Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic speech recognition using Hidden Conditional Neural Fields

Hidden Conditional Random Fields(HCRF) is a very promising approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted features, it cannot consider non-linearity among features that will be crucial for speech recognition. In this paper, we extend HCRF by incorporating gate function used in neural networks and propose a new model called Hidden Conditional Neural Fields(HCNF). Differently with conventional approaches, HCNF can be trained without any initial model and incorporate any kinds of features. Experimental results of continuous phoneme recognition on TIMIT core test set and Japanese read speach recognition task using monophone showed that HCNF was superior to HCRF and HMM trained in MPE manner.
Exact analysis of Dodgson elections: Lewis Carroll's 1876 voting system is complete for parallel access to NP

In 1876, Lewis Carroll proposed a voting system in which the winner is the candidate who with the fewest changes in voters' preferences becomes a Condorcet winner—a candidate who beats all other candidates in pairwise majority-rule elections. Bartholdi, Tovey, and Trick provided a lower bound—NP-hardness—on the computational complexity of determining the election winner in Carroll's system. We provide a stronger lower bound and an upper bound that matches our lower bound. In particular, determining the winner in Carroll's system is complete for parallel access to NP, that is, it is complete for Theta_2p for which it becomes the most natural complete problem known. It follows that determining the winner in Carroll's elections is not NP-complete unless the polynomial hierarchy collapses.
Meditation experience is associated with increased cortical thickness

Previous research indicates that long-term meditation practice is associated with altered resting electroencephalogram patterns, suggestive of long lasting changes in brain activity. We hypothesized that meditation practice might also be associated with changes in the brain's physical structure. Magnetic resonance imaging was used to assess cortical thickness in 20 participants with extensive Insight meditation experience, which involves focused attention to internal experiences. Brain regions associated with attention, interoception and sensory processing were thicker in meditation participants than matched controls, including the prefrontal cortex and right anterior insula. Between-group differences in prefrontal cortical thickness were most pronounced in older participants, suggesting that meditation might offset age-related cortical thinning. Finally, the thickness of two regions correlated with meditation experience. These data provide the first structural evidence for experience-dependent cortical plasticity associated with meditation practice.
Transient retinoic acid signaling confers anterior-posterior polarity to the inner ear

Vertebrate hearing and balance are based in complex asymmetries of inner ear structure. Here, we identify retinoic acid (RA) as an extrinsic signal that acts directly on the ear rudiment to affect its compartmentalization along the anterior-posterior axis. A rostrocaudal wave of RA activity, generated by tissues surrounding the nascent ear, induces distinct responses from anterior and posterior halves of the inner ear rudiment. Prolonged response to RA by posterior otic tissue correlates with Tbx1 transcription and formation of mostly nonsensory inner ear structures. By contrast, anterior otic tissue displays only a brief response to RA and forms neuronal elements and most sensory structures of the inner ear.
The Interaction of 'Supply', 'Demand', and 'Technological Capabilities' in terms of Medical Subject Headings: A Triple Helix Model of Medical Innovation

We develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on Triple Helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the medical context, where uncertainty poses significant challenges to the governance of innovation. The Medical Subject Headings (MeSH) of MEDLINE/PubMed provide us with publication records classified within the categories “Diseases” (C), “Drugs and Chemicals” (D), “Analytic, Diagnostic, and Therapeutic Techniques and Equipment” (E) as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of medical research areas are used as representative ‘entry perspectives’ of the medical innovation process. These are: (i) Human Papilloma Virus, (ii) RNA interference, and (iii) Magnetic Resonance Imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that points to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with medical innovation. Among the pairwise configurations in these contexts, the demand-technological capabilities (C-E) provided the strongest link, followed by the supply-demand (D-C) and the supply-technological capabilities (D-E) channels.
Numerical Investigations of Tsunami Run-Up and Flow Structure on Coastal Vegetated Beaches

Tsunami waves become hazardous when they reach the coast. In South and Southeast Asian countries, coastal forest is widely utilized as a natural approach to mitigate tsunami damage. In this study, a depth-integrated numerical model was established to simulate wave propagation in a coastal region with and without forest cover. This numerical model was based on a finite volume Roe-type scheme, and was developed to solve the governing equations with the option of treating either a wet or dry wave front boundary. The governing equations were modified by adding a drag force term caused by vegetation. First, the model was validated for the case of solitary wave (breaking and non-breaking) run-up and run-down on a sloping beach, and long periodic wave propagation was investigated on a partially vegetated beach. The simulated results agree well with the measured data. Further, tsunami wave propagation on an actual-scale slope covered by coastal forest Pandanus odoratissimus (P. odoratissimus) and Casuarina equisetifolia (C. equisetifolia) was simulated to elucidate the influence of vegetation on tsunami mitigation with a different forest open gap. The numerical results revealed that coastal vegetation on sloping beach has significant potential to mitigate the impacts from tsunami waves by acting as a buffer zone. Coastal vegetation with open gaps causes the peak flow velocity at the exit of the gap to increase, and reduces the peak flow velocity behind the forest. Compared to a forest with open gaps in a linear arrangement, specific arrangements of gaps in the forest can increase the energy attenuation from tsunami wave. The results also showed that different cost-effective natural strategies in varying forest parameters including vegetation collocations, densities, and growth stages had significant impacts in reducing the severity of tsunami damage.
Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval

