Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex-Specific Dynamics of Global Chromatin Changes in Fetal Mouse Germ Cells

Mammalian germ cells undergo global reprogramming of DNA methylation during their development. Global DNA demethylation occurs around the time when the primordial germ cells colonize the embryonic gonads and this coincides with dynamic changes in chromatin composition. Global de novo DNA methylation takes place with remarkably different dynamics between the two sexes, prospermatogonia attaining methylation during fetal stages and oocytes attaining methylation postnatally. Our hypothesis was that dynamic changes in chromatin composition may precede or accompany the wave of global DNA de novo methylation as well. We used immunocytochemistry to measure global DNA methylation and chromatin components in male and female mouse fetal germ cells compared to control somatic cells of the gonad. We found that global DNA methylation levels sharply increased in male germ cells at 17.5 days post coitum, but remained low in female germ cells at all fetal stages. Global changes in chromatin composition: i, preceded global DNA methylation in fetal germ cells; ii, sex specifically occurred in male but not in female germ cells; iii, affected active and repressive histone marks and iv, included histone tail and histone globular domain modifications. Our data suggest that dynamic changes of chromatin composition may provide a framework for the pattern of male-specific de novo DNA methylation in prospermatogonia.
Simulation Guided Hair Dynamics Modeling from Video

In this paper we present a hybrid approach to reconstruct hair dynamics from multi‐view video sequences, captured under uncontrolled lighting conditions. The key of this method is a refinement approach that combines image‐based reconstruction techniques with physically based hair simulation. Given an initially reconstructed sequence of hair fiber models, we develop a hair dynamics refinement system using particle‐based simulation and incompressible fluid simulation. The system allows us to improve reconstructed hair fiber motions and complete missing fibers caused by occlusion or tracking failure. The refined space‐time hair dynamics are consistent with video inputs and can be also used to generate novel hair animations of different hair styles. We validate this method through various real hair examples.
A closer look on traffic light detection evaluation metrics

Recognition of traffic lights is of crucial interest for intelligent vehicles. For camera based detection systems traffic lights constitute important objects that have some specialties in contrast to other traffic related objects: For a better sight of road users on traffic lights in many cases they are placed redundantly. Furthermore, at bigger intersections a particular traffic light often only denotes information for a particular lane. For a road user or intelligent vehicle only a relevant traffic light showing the information for the desired turning direction has to be found. When evaluating traffic light recognition systems for different use cases a different level of information needs to be evaluated as well. This paper provides a closer look on evaluation metrics and uses a real world dataset for providing examples of different metrics.
Connecting Technological Capabilities with Market Needs Using a Cyclic Innovation Model

One of the key limitations of current models of innovation is that they still represent variations on the familiar pipeline architecture. In addition, they are not embedded in the strategic issues of company boards and therefore remain isolated entities. Equally, the activity of entrepreneurship, while long recognized as a key factor in firm innovation management, is not captured and is inadvertently understated or only implied at best. We find that there is incongruence between current theoretical models and innovation practice. We offer a socio-technical framework that replaces the family of linear concepts by a cyclic alternative. It combines hard and soft sciences, bridges research and development and marketing communities and helps firms and policy makers to better understand the iterative nature of the innovation process.
Improving text-independent speaker recognition with GMM

The Gaussian mixture models (GMM) represent an efficient model that was broadly used in most of speaker recognition applications. This study introduces a novel method for speaker verification task. We propose a reduced feature vector employing new information detected from the speaker's voice for performing text-independent speaker verification applications using GMM. We use the power spectrum density of the speech signal to improve the system's performance. Speaker verification experiments were evaluated with the TIMIT dataset. The suggested system performance is evaluated against the baseline systems. The decrease in the error rate is well observed and the results have demonstrated the effectiveness of the new approach which avoids the use of more complex algorithms or the combination of different approaches.
Temporal correlation and probabilistic prediction based face detection framework in real time environment

