Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating speech recognition error rate without acoustic test data

We address the problem of estimating the word error rate (WER) of an automatic speech recognition (ASR) system without using acoustic test data. This is an important problem which is faced by the designers of new applications which use ASR. Quick estimate of WER early in the design cycle can be used to guide the decisions involving dialog strategy and grammar design. Our approach involves estimating the probability distribution of the word hypotheses produced by the underlying ASR system given the text test corpus. A critical component of this system is a phonemic confusion model which seeks to capture the errors made by ASR on the acoustic data at a phonemic level. We use a confusion model composed of probabilistic phoneme sequence conversion rules which are learned from phonemic transcription pairs obtained by leave-one-out decoding of the training set. We show reasonably close estimation of WER when applying the system to test sets from different domains.
The relationship between modularity and robustness in signalling networks

Many biological networks tend to have a high modularity structural property and the dynamic characteristic of high robustness against perturbations. However, the relationship between modularity and robustness is not well understood. To investigate this relationship, we examined real signalling networks and conducted simulations using a random Boolean network model. As a result, we first observed that the network robustness is negatively correlated with the network modularity. In particular, this negative correlation becomes more apparent as the network density becomes sparser. Even more interesting is that, the negative relationship between the network robustness and the network modularity occurs mainly because nodes in the same module with the perturbed node tend to be more sensitive to the perturbation than those in other modules. This result implies that dynamically similar nodes tend to be located in the same module of a network. To support this, we show that a pair of genes associated with the same disease or a pair of functionally similar genes is likely to belong to the same module in a human signalling network.
An efficient multi-objective memetic algorithm for uncertainties in distribution network expansion planning

This paper proposes an efficient Multi-objective Memetic Algorithm for distribution network expansion planning (DNEP). It may be expressed as a complex multi-objective optimization problem as well as combinatorial optimization one. Recently, deregulated and competitive power markets brought about the uncertainty of distribution systems. There are correlations between the nodal specific values. A Monte-Carlo-Simulation-based method is proposed for handling the uncertainty and correlation. Also, this paper focuses of multi-objective meta-heuristics (MOMH). It obtains many final solution candidates at the same time. Also, MOMH has specific strategies in terms of high accuracy and diversity of the set. This paper makes use of the improved Strength Pareto Evolutionary Algorithm (SPEA2) and the Controlled fast elitist Non-dominated Sorting Genetic Algorithm (CNSGA2) that are well-known for more effective methods in MOMH. As Memetic Algorithm (MA) that integrates meta-heuristic with local search (LS). This paper combines SPEA2 and CNSGA2 with Random-Multi Start Local Search (RMSLS) for improving the solution set. This is called Memetic Algorithm (MA). RMSLS plays a key role to provide many solution candidates. The proposed method has advantage to keep high accuracy and diversity of solution sets in MOMH. The proposed method is successfully applied to a sample system.
The Chk1 Protein Kinase and the Cdc25C Regulatory Pathways Are Targets of the Anticancer Agent UCN-01*

A checkpoint operating in the G2 phase of the cell cycle prevents entry into mitosis in the presence of DNA damage. UCN-01, a protein kinase inhibitor currently undergoing clinical trials for cancer treatment, abrogates G2 checkpoint function and sensitizes p53-defective cancer cells to DNA-damaging agents. In most species, the G2 checkpoint prevents the Cdc25 phosphatase from removing inhibitory phosphate groups from the mitosis-promoting kinase Cdc2. This is accomplished by maintaining Cdc25 in a phosphorylated form that binds 14-3-3 proteins. The checkpoint kinases, Chk1 and Cds1, are proposed to regulate the interactions between human Cdc25C and 14-3-3 proteins by phosphorylating Cdc25C on serine 216. 14-3-3 proteins, in turn, function to keep Cdc25C out of the nucleus. Here we report that UCN-01 caused loss of both serine 216 phosphorylation and 14-3-3 binding to Cdc25C in DNA-damaged cells. In addition, UCN-01 potently inhibited the ability of Chk1 to phosphorylate Cdc25C in vitro. In contrast, Cds1 was refractory to inhibition by UCN-01in vitro, and Cds1 was still phosphorylated in irradiated cells treated with UCN-01. Thus, neither Cds1 nor kinases upstream of Cds1, such as ataxia telangiectasia-mutated, are targets of UCN-01 action in vivo. Taken together our results identify the Chk1 kinase and the Cdc25C pathway as potential targets of G2 checkpoint abrogation by UCN-01.
DDM-VMc: the data-driven multithreading virtual machine for the cell processor

