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Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core–periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

Optimistic concurrency control, or OCC, can achieve excellent performance on uncontended workloads for main-memory transactional databases. Contention causes OCC's performance to degrade, however, and recent concurrency control designs, such as hybrid OCC/locking systems and variations on multiversion concurrency control (MVCC), have claimed to outperform the best OCC systems. We evaluate several concurrency control designs under varying contention and varying workloads, including TPCC, and find that implementation choices unrelated to concurrency control may explain much of OCC's previously-reported degradation. When these implementation choices are made sensibly, OCC performance does not collapse on high-contention TPC-C. We also present two optimization techniques, commit-time updates and timestamp splitting , that can dramatically improve the high-contention performance of both OCC and MVCC. Though these techniques are known, we apply them in a new context and highlight their potency: when combined, they lead to performance gains of 3.4X for MVCC and 3.6X for OCC in a TPC-C workload.

Modeling the spread of infectious diseases is central to the field of computational epidemiology. Two prominent approaches to modeling the contagion process include (i) simulating the spread in contact networks through Monte-Carlo processes and (ii) tracking the disease dynamics using meta-population models. In both cases, the individuals are explicitly (contact networks) or implicitly (meta-population) assumed to belong to exactly one disease state (e.g., susceptible, infected, etc.). In reality, the disease states of individuals are rarely so cleanly compartmentalized. A particular agent can exist in multiple disease states (such as infected and exposed) concurrently with varying probability. To model this stochasticity, we present a new method, that we term as the Probabilistic Infection Model (PIM). Unlike traditional models that assign exactly one state to each agent at each time step, the PIM computes the probability of each agent being in each of the infectious states. Our proposed PIM provides a more layered understanding of the dynamics of the outbreak at individual levels, by allowing the users to (i) estimate the value of R0 at individual vertices and (ii) instead of an all or none value, provides the probability of each infected state of an agent. Additionally, using our probabilistic approach the overall trajectories of the outbreaks can be computed in one simulation, as opposed to the numerous (order of hundreds) repeated simulations required for the Monte Carlo process. We demonstrate the efficacy of PIM by comparing the results of the PIM simulations with those obtained by simulating stochastic SEIR models, as well as the time required for the simulations. We present results at the system and at the individual levels for three diseases; measles and two strains of influenza. We demonstrate how the PIM can be used to study the effect of varying the transimissibility of COVID-19 on its outbreak. This paper is an extended version of a manuscript published in the proceedings of the 2020 International Conference on Computational Science (ICCS) [30] . These extensions are primarily within Sections 4 (Relationship between graph structure and probability of infection) and 5 (Effect of varying COVID-19 transmissibility on outbreak dynamics).

In networks of coupled oscillators, it is of interest to understand how interaction topology affects synchronization. Many studies have gained key insights into this question by studying the classic Kuramoto oscillator model on static networks. However, new questions arise when the network structure is time varying or when the oscillator system is multistable, the latter of which can occur when an inertial term is added to the Kuramoto model. While the consequences of evolving topology and multistability on collective behavior have been examined separately, real-world systems such as gene regulatory networks and the brain may exhibit these properties simultaneously. It is thus relevant to ask how time-varying network connectivity impacts synchronization in systems that can exhibit multistability. To address this question, we study how the dynamics of coupled Kuramoto oscillators with inertia are affected when the topology of the underlying network changes in time. We show that hysteretic synchronization behavior in networks of coupled inertial oscillators can be driven by changes in connection topology alone. Moreover, we find that certain fixed-density rewiring schemes induce significant changes to the level of global synchrony that remain even after the network returns to its initial configuration, and we show that these changes are robust to a wide range of network perturbations. Our findings highlight that the specific progression of network topology over time, in addition to its initial or final static structure, can play a considerable role in modulating the collective behavior of systems evolving on complex networks.

We propose a novel method which we call the Probabilistic Infection Model (PIM). Instead of stochastically assigning exactly one state to each agent at a time, PIM tracks the likelihood of each agent being in a particular state. Thus, a particular agent can exist in multiple disease states concurrently. Our model gives an improved resolution of transitions between states, and allows for a more comprehensive view of outbreak dynamics at the individual level. Moreover, by using a probabilistic approach, our model gives a representative understanding of the overall trajectories of simulated outbreaks without the need for numerous (order of hundreds) of repeated Monte Carlo simulations.

The quest for accurate, high-resolution, low-power-consumption, and small-footprint 3D depth cameras has driven a rapid improvement in Continuous-Wave (CW) Time-of-Flight (ToF) technology. Commercially available 3D image acquisition techniques include Stereo Vision, Structured Light, and ToF. CW ToF imaging systems offer excellent mechanical robustness, no baseline requirement, high effective depth image resolution, low computational cost, and simultaneous IR ambient light invariant intensity capture (Active Brightness). In a CW ToF camera, light from an amplitude modulated light source is backscattered by objects in the camera's field of view, and the phase delay of the amplitude envelope is measured between the emitted and reflected light. This phase difference is translated into a distance value for each pixel in the imaging array.