Thoughts and Targeted Initiatives for the Nurturing of Youth Football Reserve Talents in China

In order to strengthen the foundation for the cultivation of Chinese youth football reserve talents, a systematic review of the current ideas on the development of Chinese youth football reserve talents is conducted, and based on this, a targeted response is derived from it. The study concludes that the cultivation of Chinese youth football reserve talents should be based on the country and the world in a hierarchical and directional manner, with emphasis on the integration of the excellent Chinese traditional culture at the primary school level and the absorption of outstanding foreign achievements and experience at the secondary school level, and the promotion of three types of policy tools, namely the supply side, the demand side and the environment side, to form a protective synergy for the cultivation of youth football reserve talents, so as to build an effective and long-term development strategy that will benefit the present and the future. The aim is to speed up the construction of a reserve pool of Chinese youth football talents, improve the international competitiveness and influence of Chinese football, and contribute to the early realisation of the Chinese football dream

High-Performance and Wavelength-Reused Optical Network on Chip (ONoC) Architectures and Communication Schemes for Manycore Processor

Optical Network on Chip (ONoC) is an emerging chip-scale optical interconnection technology to realize the high-performance and power-efficient inter-core communication for many-core processors. By utilizing the silicon photonic interconnects to transmit data packets with optical signals, it can achieve ultra low communication delay, high bandwidth capacity, and low power dissipation. With the benefits of Wavelength Division Multiplexing (WDM), multiple optical signals can simultaneously be transmitted in the same optical interconnect through different wavelengths. Thus, the WDM-based ONoC is becoming a hot research topic recently. However, the maximal number of available wavelengths is restricted for the reliable and power-efficient optical communication in ONoC. Hence, with a limited number of wavelengths, the design of high-performance and power-efficient ONoC architecture is an important and challenging problem. In this thesis, the design methodology of wavelength-reused ONoC architecture is explored. With the wavelength reuse scheme in optical routing paths, high-performance and power-efficient communication is realized for many-core processors only using a small number of available wavelengths. Three wavelength-reused ONoC architectures and communication schemes are proposed to fulfil different communication requirements, i.e., network scalability, multicast communication, and dark silicon. Firstly, WRH-ONoC, a wavelength-reused hierarchical Optical Network on Chip architecture, is proposed to achieve high network scalability, namely obtaining low communication delay and high throughput capacity for hundreds of thousands of cores by reusing the limited number of available wavelengths with the modest hardware cost and energy overhead. WRH-ONoC combines the advantages of non-blocking communication in each lambda-router and wavelength reuse in all lambda-routers through the hierarchical networking. Both theoretical analysis and simulation results indicate that WRH-ONoC can achieve prominent improvement on the communication performance and scalability (e.g., 46.0% of reduction on the zero-load packet delay and 72.7% of improvement on the network throughput for 400 cores with small hardware cost and energy overhead) in comparison with existing schemes. Secondly, DWRMR, a dynamical wavelength-reused multicast scheme based on the optical multicast ring, is proposed for widely existing multicast communications in many-core processors. In DWRMR, an optical multicast ring is dynamically constructed for each multicast group and the multicast packets are transmitted in a single-send-multi-receive manner requiring only one wavelength. All the cores in the same multicast group can reuse the established multicast ring through an optical token arbitration scheme for the interactive multicast communications, thereby avoiding the frequent construction of multicast routing paths dedicatedly for each core. Simulation results indicate that DWRMR can reduce more than 50% of end-to-end packet delay with slight hardware cost, or require only half number of wavelengths to achieve the same performance compared with existing schemes. Thirdly, Dark-ONoC, a dynamically configurable ONoC architecture, is proposed for the many-core processor with dark silicon. Dark silicon is an inevitable phenomenon that only a small number of cores can be activated simultaneously while the other cores must stay in dark state (power-gated) due to the restricted power budget. Dark-ONoC periodically allocates non-blocking optical routing paths only between the active cores with as less wavelengths as possible. Thus, it can obtain high-performance communication and low power consumption at the same time. Extensive simulations are conducted with the dark silicon patterns from both synthetic distribution and real data traces. The simulation results indicate that the number of wavelengths is reduced by around 15% and the overall power consumption is reduced by 23.4% compared to existing schemes. Finally, this thesis concludes several important principles on the design of wavelength-reused ONoC architecture, and summarizes some perspective issues for the future research

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration. It is responsible for the unreliability of practical machine learning systems. For example, in online advertising, an ad can receive a click-through rate prediction of 0.1 over some population of users where its actual click rate is 0.15. In such cases, the probabilistic predictions have to be fixed before the system can be deployed. In this paper, we first introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We show that existing post-hoc calibration methods have limited improvements in the new field-level metric and other non-calibration metrics such as the AUC score. To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set. We present extensive experiments on five large-scale datasets. The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier score and AUC, as well as the proposed field-level calibration error.Comment: WWW 202

Characterization and Correction of the Scattering Background Produced by Dust on the Objective Lens of the Lijiang 10-cm Coronagraph

Scattered light from the objective lens, directly exposed to the intense sunlight, is a dominant source of stray light in internally occulted coronagraphs. The variable stray light, such as the scatter from dust on the objective lens, can produce varying scattering backgrounds in coronal images, significantly impacting image quality and data analysis. Using data acquired by the Lijiang 10-cm Coronagraph, the quantitative relationship between the distribution of dust on the objective lens and the resulting scattering backgrounds background is analyzed. Two empirical models for the scattering background are derived, and used to correct the raw coronal data. The second model, which depends on three parameters and performs better, shows that the scattering-background distribution varies with angle, weakens with increasing height, and enhances with increasing dust level on the objective lens. Moreover, we find that the dust on the center of the objective lens can contribute more significantly to the scattering background than on the edge. This study not only quantitatively confirms the significant impact of the stray light produced by dust on the objective lens of the coronagraph, but also corrects the coronal data with this stray light for the first time. Correcting for dust-scattered light is crucial for the high-precision calibration of ground-based coronagraph data, enabling a more accurate analysis of coronal structures. Furthermore, our model is envisioned to support the provision of reliable observational data for future routine coronal magnetic-field measurements using ground-based coronagraphs.Comment: 18 pages, 14 figrue

Reinforcement Learning from Statistical Feedback: the Journey from AB Testing to ANT Testing

Reinforcement Learning from Human Feedback (RLHF) has played a crucial role in the success of large models such as ChatGPT. RLHF is a reinforcement learning framework which combines human feedback to improve learning effectiveness and performance. However, obtaining preferences feedback manually is quite expensive in commercial applications. Some statistical commercial indicators are usually more valuable and always ignored in RLHF. There exists a gap between commercial target and model training. In our research, we will attempt to fill this gap with statistical business feedback instead of human feedback, using AB testing which is a well-established statistical method. Reinforcement Learning from Statistical Feedback (RLSF) based on AB testing is proposed. Statistical inference methods are used to obtain preferences for training the reward network, which fine-tunes the pre-trained model in reinforcement learning framework, achieving greater business value. Furthermore, we extend AB testing with double selections at a single time-point to ANT testing with multiple selections at different feedback time points. Moreover, we design numerical experiences to validate the effectiveness of our algorithm framework