Miracle-dz/UniFAR-FTUs huggingface dataset:
Regression-Based Context-Dependent Modeling of Deep Neural Networks for Speech Recognition

The data sparsity problem is addressed by using the decision tree state clusters as the training targets for the state-of-the- art context-dependent (CD) deep neural network (DNN) systems. The CD states within a cluster cannot be distinguished at the frame level. We surmise that the state clustering may cause an issue for the standard CD-DNNs, which has so far not been addressed in the literature. In this paper, a logistic regression framework is proposed for the CD-DNNs based on a set of broad phone classes to address both the data sparsity and the clustering problems. To address the data sparsity issue, the triphones are clustered into shorter biphones with broad phone contexts under multiple articulatory categories. A DNN is trained to discriminate the disjoint biphone clusters within each articulatory category. The regression bases are formed by the concatenated log posterior probabilities of all the broad phone DNNs. Logistic regression is used to transform the regression bases into the triphone state posteriors. Clustering of the regression parameters is used to reduce the regression model complexity while still achieving unique acoustic scores for all possible triphones. Based on some approximations, the regression model can be trained as a sparse softmax layer and its parameters can be learned by optimizing the cross-entropy criterion. The experimental results on a broadcast news transcription task reveal that the proposed regression-based CD-DNN significantly outperforms the standard CD-DNN. The best system provides a 1.3% absolute word error rate reduction compared to the best standard CD-DNN system.
On the Linear Convergence of the ADMM in Decentralized Consensus Optimization

In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum of their local objective functions over a common decision variable, where their information exchange is restricted between the neighbors. To this end, one can first obtain a problem reformulation and then apply the alternating direction method of multipliers (ADMM). The method applies iterative computation at the individual agents and information exchange between the neighbors. This approach has been observed to converge quickly and deemed powerful. This paper establishes its linear convergence rate for the decentralized consensus optimization problem with strongly convex local objective functions. The theoretical convergence rate is explicitly given in terms of the network topology, the properties of local objective functions, and the algorithm parameter. This result is not only a performance guarantee but also a guideline toward accelerating the ADMM convergence.
Efficacy of teriparatide in increasing bone mineral density in postmenopausal women with osteoporosis--an Indian experience.

BACKGROUND AND OBJECTIVE Osteoporosis is emerging as a leading cause of substantial morbidity in India, particularly in postmenopausal women. Teriparatide (recombinant human parathyroid hormone [1-34]) increases bone formation and improves bone microarchitecture, thereby reducing the risk of fractures. This study was conducted to evaluate the efficacy of teriparatide in increasing bone mineral density (BMD) in postmenopausal women with osteoporosis. MATERIAL AND METHODS A randomised, prospective, multicentre, open-label, controlled study was conducted on 82 postmenopausal women with established osteoporosis. Patients were randomly divided into control and teriparatide groups, each group consisting of 41 patients. All the patients were supplemented with 1000 mg of elemental calcium and 500 IU of vitamin D throughout the study period of 180 days. Besides, teriparatide group patients were administered teriparatide 20 microg daily subcutaneously. Lumbar spine, femoral neck and total hip BMD, bone mineral content (BMC) and bone area were measured by dual energy x-ray absorptiometry (DXA) at baseline and at the end of 6 months of treatment. Bone biomarkers, such as serum bone specific alkaline phosphatase (BSAP) and serum osteocalcin (OC), representing bone formation, and urinary deoxypyridinoline (DPD), representing bone resorption were assessed at baseline, and at 3 and 6 months of treatment. RESULTS During the study period, 9 patients (11%) were lost to follow-up--6 in control group (7.3%) and 3 in teriparatide group (3.7%). There was an excellent compliance to both oral and injectable medication. The investigational product teriparatide was well tolerated and there were no serious adverse events. In addition, there were no significant differences between the groups in the incidence of adverse events. The percentage of increase in lumbar spine BMD, which is the primary endpoint, was significantly (P < 0.001) higher in teriparatide group compared to that in control group (6.58% vs. 1.06%). Further, teriparatide significantly increased percentage of change in lumbar spine T-score (P < 0.001), BMC (P < 0.001) and bone area (P < 0.028) compared to control group at 6 months. Administration of teriparatide resulted in a significant percentage of increase in all the bone biomarkers in teriparatide group compared to control group patients at 3 and 6 months over baseline, thereby showing that there was a significant increase in bone turnover in teriparatide group of patients. CONCLUSION These results show that teriparatide is an effective and safe drug in increasing the BMD and therefore, teriparatide provides yet another new therapeutic option for reducing the risk management of osteoporosis in postmenopausal women (clinicaltrials.gov number, NCT00500409).
Development of ASG-15ME, a Novel Antibody–Drug Conjugate Targeting SLITRK6, a New Urothelial Cancer Biomarker

