nils-herrmann/clean_scirepeval huggingface dataset:
Node Re Ordering as a Means of Anomaly Detection in Time Evolving Graphs
Venue: ECML/PKDD

Anomaly detection is a vital task for maintaining and improving any dynamic system. In this paper, we address the problem of anomaly detection in time evolving graphs, where graphs are a natural representation for data in many types of applications. A key challenge in this context is how to process large volumes of streaming graphs. We propose a pre processing step before running any further analysis on the data, where we permute the rows and columns of the adjacency matrix. This pre processing step expedites graph mining techniques such as anomaly detection, PageRank, or graph coloring. In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes. The merits of our approach lie in its simplicity and resilience to challenges such as unsupervised input, large volumes and high velocities of data. We evaluate the scalability and accuracy of our method on real graphs, where our method facilitates graph processing while producing more deterministic orderings. We show that the proposed approach is capable of revealing anomalies in a more efficient manner based on node rankings. Furthermore, our method can produce visual representations of graphs that are useful for graph compression.
A multi level anomaly detection algorithm for time varying graph data with interactive visualization
Venue: Social Network Analysis and Mining

This work presents a modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in streaming graph data. Our goal is to detect changes at multiple levels of granularity, thereby identifying specific nodes and subgraphs causing a graph to appear anomalously. In particular, the framework detects changes in community membership, density, and node degree in a sequence of graphs where these are relatively stable. In route to this end, we introduce a new graph model, a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi scale graph anomaly detection. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics based approach outperforms both a distribution based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision >0.786.
International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma.
Venue: The Lancet. Oncology

Treatment of multiple myeloma has substantially changed over the past decade with the introduction of several classes of new effective drugs that have greatly improved the rates and depth of response. Response criteria in multiple myeloma were developed to use serum and urine assessment of monoclonal proteins and bone marrow assessment (which is relatively insensitive) Given the high rates of complete response seen in patients with multiple myeloma with new treatment approaches, new response categories need to be defined that can identify responses that are deeper than those conventionally defined as complete response. Recent attempts have focused on the identification of residual tumour cells in the bone marrow using flow cytometry or gene sequencing. Furthermore, sensitive imaging techniques can be used to detect the presence of residual disease outside of the bone marrow. Combining these new methods, the International Myeloma Working Group has defined new response categories of minimal residual disease negativity, with or without imaging based absence of extramedullary disease, to allow uniform reporting within and outside clinical trials. In this Review, we clarify several aspects of disease response assessment, along with endpoints for clinical trials, and highlight future directions for disease response assessments.
Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer
Venue: Science Translational Medicine

Detection of circulating tumor DNA in patients with resected stage II colon cancer provides evidence of residual disease. Footprints of persistent cancer Stage II colon cancer, which has spread through the wall of the colon but has not metastasized to the lymph nodes, can present a therapeutic dilemma. On one hand, these tumors can usually be completely removed by surgery, and the majority does not recur even without chemotherapy. On the other hand, it is difficult to determine which of these tumors will recur and to identify patients who would benefit from adjuvant chemotherapy after surgery. Tie et al. show that the presence of circulating tumor DNA in a patient's blood after surgery is a sign of persistent tumor and a greatly increased risk of relapse, suggesting that this group of patients may require chemotherapy to prevent recurrence. Detection of circulating tumor DNA (ctDNA) after resection of stage II colon cancer may identify patients at the highest risk of recurrence and help inform adjuvant treatment decisions. We used massively parallel sequencing based assays to evaluate the ability of ctDNA to detect minimal residual disease in 1046 plasma samples from a prospective cohort of 230 patients with resected stage II colon cancer. In patients not treated with adjuvant chemotherapy, ctDNA was detected postoperatively in 14 of 178 (7.9% patients, 11 (79% of whom had recurred at a median follow up of 27 months; recurrence occurred in only 16 (9.8 of 164 patients with negative ctDNA [hazard ratio (HR) 18; 95% confidence interval (CI) 7.9 to 40; P 0.001] In patients treated with chemotherapy, the presence of ctDNA after completion of chemotherapy was also associated with an inferior recurrence free survival (HR, 11; 95% CI, 1.8 to 68; P 0.001) ctDNA detection after stage II colon cancer resection provides direct evidence of residual disease and identifies patients at very high risk of recurrence.
An assessment of the impact of climate change on plant species richness through an analysis of the Normalised Difference Water Index (NDWI) in Mutirikwi Sub catchment, Zimbabwe