In this paper we present the Data-Driven Multithreading Virtual Machine for the Cell Processor (DDM-VMc). Data-Driven Multithreading is a non-blocking multithreading model that decouples the synchronization from the computation portions of a program allowing them to execute asynchronously in a data-flow manner. The core of the DDM model is the Thread Scheduling Unit (TSU), which schedules threads dynamically at runtime based on data availability. DDM-VMc implements the TSU as a software module running on the PPE core of the Cell, allowing the SPE cores to execute the program threads. DDM-VMc virtualizes the parallel resources of the Cell, handles the heterogeneity of the cores and manages the Cell memory hierarchy efficiently. We present the architecture of DDM-VMc and provide an in-depth performance analysis using a suite of standard computational benchmarks. The evaluation shows that DDM-VMc scales well and tolerates scheduling overheads and memory latencies effectively. Furthermore, DDM-VMc compares favorably with other platforms targeting the Cell processor.
Microsatellite DNA markers from HLA region ( D6S105, D6S265 and TNFa ) in autochthonous Basques from Northern Navarre (Spain)

Background : The extent of the genetic polymorphism of the HLA complex is becoming well characterized in Basque population and their subpopulations. This level of knowledge mainly concerns HLA class I loci. However, Basque population surveys dealing with HLA class II genes and/or microsatellites in the HLA region are still very scarce. Aim : The population genetics of three highly polymorphic short tandem repeat (STR) loci, D6S105, D6S265 and TNFa, from HLA region has been analysed in autochthonous (indigenous) Basques from Northern Navarre (Spain). The same blood samples have been typed for HLA class II genes from DQ/DR/DP regions and some findings from that information can be found therein. Subjects and methods : Blood samples were taken from 107 unrelated autochthonous Basques from Northern Navarre. The criterion used to define Northern Navarrese identity was that of three generations of Basque surnames and birthplaces. Results : The main features observed in Navarrese Basques were the rather high frequencies of alleles D6S105*4 and D6S265*7. A novel allele has been detected at the D6S265 locus (13: 145 bp). The most frequent haplotype was D6S105*8-D6S265*4 with a highly significant linkage disequilibrium being presented. The high frequency of allele TNFa*1 in Basques is noteworthy and this characteristic is not shared by other European populations, where TNFa*1 is absent or shows negligible values. The multidimensional scaling analysis (MDS) for TNFa allele frequencies has shown a high variability among populations and that alleles TNFa*1 ( F ST = 0.0615) and TNFa*12 ( F ST = 0.0424) seem to have significant influence over the spatial population configuration. TNFa*2 showed the lowest FST value (0.0077) because of its conspicuous homogeneous distribution all over the European populations. Conclusions : Findings shown here on HLA microsatellites and their relationships with other HLA class I and class II genes in Basques can be helpful for those studies mainly addressed at detecting associations between HLA genes and diseases in the Basque area as a whole, and particularly in its autochthonous population, settled there since remote times.
Fast and reliable top-level simulation strategy for mixed-signal integrated circuits and its application to DC-DC converters

Top-level, transient, transistor-based simulations are a critical step in the product-development cycle of mixed-signal integrated circuits. These simulations are normally performed just before fabrication and unfortunately impose cumbersome bottlenecks in the design flow. Verification is an iterative process by nature, whereby each problem found requires another simulation to ensure a proper fix is in place, and because of the complexity of a large system, minor errors can cost days, increasing design time and time-to-market. A top-level transistor-based simulation strategy is proposed with minimal time overhead. The strategy is to start with a quick, all macro-model system simulation and gradually substitute one transistor-level sub-block at a time for each additional run. For optimal results, less computationally intensive blocks, which can be determined from a proposed set of screening simulations, are replaced first. The proposed strategy was tested and applied to a buck, current-mode switching regulator, and the results show that simulation overhead is least for linear analogue functions (e.g. op-amps) and worst for high-speed nonlinear circuits (e.g. signal generators). Nonlinear and bi-stable analogue blocks such as bandgap references take more time to simulate than op-amps and less than low frequency digital functions such as power-on-reset, which in turn are less intensive than ramp and pulse generators.
A Multi-Perspective Evaluation of the NESPOLE! Speech-to-Speech Translation System

Performance and usability of real-world speech-to-speech translation systems, like the one developed within the NESPOLE! project, are affected by several aspects that go beyond the pure translation quality provided by the underlying components of the system. In this paper we describe these aspects as perspectives along which we have evaluated the NESPOLE! system. Four main issues are investigated: (1) assessing system performance under various network traffic conditions; (2) a study on the usage and utility of multi-modality in the context of multi-lingual communication; (3) a comparison of the features of the individual speech recognition engines, and (4) an end-to-end evaluation of the system.
Identifying Data Noises, User Biases, and System Errors in Geo-tagged Twitter Messages (Tweets)

