Preface to the 10th anniversary issue of the Journal on Ambient Intelligence and Smart Environments

The Editors in Chief of the JAISE journal reflect on the evolution of the technical area and the scientific community the publication has been serving for a decade

Blind identification of FIR channels with multiple users via spatio-temporal processing

A new method is proposed for blind identification of possibly nonminimum phase FIR channels with multiple users. The technique exploits the structure of the signals received by an antenna array in both the temporal and spatial frequency domains. Although in the single antenna case it is necessary to use cyclostationary signals or higher order statistics to identify the magnitude and phase of the channel, the present authors circumvent such a requirement by exploiting certain multichannel features of the array. They show that if multiple users are present, the nonminimum phase channels associated with each user can still be identified from the second-order statistics, provided additional spatial structure exists

Guest Editorial Computational and smart cameras

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Introduction to the Special Issue on Large-Scale Visual Sensor Networks: Architectures and Applications

Large–scale visual sensor networks have become progressively an essential part of our daily lives underpinning many technological, financial, and social advancements today, with applications in smart cities, traffic monitoring, environmental pollution control, public safety, and crime prevention

The Priority Structure of Bank Regulatory Capital: The Case of Subordinated Debt

The aftermath of a crisis often brings reflections on the adequacy of regulatory capital against financial shocks. Accordingly, succeeding regulatory interventions focus on strengthening the resilience of the banking system by improving the quality and quantity of capital, and subordinated debt (sub-debt) remains key to these reforms. Whether, however, the regulatory motive underpins the decision of banks to issue sub-debt is unclear. Moreover, the perceptions of shareholders on the regulatory function of sub-debt are less understood. This thesis attempts to answer these questions by first reviewing other roles of sub-debt then testing if regulation drives its issuance and finally revealing shareholder incentives that weaken its regulatory function. Contrasting capital requirement motives with other explanations, and accounting for equity issuance, we find that banks issue sub-debt primarily to improve their regulatory capital buffer. While a few non-regulatory factors, related to easier entry conditions to debt market, influence the issuance decision, their economic impact is smaller than the impact of the buffer. By exploring how variations in tail risk and size influence the sub-debt and equity issuance decisions by banks with low buffers, we show that issuance choices do not reflect risk-shifting incentives. Next, we review shareholders’ perceptions of the regulatory value of sub-debt vis-a-vis the risk-shifting and wealth-expropriation incentives associated with senior debt by comparing the reaction of stocks to these security announcements. We find that senior debt incentives are more valuable than the regulatory benefit of sub-debt. Contrary to regulatory expectations, announcement of sub-debt (capital-improving) offers are valueless even when undertaken by risky or less-capitalized banks; rather, senior debt offered by these vulnerable banks generate significant shareholder value. Pursuant to these risk-shifting motives, senior debt issuers get riskier post-issuance. These findings suggest that the broader debt priority structure harbours perverse incentives that dilute the regulatory effectiveness of sub-debt

Chemical genetics screen for enhancers of rapamycin identifies a specific inhibitor of an SCF family E3 ubiquitin ligase

The target of rapamycin (TOR) plays a central role in eukaryotic cell growth control. With prevalent hyperactivation of the mammalian TOR (mTOR) pathway in human cancers, strategies to enhance TOR pathway inhibition are needed. We used a yeast-based screen to identify small-molecule enhancers of rapamycin (SMERs) and discovered an inhibitor (SMER3) of the Skp1-Cullin-F-box (SCF)^(Met30) ubiquitin ligase, a member of the SCF E3-ligase family, which regulates diverse cellular processes including transcription, cell-cycle control and immune response. We show here that SMER3 inhibits SCF^(Met30) in vivo and in vitro, but not the closely related SCF^(Cdc4). Furthermore, we demonstrate that SMER3 diminishes binding of the F-box subunit Met30 to the SCF core complex in vivo and show evidence for SMER3 directly binding to Met30. Our results show that there is no fundamental barrier to obtaining specific inhibitors to modulate function of individual SCF complexes

On efficient use of multi-view data for activity recognition

The focus of the paper is on studying ??ve di??erent meth- ods to combine multi-view data from an uncalibrated smart camera network for human activity recognition. The multi- view classi??cation scenarios studied can be divided to two categories: view selection and view fusion methods. Selec- tion uses a single view to classify, whereas fusion merges multi-view data either on the feature- or label-level. The ??ve methods are compared in the task of classifying human activities in three fully annotated datasets: MAS, VIHASI and HOMELAB, and a combination dataset MAS+VIHASI. Classi??cation is performed based on image features com- puted from silhouette images with a binary tree structured classi??er using 1D CRF for temporal modeling. The results presented in the paper show that fusion methods outper- form practical selection methods. Selection methods have their advantages, but they strongly depend on how good of a selection criteria is used, and how well this criteria adapts to di??erent environments. Furthermore, fusion of features outperforms other scenarios within more controlled settings. But the more variability exists in camera placement and characteristics of persons, the more likely improved accu- racy in multi-view activity recognition can be achieved by combining candidate label