Influence Maximization with Bandits

We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we have data that lets us estimate these influences. However, this information is typically not initially available or is difficult to obtain. To avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm that estimates the influence probabilities as we sequentially try different seed sets. We establish bounds on the performance of this procedure under the existing edge-level feedback as well as a novel and more realistic node-level feedback. Beyond our theoretical results, we describe a practical implementation and experimentally demonstrate its efficiency and effectiveness on four real datasets.Comment: 12 page

Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin

ShadowTutor: Distributed Partial Distillation for Mobile Video DNN Inference

Following the recent success of deep neural networks (DNN) on video computer vision tasks, performing DNN inferences on videos that originate from mobile devices has gained practical significance. As such, previous approaches developed methods to offload DNN inference computations for images to cloud servers to manage the resource constraints of mobile devices. However, when it comes to video data, communicating information of every frame consumes excessive network bandwidth and renders the entire system susceptible to adverse network conditions such as congestion. Thus, in this work, we seek to exploit the temporal coherence between nearby frames of a video stream to mitigate network pressure. That is, we propose ShadowTutor, a distributed video DNN inference framework that reduces the number of network transmissions through intermittent knowledge distillation to a student model. Moreover, we update only a subset of the student's parameters, which we call partial distillation, to reduce the data size of each network transmission. Specifically, the server runs a large and general teacher model, and the mobile device only runs an extremely small but specialized student model. On sparsely selected key frames, the server partially trains the student model by targeting the teacher's response and sends the updated part to the mobile device. We investigate the effectiveness of ShadowTutor with HD video semantic segmentation. Evaluations show that network data transfer is reduced by 95% on average. Moreover, the throughput of the system is improved by over three times and shows robustness to changes in network bandwidth.Comment: Accepted at ICPP 202

Ethics of controlled human infection to study COVID-19

Development of an effective vaccine is the clearest path to controlling the coronavirus disease 2019 (COVID-19) pandemic. To accelerate vaccine development, some researchers are pursuing, and thousands of people have expressed interest in participating in, controlled human infection studies (CHIs) with severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) (1, 2). In CHIs, a small number of participants are deliberately exposed to a pathogen to study infection and gather preliminary efficacy data on experimental vaccines or treatments. We have been developing a comprehensive, state-of-the-art ethical framework for CHIs that emphasizes their social value as fundamental to justifying these studies. The ethics of CHIs in general are underexplored (3, 4), and ethical examinations of SARS-CoV-2 CHIs have largely focused on whether the risks are acceptable and participants could give valid informed consent (1). The high social value of such CHIs has generally been assumed. Based on our framework, we agree on the ethical conditions for conducting SARS-CoV-2 CHIs (see the table). We differ on whether the social value of such CHIs is sufficient to justify the risks at present, given uncertainty about both in a rapidly evolving situation; yet we see none of our disagreements as insurmountable. We provide ethical guidance for research sponsors, communities, participants, and the essential independent reviewers considering SARS-CoV-2 CHIs

Psychiatric services in primary care settings: a survey of general practitioners in Thailand

BACKGROUND: General Practitioners (GPs) in Thailand play an important role in treating psychiatric disorders since there is a shortage of psychiatrists in the country. Our aim was to examine GP's perception of psychiatric problems, drug treatment and service problems encountered in primary care settings. METHODS: We distributed 1,193 postal questionnaires inquiring about psychiatric practices and service problems to doctors in primary care settings throughout Thailand. RESULTS: Four hundred and thirty-four questionnaires (36.4%) were returned. Sixty-seven of the respondents (15.4%) who had taken further special training in various fields were excluded from the analysis, giving a total of 367 GPs in this study. Fifty-six per cent of respondents were males and they had worked for 4.6 years on average (median = 3 years). 65.6% (SD = 19.3) of the total patients examined had physical problems, 10.7% (SD = 7.9) had psychiatric problems and 23.9% (SD = 16.0) had both problems. The most common psychiatric diagnoses were anxiety disorders (37.5%), alcohol and drugs abuse (28.1%), and depressive disorders (29.2%). Commonly prescribed psychotropic drugs were anxiolytics and antidepressants. The psychotropic drugs most frequently prescribed were diazepam among anti-anxiety drugs, amitriptyline among antidepressant drugs, and haloperidol among antipsychotic drugs. CONCLUSION: Most drugs available through primary care were the same as what existed 3 decades ago. There should be adequate supply of new and appropriate psychotropic drugs in primary care. Case-finding instruments for common mental disorders might be helpful for GPs whose quality of practice was limited by large numbers of patients. However, the service delivery system should be modified in order to maintain successful care for a large number of psychiatric patients

Improving cold chain technologies through the use of phase change material

Gemstone Team FRESHVaccine-preventable diseases are responsible for about 25% of the 10 million deaths occurring annually for children under five years of age. The World Health Organization's Expanded Programmes on Immunization succeed in providing standardized guidelines for vaccine storage and distribution, but often fail to accommodate the unique infrastructure between and within countries. In order to better regulate the temperature of vaccines as they travel through countries, we have selected and characterized an appropriate phase change material (PCM) that will resist temperature fluctuations outside of a range of 2-8 °C, based on appropriate thermophysical properties. Additionally, we have integrated the selected PCM within a geometrically and thermally optimized cold box, maintaining long-term stabilization of temperatures within a range of 2-8 °C. In meeting these objectives, we have demonstrated the feasibility of a technological solution that may be readily implemented in the existing vaccine distribution supply chain, or that holds potential to be the centerpiece for new, more efficient vaccine distribution strategies