Miracle-dz/UniFAR-FTUs huggingface dataset:
Monocular vision-based object recognition for autonomous vehicle driving in a real driving environment

Nowadays, many attentions have been devoted to autonomous vehicles because the automation of driving technology has a large number of benefits, such as the minimization of risks, the improvement of mobility and ease of drivers. Among many technologies for autonomous driving, road environmental recognition is one of the key issues. In this paper, we present the test results of various object detection algorithms using single monocular camera for autonomous vehicle in real driving conditions. The vision recognition system tested in this paper has three main recognition parts: pedestrian detection, traffic sign and traffic light recognition. We use Histogram of Gradients (HOG) features and detect the pedestrians by Support Vector Machine (SVM). Also features of traffic signs are extracted by Principal Components Analysis (PCA) and canny edge detection is used for traffic lights. These two signals are classified by Neural Network (NN). Algorithms that we tested are implemented in General-Purpose computing on Graphics Processing Units (GPGPU). We show the effectiveness of these methods in real-time applications for autonomous driving.
Chemotactic Response and Adaptation Dynamics in Escherichia coli

Adaptation of the chemotaxis sensory pathway of the bacterium Escherichia coli is integral for detecting chemicals over a wide range of background concentrations, ultimately allowing cells to swim towards sources of attractant and away from repellents. Its biochemical mechanism based on methylation and demethylation of chemoreceptors has long been known. Despite the importance of adaptation for cell memory and behavior, the dynamics of adaptation are difficult to reconcile with current models of precise adaptation. Here, we follow time courses of signaling in response to concentration step changes of attractant using in vivo fluorescence resonance energy transfer measurements. Specifically, we use a condensed representation of adaptation time courses for efficient evaluation of different adaptation models. To quantitatively explain the data, we finally develop a dynamic model for signaling and adaptation based on the attractant flow in the experiment, signaling by cooperative receptor complexes, and multiple layers of feedback regulation for adaptation. We experimentally confirm the predicted effects of changing the enzyme-expression level and bypassing the negative feedback for demethylation. Our data analysis suggests significant imprecision in adaptation for large additions. Furthermore, our model predicts highly regulated, ultrafast adaptation in response to removal of attractant, which may be useful for fast reorientation of the cell and noise reduction in adaptation.
An Operator-based Approach To Upscaliiig ThePressure Equation

Permeability and porosity parameters of a porous medium are known only in a statistical sense. For risk assessment, one must perform multiple flow simulations of a single site, varying these input parameters. Because multiple simulations of large sites are computationally prohibitive, upscaling from fine to coarse scales is necessary. Traditional upscaling techniques determine a new effective or upscaled permeability field defined on a coarser scale, which is then used in a standard coarse grid discretization operator. We develop here a method of determining a new coarse grid discretization operator that provides an upscaled solution but bypasses the determination of effective permeability and porosity fields. The method has two steps. We first solve for fine scale flow information internal to each coarse grid cell. Because the problems are small, this step is relatively fast. Then we determine a modified coarse grid operator for solving the upscaled problem that includes the fine scale flow information from the first step. The method is developed for single-phase flow in the context of the mixed finite element method; therefore, the method is locally mass conservative. Unlike traditional upscaling methods (such as homogenization) we do not impose arbitrary boundary conditions on the coarse grid. We present comparisons of our method with the harmonic average permeability upscaling technique.
Associations of ABHD2 Genetic Variations with Risks for Chronic Obstructive Pulmonary Disease in a Chinese Han Population

