Wideband Optical Filters with Small Gap Coupled Subwavelength Metal Structures

In this letter, we show that the bandwidth of optical band-stop filters made of subwavelength metal structures can be significantly increased by the strong plasmonic near-field coupling through the corners of the periodic metal squares. The effect of small gap coupling on the spectral bandwidth is investigated by varying the gap size between the metal squares. An equivalent transmission line model is used to fit the transmission and reflection spectra of the metal filters. The transmission line model can characterize well the metal structures with the gap size larger than the near-field decay length. However, it fails to model the transmission and reflection spectra when the gap size reaches the decay range of the near-field in the small gaps

Confronting brane inflation with Planck and pre-Planck data

In this paper, we compare brane inflation models with the Planck data and the pre-Planck data (which combines WMAP, ACT, SPT, BAO and H0 data). The Planck data prefer a spectral index less than unity at more than 5\sigma confidence level, and a running of the spectral index at around 2\sigma confidence level. We find that the KKLMMT model can survive at the level of 2\sigma only if the parameter $\beta$ (the conformal coupling between the Hubble parameter and the inflaton) is less than $\mathcal{O}(10^{-3})$, which indicates a certain level of fine-tuning. The IR DBI model can provide a slightly larger negative running of spectral index and red tilt, but in order to be consistent with the non-Gaussianity constraints from Planck, its parameter also needs fine-tuning at some level.Comment: 10 pages, 8 figure

A Whole Process Prediction Method for Temperature Field of Fire Smoke in Large Spaces

AbstractBased on the fire development model for the whole process of localized fires in large-space buildings and assisted by the technology of FDS large eddy simulation, the temperature fields of fire smoke of localized fires in large spaces were investigated with different building heights, building areas and fire powers. It has been found that for large-space buildings with a height greater than 6 m and a building area more than 1500 m2, factors like building height and building area can slightly affect the curve trend of fire smoke, while such factor like fire power has more significant influence on the curve trend of fire smoke. Through the analysis of temperature rise curves of fire smoke in various fire scenarios, the paper proposed a whole-process prediction model for the temperature fields of fire smoke of localized fires in large-space buildings. As long as the model uses the appropriate shape coefficient, the prediction model can accurately predict the temperature fields of fire smoke of localized fires in large-space buildings

A note on on-line broadcast scheduling with deadlines

In this paper, we study an on-line broadcast scheduling problem with deadlines, in which the requests asking for the same page can be satisfied simultaneously by broadcasting this page, and every request is associated with a release time, deadline and a required page with a unit size. The objective is to maximize the number of requests satisfied by the schedule. In this paper, we focus on an important special case where all the requests have their spans (the difference between release time and deadline) less than 2. We give an optimal online algorithm, i.e., its competitive ratio matches the lower bound of the problem.postprin

A Review of Adversarial Attacks in Computer Vision

Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples. Such adversarial attacks can be invisible to human eyes, but can lead to DNN misclassification, and often exhibits transferability between deep learning and machine learning models and real-world achievability. Adversarial attacks can be divided into white-box attacks, for which the attacker knows the parameters and gradient of the model, and black-box attacks, for the latter, the attacker can only obtain the input and output of the model. In terms of the attacker's purpose, it can be divided into targeted attacks and non-targeted attacks, which means that the attacker wants the model to misclassify the original sample into the specified class, which is more practical, while the non-targeted attack just needs to make the model misclassify the sample. The black box setting is a scenario we will encounter in practice