Conventionally, the object detection algorithm proposed by Viola Jones (using Haar-like features, Integral image and AdaBoost algorithm) is implemented in majority of the face detection applications. In this article, an improvement to the application of Viola Jones algorithm in real time environment is presented by exploiting the analogy between video motion estimation and continuous object detection. Using the temporal correlation between successive frames of a real time input, various modifications improving the robustness and computational complexity of face detection are proposed. The proposed method focuses on reducing the search area for face detection on the basis of probabilistic prediction. In addition, approximation of minimum face size renders an improved performance. The modified Face Detection Framework (FDF) is applied in two scenarios, canned video sequences from public databases and real time inputs from a low resolution camera, yielding improved results in both the cases.
Involvement of the protein tyrosine phosphatase SHP-1 in Ras-mediated activation of the mitogen-activated protein kinase pathway

Ubiquitously expressed SH2-containing tyrosine phosphatases interact physically with tyrosine kinase receptors or their substrates and relay positive mitogenic signals via the activation of the Ras-mitogen-activated protein kinase (MAPK) pathway. Conversely, the structurally related phosphatase SHP-1 is predominantly expressed in hemopoietic cells and becomes tyrosine phosphorylated upon colony-stimulating factor 1 treatment of macrophages without associating with the colony-stimulating factor 1 receptor tyrosine kinase. Mice lacking functional SHP-1 (me/me and me(v)/me(v)) develop systemic autoimmune disease with accumulation of macrophages, suggesting that SHP-1 may be a negative regulator of hemopoietic cell growth. By using macrophages expressing dominant negative Ras and the me(v)/me(v) mouse mutant, we show that SHP-1 is activated in the course of mitogenic signal transduction in a Ras-dependent manner and that its activity is necessary for the Ras-dependent activation of the MAPK pathway but not of the Raf-1 kinase. Consistent with a role for SHP-1 as an intermediate between Ras and the MEK-MAPK pathway, Ras-independent activation of the latter kinases by bacterial lipopolysaccharide occurred normally in me(v)/me(v) cells. Our results sharply accentuate the diversity of signal transduction in mammalian cells, in which the same signaling intermediates can be rearranged to form different pathways.
A Hybrid Signal Processing of RR Intervals from QTc Variation Searching Arrhythmia and Improving Heart Rate Variability Assessment in Acute Large Artery Ischemic Stroke

Sudden death caused by abnormal QTc and atrial fibrillation (AF) has been reported in stroke. Heart rate variability (HRV) is reduced with missing beats of RRI during arrhythmic episode and abnormal QTc variation during acute stroke. In this study, we develop a hybrid signal processing by Pan Tompkins QRS detection and Kalman filter estimator for meaningful missing beats and searching AF with prolonged QTc. We use this hybrid model to investigate RRIs of Lead II ECG in thirty acute stroke patients with long QTc and AF (LQTc-AF) and normal QTc without AF (NQTc-nonAF) and then assess them by HRV. In LQTc-AF Kalman, higher mean heart rate with lower mean RRIs compared to NQTc-nonAF Kalman was characterized. LQTc-AF Kalman showed significant increase in SDNN, HF, SD2, SD2/SD1, and sample entropy. SDNN and HF associated with high RMSSD, pNN50, and SD1 reflect predominant parasympathetic drive for sympathovagal balance in LQTc-AF Kalman. Greater SD2, SD2/SD1, and sample entropy indicate more scatter of Poincaré plot. Compared with conventional Labchart, fractal scaling exponent of α1 (DFA) is higher in LQTc-AF Kalman. Remarkable complexity with parasympathetic drive in LQTc-AF Kalman suggests an influence of missing beats during stroke.
Essays on operational flexibilities in production planning under supply and quality uncertainty

