Reversed Indexes ≈\approx Values in Wavelet Trees

This work presents a discovery to advance the wisdom in a particular Succinct Data Structure: Wavelet Tree (Grossi, Gupta, and Vitter 2003). The discovery is first made by showing the feasibility of Reversed Indexes = Values: for integers within $[0,2^{N})$, there exists a Wavelet Tree that its compressed indexes can be equivalent to the Leibniz Binary system (Leibniz 1703), with only the bit reversal. Then we show how to strengthen the discovery by generalizing it into Reversed Indexes $\approx$ Values, by applying a longest common subsequence in bits and its patterns. Finally, we conjuncture potential implications of the above ideas by discussing its benefits, and modifications to the RAM model. The discovery reveals that: (1) the usability of Succinct Data Structure can be significantly expanded, by enabling Computation Directly on Compression; and (2) near-optimal lossless compression can still yield close connections with the Leibniz Binary System (Leibniz 1703), which breeds polymorphic functionalities within a single piece of the information. This work also provides an initial analysis of the benefits from the method (and potentially other extensions), and suggests potential modifications.Comment: This prerpint was rejected from ACM STOC 2024, and updated with the review comment

Value, Representation, Information and Communication

A new analytic framework is first formalized via the usage of the Monadology (Leibniz 1898), to expand the understanding of Zermelo-Fraenkel-choice set theory (ZFC) and Von Neumann-Bernays-Godel set theory (NBG). Implicitly, the framework levels value, representation and information separately. Given the fact that there exists a coincidental equivalence between Von Neumann universe and originally-formalized motivation in ZFC, this work hypothesizes the essential of ordered values for one monand, to carry out efficient communication with the rest. This work then focuses on the relationship among values, representation and information (and suggests potential methods for quantitative analysis). First, this framework generalizes the definition of values and representations from "Indexes approximate Values" principle by (Peng 2023) via surreal numbers (Knuth 1974). Second, credited to surreal numbers, this work recursively connects representations and information via subsets of sets. Therefore, the definition to metric space(s) is naturally formed by representations, and quantitative methods (e.g., Hausdorff Distance) can be applied for quantitative analysis among (sub)sets. Third, this framework conjectures that: as long as the metric space is (or can be formed as) complete, the existence tests can be performed via Cauchy Sequence (or its generalized methods). This work finally revisits the communication theory, and suggests new perspectives from the new analytic framework. Particularly, this work hypothesizes a (quantitative) relationship between values and representation, and conjectures that: the optimal construction of representations exists, and it can be derived as the core value of one monad via Cauchy Inequality (or its generalized methods)

Formalizing Feint Actions, and Example Studies in Two-Player Games

Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from their opponents. Such actions are regarded as widely-used tactic in most non-deterministic Two-player Games (e.g. boxing and fencing). However, existing literature does not provide comprehensive and concrete formalization on Feint actions, and their implications on Two-Player Games. We argue that a full exploration on Feint actions is of great importance towards more realistic Two-player Games. In this paper, we provide the first comprehensive and concrete formalization of Feint actions. The key idea of our work is to (1) allow automatic generation of Feint actions, via our proposed Palindrome-directed Generation of Feint actions; and (2) provide concrete principles to properly combine Feint and attack actions. Based on our formalization of Feint actions, we also explore the implications on the game strategy model, and provide optimizations to better incorporate Feint actions. Our experimental results shows that accounting for Feint actions in Non-Deterministic Games (1) brings overall benefits to the game design; and (2) has great benefits on on either game animations or strategy designs, which also introduces a great extent of randomness into randomness-demanded Game models

Feint in Multi-Player Games

This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning). Finally, our work quantitatively examines the effectiveness of our design, and the results show that our design of Feint can (1) greatly improve the reward gains from the game; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption. We conclude that our design of Feint is effective and practical, to make Multi-Player Games more interesting

Acoustic radiation force and its application for cell manipulation and ion channels activation

Sound is a stress wave that carries energy and momentum flux. Scattered sound waves can generate acoustic radiation force that can be used to manipulate particles or cells. This dissertation demonstrates the physics behind cell manipulation by ultrasound. The work begins with a detailed analysis of the mechanics of using standing surface acoustic waves to fabricate acoustic tweezers for contactless particle manipulation using acoustic radiation force. Models to design and analyze acoustic radiation force have traditionally relied on plane wave theories that cannot predict how standing surface acoustic waves can levitate cells in the direction perpendicular to the substrate. We therefore developed a revised model for how standing surface acoustic waves lead to acoustic radiation force in three dimensions. The dissertation then explored use of ultrasound for manipulating mechanosensitive ion channels in both plant and animal cells. Although evidence that such manipulation can occur is strong, it is still unclear how ultrasound activates the mechanosensitive ion channels. The dissertation therefore developed mathematical models of these forces, of how they deform the cell membrane, and of how these membrane deformations activate mechanosensitive ion channels. The modeling approach was verified in an idealized system involving measuring ion channel currents in frog oocytes that were transfected with mechanosensitive ion channels and irradiated using ultrasound. The model predicted these currents, and a modified version of the approach was then used to predict the sensitivity of stress activated ion channels in tomato trichomes to the acoustic radiation force arising from acoustic emissions by insect and other animals. The integrated modeling approach shows promise for design and analysis of experiments and tools that probe and harness the function of stress activated ion channels via ultrasound

Building BROOK: A multi-modal and facial video database for Human-Vehicle Interaction research

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github.io/BROOK/

Correlation of Influenza Virus Excess Mortality with Antigenic Variation: Application to Rapid Estimation of Influenza Mortality Burden

The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention

Succinct Representations in Collaborative Filtering: A Case Study using Wavelet Tree on 1,000 Cores

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the UI Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD’s characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results

First attempt to build realistic driving scenes using video-to-video synthesis in OpenDS framework

Existing programmable simulators enable researchers to customize different driving scenarios to conduct in-lab automotive driver simulations. However, software-based simulators for cognitive research generate and maintain their scenes with the support of 3D engines, which may affect users' experiences to a certain degree since they are not sufficiently realistic. Now, a critical issue is the question of how to build scenes into real-world ones. In this paper, we introduce the first step in utilizing video-to-video synthesis, which is a deep learning approach, in OpenDS framework, which is an open-source driving simulator software, to present simulated scenes as realistically as possible. Off-line evaluations demonstrated promising results from our study, and our future work will focus on how to merge them appropriately to build a close-to-reality, real-time driving simulator

Building BROOK: A multi-modal and facial video database for Human-Vehicle Interaction research

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github.io/BROOK/