Results on heavy ion collisions at LHCb

Heavy flavor production is important in heavy ion collisions to study both cold and hot nuclear matter effects. The LHCb experiment can make unique contribution to heavy ion physics, owing to the full particle identification of the detector in the forward region and the ability to collect fixed target data with proton or lead beams. This report describes recent results with proton-lead collision data collected in 2013 and the prospect of heavy-ion studies at LHCb.Comment: Proceeding for "Rencontres de Moriond QCD and High Energy Interactions

An Empirical Study on the Relationship between Entrepreneur’s Reputation and Financing Constraints

Information asymmetry is an important reason that causes external financing constraints. Because reputation has the function of signal transmission, a better reputation of an entrepreneur can reduce the degree of the firm information asymmetry and alleviate financing constraints of the firm. Based on the grouped sample of 94 listed companies of China from 2007 to 2009, this paper did empirical study on the relationship between entrepreneur’s reputation and financing constraints. The results show that entrepreneur’s reputation has a significant effect on firm financing activity. In other words, higher entrepreneur’s reputation leads to lower financing constraints. This study has a significant impact in helping managers and investors realize the importance of signaling effect of a good reputation in capital market. Meanwhile, it helps motivate entrepreneurs to establish good reputation, increasing the efficiency of capital market

From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction

Document-level Event Argument Extraction (EAE) requires the model to extract arguments of multiple events from a single document. Considering the underlying dependencies between these events, recent efforts leverage the idea of "memory", where the results of already predicted events are cached and can be retrieved to help the prediction of upcoming events. These methods extract events according to their appearance order in the document, however, the event that appears in the first sentence does not mean that it is the easiest to extract. Existing methods might introduce noise to the extraction of upcoming events if they rely on an incorrect prediction of previous events. In order to provide more reliable memory, we propose a simple-to-complex progressive framework for document-level EAE. Specifically, we first calculate the difficulty of each event and then, we conduct the extraction following a simple-to-complex order. In this way, the memory will store the most certain results, and the model could use these reliable sources to help the prediction of more difficult events. Experiments on WikiEvents show that our model outperforms SOTA by 1.4% in F1, indicating the proposed simple-to-complex framework is useful in the EAE task.Comment: Accepted to the Findings of EMNLP 2023 (Long Paper

The influence of conjugation in molecular tunneling junctions and nanofabrication

Het doel van dit proefschrift is om de tunneling-eigenschappen van grote-oppervlakte moleculaire juncties bestaande uit zelforganiserende monolagen (SAMs) van geconjugeerde, organische moleculen met verschillende conjugatiepatronen te onderzoeken en deze te integreren in drie-terminale juncties, met als doel het transport van lading middels tunneling te sturing met elektrische velden. In Moleculaire Electronica is het van belang om te begrijpen hoe conjugatie patronen, het transport van lading middels tunneling beïnvloeden. Eerst hebben we de eigenschappen van zelforganiserende monolagen bestaande uit oliothiopheenquarterthiopheen met een flexibele butaanthiolstaart bestudeerd met behulp van CP-AFM en EgaIn en vonden dat de SAMs, mechanisch en elektrisch robuust waren. Daarna hebben we drie moleculaire draden gebaseerd op benzodithiopheen gesynthetiseerd. We hebben het tunneling-ladingstransport van de grote-oppervlakte moleculaire juncties bestaande uit deze benzodithiopheenderivaten bestudeerd en vergeleken met het bekende anthraquinone. De quinone functionele groep introduceert niet alleen kruisconjugatie, maar remt ook het tunneling transport vanwege zijn elektronegativiteit. Later hebben we geprobeerd om organische moleculen in te bouwen in nano-gap tunneling juncties met behulp van nanoskiving, we noemen deze SAM-templated addressable nanogap electrodes (STANs). We beschreven onze inspanningen tot het fabriceren van solid-state apparaten, bevattende moleculaire tunneling juncties. Het laatste deel omvat de fabricatie van uiterst lange, vrijstaande goud nanodraden met behulp van nanoskiving. Twee toepassingen van deze goud nanodraden zijn gedemonstreerd: ten eerste als een hetedraads anemometer om de stroomsnelheid te meten door middel van de weerstand over de goud nanodraad; en ten tweede voor het oprekken van DNA moleculen in de stroom om ze te visualiseren met fluorescentiemicroscopie

Learning to Simulate: Generative Metamodeling via Quantile Regression

Stochastic simulation models, while effective in capturing the dynamics of complex systems, are often too slow to run for real-time decision-making. Metamodeling techniques are widely used to learn the relationship between a summary statistic of the outputs (e.g., the mean or quantile) and the inputs of the simulator, so that it can be used in real time. However, this methodology requires the knowledge of an appropriate summary statistic in advance, making it inflexible for many practical situations. In this paper, we propose a new metamodeling concept, called generative metamodeling, which aims to construct a "fast simulator of the simulator". This technique can generate random outputs substantially faster than the original simulation model, while retaining an approximately equal conditional distribution given the same inputs. Once constructed, a generative metamodel can instantaneously generate a large amount of random outputs as soon as the inputs are specified, thereby facilitating the immediate computation of any summary statistic for real-time decision-making. Furthermore, we propose a new algorithm -- quantile-regression-based generative metamodeling (QRGMM) -- and study its convergence and rate of convergence. Extensive numerical experiments are conducted to investigate the empirical performance of QRGMM, compare it with other state-of-the-art generative algorithms, and demonstrate its usefulness in practical real-time decision-making.Comment: Main body: 36 pages, 7 figures; supplemental material: 12 page

Representation and Matching of Articulated Shapes

We consider the problem of localizing the articulated and deformable shape of a walking person in a single view. We represent the non-rigid 2D body contour by a Bayesian graphical model whose nodes correspond to point positions along the contour. The deformability of the model is constrained by learned priors corresponding to two basic mechanisms: local non-rigid deformation, and rotation motion of the joints. Four types of image cues are combined to relate the model configuration to the observed image, including edge gradient map, foreground/background mask, skin color mask, and appearance consistency constraints. The constructed Bayes network is sparse and chain-like, enabling efficient spatial inference through Sequential Monte Carlo sampling methods. We evaluate the performance of the model on images taken in cluttered, outdoor scenes. The utility of each image cue is also empirically explored