Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase 1 study of 28-day , low-dose temozolomide and BCNU in the treatment of malignant gliomas after radiation therapy 1

We conducted a study to determine the dose-limiting toxicity of an extended dosing schedule of temozolomide (TMZ) when used with a fixed dose of BCNU, or 1,3bis(2-chloroethyl)-1-nitrosourea (carmustine), taking advantage of TMZ’s ability to deplete O-alkylguanineDNA-alkyltransferase and the synergistic activity of these two agents. Patients with malignant gliomas who had undergone radiation therapy were eligible. Patients were treated with TMZ for 28 days, followed by a 28-day rest (1 cycle). The TMZ was started at 50 mg/m and increased in 10-mg/m increments; a fixed dose of BCNU (150 mg/m) was given within 72 h of starting TMZ. A standard phase 1 dose-escalation scheme was used with 3 patients per cohort. Fourteen glioblastoma patients and 10 anaplastic astrocytoma patients were treated. The dose-limiting toxicity was myelosuppression at 90 mg/m of TMZ. The total number of cycles given was 73 (median Phase 1 study of 28-day, low-dose temozolomide and BCNU in the treatment of malignant gliomas after radiation therapy
Avatar Therapy for persistent auditory verbal hallucinations: a case report of a peer research assistant on his path toward recovery

ABSTRACT Peer support promotes the overall wellness of people with mental illness by establishing mutual partnerships throughout their different stages of recovery. In health research, investments in patient-oriented research have become prioritized. However, peer-contributed research remains relatively rare, especially in the study of psychosis. Our research team chose to include a peer research assistant in the elaboration and refinement of a novel personalized and patient-oriented psychotherapy for voice hearers, using virtual reality (Avatar Therapy (AT)). This paper details the case of a partnership between a mental health service user and clinical researchers by showcasing Mr X, the first to follow the therapy for his input and our peer support worker for future patients, in his journey toward recovery. Before AT, Mr X was unable to advance due to his voices. His participation was to initially gain from his personal expertise and invite his critique to improve AT. However, what he gained from AT was much more; his voices diminished by 80–90% and he was able to reduce his medication on four occasions. Also, he followed a university-level peer helper program and became employed. Mr X is an inspirational case of how AT may have a positive impact on one’s life.
Management of children with sexual behaviour problems, between the ages of five to nine years old, by educators and social workers

The Management of children, younger than twelve years of age, who pose a risk to other children, remains complex and confusing. When their behaviour includes sexual aggression towards other children, ignorance about how to manage them becomes even more challenging. Society has an expectation that any sexual aggressor should be punished, however, when the aggressor is younger than ten years old, different responses are necessary. The aim of this current study was to explore the management of learners with sexual behaviour problems, within the primary school setting. The main question underpinning this study was: How are children, aged five-to-nine-years, with sexual behaviour problems, managed by social workers and educators? A qualitative methodology, with focus group discussions and semi-structured interviews were used to conduct this study. Educators from primary schools, as well as social workers from the Western Cape Education Department, were purposively selected to form the sample for this study. Focus group discussions were conducted with the educators, while semi-structured interviews were conducted with the social workers. The research findings indicated that the most cases of SBPs identified by educators among the grade R to grade three learners, could be classified as very concerning behaviour. In addition, the research reflects that educators, who teach foundation phase learners, as well as social workers delivering services under the Safe School Programme, agree that trauma related behaviours are prominent. Learners identified with SBPs have been assessed to reveal behaviours such as, violence, stealing, anger, lying, attention seeking, destructive behaviour and displaying a lack of the understanding of personal boundaries. By researching the management of learners identified with SBPs by educators and social workers, it is evident that the educator’s greatest challenge is understanding which behaviours require further intervention, or would require a different response from the day to day reprimanding, given by an educator to a learner. The research reflects that the Western Cape Education Department’s ABUSE No More Manual could be used more effectively to enhance the educators understanding of the identified SBPs among learners, since these http://etd.uwc. ac.za/
Video Object Tracking Based On Extended Active Shape Models With Color Information

Tracking and recognizing non-rigid objects in video image sequences are complex tasks of increa sing importance to many applications. For the tracking of such objects in a video sequence e.g. "active shape models" can be applied. The existing active shape models are usually based on intensity information and they do not consider color information. However, active shape models are sensitive to outliers, especially in the case of partial object occlusions. In this paper, we present an extension of the active shape model for color images and we examine to what extent the use of color information can contribute to the solution of the outlier problem.
Integrating Colors, Shapes and Motions UsingActive Contour Based Visual Tracking