SLITRK6 is a member of the SLITRK family of neuronal transmembrane proteins that was discovered as a bladder tumor antigen using suppressive subtractive hybridization. Extensive immunohistochemistry showed SLITRK6 to be expressed in multiple epithelial tumors, including bladder, lung, and breast cancer as well as in glioblastoma. To explore the possibility of using SLITRK6 as a target for an antibody–drug conjugate (ADC), we generated a panel of fully human mAbs specific for SLITRK6. ADCs showed potent in vitro and in vivo cytotoxic activity after conjugation to Monomethyl Auristatin E or Monomethyl Auristatin F. The most potent ADC, ASG-15ME, was selected as the development candidate and given the product name AGS15E. ASG-15ME is currently in phase I clinical trials for the treatment of metastatic urothelial cancer. This is the first report that SLITRK6 is a novel antigen in bladder cancer and also the first report of the development of ASG-15ME for the treatment of metastatic bladder cancer. Mol Cancer Ther; 15(6); 1301–10. ©2016 AACR.
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization

We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserve the expected input to a neuron between train and test, the variance of the input differs. We thus propose a new weight initialization by correcting for the influence of dropout rates and an arbitrary nonlinearity's influence on variance through simple corrective scalars. Since Batch Normalization trained with dropout estimates the variance of a layer's incoming distribution with some inputs dropped, the variance also differs between train and test. After training a network with Batch Normalization and dropout, we simply update Batch Normalization's variance moving averages with dropout off and obtain state of the art on CIFAR-10 and CIFAR-100 without data augmentation.
Individuals with posttraumatic stress disorder show a selective deficit in generalization of associative learning.

OBJECTIVE Drawing on two different populations, Israeli police and Hungarian civilians, the present study assessed the ability of individuals with posttraumatic stress disorder (PTSD) to generalize previous learning to novel situations. Past neuroimaging studies have demonstrated diminished medial temporal lobe (MTL) activation and/or reduced hippocampal volume in individuals with PTSD. Our earlier computational models of cortico-hippocampal function and subsequent experimental tests of these models in MTL-impaired clinical populations argue that even mild hippocampal dysfunction may result in subtle impairments in generalization. Therefore, we predicted that individuals with PTSD would show impaired generalization. METHOD We compared the performance of five groups from two countries, including 19 Israeli police with PTSD and 22 trauma-exposed police without PTSD, and 22 Hungarian civilians with PTSD, 25 trauma-exposed civilians without PTSD, and 25 individuals without PTSD unexposed to the same trauma. Participants were tested on a two-phase learning paradigm, the Acquired Equivalence Task, which measures the ability to generalize past learning to novel situations. RESULTS We found that both PTSD and non-PTSD participants were capable of learning the initial stimulus-outcome associations, F(4, 108) = 1.79, p = . 14. However, as predicted, only individuals with PTSD showed a selective deficit in generalization of this learning to novel situations (F(4, 108) = 8.35, p < . 001, Partial η2 = 0.26). CONCLUSIONS Individuals with PTSD show a selective impairment in generalization of past learning similar to other clinical populations with MTL/hippocampal dysfunction. This is consistent with an emerging view of PTSD as being not only an anxiety disorder but also a learning disorder.
Implementation of G.729 Codec Based on DaVinci Technology

