Essays On Economic Growth And The Economics Of Innovation

In my dissertation, I study how legal institutions and financial system affect innovation and their impact on economic growth. This dissertation consists of two chapters. The themes of chapter 1 and 2 are intellectual property rights and the venture capital system, respectively. Chapter 1 studies the impact of intellectual property rights on the business scope of firms. Stronger intellectual property rights induce specialization and contribute to economic growth. In the United States, a sweeping legal reform in 1982 created a more pro-patent legal environment. This legal reform fostered specialization and enhanced firm performance. Around the world, countries experience faster economic growth when their innovating sectors are characterized by a higher level of specialization. An endogenous growth model with endogenous firm boundaries is developed to disentangle the relationship between legal institutions, firm boundary decisions, and economic growth. I characterize the optimal strength of patent rights and evaluate the actual patent law enforcement in the United States. The pro-patent legal reform in 1982 was welfare-enhancing, but it was too extreme. Swinging back the legal pendulum and weakening patent rights can improve welfare. Chapter 2 evaluates the contribution of venture capital (VC) to promoting entrepreneurship and spawning innovation. We assemble the stylized facts of venture capital, innovation, and economic growth. Funding by venture capitalists is positively associated with patenting activity. VC-backed firms have higher IPO values when they are floated. Following flotation, they have higher R&D-to-sales ratios and grow faster in terms of employment and sales. At the country level, VC investment is positively linked with economic growth. The relationship between venture capital and growth is examined using an endogenous growth model incorporating dynamic contracts between entrepreneurs and venture capitalists. The model is matched with stylized facts about venture capital; viz., statistics by funding round concerning the success rate, failure rate, investment rate, equity shares, and the value of an IPO. We examine how the innovative activity is affected by the capital gains tax rate. Raising capital gains taxation reduces growth and welfare

RISK TRANSMISSION AND CONTROL OF PORT-HINTERLAND SERVICE NETWORK: FROM THE PERSPECTIVE OF PREVENTIVE INVESTMENT AND GOVERNMENT SUBSIDIES

The increase in risk prevention investments in the port-hinterland service network (PHSN) effectively enhances the network’s ability to resist risks and improve the sustainability and stability of ocean transportation. Based on the construction of the PHSN risk prevention investment utility model, the equilibrium strategy, the related characteristics of each participant in the complementary networks and the complete network are analyzed. Similarly, the subsidy policy of the government under the utility maximization of the whole service network is studied. We further propose new types of subsidy strategies based on the key nodes and key groups given the resources available and the subsidy efficiency constraints imposed, while also validating the advantages of this method based on a case analysis. The results indicate that the (1) equilibrium risk prevention investment is closely related to the Katz-Bonacich centrality, network interaction intensity, cost of unit risk prevention investment and competition intensity; (2) an undifferentiated subsidy strategy cannot improve the risk prevention effectiveness of the whole network; (3) the subsidy strategy based on key nodes and key groups effectively improves the risk prevention efficiency; and (4) the subsidy strategy of key groups is superior to the subsidy strategy of key nodes. Accordingly, the results of this study provide a reference for participants and managers in the PHSN when making risk prevention investment decisions

Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds

This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. Compared with cameras and lidars, millimeter-wave radars have the advantage of not revealing privacy, having a strong anti-interference ability, and having long detection distance. The sparsity and capturing temporal-topological features of mmWave data is still a problem. However, the issue of capturing the temporal-topological coupling features under the human semantic segmentation task prevents previous advanced segmentation methods (e.g PointNet, PointCNN, Point Transformer) from being well utilized in practical scenarios. To address the challenge caused by the sparsity and temporal-topological feature of the data, we (i) introduce graph structure and topological features to the point cloud, (ii) propose a semantic segmentation framework including a global feature-extracting module and a sequential feature-extracting module. In addition, we design an efficient and more fitting loss function for a better training process and segmentation results based on graph clustering. Experimentally, we deploy representative semantic segmentation algorithms (Transformer, GCNN, etc.) on a custom dataset. Experimental results indicate that our model achieves mean accuracy on the custom dataset by $\mathbf{82.31}\%$ and outperforms the state-of-the-art algorithms. Moreover, to validate the model's robustness, we deploy our model on the well-known S3DIS dataset. On the S3DIS dataset, our model achieves mean accuracy by $\mathbf{92.6}\%$, outperforming baseline algorithms

