'She Who Disputes': A Qur'anic Precedent for Sacral Interlocution

Despite enduring differences between the Abrahamic traditions Islam, Christianity and Judaism, women of these faiths have a shared experience of exclusion from institutional theological enterprises, where women are depicted as silent subjects of faith. This thesis considers women as speaking subjects in the Qur'an within a literary reading to explore an Abrahamic interfeminist dialogue. The thesis compares how women's subjectivity has been interpreted historically in traditional Islamic scholarship with modem feminist deconstruction of androcentric language, in order to consider how women are presented as addressees of the text. Female speaking roles are explored through the language of dispute (jadala) as a thematic feature of the Qur'an, with the surah-title al-mujadilah, 'she who disputes', as pivotal character. The thesis draws on recent literary scholarship that has called for a canonical understanding of the text, whereby the Qur'an is viewed as a literary unit wherein formal structure is seen to have religious significance. The Qur'anic terms of gender and debate are read as part of an internal Qur'anic semiology that develops from the earlier to the later Qur'anic chapters through the expression of Qur'anic Sign. The chronological consideration of the Qur' an's terms of debate presented a model that critiques women's exclusion from the theological process as revealed knowledge while affirming their inclusion in the revelatory scheme not only for the Muslim addressee of Scripture, but for her Jewish and Christian counterparts as well. The thesis thus presents a novel approach of reading biblical texts in light of a Qur'anic model as an Abrahamic theology of women who speak

Efficacy and safety of anticancer drug combinations: a meta-analysis of randomized trials with a focus on immunotherapeutics and gene-targeted compounds.

Hundreds of trials are being conducted to evaluate combination of newer targeted drugs as well as immunotherapy. Our aim was to compare efficacy and safety of combination versus single non-cytotoxic anticancer agents. We searched PubMed (01/01/2001 to 03/06/2018) (and, for immunotherapy, ASCO and ESMO abstracts (2016 through March 2018)) for randomized clinical trials that compared a single non-cytotoxic agent (targeted, hormonal, or immunotherapy) versus a combination with another non-cytotoxic partner. Efficacy and safety endpoints were evaluated in a meta-analysis using a linear mixed-effects model (guidelines per PRISMA Report).We included 95 randomized comparisons (single vs. combination non-cytotoxic therapies) (59.4%, phase II; 41.6%, phase III trials) (29,175 patients (solid tumors)). Combinations most frequently included a hormonal agent and a targeted small molecule (23%). Compared to single non-cytotoxic agents, adding another non-cytotoxic drug increased response rate (odds ratio [OR]=1.61, 95%CI 1.40-1.84)and prolonged progression-free survival (hazard ratio [HR]=0.75, 95%CI 0.69-0.81)and overall survival (HR=0.87, 95%CI 0.81-0.94) (all p<0.001), which was most pronounced for the association between immunotherapy combinations and longer survival. Combinations also significantlyincreased the risk of high-grade toxicities (OR=2.42, 95%CI 1.98-2.97) (most notably for immunotherapy and small molecule inhibitors) and mortality at least possibly therapy related (OR: 1.33, 95%CI 1.15-1.53) (both p<0.001) (absolute mortality = 0.90% (single agent) versus 1.31% (combinations)) compared to single agents. In conclusion, combinations of non-cytotoxic drugs versus monotherapy in randomized cancer clinical trials attenuated safety, but increased efficacy, with the balance tilting in favor of combination therapy, based on the prolongation in survival

Impact of automated action labeling in classification of human actions in RGB-D videos

For many applications it is important to be able to detect what a human is currently doing. This ability is useful for applications such as surveillance, human computer interfaces, games and healthcare. In order to recognize a human action, the typical approach is to use manually labeled data to perform supervised training. This paper aims to compare the performance of several supervised classifiers trained with manually labeled data versus the same classifiers trained with data automatically labeled. In this paper we propose a framework capable of recognizing human actions using supervised classifiers trained with automatically labeled data in RGB-D videos.info:eu-repo/semantics/publishedVersio

Predicting human activities in sequences of actions in RGB-D videos

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.info:eu-repo/semantics/acceptedVersio

Human activity recognition from automatically labeled data in RGB-D videos

Human Activity Recognition (HAR) is an interdisciplinary research area that has been attracting interest from several research communities specialized in machine learning, computer vision, medical and gaming research. The potential applications range from surveillance systems, human computer interfaces, sports video analysis, digital shopping assistants, video retrieval, games and health-care. Several and diverse approaches exist to recognize a human action. From computer vision techniques, modeling relations between human motion and objects, marker-based tracking systems and RGB-D cameras. Using a Kinect sensor that provides the position of the main skeleton joints we extract features based solely on the motion of those joints. This paper aims to compare the performance of several supervised classifiers trained with manually labeled data versus the same classifiers trained with data automatically labeled. We propose a framework capable of recognizing human actions using supervised classifiers trained with automatically labeled data.info:eu-repo/semantics/acceptedVersio

