Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Learning for Ranking through Expected Loss Optimization

Learning to rank arises in many data mining applications, ranging from web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking model is strongly affected by the number of labeled examples in the training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming. This presents a great need for the active learning approaches to select most informative examples for ranking learning; however, in the literature there is still very limited work to address active learning for ranking. In this paper, we propose a general active learning framework, expected loss optimization (ELO), for ranking. The ELO framework is applicable to a wide range of ranking functions. Under this framework, we derive a novel algorithm, expected discounted cumulative gain (DCG) loss optimization (ELO-DCG), to select most informative examples. Then, we investigate both query and document level active learning for raking and propose a two-stage ELO-DCG algorithm which incorporate both query and document selection into active learning. Furthermore, we show that it is flexible for the algorithm to deal with the skewed grade distribution problem with the modification of the loss function. Extensive experiments on real-world web search data sets have demonstrated great potential and effectiveness of the proposed framework and algorithms.
Indexing and automatic significance analysis

Intellectual indexing proceeds on three levels: The selection of phrases occurring in the document text (sequential indexing), the posting of specific phrases from the text to generic descriptors (generic indexing), and the choice of descriptors which are implicit to the document text (symbolic indexing). Automation has been attempted on all three levels: by concordance and autoposting. Here an algorithm is proposed to solve the problem of sequential indexing which does not use any grammatical or semantic analysis, but follows the principle of emulating human judgement by evaluation of machine‐recognizable attributes of structured word assemblies (text). The algorithm is based on producing “text cuts” of a few words in length and ordering them alphabetically. Afterwards, every “text cut” which appears with a certain limit frequency or above is considered significant (by human standards). The algorithm has been applied to a text body of about 220,000 words from the NASA bibliographic file and an “established” dictionary of significant terms has been created by this algorithm. As any phrase not occurring in the established dictionary is not suppressed, but posted to a floating dictionary, from which it may, if usage increases above the limit frequency, be transferred to the established dictionary, the algorithm presents a tool for the creation and maintenance of a “self‐adaptive” data base of text information.
Learning the Taxonomy of Function Words for Parsing

Completely data-driven grammar training is prone to over-fitting. Human-defined word class knowledge is useful to address this issue. However, the manual word class taxonomy may be unreliable and irrational for statistical natural language processing, aside from its insufficient linguistic phenomena coverage and domain adaptivity. In this paper, a formalized representation of function word subcategorization is developed for parsing in an automatic manner. The function word classification representing intrinsic features of syntactic usages is used to supervise the grammar induction, and the structure of the taxonomy is learned simultaneously. The grammar learning process is no longer a unilaterally supervised training by hierarchical knowledge, but an interactive process between the knowledge structure learning and the grammar training. The established taxonomy implies the stochastic significance of the diversified syntactic features. The experiments on both Penn Chinese Treebank and Tsinghua Treebank show that the proposed method improves parsing performance by 1.6% and 7.6% respectively over the baseline.
Cerebellar vermis aplasia: Patient report and exclusion of the candidate genes EN2 and ZIC1

Cerebellar vermis aplasia (ACV, OMIM 117360) is a rare malformation of the cerebellum, with only few familial patients reported so far. Main clinical features of this rare disorder include floppiness and delayed milestones in early infancy, preceding mild cerebellar ataxia, non‐progressive clinical course, normal or slightly delayed intelligence, and occasional nystagmus. Neuroimaging reveals selective involvement of the cerebellum, which is prominent in the vermis. Because of the large preponderance of female patients, X‐linked dominant transmission was suggested by [Fenichel and Phillips (1989); Arch Neurol 46:582–583], and subsequent reports only concern female patients. Only one family with male‐to‐male transmission presenting with a generalized atrophy of the cerebellum rather than a more localized vermis aplasia has been reported so far. We report on a family in which father and son are affected by a mild form of ACV, thus confirming an autosomal mode of inheritance of the disease. Our patients showed a progressive improvement of their motor abilities, neurological examination of the father being actually normal except for a mild mental retardation. We also evaluated the potential role of two candidate genes, EN2 and ZIC1, responsible for abnormal cerebellar development in murine knock‐out models. However, molecular analysis failed to reveal any causative mutation in the coding sequence of the two genes in our patients. The understanding of the genetic basis of autosomal dominant ACV would allow a better classification of isolate cerebellar malformations and might permit to understand cell differentiation and migration in the developing central nervous system. © 2005 Wiley‐Liss, Inc.
Underestimation of Cerebral Perfusion on Flow-Sensitive Alternating Inversion Recovery Image: Semiquantitative Evaluation with Time-to-Peak Values

