Personality Characteristics of Entrepreneur and Business Survival: The Role of External Environments

Entrepreneurial is one of the solutions for a country to face an economic crisis and reduce the country's unemployment problem. However, no more than 50% of the startup business can survive in the first three years. The Open System Theory explains that there is a positive relationship between external environmental factors and business activities. This study tries to provide the complex relationship between Self-efficacy, Risk-taking propensity, Innovativeness, and business survival with technology turbulence as the moderating variable. The data collection was provided through literature review from the previous study as the predictor. The contribution of this study is the clarification of the facts of personality characteristics of entrepreneurs and the indication that external environments can moderate the personality characteristics of an entrepreneur. The result shows that risk-taking propensity and Innovativeness positively affect business survival and will be best influencing at the moderate level.Keywords: Risk-taking propensity, Technology Turbulence, Self-Efficacy, Entrepreneurship, Innovation, Business Performance, Open System Theory

Optical Map-Based Genome Scaffolding

De novo genome assembly is one of the most critical problems in computational biology. Due to the limitations of current sequencing technologies, the de novo assembly is typically carried out in two stages, namely contig (sequence) assembly and scaffolding. The scaffolding process can vastly improve the assembly contiguity and can produce chromosome-level assemblies. Despite significant algorithmic progress, the scaffolding problem can be challenging due to the high repetitive content of eukaryotic genomes, possible mis-joins in assembled contigs and the inaccuracies of the linkage information.Different types of linkage information such as paired-end/mate-pair/linked/Hi-C reads or genome-wide maps (optical, physical or genetic) are used to carry out the scaffolding process. Optical maps (in particular Bionano Genomics maps) have been extensively used in many recent large-scale genome assembly projects (e.g., goat, apple, barley, maize, quinoa, sea bass, among others).In this dissertation, we address some of the computational issues associated with genome scaffolding when optical maps are used. We propose novel algorithms for scaffolding, chimeric detection, and assembly reconciliation. First, we introduce a novel chimeric removal tool called Chimericognizer. Chimericognizer takes advantage of one or more Bionano Genomics optical maps to accurately detect and correct chimeric contigs. Experimental results show that Chimericognizer is very accurate, and significantly better than the chimeric detection method offered by the Bionano Hybrid Scaffold pipeline. Chimericognizer can also detect and correct chimeric opticalmolecules.Second, we describe a novel method called Novo&Stitch that can take advantage of optical maps to accurately carry out assembly reconciliation. Experimental results demonstrate that Novo&Stitch can double the contiguity (N50) of the input assemblies without introducing mis-joins or reducing genome completeness.Third, we introduce a scaffolding algorithm called OMGS that for the first time can take advantages of multiple optical maps. OMGS solves several optimization problems to generate scaffolds with optimal contiguity and correctness. Extensive experimental results demonstrate that our tool outperforms existing methods when multiple optical maps are available, and produces comparable scaffolds using a single optical map

Combination treatment with ethyl pyruvate and IGF-I exerts neuroprotective effects against brain injury in a rat model of neonatal hypoxic-ischemic encephalopathy

Neonatal hypoxic-ischemic (HI) brain injury causes severe brain damage in newborns. Following HI injury, rapidly accumulating oxidants injure neurons and interrupt ongoing developmental processes. The antioxidant, sodium pyruvate, has been shown to reduce neuronal injury in neonatal rats under conditions of oxygen glucose deprivation (OGD) and HI injury. In this study, we evaluated the effects of ethyl pyruvate (EP) and insulin‑like growth factor‑I (IGF‑I) alone or in combination in a similar setting. For this purpose, we used an in vitro model involving primary neonatal rat cortical neurons subjected to OGD for 2.5 h and an in vivo model involving unilateral carotid ligation in rats on post-natal day 7 with exposure to 8% hypoxia for 2.5 h. The cultured neurons were examined by lactate dehydrogenase (LDH) and cell viability assays. For the in vivo experiments, behavioral development was evaluated by the foot fault test at 4 weeks of recovery. 2,3,5‑Triphenyltetrazolium chloride monohydrate and cresyl violet staining were used to evaluate HI injury. The injured neurons were Fluoro‑Jade B-labeled, new neuroprecursors were double labeled with bromodeoxyuridine (BrdU) and doublecortin, new mature neurons were BrdU-labeled and neuronal nuclei were labeled by immunofluorescence. Under conditions of OGD, the LDH levels increased and neuronal viability decreased. Treatment with 0.5 mM EP or 25 ng/ml IGF‑I protected the neurons (P<0.05), exerting additive effects. Similarly, either the early administration of EP or delayed treatment with IGF‑I protected the neonatal rat brains against HI injury and improved neurological performance and these effects were also additive. This effect may be the result of reduced neuronal injury, and enhanced neurogenesis and maturation. On the whole, our findings demonstrate that the combination of the early administration of EP with delayed treatment with IGF‑I exerts neuroprotective effects against HI injury in neonatal rat brains