Automatic analysis of histopathological images has been widely utilized leveraging computational image-processing methods and modern machine learning techniques. Both computer-aided diagnosis (CAD) and content-based image-retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. Recently, with the ever-increasing amount of annotated medical data, large-scale and data-driven methods have emerged to offer a promise of bridging the semantic gap between images and diagnostic information. In this paper, we focus on developing scalable image-retrieval techniques to cope intelligently with massive histopathological images. Specifically, we present a supervised kernel hashing technique which leverages a small amount of supervised information in learning to compress a 10 \thinspace000-dimensional image feature vector into only tens of binary bits with the informative signatures preserved. These binary codes are then indexed into a hash table that enables real-time retrieval of images in a large database. Critically, the supervised information is employed to bridge the semantic gap between low-level image features and high-level diagnostic information. We build a scalable image-retrieval framework based on the supervised hashing technique and validate its performance on several thousand histopathological images acquired from breast microscopic tissues. Extensive evaluations are carried out in terms of image classification (i.e., benign versus actionable categorization) and retrieval tests. Our framework achieves about 88.1% classification accuracy as well as promising time efficiency. For example, the framework can execute around 800 queries in only 0.01 s, comparing favorably with other commonly used dimensionality reduction and feature selection methods.
For your eyes only

In this paper, we take a look at an enhanced approach for eye detection under difficult acquisition circumstances such as low-light, distance, pose variation, and blur. We present a novel correlation filter based eye detection pipeline that is specifically designed to reduce face alignment errors, thereby increasing eye localization accuracy and ultimately face recognition accuracy. The accuracy of our eye detector is validated using data derived from the Labeled Faces in the Wild (LFW) and the Face Detection on Hard Datasets Competition 2011 (FDHD) sets. The results on the LFW dataset also show that the proposed algorithm exhibits enhanced performance, compared to another correlation filter based detector, and that a considerable increase in face recognition accuracy may be achieved by focusing more effort on the eye localization stage of the face recognition process. Our results on the FDHD dataset show that our eye detector exhibits superior performance, compared to 11 different state-of-the-art algorithms, on the entire set of difficult data without any per set modifications to our detection or preprocessing algorithms. The immediate application of eye detection is automatic face recognition, though many good applications exist in other areas, including medical research, training simulators, communication systems for the disabled, and automotive engineering.
Wavelet-Based Finite Element Elastodynamic Analysis of Composite Beams

The paper is devoted to theoretical and computational studies on application of the multiresolutional wavelet-based homogenization approach in the Finite Element Method modeling of heterogeneous media. Multiresolutional homogenization scheme is proposed for 1D elastostatic equilibrium problem of periodic heterogeneous medium with Young modulus defined by some elementary wavelet functions and compared with the classical method; some new example of non-homogenizable periodic heterogeneous structure is shown in the context of the wavelet-based algorithm. Next, symbolic computational comparative studies for various homogenization methods are performed and used in the Finite Element analysis of the original and homogenized two, three and five-bays periodic composite beams. Theoretical and computational analysis shows that effective parameters obtained from waveletbased homogenization method are significantly different from those obtained using classical techniques; multiresolutional approach introduces significantly greater restrictions on the original functions defining material properties of analyzed heterogeneous medium. The eigenvalues obtained for periodic composite beams show that the responses of both classical and wavelet-based homogenized beams approximate well the real structural response – multiresolutional technique is more efficient for smaller numbers of the periodicity cells in the structure. The method can be extended on homogenization-based modeling of other structures and can be implemented for the forced and even stochastic vibrations.
Atmospheric boundary layer sources for upper tropospheric air over the Asian summer monsoon region