This dissertation investigates the use of operational flexibilities in production planning in order to mitigate the negative effects of supply and quality uncertainty. Uncertainties in supply and quality are commonly experienced among agro-businesses, and in particular, in the wine industry. The goal of the dissertation is to provide prescriptive solutions in mitigating such risks from the lives of agricultural businesses. The first essay of the dissertation examines the impact of supply and quality uncertainty on the investment decisions made by winemakers who lease vineyard space to grow their own fruit. At the end of the growing season, the winemaker receives an uncertain amount of highand low-quality grapes, due to varying growing conditions such as adverse weather conditions, diseases and natural disasters. High-quality grapes are used in the making of a high-end (reserve) wine, and low-quality grapes are used for the production of a low-end wine. In this study, we investigate the benefits of the downward substitution flexibility, where the winemaker uses its excess high-quality grapes for the production of its low-end wine. In addition, we examine the influence of, and the interrelationships between, three forms of operational flexibilities: downward substitution, price-setting, and fruit trading flexibilities. The second essay of the dissertation investigates the use of advance selling to mitigate quality risk in wine production. This essay examines the influence of quality uncertainty on winemakers’ decisions regarding the allocation of its wine for retail operations. Specifically, we study what proportion of the wine should be sold through regular distribution channels versus what proportion should be sold as “wine futures” in advance of bottling. Due to the intricacies of the production method, the quality of wine may vary from the moment aging begins in the barrel to the time it is bottled and sold to the general public. This study examines the use of wine futures, whereby a winemaker sells its wine while it is still in the barrel in order to reduce the quality rating risk at the time of distribution. Overall, wine futures not only allow the winemaker to pass on the quality rating risk established through expert tastings to consumers but also let them bring in cash for immediate reinvestment into the next vintage. ESSAYS ON OPERATIONAL FLEXIBILITIES IN PRODUCTION PLANNING UNDER SUPPLY AND QUALITY UNCERTAINTY
O2BC: a technique for the design of component-based applications

Component-based development (CBD) has become a much talked-about subject. While the technology of CBD-as exemplified by environments such as EJB and COM-has become increasingly mature, this has not been complemented by corresponding maturity on the methodology front. Of the few published methodologies available for the design of components, most address the process of building systems from a set of pre-built components. However, an important scenario not addressed is that of the design of custom applications. In such contexts, the CBD design question involves the creation of business components from a set of software requirements. Currently there is no published prescription that addresses this need. At best, practitioners rely on published collections of software patterns or heuristic guidelines such as those advocating correct component granularity. We propose a comprehensive design methodology for identifying two classes of business components based on an object-oriented specification of requirements. These are entity components and process components. The methodology is illustrated with an example, and a real-life case study of an auction site is also described.
Sampled-data filtering with error covariance assignment

We consider the sampled-data filtering problem by proposing a new performance criterion in terms of the estimation error covariance. An innovation approach to sampled-data filtering is presented. First, the definition of the estimation covariance e for a sampled-data system is given, then the sampled-data filtering problem is reduced to the Kalman filter design problem for a fictitious discrete-time system, and finally, an effective method is developed to design discrete-time Kalman filters in such a way that the resulting sampled-data estimation covariance achieves a prescribed value. We derive both the existence conditions and the explicit expression of the desired filters and provide an illustrative numerical example to demonstrate the directness and flexibility of the present design method.
Recognition of Eye States in Real Time Video

A composite algorithm is proposed to identify the opened and shut states of eye in real-time video. Sequentially, each frame of the video is processed first to find and separate the eye regions; then, the accumulation array of circular Hough transform is applied for detecting circles to recognize the opened state of the eye, and the upper eyelid curve is cal-culated to identify its bending direction to recognize the shut state; after that, if the state cannot be determined yet, judging by the standard deviation of distance between eyelids, the eye state of blinking is determined finally. An accuracy of 94% for eye state detection is obtained in our experiments by using the proposed algorithm. As an important part, the algorithm is going to be utilized in a fatigue warning system for drivers.
PI CONTROLLERS PERFORMANCES FOR A PROCESS MODEL WITH VARYING DELAY