Influence of dust on temperature measurement using infrared thermal imager

Temperature measurement by infrared thermal imager is an attractive technique in many fields, and it is of great importance to ensure the measurement accuracy of the infrared thermal imager. Aiming at the influence of dust on the temperature measurement of infrared thermal imager, this paper summarized the dust influence into three categories: dust on the surface of the measured object, dust on the infrared thermal imager’s lens and dust in the optical path between the measured object and the infrared thermal imager, and conducted three dust experiments. To quantify the measurement errors caused by dust, the infrared thermal image features that are affected by dust are extracted and a compensation model is established based on polynomial regression. The results indicate that dust can introduce measurement errors of infrared thermal imager and the proposed compensation method can compensate for the measurement errors caused by dust and improve the accuracy of infrared thermal imager

Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection

In this letter, we propose a novel mmWave beam selection method based on the environment semantics that are extracted from camera images taken at the user side. Specifically, we first define the environment semantics as the spatial distribution of the scatterers that affect the wireless propagation channels and utilize the keypoint detection technique to extract them from the input images. Then, we design a deep neural network with environment semantics as the input that can output the optimal beam pairs at UE and BS. Compared with the existing beam selection approaches that directly use images as the input, the proposed semantic-based method can explicitly obtain the environmental features that account for the propagation of wireless signals, and thus reduce the burden of storage and computation. Simulation results show that the proposed method can precisely estimate the location of the scatterers and outperform the existing image or LIDAR based works

Multi-User Matching and Resource Allocation in Vision Aided Communications

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve $92\%$ transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.Comment: 34 pages, 21 figure

ThIEF: Finding Genome-wide Trajectories of Epigenetics Marks

We address the problem of comparing multiple genome-wide maps representing nucleosome positions or specific histone marks. These maps can originate from the comparative analysis of ChIP-Seq/MNase-Seq/FAIRE-Seq data for different cell types/tissues or multiple time points. The input to the problem is a set of maps, each of which is a list of genomics locations for nucleosomes or histone marks. The output is an alignment of nucleosomes/histone marks across time points (that we call trajectories), allowing small movements and gaps in some of the maps. We present a tool called ThIEF (TrackIng of Epigenetic Features) that can efficiently compute these trajectories. ThIEF comes into two "flavors": ThIEF:Iterative finds the trajectories progressively using bipartite matching, while ThIEF:LP solves a k-partite matching problem on a hyper graph using linear programming. ThIEF:LP is guaranteed to find the optimal solution, but it is slower than ThIEF:Iterative. We demonstrate the utility of ThIEF by providing an example of applications on the analysis of temporal nucleosome maps for the human malaria parasite. As a surprisingly remarkable result, we show that the output of ThIEF can be used to produce a supervised classifier that can accurately predict the position of stable nucleosomes (i.e., nucleosomes present in all time points) and unstable nucleosomes (i.e., present in at most half of the time points) from the primary DNA sequence. To the best of our knowledge, this is the first result on the prediction of the dynamics of nucleosomes solely based on their DNA binding preference. Software is available at https://github.com/ucrbioinfo/ThIEF

Dynamic Gradient Reactivation for Backward Compatible Person Re-identification

We study the backward compatible problem for person re-identification (Re-ID), which aims to constrain the features of an updated new model to be comparable with the existing features from the old model in galleries. Most of the existing works adopt distillation-based methods, which focus on pushing new features to imitate the distribution of the old ones. However, the distillation-based methods are intrinsically sub-optimal since it forces the new feature space to imitate the inferior old feature space. To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features. Different from previous methods, RBCL only pushes the new features to find best-ranking positions in the old feature space instead of strictly alignment, and is in line with the ultimate goal of backward retrieval. However, the sharp sigmoid function used to make the ranking metric differentiable also incurs the gradient vanish issue, therefore stems the ranking refinement during the later period of training. To address this issue, we propose the Dynamic Gradient Reactivation (DGR), which can reactivate the suppressed gradients by adding dynamic computed constant during forward step. To further help targeting the best-ranking positions, we include the Neighbor Context Agents (NCAs) to approximate the entire old feature space during training. Unlike previous works which only test on the in-domain settings, we make the first attempt to introduce the cross-domain settings (including both supervised and unsupervised), which are more meaningful and difficult. The experimental results on all five settings show that the proposed RBCL outperforms previous state-of-the-art methods by large margins under all settings.Comment: Submitted to Pattern Recognition on Dec 06, 2021. Under Revie