Disentangling Boosted Higgs Boson Production Modes with Machine Learning

Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson, and production of a Higgs boson with a top-quark pair. Combining jet substructure and event information with modern machine learning, we demonstrate the ability to focus on particular production modes. These tools hold great discovery potential for boosted Higgs bosons produced via ggF and may also provide additional information about the Higgs Boson sector of the Standard Model in extreme phase space regions for other production modes as well.Comment: 17 pages, 9 figure

An Economy-wide Analysis of Impacts of WTO Tiered Formula for Tariff Reduction on Taiwan

In this study we use Taiwan as a case study to provide an economy-wide analysis of impacts on Taiwan of WTO tariff reduction schemes with different combinations of thresholds and reduction rates. The model we utilized in this study is Taiwan General Equilibrium Model with a WTO module (TAIGEM-WTO, hereafter) that is a multi-sectoral computable general equilibrium (CGE) model of the Taiwan's economy derived from Australian ORANI model (Dixon, Parmenter, Sutton and Vincent, 1982). Simulation results show that results are more sensitive to the scheme of tariff-reduction (i.e., Category 1, 2, and 3) than the tiered levels (i.e., A, B, C, and D) and as a strategy we should pay more attention to the arguments related to the amounts of tariff-reduction. Moreover, changes in nominal average tariff rates are more sensitive and shocks to the economy are more severe when we change the tariff reduction categories rather than the tiered levels. This conclusion also applies to the tiered reduction case when only sensitive products are considered. Finally, simulations with sector's bound rate calculated using arithmetic means have bigger effects than those using import values as weights. Therefore, sector's bound rate using import values as weights would be preferred.International Relations/Trade,

Senile cataracts and oxidative stress

AbstractIn numerous epidemiological and animal models, it can be inferred that oxidative stress is a key factor in cataract formation. Production of reactive oxygen species and reduction of endogenous antioxidants both contribute to cataract formation. In the cataractogenous process, lens proteins lose sulfhydryl groups and become thiolated or cross-linked by disulfide bonds. The resultant high molecular weight aggregates become insoluble and affect lens transparency. All these are consequences of changes in the redox state. A mixed protein-thiol and protein-protein disulfide bond precedes the morphological changes of cataract. Normally, sustained high levels of reduced glutathione provide a protective effect, while depletion of glutathione causes damage to epithelial cells and fiber cells. UV rays in the ambient environment evoke reactive oxygen species formation and also contribute to cataracts. The reduction in free UV filters and increase in their binding to lens proteins make the lens more predisposed to UV damage and oxidation. In the aqueous humor of cataract lenses, there is a decrease in antioxidant enzymes and increase in nitric oxide, which demonstrates the relationship between oxidative stress and cataracts. Though surgical intervention is the standard treatment for cataracts, experimental medical therapies for cataracts are under extensive investigation. Carnosine, a pro-drug of carnosine-N-acetylcarnosine, bendazac, ascorbic acid, and aldose reductase inhibitors are under therapeutic evaluation, and prevention of cataract formation may be possible in the future

Exploring the Universality of Hadronic Jet Classification

The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.Comment: 25 pages, 7 figures, 7 table

Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term

With advancements in natural language processing (NLP) models, automatic explanation generation has been proposed to mitigate misinformation on social media platforms in addition to adding warning labels to identified fake news. While many researchers have focused on generating good explanations, how these explanations can really help humans combat fake news is under-explored. In this study, we compare the effectiveness of a warning label and the state-of-the-art counterfactual explanations generated by GPT-4 in debunking misinformation. In a two-wave, online human-subject study, participants (N = 215) were randomly assigned to a control group in which false contents are shown without any intervention, a warning tag group in which the false claims were labeled, or an explanation group in which the false contents were accompanied by GPT-4 generated explanations. Our results show that both interventions significantly decrease participants' self-reported belief in fake claims in an equivalent manner for the short-term and long-term. We discuss the implications of our findings and directions for future NLP-based misinformation debunking strategies.Comment: EMNLP Findings 202

Anthropomorphism of AI-based Intelligent Customer Service, and Its Affective and Behavioral Consequences