Abstract Previous satellite measurements and model simulations have shown that the Asian summer monsoon (ASM) anticyclone is co-located with higher concentrations of pollutants, which are emitted in the continental atmospheric boundary layer (ABL). Backward trajectory calculations show that the air at the 150-hPa level has the maximum frequency of ABL sources within 30 days over the most intensive convection regions and their downwind areas, which are not located within the ASM anticyclone, but rather at the southern flank or periphery of the ASM anticyclone. The upper tropospheric airs originated from the ABL sources include two parts: one from the ocean, which has the dominant impact to the south of 20°N, particularly over the South China Sea (SCS) and the west tropical Pacific Ocean; and another from the continent, which is dominant between 10°N and 30°N, particularly over the Bay of Bengal (BoB), continental India, the Arabian Sea, and the Arabian Peninsula. It is the latter part that forms the higher pollutant concentration within the ASM anticyclone as shown by satellite measurements. Air in the ABL sources (both polluted and unpolluted) converges to the intensive convection region in the lower troposphere, and then traverses the middle troposphere through a wide group of upward pipes, and finally to the upper troposphere. These pipes in the middle troposphere are defined by the ASM intensive convections and cover the south of continental India, the BoB, the Tibetan Plateau, the Indochina Peninsula, the SCS, and the Philippine Sea.
Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data

Transcriptional cooperativity among several transcription factors (TFs) is believed to be the main mechanism of complexity and precision in transcriptional regulatory programs. Here, we present a Bayesian network framework to reconstruct a high-confidence whole-genome map of transcriptional cooperativity in Saccharomyces cerevisiae by integrating a comprehensive list of 15 genomic features. We design a Bayesian network structure to capture the dominant correlations among features and TF cooperativity, and introduce a supervised learning framework with a well-constructed gold-standard dataset. This framework allows us to assess the predictive power of each genomic feature, validate the superior performance of our Bayesian network compared to alternative methods, and integrate genomic features for optimal TF cooperativity prediction. Data integration reveals 159 high-confidence predicted cooperative relationships among 105 TFs, most of which are subsequently validated by literature search. The existing and predicted transcriptional cooperativities can be grouped into three categories based on the combination patterns of the genomic features, providing further biological insights into the different types of TF cooperativity. Our methodology is the first supervised learning approach for predicting transcriptional cooperativity, compares favorably to alternative unsupervised methodologies, and can be applied to other genomic data integration tasks where high-quality gold-standard positive data are scarce.
Pharmacologic Management of Perioperative Pulmonary Hypertension

Abstract: Perioperative pulmonary hypertension can originate from an established disease or acutely develop within the surgical setting. Patients with increased pulmonary vascular resistance are consequently at greater risk for complications. Despite the various specific therapies available, the ideal therapeutic approach in this patient population is not currently clear. This article describes the basic principles of perioperative pulmonary hypertension and reviews the different classes of agents used to promote pulmonary vasodilation in the surgical setting.
Transient protein-protein interactions perturb E. coli metabolome and cause gene dosage toxicity

Several genes exhibit gene dosage toxicity yet its molecular underpinnings remain unknown. Here we demonstrate that overexpression of DHFR in E. coli causes toxic metabolic imbalance triggered by interactions with several enzymes involved in 1-carbon metabolism, in particular GlyA and PurH. DHFR overexpression partially inhibits activity of these enzymes, but at physiological concentrations, PurH-DHFR interaction enhances catalytic efficiency of DHFR, implying a functional interaction in vivo. Surprisingly, overexpression of orthologous DHFRs from other bacterial species caused minimal metabolic and fitness perturbations, despite pulling out more interacting partners than overexpressed endogenous DHFR. Orthologous DHFRs were less potent in inhibiting E. coli GlyA and PurH, or gaining a catalytic improvement upon interaction with PurH, indicating a partial loss of interaction specificity due to evolutionary divergence. This study shows how protein overexpression perturbs a dynamic network of weak yet potentially functional PPI with consequences for the metabolic state of cells and their fitness.
Virologically Confirmed Population-based Burden of Hospitalization Caused by Respiratory Syncytial Virus, Adenovirus, and Parainfluenza Viruses in Children in Hong Kong

Objectives: To determine virologically confirmed hospitalization rates associated with respiratory syncytial virus (RSV), adenovirus, and parainfluenza viruses in Hong Kong children. Methods: All patients <18 years of age living on Hong Kong Island (within Hong Kong SAR) admitted for a febrile acute respiratory infection to 1 of the 2 public hospitals on 1 fixed day of the week between October 2003 and September 2006 were prospectively recruited. Hong Kong Island has a known population denominator and these 2 hospitals managed 72.5% of all general pediatric admissions for this population. Nasopharyngeal aspirates were tested for RSV, adenovirus, and parainfluenzae types 1, 2, and 3 by direct antigen detection and culture. Results: The annual hospitalization rate for RSV in infants <6 months of age was 233.4 to 311.2 per 10,000. Parainfluenza type 3 had a hospitalization rate of 27.3 to 122.8 per 10,000 in the 1 to <2 years group. Adenovirus was associated with significant hospitalization in those 6 months to 1 year (25.9–77.8 per 10,000), and in those 2 to <5 years (38.1–59.2 per 10,000). The mean duration of hospitalization for RSV was 4.04 ± 2.61 days, significantly longer than the 3.12 ± 1.41 days for adenovirus and the 2.93 ± 2.54 days for parainfluenza infections (P = 0.013 and P = 0.038, respectively). Conclusion: We documented that the overall pediatric hospitalization burden of RSV was high and comparable to that of influenza. The burden for all the studied viruses was mainly in previously healthy children <5 years of age.
“For Some Reason I Find it Hard to Work Quickly”: Introduction to the Special Issue on Sluggish Cognitive Tempo

