Miracle-dz/UniFAR-FTUs huggingface dataset:
Task dependent loss functions in speech recognition: application to named entity extraction

We present a risk-based decoding strategy for the task of Named Entity identification from speech. This approach does not select the most likely utterance produced by an ASR system, which would be the maximum a-posteriori (MAP) strategy, but instead chooses an utterance from an N-best list in an attempt to minimize the Bayes Risk under loss functions derived specifically for the Named Entity task. We describe our experimentation with three risk-based decoders corresponding to the following three performance evaluation criteria: the F-measure, the slot error rate, and the fraction of correctly identified reference slots. An unsupervised optimization is also applied to these decoders. The MAP decoder is used as the baseline for comparison. Our preliminary experiments with these task dependent decoders, using N-best lists of depth 200, show small but encouraging improvements in performance with respect to both manually tagged and machine tagged reference.
Programming in the eXtreme: Critical characteristics of Agile implementations

The prevalence of systems development project failures has been well documented. eXtreme Programming (XP) is a software development methodology that seeks to eliminate many of the shortcomings of cumbersome life cycle oriented traditional methodologies. We explore some of the basic tenets of XP and Agile methodologies and present the thoughts of two of the proponents and early participants in the “Agile revolution”, Chet Hendrickson and Ron Jeries. We analyze this interview utilizing an interpretive field study employing a hermeneutical circle technique. Our analysis suggests some of the characteristics of XP implementations are more critical than others. We propose a more concrete definition of what XP represents and suggest areas for future research.
On-line handwritten text categorization

As new innovative devices, accepting or producing on-line documents, emerge, managing facilities for these kinds of documents such as topic spotting are required. This means that we should be able to perform text categorization of on-line documents. The textual data available in on-line documents can be extracted through online recognition, a process which produces noise, i.e. errors, in the resulting text. This work reports experiments on categorization of on-line handwritten documents based on their textual contents. We analyze the effect of the word recognition rate on the categorization performances, by comparing the performances of a categorization system over the texts obtained through on-line handwriting recognition and the same texts available as ground truth. Two categorization algorithms (kNN and SVM) are compared in this work. A subset of the Reuters-21578 corpus consisting of more than 2000 handwritten documents has been collected for this study. Results show that accuracy loss is not significant, and precision loss is only significant for recall values of 60%-80% depending on the noise levels.
Application of Bland-Altman Method in Comparing Transrectal and Transabdominal Ultrasonography for Estimating Prostate Volume

Background Estimating prostate volume using less invasive transabdominal ultrasonography (TAUS) instead of transrectal ultrasonography (TRUS) is of interest in terms of identifying their agreement level. Previous reports on this subject, applied general correlation coefficient as the level of agreement. This study uses Bland-Altman method to quantify TAUS and TRUS agreement on estimating prostate volume. Methods Total prostate gland volume of 40 patients with signs and symptoms of benign prostatic hyperplasia were measured using TAUS and TRUS. The study was carried out at the Urology Research Center, Razi Hospital, Guilan University of Medical Sciences (Rasht, Iran) from March to October 2010. Both methods were performed in one session by the same experienced radiologist. Data were analyzed using Pearson correlation coefficient and Bland-Altman method. Results Total prostate volume estimated by TAUS and TRUS were 50.30±23 and 50.73±24.6 mL, respectively. The limits of agreement for the total prostate volume were -6.86/9.84 that was larger than predefined clinical acceptable margin of 5 mL. Conclusion There is a lack of agreement between TAUS and TRUS for estimating the total prostate volume. It is not recommended to apply TAUS instead of TRUS for estimating prostate volume.
Causality and Comovement between Tax Rate and Budget De cits: Further Evidence from Developing Countries

This paper examines the relationship between increasing budget de cits and tax rate for Benin, Côte d'Ivoire, Niger and Togo. This investigation is based on Johansen cointegration approach and Granger causality tests. The ndings suggest rstly that these variables are not cointegrated for the countries under study, and the bootstrap simulations of the relation show more evidence for the robustness of the results to small sample size e ects, and secondly that there is a bidirectional impact between tax rate and budget de cits. Given this bidirectional causality between these variables, variance decomposition and impulse responses are calculated to better understand the question of which e ect is greater than the other.
Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department

