9,246 research outputs found
CSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm -- {C}ommunity
{S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment
information (e.g., air pollution or temperature) in each subarea of the target
area, without aggregating sensor and location data collected by community
members. CSWA operates on top of a secured peer-to-peer network over the
community members and proposes a novel \emph{Decentralized Spatial-Temporal
Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient
Descent}. Through learning the \emph{low-rank structure} via distributed
optimization, CSWA approximates the value of the sensor data in each subarea
(both covered and uncovered) for each sensing cycle using the sensor data
locally stored in each member's mobile device. Simulation experiments based on
real-world datasets demonstrate that CSWA exhibits low approximation error
(i.e., less than C in city-wide temperature sensing task and
units of PM2.5 index in urban air pollution sensing) and performs comparably to
(sometimes better than) state-of-the-art algorithms based on the data
aggregation and centralized computation.Comment: This paper has been accepted by AAAI 2018. First two authors are
equally contribute
THE EFFECT OF DIFFERENT EXTERNAL ELASTIC COMPRESSION ON MUSCLE STRENGTH, FATIGUE, EMG AND MMG ACTIVITY
The purpose of this study was to quantify the effects of three different compression conditions on (a) performance of muscle strength/power and fatigue in lower extremity, and (b) the responses of electromyography (EMG) and mechanomyography (MMG) of rectus femoris (RF) under repeated concentric muscle actions. All subjects (N=12) performed maximal voluntary contractions (MVC) and consecutive, maximal isokinetic knee extension movements at 60°/s & 300°/s velocities with three different compression conditions. The results indicated that local elastic compression of lower extremity, while not significant in improving isokinetic strength in short period, may have a positive effect on fatigue by helping maintain long-term force production through altering muscle activity in high-velocity of locomotion
MicroRNA-124 enhances response to radiotherapy in human epidermal growth factor receptor 2-positive breast cancer cells by targeting signal transducer and activator of transcription 3
Aim To determine whether microRNA (miR)-124 enhances
the response to radiotherapy in human epidermal growth
factor receptor 2 (HER2)-positive breast cancer cells by targeting
signal transducer and activator of transcription 3
(Stat3).
Methods miR-29b expression was measured in 80 pairs of
breast tumor samples and adjacent normal tissues collected
between January 2013 and July 2014. Activity changes
of 50 canonical signaling pathways upon miR-124 overexpression
were determined using Cignal Signal Transduction
Reporter Array. Target gene of miR-124 was determined
using Targetscan and validated by Western blotting
and dual-luciferase assay. Cell death rate was assessed by
propidium iodide (PI)/Annexin V staining followed by flow
cytometry analysis. Stat3 and miR-124 expression was further
measured in 10 relapsed (non-responder) and 10 recurrence-
free HER2-positive breast cancer patients.
Results MiR-124 expression was down-regulated in HER2
positive breast cancers compared with normal tissues, and
was negatively associated with tumor size. MiR-124 overexpression
in HER2 positive breast cancer cell line SKBR3
significantly reduced the activity of Stat3 signaling pathway
compared with control transfection (P < 0.001). Bioinformatic
prediction and function assay suggested that
miR-124 directly targeted Stat3, which is a key regulator of
HER2 expression. MiR-124 overexpression down-regulated
Stat3 and potently enhanced cell death upon irradiation.
Consistently, chemical inhibitor of Stat3 also sensitized
HER2-positive breast cancer cells to irradiation. Moreover,
increased Stat3 expression and reduced miR-124 expression
were associated with a poor response to radiotherapy
in HER2-positive breast cancers.
Conclusions Weak miR-124 expression might enhance
Stat3 expression and radiotherapy resistance in HER2-positive
breast cancer cells
Detecting Floating-Point Errors via Atomic Conditions
This paper tackles the important, difficult problem of detecting program inputs that trigger large floating-point errors in numerical code. It introduces a novel, principled dynamic analysis that leverages the mathematically rigorously analyzed condition numbers for atomic numerical operations, which we call atomic conditions, to effectively guide the search for large floating-point errors. Compared with existing approaches, our work based on atomic conditions has several distinctive benefits: (1) it does not rely on high-precision implementations to act as approximate oracles, which are difficult to obtain in general and computationally costly; and (2) atomic conditions provide accurate, modular search guidance. These benefits in combination lead to a highly effective approach that detects more significant errors in real-world code (e.g., widely-used numerical library functions) and achieves several orders of speedups over the state-of-the-art, thus making error analysis significantly more practical. We expect the methodology and principles behind our approach to benefit other floating-point program analysis tasks such as debugging, repair and synthesis. To facilitate the reproduction of our work, we have made our implementation, evaluation data and results publicly available on GitHub at https://github.com/FP-Analysis/atomic-condition.ISSN:2475-142
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
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