Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic model adaptation using piece-wise energy decay curve for reverberant environments

This work presents acoustic model adaptation based on a piece-wise energy decay curve. The dual slope representation of the piece-wise curve accurately captures the early and late reflection decay which helps in precisely modeling the smearing effect caused due to reverberation. Adaptation using piece-wise decay curve leads to robust acoustic models consequently improving the recognition performance. The approach is tested on connected digits recognition task in different rooms with various reverberation times. The performance is compared with the exponential decay approach and incremental MLLR, where the proposed approach was more effective. Moreover, the combination of piece-wise adaptation with incremental MLLR is also studied and the combination is instrumental in improving the performance with respect to incremental MLLR.
Cervical Cytology Intelligent Diagnosis Based On Object Detection Technology

Objectives: (i)Explore a new method for applying artificial intelligence to cervical cytology diagnosis. (ii)Realize an automatic detection system of positive cervical squamous epithelial cell. Methods: (i) The method can be divided into two phases: training and testing. (ii) For the training phases, first of all, we collect 500 cervical cell slides. Through scanning, annotating, and extracting patches, we get the training set, validation set and test set. Then, based on the Faster R-CNN (Faster Regions with CNN features) [4], we construct a neural network model for cell detection and classification. After continuous training, validation, analysis and tuning, we finally get a well trained neural network model. (iii) During the testing phase, we use the previously well trained neural model to predict the test set which consists of 100 whole slide images of cervical cytology. The model detects the five types of target cells at first, and then counts the number of cells in each category, finally, generates a diagnosis. Results: The positive precision rate on the validation set is 0.91. On the test set, for two-class problem,the accuracy is 0.78. For fourclass problem, the accuracy is 0.70. Conclusion: Object detection technology has unique advantages in applying to cervical cytology. Through accurate detection and classification of various types of abnormal cells, as well as the statistics of each category, a comprehensive conclusion is made. This idea adopted in this study accords with doctors’ traditional diagnosis process to a certain degree. Results show that the intelligent system realized with deep learning technology has the advantages of high speed, high consistency, and well diagnostic performance. 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands.
Analyzing dependability measures at the Electronic System Level

Raising the level of abstraction to design the next generation of embedded systems has become mandatory. This design methodology is commonly referred to Electronic System Level (ESL) design. Simultaneously, dependability of embedded systems becomes a major concern. To satisfy these demands already at ESL, we present a dependability analysis approach working directly at this level. The approach analyzes the effectiveness of dependability measures in SystemC-based virtual prototypes. Errors are injected into SystemC transactions using an XML-based configuration mechanism. This is combined with the specification of the expected behavior with respect to the injected errors. The developed analysis approach allows for validation of dependability measures as well as localization of missing or buggy measures. Experimental results for a complex image processing system, which determines the position of a game controller in video data, demonstrate the advantages of our approach.
Motion-Perception Deficits and Reading Impairment

We tested the hypothesis that deficits on sensory-processing tasks frequently associated with poor reading and dyslexia are the result of impairments in external-noise exclusion, rather than motion perception or magnocellular processing. We compared the motion-direction discrimination thresholds of adults and children with good or poor reading performance, using coherent-motion displays embedded in external noise. Both adults and children who were poor readers had higher thresholds than their respective peers in the presence of high external noise, but not in the presence of low external noise or when the signal was clearly demarcated. Adults' performance in high external noise correlated with their general reading ability, whereas children's performance correlated with their language and verbal abilities. The results support the hypothesis that noise-exclusion deficits impair reading and language development and suggest that the impact of such deficits on the development of reading skills changes with age.
Implications for utilizing YouTube based community interactions for destination marketing: Investigation of a typology approach

