Miracle-dz/UniFAR-FTUs huggingface dataset:
M A ] 1 0 A pr 2 01 8 On Rational Delegations in Liquid Democracy

Liquid democracy is a proxy voting method where proxies are delegable. We propose and study a game-theoretic model of liquid democracy to address the following question: when is it rational for a voter to delegate her vote? We study the existence of pure-strategy Nash equilibria in this model, and how group accuracy is affected by them. We complement these results by means of simulations to study the effects of delegations on group’s accuracy on various network structures.
Human-guided data exploration using randomisation

An explorative data analysis system should be aware of what the user already knows and what the user wants to know of the data: otherwise the system cannot provide the user with the most informative and useful views of the data. We propose a principled way to do exploratory data analysis, where the user's background knowledge is modeled by a distribution parametrised by subsets of rows and columns in the data, called tiles. The user can also use tiles to describe his or her interests concerning relations in the data. We provide a computationally efficient implementation of this concept based on constrained randomisation. The implementation is used to model both the background knowledge and the user's information request and is a necessary prerequisite for any interactive system. Furthermore, we describe a novel linear projection pursuit method to find and show the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that our method is robust under noise and fast enough for interactive use. We also show that the method gives understandable and useful results when analysing real-world data sets. We will release an open source library implementing the idea, including the experiments presented in this paper. We show that our method can outperform standard projection pursuit visualisation methods in exploration tasks. Our framework makes it possible to construct human-guided data exploration systems which are fast, powerful, and give results that are easy to comprehend.
Automatic Caching Decision for Scientific Dataflow Execution in Apache Spark

Demands for large-scale data analysis and processing have led to the development and widespread adoption of computing frameworks that leverage in-memory data processing, largely outperforming disk-based processing systems. One such framework is Apache Spark, which adopts a lazy-evaluation execution mode. In this model, the execution of a transformation dataflow operation is delayed until its results are required by an action. Furthermore, transformation's results are not kept in memory by default, and the same transformation must be re-executed whenever required by another action. In order to spare unnecessary re-execution of entire pipelines of frequently referenced operations, Spark enables the programmer to explicitly define a cache operation to persist transformation results. However, many factors affect the efficiency of a cache in a dataflow, including the existence of other cache operations. Thus, even with a reasonably small number of transformations, choosing the optimal combination of cache operations poses a nontrivial problem. The problem is highlighted by the fact that intuitive strategies -- especially considered in isolation - may actually be harmful to the dataflow efficiency. In this work, we present an automatic procedure to compute the substantially optimal combination of cache operations given a dataflow definition and a simple model for the cost of the operations. Our results over an astronomy dataflow use case show that our algorithm is resilient to changes in the dataflow and cost model, and that it outperforms intuitive strategies, consistently deciding on a substantially optimal combination of caches.
Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images

Automated prostate segmentation from 3D MR images is very challenging due to large variations of prostate shape and indistinct prostate boundaries. We propose a novel volumetric convolutional neural network (ConvNet) with mixed residual connections to cope with this challenging problem. Compared with previous methods, our volumetric ConvNet has two compelling advantages. First, it is implemented in a 3D manner and can fully exploit the 3D spatial contextual information of input data to perform efficient, precise and volume-to-volume prediction. Second and more important, the novel combination of residual connections (i.e., long and short) can greatly improve the training efficiency and discriminative capability of our network by enhancing the information propagation within the ConvNet both locally and globally. While the forward propagation of location information can improve the segmentation accuracy, the smooth backward propagation of gradient flow can accelerate the convergence speed and enhance the discrimination capability. Extensive experiments on the open MICCAI PROMISE12 challenge dataset corroborated the effectiveness of the proposed volumetric ConvNet with mixed residual connections. Our method ranked the first in the challenge, outperforming other competitors by a large margin with respect to most of evaluation metrics. The proposed volumetric ConvNet is general enough and can be easily extended to other medical image analysis tasks, especially ones with limited training data.
Pharmaceutical Market Access: current state of affairs and key challenges – results of the Market Access Launch Excellence Inventory (MALEI)

