Zero-Shot Hashing via Transferring Supervised Knowledge

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels i.e. 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.Comment: 11 page

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

XCR1 expression distinguishes human conventional dendritic cell type 1 with full effector functions from their immediate precursors

Dendritic cells (DCs) are major regulators of innate and adaptive immune responses. DCs can be classified into plasmacytoid DCs and conventional DCs (cDCs) type 1 and 2. Murine and human cDC1 share the mRNA expression of XCR1. Murine studies indicated a specific role of the XCR1-XCL1 axis in the induction of immune responses. Here, we describe that human cDC1 can be distinguished into XCR1$^{-}$ and XCR1$^{+}$ cDC1 in lymphoid as well as nonlymphoid tissues. Steady-state XCR1$^{+}$ cDC1 display a preactivated phenotype compared to XCR1$^{-}$ cDC1. Upon stimulation, XCR1$^{+}$ cDC1, but not XCR1$^{-}$ cDC1, secreted high levels of inflammatory cytokines as well as chemokines. This was associated with enhanced activation of NK cells mediated by XCR1$^{+}$ cDC1. Moreover, XCR1$^{+}$ cDC1 excelled in inhibiting replication of Influenza A virus. Further, under DC differentiation conditions, XCR1$^{-}$ cDC1 developed into XCR1$^{+}$ cDC1. After acquisition of XCR1 expression, XCR1$^{-}$ cDC1 secreted comparable level of inflammatory cytokines. Thus, XCR1 is a marker of terminally differentiated cDC1 that licenses the antiviral effector functions of human cDC1, while XCR1$^{-}$ cDC1 seem to represent a late immediate precursor of cDC1

A single-label phenylpyrrolocytidine provides a molecular beacon-like response reporting HIV-1 RT RNase H activity

6-Phenylpyrrolocytidine (PhpC), a structurally conservative and highly fluorescent cytidine analog, was incorporated into oligoribonucleotides. The PhpC-containing RNA formed native-like duplex structures with complementary DNA or RNA. The PhpC-modification was found to act as a sensitive reporter group being non-disruptive to structure and the enzymatic activity of RNase H. A RNA/DNA hybrid possessing a single PhpC insert was an excellent substrate for HIV-1 RT Ribonuclease H and rapidly reported cleavage of the RNA strand with a 14-fold increase in fluorescence intensity. The PhpC-based assay for RNase H was superior to the traditional molecular beacon approach in terms of responsiveness, rapidity and ease (single label versus dual). Furthermore, the PhpC-based assay is amenable to high-throughput microplate assay format and may form the basis for a new screen for inhibitors of HIV-RT RNase H

Land‐use intensity and biodiversity effects on infiltration capacity and hydraulic conductivity of grassland soils in southern Germany

Evidence from experimental and established grasslands indicates that plant biodiversity can modify the water cycle. One suspected mechanism behind this is a higher infiltration capacity (ν$_{B}$) and hydraulic conductivity (K) of the soil on species-rich grasslands. However, in established and agriculturally managed grasslands, biodiversity effects cannot be studied independent of land-use effects. Therefore, we investigated in established grassland systems how land-use intensity and associated biodiversity of plants and soil animals affect νB and K at and close to saturation. On 50 grassland plots along a land-use intensity gradient in the Biodiversity Exploratory Schwäbische Alb, Germany, we measured νB with a hood infiltrometer at several matrix potentials and calculated the saturated and unsaturated K. We statistically analysed the relationship between ν$_{B}$ or K and land-use information (e.g., fertilising intensity), abiotic (e.g., soil texture) and biotic data (e.g., plant species richness, earthworm abundance). Land-use intensity decreased and plant species richness increased ν$_{B}$ and K, while the direction of the effects of soil animals was inconsistent. The effect of land-use intensity on ν$_{B}$ and K was mainly attributable to its negative effect on plant species richness. Our results demonstrate that plant species richness was a better predictor of ν$_{B}$ and K at and close to saturation than land-use intensity or soil physical properties in the established grassland systems of the Schwäbische Alb