In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

The Transmission Control Protocol.

The article reviews the book “Information Rules: A Strategic Guide to the Network Economy,” by Carl Shapiro and Hal R. Varian.

Information Rules: A Strategic Guide to the Network Economy

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Linear And Nonlinear Programming

[발표] 2015개정 실과 교육과정 ‘가정생활’ 영역 개발 방향

Spatiotemporal variations of PM2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO2, NO2, CO and O3

Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting

In this paper, we present a room-level building occupancy estimation system (BOES) utilizing low-resolution
vibration sensors that are sparsely distributed. Many ubiquitous computing and building maintenance systems
require fine-grained occupancy knowledge to enable occupant centric services and optimize space and energy
utilization. The sensing infrastructure support for current occupancy estimation systems often requires multiple
intrusive sensors per room, resulting in systems that are both costly to deploy and difficult to maintain. To
address these shortcomings, we developed BOES. BOES utilizes sparse vibration sensors to track occupancy
levels and activities. Our system has three major components. 1) It extracts features that distinguish occupant
activities from noise prone ambient vibrations and detects human footsteps. 2) Using a sequence of footsteps, the
system localizes and tracks individuals by observing changes in the sequences. It uses this tracking information to
identify when an occupant leaves or enters a room. 3) The entering and leaving room information are combined
with detected individual location information to update the room-level occupancy state of the building. Through
validation experiments in two different buildings, our system was able to achieve 99.55% accuracy for event
detection, less than three feet average error for localization, and 85% accuracy in occupancy counting.

BOES: Building Occupancy Estimation System using sparse ambient vibration monitoring

This study estimates adult mortalities attributed to PM2.5 across urban China in 2015 and the corresponding mortalities that might be avoided by meeting the yearly averaged indoor PM2.5 threshold i...

Reducing Indoor Levels of "Outdoor PM2.5" in Urban China: Impact on Mortalities.

We present a robust method for solving the map matching problem exploiting massive GPS trace data. Map matching is the problem of determining the path of a user on a map from a sequence of GPS positions of that user --- what we call a trajectory. Commonly obtained from GPS devices, such trajectory data is often sparse and noisy. As a result, the accuracy of map matching is limited due to ambiguities in the possible routes consistent with trajectory samples. Our approach is based on the observation that many regularity patterns exist among common trajectories of human beings or vehicles as they normally move around. Among all possible connected k-segments on the road network (i.e., consecutive edges along the network whose total length is approximately k units), a typical trajectory collection only utilizes a small fraction. This motivates our data-driven map matching method, which optimizes the projected paths of the input trajectories so that the number of the k-segments being used is minimized. We present a formulation that admits efficient computation via alternating optimization. Furthermore, we have created a benchmark for evaluating the performance of our algorithm and others alike. Experimental results demonstrate that the proposed approach is superior to state-of-art single trajectory map matching techniques. Moreover, we also show that the extracted popular k-segments can be used to process trajectories that are not present in the original trajectory set. This leads to a map matching algorithm that is as efficient as existing single trajectory map matching algorithms, but with much improved map matching accuracy.

/pdf/large-scale-joint-map-matching-of-gps-traces-18p3540vzv.pdf

Lin Zhang

Papers

Spatiotemporal variations of PM2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO2, NO2, CO and O3

Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting

BOES: Building Occupancy Estimation System using sparse ambient vibration monitoring

Reducing Indoor Levels of "Outdoor PM2.5" in Urban China: Impact on Mortalities.

Large-scale joint map matching of GPS traces