Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of speech synthesis based on HMM using PADAS database

This paper describes the Arabic system synthesis on hidden Markov models (HTS). The main objective of Text-to-Speech (TTS) synthesis is to convert arbitrary input text to intelligible and natural sounding speech. Our developed synthesis system uses phonemes as HMM synthesis unit. In this work, the Phonetic Arabic Database Automatically Segmented “PADAS” based on rich phonetic and balanced speech corpus is used. Prosody is applied which makes the synthesized speech sound more like human speech. In order to reduce the synthetic quality, STRAIGHT system used into the system. Besides that basic setup, a high-quality analysis/ synthesis system STRAIGHT was employed for more sophisticated speech representation. This method has several advantages. As it is parametric, it is possible to play on the HMM parameters, change the producer voice characteristics. The developed model improves the speech synthesis, naturalness and intelligibility quality in the Arabic language environment.
A Brain-specific Isoform of Small Glutamine-rich Tetratricopeptide Repeat-containing Protein Binds to Hsc70 and the Cysteine String Protein*

Small glutamine-rich tetratricopeptide repeat-containing protein (SGT) is a ubiquitously expressed cochaperone of heat shock cognate protein of 70 kDa (Hsc70). SGT binds to the C terminus of Hsc70, a site used by several tetratricopeptide repeat-containing binding partners to recruit Hsc70 into complexes of diverse function. We describe here an isoform of SGT with 60% amino acid sequence identity that we name βSGT. In contrast to the previously published αSGT, βSGT is almost exclusively expressed in brain. Both isoforms of SGT possess similar binding properties toward Hsc70 and cysteine string protein, a synaptic vesicle-associated J-domain-containing protein. In addition, SGTs oligomerize without preferences among isoforms. The distribution of protein binding motifs on SGTs reveals a modular structure. The N-terminal domains mediate oligomerization. Binding to Hsc70 is impaired by mutations of basic residues within the central tetratricopeptide repeat domain of βSGT, indicating a two-carboxylate clamp as the binding mode. The tetratricopeptide repeats are also necessary for binding to the cysteine string protein. However, this binding mode is distinct from the two-carboxylate clamp that is involved in Hsc70 binding. The C-terminal regions of SGTs might constitute independent protein interaction domains. We conclude that βSGT is likely to cooperate with αSGT as co-chaperone of Hsc70 in the brain. The modular structure of SGTs allows them to recruit client proteins to Hsc70 and to direct the resulting complex toward downstream proteins that take over the respective client proteins.
Measurement-based network link dimensioning

The ever increasing traffic demands and the current trend of network and services virtualization calls for effective approaches for optimal use of network resources. In the future Internet multiple virtual networks will coexist on top of the same physical infrastructure, and these will compete for bandwidth resources. Link dimensioning can support fair share and allocation of bandwidth. Current approaches however, are ineffective at smaller timescales or require traffic measurements that are not easy to obtain. In this thesis we focused on easy to deploy and accurate link dimensioning approaches for the future Internet. The start point of our work is a dimensioning formula, proposed in 2006, built upon the assumption of Gaussian traffic. This formula is able to accurately estimate required capacity at very small timescales. To do so it requires traffic statistics that can be obtained from packet captures. The contribution of this thesis is threefold. First, we prove that the assumption of Gaussian traffic holds for current Internet traffic and, hence, the dimensioning formula can still be applied. Second, instead of relying on costly packet captures, we develop and validate link dimensioning approaches that estimate the needed traffic statistics from measurement data obtained via technologies that are largely found in today's networks (namely, sFlow and NetFlow/IPFIX). Our approaches are able to accurately estimate required capacity at timescales as low as 1ms. Last, we propose a link dimensioning approach that uses measured data from the recent and already widely available OpenFlow. We also investigate the quality of flow-level measurements in current implementations of OpenFlow, and demonstrate that these are not yet accurate enough for link dimensioning purposes.
A joint algorithm of iterative equalization and iterative channel estimation in SC-FDE system

The channel estimation accuracy has great impact on the bit error ratio performance of the single-carrier broadband wireless communication system. In order to enhance the channel estimation accuracy when the length of pilot is limited, a joint algorithm of iterative equalization and iterative channel estimation is proposed. This algorithm makes use of the iterative block feedback equalization (IB-DFE) to combat the serious effect of the channel's deep frequency-selective fading for the single-carrier frequency domain equalization. And the soft-modulated signal feeding back from the channel decoder will be used to obtain more accurate channel estimation. Then these estimated values will be available in the next iteration of equalization to achieve better equalization performance. So the equalizer and the channel estimator will build good relationship of improving performance between each other. The simulation results show that the joint algorithm exhibits superior performance in conditions of different channel in contrast to the iterative equalization algorithm only obtaining channel estimation from pilot sequence.
Unsupervised learning of motion patterns using generative models

