Realized Volatility and Stylized Facts of Chinese Treasury Bond Market

Based on high frequency data, this paper studies the volatility stylized facts of Chinese Treasury bond market (CTBM) in detail, including the best sampling frequency selected to compute the realized volatility, the conditional and unconditional distribution of the returns, the long memory property, the intraday, inter-day pattern of the returns and volatility, the asymmetry of volatility, and so on. The main conclusions about CTBM volatility are provided. 15 minute is best sampling frequency. The RV-based conditional distribution of return is nearly normal. Both return and volatility have significant inter-day but insignificant intraday periodicity. Moreover, the volatility asymmetry existing widely in stock or exchange market is not significant in Chinese Treasury bond market. Key words: Realized volatility, Chinese Treasury bond market, High frequency data Résumé: Basé sur des données de haute fréquence, le présent article étudie en détail la volatilité des faits stylisés du Marché de bon du Trésor chinois (MBTC), comprenant la meilleure fréquence de prélèvement sélectionnée pour calculer la volatilité réalisée, la distribution conditionnelle et inconditionnelle des retours, la propriété de longue mémoire, le modèle intrajour et interjour des retours et la volatilité, l’asymétrie de volatilité, etc. Les conclusions principales sur la volatilité du MBTC sont les suivantes : 15 minutes est la meilleure fréquence de prélèvement, la distribution conditionnelle RV-basé du retour est presque normale. Le retour et l’asymétrie de volatilité ont tous les deux une périodicité inter-jour signifiante, mais une périodicité intrajour insignifiante. D’ailleurs, l’asymétrie de volatilité existant amplement dans la bourse et le marché des changes n’est pas importante sur le Marché de bon du Trésor chinois. Mots-Clés: volatilité réalisée, Marché de bon du Trésor chinois, données de haute fréquenc

Design of Air Conditioning Distributed Control System for an Office Building in Xi’an

AbstractIn this paper, the system 600 APOGEE (distributed control system) of Siemens building technologies is applied to an air conditioning controlling system in an office building. Firstly, control scheme is introduced. Then, considering the problem that air conditioning system usually loses contact with control in practice, some suggestion is presente

Classical-driving-assisted quantum synchronization in non-Markovian environments

We study the quantum phase synchronization of a driven two-level system (TLS) coupled to a structured environment and demonstrate that quantum synchronization can be enhanced by the classical driving field. We use the Husimi $Q$-function to characterize the phase preference and find the in-phase and anti-phase locking phenomenon in the phase diagram. Remarkably, we show that the classical driving enables a TLS to reach stable anti-phase locking in the Markovian regime. However, we find that the synergistic action of classical driving and non-Markovian effects significantly enhances the in-phase locking. By introducing the $S$-function and its maximal value to quantify the strength of synchronization and sketch the synchronization regions, we observe the typical signatures of the hollowed Arnold tongue in the parameter regions of synchronization. In the hollowed Arnold tongue, the synchronization regions exist both inside and outside the tongue while unsynchronized regions only lie on the boundary line. We also provide an intuitive interpretation of the above results by using the quasimode theory.Comment: 10 pages, 5 figures, revised versio

Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information

The state-of-the-art neural video codecs have outperformed the most sophisticated traditional codecs in terms of RD performance in certain cases. However, utilizing them for practical applications is still challenging for two major reasons. 1) Cross-platform computational errors resulting from floating point operations can lead to inaccurate decoding of the bitstream. 2) The high computational complexity of the encoding and decoding process poses a challenge in achieving real-time performance. In this paper, we propose a real-time cross-platform neural video codec, which is capable of efficiently decoding of 720P video bitstream from other encoding platforms on a consumer-grade GPU. First, to solve the problem of inconsistency of codec caused by the uncertainty of floating point calculations across platforms, we design a calibration transmitting system to guarantee the consistent quantization of entropy parameters between the encoding and decoding stages. The parameters that may have transboundary quantization between encoding and decoding are identified in the encoding stage, and their coordinates will be delivered by auxiliary transmitted bitstream. By doing so, these inconsistent parameters can be processed properly in the decoding stage. Furthermore, to reduce the bitrate of the auxiliary bitstream, we rectify the distribution of entropy parameters using a piecewise Gaussian constraint. Second, to match the computational limitations on the decoding side for real-time video codec, we design a lightweight model. A series of efficiency techniques enable our model to achieve 25 FPS decoding speed on NVIDIA RTX 2080 GPU. Experimental results demonstrate that our model can achieve real-time decoding of 720P videos while encoding on another platform. Furthermore, the real-time model brings up to a maximum of 24.2\% BD-rate improvement from the perspective of PSNR with the anchor H.265.Comment: 14 page

A preliminary study on the monitoring of mixed venous oxygen saturation through the left main bronchus

INTRODUCTION: The study sought to assess the feasibility and accuracy of measuring mixed venous oxygen saturation (SvO(2)) through the left main bronchus (SpO(2trachea)) METHODS: Twenty hybrid pigs of each sex were studied. After anesthesia, a Robertshaw double-lumen tracheal tube with a single-use pediatric pulse oximeter attached to the left lateral surface was introduced toward the left main bronchus of the pig by means of a fibrobronchoscope. Measurements of SpO(2trachea )and oxygen saturation from pulmonary artery samples (SvO(2blood)) were performed with an intracuff pressure of 0 to 60 cmH(2)O. After equilibration, hemorrhagic shock was induced in these pigs by bleeding to a mean arterial blood pressure of 40 mmHg. With the intracuff pressure maintained at 60 cmH(2)O, SpO(2trachea )and SvO(2blood )were obtained respectively during the pre-shock period, immediately after the onset of shock, 15 and 30 minutes after shock, and 15, 30, and 60 minutes after resuscitation. RESULTS: SpO(2trachea )was the same as SvO(2blood )at an intracuff pressure of 10, 20, 40, and 60 cmH(2)O, but was reduced when the intracuff pressure was zero (p < 0.001 compared with SvO(2blood)) in hemodynamically stable states. Changes of SpO(2trachea )and SvO(2blood )corresponded with varieties of cardiac output during the hemorrhagic shock period. There was a significant correlation between the two methods at different time points. CONCLUSION: Measurement of the left main bronchus SpO(2 )is feasible and provides similar readings to SvO(2blood )in hemodynamically stable or in low saturation states. Tracheal oximetry readings are not primarily derived from the tracheal mucosa. The technique merits further evaluation

AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization

Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to the lack of explicit 3D supervision and imprecise camera poses, resulting in suboptimal outcomes. To tackle these issues, we propose AltNeRF -- a novel framework designed to create resilient NeRF representations using self-supervised monocular depth estimation (SMDE) from monocular videos, without relying on known camera poses. SMDE in AltNeRF masterfully learns depth and pose priors to regulate NeRF training. The depth prior enriches NeRF's capacity for precise scene geometry depiction, while the pose prior provides a robust starting point for subsequent pose refinement. Moreover, we introduce an alternating algorithm that harmoniously melds NeRF outputs into SMDE through a consistence-driven mechanism, thus enhancing the integrity of depth priors. This alternation empowers AltNeRF to progressively refine NeRF representations, yielding the synthesis of realistic novel views. Additionally, we curate a distinctive dataset comprising indoor videos captured via mobile devices. Extensive experiments showcase the compelling capabilities of AltNeRF in generating high-fidelity and robust novel views that closely resemble reality