This study assesses the effects of climate change on vegetative species diversity exploring the usefulness of the Normalised Difference Water Index (NDWI) in predicting spatio temporal diversity variations. The relationship between species richness and climatic variables of rainfall and temperature is explored based on species data collected from the field over a 3 year period and climate data collected from four local weather stations. Relationship between NDWI and species diversity indices is examined to confirm the usefulness of Remote Sensing in predicting vegetative diversity. The resultant predictive model was used to estimate changes in species richness over a 27 year period (1987 2014) The species diversity data was then regressed with climatic data for the same period. The results show a significant (P<0.05) correlation between species diversity and the two climatic variables. The results also indicate that there is a significant positive (P=0.0001; a=0.05; R2=0.565) relationship between species richness and NDWI. This implies that the NDWI is essential when assessing changes in species diversity over time. The Mann Kendall test revealed a decrease, though not statistically significant, in the rainfall received within the catchment over the period and significant variability. The minimum and maximum temperatures over the period were significantly increasing. These changes in climate variables were matched with a decrease in species richness. Some species tend to be succumbing to the environmental changes influenced by climate change resulting in their changes in phenology, abundance and distribution.
Success factors for serious games to enhance learning: a systematic review
Venue: Virtual Reality

There is no doubt that an abundance of factors exists that makes learning with serious games successful. Research articles reporting on these factors, however, tend to focus on select serious game elements and do not combine all salient factors for successful learning with serious games. Addressing this gap is a necessity for the success of serious games and may even alleviate long standing debates about pedagogy over enjoyment, how much realism is enough or whether artificial intelligence is worth the cost. This article examines existing academic literature from 2000 to 2015, extracting shared serious game success factors that have had an encouraging impact on gameful learning experiences. As such, we subsequently aim to withdraw the field from a perpetual spiral of does my game work research toward more worthwhile why does my game not work research. Qualitative content analysis through the constant comparison method (CCM) analyzed a total of 63 articles from a variety of recognized electronic libraries and databases. Through this analysis, we reveal five central serious game themes: backstory and production; realism; artificial intelligence and adaptivity; interaction; and feedback and debriefing, all of which require deliberate intertwining with pedagogical content to ensure successful learning. This review unravels each of the five themes into their constituent factors and consequently presents the factors as practical guidelines that serious games producers should strive to include in their game productions. Applying these recommendations whenever serious games are considered will provide a foundation for effective gameful learning experiences.
Image sharpening detection based on multiresolution overshoot artifact analysis
Venue: Multimedia Tools and Applications

With the wide use of sophisticated photo editing tools, digital image manipulation becomes very convenient, which makes the detection of image tampering significant. Image sharpening, which aims to enhance the contrast of edges in an image, is a ubiquitous image tampering operation. The detection of image sharpening can serve as a reliable clue for image forgery. In this paper, we propose a novel image sharpening detection method based on multiresolution overshoot artifact analysis (MOAA) By building the relationship between the overshoot artifact strength and the slope of a sharpened edge, we find that although undergoing the same sharpening operation, the edge with large slope will present a stronger overshoot artifact than the one with small slope. Based on this finding, we use the nonsubsampled contourlet transform (NSCT) to classify the image edge points into three categories, i.e. weak, middle and strong edge points and measure the overshoot artifact of each category respectively. A cascaded decision strategy is adopted to decide an image is sharpened or not. Experimental results on digital images with various sharpening operators demonstrate the superiority of our proposed method when compared with state of the art approaches.
USM Sharpening Detection Based on Sparse Coding
Venue: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

