Quantitative assessment of Earth’s radiation belt modeling

The “Quantitative Assessment of Radiation Belt Modeling” focus group was in place at Geospace Environment Modeling from 2014 to 2018. The overarching goals of this focus group were to bring together the current state‐of‐the‐art models for the acceleration, transport, and loss processes in Earth's radiation belts; develop event‐specific and global inputs of wave, plasma, and magnetic field to drive these models; and combine all these components to achieve a quantitative assessment of radiation belt modeling by validating against contemporary radiation belt measurements. This article briefly reviews the current understanding of radiation belt dynamics and related modeling efforts, summarizes the activities and accomplishments of the focus group, and discusses future directions.Accepted manuscrip

Quantitative assessment of radiation belt modeling

The “Quantitative Assessment of Radiation Belt Modeling” focus group was in place at Geospace Environment Modeling from 2014 to 2018. The overarching goals of this focus group were to bring together the current state‐of‐the‐art models for the acceleration, transport, and loss processes in Earth's radiation belts; develop event‐specific and global inputs of wave, plasma, and magnetic field to drive these models; and combine all these components to achieve a quantitative assessment of radiation belt modeling by validating against contemporary radiation belt measurements. This article briefly reviews the current understanding of radiation belt dynamics and related modeling efforts, summarizes the activities and accomplishments of the focus group, and discusses future directions.Accepted manuscrip

Light Higgs boson in the NMSSM confronted with the CMS diphoton and ditau excesses

In 2018, the CMS collaboration reported a di-photon excess around 95.3 GeV with a local significance of 2.8 $\sigma$. Interestingly, the CMS collaboration also reported a di-tau excess recently at 95 $\sim$ 100 GeV with a local significance of 2.6 $\sim$ 3.1 $\sigma$. Besides, a $b\bar{b}$ excess at 98 GeV with a 2.3 $\sigma$ local significance was reported with LEP data about twenty years ago. In this work, we consider interpreting these excesses together with a light Higgs boson in the next-to-minimal supersymmetric standard model (NMSSM). We conclude that in NMSSM the 95 $\sim$ 100 GeV excesses are difficult to be satisfied simultaneously (not possible globally at $1\sigma$ level, or simultaneously at $2\sigma$ level), and we analyze two partial-satisfied scenarios: the globally $2\sigma$ scenario and small di-photon scenario. An approximate equation of global fit to the three excesses is derived, and two representative types of surviving samples are analyzed in detail. Since the mass regions of these excesses are near the Z boson, we also consider checking the light Higgs boson in the $t\bar{t}$-associated channels. The detailed results may be useful for further checking the low-mass-region excesses in the future.Comment: 9 pages, 5 figures, 2 table

A study on the Development of Biopharmaceutical Companies Based on the Context of Big Data

Based on the requirements of the computer technology background in the new era, this paper takes Chinese biopharmaceutical enterprises as the research object, outlines the general characteristics of innovation in Chinese biopharmaceutical enterprises, and analyses the current situation of innovation development in biopharmaceutical enterprises from four aspects: policy, economy, society and technology, followed by an analysis of the current problems faced by innovation in Chinese biopharmaceutical enterprises and the reasons for their generation. Based on literature research, theoretical studies and analysis of the causes of the current innovation problems of Chinese biopharmaceutical enterprises, the factors affecting the innovation performance of Chinese biopharmaceutical enterprises are summarised. The specifics of the conditional variables, as well as the outcome variables, are then identified according to the specific manifestations of poor innovation performance of biopharmaceutical firms. Finally, corresponding countermeasures are proposed in terms of the level of corporate R&D investment, the level of financing channels, the level of government policy support, and the internal management of biopharmaceutical companies, in conjunction with the characteristics of different path models. This paper combines the big data foundation to research and analyse the development of biopharmaceutical enterprises, laying a new research foundation and providing a more efficient research path for the development of the biopharmaceutical industry

Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video

Reconstructing interacting hands from monocular RGB data is a challenging task, as it involves many interfering factors, e.g. self- and mutual occlusion and similar textures. Previous works only leverage information from a single RGB image without modeling their physically plausible relation, which leads to inferior reconstruction results. In this work, we are dedicated to explicitly exploiting spatial-temporal information to achieve better interacting hand reconstruction. On one hand, we leverage temporal context to complement insufficient information provided by the single frame, and design a novel temporal framework with a temporal constraint for interacting hand motion smoothness. On the other hand, we further propose an interpenetration detection module to produce kinetically plausible interacting hands without physical collisions. Extensive experiments are performed to validate the effectiveness of our proposed framework, which achieves new state-of-the-art performance on public benchmarks.Comment: 16 page

BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization

In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. Considering the dominance of hand and body in sign language expression, we organize them as pose triplet units and feed them into the Transformer backbone in a frame-wise manner. Pre-training is performed via reconstructing the masked triplet unit from the corrupted input sequence, which learns the hierarchical correlation context cues among internal and external triplet units. Notably, different from the highly semantic word token in BERT, the pose unit is a low-level signal originally located in continuous space, which prevents the direct adoption of the BERT cross-entropy objective. To this end, we bridge this semantic gap via coupling tokenization of the triplet unit. It adaptively extracts the discrete pseudo label from the pose triplet unit, which represents the semantic gesture/body state. After pre-training, we fine-tune the pre-trained encoder on the downstream SLR task, jointly with the newly added task-specific layer. Extensive experiments are conducted to validate the effectiveness of our proposed method, achieving new state-of-the-art performance on all four benchmarks with a notable gain.Comment: Accepted by AAAI 2023 (Oral

Time-Delayed Data Informed Reinforcement Learning for Approximate Optimal Tracking Control

This paper proposes a time-delayed data informed reinforcement learning method, referred as incremental adaptive dynamic programming, to learn approximate solutions to optimal tracking control problems (OTCPs) of high-dimensional nonlinear systems. Departing from available solutions to OTCPs, our developed tracking control scheme settles the curse of complexity problem in value function approximation from a decoupled way, circumvents the learning inefficiency regarding varying desired trajectories by avoiding introducing a reference trajectory dynamics into the learning process, and requires neither an accurate nor identified dynamics using time-delayed signals. Specifically, the intractable OTCP of a high-dimensional uncertain system is first converted into multiple manageable sub-OTCPs of low-dimensional incremental subsystems constructed using time-delayed data. Then, the resulting sub-OTCPs are approximately solved by a parallel critic learning structure. The proposed tracking control scheme is developed with rigorous theoretical analysis of system stability and weight convergence, and validated experimentally on a 3-DoF robot manipulator