Miniature High-Sensitivity High-Temperature Fiber Sensor with a Dispersion Compensation Fiber-Based Interferometer

A miniature high-sensitivity, high-temperature fiber sensor with an interferometer based on a bare small-core-diameter dispersion compensation fiber (DCF) is reported. The sensing head is a single-mode-fiber (SMF) DCF configuration formed by a 4 mm long bare DCF with one end connected to the SMF by a fusion splicing technique and the other end cleaved. Due to the large mode index difference and high thermo-optic coefficient induced by two dominative interference modes, a miniature high-temperature fiber sensor with a high sensitivity of 68.6 pm/°C is obtained by monitoring the wavelength shift of the interference spectrum. This type of sensor has the features of small size, high sensitivity, high stability, simple structure, and low cost

Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

Generating Aspect-oriented Multi-document Summarization with Event-Aspect Model

In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on Rouge metric demonstrates the effectiveness and advantages of our method.

Fast DGT Based Receivers for GFDM in Broadband Channels

Generalized frequency division multiplexing (GFDM) is a recent multicarrier 5G waveform candidate with flexibility of pulse shaping filters. However, the flexibility of choosing a pulse shaping filter may result in inter carrier interference (ICI) and inter symbol interference (ISI), which becomes more severe in a broadband channel. In order to eliminate the ISI and ICI, based on discrete Gabor transform (DGT), in this paper, a transmit GFDM signal is first treated as an inverse DGT (IDGT), and then a frequency-domain DGT is formulated to recover (as a receiver) the GFDM signal. Furthermore, to reduce the complexity, a suboptimal frequency-domain DGT called local DGT (LDGT) is developed. Some analyses are also given for the proposed DGT based receivers.Comment: 28 pages, 8 figure

The supersymmetric affine Yangian

The affine Yangian of $\mathfrak{gl}_1$ is known to be isomorphic to ${\cal W}_{1+\infty}$, the $W$-algebra that characterizes the bosonic higher spin -- CFT duality. In this paper we propose defining relations of the Yangian that are relevant for the ${\cal N}=2$ superconformal version of ${\cal W}_{1+\infty}$. Our construction is based on the observation that the ${\cal N}=2$ superconformal ${\cal W}_{1+\infty}$ algebra contains two commuting bosonic ${\cal W}_{1+\infty}$ algebras, and that the additional generators transform in bi-minimal representations with respect to these two algebras. The corresponding affine Yangian can therefore be built up from two affine Yangians of $\mathfrak{gl}_1$ by adding in generators that transform appropriately.Comment: 35 pages, 5 figure