FDI, Market Structure and R&D Investments in China

FDI can be an important channel for developing countries’ ability to get access to new technology. The impact of FDI on domestically-owned firms’ technology development is less examined but it is frequently argued that technology externalities or demonstration effects could have a positive impact. Another and so far little examined effect of FDI on technology development in domestically-owned firms is through the impact on competition. We examine the effect of FDI on competition in the Chinese manufacturing sector and the effect of competition on firms’ R&D. Our analysis is conducted on a large dataset including all Chinese large and medium sized firms over the period 1998-2004. Our results show that FDI increases competition but there are no strong indications of competition affecting investments in R&D.China; FDI; Competition; R&D

Technology Development and Job Creation in China

This paper examines how Science and Technology (S&T) contribute to job creation in the Chinese manufacturing sector. The ambition of transforming China into an innovation-oriented nation and the emphasis on indigenous innovation capacity building have placed Science and Technology (S&T) high on the Chinese policy agenda. At the same time, the need for job creation is pressing, both to absorb the huge supply of underemployed people, and to enable the annual 20 million new labor market entrants to find employment. We examine the relationship between S&T and job growth in the Chinese industrial sector. S&T can be expected to have both positive and negative effects on employment. For instance, new technology might increase competitiveness and enable Chinese firms to expand their labor force. On the other hand, new technology might be labor-saving, thereby enabling Chinese firms to produce more output with fewer employees. Based on a large sample of manufacturing firms in China between 1998 and 2004, we analyze how S&T affect employment growth. Our results suggest that S&T activities have no effect on job creation.China; Science and Technology; Job-Creation

Introducing Model-based Design Methodology with LabVIEW to Teaching ARM-based Embedded System Design

This paper presents our latest experience of introducing the new topic of model-based design (MBD) concepts and tools to a Programming Tools (PT) course for educating students to be capable of utilizing modern tools for correctly developing complicated ARM-based embedded systems. It describes the course contents, student outcomes and lecture and lab preparation for teaching this topic with the emphasis on two sub-topics. Firstly, we present the details of using NI LabVIEW tool in programming ARM Cortex-M MCUs or ARM Cortex-A9 MCUs on the embedded device like NI myRIO for fast developing embedded applications. Secondly, to integrate an on-going research effort on the model-based verification into this course, we also introduce model-checking and the tools that have been utilized in the research project. This new topic helps introducing students the latest research advances which promote the wide applications of the MBD in safety-critical embedded applications. Our primary experience shows that the project-based learning approach with the graphical programming tools and selected MCUs is efficient and practical to teach the MBD of 32-bit MCUs programming

Experience of Teaching Advanced Touch Sensing Technologies

A touchscreen is an electronic visual display that users can control by touching the screen with a special stylus or fingers. It allows the rapid, accurate and direct interaction by the user with display contents, which existing keyboard and mouse systems cannot. Touchscreens are popular in many information appliances such as tablet computers, smartphones, and personal digital assistants (PDAs). In fact, many display manufactures and chip vendors around the world, such as Samsung, Chimei, Atmel, ST Microelectronics, and Texas Instruments, have recognized the trend of using touchscreens as a highly desirable user interface component and started to integrate the touch-sensing technology into their products. There are many touch sensing technologies. Among them, analog resistive, surface capacitive, projected capacitive, infrared grid, optical imaging, and surface acoustic wave are the most important ones. We feel the importance and need to teach engineering students the touch sensing technology. This paper presents our experience of teaching touch sensing technology in our second microprocessor course. This course taught the touch-sensing technology in slightly over 5 weeks. As the start of the course, we introduced the working principles of each touch technology to students. We then conducted a comparison among these technologies and their applications in real-world electronic devices. Students were taught to program a touchscreen through a series of lab exercises. The Atmel SAM 4S-EK board was the main development board employed in the course for practicing touchscreen programming. This board includes four QTouch buttons and slides which utilize capacitive sensing technology, and a resistive touch panel on a color LCD display. Atmel provides a royalty free software library for developing touch applications in C. Students learned to link the library into their applications so as to provide touch sensing capability in their projects. During the course our students have shown great interests in touch sensing technologies and are capable of incorporating touch devices to improve the human machine interface of their capstone projects

