nils-herrmann/clean_scirepeval huggingface dataset:
Safety of pembrolizumab in combination with bevacizumab in recurrent glioblastoma (rGBM)

Inhibition of vascular endothelial growth factor (VEGF) may enhance immunotherapies and decrease cerebral edema among GBM patients (pts) This multicenter, randomized phase 2 study determined safety and anti tumor activity of pembrolizumab (P) a PD 1 blocking monoclonal antibody (Mab) with and without bevacizumab (Bev) a humanized anti VEGF Mab among rGBM pts. Herein, we report the safety lead in of this study.  Pts in dose level 1 received P 200 mg Q3W and Bev 10 mg/kg Q2W. Dose de escalation levels 1 and 2 increased the P interval to 4 and 6 weeks, respectively if dose level 1 exceeded maximum tolerated dose (MTD) based on standard 3+3 criteria. Eligible pts were 18 years, had 1or 2 recurrences, no prior anti VEGF treatment, and KPS 70. The primary endpoint was MTD/recommended phase 2 dose (RP2D) determination based on dose limiting toxicity (DLT) during cycle 1. MRI scans were obtained after each 6 week cycle.  6 pts were treated (4 male, 2 female) at1st (n 2)
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation
Venue: ECCV

Discriminative methods often generate hand poses kinematically implausible, then generative methods are used to correct (or verify) these results in a hybrid method. Estimating 3D hand pose in a hierarchy, where the high dimensional output space is decomposed into smaller ones, has been shown effective. Existing hierarchical methods mainly focus on the decomposition of the output space while the input space remains almost the same along the hierarchy. In this paper, a hybrid hand pose estimation method is proposed by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method by a spatial attention mechanism and to the optimization of the generative method by hierarchical Particle Swarm Optimization (PSO) The spatial attention mechanism integrates cascaded and hierarchical regression into a CNN framework by transforming both the input (and feature space) and the output space, which greatly reduces the viewpoint and articulation variations. Between the levels in the hierarchy, the hierarchical PSO forces the kinematic constraints to the results of the CNNs. The experimental results show that our method significantly outperforms four state of the art methods and three baselines on three public benchmarks.
Fast coding unit decision and mode selection for intra frame coding in high efficiency video coding
Venue: IET Image Process.

High efficiency video coding (HEVC) is a new video coding compression standard. As the successor to H.264/AVC, it provides better performance and supports higher resolution. However, the encoding complexity increases drastically. One of the major reasons is that the coding unit (CU) in HEVC is multi sized and adjustable rather than fixed as in H.264. In addition, the number of prediction modes used in intra frame coding is expanded from 9 to 35. The authors analysed the statistical correlations of CU depth to the deviation of pixels in the largest coding unit (LCU) and rate distortion cost (RDcost) Accordingly, a fast CU decision method is proposed, which contains two steps: first, the depth to begin searching is determined according to the deviation of the LCU and then splitting the current CU further is decided according to RDcost. For intra prediction, we also propose a fast mode selection method to reduce complexity. This method can quickly determine the modes for rate distortion optimisation when the combination of most probable modes reveals the pattern direction. Software simulations show that the proposed methods reduce encoding time by more than 50% with an average of 1.4% increase of BD rate compared to reference software HM12.0.
Motif mediated protein protein interactions as drug targets
Venue: Cell Communication and Signaling

Protein protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI) Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT 199, AEG 40826 and SAH p53 8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2) B cell lymphoma 2 (Bcl 2) X linked inhibitor of apoptosis protein (XIAP) and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery.
The Synthetic Data Vault
Venue: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault (SDV) a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we also developed an algorithm that computes statistics at the intersection of related database tables. We then used a state of the art multivariate modeling approach to model this data. The SDV iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the SDV to synthesize data by sampling from any part of the database. After building the SDV, we used it to generate synthetic data for five different publicly available datasets. We then published these datasets, and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. By analyzing the outcomes, we show that synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the SDV is a viable solution for synthetic data generation.
Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting

In existing forecasting research papers support vector regression with chaotic mapping function and evolutionary algorithms have shown their advantages in terms of forecasting accuracy improvement. However, for classical particle swarm optimization (PSO) algorithms, trapping in local optima results in an earlier standstill of the particles and lost activities, thus, its core drawback is that eventually it produces low forecasting accuracy. To continue exploring possible improvements of the PSO algorithm, such as expanding the search space, this paper applies quantum mechanics to empower each particle to possess quantum behavior, to enlarge its search space, then, to improve the forecasting accuracy. This investigation presents a support vector regression (SVR) based load forecasting model which hybridizes the chaotic mapping function and quantum particle swarm optimization algorithm with a support vector regression model, namely the SVRCQPSO (support vector regression with chaotic quantum particle swarm optimization) model, to achieve more accurate forecasting performance. Experimental results indicate that the proposed SVRCQPSO model achieves more accurate forecasting results than other alternatives.
New Song In A Strange Land

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Hierarchically Gated Deep Networks for Semantic Segmentation
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Semantic segmentation aims to parse the scene structure of images by annotating the labels to each pixel so that images can be segmented into different regions. While image structures usually have various scales, it is difficult to use a single scale to model the spatial contexts for all individual pixels. Multi scale Convolutional Neural Networks (CNNs) and their variants have made striking success for modeling the global scene structure for an image. However, they are limited in labeling fine grained local structures like pixels and patches, since spatial contexts might be blindly mixed up without appropriately customizing their scales. To address this challenge, we develop a novel paradigm of multiscale deep network to model spatial contexts surrounding different pixels at various scales. It builds multiple layers of memory cells, learning feature representations for individual pixels at their customized scales by hierarchically absorbing relevant spatial contexts via memory gates between layers. Such Hierarchically Gated Deep Networks (HGDNs) can customize a suitable scale for each pixel, thereby delivering better performance on labeling scene structures of various scales. We conduct the experiments on two datasets, and show competitive results compared with the other multi scale deep networks on the semantic segmentation task.
MONGKIE: an integrated tool for network analysis and visualization for multi omics data
Venue: Biology Direct

BackgroundNetwork based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed.ResultsHere, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in house algorithms for network analysis including network clustering and over representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data to visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network based visual data mining of multi omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS independent with intrinsic support of module extension by third party developers.ConclusionWe believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi omics data sets in cancer and other diseases.ReviewersThis article was reviewed by Prof. Limsoon Wong, Prof. Soojin Yi, and Maciej M Kandula (nominated by Prof. David P Kreil)
Detecting the potential cancer association or metastasis by multi omics data analysis.
Venue: Genetics and molecular research GMR

Comprehensive multi omics data analyses have become an important means for understanding cancer incidence and progression largely driven by the availability of high throughput sequencing technologies for genomes, proteomes, and transcriptomes. However, how tumor cells from the site of origin of the cancer begin to grow in other sites of the body is very poorly understood. In order to examine potential connections between different cancers and to gain an insight into the metastatic process, we conducted a multi omics data analysis using data deposited in The Cancer Genome Atlas database. By combining somatic mutation data along with DNA methylation level and gene expression level data, we applied a Bayesian network analysis to detect the potential association among four distinct cancer types namely, Head and neck squamous cell carcinoma (Hnsc) Lung adenocarcinoma (Luad) Lung squamous cell carcinoma (Lusc) and Skin cutaneous melanoma (Skcm) Further validation based on the 'identification of somatic signatures' and the 'association rules analysis' confirmed these associations. Previous investigations have suggested that common risk factors and molecular abnormalities in cell cycle regulation and signal transduction predominate among these cancers. This evidence indicates that our study provides a rational analysis and hopefully will help shed light on the links between different cancers and metastasis as a whole.
Computer Safety, Reliability, and Security
Venue: Lecture Notes in Computer Science