The body of research investigating the sluggish cognitive tempo (SCT) construct continues to accumulate at a rapid pace. This article provides an introduction to the Special Issue on SCT, which includes 10 empirical studies that collectively make a major contribution to the SCT knowledge base. Notably, the studies in this Special Issue include participants spanning in age from 4 to 64 years and from four continents, helping to move the field toward a life span, transcultural understanding of SCT. Together, these studies demonstrate that SCT symptoms can be distinguished from ADHD symptoms as early as preschool and that SCT does not fall under the overarching umbrella of ADHD. These studies also show SCT to be associated with a range of external correlates including internalizing symptoms, learning difficulties, functional impairment, and daily life executive functioning (but not performance-based measures of executive functions). Preliminary findings of SCT in relation to thyroid functioning and tobacco exposure are reported. In addition to providing a summary of the key themes across studies included in the Special Issue, this article highlights key ways in which future research can build from these studies. There is a particular need for research utilizing longitudinal, multi-method, and multi-informant designs that can shed light on the etiologies and developmental psychopathology of SCT across the life span.
Effects of a new parallel primary healthcare centre and on-campus training programme on history taking, physical examination skills and medical students’ preparedness: a prospective comparative study in Taiwan

Objectives The primary healthcarecentre (PHCC) is the first place that medical students experience patient contact. Usually, medical students are frustrated by a lack of proper skills training for on-campus history taking (HT), physical examination (PE) and self-directed learning (SDL) to prepare for their PHCC and inhospital patient contact. For pre-clerks, this study aims to compare the effectiveness of PHCC training and PHCC training in combination with on-campus HT and PE training modules (PHCC+on-campus) on their clerkship preparedness. Design This comparative study utilised prospective, consecutive, end of pre-clerkship group objective structured clinical examination (GOSCE), beginning of clerkship OSCE and self-administered Preparation for Hospital Practice Questionnaire (PHPQ). Setting/participants 128 pre-clinical clerk volunteers (64 each year) receiving PHCC training (7 week PHCCtraining in addition to 7 week assignment based group learning, academic year 2014, controls) and PHCC training in combination with on-campus module training (academic year 2015, 7 week PHCCtraining in addition to 7 week on-campus sessions) were sequentially assessed before the module (week 1), at the end of the module (week 14) and at the beginning of clerkship (week 25). Results For overall HT and PE skills, both PHCC and PHCC+on-campus module trained pre-clerks performed better on OSCE than GOSCE. Additionally, the improvement was accompanied by higher self-reported PHPQ scores in ‘confidence/coping’ and ‘SDL’ domains. At the end of the pre-clerkship and the beginning of the clerkship stages, the degree of improvement in preparedness in ‘confidence/coping’ and ‘SDL’ domains was higher for those in the PHCC+on-campus group than for those in the PHCC group. Among the PHCC+on-campus module participants, a positive association was observed between high mean PHPQ-SDL scores and high OSCE scores. Conclusions Our study suggests that the PHCC+on-campus module, which is paired faculty led and pre-trained dyad student assisted, is effective in developing a preclinical clerk’s HT and PE skills and intensifying SDL/patient management abilities to prepare for hospital practice in clerkship.
Event-based performance perturbation: a case study

Determining the performance behavior of parallel computations requires some form of intrusive tracing measurement. The greater the need for detailed performance data, the more intrusion the measurement will cause. Recovering actual execution performance jfrom perturbed performance measurements using eventbased perturbation analysis is the topic of this paper. We show that the measurement and subsequent analysis of synchronization operations (particularly, advance and await) can produce, in practice, accurate approximations to actual performance behavior. We use as testcases three Lawrence Livermore loops that execute as parallel DOACROSS loops on an Alliant FX/80. The results of our experiments suggest that a systematic application of performance perturbation analysis techniques will allow more detailed, accurate instrumentation than traditionally believed possible.