Many social media researchers and data scientists collected geo-tagged tweets to conduct spatial analysis or identify spatiotemporal patterns of filtered messages for specific topics or events. This paper provides a systematic view to illustrate the characteristics (data noises, user biases, and system errors) of geo-tagged tweets from the Twitter Streaming API. First, we found that a small percentage (1%) of active Twitter users can create a large portion (16%) of geo-tagged tweets. Second, there is a significant amount (57.3%) of geo-tagged tweets located outside the Twitter Streaming API's bounding box in San Diego. Third, we can detect spam, bot, cyborg tweets (data noises) by examining the "source" metadata field. The portion of data noises in geo-tagged tweets is significant (29.42% in San Diego, CA and 53.47% in Columbus, OH) in our case study. Finally, the majority of geo-tagged tweets are not created by the generic Twitter apps in Android or iPhone devices, but by other platforms, such as Instagram and Foursquare. We recommend a multi-step procedure to remove these noises for the future research projects utilizing geo-tagged tweets.
Market Power, Vertical Integration and the Wholesale Price of Gasoline

This paper examines empirically the relationship between vertical integration and wholesale gasoline prices. We use discrete and differential changes in the extent of vertical integration generated by mergers in West Coast gasoline refining and retailing markets to test for incentives to raise rivals' costs. The research design allows us to test for a relationship between vertical integration and wholesale prices, controlling for horizontal market structure, cost shocks and trends. We find evidence consistent with the strategic incentive to raise competitors' input costs. This suggests that vertical integration can have a significant impact on wholesale prices.
The Role of Knowledge-based Features in Polarity Classification at Sentence Level

Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy. In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.
Female mice respond to male ultrasonic ‘songs’ with approach behaviour

The ultrasonic vocalizations of mice are attracting increasing attention, because they have been recognized as an informative readout in genetically modified strains. In addition, the observation that male mice produce elaborate sequences of ultrasonic vocalizations (‘song’) when exposed to female mice or their scents has sparked a debate as to whether these sounds are—in terms of their structure and function—analogous to bird song. We conducted playback experiments with cycling female mice to explore the function of male mouse songs. Using a place preference design, we show that these vocalizations elicited approach behaviour in females. In contrast, the playback of whistle-like artificial control sounds did not evoke approach responses. Surprisingly, the females also did not respond to pup isolation calls. In addition, female responses did not vary in relation to reproductive cycle, i.e. whether they were in oestrus or not. Furthermore, our data revealed a rapid habituation of subjects to the experimental situation, which stands in stark contrast to other species' responses to courtship vocalizations. Nevertheless, our results clearly demonstrate that male mouse songs elicit females' interest.
Impact of a Non-linear Pay-Off Function on Pairwise Stable Collaborative Oligopolies

We extend the results of Goyal and Joshi (S. Goyal and S. Joshi. Networks of collaboration in oligopoly. Games and Economic behavior, 43(1):57-85, 2003), who first considered the problem of collaboration networks of oligopolies and showed that under certain linear assumptions network collaboration produced a stable complete graph through selfish competition. We show with nonlinear cost functions and player payoff alteration that stable collaboration graphs with an arbitrary degree sequence can result. We also show a generalized non-linear extension for the result in the aforementioned paper in which the complete graph is stable. An example is provided in which the complete set of pair wise stable collaborations is computed for an oligopoly consisting of five firms.
Video classification based on low-level feature fusion model

This article presents a new system for automatically extracting high-level video concepts. The novelty of the approach lies in the feature fusion method. The system architecture is divided into three steps. The first step consists in creating sensors from a low-level (color or texture) descriptor, and a Support Vector Machine (SVM) learning to recognize a given concept (for example, “beach” or “road”). The sensor fusion step is the combination of several sensors for each concept. Finally, as the concepts depend on context, the concept fusion step models interaction between concepts in order to modify their prediction. The fusion method is based on the Transferable Belief Model (TBM). It offers an appropriate framework for modeling source uncertainty and interaction between concepts. Results obtained on TREC video protocol demonstrate the improvement provided by such a combination, compared to mono-source information.
Identification of North Sea molluscs with DNA barcoding

