Global Britain, Belt and Road Initiative, and New Southbound Policy: Which One Matters to Southeast Asia?

https://www.grips.ac.jp/list/en/facultyinfo/lim_guanie/In anticipation of the impending memberships of China, the UK, and Taiwan in the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), this paper analyses the three economies’ foreign direct investment (FDI) flows entering the region over the last 20 years. Several findings are noteworthy. Firstly, the UK outinvested China and Taiwan between 1995 and 2008. However, its preponderance has been trimmed in the years after the 2008 global financial crisis. Secondly, UK FDI is largely geared towards Singapore and Malaysia, suggesting the resilience of former colonial ties. FDI from China predominantly enters its immediate neighbours (e.g., Laos, Myanmar, and Cambodia) and those sharing sociopolitical similarities with it (i.e., Singapore and Indonesia). Taiwanese firms invested relatively more in Vietnam and the Philippines, which are adjacent to Taiwan. Thirdly, all three FDI donors invested mostly in the tertiary sector. Nevertheless, relative to China, the UK and Taiwan channelled more of their FDI towards manufacturing activities. The findings could provide essential evidence to understand or anticipate which economy will play a more significant role in the region’s political and economic affairs especially when their CPTPP membership is ratified.technical repor

EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points

Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a challenging problem to edit the scenes modeled by NeRF-based methods, especially for dynamic scenes. We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes and even support topological changes. Input with an image sequence from a single camera, our network is trained fully automatically and models topologically varying dynamics using our picked-out surface key points. Then end-users can edit the scene by easily dragging the key points to desired new positions. To achieve this, we propose a scene analysis method to detect and initialize key points by considering the dynamics in the scene, and a weighted key points strategy to model topologically varying dynamics by joint key points and weights optimization. Our method supports intuitive multi-dimensional (up to 3D) editing and can generate novel scenes that are unseen in the input sequence. Experiments demonstrate that our method achieves high-quality editing on various dynamic scenes and outperforms the state-of-the-art. Our code and captured data are available at https://chengwei-zheng.github.io/EditableNeRF/.Comment: Accepted by CVPR 202

Lokalno diskriminantna projekcija difuzije i njena primjena za prepoznavanje emocionalnog stanja iz govornog signala

The existing Diffusion Maps method brings diffusion to data samples by Markov random walk. In this paper, to provide a general solution form of Diffusion Maps, first, we propose the generalized single-graph-diffusion embedding framework on the basis of graph embedding framework. Second, by designing the embedding graph of the framework, an algorithm, namely Locally Discriminant Diffusion Projection (LDDP), is proposed for speech emotion recognition. This algorithm is the projection form of the improved Diffusion Maps, which includes both discriminant information and local information. The linear or kernelized form of LDDP (i.e., LLDDP or KLDDP) is used to achieve the dimensionality reduction of original speech emotion features. We validate the proposed algorithm on two widely used speech emotion databases, EMO-DB and eNTERFACE\u2705. The experimental results show that the proposed LDDP methods, including LLDDP and KLDDP, outperform some other state-of-the-art dimensionality reduction methods which are based on graph embedding or discriminant analysis.Postojeće metode mapiranja difuzije u uzorke podataka primjenjuju Markovljevu slučajnu šetnju. U ovom radu, kako bismo pružili općenito rješenje za mapiranje difuzije, prvo predlažemo generalizirano okruženje za difuziju jednog grafa, zasnovano na okruženju za primjenu grafova. Drugo, konstruirajući ugrađeni graf, predlažemo algoritam lokalno diskriminantne projekcije difuzije (LDDP) za prepoznavanje emocionalnog stanja iz govornog signala. Ovaj algoritam je projekcija poboljšane difuzijske mape koja uključuje diskriminantnu i lokalnu informaciju. Linearna ili jezgrovita formulacija LDDP-a (i.e., LLDDP ili KLDDP) koristi se u svrhu redukcije dimenzionalnosti originalnog skupa značajki za prepoznavanje emocionalnog stanja iz govornog signala. Predloženi algoritam testiran je nad dvama široko korištenim bazama podataka za prepoznavanje emocionalnog stanja iz govornog signala, EMO-DB i eNTERFACE\u2705. Eksperimentalni rezultati pokazuju kako predložena LDDP metoda, uključujući LLDDP i KLDDP, pokazuje bolje ponašanje od nekih drugih najsuvremenijih metoda redukcije dimenzionalnosti, zasnovanim na ugrađenim grafovima ili analizi diskriminantnosti

Relightable and Animatable Neural Avatars from Videos

Lightweight creation of 3D digital avatars is a highly desirable but challenging task. With only sparse videos of a person under unknown illumination, we propose a method to create relightable and animatable neural avatars, which can be used to synthesize photorealistic images of humans under novel viewpoints, body poses, and lighting. The key challenge here is to disentangle the geometry, material of the clothed body, and lighting, which becomes more difficult due to the complex geometry and shadow changes caused by body motions. To solve this ill-posed problem, we propose novel techniques to better model the geometry and shadow changes. For geometry change modeling, we propose an invertible deformation field, which helps to solve the inverse skinning problem and leads to better geometry quality. To model the spatial and temporal varying shading cues, we propose a pose-aware part-wise light visibility network to estimate light occlusion. Extensive experiments on synthetic and real datasets show that our approach reconstructs high-quality geometry and generates realistic shadows under different body poses. Code and data are available at \url{https://wenbin-lin.github.io/RelightableAvatar-page/}.Comment: Accepted by AAAI 202

