Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors

Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of \emph{class-specific} spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201

Connected OFCity Challenge: Addressing the Digital Divide in the Developing World

Over the past 50 years, the development of information and communications technology has provided unprecedented support to the steady economic growth of developed countries. In recent years, some of the largest growth has been reported in emerging economies, which, however, often lack adequate telecommunications infrastructure to further sustain their development. Although a number of service providers and system vendors have started to address the issue, the challenges they encounter are substantially different from those in the developed world, including an unreliable electricity grid, poor fiber infrastructure, low revenue expectations, and often a harsh climate environment. This paper reports use cases and solutions pertinent to the development of the networking infrastructure in emerging economies, provided by organizations directly involved in such activities. After providing some background information on the current state of network infrastructure and the main challenges for Africa and rural China, the paper provides details on two proposed solutions. The first focuses on the provisioning of services and network infrastructure through the development of low-cost data centers, whereas the second proposes cost-effective adaptation of both fiber and hybrid copper-fiber technology to rural areas. The article is concluded with a brief discussion on the complementarity of the two approaches

Intrauterine Infection and Preterm Labor

Preterm birth remains the leading cause of perinatal mortality and morbidity. Evidence suggests that intrauterine infection plays an important role in the pathogenesis of preterm labor. This article reviews the clinical data supporting this theory and the cellular and biochemical mechanisms by which intrauterine infection may initiate uterine contractions. The clinical and laboratory methods of diagnosing clinical chorioamnionitis and asymptomatic bacterial invasion of the intraamniotic cavity are also reviewed. Finally, the management of clinical chorioamnionitis and asymptomatic microbial invasion of the amniotic fluid and the use of adjunctive antibiotic therapy in the treatment of preterm labor are presented

How robust are value judgements of health inequality aversion? Testing for framing and cognitive effects

Background: Empirical studies have found that members of the public are inequality averse and value health gains for disadvantaged groups with poor health many times more highly than gains for better off groups. However, these studies typically use abstract scenarios that involve unrealistically large reductions in health inequality, and face-to-face survey administration. It is not known how robust these findings are to more realistic scenarios or anonymous online survey administration. Methods: This study aimed to test the robustness of questionnaire estimates of inequality aversion by comparing the following: (1) small versus unrealistically large health inequality reductions; (2) population-level versus individual-level descriptions of health inequality reductions; (3) concrete versus abstract intervention scenarios; and (4) online versus face to face mode of administration. Fifty-two members of the public participated in face-to-face discussion groups, while 83 members of the public completed an online survey. Participants were given a questionnaire instrument with different scenario descriptions for eliciting aversion to social inequality in health. Results: The median respondent was inequality averse under all scenarios. Scenarios involving small rather than unrealistically large health gains made little difference in terms of inequality aversion, as did population-level rather than individual-level scenarios. However, the proportion expressing extreme inequality aversion fell 19 percentage points when considering a specific health intervention scenario rather than an abstract scenario, and was 11-21 percentage points lower among online public respondents compared to the discussion group. Conclusions: Our study suggests that both concrete scenarios and online administration reduce the proportion expressing extreme inequality aversion but still yield median responses implying substantial health inequality aversion

Iterative, Deep Synthetic Aperture Sonar Image Segmentation

Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data, but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-the-art learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method Iterative Deep Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) A deep network estimates class assignments. 2) Low-level image features from the deep network are clustered into superpixels. 3) Superpixels are clustered into class assignments (which we call pseudo-labels) using $k$-means. 4) Resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Finally, we also develop a semi-supervised (SS) extension of IDUS called IDSS and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery.Comment: arXiv admin note: text overlap with arXiv:2107.1456

Emergent versus delayed lithotripsy for obstructing ureteral stones: a cumulative analysis of comparative studies

Objective To analyze the current evidence on the use of ureteroscopy (URS) and extracorporeal shock wave lithotripsy (ESWL) for the management of obstructing ureteral stones in emergent setting. Methods A systematic literature review was performed up to June 2016 using Pubmed and Ovid databases to identify pertinent studies. The PRISMA criteria were followed for article selection. Separate searches were done using a combinations of several search terms: "laser lithotripsy", "ureteroscopy", "extracorporeal shock wave lithotripsy", "ESWL", "rapid", "immediate", "early", "delayed", "late", "ureteral stones", "kidney stones", "renal stones". Only titles related to emergent/rapid/immediate/early (as viably defined in each study) versus delayed/late treatment of ureteral stones with either URS and/or ESWL were considered for screening. Demographics and operative outcomes were compared between emergent and delayed lithotripsy. RevMan review manager software was used to perform data analysis. Results Four studies comparing emergent (n = 526) versus delayed (n = 987) URS and six studies comparing emergent (n = 356) versus delayed (n = 355) SWL were included in the analysis. Emergent URS did not show any significant difference in terms of stone-free rate (91.2 versus 90.9%; OR 1.04; CI 0.71, 1.52; p = 0.84), complication rate (8.7% for emergent versus 11.5% for delayed; OR 0.94; CI 0.65, 1.36; p = 0.74) and need for auxiliary procedures (OR 0.85; CI 0.42, 1.7; p = 0.85) when compared to delayed URS. Emergent ESWL was associated with a higher likelihood of stone free status (OR 2.2; CI 1.55, 3.17; p < 0.001) and a lower likelihood of need for auxiliary maneuvers (OR 0.49; CI 0.33, 0.72; p < 0.001) than the delayed procedure. No differences in complication rates were noticed between the emergent and delayed ESWL (p = 0.37). Conclusions Emergent lithotripsy, either ureteroscopic or extracorporeal, can be offered as an effective and safe treatment for patients with symptomatic ureteral stone. If amenable to ESWL, based on stone and patient characteristics, an emergent approach should be strongly considered. Ureteroscopy in the emergent setting is mostly reserved for distally located stones. The implementation of these therapeutic approaches is likely to be dictated by their availability.info:eu-repo/semantics/publishedVersio