Geopolitics of Semiconductor Supply Chains: The Case of TSMC, US-China-Taiwan Relations, and the COVID-19 Crisis

As the demand for more advanced forms of technology continues to grow, so does the global importance and reliance on semiconductors. This research paper examines the impact of semiconductor shortages on both consumer and military production, as well as the geopolitical implications embedded within the global supply chain. The paper begins by providing an overview of semiconductors, the global semiconductor market, and the impact of COVID-19 on the industry’s supply chain. It also discusses the importance of the Taiwan Semiconductor Manufacturing Company (TSMC) in the industry and its role in addressing semiconductor shortages. Next, the paper discusses the impact of semiconductor shortages on the production of consumer goods such as smartphones, laptops, and automobiles. The US-China trade war and further geopolitical tensions between the US, China, and Taiwan have significantly disrupted the semiconductor supply chain, leading to shortages and price increases for consumer electronics industries. The paper also examines recent government intervention within high technology industries and specifically the semiconductor sector aimed at addressing the shortages faced during the pandemic and provides case studies of key companies affected by the semiconductor shortage. The paper then examines the impact of semiconductor shortages on military technology production, such as advanced weapons and communications systems. The disruptions in the semiconductor supply chain have highlighted the importance of semiconductor manufacturing in national security and the geopolitical implications of these disruptions. The paper discusses the impact of US-China-Taiwan geopolitical tensions on the semiconductor supply chain for military technology production, evaluates key national interests and their reactions to shortages, and provides case studies for affected countries. The paper has an overall lens of focusing on the geopolitical implications of US-China-Taiwan relations on the semiconductor industry and the role of other countries. It also upholds the sustainability focused value criterion of ensuring fair and reliable semiconductor distribution for fostering a stable and secure global supply chain. It provides future scenarios for the semiconductor industry and how past events and legislation will be either effective or ineffective in managing future supply chain issues. The paper highlights the role and need for international cooperation to address semiconductor distribution and the industry’s geopolitical implications. In conclusion, the following research provides a comprehensive analysis of the impact of semiconductor shortages on both consumer goods and military technology production and their geopolitical implications. It provides an analysis and further recommendations of government policies and interventions to address shortages and their implications and suggests future research in the field of semiconductor geopolitics. As technology continues to advance, the importance of semiconductors will only grow, making the understanding of the global supply chain and the tensions it raises even more critical

Retrieval with gene queries

BACKGROUND: Accuracy of document retrieval from MEDLINE for gene queries is crucially important for many applications in bioinformatics. We explore five information retrieval-based methods to rank documents retrieved by PubMed gene queries for the human genome. The aim is to rank relevant documents higher in the retrieved list. We address the special challenges faced due to ambiguity in gene nomenclature: gene terms that refer to multiple genes, gene terms that are also English words, and gene terms that have other biological meanings. RESULTS: Our two baseline ranking strategies are quite similar in performance. Two of our three LocusLink-based strategies offer significant improvements. These methods work very well even when there is ambiguity in the gene terms. Our best ranking strategy offers significant improvements on three different kinds of ambiguities over our two baseline strategies (improvements range from 15.9% to 17.7% and 11.7% to 13.3% depending on the baseline). For most genes the best ranking query is one that is built from the LocusLink (now Entrez Gene) summary and product information along with the gene names and aliases. For others, the gene names and aliases suffice. We also present an approach that successfully predicts, for a given gene, which of these two ranking queries is more appropriate. CONCLUSION: We explore the effect of different post-retrieval strategies on the ranking of documents returned by PubMed for human gene queries. We have successfully applied some of these strategies to improve the ranking of relevant documents in the retrieved sets. This holds true even when various kinds of ambiguity are encountered. We feel that it would be very useful to apply strategies like ours on PubMed search results as these are not ordered by relevance in any way. This is especially so for queries that retrieve a large number of documents

Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint

Due to the boom in technical compute in the last few years, the world has seen massive advances in artificially intelligent systems solving diverse real-world problems. But a major roadblock in the ubiquitous acceptance of these models is their enormous computational complexity and memory footprint. Hence efficient architectures and training techniques are required for deployment on extremely low resource inference endpoints. This paper proposes an architecture for detection of alphabets in American Sign Language on an ARM Cortex-M7 microcontroller having just 496 KB of framebuffer RAM. Leveraging parameter quantization is a common technique that might cause varying drops in test accuracy. This paper proposes using interpolation as augmentation amongst other techniques as an efficient method of reducing this drop, which also helps the model generalize well to previously unseen noisy data. The proposed model is about 185 KB post-quantization and inference speed is 20 frames per second.Comment: 6 pages, Published in IEEE RAICS 2020, see https://raics.i

CMB-S4 Science Book, First Edition

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales

Report of the Topical Group on Cosmic Probes of Dark Matter for Snowmass 2021

Cosmological and astrophysical observations currently provide the only robust, positive evidence for dark matter. Cosmic probes of dark matter, which seek to determine the fundamental properties of dark matter through observations of the cosmos, have emerged as a promising means to reveal the nature of dark matter. This report summarizes the current status and future potential of cosmic probes to inform our understanding of the fundamental nature of dark matter in the coming decade.Comment: Report of the CF3 Topical Group for Snowmass 2021; 35 pages, 10 figures, many references. V3 updates Fig 3-2 and the author lis

CMB-S4 Science Book, First Edition

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales

Profiling Topics on the Web . . .

This paper has been written with the aim of presenting motivations for my dissertation research, based on gaps in current research in text/web mining, as well as to provide an outline of the research I propose to do for my Ph.D. dissertation. The overall goal is to explore methods for knowledge discovery from the web

ABSTRACT Predicting Performance for Gene Queries

We propose a method to predict the average precision score when ranking document sets for gene queries. Compared to a baseline predictor our method reduces error by 35%. We obtain significant correlations between rankings of queries by predicted and actual average precision scores. 1

ABSTRACT Profiling Topics on the Web

The availability of large-scale data on the Web motivates the development of automatic algorithms to analyze topics and identify relationships between topics. Various approaches have been proposed in the literature. Most focus on specific entities, such as people, and not on topics in general. They are also less flexible in how they represent topics/entities. In this paper we study existing methods as well as describe preliminary research on a different approach, based on profiles, for representing general topics. Topic profiles consist of different types of features. We compare different methods for building profiles and evaluate them in terms of their information content and ability to predict relationships between topics. Our results suggest that profiles derived from the full text present in multiple pages are the most informative and that profiles derived from multiple pages are significantly better at predicting topic relationships than profiles derived from single pages