Varying delay systems represent a serious challenge in many facets of process control. A frequent issue that arises in practice is introduced by transportation delays in fixed lengths pipes at speed which varies with setpoints. Many classic control techniques can be used to deal with con- stant delays systems but they do not specifically address this structural delay variability. In this paper we present a process model (Diesel Hydrodesulfurization) that features this delay variability and explore robustness properties of a wide panel of PI controllers. A conclusion is that the re- cent method proposed by Tavakoli and Fleming compares favorably with all others, including Smith predictors, when the delay variation is not known.
Visual saliency detection using video decomposition

Estimation of salient regions in an input video is an active area of research due to its wide applications. In this paper, we propose a novel algorithm to estimate the eye gaze movement in a video using motion, color and structural cues with minimum outliers. The algorithm is generalized to capture salient information for the videos taken under different camera motions. The entire algorithm is parallelizable and ensures faster estimation of salient regions. Using different standard datasets, the estimations of proposed algorithm are compared with state-of-the-art approaches. It is observed that the proposed method produces estimations closer to the ground-truth eye tracker data with minimum outliers.
Content-based shape retrieval using different shape descriptors: a comparative study

Shape representation is a fundamental issue in the newly emerging multimedia applications. In the content based image retrieval (CBIR), shape is an important low level image feature. Many shape representations have been proposed. However, for CBIR, a shape representation should satisfy several properties such as affine invariance, robustness, compactness, low computation complexity and perceptual similarity measurement. Against these properties, in this paper we attempt to study and compare several shape descriptors which have been widely adopted for CBIR, they are: Fourier descriptors (FD), curvature scale space (CSS) descriptors (CSSD), Zernike moment descriptors (ZMD) and grid descriptors (GD). The strengths and limitations of these methods are analyzed and clarified. Retrieval results are given to show the comparison.
RASSD: A dynamically reconfigurable active storage device for energy efficient data analytics

We study the performance and energy characteristics of a reconfigurable active solid-state drive (RASSD) consisting of a tightly-coupled FPGA/SSD pair. The FPGA implements a compute node that uses partial dynamic reconfiguration to implement hardware accelerators that process data streaming from the SSD. Using K-means clustering as a representative data analytics workload, we show that a RASSD node is 1.3-15.2 times faster than a commodity, multi-core, computer, and consumes 9.4-201.9 times less energy. This suggests that using RASSD nodes in contemporary data centers can have a significant impact on performance and energy efficiency.
A Method of Synthesizing Microwave Bandpass Filters Constructed With Symmetrical or Asymmetrical Compact Microstrip Resonators

This paper presents a method of synthesizing microwave filters consisting of compact microstrip resonators (CMRs). The synthesizing method is based on the conventional insertion-loss design method and can be applied to the design of filters consisting of symmetrical and asymmetrical CMRs. The synthesizing method can help the designer to determine the appropriate layout according to the specified passband and stopband characteristics of the filter. A new type of CMR layout is proposed to suppress the spurious passband response and is applied to the implementation of a Ku-band filter. The measurement of the prototype shows approximately 1.5-dB insertion loss in the passband and a rejection greater than 40 dB at the specified frequencies. Besides, an asymmetrical CMRs filter operated at Ku-band was also designed and fabricated. The measurement result is also close to the computer simulation result. This demonstrates the effectiveness of the proposed design concept
Self‐Construal Scales Lack Validity