Objectives (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials. Data and methods We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial–patient matches and a reference standard of historical trial–patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed. Results Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial–patient matches. Discussion and conclusion By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.
Evaluation of Socio-Economic Indicators of Sustainable Development of Industrial buildings using Structural Equation Modeling

Sustainable development is the key to achieve a better future. Sustainable strategies will be successful if we enable every part of our societies to move towards sustainability. Construction industry has been one of the pioneers in this movement to reach a healthier environment, an advanced economy, and a progressed society. However, some sections have not gained much recognition; one of them is the industrial buildings section. These buildings embedded many industrial activities that are crucial to the social and economic growth of every society. The purpose of this paper is to evaluate the significance of social and economic indicators of sustainable development of industrial buildings in developing countries. To address the objective of the paper, first, the indicators of social and economic sustainability are developed through an exhaustive literature review. Then the developed indicators are evaluated through a questionnaire survey in the field of petrochemical buildings in Iran. Finally, the socio-economic indicators are analyzed and evaluated utilizing Structural Equation Modeling (SEM). Since the conducted survey is aiming to collect opinions of client and consultant professionals. This paper also determines where the client and consultant have conflicts or consensus on the importance of social and economic indicators by applying t-test and calculating mean values. The results of this paper could be considered as a basis for feasibility studies of industrial buildings in developing countries.
Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling

We propose two dynamic indexing schemes for shortest-path and distance queries on large time-evolving graphs, which are useful in a wide range of important applications such as real-time network-aware search and network evolution analysis. To the best of our knowledge, these methods are the first practical exact indexing methods to efficiently process distance queries and dynamic graph updates. We first propose a dynamic indexing scheme for queries on the last snapshot. The scalability and efficiency of its offline indexing algorithm and query algorithm are competitive even with previous static methods. Meanwhile, the method is dynamic, that is, it can incrementally update indices as the graph changes over time. Then, we further design another dynamic indexing scheme that can also answer two kinds of historical queries with regard to not only the latest snapshot but also previous snapshots. Through extensive experiments on real and synthetic evolving networks, we show the scalability and efficiency of our methods. Specifically, they can construct indices from large graphs with millions of vertices, answer queries in microseconds, and update indices in milliseconds.
Dopamine D1 versus D4 Receptors Differentially Modulate the Encoding of Salient versus Nonsalient Emotional Information in the Medial Prefrontal Cortex

Dopamine (DA) transmission plays a critical role in the processing of emotionally salient information and in associative learning and memory processes. Within the mammalian brain, neurons within the medial prefrontal cortex (mPFC) are involved critically in the encoding, expression, and extinction of emotionally salient learned information. Within the mPFC, dopaminergic transmission is involved importantly in controlling attentional and motivational processes, particularly within the context of emotionally salient sensory information. Considerable evidence suggests differential roles for DA D1-like versus D2-like receptors, including the D4 receptor subtype, in the regulation of neuronal activity and emotional processing within the mPFC. Using an olfactory fear-conditioning assay in rats, we compared the roles of DA D1 versus D4 receptor activation during the encoding and recall phases of emotional learning and memory. We report that specific activation of DA D4 receptors within the mPFC strongly potentiates the salience of normally nonsalient emotional associative fear memories and blocks the encoding of suprathreshold conditioned fear associations. However, D4 receptor activation has no effect on the recall of previously learned emotionally salient conditioned memories. In contrast, intra-mPFC D1 receptor activation failed to increase the emotional salience of subthreshold fear stimuli but completely blocked the expression of previously learned emotionally relevant information, demonstrating that DA D4 versus D1 subtype receptor transmission within the mPFC plays distinct functional roles in the processing of emotionally salient versus nonsalient associative information and differentially modulates the encoding versus recall phases of emotional memory within the mPFC.
Morphology−performance relationship of polypropylene−nanoclay composites processed by shear controlled injection moulding

Fil: Costantino, Maria Alejandra. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Mar del Plata. Instituto de Investigacion en Ciencia y Tecnologia de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingenieria; Argentina
Raloxifene as an Adjunctive Treatment for Postmenopausal Women With Schizophrenia: A 24-Week Double-Blind, Randomized, Parallel, Placebo-Controlled Trial.

