Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author's influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational Linguistics (TACL); to appea

Transcranial direct current stimulation for online gamers: A prospective single-arm feasibility study

Aim: Excessive use of online games can have negative influences on mental health and daily functioning. Although the effects of transcranial direct current stimulation (tDCS) have been investigated for the treatment of addiction, it has not been evaluated for excessive online game use. This study aimed to investigate the feasibility and tolerability of tDCS over the dorsolateral prefrontal cortex (DLPFC) in online gamers. Methods: A total of 15 online gamers received 12 active tDCS sessions over the DLPFC (anodal left/cathodal right, 2 mA for 30 min, 3 times per week for 4 weeks). Before and after tDCS sessions, all participants underwent 18F-fluoro-2-deoxyglucose positron emission tomography scans and completed the Internet Addiction Test (IAT), Brief Self Control Scale (BSCS), and Beck Depression Inventory-II (BDI-II). Results: After tDCS sessions, weekly hours spent on games (p = .02) and scores of IAT (p < .001) and BDI-II (p = .01) were decreased, whereas BSCS score was increased (p = .01). Increases in self-control were associated with decreases in both addiction severity (p = .002) and time spent on games (p = .02). Moreover, abnormal right-greater-than-left asymmetry of regional cerebral glucose metabolism in the DLPFC was partially alleviated (p = .04). Conclusions: Our preliminary results suggest that tDCS may be useful for reducing online game use by improving interhemispheric balance of glucose metabolism in the DLPFC and enhancing self-control. Larger sham-controlled studies with longer follow-up period are warranted to validate the efficacy of tDCS in gamers

Regulation of synaptic Rac1 activity, long-term potentiation maintenance, and learning and memory by BCR and ABR Rac GTPase-activating proteins

Rho family small GTPases are important regulators of neuronal development. Defective Rho regulation causes nervous system dysfunctions including mental retardation and Alzheimer's disease. Rac1, a member of the Rho family, regulates dendritic spines and excitatory synapses, but relatively little is known about how synaptic Rac1 is negatively regulated. Breakpoint cluster region (BCR) is a Rac GTPase-activating protein known to form a fusion protein with the c-Abl tyrosine kinase in Philadelphia chromosome-positive chronic myelogenous leukemia. Despite the fact that BCR mRNAs are abundantly expressed in the brain, the neural functions of BCR protein have remained obscure. We report here that BCR and its close relative active BCR-related (ABR) localize at excitatory synapses and directly interact with PSD-95, an abundant postsynaptic scaffolding protein. Mice deficient for BCR or ABR show enhanced basal Rac1 activity but only a small increase in spine density. Importantly, mice lacking BCR or ABR exhibit a marked decrease in the maintenance, but not induction, of long-term potentiation, and show impaired spatial and object recognition memory. These results suggest that BCR and ABR have novel roles in the regulation of synaptic Rac1 signaling, synaptic plasticity, and learning and memory, and that excessive Rac1 activity negatively affects synaptic and cognitive functions.This work was supported by the National Creative Research Initiative Program of the Korean Ministry of Education, Science and Technology (E.K.), Neuroscience Program Grant 2009-0081468 (S.-Y.C.), 21st Century Frontier R&D Program in Neuroscience Grant 2009K001284 (H.K.), Basic Science Research Program Grant R13-2008-009-01001-0 (Y.C.B.), and United States Public Health Service Grants HL071945 (J.G.) and HL060231 (J.G., N.H.)

Laboratory information management system for COVID-19 non-clinical efficacy trial data

Background : As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results : In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions : This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.This research was supported by the National research foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2020M3A9I2109027 and 2021M3H9A1030260)

Exploring Intimate Communication Channels for Long-Distance Relationships

Thesis (Master's)--University of Washington, 2019This thesis explores how design and technology might help to improve interpersonal connections between family members living far apart. Scentie-Talkie, a wireless prototype, uses scent to enable distant people to feel a shared presence. With an abundance of explicit communication channels available, and even more in development, this research posits that to facilitate intimate relationships over distance, more implicit and abstract platforms also merit exploration

The relationship between multiple chronic diseases and depressive symptoms among middle-aged and elderly populations: results of a 2009 korean community health survey of 156,747 participants

Abstract Background The purpose of this study was to investigate the relationship between multiple chronic diseases and depressive symptoms in middle-aged and elderly populations. Methods This study was performed using the 2009 Korean Community Health Survey, which targeted adults over the age of 40 (N = 156,747 participants, 88,749 aged 40–59 years and 67,998 aged ≥60 years). The Korean version of the Center for Epidemiologic Studies Depression Scale (CES-D-K) was used as the measurement tool for depressive symptoms (CES-D-K score over 16). Multiple chronic diseases were defined as the concurrent presence of two or more chronic diseases. Results The prevalence and risk ratios (RRs) of experiencing depressive symptoms increased in the presence of multiple chronic diseases and with the number of comorbidities. The RRs of experiencing depressive symptoms according to the presence of multiple chronic diseases were higher in the middle-aged population (adjusted RR, 1.939, 95% confidence limits (CL), 1.82-2.06) than in the elderly population (adjusted RR, 1.620, 95% CL, 1.55-1.69). In particular, middle-aged women who suffer from 4 or more chronic diseases have the highest RR (adjusted RR, 4.985, 95% CL, 4.13-6.03) for depressive symptoms. Conclusions Multiple chronic diseases are closely associated with depressive symptoms in middle-aged and elderly populations. Given the mutual relationship between multiple chronic diseases and depressive symptoms, attention to and the assessment of depressive symptoms are needed in people with multiple chronic diseases

MLogNet: A Logarithmic Quantization-Based Accelerator for Depthwise Separable Convolution

In this paper we propose a novel logarithmic quantization-based DNN (Deep Neural Network) architecture for depthwise separable convolution (DSC) networks. Our architecture is based on selective two-word logarithmic quantization (STLQ), which improves accuracy greatly over logarithmic-scale quantization while retaining the speed and area advantage of logarithmic quantization. On the other hand, it also comes with the synchronization problem due to variable-latency PEs (processing elements), which we address through a novel architecture and a compile-time optimization technique. Our architecture is dynamically reconfigurable to support various combinations of depthwise vs. pointwise convolution layers efficiently. Our experimental results using layers from MobileNetV2 and ShuffleNetV2 demonstrate that our architecture is significantly faster and more area-efficient than previous DSC accelerator architectures as well as previous accelerators utilizing logarithmic quantization

Denoising Recurrent Neural Networks for Classifying Crash-Related Events

With detailed sensor and visual data from automobiles, a data-driven model can learn to classify crash-related events during a drive. We propose a neural network model accepting time-series vehicle sensor data and forward-facing videos as input for learning classification of crash-related events and varying types of such events. To elaborate, a novel recurrent neural network structure is introduced, namely, denoising gated recurrent unit with decay, in order to deal with time-series automobile sensor data with missing value and noises. Our model detects crash and near-crash events based on a large set of time-series data collected from naturalistic driving behavior. Furthermore, the model classifies those events involving pedestrians, a vehicle in front, or a vehicle on either side. The effectiveness of our model is evaluated with more than two thousand 30-s clips from naturalistic driving behavior data. The results show that the model, including sensory encoder with denoising gated recurrent unit with decay, visual encoder, and attention mechanism, outperforms gated recurrent unit with decay, gated CNN, and other baselines not only in event classification and but also in event-type classification

Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, Curb, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.Comment: To appear at the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018

Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, Curb, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.Comment: To appear at the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018