Kernelized Multiview Projection for Robust Action Recognition

Conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features. In this paper, we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called Kernelized Multiview Projection (KMP). Computing the kernel matrices from different features/views via time-sequential distance learning, KMP can encode different features with different weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space, which allows it to be competent for various practical applications. We demonstrate KMP’s performance for action recognition on five popular action datasets and the results are consistently superior to state-of-the-art techniques

State stigmatization in urban Turkey : Managing the 'insurgent' squatter dwellers in Dikmen Valley

This paper contributes to the accounts of territorial stigmatisation by examining the state role in it in the case of Turkey, a country that suffers from growing state power. The existing debates are mainly restricted to its function as an economic strategy paving the way for capital accumulation through devaluing working‐class people and places. Drawing on textual analysis of political speeches, local newsletters and mainstream national newspapers and fieldwork material that include interviews and observations in Dikmen Valley where some squatter communities mobilised against the state‐imposed urban transformation project, I demonstrate that state conceptualisation of “problem people” targets the “insurgent” rather than the “unprofitable” groups. Stigma in urban settings functions in inciting the desire to meet the patterns deemed appropriate by the state, rather than the market. Moving from that, I argue that stigma is used as a state‐led political strategy, which is integral to the growing authoritarianism in Turkey

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal cancer with fewer than 7% of patients surviving past 5 years. T-cell immunity has been linked to the exceptional outcome of the few long-term survivors1,2, yet the relevant antigens remain unknown. Here we use genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer. Using whole-exome sequencing and in silico neoantigen prediction, we found that tumours with both the highest neoantigen number and the most abundant CD8+ T-cell infiltrates, but neither alone, stratified patients with the longest survival. Investigating the specific neoantigen qualities promoting T-cell activation in long-term survivors, we discovered that these individuals were enriched in neoantigen qualities defined by a fitness model, and neoantigens in the tumour antigen MUC16 (also known as CA125). A neoantigen quality fitness model conferring greater immunogenicity to neoantigens with differential presentation and homology to infectious disease-derived peptides identified long-term survivors in two independent datasets, whereas a neoantigen quantity model ascribing greater immunogenicity to increasing neoantigen number alone did not. We detected intratumoural and lasting circulating T-cell reactivity to both high-quality and MUC16 neoantigens in long-term survivors of pancreatic cancer, including clones with specificity to both high-quality neoantigens and predicted cross-reactive microbial epitopes, consistent with neoantigen molecular mimicry. Notably, we observed selective loss of high-quality and MUC16 neoantigenic clones on metastatic progression, suggesting neoantigen immunoediting. Our results identify neoantigens with unique qualities as T-cell targets in pancreatic ductal adenocarcinoma. More broadly, we identify neoantigen quality as a biomarker for immunogenic tumours that may guide the application of immunotherapies

Large-scale optimization with the primal-dual column generation method

The primal-dual column generation method (PDCGM) is a general-purpose column generation technique that relies on the primal-dual interior point method to solve the restricted master problems. The use of this interior point method variant allows to obtain suboptimal and well-centered dual solutions which naturally stabilizes the column generation. As recently presented in the literature, reductions in the number of calls to the oracle and in the CPU times are typically observed when compared to the standard column generation, which relies on extreme optimal dual solutions. However, these results are based on relatively small problems obtained from linear relaxations of combinatorial applications. In this paper, we investigate the behaviour of the PDCGM in a broader context, namely when solving large-scale convex optimization problems. We have selected applications that arise in important real-life contexts such as data analysis (multiple kernel learning problem), decision-making under uncertainty (two-stage stochastic programming problems) and telecommunication and transportation networks (multicommodity network flow problem). In the numerical experiments, we use publicly available benchmark instances to compare the performance of the PDCGM against recent results for different methods presented in the literature, which were the best available results to date. The analysis of these results suggests that the PDCGM offers an attractive alternative over specialized methods since it remains competitive in terms of number of iterations and CPU times even for large-scale optimization problems.Comment: 28 pages, 1 figure, minor revision, scaled CPU time

Molecular cloning of the OMP19 gene from Brucella melitensis strain H38 and its antigenicity compared to that of commercial OMP19

Brucellosis is a worldwide zoonosis, that can still be classified as endemic despite its ancient origins which causes economic losses and public health problems. Although effectively controlled by vaccination in animals, there is currently no vaccine for use in humans. Outer Membrane Proteins (OMP) that play an active immunogenic and protective role in the Brucellae family. OMP19 is present in all Brucella species as a surface antigen and is a potent immunogen responsible for Brucellosis intracellular infection. For this reason, the study was aimed to be used safely as a potential recombinant vaccine candidate against all Brucella infections, especially in humans and pregnant animals. This study evaluated a Brucella lipoprotein antigen, i.e. 19 kilodalton (kDa) outer membrane protein (OMP19), which was amplified and cloned into the pETSUMO vector system. The immunogenic power of the purified recombinant OMP19 antigen against brucellosis was compared with that of OMP19 (Raybiotech Inc, USA) in a mouse model and the obtained rOMP19 antigen was found to be similar to the commercially available recombinant protein