In recent time, YouTube has evolved into a powerful medium for social interaction. Utilizing YouTube for enhancing marketing endeavors is a strategy practiced by marketing professionals across several industries. This paper rationalizes on the different ways and means of leveraging YouTube-based platforms for effective destination marketing by the hospitality industry (hotels). More specifically, the typology of virtual communities is adapted to evaluate the YouTube platform for effective destination marketing. Comments made by YouTube users have been subjected to a content analysis and the results are reported here under the five broad clusters of virtual communities. Implications for utilizing YouTube-based community interactions for destination marketing are also highlighted as part of the outcome of this research.
Simplifying and improving qualified types

Qualified types provide a general framework for constrained type systems, with applications including type class overloading, subtyping and record calculi. This paper presents an extended version of the type inference algorithm used in previous work, that can take account of the satisfiability of constraints to obtain more accurate principal types. The new algorithm is obtained by adding two new rules, one for simplification and one for improvement of constraint sets. In particular, it permits a better treatment for the previously troublesome multiple parameter extensions of Haskell type classes. For example, a form of parametric type classes, proposed by Chen, Hudak and Odersky, can be viewed as a special case of this work.
Electrochemical Detection of Anti-Breast-Cancer Agents in Human Serum by Cytochrome P450-Coated Carbon Nanotubes

We report on the electrochemical detection of anti-cancer drugs in human serum with sensitivity values in the range of 8–925 nA/μM. Multi-walled carbon nanotubes were functionalized with three different cytochrome P450 isoforms (CYP1A2, CYP2B6, and CYP3A4). A model used to effectively describe the cytochrome P450 deposition onto carbon nanotubes was confirmed by Monte Carlo simulations. Voltammetric measurements were performed in phosphate buffer saline (PBS) as well as in human serum, giving well-defined current responses upon addition of increasing concentrations of anti-cancer drugs. The results assert the capability to measure concentration of drugs in the pharmacological ranges in human serum. Another important result is the possibility to detect pairs of drugs present in the same sample, which is highly required in case of therapies with high side-effects risk and in anti-cancer pharmacological treatments based on mixtures of different drugs. Our technology holds potentials for inexpensive multi-panel drug-monitoring in personalized therapy.
High net CO 2 and CH 4 release at a eutrophic shallow lake on a formerly drained fen

Abstract. Drained peatlands often act as carbon dioxide (CO2) hotspots. Raising the groundwater table is expected to reduce their CO2 contribution to the atmosphere and revitalise their function as carbon (C) sink in the long term. Without strict water management rewetting often results in partial flooding and the formation of spatially heterogeneous, nutrient-rich shallow lakes. Uncertainties remain as to when the intended effect of rewetting is achieved, as this specific ecosystem type has hardly been investigated in terms of greenhouse gas (GHG) exchange. In most cases of rewetting, methane (CH4) emissions increase under anoxic conditions due to a higher water table and in terms of global warming potential (GWP) outperform the shift towards CO2 uptake, at least in the short term. Based on eddy covariance measurements we studied the ecosystem–atmosphere exchange of CH4 and CO2 at a shallow lake situated on a former fen grassland in northeastern Germany. The lake evolved shortly after flooding, 9 years previous to our investigation period. The ecosystem consists of two main surface types: open water (inhabited by submerged and floating vegetation) and emergent vegetation (particularly including the eulittoral zone of the lake, dominated by Typha latifolia). To determine the individual contribution of the two main surface types to the net CO2 and CH4 exchange of the whole lake ecosystem, we combined footprint analysis with CH4 modelling and net ecosystem exchange partitioning. The CH4 and CO2 dynamics were strikingly different between open water and emergent vegetation. Net CH4 emissions from the open water area were around 4-fold higher than from emergent vegetation stands, accounting for 53 and 13 g CH4 m−2 a−1 respectively. In addition, both surface types were net CO2 sources with 158 and 750 g CO2 m−2 a−1 respectively. Unusual meteorological conditions in terms of a warm and dry summer and a mild winter might have facilitated high respiration rates. In sum, even after 9 years of rewetting the lake ecosystem exhibited a considerable C loss and global warming impact, the latter mainly driven by high CH4 emissions. We assume the eutrophic conditions in combination with permanent high inundation as major reasons for the unfavourable GHG balance.
An endogenous dAMP ligand in Bacillus subtilis class Ib RNR promotes assembly of a noncanonical dimer for regulation by dATP

