arXiv subjects

Siew Ann Cheong

Publications and source records attributed to Siew Ann Cheong.

At least 19 recordsLinked to original sources

arXiv2025

On the Statistical Physics of Wealth Distribution

Asset exchange models (AEMs) provide a physics-inspired framework for studying wealth formation. These models capture wealth distribution dynamics via pairwise money exchanges, yielding steady-state distributions from exponential to heavy-tailed power laws. However, empirical validation remains limited due to scarce real-world transaction data. Here, we bridge this gap by analyzing spectral properties of Markov transition matrices from both AEMs and Ethereum blockchain data, enabling quantitative comparison of model and empirical exchange dynamics. We assess thermodynamic equilibrium in exchange processes - specifically, detailed balance - and derive steady-state wealth distributions from transition matrices. We find that equilibrium systems' spectra contain only real eigenvalues and link Ethereum price changes to spectral shifts. We also investigate external factors (e.g., taxes), showing that advantages for richer individuals make wealth evolution path-dependent on initial distributions. Our work establishes a quantitative framework for validating AEMs with real data, advancing economic modeling and understanding of wealth formation.

physics.soc-ph↗

arXiv2025

Survey-Based Calibration of the One-Community and Two-Community Social Network Models Used for Testing Singapore's Resilience to Pandemic Lockdown

A resilient society is one capable of withstanding and thereafter recovering quickly from large shocks. Brought to the fore by the COVID-19 pandemic of 2020--2022, this social resilience is nevertheless difficult to quantify. In this paper, we measured how quickly the Singapore society recovered from the pandemic, by first modeling it as a dynamic social network governed by three processes: (1) random link addition between strangers; (2) social link addition between individuals with a friend in common; and (3) random link deletion . To calibrate this model, we carried out a survey of a representative sample of $N = 2,057$ residents and non-residents in Singapore between Jul and Sep 2022 to measure the numbers of random and social contacts gained over a fixed duration, as well as the number of contacts lost over the same duration, using phone contacts as proxy for social contacts. Lockdown simulations using the model that fits the survey results best suggest that Singapore would recover from such a disruption after 1--2 months.

physics.soc-ph↗

arXiv2024

Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries

Recently, multiple applications of machine learning have been introduced. They include various possibilities arising when image analysis methods are applied to, broadly understood, video streams. In this context, a novel tool, developed for academic educators to enhance the teaching process by automating, summarizing, and offering prompt feedback on conducting lectures, has been developed. The implemented prototype utilizes machine learning-based techniques to recognise selected didactic and behavioural teachers' features within lecture video recordings. Specifically, users (teachers) can upload their lecture videos, which are preprocessed and analysed using machine learning models. Next, users can view summaries of recognized didactic features through interactive charts and tables. Additionally, stored ML-based prediction results support comparisons between lectures based on their didactic content. In the developed application text-based models trained on lecture transcriptions, with enhancements to the transcription quality, by adopting an automatic speech recognition solution are applied. Furthermore, the system offers flexibility for (future) integration of new/additional machine-learning models and software modules for image and video analysis.

cs.AI↗

arXiv2022

Strategic differences between regional investments into graphene technology and how corporations and universities manage patent portfolios

Nowadays, patenting activities are essential in converting applied science to technology in the prevailing innovation model. To gain strategic advantages in the technological competitions between regions, nations need to leverage the investments of public and private funds to diversify over all technologies or specialize in a small number of technologies. In this paper, we investigated who the leaders are at the regional and assignee levels, how they attained their leadership positions, and whether they adopted diversification or specialization strategies, using a dataset of 176,193 patent records on graphene between 1986 and 2017 downloaded from Derwent Innovation. By applying a co-clustering method to the IPC subclasses in the patents and using a z-score method to extract keywords from their titles and abstracts, we identified seven graphene technology areas emerging in the sequence synthesis - composites - sensors - devices - catalyst - batteries - water treatment. We then examined the top regions in their investment preferences and their changes in rankings over time and found that they invested in all seven technology areas. In contrast, at the assignee level, some were diversified while others were specialized. We found that large entities diversified their portfolios across multiple technology areas, while small entities specialized around their core competencies. In addition, we found that universities had higher entropy values than corporations on average, leading us to the hypothesis that corporations file, buy, or sell patents to enable product development. In contrast, universities focus only on licensing their patents. We validated this hypothesis through an aggregate analysis of reassignment and licensing and a more detailed analysis of three case studies - SAMSUNG, RICE UNIVERSITY, and DYSON.