The human α/β hydrolase domain-containing protein 2 gene (ABHD2) plays a critical role in pulmonary emphysema, a major subset of the clinical entity known as chronic obstructive pulmonary disease (COPD). Here, we evaluated genetic variation in the ABHD2 gene in a Chinese Han population of 286 COPD patients and 326 control subjects. The rs12442260 CT/CC genotype was associated with COPD (P < 0.001) under a dominant model. In the former-smoker group, the rs12442260 TT genotype was associated with a decreased risk of developing COPD after adjusting for age, gender and pack-years (P = 0.012). Rs12442260 was also associated with pre-FEV1 (the predicted bronchodilator forced expiratory volume in the first second) in controls (P = 0.027), but with FEV1/ forced vital capacity (FVC) ratios only in COPD patients (P = 0.012) under a dominant model. Results from the current study suggest that ABHD2 gene polymorphisms contribute to COPD susceptibility in the Chinese Han population.
Gibbs Sampling Segmentation of Parallel Dependency Trees for Tree-Based Machine Translation

Abstract We present a work in progress aimed at extracting translation pairs of source and target dependency treelets to be used in a dependency-based machine translation system. We introduce a novel unsupervised method for parallel tree segmentation based on Gibbs sampling. Using the data from a Czech-English parallel treebank, we show that the procedure converges to a dictionary containing reasonably sized treelets; in some cases, the segmentation seems to have interesting linguistic interpretations.
Personalized Analysis by Validation of Monte Carlo for Application of Pathways in Cardioembolic Stroke

Background Cardioembolic stroke (CES), which causes 20% cause of all ischemic strokes, is associated with high mortality. Previous studies suggest that pathways play a critical role in the identification and pathogenesis of diseases. We aimed to develop an integrated approach that is able to construct individual networks of pathway cross-talk to quantify differences between patients with CES and controls. Material/Methods One biological data set E-GEOD-58294 was used, including 23 normal controls and 59 CES samples. We used individualized pathway aberrance score (iPAS) to assess pathway statistics of 589 Ingenuity Pathways Analysis (IPA) pathways. Random Forest (RF) classification was implemented to calculate the AUC of every network. These procedures were tested by Monte Carlo Cross-Validation for 50 bootstraps. Results A total of 28 networks with AUC >0.9 were found between CES and controls. Among them, 3 networks with AUC=1.0 had the best performance for classification in 50 bootstraps. The 3 pathway networks were able to significantly identify CES versus controls, which showed as biomarkers in the regulation and development of CES. Conclusions This novel approach could identify 3 networks able to accurately classify CES and normal samples in individuals. This integrated application needs to be validated in other diseases.
A new mouse model for the trisomy of the Abcg1–U2af1 region reveals the complexity of the combinatorial genetic code of down syndrome

Mental retardation in Down syndrome (DS), the most frequent trisomy in humans, varies from moderate to severe. Several studies both in human and based on mouse models identified some regions of human chromosome 21 (Hsa21) as linked to cognitive deficits. However, other intervals such as the telomeric region of Hsa21 may contribute to the DS phenotype but their role has not yet been investigated in detail. Here we show that the trisomy of the 12 genes, found in the 0.59 Mb (Abcg1–U2af1) Hsa21 sub-telomeric region, in mice (Ts1Yah) produced defects in novel object recognition, open-field and Y-maze tests, similar to other DS models, but induces an improvement of the hippocampal-dependent spatial memory in the Morris water maze along with enhanced and longer lasting long-term potentiation in vivo in the hippocampus. Overall, we demonstrate the contribution of the Abcg1–U2af1 genetic region to cognitive defect in working and short-term recognition memory in DS models. Increase in copy number of the Abcg1–U2af1 interval leads to an unexpected gain of cognitive function in spatial learning. Expression analysis pinpoints several genes, such as Ndufv3, Wdr4, Pknox1 and Cbs, as candidates whose overexpression in the hippocampus might facilitate learning and memory in Ts1Yah mice. Our work unravels the complexity of combinatorial genetic code modulating different aspect of mental retardation in DS patients. It establishes definitely the contribution of the Abcg1–U2af1 orthologous region to the DS etiology and suggests new modulatory pathways for learning and memory.
The star cluster population of m 51. III. Cluster disruption and formation history

