Serum Starvation Induced Cell Cycle Synchronization Facilitates Human Somatic Cells Reprogramming

Human induced pluripotent stem cells (iPSCs) provide a valuable model for regenerative medicine and human disease research. To date, however, the reprogramming efficiency of human adult cells is still low. Recent studies have revealed that cell cycle is a key parameter driving epigenetic reprogramming to pluripotency. As is well known, retroviruses such as the Moloney murine leukemia virus (MoMLV) require cell division to integrate into the host genome and replicate, whereas the target primary cells for reprogramming are a mixture of several cell types with different cell cycle rhythms. Whether cell cycle synchronization has potential effect on retrovirus induced reprogramming has not been detailed. In this study, utilizing transient serum starvation induced synchronization, we demonstrated that starvation generated a reversible cell cycle arrest and synchronously progressed through G2/M phase after release, substantially improving retroviral infection efficiency. Interestingly, synchronized human dermal fibroblasts (HDF) and adipose stem cells (ASC) exhibited more homogenous epithelial morphology than normal FBS control after infection, and the expression of epithelial markers such as E-cadherin and Epcam were strongly activated. Futhermore, synchronization treatment ultimately improved Nanog positive clones, achieved a 15–20 fold increase. These results suggested that cell cycle synchronization promotes the mesenchymal to epithelial transition (MET) and facilitates retrovirus mediated reprogramming. Our study, utilization of serum starvation rather than additional chemicals, provide a new insight into cell cycle regulation and induced reprogramming of human cells

Towards Ultra-Efficient Machine Learning for Edge Inference

Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, such as mobile phones, drones, robots and wearables. To run DNN inference directly on edge devices (a.k.a. edge inference) with a satisfactory performance, optimizing the DNN design (e.g., neural architecture and quantization policy) is crucial. However, designing an optimal DNN for even a single edge device often needs repeated de- sign iterations and is non-trivial. Worse yet, DNN model developers commonly need to serve extremely diverse edge devices. Therefore, it has become crucially important to scale up the optimization of DNNs for edge inference using automated approaches. In this dissertation, we come up with several solutions to scalably and efficiently optimize the DNN design for diverse edge devices, with increasingly flexible design consideration. Firstly, consider the fact that a large number of diverse DNN models can be generated by navigating through the design space in terms of different neural architectures and compression techniques, we look into the problem that how to select the best DNN model out of many choices for each individual edge device. We propose a novel automated and user-centric DNN selection engine, called Aquaman, which leverages users’ Quality of Experience (QoE) feedback to guide DNN selection decisions. The core of Aquaman is a machine learning-based QoE predictor which is continuously updated online, and neural bandit learning to balance exploitation and exploration. However, the assumption of a pre-existing DNN model pool in Aquaman is essentially limited and may not suit any given edge device’s best interest. Therefore, we take into consideration the design freedom of neural architectures by resorting to hardware-aware neural architecture search (NAS) for optimizing the DNN design for a given target device. NAS can thoroughly explore the model architecture search space, and automatically discover the optimal combination of building blocks, namely a model, for any target device. A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures. While building a latency predictor for each target device has been commonly used in state of the art, this is a very time-consuming process, lacking scalability in the presence of extremely diverse devices. We address the scalability challenge by exploiting latency monotonicity – the architecture latency rankings on different devices are often correlated. When strong latency monotonicity exists, we can re-use architectures searched for one proxy device on new target devices, without losing optimality. In the absence of strong latency monotonicity, we also propose an efficient proxy adaptation technique to significantly boost the latency monotonicity. Our results highlight that, by using just one proxy device, we can find almost the same Pareto-optimal architectures as the existing per-device NAS, while avoiding the prohibitive cost of building a latency predictor for each device, reducing the cost of hardware-aware NAS from O(N) to O(1). Further, besides the design flexibility of neural architectures brought by NAS (i.e. software design), exploring the hardware design space such as optimizing hardware accelerators built on FPGA or ASIC, as well as the corresponding dataflows (e.g., scheduling DNN computations and mapping them on hardware), is also critical for speeding up DNN execution. While hardware-software co-design can further optimize DNN performance, it also exponentially enlarges the search space to practically infinity, presenting significant challenges. By settling in-between the fully-decoupled approach and the fully-coupled hardware-software co-design approach, we propose a new semi-decoupled approach to reduce the size of the total co-search space by orders of magnitude, yet without losing design optimality. Our approach again builds on the latency and energy monotonicity – neural architectures’ ranking orders in terms of inference latency and energy consumption on different accelerators are highly correlated. Our results confirm that strong latency and energy monotonicity exist among different accelerator designs. More importantly, by using one candidate accelerator as the proxy and obtaining its small set of optimal architectures, we can reuse the same architecture set for other accelerator candidates during the hardware search stage