As the widely application of digital multi-media technology, G.729 has become one of the most popular audio standards. In this paper, the TMS320DM6446 which is a Davinci-based multi-core processor is chosen as our platform. We present our implementation method of G.729 algorithm which is compatible with eXpress DSP algorithm interface standard - digital media (xDAIS-DM, also called "xDM") to exploit the advantage of Davinci technology. Then we evaluate the performance of G.729 algorithm in our platform. The results show that G.729 codec can replay a high quality of speech.
PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency

The k-medoids algorithm is one of the best-known clustering algorithms. Despite this, however, it is not as widely used for big data analytics as the k-means algorithm, mainly because of its high computational complexity. Many studies have attempted to solve the efficiency problem of the k-medoids algorithm, but all such studies have improved efficiency at the expense of accuracy. In this paper, we propose a novel parallel k-medoids algorithm, which we call PAMAE, that achieves both high accuracy and high efficiency. We identify two factors---"global search" and "entire data"---that are essential to achieving high accuracy, but are also very time-consuming if considered simultaneously. Thus, our key idea is to apply them individually through two phases: parallel seeding and parallel refinement, neither of which is costly. The first phase performs global search over sampled data, and the second phase performs local search over entire data. Our theoretical analysis proves that this serial execution of the two phases leads to an accurate solution that would be achieved by global search over entire data. In order to validate the merit of our approach, we implement PAMAE on Spark as well as Hadoop and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). The results show that PAMAE significantly outperforms most of recent parallel algorithms and, at the same time, produces a clustering quality as comparable as the previous most-accurate algorithm. The source code and data are available at https://github.com/jaegil/k-Medoid.
Understanding the impact of network dynamics on mobile video user engagement

Mobile network operators have a significant interest in the performance of streaming video on their networks because network dynamics directly influence the Quality of Experience (QoE). However, unlike video service providers, network operators are not privy to the client- or server-side logs typically used to measure key video performance metrics, such as user engagement. To address this limitation, this paper presents the first large-scale study characterizing the impact of cellular network performance on mobile video user engagement from the perspective of a network operator. Our study on a month-long anonymized data set from a major cellular network makes two main contributions. First, we quantify the effect that 31 different network factors have on user behavior in mobile video. Our results provide network operators direct guidance on how to improve user engagement --- for example, improving mean signal-to-interference ratio by 1 dB reduces the likelihood of video abandonment by 2%. Second, we model the complex relationships between these factors and video abandonment, enabling operators to monitor mobile video user engagement in real-time. Our model can predict whether a user completely downloads a video with more than 87% accuracy by observing only the initial 10 seconds of video streaming sessions. Moreover, our model achieves significantly better accuracy than prior models that require client- or server-side logs, yet we only use standard radio network statistics and/or TCP/IP headers available to network operators.
Advances in Acoustic Modeling for Vietnamese LVCSR

In this paper, we present our experiments on the selection of basic phonetic units for the Vietnamese large vocabulary continuous speech recognition (LVCSR). Two acoustic models were compared. The first model has just used vowels or monophthongs as phonemes [2] while the second one, which was proposed in this paper, has explored the use of diphthongs and triphthongs as phonemes as well. The two models were trained and evaluated on a Broadcast News corpus containing 27 hours of acoustic training data and 1 hour of acoustic testing data. Moreover, an 146M-word corpus collection of newspaper was employed for building the language models. Experimental results indicate significant improvements in both word accuracy rate and time-execution. With the second acoustic model, the word accuracy rates reach 86.06% on the best case and the execution time is faster than the real-time.
Euclidean skeletons and conditional bisectors

