Miracle-dz/UniFAR-FTUs huggingface dataset:
Subspace Gaussian Mixture Models for Automatic Speech Recognition

In most of state-of-the-art speech recognition systems, Gaussian mixture models (GMMs) are used to model the density of the emitting states in the hidden Markov models (HMMs). In a conventional system, the model parameters of each GMM are estimated directly and independently given the alignment. This results a large number of model parameters to be estimated, and consequently, a large amount of training data is required to fit the model. In addition, different sources of acoustic variability that impact the accuracy of a recogniser such as pronunciation variation, accent, speaker factor and environmental noise are only weakly modelled and factorized by adaptation techniques such as maximum likelihood linear regression (MLLR), maximum a posteriori adaptation (MAP) and vocal tract length normalisation (VTLN). In this thesis, we will discuss an alternative acoustic modelling approach — the subspace Gaussian mixture model (SGMM), which is expected to deal with these two issues better. In an SGMM, the model parameters are derived from low-dimensional model and speaker subspaces that can capture phonetic and speaker correlations. Given these subspaces, only a small number of state-dependent parameters are required to derive the corresponding GMMs. Hence, the total number of model parameters can be reduced, which allows acoustic modelling with a limited amount of training data. In addition, the SGMM-based acoustic model factorizes the phonetic and speaker factors and within this framework, other source of acoustic variability may also be explored. In this thesis, we propose a regularised model estimation for SGMMs, which avoids overtraining in case that the training data is sparse. We will also take advantage of the structure of SGMMs to explore cross-lingual acoustic modelling for low-resource speech recognition. Here, the model subspace is estimated from out-domain data and ported to the target language system. In this case, only the state-dependent parameters need to be estimated which relaxes the requirement of the amount of training data. To improve the robustness of SGMMs against environmental noise, we propose to apply the joint uncertainty decoding (JUD) technique that is shown to be efficient and effective. We will report experimental results on the Wall Street Journal (WSJ) database and GlobalPhone corpora to evaluate the regularisation and cross-lingual modelling of SGMMs. Noise compensation using JUD for SGMM acoustic models is evaluated on the Aurora 4 database.
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation

This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible attribute selection scheme by automatically scoring them via an attribute attention mechanism. The semantic attributes are embedded into the hidden semantic space as the generation inputs. With the attributes properly harnessed, our proposed SAM can generate interpretable texts with regard to the input attributes. Qualitative analysis, including word semantic analysis and attention values, shows the interpretability of SAM. On several typical text datasets, we empirically demonstrate the superiority of the Semantic Attribute Modulated language model with different combinations of document attributes. Moreover, we present a style variation for the lyric generation using SAM, which shows a strong connection between the style variation and the semantic attributes.
Prospective Evaluation of a Whole-Blood Test Using Mycobacterium tuberculosis-Specific Antigens ESAT-6 and CFP-10 for Diagnosis of Active Tuberculosis

ABSTRACT A new immunodiagnostic test based on the Mycobacterium tuberculosis-specific antigens CFP-10/ESAT-6(QFT-RD1) has been launched as an aid in the diagnosis of latent tuberculosis (TB) infection (LTBI). The aim of this study was to evaluate this test for the diagnosis of active TB. Eighty-two patients with suspicion of TB and 39 healthy BCG-vaccinated persons were enrolled. Forty-eight had active TB, 25 did not, and 9 were excluded. Sensitivity and specificity of the test for active TB were evaluated in a prospective blinded manner in patients suspected of TB. The sensitivity of the QFT-RD1 was 85% (40/48; confidence interval [CI], 75 to 96), and it was higher than the sensitivity of microscopy, 42% (20/48; CI, 27 to 56; P = 0.001), and culture, 59% (27/46; CI, 44 to 73; P = 0.009). Of patients with extrapulmonary TB, 92% (12/13) were QFT-RD1 positive, whereas only 31% (4/13) were positive by microscopy and 42% (5/12) by culture (P < 0.05), and 87% (13/15) of those who were negative by both microscopy and culture were QFT-RD1 positive. By combining microscopy and culture with the QFT-RD1 test, sensitivity increased to 96% (CI, 90 to 102). Ten of 25 (40%) non-TB patients were QFT-RD1 positive, resulting in a specificity of 60%. However, 80% (8/10) of these had risk-factors for TB, indicating latent infection in this group. In healthy controls, only 3% (1/39) were QFT-RD1 positive. In conclusion, the QFT-RD1 test is sensitive for diagnosis of TB, especially in patients with negative microscopy and culture. The accuracy of the QFT-RD1 test will vary with the prevalence of LTBI. We suggest that the QFT-RD1 test could be a very useful supplementary tool for the diagnosis of TB.
Management of Type I I I Dens Invag inatus in a Mandibu lar

