A Study of the Relationship between Economic Development and Environmental Condition of Countries in the 21st Century

This project focuses on the relationship between economic status and environment conditions for countries in the 20th century. The data used is extracted from the world development indicator information, published in the World Bank website. There are two parts of analysis in this project. The first part focuses on economic status and threatened species in two research questions: Is there any association between threatened species levels and GDP levels? Does the country with better economic status have better environment condition? The methods used for these two questions are mosaic plots, chi-square independent test, Goodman-Kruskal gamma, and cluster analysis, to find associations. For the second part, this project studies the threatened species along with both natural environmental indicators and economic developmental indicators in one research question. The method is multiple regression analysis. There are two significant results in this project: First, there is statistical evidence supporting that the countries with high economic status tend to have better environment condition (fewer threatened species) than countries with low economic status; Second, it terns out that the natural environment did not affect the number of threatened species very much, no matter whether the economic developmental indicators are present or not

Analyzing the Hardware-Software Implications of Multi-modal DNN Workloads using MMBench

The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of applications called multi-modal DNNs. Multi-modal DNNs are capable of interpreting and reasoning about information from multiple modalities, making them more applicable to real-world AI scenarios. In recent research, multi-modal DNNs have outperformed the best uni-modal DNN in a wide range of applications from traditional multimedia to emerging autonomous systems. However, despite their importance and superiority, very limited research attention has been devoted to understand the characteristics of multi-modal DNNs and their implications on current computing software/hardware platforms. To facilitate research and advance the understanding of these multi-modal DNN workloads, we first present MMbench, an open-source benchmark suite consisting of a set of real-world multi-modal DNN workloads with relevant performance metrics for evaluation. Then we use MMbench to conduct an in-depth analysis on the characteristics of multi-modal DNNs. We study their implications on application and programming framework, operating and scheduling system, as well as execution hardware. Finally, we conduct a case study and extend our benchmark to edge devices. We hope that our work can provide guidance for future software/hardware design and optimization to underpin multi-modal DNNs on both cloud and edge computing platforms

Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Contemporary recommender systems predominantly rely on collaborative filtering techniques, employing ID-embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance in cold-start scenarios and long-tail user recommendations. Leveraging the capabilities of Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems by integrating open-world domain knowledge. In this paper, we propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge. We address computational complexity concerns by utilizing pretrained LLMs as item encoders and freezing LLM parameters to avoid catastrophic forgetting and preserve open-world knowledge. To bridge the gap between the open-world and collaborative domains, we design a twin-tower structure supervised by the recommendation task and tailored for practical industrial application. Through offline experiments on the large-scale industrial dataset and online experiments on A/B tests, we demonstrate the efficacy of our approach.Comment: 11 pages, 6 figure

Near-theoretical strength and deformation stabilization achieved via grain boundary segregation and nano-clustering of solutes

Grain boundary hardening and precipitation hardening are important mechanisms for enhancing the strength of metals. Here, we show that these two effects can be amplified simultaneously in nanocrystalline compositionally complex alloys (CCAs), leading to near-theoretical strength and large deformability. We develop a model nanograined (TiZrNbHf)98Ni2 alloy via thermodynamic design. The Ni solutes, which has a large negative mixing enthalpy and different electronegativity to Ti, Zr, Nb and Hf, not only produce Ni-enriched local chemical inhomogeneities in the nanograins, but also segregate to grain boundaries. The resultant alloy achieves a 2.5 GPa yield strength, together with work hardening capability and large homogeneous deformability to 65% compressive strain. The local chemical inhomogeneities impede dislocation propagation and encourage dislocation multiplication to promote strain hardening. Meanwhile, Ni segregates to grain boundaries and enhances cohesion, suppressing the grain growth and grain boundary cracking found while deforming the reference TiZrNbHf alloy. Our alloy design strategy thus opens an avenue, via solute decoration at grain boundaries combined with local chemical inhomogeneities inside the grains, towards ultrahigh strength and large plasticity in nanostructured alloys

Case report of a Li-Fraumeni syndrome-like phenotype with a de novo mutation in <i>CHEK2</i>

BACKGROUND: Cases of multiple tumors are rarely reported in China. In our study, a 57-year-old female patient had concurrent squamous cell carcinoma, mucoepidermoid carcinoma, brain cancer, bone cancer, and thyroid cancer, which has rarely been reported to date. METHODS: To determine the relationship among these multiple cancers, available DNA samples from the thyroid, lung, and skin tumors and from normal thyroid tissue were sequenced using whole exome sequencing. RESULTS: The notable discrepancies of somatic mutations among the 3 tumor tissues indicated that they arose independently, rather than metastasizing from 1 tumor. A novel deleterious germline mutation (chr22:29091846, G->A, p.H371Y) was identified in CHEK2, a Li–Fraumeni syndrome causal gene. Examining the status of this novel mutation in the patient's healthy siblings revealed its de novo origin. CONCLUSION: Our study reports the first case of Li–Fraumeni syndrome-like in Chinese patients and demonstrates the important contribution of de novo mutations in this type of rare disease