This paper deals with the determination of skeletons and conditional bisectors in discrete binary images using the Euclidean metrics. The algorithm proceeds in two steps: first, the Centers of the Euclidean Maximal Discs (CMD) included in the set to skeletonize are characterized and robustly identified. Second, a firefront propagation is simulated starting from the set boundaries, in which pixels which are not centers of maximal discs and are not crucial to homotopy preservation are removed. Not only is the resulting algorithm fast and accurate, it allows the computation of a vast variety of skeletons. Furthermore, it can be extended to provide conditional bisectors of any angular parameter (theta) . This leads to the introduction of a new morphological transformation, the bisector function, which synthesizes the information contained in all the (theta) -conditional bisectors. The interest of all these skeleton-like transformations is illustrated on the segmentation of binary images of glass fibers.
Strategy Mining

Strategy mining is a new area of research about discovering strategies for decision-making. It is motivated by how similarity is assessed in retrospect in law. In the legal domain, when both case facts and court decisions are present, it is often useful to assess similarity by accounting for both case facts and case outcomes. In this paper, we formulate the strategy mining problem as a clustering problem with the goal of finding clusters that represent disparate conditional dependency of decision labels on other features. Existing clustering algorithms are inappropriate to cluster dependency because they either assume feature independence, such as K-means, or only consider the co-occurrence of features without explicitly modeling the special dependency of the decision label on other features, such as Latent Dirichlet Allocation (LDA). We propose an Expectation Maximization (EM) style unsupervised learning algorithm for dependency clustering. Like EM, our algorithm is grounded in statistical learning theory. It minimizes the empirical risk of decision tree learning. Unlike other clustering algorithms, our algorithm is irrelevantfeature resistant, and its learned clusters modeled by decision trees are strongly interpretable and predictive. We systematically evaluate both the convergence property and solution quality of our algorithm using a common law dataset comprised of actual cases. Experimental results show that our algorithm significantly outperforms K-means and LDA on clustering dependency.
Coronary artery disease in patients with atypical chest pain with and without diabetes mellitus assessed with coronary CT angiography

Introduction Coronary artery disease (CAD) in diabetes mellitus (DM) is often widespread when diagnosed. Non-invasive coronary calcium scoring and coronary CT angiography (CAC-score/CCTA) are accurate in the detection of CAD. This study compared CAD characteristics as identified by CCTA between patients with and without DM with atypical chest pain. Methods CAD was defined as CAC-score >0 and/or presence of coronary plaque. Several CAD characteristics (number of affected segments, obstructive (>50% stenosis) CAD and CAD distribution) were compared on a per patient and segment basis. Subanalysis of duration of DM (<5 or >5 years) and gender was performed. Results A total of 1148 patients (63.3% men, mean age 57.7±10.7), of whom 99 (8.6%) suffered from DM, were referred for CCTA. There was no difference in the prevalence of CAD between patients with and without DM (53.5% vs 50.9%, p=0.674). However patients with DM showed more affected coronary segments compared with patients without DM (2.5±3.4 vs 1.7±2.4, p=0.003). Multivariate analysis indicated that DM was an independent predictor of obstructive CAD (OR 2.16, 95% CI 1.23 to 3.78), as were age, women, and Diamond-Forrester score. In our study, obstructive CAD was more prevalent in women than in men (DM 40.0% vs 14.1%, p=0.003; non-DM 16.8% vs 8.4%, p<0.001). Patients suffering from DM >5 years showed more distal plaques (11.2% vs 7.7%, p=0.030). Conclusions Patients with atypical chest pain and DM showed more extensive CAD, as well as more obstructive CAD, particularly in women. Diabetes duration (>5 years) was not associated with more obstructive coronary disease or different plaque morphology, although more distal disease was present.
Performance-Based Mix Design for Porous Friction Courses

ii List of Tables . ix List of Figures . xiv Chapter 1 – Introduction . 1 Background . 1 Objective and Scope . 3 Chapter 2 – Literature Review . 4 Introduction . 4 Benefits of Porous Mixtures . 4 Reduced Hydroplaning and Improved Friction . 5 Backsplash, Spray and Glare Reduction . 6 Pavement Noise Reduction . 7 Mix Designs . 9 Suitable Materials . 9 Design Gradation Selection . 12 Determining the Optimum Asphalt Binder Content . 15
Zoledronic Acid Restores Doxorubicin Chemosensitivity and Immunogenic Cell Death in Multidrug-Resistant Human Cancer Cells

