Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional neural network bottleneck features for bi-directional generalized variable parameter HMMs

Recently, convolutional neural networks (CNNs) have been applied successfully to acoustic modelling in speech recognition. As the bottleneck features from CNNs contain inherently discriminative and rich context information, the standard approach is to augment the conventional acoustic features with the CNN bottleneck features in a tandem framework. To better capture the highly complex relationship between them, a novel bidirectional generalized variable parameter HMM (GVP-HMM) based approach is proposed in this paper. In this approach, the trajectories of continuous acoustic features space HMM parameters, as well as the model space linear transforms against CNN bottleneck features are modelled by polynomial functions. The optimal GVP-HMM model structure for each direction, which is determined by the locally varying polynomial parameters and degrees, can be automatically learnt using model selection techniques. The proposed bi-directional GVP-HMM based approach gave a word error rate of 12.22% on the Aurora 4 task. In particular, a significant error rate reduction of 18.09% relative was obtained over the baseline tandem HMM system using CNN bottleneck features on the secondary microphone channel condition.
“Man in the Anthropocene”: Max Frisch's Environmental History

The aesthetic practices in Max Frisch's late story Der Mensch erscheint im Holozän (Man in the Holocene [1979]) lend themselves to a reflection on the current global environmental crisis and its anthropological and epistemological repercussions. Frisch's visual and narrative artwork anticipates central issues in the current Anthropocene debate, in which the humanities have made incisive interventions. I bring these interventions to bear on close readings of Frisch's intermedia aesthetics, unearthing an environmental reflexivity that revolves around issues of time and history, place and identity, nature and human knowledge, metamorphosis and anthropogenic transformation. I thus invite us to reconsider in the light of the anthropological and sociopolitical imaginaries of the Anthropocene some ways in which the literature of the past half century has negotiated the relation between human beings and their natural environments.
DSP core verification using automatic test case generation

The verification methodology for a TMS320C25 compatible embedded DSP core is described. The DSP core has been implemented in synthesizable VHDL and has been cosimulated with the original DSP to verify correct behavior. Automatic test case generation together with hand-crafted code has been used as a means of providing stimuli to achieve increased RTL-simulation coverage. The cosimulation environment for this verification and the process of automatic test case generation is described in detail. Experimental results in terms of simulation coverage are discussed. Finally, a classification of all identified design flaws in the implementation is given and error-prone parts of the HDL design are identified.
CloneDiff: semantic differencing of clones

Clone detection provides a scalable and efficient way to detect similar codes, while program differencing is a powerful and effective way to analyze similar codes. CloneDiff, a Program Dependence Graphs (PDGs) differencing tool, complements clone detection with program differencing for the purpose of characterizing clones. It captures semantic information of clones from PDGs, and uses graph matching techniques to compute a precise characterization of clones in terms of a category of semantic differences.
A reduced jitter-sensitivity clock generation technique for continuous-time ΣΔ modulators

A clock generation technique for reducing the clock-jitter sensitivity of Switched current (SI) Return-to-Zero (RZ) DAC in CT ΣΔ modulators is presented in this paper. While realizing the clock-jitter insensitivity, this technique ensures that the feedback period can be utilized more efficiently so that the amplitude of feedback current can be reduced. The proposed technique employs simple digital elements to generate a fixed-pulse-width feedback control clock. It was verified in a 2nd order, 1-bit CT ΣΔ modulator with SI RZ feedback. Simulation result shows that the clock-jitter tolerance using the proposed technique is up to 2% of a clock cycle with SNDR larger than 62dB. While using the traditional clock generation method, clock-jitter tolerance is only 0.1% of a clock cycle.
Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks

The Wireless Sensor Network similarity search problem has received considerable research attention due to sensor hardware imprecision and environmental parameter variations. Most of the state-of-the-art distributed data centric storage (DCS) schemes lack optimization for similarity queries of events. In this paper, a DCS scheme with metric based similarity searching (DCSMSS) is proposed. DCSMSS takes motivation from vector distance index, called iDistance, in order to transform the issue of similarity searching into the problem of an interval search in one dimension. In addition, a sector based distance routing algorithm is used to efficiently route messages. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries.
Modulation of Rolandic Beta-Band Oscillations during Motor Simulation of Joint Actions

