A survey of multilinear subspace learning for tensor data

Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning (MSL) for dimensionality reduction of multidimensional data directly from their tensorial representations. It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.     

Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing

Content distribution networks (CDNs) improve scalability and reliability, by replicating content to the “edge” of the Internet. Apart from the pure networking issues of the CDNs relevant to the establishment of the infrastructure, some very crucial data management issues must be resolved to exploit the full potential of CDNs to reduce the “last mile” latencies. A very important issue is the selection of the content to be prefetched to the CDN servers. All the approaches developed so far, assume the existence of adequate content popularity statistics to drive the prefetch decisions. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. To address this issue, we develop self-adaptive techniques to select the outsourced content in a CDN infrastructure, which requires no apriori knowledge of request statistics. We identify clusters of “correlated” Web pages in a site, called Web site communities, and make these communities the basic outsourcing unit. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed techniques are very robust and effective in reducing the user-perceived latency, performing very close to an unfeasible, off-line policy, which has full knowledge of the content popularity.     

Novel Insights into Factor D Inhibition

Complement-mediated diseases or complementopathies, such as Paroxysmal nocturnal hemoglobinuria (PNH), cold agglutinin disease (CAD), and transplant-associated thrombotic microangiopathy (TA-TMA), demand advanced complement diagnostics and therapeutics be adopted in a vast field of medical specialties, such as hematology, transplantation, rheumatology, and nephrology. The miracle of complement inhibitors as “orphan drugs” has dramatically improved morbidity and mortality in patients with otherwise life-threatening complementopathies. Efficacy has been significantly improved by upstream inhibition in patients with PNH. Different molecules may exert diverse characteristics in vitro and in vivo. Further studies remain to show safety and efficacy of upstream inhibition in other complementopathies. In addition, cost and availability issues are major drawbacks of current treatments. Therefore, further developments are warranted to address the unmet clinical needs in the field of complementopathies. This state-of-the-art narrative review aims to delineate novel insights into factor D inhibition as a promising target for complementopathies.     

Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues,databases, and tools

While detecting and interpreting temporal patterns of nonverbal behavioural cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human‐centred interaction they are bound to experience. This paper surveys the nonverbal behavioural cues that could be present during displays of agreement and disagreement; discusses a number of methods that could be used or adapted to detect these suggested cues; lists some publicly available databases these tools could be trained on for the analysis of spontaneous, audiovisual instances of agreement and disagreement, it examines the few existing attempts at agreement and disagreement classification, and finally discusses the challenges in automatically detecting agreement and disagreement.     

Preparation and nonlinear optical response of novel palladium-containing micellar nanohybrids

The synthesis and characterization of novel micellar nanohybrids with controllable sizes consisting of palladium nanosized core and covered by well-defined poly(lauryl methacrylate)-block-poly(2-(acetoacetoxy)ethyl methacrylate) (pLauMA-b-pAEMA) diblock copolymers are reported. Their nonlinear optical (NLO) response (i.e. nonlinear refraction and absorption and the third-order susceptibility χ⁽³⁾) is also studied under 35 ps laser excitation both in the visible and in the infrared and found to be insensitive to the size of the micelles while it was found to be greatly affected by the molar fraction of the AEMA block. To the best of our knowledge it is the first time that such Pd micellar nanohybrids are synthesized and investigated towards their NLO properties. The magnitude of the nonlinear optical response of these nanohybrids renders them promising candidates for potential optoelectronic applications.     

Numerical and analytical investigation of collapse loads of laced built-up columns

Built-up columns are often used in steel buildings and bridges providing economical solutions in cases of large spans and/or heavy loads. Two main effects should be taken into account in their design that differentiate them from other structural members. One is the significant influence of shear deformations due to their reduced shear rigidity. The second is the interaction between global and local buckling. These effects are addressed here from both a numerical and an analytical point of view for laced built-up columns. It is concluded that the largest loss of capacity occurs when the local and global Euler critical stresses and the yield stress all coincide. This reduction in capacity becomes more prominent in the presence of imperfections, reaching magnitudes in the order of 50%. Despite the detrimental effects of mode interaction many major design codes do not provide sufficient pertinent guidance. In order to address this issue, a simple analytical method is proposed for calculating the collapse load of laced built-up members taking into account the above effects as well as global imperfections, local out-of straightness and plasticity, which is then verified by means of nonlinear finite element analysis, using either beam or shell elements. The proposed method is found to provide improved accuracy in comparison to EC3 specifications in cases of global elastic failure.