Durable tumor cell eradication by chemotherapy is challenged by the development of multidrug-resistance (MDR) and the failure to induce immunogenic cell death. The aim of this work was to investigate whether MDR and immunogenic cell death share a common biochemical pathway eventually amenable to therapeutic intervention. We found that mevalonate pathway activity, Ras and RhoA protein isoprenylation, Ras- and RhoA-downstream signalling pathway activities, Hypoxia Inducible Factor-1alpha activation were significantly higher in MDR+ compared with MDR− human cancer cells, leading to increased P-glycoprotein expression, and protection from doxorubicin-induced cytotoxicity and immunogenic cell death. Zoledronic acid, a potent aminobisphosphonate targeting the mevalonate pathway, interrupted Ras- and RhoA-dependent downstream signalling pathways, abrogated the Hypoxia Inducible Factor-1alpha-driven P-glycoprotein expression, and restored doxorubicin-induced cytotoxicity and immunogenic cell death in MDR+ cells. Immunogenic cell death recovery was documented by the ability of dendritic cells to phagocytise MDR+ cells treated with zoledronic acid plus doxorubicin, and to recruit anti-tumor cytotoxic CD8+ T lymphocytes. These data indicate that MDR+ cells have an hyper-active mevalonate pathway which is targetable with zoledronic acid to antagonize their ability to withstand chemotherapy-induced cytotoxicity and escape immunogenic cell death.
Protocol to Establish Axenic Cultures for Diatoms of Fresh Water

Diatom axenic cultures have great potential importance in ecological, whole genome and other single genera based studies. The generation of axenic diatom cultures like other cell culture requires special treatment and requirement of macro, micronutrients vitamins and pH. However, since silica is the most important component of diatom cell wall it is most important component of the media too. The f/2 media with little modification served as the best media for fresh water diatoms collected from water bodies of Northern western India with culture conditions standardized at temperature 22°C, light intensity 18000 lumen per m2, 16 hours/8 hours of light:dark conditions. The present study demonstrates the protocol to hinder the growth of fungi, bacteria and other green algae which contaminates the diatom culture. It was observed that antibiotic mixture of penicillin G and streptomycin in the ratio of 1:0.5 removed most of the bacterial growth in the culture media. The fungal spores were very rigid to remove from the culture plate and treatment with amphotericin B (2.5mg/L) did not reduce the appearance of fungal mycelium; however multiple centrifugation and washing with milliQ water gave relief from fungal spores. The axenicity of diatom culture is further confirmed using 16S rRNA gene based primers which showed that on blasting the amplicon the multiple sequence alignment had phylogeny resembling different diatom genera and species.
Broadband Sb/B4C multilayer mirrors for XUV spectroscopy applications

We have numerically optimized several broadband aperiodic normal-incidence multilayer mirrors based on Sb/B4C for the 85 – 125 Å spectral domain for spectroscopy applications below the silicon L-edge (λ = 125 Å). Three multilayer mirrors were then synthesized. The designed multilayer structures were optimized for a maximum uniform reflectivity in the 90 – 100, 95 – 105 and 100 – 120 Å wavebands, respectively. All the Sb/B4C multilayer mirrors were synthesized via magnetron sputtering in argon medium. The multilayers reflection spectra were evaluated with the use of a laboratory XUV spectrograph employing a laser-plasma radiation source, the mirror under study, a transmission diffraction grating and a backside-illuminated CCD matrix. The experimentally recorded spectra were compared with the theoretical ones. Numerical calculations of aperiodic Sb/B4C multilayer structures with different layer densities are presented. Effects of lower densities and small random variations of the individual layer widths on the reflection spectra are discussed.