cs.DL↗

arXiv2021

Coevolution of theoretical and applied research: a case study of graphene research by temporal and geographic analysis

As a part of science of science (SciSci) research, the evolution of scientific disciplines has been attracting a great deal of attention recently. This kind of discipline level analysis not only give insights of one particular field but also shed light on general principles of scientific enterprise. In this paper we focus on graphene research, a fast growing field covers both theoretical and applied study. Using co-clustering method, we split graphene literature into two groups and confirm that one group is about theoretical research (T) and another corresponds to applied research (A). We analyze the proportion of T/A and found applied research becomes more and more popular after 2007. Geographical analysis demonstrated that countries have different preference in terms of T/A and they reacted differently to research trend. The interaction between two groups has been analyzed and shows that T extremely relies on T and A heavily relies on A, however the situation is very stable for T but changed markedly for A. No geographic difference is found for the interaction dynamics. Our results give a comprehensive picture of graphene research evolution and also provide a general framework which is able to analyze other disciplines.

cs.DL↗

arXiv2020

Spatial-Temporal Dataset of COVID-19 Outbreak in China

We present Coronavirus disease 2019 (COVID-19) statistics in China dataset: daily statistics of the COVID-19 outbreak in China at the city/county level. For each city/country, we include the six most important numbers for epidemic research: daily new infections, accumulated infections, daily new recoveries, accumulated recoveries, daily new deaths, and accumulated deaths. We cross validate the dataset and the estimate error rate is about 0.04%. We then give several examples to show how to trace the spreading in particular cities or provinces, and also contrast the development of COVID-19 in all cities in China at the early, middle and late stages. We hope this dataset can help researchers around the world better understand the spreading dynamics of COVID-19 at a regional level, to inform intervention and mitigation strategies for policymakers.

q-bio.PE↗

arXiv2019

The role of mainstreamness and interdisciplinarity for the relevance of scientific papers

There is demand from science funders, industry, and the public that science should become more risk-taking, more out-of-the-box, and more interdisciplinary. Is it possible to tell how interdisciplinary and out-of-the-box scientific papers are, or which papers are mainstream? Here we use the bibliographic coupling network, derived from all physics papers that were published in the Physical Review journals in the past century, to try to identify them as mainstream, out-of-the-box, or interdisciplinary. We show that the network clusters into scientific fields. The position of individual papers with respect to these clusters allows us to estimate their degree of mainstreamness or interdisciplinary. We show that over the past decades the fraction of mainstream papers increases, the fraction of out-of-the-box decreases, and the fraction of interdisciplinary papers remains constant. Studying the rewards of papers, we find that in terms of absolute citations, both, mainstream and interdisciplinary papers are rewarded. In the long run, mainstream papers perform less than interdisciplinary ones in terms of citation rates. We conclude that to avoid a trend towards mainstreamness a new incentive scheme is necessary.

cs.DL↗

arXiv2019

The Golden Eras of Graphene Science and Technology: Bibliographic Evidences From Journal and Patent Publications

Today's scientific research is an expensive enterprise funded largely by taxpayers' and corporate groups' monies. It is a critical part in the competition between nations, and all nations want to discover fields of research that promise to create future industries, and dominate these by building up scientific and technological expertise early. However, our understanding of the value chain going from science to technology is still in a relatively infant stage, and the conversion of scientific leadership into market dominance remains very much an alchemy rather than a science. In this paper, we analyze bibliometric records of scientific journal publications and patents related to graphene, at the aggregate level as well as on the temporal and spatial dimensions. We find the present leaders of graphene science and technology emerged rather late in the race, after the initial scientific leaders lost their footings. More importantly, notwithstanding the amount of funding already committed, we find evidences that suggest the 'Golden Eras' of graphene science and technology were in 2010 and 2012 respectively, in spite of the continued growth of journal and patent publications in this area.

cs.DL↗

arXiv2018

Using Machine Learning to Predict the Evolution of Physics Research

The advancement of science as outlined by Popper and Kuhn is largely qualitative, but with bibliometric data it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore it is also important to allocate finite resources to research topics that have growth potential, to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analysing the APS publication data set from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increases significantly for merging events, and decreases significantly for splitting events. Our results represent a first step from a descriptive understanding of the Science of Science (SciSci), towards one that is ultimately prescriptive.