The visual tracking is the major process in finding the spot of moving object over time using a camera. Object tracking is challenging task when the object moves fast relative to the frame rate. The active contour algorithm is used for tracking the object in a given frame of an image sequence. In videos particular object motion can be tracked by using stationary cameras but in moving camera the particular object cannot be extracted from background subtraction. Active contour based visual tracking using level sets is proposed which does not consider the camera is stationary (or) moving . The optical flow based algorithm is used for initializing contours at first frame. The correlations between the values of neighboring pixels for posterior probability estimation is done by Markov random field theory in the color based contour evolution. The Independent Component Analysis (ICA) is used to deal 999with noise (or) partial occlusion to obtain the more accurate contours in the shape based contour. To handle the abrupt motions the particle swarm optimization is used to track the object from first frame to last frame and it is applied to current frame to produce an initial contour in next frame. This visual tracking can be used in real time applications like vehicle guidance and control, surveillance and identification, user interface, video processing and medical applications.
Beyond a psychologistic trauma: a sense-based approach to the study of affect

ABSTRACT Taking trauma as an affect, this article discusses how body-matter assemblages can impact affect. Based on an ethnography of conflict-related loss and grieving from postwar Sri Lanka, it examines nuances of affective becomings, explicating a complex process that involves bodies, things, and spaces charged with intensities and in motion. The article also highlights the methodological challenges of studying such bodily processes. Based on that discussion, the article summons an approach to the study of trauma that re-engages with senses as a means of studying the nonverbal and nonconscious.
Effects of experimental fires on litter decomposition in a seasonally dry Amazonian forest

Abstract: Litter decomposition is a fundamental process for nutrient cycling but we have a limited understanding of this process in disturbed tropical forests. We studied litter decomposition over a 10-mo period in a seasonally dry Amazon forest in Mato Grosso, Brazil. The study plots (50 ha each) included unburned forest (UF), once-burned (BF1) and forest burned annually for 3 y (BF3). We measured understorey density, litter depth, canopy openness, temperature and relative humidity in the plots. Decomposition experiments took place using 720 litterbags filled with approximately 10 g of natural abscised oven-dried leaves. To test the effects of fire on soil meso- and macrofauna, the litterbags had either a fine (2 mm) or coarse (with 1-cm holes in side) mesh size. Litterbags were collected and reweighed 2, 4, 6 and 8 mo after being placed on the forest floor. All forest structure variables were significantly different across plots: BF3 was hotter, less humid, had the highest degree of canopy openness, lowest understorey density and the shallowest litter depth. Litter decomposition (mass loss) was similar in the once-burned and unburned plots, but declined more slowly in BF3. In addition, decomposition was slower in fine-mesh litterbags than coarse-mesh litterbags in BF3, but there was no difference between mesh sizes in BF1 and UF. It is likely that changes in forest structure and microclimate explain the lower decomposition rates in BF3. These results show the importance of recurrent fires, but suggest that single understorey fires may not have long-term negative effects on some ecological processes in seasonally dry Amazonian forests.
On-road vehicle detection using Gabor filters and support vector machines

On-road vehicle detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for rear-view vehicle detection. Specifically, we propose using Gabor filters for vehicle feature extraction and support vector machines (SVM) for vehicle detection. Gabor filters provide a mechanism for obtaining some degree of invariance to intensity due to global illumination, selectivity in scale, and selectivity in orientation. Basically, they are orientation and scale tunable edge and line detectors. Vehicles do contain strong edges and lines at different orientation and scales, thus, the statistics of these features (e.g., mean, standard deviation, and skewness) could be very powerful for vehicle detection. To provide robustness, these statistics are not extracted from the whole image but rather are collected from several subimages obtained by subdividing the original image into subwindows. These features are then used to train a SVM classifier. Extensive experimentation and comparisons using real data, different features (e.g., based on principal components analysis (PCA)), and different classifiers (e.g., neural networks (NN)) demonstrate the superiority of the proposed approach which has achieved an average accuracy of 94.81% on completely novel test images.
Photodesorption of water ice - A molecular dynamics study

