517 research outputs found
Effects of epidural compression on stellate neurons and thalamocortical afferent fibers in the rat primary somatosensory cortex
A number of neurological disorders such as epidural hematoma can cause compression of cerebral cortex. We here tested the hypothesis that sustained compression of primary somatosensory cortex may affect stellate neurons and thalamocortical afferent (TCA) fibers. A rat model with barrel cortex subjected to bead epidural compression was used. Golgi‑Cox staining analyses showed the shrinkage of dendritic arbors and the stripping of dendritic spines of stellate neurons for at least 3 months post‑lesion. Anterograde tracing analyses exhibited a progressive decline of TCA fiber density in barrel field for 6 months post‑lesion. Due to the abrupt decrease of TCA fiber density at 3 days after compression, we further used electron microscopy to investigate the ultrastructure of TCA fibers at this time. Some TCA fiber terminal profiles with dissolved or darkened mitochondria and fewer synaptic vesicles were distorted and broken. Furthermore, the disruption of mitochondria and myelin sheath was observed in some myelinated TCA fibers. In addition, expressions of oxidative markers 3‑nitrotyrosine and 4‑hydroxynonenal were elevated in barrel field post‑lesion. Treatment of antioxidant ascorbic acid or apocynin was able to reverse the increase of oxidative stress and the decline of TCA fiber density, rather than the shrinkage of dendrites and the stripping of dendritic spines of stellate neurons post‑lesion. Together, these results indicate that sustained epidural compression of primary somatosensory cortex affects the TCA fibers and the dendrites of stellate neurons for a prolonged period. In addition, oxidative stress is responsible for the reduction of TCA fiber density in barrels rather than the shrinkage of dendrites and the stripping of dendritic spines of stellate neurons
Interpretations of Domain Adaptations via Layer Variational Analysis
Transfer learning is known to perform efficiently in many applications
empirically, yet limited literature reports the mechanism behind the scene.
This study establishes both formal derivations and heuristic analysis to
formulate the theory of transfer learning in deep learning. Our framework
utilizing layer variational analysis proves that the success of transfer
learning can be guaranteed with corresponding data conditions. Moreover, our
theoretical calculation yields intuitive interpretations towards the knowledge
transfer process. Subsequently, an alternative method for network-based
transfer learning is derived. The method shows an increase in efficiency and
accuracy for domain adaptation. It is particularly advantageous when new domain
data is sufficiently sparse during adaptation. Numerical experiments over
diverse tasks validated our theory and verified that our analytic expression
achieved better performance in domain adaptation than the gradient descent
method.Comment: Published at ICLR 202
Secure bootstrapping and routing in an IPv6-based ad hoc network
The mobile ad hoc network (MANET), which is characterized by an infrastructureless architecture and multi-hop communication, has attracted a lot of attention recently. In the evolution of IP networks to version 6, adopting the same protocol would guarantee the success and portability of MANETs. In this paper, we propose a secure bootstrapping and routing protocol for MANETs. Mobile hosts can autoconfigure and even change their IP addresses based on the concept of CGA (cryptographically generated address), but they can not hide their identities easily. The protocol is modified from DSR (dynamic source routing) to support secure routing. The neighbor discovery and domain name registration in IPv6 are incorporated and enhanced with security functions. The protocol is characterized by the following features: (i) it is designed based on IPv6, (ii) relying on a DNS server, it allows bootstrapping a MANET with little pre-configuration overhead, so network formation is light-weight, and (iii) it is able to resist a variety of security attacks
Relationships between Connectedness, Performance Proficiency, Satisfaction, and Online Learning Continuance
Maintaining momentum is vital in terms of how soon students can complete a program, especially for those who are in the early stage of taking online courses. This study attempted to extend the existing literature by examining the influence of online students’ perceived sense of connectedness, performance proficiency, and satisfaction on their intentions to continue an online learning course. A quantitative survey approach was adopted to test our hypothesized structural model. Three hundred and sixty-nine students who had taken fewer than three fully online courses participated in this study. The results revealed that three out of four testing hypotheses were all supported at the 0.01 significance level, and one of the path coefficients indicated that online students’ confidence in their ability or competency to perform academic tasks did not directly influence their intention to take future online courses. Instead, the influence of performance proficiency on online learning continuance intention was mediated through the factor of satisfaction. In addition, satisfaction was found to have a significantly direct impact on online learning continuance intention, suggesting that when students taking online courses are satisfied with their online learning experience, the likelihood for them to continue taking other online courses is higher
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization
Domain Generalization (DG) aims to learn a generalizable model on the unseen
target domain by only training on the multiple observed source domains.