physics.soc-ph↗

arXiv2017

Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks

Even as we advance the frontiers of physics knowledge, our understanding of how this knowledge evolves remains at the descriptive levels of Popper and Kuhn. Using the APS publications data sets, we ask in this letter how new knowledge is built upon old knowledge. We do so by constructing year-to-year bibliographic coupling networks, and identify in them validated communities that represent different research fields. We then visualize their evolutionary relationships in the form of alluvial diagrams, and show how they remain intact through APS journal splits. Quantitatively, we see that most fields undergo weak Popperian mixing, and it is rare for a field to remain isolated/undergo strong mixing. The sizes of fields obey a simple linear growth with recombination. We can also reliably predict the merging between two fields, but not for the considerably more complex splitting. Finally, we report a case study of two fields that underwent repeated merging and splitting around 1995, and how these Kuhnian events are correlated with breakthroughs on BEC, quantum teleportation, and slow light. This impact showed up quantitatively in the citations of the BEC field as a larger proportion of references from during and shortly after these events.

physics.soc-ph↗

arXiv2016

Grown-in beryllium diffusion in indium gallium arsenide: An ab initio, continuum theory and kinetic Monte Carlo study

A roadblock in utilizing InGaAs for scaled-down electronic devices is its anomalous dopant diffusion behavior; specifically, existing models are not able to explain available experimental data on beryllium diffusion consistently. In this paper, we propose a comprehensive model, taking self-interstitial migration and Be interaction with Ga and In into account. Density functional theory (DFT) calculations are first used to calculate the energy parameters and charge states of possible diffusion mechanisms. Based on the DFT results, continuum modeling and kinetic Monte Carlo simulations are then performed. The model is able to reproduce experimental Be concentration profiles. Our results suggest that the Frank-Turnbull mechanism is not likely, instead, kick-out reactions are the dominant mechanism. Due to a large reaction energy difference, the Ga interstitial and the In interstitial play different roles in the kick-out reactions, contrary to what is usually assumed. The DFT calculations also suggest that the influence of As on Be diffusion may not be negligible.

cond-mat.mtrl-sci↗

arXiv2014

Quantitative Comparison Between Crowd Models for Evacuation Planning and Evaluation

Crowd simulation is rapidly becoming a standard tool for evacuation planning and evaluation. However, the many crowd models in the literature are structurally different, and few have been rigorously calibrated against real-world egress data, especially in emergency situations. In this paper we describe a procedure to quantitatively compare different crowd models or between models and real-world data. We simulated three models: (1) the lattice gas model, (2) the social force model, and (3) the RVO2 model, and obtained the distributions of six observables: (1) evacuation time, (2) zoned evacuation time, (3) passage density, (4) total distance traveled, (5) inconvenience, and (6) flow rate. We then used the DISTATIS procedure to compute the compromise matrix of statistical distances between the three models. Projecting the three models onto the first two principal components of the compromise matrix, we find the lattice gas and RVO2 models are similar in terms of the evacuation time, passage density, and flow rates, whereas the social force and RVO2 models are similar in terms of the total distance traveled. Most importantly, we find that the zoned evacuation times of the three models to be very different from each other. Thus we propose to use this variable, if it can be measured, as the key test between different models, and also between models and the real world. Finally, we compared the model flow rates against the flow rate of an emergency evacuation during the May 2008 Sichuan earthquake, and found the social force model agrees best with this real data.

cs.MA↗

arXiv2012

Epidemic reemergence in adaptive complex networks

The dynamic nature of system gives rise to dynamical features of epidemic spreading, such as oscillation and bistability. In this paper, by studying the epidemic spreading in growing networks, in which susceptible nodes may adaptively break the connections with infected ones yet avoid getting isolated, we reveal a new phenomenon - \emph{epidemic reemergence}, where the number of infected nodes is incubated at a low level for a long time and then bursts up for a short time. The process may repeat several times before the infection finally vanishes. Simulation results show that all the three factors, namely the network growth, the connection breaking and the isolation avoidance, are necessary for epidemic reemergence to happen. We present a simple theoretical analysis to explain the process of reemergence in detail. Our study may offer some useful insights helping explain the phenomenon of repeated epidemic explosions.

physics.soc-ph↗

arXiv2012

A hierarchy of self-consistent stochastic boundary conditions for Ising lattice simulations