Context. Absorption of ultraviolet radiation by water ice coating interstellar grains can lead to dissociation and desorption of the ice molecules. These processes are thought to be important in the gas-grain chemistry in molecular clouds and protoplanetary disks, but very few quantitative studies exist. Aims. We compute the photodesorption efficiencies of amorphous water ice and elucidate the mechanisms by which desorption occurs. Methods. Classical molecular dynamics calculations were performed for a compact amorphous ice surface at 10 K thought to be representative of interstellar ice. Dissociation and desorption of H2O molecules in the top six monolayers are considered following absorption into the first excited electronic state with photons in the 1300−1500 A range. The trajectories of the H and OH photofragments are followed until they escape or become trapped in the ice. Results. The probability for H2O desorption per absorbed UV photon is 0.5−1% in the top three monolayers, then decreases to 0.03% in the next two monolayers, and is negligible deeper into the ice. The main H2O removal mechanism in the top two monolayers is through separate desorption of H and OH fragments. Removal of H2O molecules from the ice, either as H2O itself or its products, has a total probability of 2−3% per absorbed UV photon in the top two monolayers. In the third monolayer the probability is about 1% and deeper into the ice the probability of photodesorption falling to insignificant numbers. The probability of any removal of H2 Op er incident photon is estimated to be 3.7 × 10 −4 , with the probability for photodesorption of intact H2O molecules being 1.4 × 10 −4 per incident photon. When no desorption occurs, the H and OH products can travel up to 70 and 60 A inside or on top of the surface, respectively, during which they can react with other species, such as CO, before they become trapped.
SEMANTIC CONCEPTS FOR CONTENT FILTERING ON VIDEO SHARING SOCIAL NETWORKS

In this work we are concerned in use semantic concepts to filter spam videos on video sharing social networks. Specifically, we investigate how much semantic-based information analysis, based on content-based visual information retrieval (CBVIR), can aid in detecting spam videos. This is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information. In addition, a spam video is by nature context-dependent, forcing us to take into account the context of the videos within their threads in the classification. Web services for the sharing of clips of video are extremely popular; and the recent boom of intelligent mobile devices such as smartphones and tablets equipped with fast network access and good-quality cameras and displays has reduced the interval between content creation and broadcasting. That proliferation of new content, and the immediacy of its availability, is not without problems, creating a demand for mechanisms to control abuses and terms-of-use violations. We propose an approach based on bags-of-topic-differences, which improves considerably over the use of the baseline bags-of-words model, by allowing us to incorporate the context of the video in the representation. Our model is evaluated in challenging video dataset, showing very encouraging results.
Analysis of Tradeoffs among Verification Accuracy, Memory Consumption, and Execution Time in the GH-Based Fuzzy Fingerprint Vault

The fuzzy fingerprint vault solution has been proposed as a way to protect the registered fingerprint template, and the geometric hashing solution has been applied to implement it. Although this Geometric Hashing-based fuzzy fingerprint vault solution can solve the auto-alignment problem and provide higher security, it requires more memory space due to the large size of the hash table. To solve this problem, we make the GH-based fuzzy fingerprint vault to be configurable, and study the tradeoffs among the verification accuracy, the memory consumption, and the execution time of it. The experimental results show that the configurable approach can either maximize the verification accuracy or minimize the memory consumption and/or the execution time by setting a performance parameter properly.
Survival analysis for freshness in microblogging search

Freshness of information in real-time search is central in social networks, news, blogs and micro-blogs. Nevertheless, there is not a clear experimental evidence that shows what principled approach effectively combines time and content. We introduce a novel approach to model freshness using a survival analysis of relevance over time. In such models, freshness is measured by the tail probability of relevance over time. We also assume that the probability distributions for freshness are heavy-tailed. The heavy-tailed models of freshness are shown to be highly effective on the micro-blogging test collection of TREC 2011. The improvements over the state-of-the-art time-based models are statistically significant or moderately significant.
Photoreceptor differentiation in Drosophila: From immature neurons to functional photoreceptors

How a pool of equipotent cells acquires a multitude of distinct fates is a major question in developmental biology. The study of photoreceptor (PR) cell differentiation in Drosophila has been used to address this question. PR differentiation is a process that extends over a period of 5 days: It begins in the larval eye imaginal disc when PRs are recruited and commit to particular PR fates, and it culminates in the pupal eye disc with the morphogenesis of the rhabdomeres and the initiation of rhodopsin expression. Several models for PR specification agree that the Ras and Notch signaling pathways are important for the specification of different PR subtypes (Freeman [1997] Development 124:261–270; Cooper and Bray [ 2000 ] Curr. Biol. 10:1507–1510; Tomlinson and Struhl [ 2001 ] Mol. Cell. 7:487–495). In the first part of this review, we briefly describe the different signaling pathways and transcription factors required for the specification and differentiation of the different PR subtypes in the larval eye disc. In the second part, we review the roles of several transcription factors, which are required for the terminal photoreceptor differentiation and rhodopsin expression. Developmental Dynamics 232:585–592, 2005. © 2005 Wiley‐Liss, Inc.
Spatial Data Reconstruction via ADMM and Spatial Spline Regression