Objectives To take inventory of the current state of affairs of Market Access Launch Excellence in the life sciences industry. To identify key gaps and challenges for Market Access (MA) and discuss how they can be addressed. To generate a baseline for benchmarking MA launch excellence. Methodology An online survey was conducted with pharmaceutical executives primarily working in MA, marketing, or general management. The survey aimed to evaluate MA excellence prerequisites across the product life cycle (rated by importance and level of implementation) and to describe MA activity models in the respective companies. Composite scores were calculated from respondents’ ratings and answers. Results Implementation levels of MA excellence prerequisites generally lagged behind their perceived importance. Item importance and the respective level of implementation correlated well, which can be interpreted as proof of the validity of the questionnaire. The following areas were shown to be particularly underimplemented: 1) early integration of MA and health economic considerations in research and development decision making, 2) developing true partnerships with payers, including the development of services ‘beyond the pill’, and 3) consideration of human resource and talent management. The concept of importance-adjusted implementation levels as a hybrid parameter was introduced and shown to be a viable tool for benchmarking purposes. More than 70% of respondents indicated that their companies will invest broadly in MA in terms of capital and headcount within the next 3 years. Conclusions MA (launch) excellence needs to be further developed in order to close implementation gaps across the entire product life cycle. As MA is a comparatively young pharmaceutical discipline in a complex and dynamic environment, this effort will require strategic focus and dedication. The Market Access Launch Excellence Inventory benchmarking tool may help guide decision makers to prioritize their endeavors.
In-Vitro Analysis of Glucose and Quercetin Effects on m-TOR and Nrf-2 Expression in HepG2 Cell Line (Diabetes and Cancer Connection)

ABSTRACT Some types of cancers show a strong relationship with diabetes and play a central role in mortality in the patient population suffering from diabetes mellitus. In this study, HepG2 cells have been used to investigate the toxic effects of hyperglycemia and/or quercetin (Q) on mammalian target of rapamycin (m-TOR) and nuclear factor erythroid 2-related factor 2 (Nrf-2) expression as central molecules involved in cancer. HepG2 cells were cultured with different concentrations of glucose (5.5, 30, and 50 mM) and/or Q (25 µM) for 48 and 72 h. Effects of glucose and/or Q on m-TOR and Nrf-2 expression were assayed by quantitative real-time PCR (qRT-PCR). qRT-PCR results revealed that 30 and 50 mM of glucose increased m-TOR expression at 48 h, although after 72 h, only 30 mM had an increasing effect. At 50 mM, glucose-induced Nrf-2 gene expression after both 48 and 72 h. The results also showed that 25 µM of Q reduced m-TOR and Nrf-2 expression at both 30 and 50 mM after 48 and 72 h incubation. Q has potential effects on reducing oxidative stress caused by hyperglycemia and during diabetes may be able to modulate some carcinogenic signaling pathways.
Dissociated Role of D-Serine in Extinction During Consolidation vs. Reconsolidation of Context Conditioned Fear

Extinction-based exposure therapy is widely used for the treatment of anxiety disorders, such as post-traumatic stress disorder (PTSD). D-serine, an endogenous co-agonist at the glycine-binding site of the N-methyl-D-aspartate-type glutamate receptor (NMDAR), has been shown to be involved in extinction of fear memory. Recent findings suggest that the length of time between the initial learning and an extinction session is a determinant of neural mechanism involved in fear extinction. However, how D-serine is involved in extinction of fear memory at different timings remains unclear. In the present study, we investigated the role of D-serine in immediate, delayed and post-retrieval extinction (P-RE) of contextual fear memory using wild-type (WT) and serine racemase (SRR) knockout (KO) mice that exhibit 90% reduction in D-serine content in the hippocampus. We found that SRR disruption impairs P-RE, facilitates immediate extinction (IE), but has no effect on delayed extinction (DE) of contextual fear memories. The impaired P-RE of contextual fear memory in SRRKO mice was associated with increased expression of the GluA1 subunit of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptor (AMPAR) in the hippocampal synaptic membrane fraction after P-RE, and this increase of AMPAR and impaired P-RE were rescued by the administration of D-serine to SRRKO mice. Our findings suggest that D-serine is differentially involved in the regulation of contextual fear extinction depending on the timing of behavioral intervention, and that combining D-serine or other drugs, enhancing the NMDAR function, with P-RE may achieve optimal outcomes for the treatment of PTSD.
Prophylactic antibiotics at the time of elective cholecystectomy are effective in reducing the post-operative infective complications: a systematic review and meta-analysis.