Magnetic properties of Fe3O4 nanoparticles coated with oleic and dodecanoic acids

Magnetic nanoparticles (NP) of magnetite (Fe3O4) coated with oleic acid (OA) and dodecanoic acid (DA) were synthesized and investigated through Transmission Electron Microscopy (TEM),magnetization M, and ac magnetic susceptibility measurements. The OA coated samples were produced with different magnetic concentrations (78, 76, and 65%) and the DA sample with 63% of Fe3O4. Images from TEM indicate that the NP have a nearly spherical geometry and mean diameter ~ 5.5 nm. Magnetization measurements, performed in zero field cooled (ZFC) and field cooled (FC) processes under different external magnetic fields H, exhibited a maximum at a given temperature TB in the ZFC curves, which depends on the NP coating (OA or DA), magnetite concentration, and H. The temperature TB decreases monotonically with increasing H and, for a given H, the increase in the magnetite concentration results in an increase of TB. The observed behavior is related to the dipolar interaction (DI) between NP which seems to be an important mechanism in all samples studied. This is supported by the results of the ac magnetic susceptibility Xac measurements, where the temperature in which X' peaks for different frequencies follows the Vogel-Fulcher model, a feature commonly found in systems with dipolar interactions. Curves of H vs. TB/TB(H=0) for samples with different coatings and magnetite concentrations collapse into a universal curve, indicating that the qualitative magnetic behavior of the samples may be described by the NP themselves, instead of the coating or the strength of the dipolar interaction. Below TB, M vs. H curves show a coercive field (HC) that increases monotonically with decreasing temperature. The saturation magnetization (MS) follows the Bloch's law and values of MS at room temperature as high as 78 emu/g were estimated, a result corresponding to ~80% of the bulk value. The overlap of M/MS vs. H/T curves for a given sample and the low HC at high temperatures suggest superparamagnetic behavior in all samples studied. The overlap of M/MS vs. H curves at constant temperature for different samples indicates that the NP magnetization behavior is preserved, independently of the coating and magnetite concentration.Comment: 8 pages and 9 figure

Role of dipolar interactions in a system of Ni nanoparticles studied by magnetic susceptibility measurements

The role of dipolar interactions among Ni nanoparticles (NP) embedded in an amorphous SiO2/C matrix with different concentrations has been studied performing ac magnetic susceptibility Chi_ac measurements. For very diluted samples, with Ni concentrations < 4 wt % Ni or very weak dipolar interactions, the data are well described by the Neel-Arrhenius law. Increasing Ni concentration to values up to 12.8 wt % Ni results in changes in the Neel-Arrhenius behavior, the dipolar interactions become important, and need to be considered to describe the magnetic response of the NPs system. We have found no evidence of a spin-glasslike behavior in our Ni NP systems even when dipolar interactions are clearly present.Comment: 7 pages, 5 figures, 3 table

Measurement of the 0.511 MeV gamma ray line from the Galactic Center

The detection of the 0.511 MeV electron positron annihilation line coming from the Galactic Center to provide the means to estimate the rate of positron production and to test some theoretical sources of positrons is addressed. The results of the measurements of the 0.511 MeV line flux made with a gamma ray experiment on board a stratospheric balloon are presented. The detector field of view looked at the galactic longitude range -31 deg l(II) +41 deg. The observed flux is 0.0067 (+ or - 0.0005) photons 1/cm(2)5 which is in very good agreement with the expected flux when assuming that the Galactic Center is a line source emitting uniformly

Automatic human activity segmentation and labeling in RGBD videos

Human activity recognition has become one of the most active research topics in image processing and pattern recognition. Manual analysis of video is labour intensive, fatiguing, and error prone. Solving the problem of recognizing human activities from video can lead to improvements in several application fields like surveillance systems, human computer interfaces, sports video analysis, digital shopping assistants, video retrieval, gaming and health-care. This paper aims to recognize an action performed in a sequence of continuous actions recorded with a Kinect sensor based on the information about the position of the main skeleton joints. The typical approach is to use manually labeled data to perform supervised training. In this paper we propose a method to perform automatic temporal segmentation in order to separate the sequence in a set of actions. By measuring the amount of movement that occurs in each joint of the skeleton we are able to find temporal segments that represent the singular actions.We also proposed an automatic labeling method of human actions using a clustering algorithm on a subset of the available features.info:eu-repo/semantics/acceptedVersio