A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge

A vector database is used to store high-dimensional data that cannot be characterized by traditional DBMS. Although there are not many articles describing existing or introducing new vector database architectures, the approximate nearest neighbor search problem behind vector databases has been studied for a long time, and considerable related algorithmic articles can be found in the literature. This article attempts to comprehensively review relevant algorithms to provide a general understanding of this booming research area. The basis of our framework categorises these studies by the approach of solving ANNS problem, respectively hash-based, tree-based, graph-based and quantization-based approaches. Then we present an overview of existing challenges for vector databases. Lastly, we sketch how vector databases can be combined with large language models and provide new possibilities

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is determined by dice rolling. Visit our website http://zhuhao.me/fewre

Review of Methods Used for Microalgal Lipid-Content Analysis

AbstractThis paper provides a brief overview of most recent strategies that used to analyze microalgal lipid content, including NIR spectroscopy and TD-NMR methods etc. Common methods like gravimetric quantification and staining quantification are also introduced in this report. The physiology background of microalgal lipid accumulation is stated in order to clarify the purpose of each individual analytical method. After all, online lipid content measurement method that has good accuracy has the best chance to be generalized for all the lipid analyzing researches

Chiral Dirac-like fermion in spin-orbit-free antiferromagnetic semimetals

Dirac semimetal is a phase of matter, whose elementary excitation is described by the relativistic Dirac equation. In the limit of zero mass, its parity-time symmetry enforces the Dirac fermion in the momentum space, which is composed of two Weyl fermions with opposite chirality, to be non-chiral. Inspired by the flavor symmetry in particle physics, we theoretically propose a massless Dirac-like equation yet linking two Weyl fields with the identical chirality by assuming SU(2) isospin symmetry, independent of the space-time rotation exchanging the two fields. Dramatically, such symmetry is hidden in certain solid-state spin-1/2 systems with negligible spin-orbit coupling, where the spin degree of freedom is decoupled with the lattice. Therefore, the existence of the corresponding quasiparticle, dubbed as flavor Weyl fermion, cannot be explained by the conventional (magnetic) space group framework. The four-fold degenerate flavor Weyl fermion manifests linear dispersion and a Chern number of 2, leading to a robust network of topologically protected Fermi arcs throughout the Brillouin zone. For material realization, we show that the transition-metal chalcogenide CoNb3S6 with experimentally confirmed collinear antiferromagnetic order is ideal for flavor Weyl semimetal under the approximation of vanishing spin-orbit coupling. Our work reveals a counterpart of the flavor symmetry in magnetic electronic systems, leading to further possibilities of emergent phenomena in quantum materials.Comment: 27 pages and 5 figure

Estimating global cropland production from 1961 to 2010

Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17–45 % to the increase in global atmospheric CO2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation–Global Atmosphere–Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr−1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr−1 (∼  45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history

Experimental demonstration of RGB LED-based optical camera communications

Red, green, and blue (RGB) light-emitting diodes (LEDs) are widely used in everyday illumination, particularly where color-changing lighting is required. On the other hand, digital cameras with color filter arrays over image sensors have been also extensively integrated in smart devices. Therefore, optical camera communications (OCC) using RGB LEDs and color cameras is a promising candidate for cost-effective parallel visible light communications (VLC). In this paper, a single RGB LED-based OCC system utilizing a combination of undersampled phase-shift on off keying (UPSOOK), wavelength-division multiplexing (WDM), and multiple-input multiple-output (MIMO) techniques is designed, which offers higher space efficiency (3 bits/Hz/LED), long-distance, and nonflickering VLC data transmission. A proof-of-concept test bed is developed to assess the bit-error-rate performance of the proposed OCC system. The experimental results show that the proposed system using a single commercially available RGB LED and a standard 50-frame/s camera is able to achieve a data rate of 150 bits/s over a range of up to 60 m