We describe a hierarchy of stochastic boundary conditions (SBCs) that can be used to systematically eliminate finite size effects in Monte Carlo simulations of Ising lattices. For an Ising model on a $100 \times 100$ square lattice, we measured the specific heat, the magnetic susceptibility, and the spin-spin correlation using SBCs of the two lowest orders, to show that they compare favourably against periodic boundary conditions (PBC) simulations and analytical results. To demonstrate how versatile the SBCs are, we then simulated an Ising lattice with a magnetized boundary, and another with an open boundary, measuring the magnetization, magnetic susceptibility, and longitudinal and transverse spin-spin correlations as a function of distance from the boundary.

cond-mat.stat-mech↗

arXiv2012

Understanding agent-based models of financial markets: a bottom-up approach based on order parameters and phase diagrams

We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby $N$ independent traders buy and sell $M$ stocks through an order book that acts as a clearing house. The price of a stock increases whenever it is bought and decreases whenever it is sold. Price changes are updated by the order book before the next transaction takes place. In this deterministic model, all traders based their buy decisions on a call utility function, and all their sell decisions on a put utility function. We then make the agent-based model more realistic, by either having a fraction $f_b$ of traders buy a random stock on offer, or a fraction $f_s$ of traders sell a random stock in their portfolio. Based on our simulations, we find that it is possible to identify useful order parameters from the steady-state price distributions of all three models. Using these order parameters as a guide, we find three phases: (i) the dead market; (ii) the boom market; and (iii) the jammed market in the the phase diagram of the deterministic model. Comparing the phase diagrams of the stochastic models against that of the deterministic model, we realize that the primary effect of stochasticity is to eliminate the dead market phase.

q-fin.TR↗

arXiv2011

Understanding finite size effects in quasi-long-range orders for exactly solvable chain models

In this paper, we investigate how much of the numerical artefacts introduced by finite system size and choice of boundary conditions can be removed by finite size scaling, for strongly-correlated systems with quasi-long-range order. Starting from the exact ground-state wave functions of hardcore bosons and spinless fermions with infinite nearest-neighbor repulsion on finite periodic chains and finite open chains, we compute the two-point, density-density, and pair-pair correlation functions, and fit these to various asymptotic power laws. Comparing the finite-periodic-chain and finite-openchain correlations with their infinite-chain counterparts, we find reasonable agreement among them for the power-law amplitudes and exponents, but poor agreement for the phase shifts. More importantly, for chain lengths on the order of 100, we find our finite-open-chain calculation overestimates some infinite-chain exponents (as did a recent density-matrix renormalization-group (DMRG) calculation on finite smooth chains), whereas our finite-periodic-chain calculation underestimates these exponents. We attribute this systematic difference to the different choice of boundary conditions. Eventually, both finite-chain exponents approach the infinite-chain limit: by a chain length of 1000 for periodic chains, and > 2000 for open chains. There is, howwever, a misleading apparent finite size scaling convergence at shorter chain lengths, for both our finite-chain exponents, as well as the finite-smooth-chain exponents. Implications of this observation are discussed.

cond-mat.str-el↗

arXiv2011

Causal Links Between US Economic Sectors

In this paper, we perform a comparative segmentation and clustering analysis of the time series for the ten Dow Jones US economic sector indices between 14 February 2000 and 31 August 2008. From the temporal distributions of clustered segments, we find that the US economy took one and a half years to recover from the mid-1998-to-mid-2003 financial crisis, but only two months to completely enter the present financial crisis. We also find the oil & gas and basic materials sectors leading the recovery from the previous financial crisis, while the consumer goods and utilities sectors led the descent into the present financial crisis. On a macroscopic level, we find sectors going earlier into a crisis emerge later from it, whereas sectors going later into the crisis emerge earlier. On the mesoscopic level, we find leading sectors experiencing stronger and longer volatility shocks, while trailing sectors experience weaker and shorter volatility shocks. In our shock-by-shock causal-link analysis, we also find shorter delays between corresponding shocks in more closely related economic sectors. In addition, our analysis reveals evidences for complex sectorial structures, as well as nonlinear amplification in the propagating volatility shocks. From a perspective relevant to public policy, our study suggests an endogeneous sectorial dynamics during the mid-2003 economic recovery, in contrast to strong exogeneous driving by Federal Reserve interest rate cuts during the mid-2007 onset. Most interestingly, we find for the sequence of closely spaced interest rate cuts instituted in 2007/2008, the first few cuts effectively lowered market volatilities, while the next few cuts counter-effectively increased market volatilities. Subsequent cuts evoked little response from the market.

q-fin.GN↗

arXiv2010

Will the US Economy Recover in 2010? A Minimal Spanning Tree Study

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil and gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.

q-fin.ST↗