Applications in secure components (such as smartcards, mobile phones or secure dongles) must be hardened against fault injection to guarantee security even in the presence of a malicious fault. Crafting applications robust against fault injection is an open problem for all actors of the secure application development life cycle, which prompted the development of many simulation tools. A major difficulty for these tools is the absence of representative codes, criteria and metrics to evaluate or compare obtained results. We present FISSC, the first public code collection dedicated to the analysis of code robustness against fault injection attacks. FISSC provides a framework of various robust code implementations and an approach for comparing tools based on predefined attack scenarios.
Reliability and Safety of Nuclear Power Plant Instrumentation and Control Systems: New Challenges and Solutions
Venue: 2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO)

The paper is devoted to some aspects of reliability of Instrumentation and Control Systems (I&Cs) for Nuclear Power Plants (NPP) A few challenges regarding regulation, assessment and assure of NPP I&Cs reliability and safety are analyzed considering: the most known accidents in NPPs including the last of them at the Fukushima Daiichi, a progress in development and implementation of information technologies and computer components (hardware, software, FPGA) Tasks and solutions for NPP I&C reliability and safety which follow from these challenges are described. In particular, related standards, the problems of assessment and implementation of diversity principle and safety assurance are analyzed. Security informed safety approach is discussed.
Introduction to this Special Issue on "Human Machine Interaction and Cooperation in Safety Critical Systems"
Venue: i com

Mission and safety critical domains are more and more characterized by interactive and multimedia systems varying from large scale technologies (e. g. airplanes) to wearable devices (e. g. smartglasses) operated by professional staff or volunteering laypeople. While technical availability, reliability and security of computer based systems are of utmost importance, outcomes and performances increasingly depend on sufficient human machine interaction or even cooperation to a large extent. While this i com Special Issue on "Human Machine Interaction and Cooperation in Safety Critical Systems" presents recent research results from specific application domains like aviation, automotive, crisis management and healthcare, this introductory paper outlines the diversity of users, technologies and interaction or cooperation models involved.
The Effects of Dietary Iron and Capsaicin on Hemoglobin, Blood Glucose, Insulin Tolerance, Cholesterol, and Triglycerides, in Healthy and Diabetic Wistar Rats
Venue: PloS one

Our aim was to assess the effects of dietary iron, and the compound capsaicin, on hemoglobin as well as metabolic indicators including blood glucose, cholesterol, triglycerides, insulin, and glucose tolerance.  Our animal model was the Wistar rat, fed a chow diet, with or without experimentally induced diabetes. Diabetic males were fed control, low, or high iron diets, the latter, with or without capsaicin. Healthy rats were fed identical diets, but without the capsaicin supplement. We then measured the parameters listed above, using the Student t test and ANOVA, to compare groups.  Healthy rats fed a low iron diet exhibited significantly reduced total cholesterol and triglyceride levels, compared with rats fed a control diet. Significantly reduced blood lipid was also provoked by low dietary iron in diabetic rats, compared with those fed a control diet. Insulin, and glucose tolerance was only improved in healthy rats fed the low iron diet. Significant increases in total cholesterol were found in diabetic rats fed a high iron diet, compared with healthy rats fed the same diet, although no statistical differences were found for triglycerides. Hemoglobin levels, which were not statistically different in diabetic versus healthy rats fed the high iron diet, fell when capsaicin was added. Capsaicin also provoked a fall in the level of cholesterol and triglycerides in diabetic animals, versus diabetics fed with the high iron diet alone. In conclusion, low levels of dietary iron reduced levels of serum triglycerides, hemoglobin, and cholesterol, and significantly improved insulin, and glucose tolerance in healthy rats. In contrast, a high iron diet increased cholesterol significantly, with no significant changes to triglyceride concentrations. The addition of capsaicin to the high iron diet (for diabetic rats) further reduced levels of hemoglobin, cholesterol, and triglycerides. These results suggest that capsaicin, may be suitable for the treatment of elevated hemoglobin, in patients.
Evaluation of a high nutritional quality snack based on oat flakes and inulin: effects on postprandial glucose, insulin and ghrelin responses of healthy subjects.
Venue: Food function