Background The objective of this article is to evaluate the role of prophylactic antibiotics in preventing the infective complications in patients undergoing elective laparoscopic cholecystectomy (ELC). Methods A systematic review of the literature on the published randomized, controlled reporting the role of prophylactic antibiotics in preventing the infective complications in patients undergoing ELC was undertaken using the principles of meta-analysis. Results Twenty-five RCTs on 6,138 patients evaluating the infective complications in patients undergoing ELC were systematically analysed. There were 3,099 patients in antibiotics group and 3,039 patients in no-antibiotics group. The risk of surgical site infection (SSI) [odds ratio (OR), 0.75 (95% CI, 0.52-1.07), P=0.11], distant infection [OR, 0.66 (95% CI, 0.21-2.14), P=0.49] and residual abscess [OR, 0.93 (95% CI, 0.23-3.81), P=0.92] was lower in the antibiotics group but statistical significance was not reached. However, the risk of overall all type of infective complications was statistically lower [OR, 0.69 (95% CI, 0.50-0.95), P=0.02] in the antibiotics group. Subsequently, this was reflected into the reduced length of hospitalization [standardized mean difference (SMD), -0.32 (95% CI, -0.54--0.10), P=0.004] in the antibiotics group. Conclusions Use of prophylactic antibiotics at the time of induction in patients undergoing ELC has clinically proven advantage of reducing the post-operative infective complications.
Encoder Generator Real / Fake ? Discriminator Net Bird ? Cat ? Dog ?

The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image editing, synthesizing high resolution images, generating videos, etc. These networks and the corresponding learning scheme can handle various visual space mappings. We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discovery in object detection pipelines. A crucial advantage of our method is that it learns a new deep similarity metric, to distinguish multiple objects in one image. We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object, or as a modified deep CNN to represent images for object detection in supervised and weakly supervised scheme. Our ranking GAN offers a novel way to search through images for object specific patterns. We have conducted experiments for different scenarios and demonstrate the method performance for object synthesizing and weakly supervised object detection and classification using the MS-COCO and PASCAL VOC datasets.
A Bezier Curve Cohort Selection Strategy for Face Pair Matching

The matching of two face images without any prior information is very much challenging task unlike a verification or identification system where already some knowledge about the images of each subjects are stored in the system's database. This paper proposes a methodology to enrich the performance of a face pair matching system by utilizing the complementary information collected from a set of cohort face images with the help of Bezier Curve cohort selection algorithm. A pair of face images is given as input to the system. Each image is compared with a predefined cohort pool to form two separate set of cohort scores. Further these set of cohort scores are passed through Bezier curve cohort selection method which provide two suitable cohort subsets. Afterwards a cross normalization is accomplished in conjunction with T-norm score normalization method then the absolute normalized difference between the paired face images is determined. On the basis of this normalized difference, it is finally decided whether the input face pair is from same person or not. The system is investigated with FEI face database and the results are quite impressive.
Parallelizing Coherent Rule Mining Algorithm on CUDA

In data mining, association rules are discovered from the domain when the minimum support threshold value is given by domain expert. Minimum support threshold affects the number of rules generated and the quality of rules. So In this work, a framework is proposed to discover domain knowledge in terms of coherent rules. This rules are discovered from properties of propositional logic, and does not require minimum support threshold.The execution of association rule mining algorithm involves processing of huge data.Handling such amount of data having millions of rows is very challenging. High-end computers and serverside machines are used to process such algorithms for enormous amount of data but are very expensive and accessible to only a few. In comparison graphics processing units (GPUs) have much high computation power and are also less expensive. So, GPU acts as the high performance co-processors. So, the implementation of association rule mining algorithm on GPU will provides the high performance computing and increase in the speedup.
Key Share Management to Protect Data Over Cloud

: Cloud computing is not a new technology it is a new technique of computing. Data which is store over cloud is of sensitive nature that must be protect over cloud from being read and modify by intruder, User data may be stored in a cloud to take advantage of its scalability, accessibility, and economics. This triggered a lot of research activities, resulting in a quantity of proposals targeting the various cloud security threats. According to propose method works on key management and encrypt the data while sending that to the server and automatically get vanished based on passage of time or user activity. The process does not require additional coordination by the data owner, which is of advantage to a very large population of resource-constrained mobile users. By putting the access limit for users over the data can achieve good data confidentiality and integrity. The rate of expiration may be controlled through the initial allocation of shares and the heuristics for removal. A simulation of the scheme and also its implementation on commercial mobile and cloud platforms demonstrate its practical performance.
Microstructural Integrity of a Pathway Connecting the Prefrontal Cortex and Amygdala Moderates the Association Between Cognitive Reappraisal and Negative Emotions

