Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees

This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The SENC problem can be decomposed into three sub problems: detecting emerging new classes, classifying for known classes, and updating models to enable classification of instances of the new class and detection of more emerging new classes. The proposed method employs completely random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods

Order flow dynamics around extreme price changes on an emerging stock market

We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the bid-ask spread, and the volume imbalance increase before the extreme events and decay slowly as a power law, which forms a well-established peak. The volume of buy market orders increases faster and the corresponding peak appears earlier than for sell market orders around positive events, while the volume peak of sell market orders leads buy market orders in the magnitude and time around negative events. When orders are divided into four groups according to their aggressiveness, we find that the behaviors of order volume and order number are similar, except for buy limit orders and canceled orders that the peak of order number postpones two minutes later after the peak of order volume, implying that investors placing large orders are more informed and play a central role in large price fluctuations. We also study the relative rates of different types of orders and find differences in the dynamics of relative rates between buy orders and sell orders and between individual investors and institutional investors. There is evidence showing that institutions behave very differently from individuals and that they have more aggressive strategies. Combing these findings, we conclude that institutional investors are more informed and play a more influential role in driving large price fluctuations.Comment: 22 page

Preferred numbers and the distribution of trade sizes and trading volumes in the Chinese stock market

The distribution of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individual stocks exhibits jumps, which is caused by the number preference of traders when placing orders. We analyze the applicability of the "$q$-Gamma" function for fitting the distribution by the Cram\'{e}r-von Mises criterion. The empirical PDFs of trading volumes at different timescales $\Delta{t}$ ranging from 1 min to 240 min can be well modeled. The applicability of the $q$-Gamma functions for multiple trades is restricted to the transaction numbers $\Delta{n}\leqslant8$. We find that all the PDFs have power-law tails for large volumes. Using careful estimation of the average tail exponents $\alpha$ of the distribution of trade sizes and trading volumes, we get $\alpha>2$, well outside the L{\'e}vy regime.Comment: 7 pages, 5 figures and 4 table

Banach spaces not antiproximinal in their second dual

AbstractWe prove that (l1,¦·¦) is not antiproximinal in (l1,¦·¦)∗∗, where ¦·¦ is the norm constructed in [1]. This fact shows that Davidson's equivalent norm fails to deliver on his promise

Implementing and Investigating Refractoriness in LGMD Neural Networks

Collision can be threatening for animals including human beings. Thus, reliable and accurate collision perception is vital in plenty of aspects. Taking inspiration from nature, the computational methods of lobula giant movement detectors (LGMDs) identified in flying locust’s visual pathways have positively demonstrated impacts on addressing this problem. However, collision perception methods based on visual cues are still challenged by several factors in physical world including ultra-fast approaching linear velocity and noisy signals. The current visual-cue-based LGMD neural networks could show ineffectiveness or generate false positive, especially when objects approach at fast velocity and when the video signals are polluted by noises. Hence, how ultra-fast approaching object in a colliding way can be detected remains to be further improved. Neural refractoriness, also known as refractory period (RP), a common mechanism inside animals’ neural system studied for decades, though it has been considered to play a significant role in stabilising a neuron, has not been researched in the aforementioned LGMD neural networks for accurate and reliable collision perception. In this thesis, a novel method phenomenologically simulating neural refractoriness inside animals’ neural systems is proposed and is further investigated on its functionality and efficacy when it is combined with the classic LGMD1 and LGMD2 neuronal networks for collision perception. Our systematically experimental results demonstrate that, mimicking refractoriness not only enhances the LGMD1 models in terms of reliability and stability when facing ultra-fast approaching objects, but also improves its performance against visual stimuli polluted by Gaussian or Salt & Pepper noise. Potential proof of LGMD2 neural network’s reliability and its capability to adapt to cluttered physical world is also provided. This research shows that, modelling of refractoriness can be effective and benefiting in collision perception neuronal networks, and be promising to address the aforementioned challenges for collision perception

Determination of cyclovirobuxine D in human plasma by liquid chromatography tandem mass spectrometry and application in a pharmacokinetic study

AbstractA sensitive and reliable method based on liquid chromatography tandem mass spectrometry (LC–MS/MS) for the quantitation of cyclovirobuxine D in human plasma has been developed and validated. Sample preparation by solid phase extraction was followed by separation on a CN column with a mobile phase of methanol–water (95:5, v/v) containing 0.2% formic acid. Mass spectrometric detection in the positive ion mode was carried out by selected reaction monitoring (SRM) of the transitions at m/z 403.0→372.0 for cyclovirobuxine D and m/z 325.0→234.0 for citalopram (internal standard). The method was linear in the range 10–200ng/L with LLOQ of 10ng/L, recovery >85%, and no significant matrix effects. Intra- and inter-day precisions were all <9% with accuracies of 94.0–104.8%. The method was successfully applied to a pharmacokinetic study involving a single oral administration of a 2mg cyclovirobuxine D tablet to twenty-two healthy Chinese volunteers

Optimization of Roller Velocity for Quenching Machine Based on Heat Transfer Mathematical Model

During quenching process of steel plate, control parameters are important to product quality. In this work, heat transfer mathematical model has been developed for roller-type quenching machine to predict the temperature field of plate at first, and then an optimization schedule considering quenching technology and equipment limitations is developed firstly based on the heat transfer mathematical model with considering the shortest quenching time. A numerical simulation is performed during optimization process to investigate the effects of roller velocity on the temperature of representative plate. Based on the optimization method, study is also performed for different thickness of plate to obtain the corresponding roller velocity. The results show that the optimized roller velocity can be achieved for the roller-type continuous quenching machine based on the heat transfer mathematical model. With the increasing of plate’s thickness, the optimized roller velocity decreases exponentially