176 research outputs found
Lung cancer diagnosis with quantitative DIC microscopy and support vector machine
We report the study of lung squamous cell carcinoma diagnosis using the TI-DIC microscopy and the scattering-phase theorem. The spatially resolved optical properties of tissue are computed from the 2D phase map via the scattering-phase theorem. The scattering coefficient, the reduced scattering coefficient, and the anisotropy factor are all found to increase with the grade of lung cancer. The retrieved optical parameters are shown to distinguish cancer cases from the normal cases with high accuracy. This label-free microscopic approach applicable to fresh tissues may be promising for in situ rapid cancer diagnosis. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only
Federated Learning on Non-iid Data via Local and Global Distillation
Most existing federated learning algorithms are based on the vanilla FedAvg
scheme. However, with the increase of data complexity and the number of model
parameters, the amount of communication traffic and the number of iteration
rounds for training such algorithms increases significantly, especially in
non-independently and homogeneously distributed scenarios, where they do not
achieve satisfactory performance. In this work, we propose FedND: federated
learning with noise distillation. The main idea is to use knowledge
distillation to optimize the model training process. In the client, we propose
a self-distillation method to train the local model. In the server, we generate
noisy samples for each client and use them to distill other clients. Finally,
the global model is obtained by the aggregation of local models. Experimental
results show that the algorithm achieves the best performance and is more
communication-efficient than state-of-the-art methods.Comment: Accpeted in IEEE ICWS 202
Design and implementation of an RFID-based customer shopping behavior mining system
Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that ShopMiner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference.Department of Computing2016-2017 > Academic research: refereed > Publication in refereed journalbcr
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