BACKGROUND AND PURPOSE: We assessed the underestimation of cerebral perfusion measured by the flow-sensitive alternating inversion recovery (FAIR) technique in patients with carotid stenosis and compared the technique with dynamic susceptibility contrast (DSC) MR images. MATERIALS AND METHODS: We studied 42 areas of decreased cerebral blood flow (CBF) using 3 FAIR images with different inversion times (TIs) in 42 consecutive patients with unilateral carotid stenosis of more than 50%. The width of decreased CBF area (wCBF) was qualitatively assessed. We analyzed the ratio of CBF (rCBF) and the time-to-peak (TTP) difference (dTTP) between the ipsilateral hemisphere to carotid stenosis and contralateral normal area using regions of interest (ROIs) at the same location. RESULTS: In the areas with more prolonged TTP (dTTP ≥3.2 s), the wCBF obtained from the FAIR images with TI of 1600 ms was smaller than those from the FAIR images with a TI of 800 ms and 1200 ms in all cases. The mean rCBF obtained from the FAIR images with a TI of 1200 ms was significantly lower than that obtained from the FAIR images with a TI of 1600 ms (P < .01) in the areas with more prolonged TTP. In the areas with less prolonged TTP (dTTP <3.2 s), the wCBF and mean rCBF were not significantly different between the 2 FAIR images (TI, 1200 and 1600 ms). CONCLUSION: If TTP is delayed significantly (dTTP ≥3.2 s), the FAIR with intermediate or short TI showed underestimation of perfusion in the same area with delay in TTP.
Evaluation Studies of Robotic Rollators by the User Perspective: A Systematic Review

Background: Robotic rollators enhance the basic functions of established devices by technically advanced physical, cognitive, or sensory support to increase autonomy in persons with severe impairment. In the evaluation of such ambient assisted living solutions, both the technical and user perspectives are important to prove usability, effectiveness and safety, and to ensure adequate device application. Objective: The aim of this systematic review is to summarize the methodology of studies evaluating robotic rollators with focus on the user perspective and to give recommendations for future evaluation studies. Methods: A systematic literature search up to December 31, 2014, was conducted based on the Cochrane Review methodology using the electronic databases PubMed and IEEE Xplore. Articles were selected according to the following inclusion criteria: evaluation studies of robotic rollators documenting human-robot interaction, no case reports, published in English language. Results: Twenty-eight studies were identified that met the predefined inclusion criteria. Large heterogeneity in the definitions of the target user group, study populations, study designs and assessment methods was found across the included studies. No generic methodology to evaluate robotic rollators could be identified. We found major methodological shortcomings related to insufficient sample descriptions and sample sizes, and lack of appropriate, standardized and validated assessment methods. Long-term use in habitual environment was also not evaluated. Conclusions: Apart from the heterogeneity, methodological deficits in most of the identified studies became apparent. Recommendations for future evaluation studies include: clear definition of target user group, adequate selection of subjects, inclusion of other assistive mobility devices for comparison, evaluation of the habitual use of advanced prototypes, adequate assessment strategy with established, standardized and validated methods, and statistical analysis of study results. Assessment strategies may additionally focus on specific functionalities of the robotic rollators allowing an individually tailored assessment of innovative features to document their added value.
Privacy preserving serial data publishing by role composition

Previous works about privacy preserving serial data publishing on dynamic databases have relied on unrealistic assumptions of the nature of dynamic databases. In many applications, some sensitive values changes freely while others never change. For example, in medical applications, the disease attribute changes with time when patients recover from one disease and develop another disease. However, patients do not recover from some diseases such as HIV. We call such diseases permanent sensitive values. To the best of our knowledge, none of the existing solutions handle these realistic issues. We propose a novel anonymization approach called HD-composition to solve the above problems. Extensive experiments with real data confirm our theoretical results.
An Analyzable Memory Controller for Hard Real-Time CMPs

Multicore processors (CMPs) represent a good solution to provide the performance required by current and future hard real-time systems. However, it is difficult to compute a tight WCET estimation for CMPs due to interferences that tasks suffer when accessing shared hardware resources. We propose an analyzable JEDEC-compliant DDRx SDRAM memory controller (AMC) for hard real-time CMPs, that reduces the impact of memory interferences caused by other tasks on WCET estimation, providing a predictable memory access time and allowing the computation of tight WCET estimations.
Lipase-Catalyzed Kinetic Resolution of Aryltrimethylsilyl Chiral Alcohols

Lipase-catalyzed kinetic resolution of aryltrimethylsilyl chiral alcohols through a transesterification reaction was studied. The optimal conditions found for the kinetic resolution of m- and p-aryltrimethylsilyl chiral alcohols, led to excellent results, high conversions (c = 50%), high enantiomeric ratios (E > 200) and enantiomeric excesses for the remaining (S)-alcohol and (R)-acetylated product (>99%). However, kinetic resolution of o-aryltrimethylsilyl chiral alcohols did not occur under the same conditions applied to the other isomers.
WearCom : A Wearable Communication Space

