Foreign Direct Investment Inequality (FDI) and Convergence of Growth: Evidence from Yangtze River Delta

The Yangtze River Delta (YRD) Economic Circle has experienced a long period of development. From the original Shanghai Economic Zone in 1983, altogether 10 cities encompassing Suzhou, Wuxi, Changzhou, Nantong and Hangzhou, Jiaxing, Huzhou, Ningbo and Shaoxing surrounding the Shanghai Core, to the final stage in 2003 when the latest leaguer, Taizhou of Zhejiang Province joined the family, the YRD has been experiencing a long time of expansion. Focusing on the aggregation, see Table 1, the YRD attracted almost half the FDI in the nationwide scale, over 1/3 export and import, around 6 percent of the fixed investment and produced nearly 1/5 of the GDP. We should say this was, and is a miracle in the river of regional economic growth

Multi-Agent Combinatorial Path Finding with Heterogeneous Task Duration

Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for multiple agents from their initial locations to destinations, visiting a set of intermediate target locations in the middle of the paths, while minimizing the sum of arrival times. While a few approaches have been developed to handle MCPF, most of them simply direct the agent to visit the targets without considering the task duration, i.e., the amount of time needed for an agent to execute the task (such as picking an item) at a target location. MCPF is NP-hard to solve to optimality, and the inclusion of task duration further complicates the problem. This paper investigates heterogeneous task duration, where the duration can be different with respect to both the agents and targets. We develop two methods, where the first method post-processes the paths planned by any MCPF planner to include the task duration and has no solution optimality guarantee; and the second method considers task duration during planning and is able to ensure solution optimality. The numerical and simulation results show that our methods can handle up to 20 agents and 50 targets in the presence of task duration, and can execute the paths subject to robot motion disturbance

A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems

Resistive (or memristive) switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses) with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i) the natural variability of set/reset processes in the nanoscale switch, and (ii) the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns

A Study of Protein Expression and Enzyme Selectivity Using Unnatural Amino Acids

A study of substrate selectivity of aminoacyl-tRNA synthetases, from both bacterial and eukaryotic organisms, using unnatural amino acids is described and discussed in this thesis. The first part of this thesis involved an investigation of the substrate selectivity of bacterial (E. coli) aminoacyl-tRNA synthetases, by protein synthesis using unnatural amino acids via bacterial (E. coli) wild-type machinery. The levels of unnatural amino acid incorporation obtained are discussed in relation to the substrate selectivity of aminoacyl-tRNA synthetases. This work clearly shows that the E. coli TrpRS (Chapter One), the E. coli PheRS (Chapter Two) and the E. coli TyrRS (Chapter Three) display some degree of substrate promiscuity. An HPLC technique has been used to determine the relative rate of unnatural amino acid activation by E. coli TyrRS for a series of meta- and ortho-substituted tyrosines. This work, which is discussed in Chapter Four, shows that the reaction rate of aminoacylation is significantly affected by the substituent on the aromatic ring of tyrosine. The other part involved a study of the substrate selectivity of eukaryotic aminoacyl-tRNA synthetase. Although the original work, which was conducted to investigate the substrate selectivity of eukaryotic PheRS and TyrRS by incorporating the unnatural amino acids into protein with a eukaryotic protein synthesis system and is discussed in Chapter Five, had been unsuccessful, an investigation of the human TyrRS with a series of meta- and ortho-substituted tyrosines using the ITC technique, which is discussed in Chapter Six, shows some degree of substrate promiscuity of human TyrRS. In addition, a selectivity comparison made between the bacterial (E. coli) and the eukaryotic (human) TyrRSs, which is also discussed in Chapter Six, shows that there is difference between these two enzymes regarding substrate selectivity, resulting in the discovery of two organic compounds that could potentially be developed as antimicrobial agents

Improving Top- N

Recommender systems become increasingly significant in solving the information explosion problem. Data sparse is a main challenge in this area. Massive unrated items constitute missing data with only a few observed ratings. Most studies consider missing data as unknown information and only use observed data to learn models and generate recommendations. However, data are missing not at random. Part of missing data is due to the fact that users choose not to rate them. This part of missing data is negative examples of user preferences. Utilizing this information is expected to leverage the performance of recommendation algorithms. Unfortunately, negative examples are mixed with unlabeled positive examples in missing data, and they are hard to be distinguished. In this paper, we propose three schemes to utilize the negative examples in missing data. The schemes are then adapted with SVD++, which is a state-of-the-art matrix factorization recommendation approach, to generate recommendations. Experimental results on two real datasets show that our proposed approaches gain better top-N performance than the baseline ones on both accuracy and diversity

An Enhanced IEEE1588 Clock Synchronization for Link Delays Based on a System-on-Chip Platform

The clock synchronization is considered as a key technology in the time-sensitive networking (TSN) of 5G fronthaul. This paper proposes a clock synchronization enhancement method to optimize the link delays, in order to improve synchronization accuracy. First, all the synchronization dates are filtered twice to get the good calculation results in the processor, and then FPGA adjust the timer on the slave side to complete clock synchronization. This method is implemented by Xilinx Zynq UltraScale+ MPSoC (multiprocessor system-on-chip), using FPGA+ARM software and hardware co-design platform. The master and slave output Pulse Per-Second (PPS) signals. The synchronization accuracy was evaluated by measuring the time offset between PPS signals. Contraposing the TSN, this paper compares the performance of the proposed scheme with some previous methods to show the efficacy of the proposed work. The results show that the slave clock of proposed method is synchronized with the master clock, leading to better robustness and significant improvement in accuracy, with time offset within the range of 40 nanoseconds. This method can be applied to the time synchronization of the 5G open fronthaul network and meets some special service needs in 5G communication

Restoration and Management of Healthy Wetland Ecosystems

fi=vertaisarvioitu|en=peerReviewed

The growth and shrinkage of water droplets at the oil-solid interface.

HYPOTHESIS: The mechanism for the spontaneous formation of water droplets at oil/solid interfaces immersed in water is currently unclear. We hypothesize that growth and shrinkage of droplets are kinetically controlled by diffusion of water through the oil, driven by differences in chemical potential between the solid substrate and the aqueous reservoir. EXPERIMENTS: The formation, growth and shrinkage of water droplets at an immersed oil/solid interface are investigated theoretically and experimentally with three silicone oils. The surface is hydrophobic and the droplets formed are truncated spheres with radius, a, less than 10 μm. The expansion and contraction of the droplets can be controlled by adjusting the difference in chemical potential. The growth kinetics are modelled in terms of water migration through the oil layer which predicts a2∝t. FINDINGS: This is the first study of possible mechanisms for the formation of such interfacial droplets. Several possible causes are shown to be unfavourable, negligible, or are eliminated by careful experiments controlling key parameters (such as oil viscosity, substrate chemistry). The rate constant for mass transport is proportional to difference in chemical potential and an estimate shows dissociation of surface groups on the substrate provides a driving chemical potential of the right magnitude.National Natural Science Foundation of China (21872078

Adaptive Policy with Wait-kk Model for Simultaneous Translation

Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-$k$ policy coupled with a standalone wait-$k$ translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-$k$ model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-$k$ model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.Comment: Accept to EMNLP 2023 main conference. 17 pages, 12 figures, 5 table