A New Deep-Neural-Network--Based Missing Transverse Momentum Estimator, and its Application to W Recoil

This dissertation presents the first Deep-Neural-Network–based missing transverse momentum (pTmiss) estimator, called “DeepMET”. It utilizes all reconstructed particles in an event as input, and assigns an individual weight to each of them. The DeepMET estimator is the negative of the vector sum of the weighted transverse momenta of all input particles. Compared with the pTmiss estimators currently utilized by the CMS Collaboration, DeepMET is found to improve the pTmiss resolution by 10-20%, and is more resilient towards the effect of additional proton-proton interactions accompanying the interaction of interest. DeepMET is demonstrated to improve the resolution on the recoil measurement of the W boson and reduce the systematic uncertainties on the W mass measurement by a large fraction compared with other pTmiss estimators

Semi-supervised Graph Neural Networks for Pileup Noise Removal

The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton-proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the ground truth information from simulation and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies

How to Choose Interesting Points for Template Attacks?

Template attacks are widely accepted to be the most powerful side-channel attacks from an information theoretic point of view. For template attacks, many papers suggested a guideline for choosing interesting points which is still not proven. The guideline is that one should only choose one point as the interesting point per clock cycle. Up to now, many different methods of choosing interesting points were introduced. However, it is still unclear that which approach will lead to the best classification performance for template attacks. In this paper, we comprehensively evaluate and compare the classification performance of template attacks when using different methods of choosing interesting points. Evaluation results show that the classification performance of template attacks has obvious difference when different methods of choosing interesting points are used. The CPA based method and the SOST based method will lead to the best classification performance. Moreover, we find that some methods of choosing interesting points provide the same results in the same circumstance. Finally, we verify the guideline for choosing interesting points for template attacks is correct by presenting a new way of conducting template attacks

Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue

E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences for the items they are seeking so as to provide appropriate recommendations. Conversational recommender systems (CRSs) learn user representation and provide accurate recommendations based on dialogue context, but rely on external knowledge. Large language models (LLMs) generate responses that mimic pre-sales dialogues after fine-tuning, but lack domain-specific knowledge for accurate recommendations. Intuitively, the strengths of LLM and CRS in E-commerce pre-sales dialogues are complementary, yet no previous work has explored this. This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS. We conduct extensive experiments on a real-world dataset of Ecommerce pre-sales dialogues. We analyze the impact of two collaborative approaches with two CRSs and two LLMs on four tasks of Ecommerce pre-sales dialogue. We find that collaborations between CRS and LLM can be very effective in some cases.Comment: EMNLP 2023 Finding

Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs

Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation. Unlike previous small models (e.g., BART, T5), current LLMs make fewer silly mistakes but more sophisticated ones, such as imposing cause and effect, adding false details, overgeneralizing, etc. These hallucinations are challenging to detect through traditional methods, which poses great challenges for improving the factual consistency of text summarization. In this paper, we propose an adversarially DEcoupling method to disentangle the Comprehension and EmbellishmeNT abilities of LLMs (DECENT). Furthermore, we adopt a probing-based efficient training to cover the shortage of sensitivity for true and false in the training process of LLMs. In this way, LLMs are less confused about embellishing and understanding; thus, they can execute the instructions more accurately and have enhanced abilities to distinguish hallucinations. Experimental results show that DECENT significantly improves the reliability of text summarization based on LLMs

Ginsenoside Rh1 Improves the Effect of Dexamethasone on Autoantibodies Production and Lymphoproliferation in MRL/lpr Mice

Ginsenoside Rh1 is able to upregulate glucocorticoid receptor (GR) level, suggesting Rh1 may improve glucocorticoid efficacy in hormone-dependent diseases. Therefore, we investigated whether Rh1 could enhance the effect of dexamethasone (Dex) in the treatment of MRL/lpr mice. MRL/lpr mice were treated with vehicle, Dex, Rh1, or Dex + Rh1 for 4 weeks. Dex significantly reduced the proteinuria and anti-dsDNA and anti-ANA autoantibodies. The levels of proteinuria and anti-dsDNA and anti-ANA autoantibodies were further decreased in Dex + Rh1 group. Dex, Rh1, or Dex + Rh1 did not alter the proportion of CD4+ splenic lymphocytes, whereas the proportion of CD8+ splenic lymphocytes was significantly increased in Dex and Dex + Rh1 groups. Dex + Rh1 significantly decreased the ratio of CD4+/CD8+ splenic lymphocytes compared with control. Con A-induced CD4+ splenic lymphocytes proliferation was increased in Dex-treated mice and was inhibited in Dex + Rh1-treated mice. Th1 cytokine IFN-γ mRNA was suppressed and Th2 cytokine IL-4 mRNA was increased by Dex. The effect of Dex on IFN-γ and IL-4 mRNA was enhanced by Rh1. In conclusion, our data suggest that Rh1 may enhance the effect of Dex in the treatment of MRL/lpr mice through regulating CD4+ T cells activation and Th1/Th2 balance

Extinction risk of Chinese angiosperms varies between woody and herbaceous species

Aim: Understanding how species' traits and environmental contexts relate to extinction risk is a critical priority for ecology and conservation biology. This study aims to identify and explore factors related to extinction risk between herbaceous and woody angiosperms to facilitate more effective conservation and management strategies and understand the interactions between environmental threats and species' traits. Location: China. Taxon: Angiosperms. Methods: We obtained a large dataset including five traits, six extrinsic variables, and 796,118 occurrence records for 14,888 Chinese angiosperms. We assessed the phylogenetic signal and used phylogenetic generalized least squares regressions to explore relationships between extinction risk, plant traits, and extrinsic variables in woody and herbaceous angiosperms. We also used phylogenetic path analysis to evaluate causal relationships among traits, climate variables, and extinction risk of different growth forms. Results: The phylogenetic signal of extinction risk differed among woody and herbaceous species. Angiosperm extinction risk was mainly affected by growth form, altitude, mean annual temperature, normalized difference vegetation index, and precipitation change from 1901 to 2020. Woody species' extinction risk was strongly affected by height and precipitation, whereas extinction risk for herbaceous species was mainly affected by mean annual temperature rather than plant traits. Main conclusions: Woody species were more likely to have higher extinction risks than herbaceous species under climate change and extinction threat levels varied with both plant traits and extrinsic variables. The relationships we uncovered may help identify and protect threatened plant species and the ecosystems that rely on them

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table