Significance Negative feedback regulation of ribonucleotide reductase (RNR) activity by dATP is important for maintaining balanced intracellular 2ʹ-deoxynucleoside triphosphate (dNTP) pools essential for the high fidelity of DNA replication and repair. To date, this type of allostery has been nearly universally associated with dATP binding to the N-terminal ATP-cone domain of the class Ia RNR large subunit (canonical α2), resulting in an altered quaternary structure that is unable to productively bind the second subunit (β2). Here, we report our studies on activity inhibition by dATP of the Bacillus subtilis class Ib RNR, which lacks a traditional ATP-cone domain. This unprecedented allostery involves deoxyadenosine 5′-monophosphate (dAMP) binding to a newly identified site in a partial N-terminal cone domain, forming an unprecedented noncanonical α2. The high fidelity of DNA replication and repair is attributable, in part, to the allosteric regulation of ribonucleotide reductases (RNRs) that maintains proper deoxynucleotide pool sizes and ratios in vivo. In class Ia RNRs, ATP (stimulatory) and dATP (inhibitory) regulate activity by binding to the ATP-cone domain at the N terminus of the large α subunit and altering the enzyme’s quaternary structure. Class Ib RNRs, in contrast, have a partial cone domain and have generally been found to be insensitive to dATP inhibition. An exception is the Bacillus subtilis Ib RNR, which we recently reported to be inhibited by physiological concentrations of dATP. Here, we demonstrate that the α subunit of this RNR contains tightly bound deoxyadenosine 5′-monophosphate (dAMP) in its N-terminal domain and that dATP inhibition of CDP reduction is enhanced by its presence. X-ray crystallography reveals a previously unobserved (noncanonical) α2 dimer with its entire interface composed of the partial N-terminal cone domains, each binding a dAMP molecule. Using small-angle X-ray scattering (SAXS), we show that this noncanonical α2 dimer is the predominant form of the dAMP-bound α in solution and further show that addition of dATP leads to the formation of larger oligomers. Based on this information, we propose a model to describe the mechanism by which the noncanonical α2 inhibits the activity of the B. subtilis Ib RNR in a dATP- and dAMP-dependent manner.
Leakage power optimization with dual-V/sub th/ library in high-level synthesis

In this paper we address the problem of module selection during high-level synthesis. We present a heuristic algorithm for leakage power optimization based on the maximum weight independent set problem. A dual threshold voltage (V/sub th/) technique is used to reduce leakage energy consumption in a data flow graph. Experiments are performed on a data-path dominated test suite of six benchmarks. Our approach achieves an average of 70.9% leakage power reduction, which is very close to the optimal results from an integer linear programming approach.
Islanding detection in distributed generation

In the present times, distributed generation has acquired lots of importance in the power industry. The consequences on the power system develop further significance with the increase in the contribution of distribution energy resources to the electric power production. Nowadays islanding has become a major issue with increase in use of Distributed Generators (DG) in power system. When a part of distributed system becomes electrically separated from the rest of the system and the DG(s) in the isolated portion continues to power the local loads, then this condition is termed as Islanding condition. In this paper, for the detection of islanding in distributed generation, we have used the wavelet transform which is due to the negative sequence component of current and voltage. The islanding condition is further detected when the detailed coefficient at the level-1 (d1) evidently isolates the event.
Validating optimizations of concurrent C/C++ programs

