Transductive Multi-View Zero-Shot Learning

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Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

Source origins, modeled profiles, and apportionments of halogenated hydrocarbons in the greater Pearl River Delta region, southern China

We analyze 16-month data of 13 major halocarbons measured at a southern China coastal site in the greater Pearl River Delta (PRD). A total of 188 canister air samples were collected from August 2001 to December 2002. Overall inspection indicated that CH2Cl2, C2Cl 4, and C2HCl3 had similar temporal variations while CFC-11, CFC-12, and CFC-113 showed the same emission patterns during the sampling period. Diurnal variations of halocarbons presented different patterns during ozone episode days, mainly related to emission strength, atmospheric dispersion, and photochemical lifetimes. For further statistics and source appointment, Lagrangian backward particle release simulations were conducted to help understand the potential source regions of all samples and classify them into different categories, including local Hong Kong, inner PRD, continental China, and marine air masses. With the exception of HCFC-142b, the mixing ratios of all halocarbons in marine air were significantly lower than those in urban and regional air (p < 0.01), whereas no significant difference was found between urban Hong Kong and inner PRD regional air, reflecting the dominant impact of the greater PRD regional air on the halocarbon levels. The halocarbon levels in this region were significantly influenced by anthropogenic sources, causing the halocarbon mixing ratios in South China Sea air to be higher than the corresponding background levels, as measured by global surface networks and by airborne missions such as Transport and Chemical Evolution Over the Pacific. Interspecies correlation analysis suggests that CHCl3 is mainly used as a solvent in Hong Kong but mostly as a feedstock for HCFC-22 in the inner PRD. Furthermore, CH3Cl is often used as a refrigerant and emitted from biomass/biofuel burning in the inner PRD. A positive matrix factorization receptor model was applied to the classified halocarbon samples in the greater PRD for source profiles and apportionments. Seven major sources were identified and quantified. Emissions from solvent use were the most significant source of halocarbons (71 ± 9%), while refrigeration was the second largest contributor (18 ± 2%). By further looking at samples from the inner PRD and from urban Hong Kong separately, we found that more solvent was used in the dry cleaning industry in Hong Kong, whereas the contribution of cleaning solvent in the electronic industry was higher in the inner PRD. Besides the two common sources of solvent use and refrigeration, the contributions of biomass/biofuel burning and feedstock in chemical manufacturing was remarkable in the inner PRD but negligible in Hong Kong. These findings are of help to effectively control and phase out the emissions of halocarbons in the greater PRD region of southern China Copyright 2009 by the American Geophysical Union

Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels.

The problem of estimating subjective visual properties from image and video has attracted increasing interest. A subjective visual property is useful either on its own (e.g. image and video interestingness) or as an intermediate representation for visual recognition (e.g. a relative attribute). Due to its ambiguous nature, annotating the value of a subjective visual property for learning a prediction model is challenging. To make the annotation more reliable, recent studies employ crowdsourcing tools to collect pairwise comparison labels because human annotators are much better at ranking two images/videos (e.g. which one is more interesting) than giving an absolute value to each of them separately. However, using crowdsourced data also introduces outliers. Existing methods rely on majority voting to prune the annotation outliers/errors. They thus require large amount of pairwise labels to be collected. More importantly as a local outlier detection method, majority voting is ineffective in identifying outliers that can cause global ranking inconsistencies. In this paper, we propose a more principled way to identify annotation outliers by formulating the subjective visual property prediction task as a unified robust learning to rank problem, tackling both the outlier detection and learning to rank jointly. Differing from existing methods, the proposed method integrates local pairwise comparison labels together to minimise a cost that corresponds to global inconsistency of ranking order. This not only leads to better detection of annotation outliers but also enables learning with extremely sparse annotations. Extensive experiments on various benchmark datasets demonstrate that our new approach significantly outperforms state-of-the-arts alternatives.Comment: 14 pages, accepted by IEEE TPAM

Microwave studies of the fractional Josephson effect in HgTe-based Josephson junctions