an invagination of enamel organ into dental papilla during tooth development. The endodontic treatment of invaginated teeth may be challenging due to difficulties in accessing the root canals and also due to complex variations of internal morphology. This article presents the endodontic management and follow-up in a rare case of right mandibular second premolar with Oehlers’ type III dens invaginatus. The result of cold pulp testing was positive for this tooth but it was associated to a sinus tract and periapical lesion. Herein, it is described the root canal therapy of this tooth combined with periapical surgery, emphasizing the importance of proper diagnosis and planning by using cone beam computed tomography (CBCT). This case report presents the proper periapical healing 6 months after the combination of nonsurgical and surgical treatments. It also shows that CBCT is an important auxiliary examination to avoid errors in diagnosis and subsequent treatment of dental anomalies. Management of Type I I I Dens Invag inatus in a Mandibu lar P r e m o l a r : A C a s e R e p o r t
The effect of topical corticosteroids in combination with alefacept on circulating T‐cell subsets in psoriasis

Background: Novel therapies against psoriasis are emerging. Alefacept is such a treatment. It selectively targets the memory effector population of T cells and thereby diminishes the psoriatic plaques. In some cases, however, the use of alefacept as a monotherapy is not sufficient. Objective: In the present study we investigate the safety and efficacy of adding topical steroids to alefacept treatment during the initial 4 weeks. Methods: Peripheral blood was obtained from all patients and the presence of specific T‐cell subsets was assessed by flow cytometry. Fourteen patients were included and treated with 15 mg alefacept intramuscularly for a period of 12 weeks. Each of them was randomized to use either betamethasone‐dipropionate cream or a vehicle cream during the first 4 weeks of the alefacept course. Results: Additional topical corticosteroid treatment during the first 4 weeks of alefacept treatment does not have a beneficial effect on the clinical efficacy. Marked changes were seen in the absolute cell counts of various of the analysed T‐cell subsets in peripheral blood after 12 weeks of alefacept, either with or without additional local steroid application. The CD45RO+, CD8+CD45RO+, CD8+CD161+, CD4+CD25+, CD4+CLA+ and CD8+CLA+ populations showed a statistically significant decrease immediately after the treatment period. Further analysis revealed that the addition of local steroid therapy to alefacept results in marked decreases of all T‐cell subsets analysed in this study, in contrast to the addition of the vehiculum only. Conclusion: Alefacept selectively targets the CD45RO+ lymphocyte population, as well as some other subpopulations of lymphocytes. This effect is independent of the use of additional topical therapy during the first 4 weeks. The extent of the decrease, on the contrary, is dependent on the use of corticosteroids.
Detecting bridging faults with stuck-at test sets

A method is described that provides high detection of bridging faults without requiring extensive fault simulation. Bridging fault coverage can be increased by doing fault simulation and test generation for bridging faults that are identified as hard to detect. These bridging faults occur between nodes that rarely, if ever, differ, or that seldom change value. In addition, if the nodes in the fault-free circuits toggle often, feedback faults are easier to detect. This is true even if the nodes involved always have equal values. Methods for identifying such nodes have been presented. These methods use results available from fault-free simulations. A simple solution is to randomly reorder the test vectors to increase toggling and therefore increase bridging fault coverage. As a result, computer time for test generation will be only slightly greater than the time required for stuck-at fault generation alone. <<ETX>>
Obesity: the protein leverage hypothesis