The Use of BeagleBone Black Board in Engineering Design and Development

The BeagleBone Black (BBB) board is a low cost, powerful expandable computer launched by a community of developers sponsored by Texas Instruments in the early 2013. It is the newest product in the Beagle family. This board features a powerful TI Sitara™ ARM Cortex™-A8 processor which runs at 1 GHz. And a 2 GB on-board flash memory acts as the “hard drive” for the board to host a Linux operating system and other software development tools. The size of the board is small enough to fit in a mint tin box. It can be used for a variety of projects from high school fair projects to prototypes of very complex embedded systems. With a user-friendly, browser-based Bonescript programming environment called Cloud9, a learner can easily program the BBB board to rapidly prototype electronic systems that interface with real-world applications. Afterwards, as the knowledge of users develops, the board provides more complicated interfaces including C/C++ functions to access digital and analog pins aboard the ARM Cortex A8 microprocessor. The full power and capability of the BBB board may be programmed in the underlying onboard Linux operating system, such as Angstrom or Ubuntu. Moreover, the Beagle community provides a useful repository of example projects, forums and hardware/software documentation. This paper presents our work of employing the BBB board in designing engineering senior projects, and uses a case study of robot car with voice recognition senior project to compares it with Raspberry Pi and Arduino in educating engineering students to construct embedded systems. Our primary experiences demonstrate that the BBB board is an easy-to-use and cost-effective development kit which can be employed by college-level engineering students for their capstone design projects

Diff-Privacy: Diffusion-based Face Privacy Protection

Privacy protection has become a top priority as the proliferation of AI techniques has led to widespread collection and misuse of personal data. Anonymization and visual identity information hiding are two important facial privacy protection tasks that aim to remove identification characteristics from facial images at the human perception level. However, they have a significant difference in that the former aims to prevent the machine from recognizing correctly, while the latter needs to ensure the accuracy of machine recognition. Therefore, it is difficult to train a model to complete these two tasks simultaneously. In this paper, we unify the task of anonymization and visual identity information hiding and propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding. Extensive experiments have been conducted to validate the effectiveness of our proposed framework in protecting facial privacy.Comment: 17page

All-to-key Attention for Arbitrary Style Transfer

Attention-based arbitrary style transfer studies have shown promising performance in synthesizing vivid local style details. They typically use the all-to-all attention mechanism -- each position of content features is fully matched to all positions of style features. However, all-to-all attention tends to generate distorted style patterns and has quadratic complexity, limiting the effectiveness and efficiency of arbitrary style transfer. In this paper, we propose a novel all-to-key attention mechanism -- each position of content features is matched to stable key positions of style features -- that is more in line with the characteristics of style transfer. Specifically, it integrates two newly proposed attention forms: distributed and progressive attention. Distributed attention assigns attention to key style representations that depict the style distribution of local regions; Progressive attention pays attention from coarse-grained regions to fine-grained key positions. The resultant module, dubbed StyA2K, shows extraordinary performance in preserving the semantic structure and rendering consistent style patterns. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate the superior performance of our approach

The Estimation of Nodal Power Supply Reliability through the Network Connectivity by Complex Network Method

The paper studies the reliability of the power system from the perspective of node loads. The reliability of the whole system can be estimated by evaluating the power supply reliability of each node. A measure, connectivity observed at load node (Ci), is proposed. Ci is calculated through a recursion equation by evaluating the generation capacity that can be transferred from the further neighbor to the nearest neighbor of load node i. IEEE-30 bus system is taken as a test system. We calculated the index of 7 load nodes at 2 different load levels with different N-1 failures. The test results show that the variation of the index and that of the percentage load shedding at selected load nodes show good consistency

Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID

Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing works mainly focus on alleviating the modality gap by aligning instance-level features of the unlabeled samples. However, the relationships between cross-modality clusters are not well explored. To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Specifically, we design a Many-to-many Bilateral Cross-Modality Cluster Matching (MBCCM) algorithm through optimizing the maximum matching problem in a bipartite graph. Then, the matched pairwise clusters utilize shared visible and infrared pseudo-labels during the model training. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Meanwhile, the cross-modality Consistency Constraint (CC) is proposed to explicitly reduce the large modality discrepancy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing state-of-the-art approaches by a large margin of 8.76% mAP on average

TransFA: Transformer-based Representation for Face Attribute Evaluation

Face attribute evaluation plays an important role in video surveillance and face analysis. Although methods based on convolution neural networks have made great progress, they inevitably only deal with one local neighborhood with convolutions at a time. Besides, existing methods mostly regard face attribute evaluation as the individual multi-label classification task, ignoring the inherent relationship between semantic attributes and face identity information. In this paper, we propose a novel \textbf{trans}former-based representation for \textbf{f}ace \textbf{a}ttribute evaluation method (\textbf{TransFA}), which could effectively enhance the attribute discriminative representation learning in the context of attention mechanism. The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning. Specially, the hierarchical identity-constraint attribute loss is designed to train the end-to-end architecture, which could further integrate face identity discriminative information to boost performance. Experimental results on multiple face attribute benchmarks demonstrate that the proposed TransFA achieves superior performances compared with state-of-the-art methods