Reconstructing fine-grained spatial densities from coarse-grained measurements, namely the aggregate observations recorded for each subregion in the spatial field of interest, is a critical problem in many real world applications. In this paper, we propose a novel Constrained Spatial Smoothing (CSS) approach for the problem of spatial data reconstruction. We observe that local continuity exists in many types of spatial data. Based on this observation, our approach performs sparse recovery via a finite element method, while in the meantime enforcing the aggregated observation constraints through an innovative use of the Alternating Direction Method of Multipliers (ADMM) algorithm framework. Furthermore, our approach is able to incorporate external information as a regression add-on to further enhance recovery performance. To evaluate our approach, we study the problem of reconstructing the spatial distribution of cellphone traffic volumes based on aggregate volumes recorded at sparsely scattered base stations. We perform extensive experiments based on a large dataset of Call Detail Records and a geographical and demographical attribute dataset from the city of Milan, and compare our approach with other methods such as Spatial Spline Regression. The evaluation results show that our approach significantly outperforms various baseline approaches. This proves that jointly modeling the underlying spatial continuity and the local features that characterize the heterogeneity of different locations can help improve the performance of spatial recovery.
Automatic test-based assessment of programming: A review

Systems that automatically assess student programming assignments have been designed and used for over forty years. Systems that objectively test and mark student programming work were developed simultaneously with programming assessment in the computer science curriculum. This article reviews a number of influential automatic assessment systems, including descriptions of the earliest systems, and presents some of the most recent developments. The final sections explore a number of directions automated assessment systems may take, presenting current developments alongside a number of important emerging e-learning specifications.
CloudDB AutoAdmin: Towards a Truly Elastic Cloud-Based Data Store

In this paper, we present the design and the architecture of the CloudDB AutoAdmin system which aims to fill the existing gaps between the provided cloud database services and the requirements of the consumer applications. In particular, it focuses on facilitating the job of the cloud database consumers in implementing database applications as distributed, scalable, and elastic services with a minimum effort on the side of the application developer and a limited footprint in the application code.
A Multi-objective GA-based Fuzzy Modeling Approach for Constructing Pareto-optimal Fuzzy systems

— An approach to construct multiple Pareto-optimal fuzzy systems based on a multi-objective genetic algorithm is proposed in this paper. First, in order to obtain a good initial fuzzy system, a modified fuzzy clustering algorithm is used to identify the antecedents of fuzzy system, while the consequents are designed separately to reduce computational burden. Second, a Pareto multi-objective genetic algorithm based on NSGA-II and the interpretability-driven simplification techniques are used to evolve the initial fuzzy system iteratively with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets. Resultantly, multiple Pareto-optimal fuzzy systems are obtained. The proposed approach is applied to two benchmark problems, and the results show its validity.
Establishing P2P-Overlays for Ad Hoc Networks Using Provider Subscription

Multi-hop ad hoc networks are envisioned to be an integral part of future mobile networks. Today, however, ad hoc networks lack the necessary resilience and reliability. This work proposes to use P2P routing overlays to enhance the dependability and performance of multi-hop ad hoc networks for devices that are subscribed to a provider.
Prospect Theory Goes to War: Loss-Aversion and the Duration of Military Combat

This paper contributes to the empirical foundation of prospect theory in real-life international relations by testing two of its major implications in the field of military conflict. Using duration analysis for a data set of twentieth century battles, it is shown how the experience of losses contributes positively to the preparedness to continue fighting, up to a point where casualties clearly outweigh any direct utility drawn from ordinary expected-utility theory. Moreover, the empirical results also indicate that the relative position compared to the opponent's is clearly less important for the decision whether to stop a battle or not than the change of one's own position compared to the beginning of the fight.
Speed Prediction of DC Shunt Motor by using Artificial Neural Network.

This paper explicitly and effectively examines the application of ANN to speed forecast of dc motors. Artificial neural networks (ANN's) is an approach to evolve an efficient model for prediction of DC motor speed, based on a set of input conditions. Neural network algorithms are developed for use as a direct modeling method, to predict DC motor speed control operations. The training of the networks is performed with experimental machining data. Levenberg- Marquardt standard back-propagation algorithm with normalized preprocessed data is used to predict and show the pattern of correlation of input in relation to the output. All machine manufactures and industrial engineers often find ways of minimizing engineering time, reducing prototyping cost and optimizing product quality by expanding the data set for future year development. Keywords—Artificial neural network (ANN), DC shunt Motor, Error chart, Feed-forward Back-propagation, MATLAB and Speed prediction .