This work introduces a non-supervised algorithm for learning generative models for classification/recognition of human activities (specifically, pedestrian trajectories) with application to video surveillance. The proposed algorithm comprises two main features: (? ) a set of low level dynamical models of the trajectories, estimated in unsupervised manner using the expectation-maximization (EM) algorithm and automatic model selection using the minimum message length (MML) criterion; (ii) a switching dynamical model described by an hidden Markov model (HMM) used to characterize the higher level activities. The hierarchical model with these two levels is herein denoted as switched dynamical hidden Markov model (SD-HMM). We illustrate the performance of the proposed technique for human activity recognition in a university campus.
Quadrupole mass filter

An ion trap mass spectrometer according to an embodiment of the present invention comprises: an electron emission source; an ion trap which stores ions ionized through a collision with electrons emitted from the electron emission source; a secondary ion filter which blocks secondary ions by the ions selectively emitted from the ion trap; an ion detector which detects the ions selectively emitted from the ion trap, wherein the electron emission source, the ion trap, the secondary ion filter, and the ion detector are coaxially arranged, and the ion trap and the secondary ion filter form a quadrupole. According to the present invention, the ion trap mass spectrometer can meter a pure mass spectrum by excluding secondary ions which cause a background noise signal in a process of detecting ions.
Analysis on a Localized Pruning Method for Connected Dominating Sets

While restricted rule-k has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is presented. Assume N nodes are deployed uniformly and randomly in a square of size L(subscript N)×L(subscript N) (where N and L(subscript N)→∞∞); three results are obtained. (1) It is proved that the node degree distribution of such a network follows a Poisson distribution. (2) The expected size of a CDS that is derived by the restricted pruning rule-k is a decreasing function with respect to the node density (The Symbol is abbreviated). For (The Symbol is abbreviated)≥30. it is found that the expected size is close to N/(The Symbol is abbreviated). (3) It is proved that the lower bound on the expected size of a CDS for a Poissonian network of node density (The Symbol is abbreviated) is given by (The equation is abbreviated)N. The second result is of paramount importance for practitioners. It provides the information about the expected size of a CDS when the node density (The Symbol is abbreviated) is between 6 and 30. The data (expected CDS size) for this range can hardly be provided by simulations.
Prostate cancer stem cells: deciphering the origins and pathways involved in prostate tumorigenesis and aggression

The cells of the prostate gland are dependent on cell signaling pathways to regulate their growth, maintenance and function. However, perturbations in key signaling pathways, resulting in neoplastic transformation of cells in the prostate epithelium, are likely to generate subtypes of prostate cancer which may subsequently require different treatment regimes. Accumulating evidence supports multiple sources of stem cells in the prostate epithelium with distinct cellular origins for prostate tumorigenesis documented in animal models, while human prostate cancer stem-like cells (PCSCs) are typically enriched by cell culture, surface marker expression and functional activity assays. As future therapies will require a deeper understanding of its cellular origins as well as the pathways that drive PCSC maintenance and tumorigenesis, we review the molecular and functional evidence supporting dysregulation of PI3K/AKT, RAS/MAPK and STAT3 signaling in PCSCs, the development of castration resistance, and as a novel treatment approach for individual men with prostate cancer.
Multi-Sensor Image Enhancement and Fusion for Vision Clarity Using Contourlet Transform

A multi- sensor pixel based image fusion system using multi-resolution (MR) approach is proposed. Images captured by visual cameras are fused with those captured by night vision cameras for visibility improvement of a surveillance system and for driving at night or in bad weather conditions. Images captured by visual cameras in insufficient illumination generally have dark shadows and low contrast. This problem seriously affects other further processing schemes. Before fusion a pre visual image contrast enhancement process is done. Multi-resolution decomposition is performed at each input image and they are combined according to the predefined fusion rules. Different fusion rules are defined for the approximate and detail coefficients. The Contourlet transform is used for the multi-resolution decomposition. Contourlet transform has better performance in representing the image salient features such as edges, lines, curves and contours because of its anisotropy and directionality. It is therefore well-suited for multi-resolution image enhancement and fusion. The simulation results show that this approach gives encouraging results for applications in low light and/or non uniform lighting conditions. The composite image contains almost all of the salient features of the input images and it appears to be natural.
Identifying parallelism in programs with cyclic graphs