We present a validator for checking the correctness of LLVM compiler optimizations on C11 programs as far as concurrency is concerned. Our validator checks that optimizations do not change memory accesses in ways disallowed by the C11 and/or LLVM memory models. We use a custom C11 concurrent program generator to trigger multiple LLVM optimizations and evaluate the efficacy of our validator. Our experiments highlighted the difference between the C11 and LLVM memory models, and uncovered a number of previously unknown compilation errors in the LLVM optimizations involving the C11 concurrency primitives.
1 Azithromycin resistance in Shigella spp . in Southeast Asia 1 2

Darton, Thomas C; Tuyen, Ha Thanh; The, Hao Chung; Newton, Paul N; Dance, David AB; Phetsouvanh, Rattanaphone; Davong, Viengmon; Campbell, James I; Hoang, Nguyen Van Minh; Thwaites, Guy E; +3 more. Parry, Christopher M; Thanh, Duy Pham; Baker, Stephen; (2018) Azithromycin Resistance in Shigella spp. in Southeast Asia. Antimicrobial agents and chemotherapy, 62 (4). ISSN 0066-4804 DOI: https://doi.org/10.1128/AAC.01748-17 Downloaded from: http://researchonline.lshtm. ac.uk/id/eprint/4646321/ DOI: https://doi.org/10.1128/AAC.01748-17
Why Crises Happen - Nonstationary Macroeconomics

A Real Business Cycle model of the UK is developed to account for the behaviour of UK nonstationary macro data. The model is tested by the method of indirect inference, bootstrapping the errors to generate 95% confidence limits for a VECM representation of the data; we find the model can explain the behaviour of main variables (GDP, real exchange rate, real interest rate) but not that of detailed GDP components. We use the model to explain how 'crisis' and 'euphoria' are endemic in capitalist behaviour due to nonstationarity; and we draw some policy lessons.
Protest Against Waste Contamination in the ‘Land of Fires’: Psychological Antecedents for Activists and Non‐activists

The ‘Land of Fires’ is a district in Italy characterized by illegal waste disposal, waste burning, and citizen protests over contamination. This study investigates the relevance of several psycho-social factors that predict citizens' intention to protest, taking into account different research traditions. In addition, we hypothesize the effect of protest antecedents to be moderated by past participation behaviour (i.e. the level of activism). Hence, our study is a first attempt to explore the effect of protest antecedents as a function of the individual level of activism through a cross-sectional survey study (N = 306). The results show significant effects of collective identity, sense of injustice, costs of protest, and perceived risk (cognitive dimension) on intention to protest. The effects of collective efficacy and perceived risk (affective dimension) are moderated by participants' level of activism, and these effects are significant only for non-activists. The relevance of this new approach is discussed, as well as practical implications and possible further developments. Copyright © 2016 John Wiley & Sons, Ltd.
Correction of errors in a verb modality corpus for machine translation with a machine-learning method

In recent years, various types of tagged corpora have been constructed and much research using tagged corpora has been done. However, tagged corpora contain errors, which impedes the progress of research. Therefore, the correction of errors in corpora is an important research issue. In this study we investigate the correction of such errors, which we call corpus correction. Using machine-learning methods, we applied corpus correction to a verb modality corpus for machine translation. We used the maximum-entropy and decision-list methods as machine-learning methods. We compared several kinds of methods for corpus correction in our experiments, and determined which is most effective by using a statistical test. We obtained several noteworthy findings: (1) Precision was almost the same for both detection and correction, so it is more convenient to do both correction and detection, rather than detection only. (2) In general, the maximum-entropy method worked better than the decision-list method; but the two methods had almost the same precision for the top 50 pieces of extracted data when closed data was used. (3) In terms of precision, the use of closed data was better than the use of open data; however, in terms of the total number of extracted errors, the use of open data was better than the use of closed data. Based on our analysis of these results, we developed a good method for corpus correction. We confirmed the effectiveness of our method by carrying out experiments on machine translation. As corpus-based machine translation continues to be developed, the corpus correction we discuss in this article should prove to be increasingly significant.
Cellular cardiac electrophysiology modeling with Chaste and CellML