Cognitive reappraisal is a commonly used form of emotion regulation that utilizes frontal-executive control to reframe an approaching emotional event to moderate its potential psychological impact. Use of cognitive reappraisal has been associated with diminished experience of anxiety and depressive symptoms, as well as greater overall well-being. Using data from a study of 647 healthy young adults, we provide initial evidence that an association between typical use of cognitive reappraisal in daily life and the experience of anxiety and depressive symptoms is moderated by the microstructural integrity of the uncinate fasciculus, which provides a major anatomical link between the amygdala and prefrontal cortex. Our findings are consistent with the nature of top-down regulation of bottom-up negative emotions and suggest the uncinate fasciculus may be a useful target in the search for biomarkers predicting not only disorder risk but also response to psychotherapy utilizing cognitive reappraisal.
Learning to Multitask

Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given multitask problem, we propose a learning framework called learning to multitask (L2MT). To achieve the goal, L2MT exploits historical multitask experience which is organized as a training set consists of several tuples, each of which contains a multitask problem with multiple tasks, a multitask model, and the relative test error. Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation. Given a new multitask problem, the estimation function is used to identify a suitable multitask model. Experiments on benchmark datasets show the effectiveness of the proposed L2MT framework.
Policy Reuse in Deep Reinforcement Learning

Driven by recent developments in Artificial Intelligence research, a promising new technology for building intelligent agents has evolved. The approach is termed Deep Reinforcement Learning and combines the classic field of Reinforcement Learning (RL) with the representational power of modern Deep Learning approaches. It is very well suited for single task learning but needs a long time to learn any new task. To speed up this process, we propose to extend the concept to multi-task learning by adapting Policy Reuse, a Transfer Learning approach from classic RL, to use with Deep Q-Networks.
How to Cure Cancer (in images) with Unpaired Image Translation

We discuss how distribution matching losses, such as those used in CycleGAN, when used to translate images from one domain to another can lead to mis-diagnosis of medical conditions. It seems appealing to use these methods for image translation from the source domain to the target domain without requiring paired data. However, the way these models function is through matching distributions of the source and target domains. This can cause issues especially when the percentage of known and unknown labels (e.g. sick and healthy labels) differ between the two domains. When the output of the model is an image, current methods do not guarantee that the known and unknown labels have been preserved. Therefore until alternative solutions are proposed to maintain the accuracy of the translated features, such translated images should not be presented to doctors for medical diagnosis. However, recent papers are using these models as if this is the goal.
An Efficient Method for Image Denoising Using Orthogonal Wavelet Transform

: Digital images are noisy due to environmental disturbances. To ensure image quality, image processing of noise reduction is a very important step before analysis or using images. Image denoising is one such powerful methodology which is deployed to remove the noise through the manipulation of the image data to produce very high quality images. In this paper, we analyzed several methods of noise removal from degraded images with Gaussian noise and salt & pepper by using adaptive wavelet threshold and compare the results in term of PSNR and MSE. After simulation can find that stein unbiased risk estimator is one of the best techniques for removing the noise from the image in terms of PSNR.
Explainable Image Understanding Using Vision and Reasoning

Image Understanding is fundamental to intelligent agents.Researchers have explored Caption Generation and VisualQuestion Answering as independent aspects of Image Understanding (Johnson et al. 2015; Xiong, Merity, and Socher2016). Common to most of the successful approaches, are the learning of end-to-end signal mapping (image-to-caption, image and question to answer). The accuracy is impressive. It is also important to explain a decision to end-user(justify the results, and rectify based on feedback). Very recently, there has been some focus (Hendricks et al. 2016;Liu et al. ) on explaining some aspects of the learning systems. In my research, I look towards building explainableImage Understanding systems that can be used to generate captions and answer questions. Humans learn both from examples (learning) and by reading (knowledge). Inspired by such an intuition, researchers have constructed Knowledge-Bases that encode (probabilistic) commonsense and background knowledge. In this work, we look towards efficiently using this probabilistic knowledge on top of machine learning capabilities, to rectify noise in visual detections and generate captions or answers to posed questions.
Automated visual inspection of an airplane exterior

This paper deals with the inspection of an airplane using a Pan-Tilt-Zoom camera mounted on a mobile robot moving around the airplane. We present image processing methods for detection and inspection of four different types of items on the airplane exterior. Our detection approach is focused on the regular shapes such as rounded corner rectangles and ellipses, while inspection relies on clues such as uniformity of isolated image regions, convexity of segmented shapes and periodicity of the image intensity signal. The initial results are promising and demonstrate the feasibility of the envisioned robotic system.