In this work we concentrate on the evolution of the cluster population of the interacting galaxy M 51 (NGC 5194), more precisely the timescale of cluster disruption and possible variations in the cluster formation rate. We present a method to compare observed age vs. mass number density diagrams with predicted populations including various physical input parameters like the cluster initial mass function, cluster disruption, cluster formation rate and star bursts. If we assume that the cluster formation rate increases at the moments of the encounters with NGC 5195, we find an increase in the cluster formation rate of a factor of $\mbox{$3.0^{+4.6}_{-1.2}$}$, combined with a disruption timescale which is slightly higher than when assuming a constant formation rate ($t_4 = \mbox{$2.0^{+2.3}_{-1.1}\times10^8$}$ yr vs. $\mbox{$1.0^{+0.6}_{-0.5}\times10^8$}$ yr). The measured cluster disruption time is a factor of 5 shorter than expected on theoretical grounds. This implies that the disk of M 51 is not a preferred location for survival of young globular clusters, since even clusters with masses on the order of $10^6~\mbox{${M}_{\odot}$}$ will be destroyed within a few Gyr.
On the Performance Bound of Sparse Estimation With Sensing Matrix Perturbation

This paper focuses on the sparse estimation in the situation where both the the sensing matrix and the measurement vector are corrupted by additive Gaussian noises. The performance bound of sparse estimation is analyzed and discussed in depth. Two types of lower bounds, the constrained Cramér-Rao bound (CCRB) and the Hammersley-Chapman-Robbins bound (HCRB), are discussed. It is shown that the situation with sensing matrix perturbation is more complex than the one with only measurement noise. For the CCRB, its closed-form expression is deduced. It demonstrates a gap between the maximal and nonmaximal support cases. It is also revealed that a gap lies between the CCRB and the MSE of the oracle pseudoinverse estimator, but it approaches zero asymptotically when the problem dimensions tend to infinity. For a tighter bound, the HCRB, despite the difficulty in obtaining a simple expression for general sensing matrix, a closed-form expression in the unit sensing matrix case is derived for a qualitative study of the performance bound. It is shown that the gap between the maximal and nonmaximal cases is eliminated for the HCRB. Numerical simulations are performed to verify the theoretical results in this paper.
Design and implementation of system prediction and traffic conditions visualization in two dimensional map (Case study: Bandung city)

Traffic jam today in the city is one of the focus from the government, because it has a very big impact, from wasting a lot of time, the fuel until air pollution. Searching information about traffic conditions in the city has been widely available through the Twitter account @infobdg, with hashtag #lalinbdg and from the website bandung247.com. However, the rapid development of online information services resulted in the lack of time to read the complete information and for extracting the right information. To resolve these problems, it is necessary to design and implementation the visualization of traffic conditions in the form of map, which the information obtained from Twitter, cctv, user and operator prfm (the radio information about traffic conditions in Bandung). And then designing the predictive system for traffic conditions based on information from Twitter, so the people can predict traffic conditions for one week ahead. Making the visualization begins with taking a summary of the information that will be used for the visualization on the map. From the information Twitter, cctv, user and operator has been able to display traffic conditions in real time and predictive traffic conditions to allow users to know the traffic conditions.
Doing Research in Business Management; How to Choose Your Philosophy and Methodology?

Qualitative research has experienced an upsurge in business management fields of inquiry in the recent past. A methodology is selection, justification and sequential arranging of activities, procedures and tasks in a research project. Research methodology can no longer be confined to a set of universally applicable rules, conventions and traditions. A research paradigm is a set of propositions that explains how the world is perceived. There are three basic paradigms: positivist, interpretive and critical. Positivists believe that research should be objective and value-free, therefore, mainly they depend on quantitative analysis while paradigms other than positivism often guide qualitative research. Interpretivists seek understanding of the world in multiple realities and often these subjective meanings are negotiated socially and historically while critical theorists aim to criticize social reality, emancipate, empower and liberate people, and propose solutions to social problems. Qualitative ‘approaches to research’, ‘strategies of inquiry’ and ‘varieties of methodologies’ classified into five ‘types’ or ‘traditions’ namely; biography, phenomenology, grounded theory, ethnography and case study. The major criticism made of qualitative methods is that they are impressionistic and non-verifiable, post-positivists who reject this charge claiming that qualitative data is auditable and therefore dependable. Further, quantitative research is considered hard-nosed, data-driven, outcome-oriented, and truly scientific, however, even qualitative research can be hard-nosed, data-driven, outcome-oriented, and truly scientific. The less structured qualitative methodologies reject many of the positivists’ constructions over what constitutes rigour, favouring instead the flexibility, creativity and otherwise inaccessible insights afforded by alternative routes of inquiry that embrace storytelling, recollection, and dialogue.
Discovering Topic Evolution Topology in a Microblog Corpus