Although a variety of DG methods have focused on extracting domain-invariant
features, the domain-specific class-relevant features have attracted attention
and been argued to benefit generalization to the unseen target domain. To take
into account the class-relevant domain-specific information, in this paper we
propose an Information theory iNspired diSentanglement and pURification modEl
(INSURE) to explicitly disentangle the latent features to obtain sufficient and
compact (necessary) class-relevant feature for generalization to the unseen
domain. Specifically, we first propose an information theory inspired loss
function to ensure the disentangled class-relevant features contain sufficient
class label information and the other disentangled auxiliary feature has
sufficient domain information. We further propose a paired purification loss
function to let the auxiliary feature discard all the class-relevant
information and thus the class-relevant feature will contain sufficient and
compact (necessary) class-relevant information. Moreover, instead of using
multiple encoders, we propose to use a learnable binary mask as our
disentangler to make the disentanglement more efficient and make the
disentangled features complementary to each other. We conduct extensive
experiments on four widely used DG benchmark datasets including PACS,
OfficeHome, TerraIncognita, and DomainNet. The proposed INSURE outperforms the
state-of-art methods. We also empirically show that domain-specific
class-relevant features are beneficial for domain generalization.Comment: 10 pages, 4 figure
Review on the Conflicts between Offshore Wind Power and Fishery Rights: Marine Spatial Planning in Taiwan
In recent years, Taiwan has firmly committed itself to pursue the green energy transition and a nuclear-free homeland by 2025, with an increase in renewable energy from 5% in 2016 to 20% in 2025. Offshore wind power (OWP) has become a sustainable and scalable renewable energy source in Taiwan. Maritime Spatial Planning (MSP) is a fundamental tool to organize the use of the ocean space by different and often conflicting multi-users within ecologically sustainable boundaries in the marine environment. MSP is capable of definitively driving the use of offshore renewable energy. Lessons from Germany and the UK revealed that MSP was crucial to the development of OWP. This paper aims to evaluate how MSP is able to accommodate the exploitation of OWP in Taiwan and contribute to the achievement of marine policy by proposing a set of recommendations. It concludes that MSP is emerging as a solution to be considered by government institutions to optimize the multiple use of the ocean space, reduce conflicts and make use of the environmental and economic synergies generated by the joint deployment of OWP facilities and fishing or aquaculture activities for the conservation and protection of marine environments.Peer Reviewe
Lexical Retrieval Hypothesis in Multimodal Context
Multimodal corpora have become an essential language resource for language
science and grounded natural language processing (NLP) systems due to the
growing need to understand and interpret human communication across various
channels. In this paper, we first present our efforts in building the first
Multimodal Corpus for Languages in Taiwan (MultiMoco). Based on the corpus, we
conduct a case study investigating the Lexical Retrieval Hypothesis (LRH),
specifically examining whether the hand gestures co-occurring with speech
constants facilitate lexical retrieval or serve other discourse functions. With
detailed annotations on eight parliamentary interpellations in Taiwan Mandarin,
we explore the co-occurrence between speech constants and non-verbal features
(i.e., head movement, face movement, hand gesture, and function of hand
gesture). Our findings suggest that while hand gestures do serve as
facilitators for lexical retrieval in some cases, they also serve the purpose
of information emphasis. This study highlights the potential of the MultiMoco
Corpus to provide an important resource for in-depth analysis and further
research in multimodal communication studies
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