Chaste is an open-source C++ library for computational biology that has well-developed cardiac electrophysiology tissue simulation support. In this paper, we introduce the features available for performing cardiac electrophysiology action potential simulations using a wide range of models from the Physiome repository. The mathematics of the models are described in CellML, with units for all quantities. The primary idea is that the model is defined in one place (the CellML file), and all model code is auto-generated at compile or run time; it never has to be manually edited. We use ontological annotation to identify model variables describing certain biological quantities (membrane voltage, capacitance, etc.) to allow us to import any relevant CellML models into the Chaste framework in consistent units and to interact with them via consistent interfaces. This approach provides a great deal of flexibility for analysing different models of the same system. Chaste provides a wide choice of numerical methods for solving the ordinary differential equations that describe the models. Fixed-timestep explicit and implicit solvers are provided, as discussed in previous work. Here we introduce the Rush–Larsen and Generalized Rush–Larsen integration techniques, made available via symbolic manipulation of the model equations, which are automatically rearranged into the forms required by these approaches. We have also integrated the CVODE solvers, a ‘gold standard’ for stiff systems, and we have developed support for symbolic computation of the Jacobian matrix, yielding further increases in the performance and accuracy of CVODE. We discuss some of the technical details of this work and compare the performance of the available numerical methods. Finally, we discuss how this is generalized in our functional curation framework, which uses a domain-specific language for defining complex experiments as a basis for comparison of model behavior.
Expected vectorless Teacher-Student Swap (TSS) test method with dual power supply voltages for 0.3V homogeneous multi-core LSI’s

A teacher-student swap (TSS) test method with the dual supply voltage (VDD) for the ultra low VDD homogeneous multi-core LSIpsilas is proposed and the test chips are fabricated in 90 nm CMOS. In this method, two same cores with different power supply voltages test each other by comparing their outputs, which eliminates the need for the expected vector. When VDD is less than 0.3 V, the die-to-die power reduction by the dual VDD in the 5 chips was from 18% to 48%. In order to manage the large die-to-die variations at low VDD, the fine grain dual VDD with TSS test method is a promising approach without increasing the test cost.
Primal-Dual Decomposition by Operator Splitting and Applications to Image Deblurring

We present primal-dual decomposition algorithms for convex optimization problems with cost functions $f(x)+g(Ax)$, where $f$ and $g$ have inexpensive proximal operators and $A$ can be decomposed as a sum of two structured matrices. The methods are based on the Douglas--Rachford splitting algorithm applied to various splittings of the primal-dual optimality conditions. We discuss applications to image deblurring problems with nonquadratic data fidelity terms, different types of convex regularization, and simple convex constraints. In these applications, the primal-dual splitting approach allows us to handle general boundary conditions for the blurring operator. Numerical results indicate that the primal-dual splitting methods compare favorably with the alternating direction method of multipliers, the Douglas--Rachford algorithm applied to a reformulated primal problem, and the Chambolle--Pock primal-dual algorithm.
An algorithm for source location in directed graphs

Ito, Makino, Arata, Honami, Itatsu, and Fujishige [6] provided a theoretical answer to a source location problem by proving that the minimum cardinality of a subset R of nodes in an edge-capacitated directed graph D = (V,A) so that the maximum flow-amount from R to every node v ∈ V − R is at least k and the maximum flow amount from every node v ∈ V − R to R is at least l is equal to the maximum number of pairwise disjoint deficient sets where a subset of nodes is deficient if its in-capacity is less than k or its out-capacity is less than l. They also showed how this theorem gave rise to a polynomial time algorithm to compute the optima in question in case the demands k and l are fixed, and posed as an open problem of developing an algorithm that is (strongly) polynomial not only in the size of the digraph but in k and l, as well. The present work describes such an algorithm.