Listening to bluetooth beacons for epidemic risk mitigation.

The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address these limitations.

Pooled testing of traced contacts under superspreading dynamics.

Testing is recommended for all close contacts of confirmed COVID-19 patients. However, existing pooled testing methods are oblivious to the circumstances of contagion provided by contact tracing. Here, we build upon a well-known semi-adaptive pooled testing method, Dorfman's method with imperfect tests, and derive a simple pooled testing method based on dynamic programming that is specifically designed to use information provided by contact tracing.

Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively.

We perform a large-scale randomized controlled trial to evaluate the potential of machine learning-based instruction sequencing to improve memorization while allowing the learners the freedom to choose their review times. After controlling for the length and frequency of study, we find that learners for whom a machine learning algorithm determines which questions to include in their study sessions remember the content over ~69% longer. We also find that the sequencing algorithm has an effect on users' engagement.

Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach.

In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018.

Enhancing human learning via spaced repetition optimization.

Spaced repetition is a technique for efficient memorization which uses repeated review of content following a schedule determined by a spaced repetition algorithm to improve long-term retention. However, current spaced repetition algorithms are simple rule-based heuristics with a few hard-coded parameters. Here, we introduce a flexible representation of spaced repetition using the framework of marked temporal point processes and then address the design of spaced repetition algorithms with provable guarantees as an optimal control problem for stochastic differential equations with jumps.

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting.

In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space.

Acute Abdomen Secondary to Ruptured Epithelial Ovarian Cancer during Pregnancy: The Relevance of Teamwork.

Acute abdomen secondary to epithelial ovarian cancer rupture during pregnancy is a rare event. Our aim is to present how the work of a coordinated multidisciplinary team in a case of ruptured epithelial ovarian cancer during pregnancy is feasible to obtain the best results possible. A 34-year-old woman during the 37th week of her first gestation presented with an acute abdomen.

Solar Radiation Stress in Natural Acidophilic Biofilms of Euglena mutabilis Revealed by Metatranscriptomics and PAM Fluorometry.

The daily photosynthetic performance of a natural biofilm of the extreme acidophilic Euglena mutabilis from Río Tinto (SW, Spain) under full solar radiation was analyzed by means of pulse amplitude-modulated (PAM) fluorescence measurements and metatrascriptomic analysis. Natural E. mutabilis biofilms undergo large-scale transcriptomic reprogramming during midday due to a dynamic photoinhibition and solar radiation stress.

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm.

Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called . Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees? Despite the increasing availability of cascade-data and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuous-time diffusion models using an [Formula: see text]-regularized likelihood maximization framework.

Shaping Social Activity by Incentivizing Users.

Events in an online social network can be categorized roughly into events, where users just respond to the actions of their neighbors within the network, or events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level.

Scalable Influence Estimation in Continuous-Time Diffusion Networks.

If a piece of information is released from a media site, can we predict whether it may spread to one million web pages, in a month ? This influence estimation problem is very challenging since both the time-sensitive nature of the task and the requirement of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuous-time diffusion networks. Our algorithm can estimate the influence of every node in a network with || nodes and || edges to an accuracy of using = (1/) randomizations and up to logarithmic factors (||+||) computations.