Sequence‐based specimen identification, known as DNA barcoding, is a common method complementing traditional morphology‐based taxonomic assignments. The fundamental resource in DNA barcoding is the availability of a taxonomically reliable sequence database to use as a reference for sequence comparisons. Here, we provide a reference library including 579 sequences of the mitochondrial cytochrome c oxidase subunit I for 113 North Sea mollusc species. We tested the efficacy of this library by simulating a sequence‐based specimen identification scenario using Best Match, Best Close Match (BCM) and All Species Barcode (ASB) criteria with three different threshold values. Each identification result was compared with our prior morphology‐based taxonomic assignments. Our simulation resulted in 87.7% congruent identifications (93.8% when excluding singletons). The highest number of congruent identifications was obtained with BCM and ASB and a 0.05 threshold. We also compared identifications with genetic clustering (Barcode Index Numbers, BINs) computed by the Barcode of Life Datasystem (BOLD). About 68% of our morphological identifications were congruent with BINs created by BOLD. Forty‐nine sequences were clustered in 16 discordant BINs, and these were divided in two classes: sequences from different species clustered in a single BIN and conspecific sequences divided in more BINs. Whereas former incongruences were probably caused by BOLD entries in need of a taxonomic update, the latter incongruences regarded taxa requiring further investigations. These include species with amphi‐Atlantic distribution, whose genetic structure should be evaluated over their entire range to produce a reliable sequence‐based identification system.
In vitro versus in vivo metabolic activation of mutagens.

Mutagenicity testing is beginning to find a place as a factor in evaluation of the safety of compounds to which the human population is exposed. The mutation systems selected for such tests should ideally involve in vivo studies with mammals and encompass scoring of a broad spectrum of mutational events including both transmissible chromosome aberrations and point mutations. The frequency of chromosome aberrations obtained after a certain treatment can be assessed quantitatively without prohibitive costs and effort. At the present time there is only one proven system which can be used to screen for specific locus mutations in mammals in vivo; the seven specific locus system of Russell. On the other hand there are many microbial systems in which point mutation frequencies can be easily determined quantitatively. It is a known fact, however, that mammals can convert nonmutagenic compounds (promutagens) to highly mutagenic metabolites and that these promutagens are not mutagenic in microorganisms (1). In order to combine the drug metabolism of the mammal with the excellence of the microbial systems for measuring point mutations, the host-mediated assay (1) and the microsomal system (2) were developed. In the host-mediated assay the
Activation of Leukocytes During the Uteroplacental Passage in Preeclampsia

Endothelial dysfunction and inflammation appear to play a major role in the pathogenesis of preeclampsia. We hypothesize that a chronic inflammation in the decidua and placenta during preeclampsia may lead to a local leukocyte activation in this compartment. Venous blood was sampled simultaneously from antecubital and uterine veins during cesarean sections in 30 women with preeclampsia, 29 with uncomplicated pregnancies, and from 17 nonpregnant women. The expression of adhesion molecules and complement-related markers on neutrophils and monocytes was analyzed by flow cytometry. In patients with preeclampsia, neutrophil expression of the integrins CD11a, CD11b, and CD11c and of the complement related markers CD35 and CD59 was significantly higher in samples from uterine than from antecubital veins. No differences were found in nonpregnant women. On monocytes the expression of the Sialyl Lewisx antigen, the integrins CD11a, CD11c, and CD49d, and the complement-related markers CD46 and CD59 was higher in samples from uterine than from antecubital veins during preeclampsia, but not in uncomplicated pregnancies, whereas in nonpregnant women CD31 was decreased. Our findings suggest activation of neutrophils and monocytes taking place during the uteroplacental passage in preeclamptic, but not in normal pregnancies. Such a local inflammatory response involving enhanced leukocyte/endothelial interaction may contribute to the pathogenesis of this disorder.
The Application of Regulatory Cascades in Streptomyces: Yield Enhancement and Metabolite Mining

Streptomyces is taken as an important resource for producing the most abundant antibiotics and other bio-active natural products, which have been widely used in pharmaceutical and agricultural areas. Usually they are biosynthesized through secondary metabolic pathways encoded by cluster situated genes. And these gene clusters are stringently regulated by interweaved transcriptional regulatory cascades. In the past decades, great advances have been made to elucidate the regulatory mechanisms involved in antibiotic production in Streptomyces. In this review, we summarized the recent advances on the regulatory cascades of antibiotic production in Streptomyces from the following four levels: the signals triggering the biosynthesis, the global regulators, the pathway-specific regulators and the feedback regulation. The production of antibiotic can be largely enhanced by rewiring the regulatory networks, such as overexpression of positive regulators, inactivation of repressors, fine-tuning of the feedback and ribosomal engineering in Streptomyces. The enormous amount of genomic sequencing data implies that the Streptomyces has potential to produce much more antibiotics for the great diversities and wide distributions of biosynthetic gene clusters in Streptomyces genomes. Most of these gene clusters are defined cryptic for unknown or undetectable natural products. In the synthetic biology era, activation of the cryptic gene clusters has been successfully achieved by manipulation of the regulatory genes. Chemical elicitors, rewiring regulatory gene and ribosomal engineering have been employed to crack the potential of cryptic gene clusters. These have been proposed as the most promising strategy to discover new antibiotics. For the complex of regulatory network in Streptomyces, we proposed that the discovery of new antibiotics and the optimization of industrial strains would be greatly promoted by further understanding the regulatory mechanism of antibiotic production.