The obesity epidemic is among the greatest public health challenges facing the modern world. Regarding dietary causes, most emphasis has been on changing patterns of fat and carbohydrate consumption. In contrast, the role of protein has largely been ignored, because (i) it typically comprises only ∼15% of dietary energy, and (ii) protein intake has remained near constant within and across populations throughout the development of the obesity epidemic. We show that, paradoxically, these are precisely the two conditions that potentially provide protein with the leverage both to drive the obesity epidemic through its effects on food intake, and perhaps to assuage it. We formalize this hypothesis in a mathematical model. Some supporting epidemiological, experimental and animal data are presented, and predictions are made for future testing.
TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter

This paper describes the system we submitted to SemEval-2017 Task 4 (Sentiment Analysis in Twitter), specifically subtasks A, B, and D. Our main focus was topic-based message polarity classification on a two-point scale (subtask B). The system we submitted uses a Support Vector Machine classifier with rich set of features, ranging from standard to more creative, task-specific features, including a series of rating-based features as well as features that account for sentimental reminiscence of past topics and deceased famous people. Our system ranked 14th out of 39 submissions in subtask A, 5th out of 24 submissions in subtask B, and 3rd out of 16 submissions in subtask D.
Quantum particle in a washboard potential. I. Linear mobility and the Einstein relation.

The Einstein-Kubo relation between the diffusion constant {ital D} and the linear mobility {nu} is investigated for a dissipative quantum particle in a potential {ital V}({ital x})={minus}{ital Fx}+{ital V}{sub 0}cos({ital k}{sub 0}{ital x}). This system is directly related to a current-biased Josephson junction. It is shown that {ital D}={ital kT}{nu} holds to all order in {ital V}{sub 0} for a general dissipation spectrum {ital J}({omega}), as long as {ital J}({omega}) is linear near {omega}{r arrow}0 and is not pathological. A generalized version of the Einstein relation is shown to hold also for sub-Ohmic dissipation, {ital J}({omega}){similar to}{omega}{sup {ital s}}, 0{lt}{ital s}{lt}1 at {omega}{r arrow}0 where the motion is subdiffusive. The super-Ohmic case 1{lt}{ital s}{lt}2 is also discussed.
A Phase I clinical trial of immunotherapy with interferon-gamma gene-modified autologous melanoma cells: monitoring the humoral immune response.

BACKGROUND Tumor cells transduced with cytokine genes provide immunogenic vaccines for cancer immunotherapy. METHODS A Phase I clinical trial was conducted for the specific active immunization of melanoma patients with interferon-gamma (IFN-gamma) gene-modified autologous melanoma tumor cells. Short term melanoma cultures were transduced retrovirally with the gene for human IFN-gamma. The genetically modified melanoma cells secreted biologically active IFN-gamma and showed enhanced expression of major histocompatibility complex class I and class II surface antigens. These cells were inactivated by irradiation (50 gray) and were cryopreserved for the vaccine. Twenty melanoma patients were enrolled in this clinical trial. The immunizations were administered in escalating doses once every 2 weeks for 3 months. The first and second injections consisted of 2 million cells, followed by 6 million for the third and fourth injections, and then 18 million for the fifth and sixth injections. The humoral immune responses of the patients were assessed by enzyme-linked immunoadsorbent assay, radioimmunoassay, and radioimmunoprecipitation. RESULTS Thirteen of the 20 patients completed the immunization protocol. Eight of these 13 patients showed a humoral immunoglobulin (Ig)G response against autologous and allogeneic melanoma cells. The other five patients either had no detectable antimelanoma antibodies or showed a weak IgG response that did not rise significantly above the preimmune level. All the sera contained low or undetectable levels of antimelanoma IgM antibodies. The IgG response increased progressively in titer during the course of immunization. The positive sera showed preferentially strong binding to melanoma cell lines and some cross-reactivity to nonmelanoma tumors. A 75-80 kD antigen on melanoma cells was immunoprecipitated by postimmune sera of 3 of the responding patients. Preimmune sera from these three patients and sera from other patients immunized with a standard nontransduced melanoma cell vaccine failed to precipitate this antigen. Two patients with significant increases in serum IgG had clinical tumor regression, and two additional patients with low serum IgG response had transient shrinkage of nodular disease during therapy. CONCLUSIONS These data suggest that gene therapy with IFN-gamma-transduced melanoma cells is safe and worthy of further investigation in patients with less advanced stage malignant melanoma. The ability to monitor changes in the humoral responses of the immunized patients has been demonstrated.
Road vanishing point detection using weber adaptive local filter and salient-block-wise weighted soft voting

