 1996 and June 2000 were reviewed. A total of 303 operations were performed in 284 patients with aneurysms; 24 patients also underwent postoperative angiography. Findings on intraoperative angiographic studies prompted reexploration and clip readjustment in 37 (11%) of the 337 aneurysms clipped. Angiography revealed parent vessel occlusion in 10 cases (3%), residual aneurysm in 22 cases (6.5%), and both residual lesion and parent vessel occlusion in five cases (1.5%). When compared with subsequent postoperative imaging, false-negative results were found on two intraoperative angiograms (8.3%) and a false-positive result was found on one (4.2%). Postoperative angiograms obtained in both false-negative cases revealed residual anterior communicating artery aneurysms. Both of these aneurysms also subsequently rebled, requiring reoperation. In the group that underwent intraoperative angiography, in 303 operations eight patients (2.6%) suffered complications, of which only one was neurological. CONCLUSIONS In the surgical treatment of intracranial aneurysms, the use of routine intraoperative angiography is safe and helpful in a significant number of cases, although it does not replace careful intraoperative inspection of the surgical field.
Qualitative navigation for mobile robots in indoor environments

This article describes a novel qualitative navigation method for mobile robots in indoor environments. The approach is based on qualitative representations of variations in sensor behavior between adjacent regions in space. These representations are used to localize and guide planning and reaction. Off-line, the system accepts as input a line-based diagram of the environment and generates a map based on a simple qualitative model of sensor behavior. During execution, the robot controller integrates this map into a reaction module. This architecture has been tested both in simulation and on a real mobile robot. Results from both trials are provided.
Analysis of semantic features in free-form objects reconstruction

Abstract One of the biggest challenges associated with design and digital reconstruction of free forms comes from uniqueness and unrepeatability of these shapes. During digital reconstruction of these forms, the designer has to choose the right set of geometric features and then compose them in a way that will enable the most accurate reconstruction of the geometry. While doing this, the designer primarily relies on personal experience gained through work with free-form objects of similar geometry. In our opinion, the analysis of free-form objects geometry should rely upon semantic interpretation of their geometric and other features, and the greatest challenge of automation of digital reconstruction and free-form object design in general is closely related to automation of semantic interpretation of geometric and other free-form object features. In this paper, a case of chest bone implant digital reconstruction is presented, where a new semantic model called the active semantic model was used for modeling the meaning of geometric elements, that is, the semantic features of a free-form object. The active semantic model and its analogy-based reasoning algorithms have shown themselves as applicable for the automation of semantic interpretation of the unique, unrepeatable, and unpredictable forms of chest bone. Moreover, this semantic model showed the potential to help automate selecting and composing of geometric features for efficient digital reconstruction of the geometry of free forms.
Integrating Semantic Features for Enhancing Arabic Named Entity Recognition

Named Entity Recognition (NER) is currently an essential research area that supports many tasks in NLP. Its goal is to find a solution to boost accurately the named entities identification. This paper presents an integrated semantic-based Machine learning (ML) model for Arabic Named Entity Recognition (ANER) problem. The basic idea of that model is to combine several linguistic features and to utilize syntactic dependencies to infer semantic relations between named entities. The proposed model focused on recognizing three types of named entities: person, organization and location. Accordingly, it combines internal features that represented linguistic features as well as external features that represent the semantic of relations between the three named entities to enhance the accuracy of recognizing them using external knowledge source such as Arabic WordNet ontology (ANW). We introduced both features to CRF classifier, which are effective for ANER. Experimental results show that this approach can achieve an overall F-measure around 87.86% and 84.72% for ANERCorp and ALTEC datasets respectively.