Self-construal is thought to mediate and explain the effects of culture on a wide variety of outcome variables. A meta-analysis of published cross-cultural self-construal research is reported in this article, and the results across studies suggests that the evidence for the predicted cultural differences is weak, inconsistent, or nonexistent. The results of 3 priming experiments (N = 121, N = 99, and N = 361) suggest that (a) priming does not account for the inconsistent results observed in the meta-analysis, (b) that scores on a self-construal scale appear to be measuring trait-like constructs that are not sensitive to priming, and (c) that measures of self-construals lack convergent validity. The results of several measurement studies (N = 121, 223, 230, 323, 214, 206, 126, 204, 148, 141, and 150) were inconsistent with the a priori two-factor measurement model in every case. Self-construal scales were found to be radically multidimensional and highly unstable within and across cultures. These results lead us to conclude that catastrophic validity problems exist in research involving the use of self-construal scales in cross-cultural research.
Profile guided optimization for dataflow predication

Dataflow predication provides a lightweight full support for predicated execution in dataflow-like architectures. One of its major overhead is the large amounts of fanout trees for distributing predicates to all dependant instructions. Conventional optimizations are predicating only the heads or tails of dataflow chains. Predicating tails offers more speculation but leads to resource contentions and power consumption increasing. Predicating heads is power efficient but reduces speculation and instruction level parallelism. This paper introduces a profile guided technique to combine these optimizations. It uses profiling feedback to guide the compiler in deciding to predicate at the head or tail. By predicating tails on hot paths and predicating heads on infrequent paths, this technique can get performance, power and resource efficiency. Performance evaluation result shows that profile guided optimization performs better in removing fanout trees. It has 10.6% speedup over always predicating heads and 2.5% speedup over always predicating tails in performance.
Protein p38: an integral membrane protein specific for small vesicles of neurons and neuroendocrine cells

An intrinsic membrane protein of brain synaptic vesicles with Mr 38,000 (p38, synaptophysin) has recently been partially characterized (Jahn, R., W. Schiebler, C. Ouimet, and P. Greengard, 1985, Proc. Natl. Acad. Sci. USA, 83:4137-4141; Wiedenmann, B., and W. W. Franke, 1985, Cell, 41:1017-1028). We have now studied the presence of p38 in a variety of tissues by light and electron microscopy immunocytochemistry and by immunochemistry. Our results indicate that, within the nervous system, p38, like the neuron-specific phosphoprotein synapsin I, is present in virtually all nerve terminals and is selectively associated with small synaptic vesicles (SSVs). No p38 was detectable on large dense-core vesicles (LDCVs). p38 and synapsin I were found to be present in similar concentrations throughout the brain. Outside the nervous system, p38 was found in a variety of neuroendocrine cells, but not in any other cell type. In neuroendocrine cells p38 was localized on a pleiomorphic population of small, smooth-surfaced vesicles, which were interspersed among secretory granules and concentrated in the Golgi area, but not on the secretory granules themselves. Immunoblot analysis of endocrine tissues and cell lines revealed a band with a mobility slightly different from that of neuronal p38. This difference was attributable to a difference in glycosylation. The finding that p38, like synapsin I, is a component of SSVs of virtually all neurons, but not of LDCVs, supports the idea that SSVs and LDCVs are organelles of two distinct pathways for regulated neuronal secretion. In addition, our results indicate the presence in a variety of neuroendocrine cells of an endomembrane system, which is related to SSVs of neurons but is distinct from secretory granules.
Opinion classification with tree kernel SVM using linguistic modality analysis

We propose a method for classifying opinions which captures the role of linguistic modalities in the sentence. We use features than simple bag-of-words or opinion-holding predicates. The method is based on a machine learning and utilizes opinion-holding predicates and linguistic modalities as features. Two different detectors help to classify the opinions: the opinion-holding predicate detector and the modality detector. An opinion in the target is first parsed into a dependency structure, and then the opinion-holding predicates and modalities stick onto the leaf nodes of the dependency tree. The whole tree is regarded as input features of the opinion, and it becomes the input of tree kernel support vector machines. We have applied method to opinions in Japanese about television programs, and have confirmed the effectiveness of the method against conventional bag-of-words features, or against simple opinion-holding predicates features