Gaze-Adaptive Lenses for Feature-Rich Information Spaces

ABSTRACT We examine whether financial analysts are subject to limited attention. We find that when analysts have another firm in their coverage portfolio announcing earnings on the same day as the sample firm (a “concurrent announcement”), they are less likely to issue timely earnings forecasts for the sample firm's subsequent quarter than analysts without a concurrent announcement. Among the analysts who issue timely earnings forecasts, the thoroughness of their work decreases as their number of concurrent announcements increases. In addition, analysts are more sluggish in providing stock recommendations and less likely to ask questions in earnings conference calls as their number of concurrent announcements increases. Moreover, when analysts face concurrent announcements, they tend to allocate their limited attention to firms that already have rich information environments, leaving behind firms in need of attention. Overall, our evidence suggests that even financial analysts, who serve as information specialists, are subject to limited attention. JEL Classifications: G10; G11; G17; G14. Data Availability: Data are publicly available from the sources identified in the paper.

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Seeing, Knowing, and Doing

10.1093/oso/9780197503508.001.0001 ◽

2020 ◽

Author(s):

Robert Audi

Keyword(s):

Practical Reasoning ◽

Practical Knowledge ◽

A Priori ◽

Human Action ◽

A Priori Knowledge ◽

Rational Action ◽

Reasons For Action ◽

Discriminative Response ◽

Rich Information ◽

Priori Knowledge

This book provides an overall theory of perception and an account of knowledge and justification concerning the physical, the abstract, and the normative. It has the rigor appropriate for professionals but explains its main points using concrete examples. It accounts for two important aspects of perception on which philosophers have said too little: its relevance to a priori knowledge—traditionally conceived as independent of perception—and its role in human action. Overall, the book provides a full-scale account of perception, presents a theory of the a priori, and explains how perception guides action. It also clarifies the relation between action and practical reasoning; the notion of rational action; and the relation between propositional and practical knowledge. Part One develops a theory of perception as experiential, representational, and causally connected with its objects: as a discriminative response to those objects, embodying phenomenally distinctive elements; and as yielding rich information that underlies human knowledge. Part Two presents a theory of self-evidence and the a priori. The theory is perceptualist in explicating the apprehension of a priori truths by articulating its parallels to perception. The theory unifies empirical and a priori knowledge by clarifying their reliable connections with their objects—connections many have thought impossible for a priori knowledge as about the abstract. Part Three explores how perception guides action; the relation between knowing how and knowing that; the nature of reasons for action; the role of inference in determining action; and the overall conditions for rational action.

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A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Agriculture ◽

10.3390/agriculture11040359 ◽

2021 ◽

Vol 11 (4) ◽

pp. 359

Author(s):

Kai Ye ◽

Yangheran Piao ◽

Kun Zhao ◽

Xiaohui Cui

Keyword(s):

Decision Making ◽

Essential Role ◽

Online Discussion ◽

Professional Community ◽

Experimental Results ◽

Agricultural Products ◽

Price Prediction ◽

Price Data ◽

Rich Information ◽

Lstm Network

Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

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A New Analysis Tool for Continuous Glucose Monitor Data

Journal of Diabetes Science and Technology ◽

10.1177/19322968211028909 ◽

2021 ◽

pp. 193229682110289

Author(s):

Evan Olawsky ◽

Yuan Zhang ◽

Lynn E Eberly ◽

Erika S Helgeson ◽

Lisa S Chow

Keyword(s):

Glucose Monitoring ◽

Glycemic Variability ◽

R Package ◽

Analysis Tool ◽

Continuous Glucose Monitor ◽

Analysis Tools ◽

R Shiny ◽

Primary Driver ◽

Rich Information ◽

Web App

Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

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Interactive Exploration of Multi-Dimensional Information Spaces with Preference Support

ACM SIGIR Forum ◽

10.1145/2701583.2701602 ◽

2014 ◽

Vol 48 (2) ◽

pp. 118-118

Author(s):

Panagiotis Papadakos

Keyword(s):

Information Spaces ◽

Interactive Exploration

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651 Longitudinal, unobtrusive, and ecologically valid sleep metric estimation from a smart bed to predict the pathology of COVID-19

SLEEP ◽

10.1093/sleep/zsab072.649 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A255-A255

Author(s):

Dmytro Guzenko ◽

Gary Garcia ◽

Farzad Siyahjani ◽

Kevin Monette ◽

Susan DeFranco ◽

...