UNLABELLED The potential therapeutic utility of estrogens in schizophrenia is increasingly being recognized. Raloxifene, a selective estrogen receptor modulator, appears to act similarly to estrogens on dopamine and serotonin brain systems. One previous trial by our team found that raloxifene was useful to improve negative, positive, and general psychopathological symptoms, without having the negative side effects of estrogens. In this study, we assess the utility of raloxifene in treating negative and other psychotic symptoms in postmenopausal women with schizophrenia exhibiting prominent negative symptoms. This was a 24-week, randomized, parallel, double-blind, placebo-controlled study. Patients were recruited from the inpatient and outpatient departments of Parc Sanitari Sant Joan de Déu, Hospital Universitari Institut Pere Mata, and Corporació Sanitària Parc Taulí. Seventy postmenopausal women with schizophrenia (DSM-IV) were randomized to either adjunctive raloxifene (38 women) or adjunctive placebo (32 women). Psychopathological symptoms were assessed at baseline and at weeks 4, 12, and 24 with the Positive and Negative Syndrome Scale (PANSS) and the Scale for the Assessment of Negative Symptoms (SANS). The addition of raloxifene (60 mg/d) to regular antipsychotic treatment significantly reduced negative (P = .027), general (P = .003), and total symptomatology (P = .005) measured with the PANSS during the 24-week trial, as compared to women receiving placebo. Also Alogia SANSS subscale improved more in the raloxifene (P = .048) than the placebo group. In conclusion, raloxifene improved negative and general psychopathological symptoms, compared with antipsychotic medication alone, in postmenopausal women with schizophrenia. These data replicate our previous results with a larger sample and a longer follow-up. TRIAL REGISTRATION NCT01573637.
Predicting Protein Levels from Tandem Mass Spectrometry Data

This paper addresses a central problem of Proteomics: estimating the amounts of each of the thousands of proteins in a cell culture or tissue sample. Although laboratory methods have been developed for this problem, we seek a simple method, one that does not involve intricate, complex or expensive laboratory procedures. Instead, our aim is to use machinelearning techniques to infer protein levels from the relatively cheap and abundant data available from high-throughput tandem mass spectrometry (MS/MS). In this paper, we develop and evaluate several techniques for tackling this problem. Specifically, we develop three generative models of MS/MS data, and for each, we develop a family of methods for efficiently fitting the model to data. We prove that each method is correct in that it achieves a well-defined optimization criterion. In addition, to evaluate their biological relevance, we test each method on three real-world datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse.
Delivery Context Access for Mobile Browsing

The usage of context data to facilitate adaptive applications is gaining widespread prominence. We describe an architecture for adaptive Web applications specially suited for mobile devices. This approach is based on an ongoing standardization effort within World Wide Web Consortium (W3C) for client side device context access. The specification, delivery context: interfaces (DCI) is intended to be used as an access mechanism for context consumers. We augment this approach by providing additional interfaces for context providers towards data provisioning to DCI and usage of a dynamic profile generation mechanism that relies on DCI for data serialization. This complements approaches such as utilizing user agent profiles used in server side adaptation. We also provide details of a proof-of-concept implementation for DCI along with two adaptive applications and outline our future plans for this paper
Dimensionality reduction by self organizing maps that preserve distances in output space

Dimensionality Reduction is a key issue in many scientific problems, in which data is originally given by high dimensional vectors, all of which lie however over a fewer dimensional manifold. Therefore, they can be represented by a reduced number of values that parametrize their position over the mentioned non-linear manifold. This dimensionality reduction is essential not only for representing and managing data, but also for its understanding at a high interpretation level, similar to the way it is performed by the mammal cortex. This paper presents an algorithm for representing the data that lie on a non-linear manifold by the reduced number of their coordinates along a grid or map of neurons extended over this manifold. This map is generated by a Self-organization learning process whose key feature is the fact that the winning neuron is selected in order to preserve distances of input data when they are represented by their coordinates in the output map. Unlike other methods, the proposed algorithm has important features, that namely the intrinsic dimensionality is obtained simultaneously in the learning process itself, it doesn't require a long course positioning phase, and it seeks to maintain the data structure from the beginning, not leaving it as an ulterior fact to be proven. The algorithm has proven to efficiently solve classical dimensionality reduction problems, and has also showed that it can be useful for realistic problems, such as face images classification or document indexing.
The Influence of HIV-1 Subtype in the Response to Therapeutic Dendritic Cell Vaccine