Topic evolution is an important research topic in natural language processing. Existing work has been focused on topic evolution of long text such as Web pages, news and blogs. In this paper, we aim to derive topic evolution summaries and its topology structures from short text like microblogs. We propose a microblog topic evolution algorithm based on retweeting relationships (MTERR). Firstly, we utilize topic models to generate microblog topics by incorporating temporal characteristics and retweeting features. Secondly, we build a topic correlation function to generate a topic evolution topological graph by considering both topic content and retweeting relationships. Experiments on the real-world event datasets show the effectiveness of our proposed methods.
Microglial AGE-albumin is critical for neuronal death in Parkinson’s disease: a possible implication for theranostics

Advanced glycation end products (AGEs) are known to play an important role in the pathogenesis of neurodegenerative diseases, including Parkinson’s disease (PD), by inducing protein aggregation and cross-link, formation of Lewy body, and neuronal death. In this study, we observed that AGE-albumin, the most abundant AGE product in the human PD brain, is synthesized in activated microglial cells and accumulates in the extracellular space. AGE-albumin synthesis in human-activated microglial cells is distinctly inhibited by ascorbic acid and cytochalasin treatment. Accumulated AGE-albumin upregulates the receptor to AGE, leading to apoptosis of human primary dopamine (DA) neurons. In animal experiments, we observed reduced DA neuronal cell death by treatment with soluble receptor to AGE. Our study provides evidence that activated microglial cells are one of the main contributors in AGE-albumin accumulation, deleterious to DA neurons in human and animal PD brains. Finally, activated microglial AGE-albumin could be used as a diagnostic and therapeutic biomarker with high sensitivity for neurodegenerative disorders, including PD.
Secreted Endothelial Cell Factors Immobilized on Collagen Scaffolds Enhance the Recipient Endothelial Cell Environment

Abstract Strategies to design novel vascular scaffolds are a continuing aim in tissue engineering and often such designs encompass the use of recombinant factors to enhance the performance of the scaffold. The established use of cell secretion utilized in feeder systems and conditioned media offer a source of paracrine factors, which has potential to be used in tissue-engineered (TE) scaffolds. Here we utilize this principle from endothelial cells (ECs), to create a novel TE scaffold by harnessing secreted factors and immobilizing these to collagen scaffolds. This research revealed increased cellular attachment and positive angiogenic gene upregulation responses in recipient ECs grown on these conditioned scaffolds. Also, the conditioning method did not affect the mechanical structural integrity of the scaffolds. These results may advocate the potential use of this system to improve vascular scaffolds' in vivo performance. In addition, this process may be a future method utilized to improve other tissue engineering scaffold therapies.
Robust model predictive control approach to time-delay systems with structured uncertainty

In this paper, robust model predictive control approach is proposed for time-delay systems with structured uncertainty, which has rarely been addressed in the literature. Using the control invariant set and linear matrix inequalities (LMIs), the control approach could stabilized the time-delay systems with structured uncertainty. The effectiveness of the proposed approach is verified by simulation examples.
Neural mechanisms underlying the emergence of rhythmic and stereotyped vocalizations in juvenile songbirds

