Observation of fractional topological numbers at photonic edges and corners

Topological phases of matter are featured with exotic edge states. However, the fractional topological numbers at edges, though predicted long ago by Jackiw and Rebbi, remain elusive in topological photonic systems. Here, we report on the observation of fractional topological numbers at the topological edges and corners in one- and two-dimensional photonic crystals. The fractional topological numbers are determined via the measurements of the photonic local density-of-states. In one-dimensional photonic crystals, we witness a rapid change of the fractional topological number at the edges rising from 0 to 1/2 when the photonic band gap experiences a topological transition, confirming the well-known prediction of Jackiw and Rebbi. In two-dimensional systems, we discover that the fractional topological number in the corner region varies from 0 to 1/2 and 1/4 in different photonic band gap phases. Our study paves the way toward topological manipulation of fractional quantum numbers in photonics.Comment: All comments are welcom

Hybrid topological photonic crystals

Photonic topological phases offering unprecedented manipulation of electromagnetic waves have attracted much research interest which, however, have been mostly restricted to a single band gap. Here, we report on the experimental discovery of hybrid topological photonic crystals which host simultaneously quantum anomalous Hall and valley Hall phases in different photonic band gaps. The underlying hybrid topological phase manifests itself in the edge responses as the coexistence of the chiral edge states and valley Hall edge states in different frequency ranges. We experimentally verify such an emergent phenomenon and show that such a feature enables novel multiplexing of photon transport in the edge channels. Our study reveals a situation with coexisting topology of distinct nature in a single photonic system that may enable frequency-dependent filtering and manipulation of topological edge photons

Physiological expression of olfactory discrimination rule learning balances whole-population modulation and circuit stability in the piriform cortex network

Once trained, rats are able to execute particularly difficult olfactory discrimination tasks with exceptional accuracy. Such skill acquisition, termed “rule learning”, is accompanied by a series of long‐lasting modifications to three cellular properties which modulate pyramidal neuron activity in piriform cortex; intrinsic excitability, synaptic excitation, and synaptic inhibition. Here, we explore how these changes, which are seemingly contradictory at the single‐cell level in terms of their effect on neuronal excitation, are manifested within the piriform cortical neuronal network to store the memory of the rule, while maintaining network stability. To this end, we monitored network activity via multisite extracellular recordings of field postsynaptic potentials (fPSPS) and with single‐cell recordings of miniature inhibitory and excitatory synaptic events in piriform cortex slices. We show that although 5 days after rule learning the cortical network maintains its basic activity patterns, synaptic connectivity is strengthened specifically between spatially proximal cells. Moreover, while the enhancement of inhibitory and excitatory synaptic connectivity is nearly identical, strengthening of synaptic inhibition is equally distributed between neurons while synaptic excitation is particularly strengthened within a specific subgroup of cells. We suggest that memory for the acquired rule is stored mainly by strengthening excitatory synaptic connection between close pyramidal neurons and runaway synaptic activity arising from this change is prevented by a nonspecific enhancement of synaptic inhibition

Effectiveness of Nutritional Advice for Community-Dwelling Obese Older Adults With Frailty: A Systematic Review and Meta-Analysis

Objectives: This systematic review was aimed to examine the effectiveness of nutritional advise interventions compared with usual care, or exercise, or exercise combined with nutritional advice as a means of improving the body weight, body composition, physical function, and psychosocial well-being of frail, obese older adults. Methods: CINAHL, Cochrane Library, Embase, MEDLINE, PsycINFO, and Scopus databases were searched to identify relevant studies. The quality of the included studies was assessed using Cochrane's risk of bias tool 2. Meta-analysis was performed with respect to body weight and fat mass. Other outcomes were synthesized narratively. Results: Eight articles (from two studies) with a total of 137 participants were included in the review. The results revealed that nutritional advice was more effective than exercise in reducing body weight and fat mass. The nutritional advice was also beneficial in enhancing physical function and psychosocial well-being. However, it was less effective than exercise or combined interventions in increasing muscle strength and preventing lean mass loss. Conclusions: Nutritional advice is an essential intervention for reducing body weight and fat mass, for enhancing physical function, and for improving the psychosocial well-being of obese older adults experiencing frailty. The limited number of studies included in this review suggests that there is a need for more well-designed interventional studies in order to confirm these findings

ASSESSMENT OF THERMAL COMFORT IN SEMI-OUTDOOR SPACES: THE STAR VISTA

Bachelor'sBACHELOR OF SCIENCE (PROJECT AND FACILITIES MANAGEMENT

Efficient-PrototypicalNet with self knowledge distillation for few-shot learning

The focus of recent few-shot learning research has been on the development of learning methods that can quickly adapt to unseen tasks with small amounts of data and low computational cost. In order to achieve higher performance in few-shot learning tasks, the generalizability of the method is essential to enable it generalize well from seen tasks to unseen tasks with limited number of samples. In this work, we investigate a new metric-based few-shot learning framework which transfers the knowledge from another effective classification model to produce well generalized embedding and improve the effectiveness in handling unseen tasks. The idea of our proposed Efficient-PrototypicalNet involves transfer learning, knowledge distillation, and few-shot learning. We employed a pre-trained model as a feature extractor to obtain useful features from tasks and decrease the task complexity. These features reduce the training difficulty in few-shot learning and increase the performance. Besides that, we further apply knowledge distillation to our framework and achieve extra performance improvement. The proposed Efficient-PrototypicalNet was evaluated on five benchmark datasets, i.e., Omniglot, miniImageNet, tieredImageNet, CIFAR-FS, and FC100. The proposed Efficient-PrototypicalNet achieved the state-of-the-art performance on most datasets in the 5-way K-shot image classification task, especially on the miniImageNet dataset