Successful joint actions require precise temporal and spatial coordination between individuals who aim to achieve a common goal. A growing number of behavioral data suggest that to efficiently couple and coordinate a joint task, the actors have to represent both own and the partner’s actions. However it is unclear how the motor system is specifically recruited for joint actions. To find out how the goal and the presence of the partner’s hand can impact the motor activity during joint action, we assessed the functional state of 16 participants’ motor cortex during observation and associated motor imagery of joint actions, individual actions, and non-goal-directed actions performed with either 1 or 2 hands. As an indicator of the functional state of the motor cortex, we used the reactivity of the rolandic magnetoencephalographic (MEG) beta rhythm following median-nerve stimulation. Motor imagery combined with action observation was associated with activation of the observer’s motor cortex, mainly in the hemisphere contralateral to the viewed (and at the same time imagined) hand actions. The motor-cortex involvement was enhanced when the goal of the actions was visible but also, in the ipsilateral hemisphere, when the partner’s hand was visible in the display. During joint action, the partner’s action, in addition to the participant’s own action, thus seems to be represented in the motor cortex so that it can be triggered by the mere presence of an acting hand in the peripersonal space.
A modular neural network architecture for sequential paraphrasing of script-based stories

Sequential recurrent neural networks have been applied to a fairly high-level cognitive task, i.e. paraphrasing script-based stories. Using hierarchically organized modular subnetworks, which are trained separately and in parallel, the complexity of the task is reduced by effectively dividing it into subgoals. The system uses sequential natural language input and output and develops its own I/O representations for the words. The representations are stored in an external global lexicon and are adjusted in the course of training by all four subnetworks simultaneously, according to the FGREP-method. By concatenating a unique identification with the resulting representation, an arbitrary number of instances of the same word type can be created and used in the stories. The system is able to produce a fully expanded paraphrase of the story from only a few sentences, i.e. the unmentioned events are inferred. The word instances are correctly bound to their roles, and simple plausible inferences of the variable content of the story are made in the process. <<ETX>>
Transcriptional activity of the short gastrulation primary enhancer in the ventral midline requires its early activity in the presumptive neurogenic ectoderm

The short gastrulation (sog) shadow enhancer directs early and late sog expression in the neurogenic ectoderm and the ventral midline of the developing Drosophila embryo, respectively. Here, evidence is presented that the sog primary enhancer also has both activities, with the late enhancer activity dependent on the early activity. Computational analyses showed that the sog primary enhancer contains five Dorsal (Dl)-, four Zelda (Zld)-, three Bicoid (Bcd)-, and no Single-minded (Sim)-binding sites. In contrast to many ventral midline enhancers, the primary enhancer can direct lacZ expression in the ventral midline as well as in the neurogenic ectoderm without a canonical Simbinding site. Intriguingly, the impaired transcriptional synergy between Dl and either Zld or Bcd led to aberrant and abolished lacZ expression in the neurogenic ectoderm and in the ventral midline, respectively. These findings suggest that the two enhancer activities of the sog primary enhancer are functionally consolidated and geographically inseparable. [BMB Reports 2016; 49(10): 572-577]
Three-Dimensional Organization of the Recurrent Axon Collateral Network of the Substantia Nigra Pars Reticulata Neurons in the Rat

The substantia nigra pars reticulata (SNR) constitutes a major output nucleus of the basal ganglia where the final stage of information processing within this system takes place. In this study, using juxtacellular labeling and three-dimensional reconstruction methods, we investigated the spatial organization of the intranigral innervation provided by single GABAergic projection neurons from the sensory-motor subdivision of the rat SNR. Confirming previous observations, most labeled SNR cells were found to possess a local axonal network innervating the pars reticulata and pars compacta (SNC). Within the SNR, axons of these cells were distributed along curved laminas enveloping a dorsolaterally located core, thus mostly respecting the onion-like compartmentalization of this nucleus. Although the axonal projection field mostly remained confined to the dendritic field of the parent neuron, it usually extended beyond its limits in caudal, lateral, and/or dorsal directions. Because SNR cells are GABAergic, this pattern of axonal projection suggests the existence of lateral inhibitory interactions between neurons belonging to the same as well as to adjacent functional subdivisions. Axonal projections of SNR cells to the SNC formed longitudinal bands. These bands partly occupied the SNC region projecting to the striatal sector from which parent SNR cells receive their afferents. These data indicate that SNR cells contribute to an indirect nigrostriatal loop circuit through which the striatum could upregulate its level of dopaminergic transmission via a disinhibition of nigrostriatal neurons. Spatial relationships between elements of this indirect nigrostriatonigral circuit indicate that this circuit operates in both a closed and open loop manner.
The potential of omniscient debugging for aspect-oriented programming languages

Aspect-oriented programming improve program modularity and meanwhile decreases program comprehensibility, because it can alter the program behavior implicitly. Sometimes, the implicit behavior even varies in different runtime context. To fix bugs related to aspect-oriented entities, programmers need to fully comprehend their actual behavior before taking treatments. However, in some commonly encountered debugging scenarios, existing tools fall short in providing desired information. In this paper, we have described two AO-specific debugging scenarios that require to use the program execution history. We discuss our design ideas, which are (1) a model defining AO-specific events and (2) a visualization consisting of three states, which shows different levels of details.
Genetic Function Approximation and Bayesian Models for the Discovery of Future HDAC8 Inhibitors