Wearable computers provide constant access to computing and communications resources. We apply traditional virtual reality techniques to develop a wearable body-stabilized space which allows multiple remote people to communicate with a wearable computer user. By combining communication and computing facilities we are able to use spatialised 3D graphics and audio cues to aid communication. The result is a portable augmented reality communication space with audio enabled avatars of the remote collaborators surrounding the user. The wearable user can use natural head motion to attend to the collaborators they wish to communicate with while being aware of other conversations, and remote users can freely connect or disconnect from the communication space.
New low-power techniques: Leakage Feedback with Stack & Sleep Stack with Keeper

For the most recent CMOS feature sizes (e.g., 90 nm and 65 nm), leakage power dissipation has become an overriding concern for VLSI circuit designers. ITRS reports that leakage power dissipation may come to dominate total power consumption [1]. We are doing comparable analysis of different low power, leakage current reduction techniques like SLEEP approach, STACK, ZIGZAG & some new techniques like, SLEEPY-STACK, LEAKAGE FEEDBACK approach and SLEEPY KEEPER techniques and, after that to combine the advantages of above written techniques, we propose two novel approaches from simulation study, named "Leakage Feedback with Stack (LFS)" & "Sleep Stack with Keeper (SSK)" which reduces leakage current while saving exact logic state. But based on simulations result with a full adder circuit, "Sleep-Stack with keeper approach" achieves up to 76–80 % less power consumption.
Option Pricing Under GARCH Processes Using PDE Methods

In this paper, we propose a partial differential equation formulation for the value of an option when the underlying asset price is described by a discrete-time GARCH process. Our numerical approach involves a spectral Fourier-Chebyshev interpolation. Numerical illustrations are provided, and the results are compared with other available valuation methods. Our numerical procedure converges exponentially fast and allows for the efficient computation of option prices, achieving a high level of precision in a few seconds of computing time.
A Resource and Tool for Super-sense Tagging of Italian Texts

A SuperSense Tagger is a tool for the automatic analysis of texts that associates to each noun, verb, adjective and adverb a semantic category within a general taxonomy. The developed tagger, based on a statistical model (Maximum Entropy), required the creation of an Italian annotated corpus, to be used as a training set, and the improvement of various existing tools. The obtained results significantly improved the current state-of-the art for this particular task.
Sample selection based on maximum entropy for support vector machines

It is always true that in the classification problems, unlabeled data is abundant while the cost for labeling data is expensive. In addition, large data sets often contain redundancy hence degrade the performance of the classifiers. In order to guarantee the generalization capability of the classifiers, a certain number of suitable unlabeled samples need to be selected out and labeled. This process is referred to as sample selection. In this paper, we propose an active learning model of sample selection for support vector machines based on the measurement of neighborhood entropy. In order to evaluate the capability of the generated SVMs, experiments have been conducted on several benchmark data sets. Comparisons between our proposed method and the random selecting method have also been conducted.
Terms of the specialized nursing language for the care of ostomates.

Objectives to identify terms of the specialized nursing language for the care of ostomates from the literature of the area, and to map the identified terms with terms of the International Classification for Nursing Practice (ICNP®). Method descriptive study of quantitative approach guided by the guidelines for the elaboration of terminology subsets of the ICNP®. The terms were collected in 49 scientific articles, extracted using a computational tool, selected according to the relevance for the theme, and normalized and mapped with the ICNP®. Results 20,668 terms were extracted. The standardization process resulted in 425 relevant terms (151 were constant in ICNP® and 274 were not contained in ICNP®), of which 154 were similar, 19 were more comprehensive, 50 were more restricted, and 51 were not in concordance. Conclusion the use of standardized language can minimize the ambiguities and redundancies identified in the mapping. The existence of terms not in concordance with the ICNP® reinforces the need for constant updating of this classification.
Neural Joint Learning for Classifying Wikipedia Articles into Fine-grained Named Entity Types

This paper addresses the task of assigning ﬁne-grained NE type labels to Wikipedia articles. To address the data sparseness problem, which is salient particularly in ﬁne-grained type clas-siﬁcation, we introduce a multi-task learning framework where type classiﬁers are all jointly learned by a neural network with a hidden layer. In addition, we also propose to learn article vectors (i.e. entity embeddings) from Wikipedia’s hypertext structure using a Skip-gram model and incorporate them into the input feature set. To conduct large-scale practical experiments, we created a new dataset containing over 22,000 manually labeled instances. The dataset is available. The results of our experiments show that both ideas gained their own statistically signiﬁcant improvement separately in classiﬁcation accuracy.
UnURL: Unsupervised Learning from URLs