Keyword(s):

Heart Rate ◽

Sleep Quality ◽

Predictive Model ◽

Sleep Duration ◽

Wearable Sensors ◽

Test Results ◽

Cloud Data ◽

Cardiorespiratory Function ◽

Group Data ◽

Rich Information

Abstract Introduction Pathophysiologic responses to viral respiratory challenges such as SARS-CoV-2 may affect sleep duration, quality and concomitant cardiorespiratory function. Unobtrusive and ecologically valid methods to monitor longitudinal sleep metrics may therefore have practical value for surveillance and monitoring of infectious illnesses. We leveraged sleep metrics from Sleep Number 360 smart bed users to build a COVID-19 predictive model. Methods An IRB approved survey was presented to opting-in users from August to November 2020. COVID-19 test results were reported by 2003/6878 respondents (116 positive; 1887 negative). From the positive group, data from 82 responders (44.7±11.3 yrs.) who reported the date of symptom onset were used. From the negative group, data from 1519 responders (48.4±12.9 yrs.) who reported testing dates were used. Sleep duration, sleep quality, restful sleep duration, time to fall asleep, respiration rate, heart rate, and motion level were obtained from ballistocardiography signals stored in the cloud. Data from January to October 2020 were considered. The predictive model consists of two levels: 1) the daily probability of staying healthy calculated by logistic regression and 2) a continuous density Hidden Markov Model to refine the daily prediction considering the past decision history. Results With respect to their baseline, significant increases in sleep duration, average breathing rate, average heart rate and decrease in sleep quality were associated with symptom exacerbation in COVID-19 positive respondents. In COVID-19 negative respondents, no significant sleep or cardiorespiratory metrics were observed. Evaluation of the predictive model resulted in cross-validated area under the receiving-operator curve (AUC) estimate of 0.84±0.09 which is similar to values reported for wearable-sensors. Considering additional days to confirm prediction improved the AUC estimate to 0.93±0.05. Conclusion The results obtained on the smart bed user population suggest that unobtrusive sleep metrics may offer rich information to predict and track the development of symptoms in individuals infected with COVID-19. Support (if any):

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Cellular lensing and near infrared fluorescent nanosensor arrays to enable chemical efflux cytometry

Nature Communications ◽

10.1038/s41467-021-23416-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Soo-Yeon Cho ◽

Xun Gong ◽

Volodymyr B. Koman ◽

Matthias Kuehne ◽

Sun Jin Moon ◽

...

Keyword(s):

Near Infrared ◽

Single Cells ◽

Microfluidic Channel ◽

Human Monocyte ◽

Cellular Heterogeneity ◽

Label Free ◽

Nanotube Array ◽

Chemical Cytometry ◽

Rich Information ◽

Non Destructive

AbstractNanosensors have proven to be powerful tools to monitor single cells, achieving spatiotemporal precision even at molecular level. However, there has not been way of extending this approach to statistically relevant numbers of living cells. Herein, we design and fabricate nanosensor array in microfluidics that addresses this limitation, creating a Nanosensor Chemical Cytometry (NCC). nIR fluorescent carbon nanotube array is integrated along microfluidic channel through which flowing cells is guided. We can utilize the flowing cell itself as highly informative Gaussian lenses projecting nIR profiles and extract rich information. This unique biophotonic waveguide allows for quantified cross-correlation of biomolecular information with various physical properties and creates label-free chemical cytometer for cellular heterogeneity measurement. As an example, the NCC can profile the immune heterogeneities of human monocyte populations at attomolar sensitivity in completely non-destructive and real-time manner with rate of ~600 cells/hr, highest range demonstrated to date for state-of-the-art chemical cytometry.

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Using Handhelds to Search in Physical and Digital Information Spaces

10.1109/ubicomm.2008.9 ◽

2008 ◽

Author(s):

Spyros Veronikis ◽

Dimitris Gavrilis ◽

Kyriaki Zoutsou ◽

Christos Papatheodorou

Keyword(s):

Digital Information ◽

Information Spaces

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Restriction of cytosolic sucrose hydrolysis profoundly alters development, metabolism and gene expression in Arabidopsis roots

Journal of Experimental Botany ◽

10.1093/jxb/eraa581 ◽

2020 ◽

Author(s):

Cristina Pignocchi ◽

Alexander Ivakov ◽

Regina Feil ◽

Martin Trick ◽

Marilyn Pike ◽

...

Keyword(s):

Growth And Development ◽

Low Carbon ◽

Sucrose Hydrolysis ◽

Developmental Defects ◽

Altered Expression ◽

Signalling Molecules ◽

Exogenous Glucose ◽

Rich Information ◽

Sugar Levels ◽

Reduced Activity

Abstract Plant roots depend on sucrose imported from leaves as the substrate for metabolism and growth. Sucrose and hexoses derived from it are also signalling molecules that modulate growth and development, but the importance for signalling of endogenous changes in sugar levels is poorly understood. We report that reduced activity of cytosolic invertase, which converts sucrose to hexoses, leads to pronounced metabolic, growth and developmental defects in roots of Arabidopsis (Arabidopsis thaliana) seedlings. In addition to altered sugar and downstream metabolite levels, roots of cinv1 cinv2 mutants have reduced elongation rates, cell and meristem size, abnormal meristematic cell division patterns, and altered expression of thousands of genes of diverse functions. Provision of exogenous glucose to mutant roots repairs relatively few of the defects. The extensive transcriptional differences between mutant and wild-type roots have hallmarks of both high sucrose and low hexose signalling. We conclude that the mutant phenotype reflects both low carbon availability for metabolism and growth and complex sugar signals derived from elevated sucrose and depressed hexose levels in the cytosol of mutant roots. Such reciprocal changes in endogenous sucrose and hexose levels potentially provide rich information about sugar status that translates into flexible adjustments of growth and development.

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