8,904 research outputs found
Kappa-Opioid Receptors in the Caudal Nucleus Tractus Solitarius Mediate 100 Hz Electroacupuncture-Induced Sleep Activities in Rats
Previous results demonstrated that 10 Hz electroacupuncture (EA) of Anmian acupoints in rats during the dark period enhances slow wave sleep (SWS), which involves the induction of cholinergic activity in the caudal nucleus tractus solitarius (NTS) and subsequent activation of opioidergic neurons and μ-receptors. Studies have shown that different kinds of endogenous opiate peptides and receptors may mediate the consequences of EA with different frequencies. Herein, we further elucidated that high-frequency (100 Hz)-EA of Anmian enhanced SWS during the dark period but exhibited no direct effect on rapid eye movement (REM) sleep. High-frequency EA-induced SWS enhancement was dose-dependently blocked by microinjection of naloxone or κ-receptor antagonist (nor-binaltorphimine) into the caudal NTS, but was affected neither by μ- (naloxonazine) nor δ-receptor antagonists (natatrindole), suggesting the role of NTS κ-receptors in the high-frequency EA-induced SWS enhancement. Current and previous results depict the opioid mechanisms of EA-induced sleep
When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
Discovering and exploiting the causality in deep neural networks (DNNs) are
crucial challenges for understanding and reasoning causal effects (CE) on an
explainable visual model. "Intervention" has been widely used for recognizing a
causal relation ontologically. In this paper, we propose a causal inference
framework for visual reasoning via do-calculus. To study the intervention
effects on pixel-level features for causal reasoning, we introduce pixel-wise
masking and adversarial perturbation. In our framework, CE is calculated using
features in a latent space and perturbed prediction from a DNN-based model. We
further provide the first look into the characteristics of discovered CE of
adversarially perturbed images generated by gradient-based methods
\footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}.
Experimental results show that CE is a competitive and robust index for
understanding DNNs when compared with conventional methods such as
class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for
human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds
promises for detecting adversarial examples as it possesses distinct
characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal
Intervention Meets Adversarial Examples and Image Masking for Deep Neural
Networks" as the v3 official paper title in IEEE Proceeding. Please use it in
your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released
on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm
Do environmental regulations cause enterprises to exit from market? Quasi-natural experiments based on China’s Cleaner Production Standards
Taking the implementation of Cleaner Production Standards at
the industry level in China as a quasi-natural experiment, the
impact of these standards on enterprises’ exit behavior was
empirically analyzed by using the Difference-in-Differences
method. Results suggested that the implementation of Cleaner
Production Standards reduced the probability of enterprises exiting
the market. A parallel trend test, Propensity Score Matching
(PSM), and the exclusion of other policy factors were then used
to verify the robustness of this finding. The impact mechanism
test showed that implementation of the standards reduced the
probability of enterprises exiting the market through improving
total factor productivity and promoting enterprise product innovation.
The heterogeneity test revealed that, on the one hand, the
implementation of Cleaner Production Standards can reduce the
probability of R&D intensive industries and medium-sized enterprises
exiting the market, and protect innovative and moderately
sized enterprises. On the other hand, the implementation of
Cleaner Production Standards can increase the probability of
state-owned enterprises and small-scale enterprises exiting the
market and optimize the allocation of resources among enterprises.
This paper has important implications for China’s future
approach to environmental policy formulation as well as the optimization
of domestic enterprise structur
Profit Maximization by Forming Federations of Geo-Distributed MEC Platforms
This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaMulti-access edge computing (MEC) as an emerging
technology which provides cloud service in the edge of multi-radio
access networks aims to reduce the service latency experienced
by end devices. When individual MEC systems do not have
adequate resource capacity to fulfill service requests, forming
MEC federations for resource sharing could provide economic
incentive to MEC operators. To this end, we need to maximize
social welfare in each federation, which involves efficient federation
structure generations, federation profit maximization by
resource provisioning configuration, and fair profit distribution
among participants. We model the problem as a coalition game
with difference from prior work in the assumption of latency
and locality constraints and also in the consideration of various
service policies/demand preferences. Simulation results show that
the proposed approach always increases profits. If local requests
are served with local resource with priority, federation improves
profits without sacrificing request acceptance rates.This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant numbers 106-2221-E-009-004 and by the H2020 collaborative Europe/Taiwan
research project 5G-CORAL (grant number 761586)
Part2Word: Learning Joint Embedding of Point Clouds and Text by Matching Parts to Words
It is important to learn joint embedding for 3D shapes and text in different
shape understanding tasks, such as shape-text matching, retrieval, and shape
captioning. Current multi-view based methods learn a mapping from multiple
rendered views to text. However, these methods can not analyze 3D shapes well
due to the self-occlusion and limitation of learning manifolds. To resolve this
issue, we propose a method to learn joint embedding of point clouds and text by
matching parts from shapes to words from sentences in a common space.
Specifically, we first learn segmentation prior to segment point clouds into
parts. Then, we map parts and words into an optimized space, where the parts
and words can be matched with each other. In the optimized space, we represent
a part by aggregating features of all points within the part, while
representing each word with its context information, where we train our network
to minimize the triplet ranking loss. Moreover, we also introduce cross-modal
attention to capture the relationship of part-word in this matching procedure,
which enhances joint embedding learning. Our experimental results outperform
the state-of-the-art in multi-modal retrieval under the widely used benchmark
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