In this study, a novel and efficient technique is proposed for road vanishing point detection in challenging scenes. Currently, most existing texture-based methods detect the vanishing point using pixel wise texture orientation estimation and voting map generation, which suffers from high computational complexity. Since only road trails (e.g. road edges, ruts, and tire tracks) would contribute informative votes to vanishing point detection, the Weber adaptive local filter is proposed to distinguish road trails from background noise, which is envisioned to reduce the workload and to eliminate uninformative votes introduced by the background noise. Furthermore, instead of using the conventional pixel-wise voting scheme, the salient-block-wise weighted soft voting is developed to eliminate most of the noise votes introduced by incorrectly estimated pixel-wise texture orientations, and to further reduce the computation time of voting stage as well. The experimental results on the benchmark dataset demonstrate that the proposed method shows superior performance. The authors’ method is about ten times faster in detection speed and outperforms by 3.6% in detection accuracy, when compared with a well-known state-of-the-art approach.
A simulation tool for tuning IP network parameters based on fluid-flow models and parallel genetic algorithms

In the current Internet scenario increasing interest is being shown in quality of service (QoS) issues, so simulation and parameter tuning are becoming very hot topics. In this paper we present a user-friendly, easily expandable and time-saving tool, which is able to perform network simulation through fluid-flow models and parameter tuning with parallel genetic algorithms. All the network elements involved (TCP sources, links, routers and so on) are modeled with the fluid-flow approach as independent systems: their outputs only depend on their inputs and internal state variables. Network elements can be connected to each other by a powerful graphical interface. Subsequently the user can select network parameters to be optimized in order to achieve prearranged QoS guarantees. During this phase the tool adopts a parallel implementation of a genetic algorithm (GA) which reduces the execution time required for optimization. A case study is also presented.
GENETICS OF PSORIASIS SUSCEPTIBILITY AND TREATMENT RESPONSE

Psoriasis is a chronic, recurrent skin disease that has an unknown trigger. Recent development in understanding the disease mechanisms led to the idea that psoriasis has a genetic background on which external triggers act to develop the psoriatic lesions. This review presents the newest information regarding the genetic variants of the psoriatic patients and their influence on disease evolution and treatment response.
Synchronizer Behavior and Analysis

Synchronizer characterization is non-trivial. The exponential response to parameter changes makes this task a challenge, which is further hampered by numerical instability and precision limitations of circuit simulators. The analysis of multi-stage synchronizers is extremely difficult due to the compounding of these exponential factors. We present results and discoveries from analyzing a variety of synchronizer circuits, and show some problematic circuit behaviors that can arise from synchronizer operation. We also present three ways to cross-check the analysis results, including a formula for calculating the metastable characteristics of a multi-stage synchronizer from its component stages.
The role of positron emission mammography in breast cancer imaging and management