Web pages are identified by their URLs. For authoritative web pages, pages that are focused on a specific topic, webmasters tend to use URLs which summarize the page. URL information is good for clustering because, they are small and ubiquitous, making techniques based on just URL information magnitudes faster than those which make use of the text content as well. We present a system that makes use of only URL information to perform clustering of web search result sets, clustering of general web document corpora and topic identification of topical URL corpora. This research prototype which we call UnURL is, to the best of our knowledge, the first attempt on using unsupervised machine learning techniques on URLs.
Loss of BMP2, Smad8, and Smad4 expression in prostate cancer progression

The role of the bone morphogenetic protein (BMP) pathway in prostate cancer (PC) is unclear. This study aimed to characterize aspects of the BMP pathway in PC by assessing BMP2, Smad8, and Smad4 expression in normal, hyperplastic, and malignant prostate tissue, and to correlate findings with progression to PC.
Discriminating projections for estimating face age in wild images

Despite the fundamental variability of human appearance, the last several years have seen considerable advances in age estimation from images of faces. Many of these advances have been made possible by artificially removing external sources of variability-they focus on highly constrained images from datasets such as the MORPH face database and FG-NET. We introduce a novel approach to estimating age from a single “wild” image, where pose, illumination, expression, face size, and face occlusions are not managed. Our method is able to reduce the effects of variations that already exist within in image. Using pose-specific projections, we map image features into a latent space that is pose-insensitive and age-discriminative. Age estimation is then performed using a multi-class SVM. We show that our approach outperforms other published results on the Images of Groups dataset (Gallagher and Chen, 2009), which is the only age-related dataset with a non-trivial number of off-axis “wild” face images. We also show results that are competitive with recent age estimation algorithms on the mostly-frontal FG-NET dataset, and we experimentally demonstrate that our feature projections introduce insensitivity to pose.
Hierarchical non-uniform unit selection based on prosodic structure

In speech synthesis systems based on wave concatenation, using longer units can generate more natural synthetic speech. In order to improve the usage of longer units in the corpus, this paper proposed a hierarchical non-uniform unit selection framework. Each layer included in the framework is an independent searching procedure which searches for different sized units and adopts suitable naturalness measuring functions related to the unit type. We have applied it to our Mandarin speech synthesis system according to the Chinese prosodic structure with respect to the statistical result in our corpus. Experiment result shows it outperforms our previous system.
Dual band single layer microstrip antenna with circular polarization for WiMAX application

A new configuration of a square patch Microstrip antenna for a single-feed to achieve a dual band frequency for WiMax application with circular polarization (CP) is proposed. A single layer is used for the antenna. Dual bandwidths and CP is achieved by designing asymmetrical slot and two different truncated corners. The radiation patterns demonstrate well cross polarization rejection and low back lobe radiations in directional orientation. The antenna has small size as well as good impedance matching bandwidth and is easy to fabricate. The compact microstrip antenna showed a good radiation characteristic with an impedance bandwidth of 4.6% at the lower band (2.5 GHz) and 4.3% at the upper band (5.7 GHz) and axial ratio of 2.1% and 0.9% at the lower and upper band, respectively.
A Flexible Content-Based Approach to Adaptive Image Compression

Recent research in image compression has focused on lossy compression algorithms. However, the baseline implementations of such algorithms generally use a universal quantization process that results in poor image quality for certain types of images, particularly mixed-content images. This paper addresses this image quality issue by presenting a new algorithm that provides flexible and customizable image quality preservation by introducing an adaptive thresholding and quantization process based on content information such as edge and texture characteristics from the actual image. The algorithm is designed to improve visual quality based on the human vision system. Experimental results from the compression of various test images show noticeable improvements both quantitatively and qualitatively relative to baseline implementations as well as other adaptive techniques
Shear-Transfer Strength of Reinforced Concrete

Many methods for solving the shear-transfer problem in shear-friction specimens and across cold joints in composite beams are partially empirical. This limits their application to other types of shear problems. In this paper, a recently developed model for the calculation of shear strength in reinforced concrete membrane elements subjected to in-plane stresses and in beams subjected to shear and torsion is applied to the shear-transfer problem. The modeling is different from the commonly used shear-friction concept, and relates the strength at a shear interface to the state of stress in a membrane element along this interface. The shear strength thus is related to the steel parallel to the shear-transfer plane as well as the concrete strength and clamping steel. Good agreement is found between the calculations of the simple model and the experimental results from 114 normal weight pushoff specimens and 15 composite beams from the literature. The model is also used to derive the empirically based coefficients of existing methods that relate the shear-transfer strength to the square root of the clamping stress. The findings from this study suggest that the new model offers a good combination of simplicity, generality and accuracy.