Who is the Real Hero? Measuring Developer Contribution via Multi-dimensional Data Integration

Proper incentives are important for motivating developers in open-source communities, which is crucial for maintaining the development of open-source software healthy. To provide such incentives, an accurate and objective developer contribution measurement method is needed. However, existing methods rely heavily on manual peer review, lacking objectivity and transparency. The metrics of some automated works about effort estimation use only syntax-level or even text-level information, such as changed lines of code, which lack robustness. Furthermore, some works about identifying core developers provide only a qualitative understanding without a quantitative score or have some project-specific parameters, which makes them not practical in real-world projects. To this end, we propose CValue, a multidimensional information fusion-based approach to measure developer contributions. CValue extracts both syntax and semantic information from the source code changes in four dimensions: modification amount, understandability, inter-function and intra-function impact of modification. It fuses the information to produce the contribution score for each of the commits in the projects. Experimental results show that CValue outperforms other approaches by 19.59% on 10 real-world projects with manually labeled ground truth. We validated and proved that the performance of CValue, which takes 83.39 seconds per commit, is acceptable to be applied in real-world projects. Furthermore, we performed a large-scale experiment on 174 projects and detected 2,282 developers having inflated commits. Of these, 2,050 developers did not make any syntax contribution; and 103 were identified as bots

An Empirical Study of Malicious Code In PyPI Ecosystem

PyPI provides a convenient and accessible package management platform to developers, enabling them to quickly implement specific functions and improve work efficiency. However, the rapid development of the PyPI ecosystem has led to a severe problem of malicious package propagation. Malicious developers disguise malicious packages as normal, posing a significant security risk to end-users. To this end, we conducted an empirical study to understand the characteristics and current state of the malicious code lifecycle in the PyPI ecosystem. We first built an automated data collection framework and collated a multi-source malicious code dataset containing 4,669 malicious package files. We preliminarily classified these malicious code into five categories based on malicious behaviour characteristics. Our research found that over 50% of malicious code exhibits multiple malicious behaviours, with information stealing and command execution being particularly prevalent. In addition, we observed several novel attack vectors and anti-detection techniques. Our analysis revealed that 74.81% of all malicious packages successfully entered end-user projects through source code installation, thereby increasing security risks. A real-world investigation showed that many reported malicious packages persist in PyPI mirror servers globally, with over 72% remaining for an extended period after being discovered. Finally, we sketched a portrait of the malicious code lifecycle in the PyPI ecosystem, effectively reflecting the characteristics of malicious code at different stages. We also present some suggested mitigations to improve the security of the Python open-source ecosystem.Comment: Accepted by the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE2023

Development of a novel vector for cloning and expressing extremely toxic genes in Escherichia coli

Background: Escherichia coli has been widely used as a host to clone and express heterologous genes. However, there are few vectors available for cloning and expressing extremely toxic genes, which limits further basic and applied research on extremely toxic proteins. Results: In this study, a novel vector pAU10 was constructed in E. coli. pAU10 utilizes the combination of the efficient but highly repressible T7-lacO promoter/operator and the strong rrnBT2 transcriptional terminator upstream of the T7 promoter to strictly control unwanted transcription of the extremely toxic gene; in addition, the trp promoter/operator is oriented opposite to the T7 promoter to control the production of the antisense RNA that may block the translation of leaky mRNA. Without the supplementation of IPTG and L-tryptophan in the culture medium, transcription of the extremely toxic gene by the T7 promoter is highly repressed, and the trp promoter produces the antisense RNA, which strictly prevents unwanted expression of the extremely toxic protein in E. coli. With the supplementation of IPTG and L-tryptophan, the T7 promoter efficiently transcribes the extremely toxic gene, and the trp promoter does not produce the antisense RNA, ensuring efficient expression of the extremely toxic protein in E. coli. Tight regulation and efficiency of expression of an extremely toxic gene cloned in the vector pAU10 were confirmed by cloning and expressing the restriction endonuclease-encoding gene bamHI without its corresponding methylase gene in E. coli JM109(DE3). Conclusion: pAU10 is a good vector used for cloning and expressing extremely toxic genes in E. coli

Investigation on coming out phenomenon of the shaft from the sleeve by 2-D plate model approach

The ceramics roller can to be used in the heating furnace because of high temperature resistance. The roller consists of ceramics sleeve and steel shaft connected by shrink fitting. Since ceramics is brittle, it should be noted that only low shrink fitting ratio can be applied for the connection. Therefore, coming out of the shaft from the sleeve during rotation should be investigated in this study. In this study, the finite element analysis is applied to simulate this phenomenon. In the previous study, mechanism of coming out has been considered by using 3-D model. However, since 3-D model analysis needs large computational time, only small number of cycle can be considered, and therefore, the coming out phenomenon cannot be predicted easily. In this research, the 2-D plate model approach is proposed in order to reduce computational time, considering the upper and lower alternate load, repeatedly. Then, the effects of the magnitude of the load and shrink fitting ratio are investigated systematically. Finally, the simulation of the coming out phenomenon can be carried out for much larger number of cycles

SNP discovery by high-throughput sequencing in soybean

<p>Abstract</p> <p>Background</p> <p>With the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies (Roche GS/454, Illumina GA/Solexa, and ABI/SOLiD), have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology.</p> <p>Results</p> <p>A total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp.</p> <p>Conclusions</p> <p>We have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops.</p