Recently, as many users turn to social media to interact with service providers, organizations apply Artificial intelligence (AI) to improve the efficiency and effectiveness of the operation. This type of customer service system is called intelligent customer service (ICS) which one of the most commonly adopted tools is chatbot. Since chatbot is AI-empowered, whether this system can effectively interact with customers and solve their problems is critical. However, the quality of ICS has received significant attention recently, and a lack of systematic study on the outcomes of anthropomorphism leaves this question unanswered in an ICS context. Based on a cognitive-affective-behavioral framework, this study attempts to understand whether anthropomorphism can promote desired behaviors (including usage and citizen-ship behaviors) through enhancing affective out-comes, such as satisfaction and identity. Data collected from 183 chatbot-ICS users, this study illustrates how anthropomorphism can increase quality, enhance satisfaction and identity. Furthermore, we also show that satisfaction and identity lead to further usage and citizenship behaviors. This highlights the importance of increasing anthropomorphism for the chatbot-ICS

The integrin-binding defective FGF2 mutants potently suppress FGF2 signalling and angiogenesis.

We recently found that integrin αvβ3 binds to fibroblast growth factor (FGF)-αvβ31 (FGF1), and that the integrin-binding defective FGF1 mutant (Arg-50 to glutamic acid, R50E) is defective in signalling and antagonistic to FGF1 signalling. R50E suppressed angiogenesis and tumour growth, suggesting that R50E has potential as a therapeutic. However, FGF1 is unstable, and we had to express R50E in cancer cells for xenograft study, since injected R50E may rapidly disappear from circulation. We studied if we can develop antagonist of more stable FGF2. FGF2 is widely involved in important biological processes such as stem cell proliferation and angiogenesis. Previous studies found that FGF2 bound to αvβ3 and antagonists to αvβ3 suppressed FGF2-induced angiogenesis. However, it is unclear how FGF2 interacts with integrins. Here, we describe that substituting Lys-119/Arg-120 and Lys-125 residues in the predicted integrin-binding interface of FGF2 to glutamic acid (the K119E/R120E and K125E mutations) effectively reduced integrin binding to FGF2. These FGF2 mutants were defective in signalling functions (ERK1/2 activation and DNA synthesis) in NIH3T3 cells. Notably they suppressed, FGF2 signalling induced by WT FGF2 in endothelial cells, suggesting that the FGF2 mutants are antagonists. The FGF2 mutants effectively suppressed tube formation in vitro, sprouting in aorta ring assays ex vivo and angiogenesis in vivo The positions of amino acids critical for integrin binding are different between FGF1 and FGF2, suggesting that they do not interact with integrins in the same manner. The newly developed FGF2 mutants have potential as anti-angiogenic agents and useful tools for studying the role of integrins in FGF2 signalling

Microwave plasma-assisted photoluminescence enhancement in nitrogendoped ultrananocrystalline diamond film

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Factors associated with outcomes of second-line treatment for EGFR-mutant non-small-cell lung cancer patients after progression on first- or second-generation EGFR-tyrosine kinase inhibitor treatment

PurposeEpidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are standard first-line treatments for advanced EGFR-mutant non-small-cell lung cancer (NSCLC) patients. However, factors associated with outcomes after progression on first-line therapy are seldom investigated.Materials and methodsFrom January 2016 to December 2020, we enrolled 242 EGFR-mutant stage IIIB–IV NSCLC patients who progressed on first- or second-generation EGFR-TKI treatments, and 206 of them receive second-line treatments after disease progression. The factors that predict the survival outcomes of different second-line treatments after disease progression were evaluated. Clinical and demographic characteristics, including metastatic sites, neutrophil-to-lymphocyte ratio (NLR) at first-line progression, and second-line treatment regimens, and whether re-biopsied after disease progression or not, were reviewed for outcome analysis.ResultsThe univariate analysis showed that the PFS was shorted in male patients (p =0.049), patients with ECOG performance state ≥ 2 (p =0.014), former smokers (p =0.003), patients with brain metastasis (p =0.04), second-line chemotherapy or EGFR-TKIs other than osimertinib (p =0.002), and NLR ≥5.0 (p=0.024). In addition, second-line osimertinib was associated with longer OS compared to chemotherapy and other EGFR-TKI treatment (p =0.001). In the multivariate analysis, only second-line osimertinib was an independent predictor of PFS (p =0.023). Re-biopsy after first-line treatment was associated with a trend of better OS. Patients with NLR ≥5.0 at disease progression had shorter OS than patients with NLR <5.0 (p = 0.008).ConclusionThe benefits of osimertinib necessitate that aggressive re-biopsy after progression on first- or second-generation EGFR-TKI treatment is merited for appropriate second-line treatments to provide better outcomes for these patients