April 2008 Breast cancer is one of the most frequently diagnosed cancers in women, touching the lives of roughly 213,000 women in 2006. In addition to invasive breast cancer, approximately 62,000 cases of in situ breast cancer were projected to occur in 2006, with 85% of these being ductal carcinoma in situ. Even with these increased numbers, mortality is decreasing. Screening mammography has been the gold standard for breast cancer surveillance for 3 decades and is credited with decreasing mortality by 33%. Because mammography has resulted in earlier detection of breast cancer, more patients today are candidates for, and choose, breast-conserving surgery instead of mastectomy. However, mammography is frequently inadequate as a planning tool for lumpectomy. In addition, it has very limited value in women with dense breasts. This may explain the finding that residual cancer can be found in as many as 30% to 60% of patients after lumpectomy, resulting in a second trip to the operating room. One goal of molecular imaging is to preoperatively identify which patients are best served by lumpectomy and define the margins for surgery by identifying metabolic abnormalities in tissue, potentially decreasing the number of second surgeries needed for resection of residual disease. Any technology that could more precisely map the extent of both invasive and noninvasive disease would lead to more precise surgery. This article will review the history of molecular imaging in breast cancer with a special emphasis on the use of the new technology positron emission mamography (PEM), in preand postoperative breast cancer management.
Identifying Cancer Biomarkers Via Node Classification within a Mapreduce Framework

Big data are giving new research challenges in the life sciences domain because of their variety, volume, veracity, velocity, and value. Predicting gene biomarkers is one of the vital research issues in bioinformatics field, where microarray gene expression and network based methods can be used. These datasets suffer from the huge data voluminous, causing main memory problems. In this paper, a Random Committee Node Classifier algorithm (RCNC) is proposed for identifying cancer biomarkers, which is based on microarray gene expression data and Protein-Protein Interaction (PPI) data. Data are enriched from other public databases, such as IntACT1 and UniProt2 and Gene Ontology3 (GO). Cancer Biomarkers are identified when applied to different datasets with an accuracy rate an accuracy rate 99.16%, 99.96% precision, 99.24% recall, 99.16% F1-measure and 99.6 ROC. To speed up the performance, it is run within a MapReduce framework, where RCNC MapReduce algorithm is much faster than RCNC sequential algorithm when having large datasets.
'These sorts of people don't do very well': race and allocation of health care resources.

Recent literature has highlighted issues of racial discrimination in medicine. In order to explore the sometimes subtle influence of racial determinants in decisions about resource allocation, we present the case of a 53-year-old Australian Aboriginal woman with end-stage renal failure. The epidemiology of renal failure in the Australian Aboriginal population and amongst other indigenous peoples is discussed. We show that the use of utilitarian outcome criteria for resource allocation may embody subtle racial discrimination where consideration is not given to issues of justice, race, culture and gender. It is only where the processes by which resources are allocated are transparent, clearly defined and based upon consultation with individual patients that issues and justice are likely to be adequately addressed.
Dimmable and Cost-Effective DC Driving Technique for Flicker Mitigation in LED Lighting

In LED lighting systems, consideration about dimming and flicker is necessary. It is found that conventional DC-mode technique produces flicker-free light and high luminous efficacy but poor dimming function, whereas pulse-width-modulation (PWM) technique offers better dimming flexibility but inherent flicker in comparison to DC technique. Although increasing the PWM dimming frequency could make the flicker invisible to the human eyes, the flicker still exists, and the potential health risks should not be ignored. In this paper, a cost-effective DC driving technique is proposed to improve dimming flexibility of existing DC technique while retaining flicker-free and high luminous efficacy. A current-controlled buck converter is operated in discontinuous conduction mode (DCM). LED current is regulated by the number of inductor current pulses during the dimming period. A method to distribute PWM pulses over the dimming period is utilized to ensure a small output voltage ripple without a large output capacitor for the buck converter. A RGB LED lamp was used in the experimental verifications. The results show that linear constant DC through LEDs could be obtained and flicker could not be found. The main tradeoff is the reduction of control precision, which is acceptable for LED outdoor lighting systems.