Background: Histone deacetylase (HDAC) 8 is one of its family members catalyzes the removal of acetyl groups from N-terminal lysine residues of histone proteins thereby restricts transcription factors from being expressed. Inhibition of HDAC8 has become an emerging and effective anti-cancer therapy for various cancers. Application computational methodologies may result in identifying the key components that can be used in developing future potent HDAC8 inhibitors. Results: Facilitating the discovery of novel and potential chemical scaffolds as starting points in the future HDAC8 inhibitor design, quantitative structure-activity relationship models were generated with 30 training set compounds using genetic function approximation (GFA) and Bayesian algorithms. Six GFA models were selected based on the significant statistical parameters calculated during model development. A Bayesian model using fingerprints was developed with a receiver operating characteristic curve cross-validation value of 0.902. An external test set of 54 diverse compounds was used in validating the models. Conclusions: Finally two out of six models based on their predictive ability over the test set compounds were selected as final GFA models. The Bayesian model has displayed a high classifying ability with the same test set compounds and the positively and negatively contributing molecular fingerprints were also unveiled by the model. The effectively contributing physicochemical properties and molecular fingerprints from a set of known HDAC8 inhibitors were identified and can be used in designing future HDAC8 inhibitors.
Unsupervised Online Bayesian Autonomic Happy Internet-of-Things Management

In Happy IoT, the revenue of service providers synchronizes to the unobservable and dynamic usage-contexts (e.g. emotion, environmental information, etc.) of Smart-device users. Hence, the usage-context-estimation from the unreliable Smart-device sensed data is justified as an unsupervised and non-linear optimization problem. Accordingly, Autonomic Happy IoT Management is aimed at attracting initial user-groups based on the common interests (i.e. recruitment ), then uncovering their latent usage-contexts from unreliable sensed data (i.e. revenue-renewal ) and synchronizing to usage-context dynamics (i.e. stochastic monetization). In this context, we have proposed an unsupervised online Bayesian mechanism, namely Whiz (Greek word, meaning Smart), in which, (a) once latent user-groups are initialized (i.e measurement model ), (b) usage-context is iteratively estimated from the unreliable sensed data (i.e. learning model ), (c) followed by online filtering of Bayesian knowledge about usage-context (i.e. filtering model ). Finally, we have proposed an Expectation Maximization (EM)-based iterative algorithm Whiz, which facilitates Happy IoT by solving (a) recruitment, (b) revenue-renewal and (c) stochastic- monetization problems with (a) measurement, (b) learning, and (c) filtering models, respectively.
A Survey of Adaptive Layered Video Multicast using MPEG-2 Streams

Adaptive Layered Video Multicast is a combination of layered video encoding and layered multicast. The layered video encoding method encodes (or transcodes) a video stream into multiple layers of data. With the layered multicast mechanism, these layers are transmitted over the network, automatically adapting to changes in the available channel resources, such as bandwidth, latency, or error rate. We present an overview over this technology, and some possible methods of using a pre-existing MPEG-2 bitstream with this transmission scheme. These methods do not require to fully decode and re-encode the bitstream, which results in considerably faster and less resource-intensive processing.
SAAT: reverse engineering for performance analysis

It is essential to understand both the static and dynamic aspects of existing software for performance analysis. Software reverse engineering reestablishes the structure and behavior of software and helps with that understanding. Researchers in reverse engineering, however, have focused on identifying components and on static relationships. Efforts on performance engineering are being made to represent software behavior and simulate it. However, no one has tried to extract a simulated model from existing software automatically. We introduce SAAT, a tool developed at our research center. SAAT analyzes the dynamic aspects of software and creates a simulated model for performance analysis. We explain how the model can be generated, using a case study of UPnP middleware. This paper contributes to the bridge between performance analysis and reverse engineering
Discontinuing neuroleptic medication for psychosis : a systematic review of functional outcomes and a qualitative exploration of personal accounts

12 Introduction . 14 Methods . 17 Search strategy and selection criteria . 17 Inclusion criteria. 19 Quality appraisal . 19 Results . 20 Descriptive synthesis . 26 Data synthesis . 29 Discussion . 37 Conclusions . 41 References . 43
The Effect of Aligned and Random Electrospun Fibrous Scaffolds on Rat Mesenchymal Stem Cell Proliferation