In the present study, we investigated the influence of HIV-1 subtype in the response to the dendritic cell (DC) therapeutic vaccine for HIV. HIV-1 viral load and TCD8+/TCD4+ cell counts for up to 48 weeks after vaccination. Out of 19 immunized subjects, 13 were infected by subtype B, 5 by subtype F, and 1 by subtype D. Overall, 42.1% (8/19) achieved a viral load decline of ≥ 1 log10 sustained up to 48 weeks after immunization. Such magnitude of viral load drop was seen in 80% (4/5) of subtype F infected patients, and in 23.0% (3/13) of the subtype B infected ones (p=0.08). Moreover, mean viral load decline was 1.32 log10, for subtype F infected individuals compared to 0.5 log10 among subtype B infected patients (p=0.01). The variation in TCD4+ cell count was not related to HIV-1 subtype. Larger studies are necessary to confirm the efficacy of this immunotherapy and the differential response according to the background genetic diversity of HIV-1.
Organizational Learning and Capabilities for Onshore and Offshore Business Process Outsourcing

This paper identifies and analyzes firm-level characteristics that facilitate onshore and offshore business process outsourcing (BPO). We use organizational learning and capabilities to develop a conceptual model. We test the conceptual model with archival data on a broad cross section of U. S. firms. Our empirical findings indicate that firms with experience in onshore information technology (IT) outsourcing and capabilities related to IT coordination applications and process codification are more likely to engage in BPO, and firms with experience in internationalization are more likely to engage in offshore BPO. We also find that IT coordination applications have a greater impact on onshore BPO than on offshore BPO, and the effect of process codification is partly mediated through IT outsourcing.
3D face recognition with asymptotic cones based principal curvatures

The classical curvatures of smooth surfaces (Gaussian, mean and principal curvatures) have been widely used in 3D face recognition (FR). However, facial surfaces resulting from 3D sensors are discrete meshes. In this paper, we present a general framework and define three principal curvatures on discrete surfaces for the purpose of 3D FR. These principal curvatures are derived from the construction of asymptotic cones associated to any Borel subset of the discrete surface. They describe the local geometry of the underlying mesh. First two of them correspond to the classical principal curvatures in the smooth case. We isolate the third principal curvature that carries out meaningful geometric shape information. The three principal curvatures in different Borel subsets scales give multi-scale local facial surface descriptors. We combine the proposed principal curvatures with the LNP-based facial descriptor and SRC for recognition. The identification and verification experiments demonstrate the practicability and accuracy of the third principal curvature and the fusion of multi-scale Borel subset descriptors on 3D face from FRGC v2.0.
A Survey On Sketch Based Image Retrieval Technique

Recently, content based image retrieval is becoming a way of fast retrieval. Users are not satisfied with the usual information retrieval techniques. Retrieving the relevant information from keywords is very major task and this technique has been focused by researchers in many ways. Sketch is more natural and informative, that breaking down the language barrier and one of the strong media for communication in old era as well as in current scenarios. Hence currently sketch based image retrieval (SBIR) is also in focus for research purpose. Existing system descriptor SBIR is derived from two classic descriptor namely, SHoG and GF-HOG. This descriptor used to capture pixel-level features from image patches. It is very difficult task to isolate the features those are corresponds to certain edges from whole patches. Whereas, a line-based descriptor is highly adorable to tone down the impact of noisy edges but it suffers from noise problem. For effectiveness of SBIR it is mandatory to describe lines and their relationships, rather than describing image patches. For that matter some latest techniques should be discovered that attains the smart way of sketch matching with photo realistic images. Here based on review and research, histogram of line relationship (HLR) descriptor is having good hopes. A good object boundary selection algorithm and HLR and some other processes should be discovered so that work can be carried out on reducing the impact of noisy edges by selecting the shaping edges that best correspond to the object boundaries.