Degree Matters: The Impact of a Leader’s Foreign Education on His Country’s Economic Development

I analyze the correlation between a nation leader’s foreign education experience and their nation’s GDP growth and economic freedom in African, Asian, and South American countries. There is a statistically significant correlation between a leader’s foreign education and the country’s GDP growth rate, especially in Africa. Data also shows that a leader’s foreign education is positively correlated with his country’s economic freedom. Despite the fact that the regressions can only demonstrate correlation as opposed to causation relationships among variables, further analysis of the results concludes that a leader’s education and the country’s development are reciprocal. The findings of this paper shine light on future policy directions for developing countries

Over expression of Zmda1-1 gene increases seed mass of corn

Genetic engineering of seed size and increasing biomass in crop plants has an important significant contribution to the world. Arabidopsis DA1 is one of the key factors that negatively control seed and organ size by restricting the period of cell proliferation, and the mutant of Arabidopsis DA1, da1-1 (DA1R358K) can dramatically increase the size of seed. However, it is not clear whether overexpression of Zmda1-1, the mutant of ZmDA1 which is homology of DA1 in Arabidopsis, has the same biological effect as da1-1 in Arabidopsis. Therefore, in this study, the plant expression vector harboring both Zmda1-1 driven by the corn ubiquitin promoter and a PAT selectable marker gene driven by 35S CAMV promoter was constructed and introduced into maize inbred line ‘ji444’ using pollen-tube-pathway method. Screened with herbicide phosphinothricin (PPT), 22 seedlings of 2563 transformed samples survived, and 21 independence lines of which were positive in polymerase chain reaction (PCR) analysis, and the transformation rate of T0 generation was about 0.82%. Further PCR-southern blotting results proved that the Zmda1-1 had integrated into maize genome, and the Zmda1-1 had expression in low level by reverse transcription-polymerase chain reaction (RT-PCR) analysis. The seed mass of transgenic maize increased at an average of 33.6% of empty vector control lines, and the harvest yield was increased by 23.6 to 114.1% in different lines than empty vector control lines. The result suggests that Zmda1-1 can be used to engineer higher harvest yield in crops plant, thus providing the first successful example of increasing the harvest yield of maize by transgenic technology.Key words: Transgenic maize, pollen-tube pathway, Zmda1-1, seed mass

AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing

With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensive attention. And these applications necessitate the mobile edge computing scheme, i.e., offloading partial computation from mobile devices to edge devices for inference acceleration and transmission load reduction. The current practices have relied on collaborative DNN partition and offloading to satisfy the predefined latency requirements, which is intractable to adapt to the dynamic deployment context at runtime. AdaMEC, a context-adaptive and dynamically-combinable DNN deployment framework is proposed to meet these requirements for mobile edge computing, which consists of three novel techniques. First, once-for-all DNN pre-partition divides DNN at the primitive operator level and stores partitioned modules into executable files, defined as pre-partitioned DNN atoms. Second, context-adaptive DNN atom combination and offloading introduces a graph-based decision algorithm to quickly search the suitable combination of atoms and adaptively make the offloading plan under dynamic deployment contexts. Third, runtime latency predictor provides timely latency feedback for DNN deployment considering both DNN configurations and dynamic contexts. Extensive experiments demonstrate that AdaMEC outperforms state-of-the-art baselines in terms of latency reduction by up to 62.14% and average memory saving by 55.21%

Complete Genome Sequence of Clostridium estertheticum DSM 8809, a Microbe Identified in Spoiled Vacuum Packed Beef

peer-reviewedBlown pack spoilage (BPS) is a major issue for the beef industry. Etiological agents of BPS involve members of a group of Clostridium species, including Clostridium estertheticum which has the ability to produce gas, mostly carbon dioxide, under anaerobic psychotrophic growth conditions. This spore-forming bacterium grows slowly under laboratory conditions, and it can take up to 3 months to produce a workable culture. These characteristics have limited the study of this commercially challenging bacterium. Consequently information on this bacterium is limited and no effective controls are currently available to confidently detect and manage this production risk. In this study the complete genome of C. estertheticum DSM 8809 was determined by SMRT® sequencing. The genome consists of a circular chromosome of 4.7 Mbp along with a single plasmid carrying a potential tellurite resistance gene tehB and a Tn3-like resolvase-encoding gene tnpR. The genome sequence was searched for central metabolic pathways that would support its biochemical profile and several enzymes contributing to this phenotype were identified. Several putative antibiotic/biocide/metal resistance-encoding genes and virulence factors were also identified in the genome, a feature that requires further research. The availability of the genome sequence will provide a basic blueprint from which to develop valuable biomarkers that could support and improve the detection and control of this bacterium along the beef production chain

Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning

In recent years, the explosion of web videos makes text-video retrieval increasingly essential and popular for video filtering, recommendation, and search. Text-video retrieval aims to rank relevant text/video higher than irrelevant ones. The core of this task is to precisely measure the cross-modal similarity between texts and videos. Recently, contrastive learning methods have shown promising results for text-video retrieval, most of which focus on the construction of positive and negative pairs to learn text and video representations. Nevertheless, they do not pay enough attention to hard negative pairs and lack the ability to model different levels of semantic similarity. To address these two issues, this paper improves contrastive learning using two novel techniques. First, to exploit hard examples for robust discriminative power, we propose a novel Dual-Modal Attention-Enhanced Module (DMAE) to mine hard negative pairs from textual and visual clues. By further introducing a Negative-aware InfoNCE (NegNCE) loss, we are able to adaptively identify all these hard negatives and explicitly highlight their impacts in the training loss. Second, our work argues that triplet samples can better model fine-grained semantic similarity compared to pairwise samples. We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs. The proposed TPM-CL designs an adaptive token masking strategy with cross-modal interaction to model subtle semantic differences. Extensive experiments demonstrate that the proposed approach outperforms existing methods on four widely-used text-video retrieval datasets, including MSR-VTT, MSVD, DiDeMo and ActivityNet.Comment: Accepted by ACM MM 202

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation. However, the field of pathology has largely remained untapped in this regard, despite the growing need for accurate, timely, and personalized diagnostics. To bridge the gap in pathology MLLMs, we present the PathAsst in this study, which is a generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. To develop PathAsst, we collect over 142K high-quality pathology image-text pairs from a variety of reliable sources, including PubMed, comprehensive pathology textbooks, reputable pathology websites, and private data annotated by pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data, specifically tailored for the invocation of the pathology-specific models, allowing the PathAsst to effectively interact with these models based on the input image and user intent, consequently enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is trained based on Vicuna-13B language model in coordination with the CLIP vision encoder. The results of PathAsst show the potential of harnessing the AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We are committed to open-sourcing our meticulously curated dataset, as well as a comprehensive toolkit designed to aid researchers in the extensive collection and preprocessing of their own datasets. Resources can be obtained at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.Comment: 13 pages, 5 figures, conferenc