Dependence analysis algorithms have been proposed to identify parallelism in programs with tree-like data structures. However, they can not analyze the dependence of statements if recursive data structures of programs are cyclic. This paper presents a technique to identify parallelism in programs with cyclic graphs. The technique consists of three steps: (1) Traversal patterns that loops or recursive procedures traverse graphs are identified, and the statements that construct the links of traversal patterns are located by definition-use chains of recursive data structures; (2) Shape analysis is performed to estimate possible shapes of traversal patterns; (3) Dependence analysis is performed to identify parallelism using the result of shape analysis. This approach can identify parallelism in programs with cyclic data structures due to the facts that many programs follow acyclic structures (i.e. traversal patterns) to access all nodes on the cyclic data structures. Once the traversal patterns are isolated from the overall data structures, dependence analysis can be applied to identify parallelism.
FaceSync: A Linear Operator for Measuring Synchronization of Video Facial Images and Audio Tracks

FaceSync is an optimal linear algorithm that finds the degree of synchronization between the audio and image recordings of a human speaker. Using canonical correlation, it finds the best direction to combine all the audio and image data, projecting them onto a single axis. FaceSync uses Pearson's correlation to measure the degree of synchronization between the audio and image data. We derive the optimal linear transform to combine the audio and visual information and describe an implementation that avoids the numerical problems caused by computing the correlation matrices.
Ethanol and Isopropanol in Concentrations Present in Hand Sanitizers Sharply Reduce Excystation of Giardia and Entamoeba and Eliminate Oral Infectivity of Giardia Cysts in Gerbils

ABSTRACT Enteric protozoan parasites, which are spread by the fecal-oral route, are important causes of diarrhea (Giardia duodenalis) and amebic dysentery (Entamoeba histolytica). Cyst walls of Giardia and Entamoeba have a single layer composed of fibrils of β-1,3-linked GalNAc and β-1,4-linked GlcNAc (chitin), respectively. The goal here was to determine whether hand sanitizers that contain ethanol or isopropanol as the active microbicide might reduce transmission of these parasites. We found that treatment with these alcohols with or without drying in a rotary evaporator (to model rapid evaporation of sanitizers on hands) kills 85 to 100% of cysts of G. duodenalis and 90 to 100% of cysts of Entamoeba invadens (a nonpathogenic model for E. histolytica), as shown by nuclear labeling with propidium iodide and failure to excyst in vitro. Alcohols with or without drying collapsed the cyst walls of Giardia but did not collapse the cyst walls of Entamoeba. To validate the in vitro results, we showed that treatment with alcohols eliminated oral infection of gerbils by 1,000 G. duodenalis cysts, while a commercial hand sanitizer (Purell) killed E. invadens cysts that were directly applied to the hands. These results suggest that expanded use of alcohol-based hand sanitizers might reduce the transmission of Giardia and Entamoeba.
Coping with CDMA Asynchronicity in Compressive Sensing Multi-User Detection

The growing field of Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has been shown for a synchronous CDMA transmission that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). However, many practical applications show some degree of asynchronicity. In order to reduce transmitter complexity, we propose an enhanced CS MUD that detects the delay in addition to activity and data. This solves synchronicity issues for scenarios with a known maximum delay, without requiring signaling or pre-compensation of asynchronicity.
Two Forkhead transcription factors regulate cardiac progenitor specification by controlling the expression of receptors of the fibroblast growth factor and Wnt signaling pathways

Cardiogenesis involves the coordinated regulation of multiple biological processes by a finite set of transcription factors (TFs). Here, we show that the Forkhead TFs Checkpoint suppressor homologue (CHES-1-like) and Jumeau (Jumu), which govern cardiac progenitor cell divisions by regulating Polo kinase activity, play an additional, mutually redundant role in specifying the cardiac mesoderm (CM) as eliminating the functions of both Forkhead genes in the same Drosophila embryo results in defective hearts with missing hemisegments. This process is mediated by the Forkhead TFs regulating the fibroblast growth factor receptor Heartless (Htl) and the Wnt receptor Frizzled (Fz): CHES-1-like and jumu exhibit synergistic genetic interactions with htl and fz in CM specification, thereby implying that they function through the same genetic pathways, and transcriptionally activate the expression of both receptor-encoding genes. Furthermore, ectopic overexpression of either htl or fz in the mesoderm partially rescues the defective CM specification phenotype in embryos lacking both Forkhead genes. Together, these data emphasize the functional redundancy that leads to robustness in the cardiac progenitor specification process, and illustrate the pleiotropic functions of Forkhead TFs in different aspects of cardiogenesis. Summary: Checkpoint suppressor homologue and Jumeau, which are known to govern cardiac progenitor cell divisions, play additional, mutually redundant roles in specifying cardiac mesoderm in Drosophila.
Gradually DropIn Layers to Train Very Deep Neural Networks: Theory and Implementation

