309 research outputs found
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Sequential Modelling and Inference of High-frequency Limit Order Book with State-space Models and Monte Carlo Algorithms
The high-frequency limit order book (LOB) market has recently attracted increasing research attention from both the industry and the academia as a result of expanding algorithmic trading. However, the massive data throughput and the inherent complexity of high-frequency market dynamics also present challenges to some classic statistical modelling approaches. By adopting powerful state-space models from the field of signal processing as well as a number of Bayesian inference algorithms such as particle filtering, Markov chain Monte Carlo and variational inference algorithms, this thesis presents my extensive research into the high-frequency limit order book covering a wide scope of topics.
Chapter 2 presents a novel construction of the non-homogeneous Poisson process to allow online intensity inference of limit order transactions arriving at a central exchange as point data. Chapter 3 extends a baseline jump diffusion model for market fair-price process to include three additional model features taken from real-world market intuitions. In Chapter 4, another price model is developed to account for both long-term and short-term diffusion behaviours of the price process. This is achieved by incorporating multiple jump-diffusion processes each exhibiting a unique characteristic. Chapter 5 observes the multi-regime nature of price diffusion processes as well as the non-Markovian switching behaviour between regimes. As such, a novel model is proposed which combines the continuous-time state-space model, the hidden semi-Markov switching model and the non-parametric Dirichlet process model. Additionally, building upon the general structure of the particle Markov chain Monte Carlo algorithm, I further propose an algorithm which achieves sequential state inference, regime identification and regime parameters learning requiring minimal prior assumptions. Chapter 6 focuses on the development of efficient parameter-learning algorithms for state-space models and presents three algorithms each demonstrating promising results in comparison to some well-established methods.
The models and algorithms proposed in this thesis not only are practical tools for analysing high-frequency LOB markets, but can also be applied in various areas and disciplines beyond finance
Accurate position tracking with a single UWB anchor
Accurate localization and tracking are a fundamental requirement for robotic
applications. Localization systems like GPS, optical tracking, simultaneous
localization and mapping (SLAM) are used for daily life activities, research,
and commercial applications. Ultra-wideband (UWB) technology provides another
venue to accurately locate devices both indoors and outdoors. In this paper, we
study a localization solution with a single UWB anchor, instead of the
traditional multi-anchor setup. Besides the challenge of a single UWB ranging
source, the only other sensor we require is a low-cost 9 DoF inertial
measurement unit (IMU). Under such a configuration, we propose continuous
monitoring of UWB range changes to estimate the robot speed when moving on a
line. Combining speed estimation with orientation estimation from the IMU
sensor, the system becomes temporally observable. We use an Extended Kalman
Filter (EKF) to estimate the pose of a robot. With our solution, we can
effectively correct the accumulated error and maintain accurate tracking of a
moving robot.Comment: Accepted by ICRA202
Response and Simulation of Vegetation in Desert Scenic Spot to Tourists’ Trampling Disturbance
A large number of tourists had a devastating effect to the scenic area. Shapotou and Huangshagudu scenic spots in Ningxia were selected as the research areas. The fait accompli method was used to investigate the response of footpath in the above scenic spots to tourists’ stampede interference. Three different angles and different vegetation types of quadrats were set up to simulate tourists’ stampede mode and observe the vegetation recovery after stampede. The results showed that: (1) The tourist trampling disturbance mainly were limited 0-4 meters distance from the tourist trails, but there was difference for different tourist trails. (2) The index of land cover impact (ILCI) of the investigating sections indicated the 1 meter distance from tourist trail is seriously disturbed; Because of the palisade on both sides of the plank road, the average value of ILCI in north of Shapotou (investigating section 4) is less than 44.9%.(3)With the increase of tourist activity,the coverage of vegetation decline, the height of plant reduce, the quantity and kinds decrease,soil crust fragmentation increase.(4)Because of different angle of sand dune, the impact of tourist activity to vegetation and soil is different.Vegetation and biological crust of sample C which is the biggest angle suffer from the devastation.(5)Based on limits of acceptable change (LAC) visitor questionnaires, the limit of acceptable change in ground coverage was 16.4%. The vegetation coverage should be below this level at desert scenic spots. It shows that there is natural incompatible relationship between tourist demand to the empty and desolate desert and desert ecological management. The results also indicated that the current tourism disturbance had some negative effect on the tourist experience and ecosystem.
Facility Location Games with Ordinal Preferences
We consider a new setting of facility location games with ordinal
preferences. In such a setting, we have a set of agents and a set of
facilities. Each agent is located on a line and has an ordinal preference over
the facilities. Our goal is to design strategyproof mechanisms that elicit
truthful information (preferences and/or locations) from the agents and locate
the facilities to minimize both maximum and total cost objectives as well as to
maximize both minimum and total utility objectives. For the four possible
objectives, we consider the 2-facility settings in which only preferences are
private, or locations are private. For each possible combination of the
objectives and settings, we provide lower and upper bounds on the approximation
ratios of strategyproof mechanisms, which are asymptotically tight up to a
constant. Finally, we discuss the generalization of our results beyond two
facilities and when the agents can misreport both locations and preferences
Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization
Most current multi-modal summarization methods follow a cascaded manner,
where an off-the-shelf object detector is first used to extract visual
features, then these features are fused with language representations to
generate the summary with an encoder-decoder model. The cascaded way cannot
capture the semantic alignments between images and paragraphs, which are
crucial to a precise summary. In this paper, we propose ViL-Sum to jointly
model paragraph-level \textbf{Vi}sion-\textbf{L}anguage Semantic Alignment and
Multi-Modal \textbf{Sum}marization. The core of ViL-Sum is a joint multi-modal
encoder with two well-designed tasks, image reordering and image selection. The
joint multi-modal encoder captures the interactions between modalities, where
the reordering task guides the model to learn paragraph-level semantic
alignment and the selection task guides the model to selected summary-related
images in the final summary. Experimental results show that our proposed
ViL-Sum significantly outperforms current state-of-the-art methods. In further
analysis, we find that two well-designed tasks and joint multi-modal encoder
can effectively guide the model to learn reasonable paragraphs-images and
summary-images relations
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