The rise of topological phases of matter is strongly connected to their potential to host Majorana bound states, a powerful ingredient in the search for a robust, topologically protected, quantum information processing. In order to produce such states, a method of choice is to induce superconductivity in topological insulators. The engineering of the interplay between superconductivity and the electronic properties of a topological insulator is a challenging task and it is consequently very important to understand the physics of simple superconducting devices such as Josephson junctions, in which new topological properties are expected to emerge. In this article, we review recent experiments investigating topological superconductivity in topological insulators, using microwave excitation and detection techniques. More precisely, we have fabricated and studied topological Josephson junctions made of HgTe weak links in contact with two Al or Nb contacts. In such devices, we have observed two signatures of the fractional Josephson effect, which is expected to emerge from topologically-protected gapless Andreev bound states. We first recall the theoretical background on topological Josephson junctions, then move to the experimental observations. Then, we assess the topological origin of the observed features and conclude with an outlook towards more advanced microwave spectroscopy experiments, currently under development.Comment: Lectures given at the San Sebastian Topological Matter School 2017, published in "Topological Matter. Springer Series in Solid-State Sciences, vol 190. Springer

A comparison of methodological frameworks for digital learning game design

Methodological frameworks guide the design of digital learning game based on well founded learning theories and instructional strategies. This study presents a comparison of five methodological frameworks for digital learning game design, highlighting their similarities and differences. The objective is to support the choice of an adequate framework, aiming to promote them as a way to foster principled digital learning games design. This paper concludes that: (i) interactivity, engagement and increasing complexity of challenges are fundamental factors to digital learning game design; (ii) the pedagogical base, the target, the possibility of doing game assessment and the presence of practical guidelines are the selection criteria that influence most the choice of a methodological framework, and (iii) the development of digital learning games - preferably by different research teams - is needed to provide empirical evidence of the utility of framework-based design

Sensing electric fields using single diamond spins

The ability to sensitively detect charges under ambient conditions would be a fascinating new tool benefitting a wide range of researchers across disciplines. However, most current techniques are limited to low-temperature methods like single-electron transistors (SET), single-electron electrostatic force microscopy and scanning tunnelling microscopy. Here we open up a new quantum metrology technique demonstrating precision electric field measurement using a single nitrogen-vacancy defect centre(NV) spin in diamond. An AC electric field sensitivity reaching ~ 140V/cm/\surd Hz has been achieved. This corresponds to the electric field produced by a single elementary charge located at a distance of ~ 150 nm from our spin sensor with averaging for one second. By careful analysis of the electronic structure of the defect centre, we show how an applied magnetic field influences the electric field sensing properties. By this we demonstrate that diamond defect centre spins can be switched between electric and magnetic field sensing modes and identify suitable parameter ranges for both detector schemes. By combining magnetic and electric field sensitivity, nanoscale detection and ambient operation our study opens up new frontiers in imaging and sensing applications ranging from material science to bioimaging

Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer.

Although targeted therapies often elicit profound initial patient responses, these effects are transient due to residual disease leading to acquired resistance. How tumors transition between drug responsiveness, tolerance and resistance, especially in the absence of preexisting subclones, remains unclear. In epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma cells, we demonstrate that residual disease and acquired resistance in response to EGFR inhibitors requires Aurora kinase A (AURKA) activity. Nongenetic resistance through the activation of AURKA by its coactivator TPX2 emerges in response to chronic EGFR inhibition where it mitigates drug-induced apoptosis. Aurora kinase inhibitors suppress this adaptive survival program, increasing the magnitude and duration of EGFR inhibitor response in preclinical models. Treatment-induced activation of AURKA is associated with resistance to EGFR inhibitors in vitro, in vivo and in most individuals with EGFR-mutant lung adenocarcinoma. These findings delineate a molecular path whereby drug resistance emerges from drug-tolerant cells and unveils a synthetic lethal strategy for enhancing responses to EGFR inhibitors by suppressing AURKA-driven residual disease and acquired resistance

Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation

Abstract. Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. Such a semantic representation is shared between an annotated auxiliary dataset and a target dataset with no annotation. A projection from a low-level feature space to the seman-tic space is learned from the auxiliary dataset and is applied without adaptation to the target dataset. In this paper we identify an inher-ent limitation with this approach. That is, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift prob-lem and propose a novel framework, transductive multi-view embedding, to solve it. It is ‘transductive ’ in that unlabelled target data points are explored for projection adaptation, and ‘multi-view ’ in that both low-level feature (view) and multiple semantic representations (views) are embedded to rectify the projection shift. We demonstrate through ex-tensive experiments that our framework (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complemen-tarity of multiple semantic representations, (3) achieves state-of-the-art recognition results on image and video benchmark datasets, and (4) en-ables novel cross-view annotation tasks.

The impact of point mutations in the human androgen receptor : classification of mutations on the basis of transcriptional activity

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