We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network’s depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the new layers for both feedforward and backpropagation. We show that deep networks, which are untrainable with conventional methods, will converge with DropIn layers interspersed in the architecture. In addition, we demonstrate that DropIn provides regularization during training in an analogous way as dropout. Experiments are described with the MNIST dataset and various expanded LeNet architectures, CIFAR-10 dataset with its architecture expanded from 3 to 11 layers, and on the ImageNet dataset with the AlexNet architecture expanded to 13 layers and the VGG 16-layer architecture.
Influence of local climate and climate change on aeroterrestrial phototrophic biofilms

Aeroterrestrial phototrophic biofilms colonize natural and man-made surfaces and may damage the material they settle on. The occurrence of biofilms varies between regions with different climatic conditions. The aim of this study was to evaluate the influence of meteorological factors on the growth of aeroterrestrial phototrophs. Phototrophic biomass was recorded on roof tiles at six sites within Germany five times over a period of five years and compared to climatic parameters from neighboring weather stations. All correlating meteorological factors influenced water availability on the surface of the roof tiles. The results indicate that the frequency of rainy days and not the mean precipitation per season is more important for biofilm proliferation. It is also inferred that the macroclimate is more important than the microclimate. In conclusion, changed (regional) climatic conditions may determine where in central Europe global change will promote or inhibit phototrophic growth in the future.
Temporal Relevance Profiles for Tweet Search

When searching tweets, users may know something about the temporal characteristics of the information they’re after. For example, based on external knowledge, a searcher might prefer more recent results or results within a particular time interval. However, most search applications do not allow the user to explicitly supply this information, and neither do most retrieval models have a mechanism to incorporate this additional evidence. In this paper, we introduce the notion of a temporal relevance profile, which a user explicitly includes alongside a keyword search query. We propose alternative representations of temporal relevance profiles and how existing retrieval models might take advantage of this data. Oracle experiments on microblog track data from TREC 2011 and 2012 empirically demonstrate that this approach has the potential to significantly increase the quality of retrieved results.
New outlier detection method based on fuzzy clustering

In this paper, a new efficient method for outlier detection is proposed. The proposed method is based on fuzzy clustering techniques. The c-means algorithm is first performed, then small clusters are determined and considered as outlier clusters. Other outliers are then determined based on computing differences between objective function values when points are temporarily removed from the data set. If a noticeable change occurred on the objective function values, the points are considered outliers. Test results were performed on different well-known data sets in the data mining literature. The results showed that the proposed method gave good results.
Agent-based interaction analysis of consumer behavior

Our goal is to create a virtual consumer population that can be used for simulating the effects of marketing strategies in a competing market context. That requires having a consumers' behavioral model allowing the representation of observed individual behaviors and the simulation of a large population of consumers. That also requires finding the parameters' values characterizing the virtual population that reproduces real market evolutions. This paper proposes a consumer behavioral model based on a set of behavioral primitives such as imitation, conditioning and innovativeness, which are founded on the new concept of behavioral attitude. It shows that this model provides an interpretation of the main concepts and cognitive features, issued from marketing research and psycho-sociology works on consumption. The paper presents also the CUstomer BEhavior Simulator (CUBES), which has been realized for implementing the customer model and leading multi-agents simulations. It shows how genetic algorithms (GA), in addition to multi-agent systems, are used to fit the characteristics of the virtual consumers' population into a global realistic market behavior.
Distress Tolerance, Experiential Avoidance, and Negative Affect: Implications for Understanding Eating Behavior and BMI

Distress tolerance and experiential avoidance are important aspects of the coping process. In the current study, both were examined in relation to Body Mass Index and selfreported disturbances in mood and eating behavior. Distress tolerance was measured behaviorally and via self-report to elucidate the manner in which a) the ability to tolerate emotional distress, and b) the ability to persist behaviorally in the presence of stressinducing stimuli were related to self-reported levels of depression, anxiety, maladaptive eating habits, and bodily concerns. A sample of 73 undergraduate students participated, and height, weight, and waist circumference were measured. Increased experiential avoidance was associated with increased weight status; however, this was true only for the morbidly obese group (n = 1). Increased experiential avoidance and decreased selfefficacy were significantly associated with less rewarding eating experiences. Individuals with lower distress tolerance reported increased depression, anxiety, and experiential avoidance, and were more likely to indicate eating disturbances and concerns on selfreport measures, although distress tolerance generally was unrelated to eating behaviors as indexed on food diaries. These results were not replicated utilizing a behavioral measure of distress tolerance. Future directions for research designed to examine these variables in overweight and obese populations are discussed.