Personality and US presidential choices: a study of the protracted Afghanistan war

The 20-year-long US war in Afghanistan, which started in 2001 and ended in 2021, resulted in significant civilian casualties, US military deaths and financial costs. This protracted war raised the question of why the war endured for so long despite such terrible costs. In order to answer this question, this thesis explores the causal relationship between the personalities and leadership styles of US presidents George Walker Bush and Barack Obama and their decision-making relating to US continuation of this war. Bush’s and Obama’s personalities and leadership styles are examined using Leadership Trait Analysis (LTA). Further personality-based expectations relating to the two presidents’ policy orientations and decision-making are developed based on their scores on the seven LTA traits. These expectations are examined in two case studies of five major occasions for decision and two subsequent policy changes relating to the Afghanistan war. The findings confirm that Bush’s and Obama’s personalities help understand and explain their continuation of the Afghanistan war. First, their war orientations are consistent with the expectations based on their distrust of others. Another trait, in-group bias, also helps explain their continuation of this war. Second, the different ways in which the two presidents managed their decision-making processes and shaped the policy outcomes are mainly consistent with the expectations based on their personalities. Third, leaders’ openness to divergent voices in decision-making is based more on their conceptual complexity and can be influenced by their task focus and inexperience in different ways. Findings from this thesis contribute to the existing scholarship on the post-9/11 US foreign policy in Afghanistan, especially US continuation of the US-Afghanistan war. Furthermore, this thesis makes two main theoretical contributions to LTA theory. First, it explores and identifies the causal relationship between leaders’ distrust of others and their continuation of the war. Second, it examines and identifies factors (leaders’ task focus and inexperience) that influence the effects of leaders’ conceptual complexity on their openness to divergent opinions in decision-making

Exploring the Design Space of Immersive Urban Analytics

Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft HoloLens. These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence. In this paper, we propose an theoretical model to characterize the visualizations in immersive urban analytics. Further more, based on our comprehensive and concise model, we contribute a typology of combination methods of 2D and 3D visualizations that distinguish between linked views, embedded views, and mixed views. We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations. Finally, based on existing works, possible future research opportunities are explored and discussed.Comment: 23 pages,11 figure

Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FGNET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention sentence-specific context. This is inadequate for highly overlapping and noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro f1 and micro f1 respectively

HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.Comment: 9 pages, 2 figure

GPTSee: Enhancing Moment Retrieval and Highlight Detection via Description-Based Similarity Features

Moment retrieval (MR) and highlight detection (HD) aim to identify relevant moments and highlights in video from corresponding natural language query. Large language models (LLMs) have demonstrated proficiency in various computer vision tasks. However, existing methods for MR\&HD have not yet been integrated with LLMs. In this letter, we propose a novel two-stage model that takes the output of LLMs as the input to the second-stage transformer encoder-decoder. First, MiniGPT-4 is employed to generate the detailed description of the video frame and rewrite the query statement, fed into the encoder as new features. Then, semantic similarity is computed between the generated description and the rewritten queries. Finally, continuous high-similarity video frames are converted into span anchors, serving as prior position information for the decoder. Experiments demonstrate that our approach achieves a state-of-the-art result, and by using only span anchors and similarity scores as outputs, positioning accuracy outperforms traditional methods, like Moment-DETR.Comment: 5 pages, 3 figure

Characterization of Electronic Cigarette Aerosol and Its Induction of Oxidative Stress Response in Oral Keratinocytes.

In this study, we have generated and characterized Electronic Cigarette (EC) aerosols using a combination of advanced technologies. In the gas phase, the particle number concentration (PNC) of EC aerosols was found to be positively correlated with puff duration whereas the PNC and size distribution may vary with different flavors and nicotine strength. In the liquid phase (water or cell culture media), the size of EC nanoparticles appeared to be significantly larger than those in the gas phase, which might be due to aggregation of nanoparticles in the liquid phase. By using in vitro high-throughput cytotoxicity assays, we have demonstrated that EC aerosols significantly decrease intracellular levels of glutathione in NHOKs in a dose-dependent fashion resulting in cytotoxicity. These findings suggest that EC aerosols cause cytotoxicity to oral epithelial cells in vitro, and the underlying molecular mechanisms may be or at least partially due to oxidative stress induced by toxic substances (e.g., nanoparticles and chemicals) present in EC aerosols

Antonym-Synonym Classification Based on New Sub-space Embeddings

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance

Association Studies of Regional Scientific and Technology Talent Coupled with the High-Tech Industry

Based on the theory of coupled systems, we use gray relational analysis to build a complex system that regional technology talent coupled with regional high-tech industry. We examine coupling relations of regional technology talent with the high-tech industry and analyze the law of the coupling of them. Results indicated (1) the degree of coupling of the regional technology talent and the high-tech industry system is relatively high, which has much close relationship. (2) China’s central and western provinces are mostly classified to low-level coupling and antagonistic stage, the degree of coupling of each regional technology talent and high-tech industry interact is significantly different, and we found that regional distribution has corresponding relationship with the level of economic development.Key words: Technology talent; High-tech industry; Coupling degree; Couplin

E-cigarette aerosols induce unfolded protein response in normal human oral keratinocytes.

Objective: Since the introduction in 2004, global usage of e-cigarettes (ECs) has risen exponentially. However, the risks of ECs on oral health are uncertain. The purpose of this study is to understand if EC aerosol exposure impacts the gene pathways of normal human oral keratinocytes (NHOKs), particularly the unfolded protein response (UPR) pathway. Materials and methods: EC aerosols were generated reproducibly with a home-made puffing device and impinged into the culture medium for NHOKs. DNA microarrays were used to profile the gene expression changes in NHOKs treated with EC aerosols, and the Ingenuity Pathway Analysis (IPA) was used to reveal signaling pathways altered by the EC aerosols. Quantitative PCR was used to validate the expression changes of significantly altered genes. Results: DNA microarray profiling followed by IPA revealed a number of signaling pathways, such as UPR, cell cycle regulation, TGF-β signaling, NRF2-mediated oxidative stress response, PI3K/AKT signaling, NF-κB signaling, and HGF signaling, activated by EC aerosols in NHOKs. The UPR pathway genes, C/EBP homologous protein (CHOP), activating transcription factor 4 (ATF4), X box binding protein 1 (XBP1), and inositol-requiring enzyme 1 alpha (IRE1α) were all significantly up-regulated in EC aerosol-treated NHOKs whereas immunoglobulin heavy-chain binding protein (BIP) and PRKR-like ER kinase (PERK) were slightly up-regulated. qPCR analysis results were found to be well correlated with those from the DNA microarray analysis. The most significantly changed genes in EC aerosol-treated NHOKs versus untreated NHOKs were CHOP, ATF4, XBP1, IRE1α and BIP. Meanwhile, Western blot analysis confirmed that CHOP, GRP78 (BIP), ATF4, IRE1α and XBP1s (spliced XBP1) were significantly up-regulated in NHOKs treated with EC aerosols. Conclusion: Our results indicate that EC aerosols up-regulate the UPR pathway genes in NHOKs, and the induction of UPR response is mediated by the PERK - EIF2α - ATF4 and IRE1α - XBP1 pathways