Objective: The development of combining mesenchymal stem cells (MSCs) with surface modified three-dimensional (3D) biomaterial scaffold provides a desirable alternative for replacement of damaged and diseased tissue. Nanofibrous scaffolds serve as suitable environment for cell attachment and proliferation due to their similarity to the physical dimension of the natural extracellular matrix. In this study the properties of plasma treated poly-C-caprolactone nanofiber scaffolds (p-PCL) and unaltered PCL scaffolds were compared, and then p-PCL scaffolds were evaluated for MSC culture. Materials and Methods: Aligned and random PCL nanofibrus scaffolds were fabricated by electrospining and their surface modified with O2 plasma treatment to enhance MSC proliferation, adhesion and interaction. Chemical and mechanical characterizations were carried out using scanning electron microscopy (SEM), water contact angle and tensile testing. Cell adhesion and morphology were evaluated using SEM 1 day after culture. Statistical analysis was carried out using one way analysis of variance (ANOVA). Results: The proliferation of MSCs were evaluated using 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide(MTT) assay on day 1, 3, and 5 after cell culture. Results showed that the numbers of cells that had grown on PCL nanofibrous scaffolds were significantly higher than those of control surfaces without nanofibers. Furthermore, the proliferation of MSCs on random nanofiber was significantly higher compared to that on aligned nanofiber. Conclusion: This study showed that while both aligned and random plasma treated PCL nanofibrous scaffold are more suitable substrates for MSC growth than tissue culture plates, random nanofiber best supported the proliferation of MSCs.
N‐FindR method versus independent component analysis for lithological identification in hyperspectral imagery

The current study addresses the problem of the identification of each natural material present in each pixel of a hyperspectral image. Two end member extraction methods from hyperspectral imagery were studied: the N‐FindR method and the independent component analysis (ICA). The N‐FindR is an automatic technique that selects extreme points (end members) of an n‐dimensional scatter plot. It assumes the existence of pure pixels in the distribution, which is infrequent in practice. ICA is a blind source separation technique studied in the signal processing community, which allows each spectrum of natural elements (end member spectra) to be extracted from the observation of some linear combinations of these. It considers a more realistic situation than N‐FindR, assuming a spectra mixture for all the pixels. To increase the robustness of ICA, continuum‐removed reflectance spectra were used and an iterative algorithm was introduced that takes into account a major part of the available information. The end member abundances were estimated by the fully constrained least squares spectral mixture analysis (FLCS). The end member identification and quantification were carried out on two surficial formations of a semi arid region located in the Rehoboth region, in Namibia, from hyperspectral Hyperion data. It appears that the two end member extraction methods have a similar potential. Whichever end member extraction method is used, the analysis of the rock abundance maps produces a lot of geological information: the distribution of natural elements is in line with the field observations and allows the description of the formation processes of surficial units.
Support for pre-registration midwifery studentsand mentors in clinical practice: a small scaleevaluation of the duty teacher role

At least 50% of the pre-registration midwifery education programme in the United Kingdom (UK) is based in clinical practice. Midwifery lecturers are therefore expected to spend a proportion of their time supporting student learning in practice. A duty teacher role was designed to provide indirect support for practice-based learning through placement visits mainly associated with offering pastoral support and academic guidance, narrowing the theory–practice gap, and supporting students and mentors in the use of practice assessment documentation and in the event of failing students. This role involved a visible presence within two maternity units where a ‘duty teacher’ would be available for one day per week at each of the units. A small scale evaluation was conducted to explore experiences and perceptions of the role. A convenience sample of 26 midwives and six student midwives were interviewed. Thematic content analysis revealed three broad themes: lack of understanding of the role, mismatched ideals, and partnership working. Lack of clarity resulted in the duty teacher not being utilised for the intended purpose, and therefore having limited impact on practice learning. However, the regular and frequent clinical visits were valued particularly by those clinicians who were not working as a sign-off mentor.
Generalized Maximum Entropy Estimators: Applications to the Portland Cement Dataset

Consider the linear regression model y= X￻+ u in the usual notation. In many applications the design matrix X is frequently subject to severe multicollinearity. In this paper an alternative estimation methodology, maximum entropy is given and used to estimate the parameters in a linear regression model when the basic data are ill-conditioned. We de- scribed the generalized maximum entropy (GME) estimator, imposing sign restrictions of parameters and imposing cross parameter restrictions for GME. Mean squared error (mse) values of the estimators are estimated by the bootstrap method. We compared the generalized maximum entropy (GME) estimator, least squares and inequality restricted least squares (IRLS) estimator on the widely analyzed dataset on Portland cement.
MP-HA: Multicycles protocol for hospital automation over multicast addressing

This paper presents a Multicycles Protocol for Hospital Automation (MP-HA) that works over multicast addressing and uses a Master-Slave architecture. The protocol creates a segmented logical network based on multicast addressing associated to hospital beds. The objective of MP-HA is to ensure the determinism on network through medium access control mechanism increasing the transmission throughput. Thus, it creates a periodical environment making use of parallel cycles which is called multicycles.