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Despite its massive potential, Raman imaging represents just a modest fraction of all research and clinical microscopy to date. This is due to the ultralow Raman scattering cross-sections of most biomolecules that impose low-light or photon-sparse conditions. Bioimaging under such conditions is suboptimal, as it either results in ultralow frame rates or requires increased levels of irradiance. Here, we overcome this tradeoff by introducing Raman imaging that operates at both video rates and 1,000-fold lower irradiance than state-of-the-art methods. To accomplish this, we deployed a judicially designed Airy light-sheet microscope to efficiently image large specimen regions. Further, we implemented subphoton per pixel image acquisition and reconstruction to confront issues arising from photon sparsity at just millisecond integrations. We demonstrate the versatility of our approach by imaging a variety of samples, including the three-dimensional (3D) metabolic activity of single microbial cells and the underlying cell-to-cell variability. To image such small-scale targets, we again harnessed photon sparsity to increase magnification without a field-of-view penalty, thus, overcoming another key limitation in modern light-sheet microscopy.

The unique topology of Weyl semimetals’ band structure has been recently shown to lead to a host of novel optoelectronic properties. Among them is the prospect of polarization-dependent photocurrents, most notably the circular photogalvanic effect arising from the spin texture of the Weyl cones in the presence of symmetry breaking. Here we show that these helicity-dependent photocurrent processes can be employed to realize fully integrated polarimetric detection systems. In this respect, a TaAs-based polarimeter is demonstrated involving three pixels that can uniquely map the polarization state of light on the Poincaré sphere. Our work could enable a class of optoelectronic devices that directly respond to the polarization of incident light, while paving the way toward a better understating of light–matter interactions in Weyl semimetals.

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relieve the dilemma. Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph-fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.

Smartphones have become increasingly prevalent and important in our daily lives. To meet users' expectations about the Quality of Experience (QoE) of mobile applications (apps), it is essential to obtain a comprehensive understanding of app QoE and identify the critical factors that affect it. However, effectively and systematically studying the QoE of popular mobile apps such as Facebook and YouTube still remains a challenging task, largely due to a lack of a controlled and reproducible measurement methodology, and limited insight into the complex multi-layer dynamics of the system and network stacks. In this paper, we propose QoE Doctor, a tool that supports accurate, systematic, and repeatable measurements and analysis of mobile app QoE. QoE Doctor uses UI automation techniques to replay QoE-related user behavior, and measures the user-perceived latency directly from UI changes. To better understand and analyze QoE problems involving complex multi-layer interactions, QoE Doctor supports analysis across the application, transport, network, and cellular radio link layers to help identify the root causes. We implement QoE Doctor on Android, and systematically quantify various factors that impact app QoE, including the cellular radio link layer technology, carrier rate-limiting mechanisms, app design choices and user-side configuration options.

Understanding how network and application behavior patterns impact client performance on mobile devices is a difficult yet important problem to solve. Often, we are most interested in the performance experienced by end users, but accurately and effectively measuring performance on uncontrolled mobile devices in the wild continues to be a challenging problem. In this paper, we have developed a tool to allow us to more accurately characterize RRC states using client-based measurements. To do so, we have made use of and contributed to an open-source framework called Mobilyzer, which facilitates collecting complex network measurements on client devices without impacting the user. We have deployed our tool on unmodified devices in 23 countries worldwide to directly measure the impact of RRC state machine configurations on individual packets and network protocols. Demonstrating the value of client-based measurements, our large-scale measurement study allowed us to uncover previously unknown performance problems that can increase network latency by several seconds and increase packet losses by an order of magnitude.

To conserve power while ensuring good performance on resource-constrained mobile devices, devices transition between different Radio Resource Control (RRC) states in response to network traffic and according to parameters specific to network operators. As RRC states significantly affect application power consumption and performance, it is important to understand how RRC state timers interact with network traffic patterns. In this paper, we show that the impact of RRC states on performance is significantly more complex and diverse than found in previous work. To do so, we introduce an open-source tool that allows the impact of RRC states on network and application performance to be measured in a robust and accurate manner on unmodified user devices, and deploy the tool in 23 countries around the world to test a broad range of cellular network technologies. We detect previously unknown performance problems which increase network latencies by up to several seconds and for LTE, can increase packet losses by an order of magnitude. Through an in-depth cross-layer analysis of several carriers, we examine the lower-layer causes of these problems. We determine that the highly complex state transitions of certain carriers, and in particular poor interactions between state demotions and network traffic, can lead to substantial, unexpected latencies.

The frequency response of the photocurrent generated by TaAs Weyl semimetal is studied to seek the frequency limitation of a novel polarimeter design which utilizes a polarization dependent effect.