The consumption of high nutritional value snacks may favorably affect the diet quality. Biscuits manufactured with oat flakes and maltitol were assessed for glycemic, insulinemic and ghrelin responses. Enrichment with inulin, a fructooligosachararide (FOS) which acts as soluble fiber, was performed in an attempt to further increase the dietary fiber content and examine potential additional postprandial benefits. Eleven healthy subjects participated in the study and consumed either 80 g oat biscuits (OB) or 81 g oat biscuits with 4% inulin (OBIN) or a solution containing 50 g of glucose (reference food) each yielding 50 g of available carbohydrates. Venous blood samples were collected before consumption and at 15, 30, 45, 60, 90, 120 and 180 min postprandially. The developed products were also evaluated for physicochemical properties, including porosity, density, texture, color, sensory attributes and microstructure (by scanning electron microscopy) Both biscuits demonstrated a low glycemic index (GI) which was found to be 32.82 8.07 for OB and 45.68 9.64 for OBIN. Compared to OB, OBIN demonstrated higher insulin response at 45 and 60 min and higher ghrelin suppression at 60 and 120 min postprandially (P 0.05) Furthermore, OBIN demonstrated increased hardness and color values, lower porosity, and higher rate of starch granule gelatinization, without significantly altering the sensory attributes. Biscuits formulated with oat flakes and maltitol with or without 4% inulin can be classified as low GI foods. Inulin addition significantly lowered the ghrelin response to OBIN, suggesting an advantage of OBIN in the modulation of satiety; however, no further benefits regarding glucose and insulin responses were observed.
Learning First Order Logic Embeddings via Matrix Factorization
Venue: IJCAI

Many complex reasoning tasks in Artificial Intelligence (including relation extraction, knowledge base completion, and information integration) can be formulated as inference problems using a probabilistic first order logic. However, due to the discrete nature of logical facts and predicates, it is challenging to generalize symbolic representations and represent first order logic formulas in probabilistic relational models. In this work, we take a rather radical approach: we aim at learning continuous low dimensional embeddings for first order logic from scratch. In particular, we first consider a structural gradient based structure learning approach to generate plausible inference formulas from facts; then, we build grounded proof graphs using background facts, training examples, and these inference formulas. To learn embeddings for formulas, we map the training examples into the rows of a binary matrix, and inference formulas into the columns. Using a scalable matrix factorization approach, we then learn the latent continuous representations of examples and logical formulas via a low rank approximation method. In experiments, we demonstrate the effectiveness of reasoning with first order logic embeddings by comparing with several state of the art baselines on two datasets in the task of knowledge base completion.
Formalization of the Resolution Calculus for First Order Logic
Venue: ITP

A formalization in Isabelle/HOL of the resolution calculus for first order logic is presented. Its soundness and completeness are formally proven using the substitution lemma, semantic trees, Herbrand's theorem, and the lifting lemma. In contrast to previous formalizations of resolution, it considers first order logic with full first order terms, instead of the propositional case.
Finding Finite Models in Multi Sorted First Order Logic
Venue: SAT

This work extends the existing MACE style finite model finding approach to multi sorted first order logic. This existing approach iteratively assumes increasing domain sizes and encodes the related ground problem as a SAT problem. When moving to the multi sorted setting each sort may have a different domain size, leading to an explosion in the search space. This paper focusses on methods to tame that search space. The key approach adds additional information to the SAT encoding to suggest which domains should be grown. Evaluation of an implementation of techniques in the Vampire theorem prover shows that they dramatically reduce the search space and that this is an effective approach to find finite models in multi sorted first order logic.