Effects of electrical charging on the mechanical Q of a fused silica disk

We report on the effects of an electrical charge on mechanical loss of a fused silica disk. A degradation of Q was seen that correlated with charge on the surface of the sample. We examine a number of models for charge damping, including eddy current damping and loss due to polarization. We conclude that rubbing friction between the sample and a piece of dust attracted by the charged sample is the most likely explanation for the observed loss.Comment: submitted to Review of Scientific Instrument

Analysis of AC loss in superconducting power devices calculated from short sample data

A method to calculate the AC loss of superconducting power devices from the measured AC loss of a short sample is developed. In coils and cables the magnetic field varies spatially. The position dependent field vector is calculated assuming a homogeneous current distribution. From this field profile and the transport current, the local AC loss is calculated. Integration over the conductor length yields the AC loss of the device. The total AC loss of the device is split up in different components. Magnetization loss, transport current loss and the loss due to the combined action of field and current all contribute to the AC loss of the device. Because ways to reduce the AC loss depend on the loss mechanism it is important to know the relative contribution of each component. The method is demonstrated on a prototype transformer coil wound from Bi/sub 2/Sr/sub 2/Ca/sub 2/Cu/sub 3/O/sub x//Ag superconducting tape. Differences between the model assumptions and devices are pointed out. Nevertheless, within the uncertainty margins the calculated AC loss is in agreement with the measured loss of the coil

Support Neighbor Loss for Person Re-Identification

Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks (CNN). The majority of deep re-ID methods focus on designing new CNN architectures, while less attention is paid on investigating the loss functions. Verification loss and identification loss are two types of losses widely used to train various deep re-ID models, both of which however have limitations. Verification loss guides the networks to generate feature embeddings of which the intra-class variance is decreased while the inter-class ones is enlarged. However, training networks with verification loss tends to be of slow convergence and unstable performance when the number of training samples is large. On the other hand, identification loss has good separating and scalable property. But its neglect to explicitly reduce the intra-class variance limits its performance on re-ID, because the same person may have significant appearance disparity across different camera views. To avoid the limitations of the two types of losses, we propose a new loss, called support neighbor (SN) loss. Rather than being derived from data sample pairs or triplets, SN loss is calculated based on the positive and negative support neighbor sets of each anchor sample, which contain more valuable contextual information and neighborhood structure that are beneficial for more stable performance. To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets. To reduce intra-class variance, the distance between the anchor's nearest positive neighbor and furthest positive sample is penalized. Integrating SN loss on top of Resnet50, superior re-ID results to the state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201

Complex permittivity measurements of lunar samples at microwave and millimeter wavelengths

The relative dielectric constant and loss tangent of lunar sample 14163,164 (fine dust) were determined as a function of density at 9.375, 24, 35, and 60 GHz. In addition, such measurements have also been performed on lunar sample 14310,74 (solid rock) at 9.375 GHz. The loss tangent was found to be frequency independent at these test frequencies and had a value of 0.015 for the lunar dust sample

Passive Learning with Target Risk

In this paper we consider learning in passive setting but with a slight modification. We assume that the target expected loss, also referred to as target risk, is provided in advance for learner as prior knowledge. Unlike most studies in the learning theory that only incorporate the prior knowledge into the generalization bounds, we are able to explicitly utilize the target risk in the learning process. Our analysis reveals a surprising result on the sample complexity of learning: by exploiting the target risk in the learning algorithm, we show that when the loss function is both strongly convex and smooth, the sample complexity reduces to \O(\log (\frac{1}{\epsilon})), an exponential improvement compared to the sample complexity \O(\frac{1}{\epsilon}) for learning with strongly convex loss functions. Furthermore, our proof is constructive and is based on a computationally efficient stochastic optimization algorithm for such settings which demonstrate that the proposed algorithm is practically useful

Lithium Sulphur Batteries

S+C cathode material was prepared by simple solid-state reaction in ball mill. Content of sulphur was approximately 80 wt. % in final sample. Cyclic voltammetry and galvanostatic charge/discharge techniques were used for characterization of the samples. Initial discharge capacity observed for S+C sample was 600 mAh/gsulfur. Capacity loss for S+C sample after 30th cycles was 66 %. Cycling loss is due to insoluble polysulfide formation. In this paper I present fundamental characteristics of Li-S batteries. This paper presents a principle of Li-S batteries, fundamental measurement and their evaluation. I present the techniques of measurement and preparation of cathode materials

Nash--Moser iteration and singular perturbations

We present a simple and easy-to-use Nash--Moser iteration theorem tailored for singular perturbation problems admitting a formal asymptotic expansion or other family of approximate solutions depending on a parameter \eps\to 0. The novel feature is to allow loss of powers of \eps as well as the usual loss of derivatives in the solution operator for the associated linearized problem. We indicate the utility of this theorem by describing sample applications to (i) systems of quasilinear Schr\"odinger equations, and (ii) existence of small-amplitude profiles of quasilinear relaxation systems.Comment: Final version; general presentation completely revised; new sample application to systems of quasilinear Schr\"odinger equation