A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

Bubbling Over: Soda Consumption and Its Link to Obesity in California

Based on 2005 Health Interview Survey data, examines the link between soda consumption and the prevalence of overweight and obese adults and children, geographical differences in soda consumption, and the social and environmental factors that affect it

Adaptive Energy-Efficient Power Allocation in Green Interference Alignment Based Wireless Networks

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum especially at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation and transmission-mode adaptation for green IAbased wireless networks. The power allocation problem for IA is first analyzed, then we propose a power allocation scheme that optimizes the energy efficiency of IA-based wireless networks. When SNR is low, the transmitted power of some users may become zero. Thus the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the energy efficiency of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the energy efficiency of IAbased wireless networks

Health Status of the Uninsured: Opportunities for Improvement

Provides estimates of the geographic variations in the rates of self-reported fair or poor health status, asthma, and hypertension among uninsured children and adults in California

Reach of Supplemental Nutrition Assistance Program-Education (SNAP-Ed) interventions and nutrition and physical activity-related outcomes, California, 2011-2012.

IntroductionThis study combined information on the interventions of the US Department of Agriculture's Supplemental Nutrition Assistance Program-Education with 5,927 interview responses from the California Health Interview Survey to investigate associations between levels of intervention reach in low-income census tracts in California and self-reported physical activity and consumption of fruits and vegetables, fast food, and sugar-sweetened beverages.MethodsWe determined 4 levels of intervention reach (low reach, moderate reach, high reach, and no intervention) across 1,273 program-eligible census tracts from data on actual and eligible number of intervention participants. The locations of California Health Interview Survey respondents were geocoded and linked with program data. Regression analyses included measures for sex, age, race/ethnicity, and education.ResultsAdults and children from high-reach census tracts reported eating more fruits and vegetables than adults and children from no-intervention census tracts. Adults from census tracts with low, moderate, or high levels of reach reported eating fast food less often than adults from no-intervention census tracts. Teenagers from low-reach census tracts reported more physical activity than teenagers in no-intervention census tracts.ConclusionThe greatest concentration of Supplemental Nutrition Assistance Program-Education interventions was associated with adults and children eating more fruits and vegetables and adults eating fast food less frequently. These findings demonstrate the potential impact of such interventions as implemented by numerous organizations with diverse populations; these interventions can play an important role in addressing the obesity epidemic in the United States. Limitations of this study include the absence of measures of exposure to the intervention at the individual level and low statistical power for the teenager sample

Energy Efficient Massive MIMO System Design for Smart Grid Communications

Communication technologies are critical in achieving potential advantages of smart gird (SG), as they enable electric utilities to interact with their devices and customers. This paper focuses on the integration of a massive multiple-input multiple-output (MIMO) technique into a SG communication architecture. Massive MIMO has the benefits of offering higher data rates, whereas operating a large number of antennas in practice could increase the system complexity and energy consumption. We propose to use antenna selection to preserve the gain provided by the large number of antennas, and investigate an energy efficient massive MIMO system design for SG communications. Specifically, we derive a closed-form asymptotic approximation to the system energy efficiency function in consideration of channel spatial correlation, which exhibits an excellent level of accuracy for a wide range of system dimensions in SG communication scenarios. Based on the accurate approximation, we propose a novel antenna selection scheme aiming at maximizing the system energy efficiency, using only the long-term channel statistics. Simulation results show that the proposed antenna selection scheme can always achieve an energy efficiency gain compared to other selection schemes or baseline systems without antenna selection, and thus is particularly valuable for enabling an energy efficient communication system of the SG

Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution

Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical structures and aiding in accurate diagnosis. Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. However, existing deep learning-based SR methods predominantly rely on convolutional neural networks (CNNs), which inherently limit the expressive capabilities of these models and therefore make it challenging to discover potential relationships between different image features. To overcome this limitation, we propose an A-network that utilizes multiple convolution operator feature extraction modules (MCO) for extracting image features using multiple convolution operators. These extracted features are passed through multiple sets of cross-feature extraction modules (MSC) to highlight key features through inter-channel feature interactions, enabling subsequent feature learning. An attention-based sparse graph neural network module is incorporated to establish relationships between pixel features, learning which adjacent pixels have the greatest impact on determining the features to be filled. To evaluate our model's effectiveness, we conducted experiments using different models on data generated from multiple datasets with different degradation multiples, and the experimental results show that our method is a significant improvement over the current state-of-the-art methods.Comment: 12 pages, 6 figure

Many Uninsured Children Qualify for Medi-Cal or Healthy Families

Examines the public health insurance eligibility of children in California who did not have health insurance coverage for some or all of the year in 2002, to highlight the geographic variations in children's uninsured eligibility rates