Complex motor behaviors in humans, such as speech, are not innate, but instead are learned. How does the brain construct neural circuits that generate these motor behaviors during learning? To understand the neural mechanisms underlying learned motor skills, I use vocal learning in songbirds as a model. While previous studies have shown that a premotor area in the songbird brain, HVC, is important for stereotyped adult song, the role of HVC in juvenile song is less known. This thesis characterizes how activity in HVC develops during song learning in juvenile birds. Early in song learning, temporal structure emerged in HVC. During the earliest vocalization of juvenile birds (subsong), HVC neurons exhibit bursts of action potentials. However, only half of the neurons show bursts that are temporally aligned to syllables, and most of these bursts are clustered around onsets of subsong syllables. Over several days, as the bird starts producing the earliest stereotyped vocalization called protosyllables, HVC neurons start exhibiting rhythmic bursts at 5-10 Hz. These rhythmic bursts are aligned to protosyllables, and bursts from different neurons are active at different latencies relative to protosyllables. Thus, as a population, HVC neurons start forming a rhythmic neural sequence. As the bird matures, multiple distinct syllable types emerge from a protosyllable. During this process, some neurons are active only during a specific syllable type ('specific neurons') while others are active during both syllable types ('shared neurons'). These shared neurons are active at similar latencies for both syllable types, and therefore form a shared neural sequence. Over development, fraction of shared neurons decrease and more neurons become specific. These results demonstrate that splitting of a neural sequence into multiple sequences underlies the emergence of a multiple syllable types. Moreover, this sequence splitting is observed during different song learning strategies, suggesting that this is a fundamental neural mechanism for song learning. This work demonstrates how the growth of a rhythmic neural sequence and its subsequence splitting gives rise to complex vocalization in songbirds. This may be a general neural mechanism in which the brain constructs neural circuits during learning of a complex motor behavior. Thesis supervisor: Michale S. Fee Title: Glenn V. and Phyllis F. Dorflinger Professor of Neuroscience
Feature selection algorithms: a survey and experimental evaluation

In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work assesses the performance of several fundamental algorithms found in the literature in a controlled scenario. A scoring measure ranks the algorithms by taking into account the amount of relevance, irrelevance and redundance on sample data sets. This measure computes the degree of matching between the output given by the algorithm and the known optimal solution. Sample size effects are also studied.
Structural insights into proteoglycan-shaped Hedgehog signaling

Significance The Hedgehog (Hh) signaling pathway plays key roles during embryonic development and remains active in adults. Mutations in the genes encoding the Hh signaling pathway proteins lead to developmental disorders and cancer. The glycosaminoglycan (GAG) chains of proteoglycans at the cell surface shape Hh gradients and signal transduction. We determined the crystal structures of Hh proteins with two different GAG chains, heparin and chondroitin sulfate. The GAG-binding site we identified in the Hh protein is previously not identified and the majority of Hh residues forming this GAG-binding site have been previously implicated in developmental diseases. Analysis of the crystal packing, combined with biophysical experiments, revealed GAG-dependent Hh multimerization and suggests a unique mechanism of Hh signaling regulation. Hedgehog (Hh) morphogens play fundamental roles during embryogenesis and adulthood, in health and disease. Multiple cell surface receptors regulate the Hh signaling pathway. Among these, the glycosaminoglycan (GAG) chains of proteoglycans shape Hh gradients and signal transduction. We have determined crystal structures of Sonic Hh complexes with two GAGs, heparin and chondroitin sulfate. The interaction determinants, confirmed by site-directed mutagenesis and binding studies, reveal a previously not identified Hh site for GAG binding, common to all Hh proteins. The majority of Hh residues forming this GAG-binding site have been previously implicated in developmental diseases. Crystal packing analysis, combined with analytical ultracentrifugation of Sonic Hh–GAG complexes, suggests a potential mechanism for GAG-dependent Hh multimerization. Taken together, these results provide a direct mechanistic explanation of the observed correlation between disease and impaired Hh gradient formation. Moreover, GAG binding partially overlaps with the site of Hh interactions with an array of protein partners including Patched, hedgehog interacting protein, and the interference hedgehog protein family, suggesting a unique mechanism of Hh signaling modulation.