Marketing and Price strategies for China Telecom Company : a case study of differences between broadband price and area in China

Purpose: The purpose of this dissertation is to explore the relationship between the cities’ GDP and prices. We would like to find out the differences between China Telecom’s broadband prices and areas. If there are differences, are those differences considered from the cities’ GDP? The outcome of this dissertation will provide information about new project price of China Telecom after Network three in one. Design/methodology/approach: The study deals with the relationship between China Telecom Company telecommunication prices and areas. This refers to the use of China Telecom Company’s broadband prices and the information from three different areas. The hypotheses are tested with survey data from three different areas in China. Findings: The results show that the cities’ GDP and the price of China Telecom service are related. Originality/value: This thesis will explore the influencing factors the price in telecommunication industry

A clipping algorithm for real-scene 3D models

The development of unmanned aerial vehicle (UAV) oblique photogrammetric technology provides a good foundation for the rapid construction of large-scale and high-definition real-scene 3D models. However, due to the limitations of the modeling process, irrelevant feature data cannot be eliminated in the modeling stage. The built models contain irrelevant features and model distortions caused by errors. At present, most existing clipping algorithms cannot effectively clip real-scene 3D models that are organized as a whole or with levels of detail (LODs). Therefore, this paper proposes a novel algorithm for clipping real-scene 3D models from any perspective based on clipping boundary lines that fit the surfaces of the models. The results of the clipping experiments for 3D models constructed with oblique UAV images show that this algorithm can effectively clip any part of the 3D models, that the clipping results of each level model closely fit the corresponding clipping boundary lines, and that the accuracy of the clipping results is very high. Additionally, the time complexity of the algorithm is O(n2). In conclusion, the algorithm proposed in this paper provides correct and effective clipping results for real-scene 3D models with LODs that are constructed with photogrammetric or 3D laser scanning data

Research on A Collaborative Control Strategy of An Urban Expressway Merging Bottleneck Area

The conflict between the mainline and incoming traffic flow in the merging area of an urban expressway makes it easier to form a traffic bottleneck than the basic road section. When the merging bottleneck occurs, the overall efficiency is affected. This paper establishes an improved Cell Transmission Model (CTM) using Genetic Algorithms (GA) and Mean Absolute Percentage Error (MAPE) for parameter calibration and validation. Based on the joint optimization goal of efficiency and safety, a collaborative control strategy is established. The strategy is verified by VISSIM. The results show that the total travel time is reduced by 7.34%, and the total turnover is increased by 6.06% by applying the collaborative control strategy during the peak period. Therefore, the cooperative control strategy of the merging bottleneck proposed can improve the traffic state at the merging bottleneck and improve the efficiency and safety level of the expressway

Research on A Collaborative Control Strategy of An Urban Expressway Merging Bottleneck Area

The conflict between the mainline and incoming traffic flow in the merging area of an urban expressway makes it easier to form a traffic bottleneck than the basic road section. When the merging bottleneck occurs, the overall efficiency is affected. This paper establishes an improved Cell Transmission Model (CTM) using Genetic Algorithms (GA) and Mean Absolute Percentage Error (MAPE) for parameter calibration and validation. Based on the joint optimization goal of efficiency and safety, a collaborative control strategy is established. The strategy is verified by VISSIM. The results show that the total travel time is reduced by 7.34%, and the total turnover is increased by 6.06% by applying the collaborative control strategy during the peak period. Therefore, the cooperative control strategy of the merging bottleneck proposed can improve the traffic state at the merging bottleneck and improve the efficiency and safety level of the expressway