USM sharpening is one of the most widely used sharpening methods. The detection of USM sharpening has attracted much concern in image forensics. Previous research has demonstrated that USM sharpening will cause overshoot artifacts along image edges. In order to detect such artifacts accurately and comprehensively, a sparse coding based local feature representation is proposed in this paper. The K SVD algorithm is adopted to build an over complete dictionary characterizing the local textures along image edges. Then the sparse code for each local patch is calculated by the Orthogonal Matching Pursuit (OMP) algorithm and the overall feature for the image is constructed by max pooling over local features. Experimental results have demonstrated the superior performance of the proposed approach compared with some state of the art methods investigated.
Implementation in the NCEP GFS of a Hybrid Eddy Diffusivity Mass Flux (EDMF) Boundary Layer Parameterization with Dissipative Heating and Modified Stable Boundary Layer Mixing

AbstractThe current operational eddy diffusivity countergradient (EDCG) planetary boundary layer (PBL) scheme in the NCEP Global Forecast System (GFS) tends to underestimate the PBL growth in the convective boundary layer (CBL) To improve CBL growth, an eddy diffusivity mass flux (EDMF) PBL scheme is developed, where the nonlocal transport by large turbulent eddies is represented by a mass flux (MF) scheme and the local transport by small eddies is represented by an eddy diffusivity (ED) scheme. For the vertical momentum mixing, the MF scheme is modified to include the effect of the updraft induced pressure gradient force. While the EDMF scheme displays better CBL growth than the EDCG scheme, it tends to overproduce the amount of low clouds and degrades wind vector forecasts over the tropical ocean where strongly unstable PBLs are rarely found. In order not to degrade the forecast skill in the tropics, a hybrid scheme is developed, where the EDMF scheme is applied only for the strongly unstable PBL, whil.
Genetic Fuzzy System (GFS) based wavelet co occurrence feature selection in mammogram classification for breast cancer diagnosis

Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co occurrence feature (WCF) using Genetic Fuzzy System (GFS) in mammogram classification problem. The performance of GFS algorithm is explained using mini MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64% Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75% For third level, 16 features are selected from 54 features and average success rate is improved to 64.98% Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47% Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.
Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation.
Venue: Monthly weather review

There are many issues regarding the assimilation of satellite precipitation data into numerical models, including the non Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, it is not easy to improve the model forecast beyond a few hours by assimilating precipitation. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low resolution NCEP Global Forecasting System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA) The samples are constructed using the same model with the same forecast period, observation variables, and resolution as planned in the follow on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is problematic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6 hour accumulated precipitation, should be advantageous for precipitation assimilation.
The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP.
Venue: Geoscientific model development

The NOAA National Centers for Environmental Prediction (NCEP) implemented NEMS GFS Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC) NGAC Version 1.0 has been providing 5 day dust forecasts at 1degx1deg resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC) since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.
Dry layers in the tropical troposphere observed during CONTRAST and global behavior from GFS analyses

The Convective Transport of Active Species in the Tropics (CONTRAST) experiment was an aircraft based field campaign conducted from Guam (14degN, 145degE) during January February 2014. Aircraft measurements included over 80 vertical profiles from the boundary layer to the upper troposphere ~15 km) A large fraction of these profiles revealed layered structures with very low water vapor (relative humidity <20% and enhanced ozone, primarily in the lower middle troposphere ~3 9 km) Comparing CONTRAST water vapor measurements with co located profiles from National Centers for Environmental Prediction Global Forecast System (GFS) analyses, we find good agreement for dry layers, including profile by profile comparisons and statistical behavior. We then utilize GFS data to evaluate the frequency of occurrence and 3 D structure of dry layers for the CONTRAST period to provide perspective to the campaign measurements and evaluate the global climatological behavior based on a longer record. GFS data show that dry layers occur ~50 80% of the time in the subtropical troposphere, maximizing on the equatorward side of the subtropical jets in the winter hemisphere. Subtropical dry layers occur most frequently over isentropic levels ~320 340 K, which extend into the extratropical upper troposphere lower stratosphere (UTLS) Similar statistical behavior of dry, ozone rich layers is found in long term balloon measurements from Reunion Island (21degS, 56degE) The climatologically frequent occurrence of dry, ozone rich layers, plus their vertical and spatial structures linked to the subtropical jets, all suggest that dry layers are linked to quasi isentropic transport from the extratropical UTLS and suggest a ubiquitous UTLS influence on the subtropical middle troposphere.
An Evaluation of QPF from the WRF, NAM, and GFS Models Using Multiple Verification Methods over a Small Domain

ARW model was run over a small domain centered on Iowa for 9 months with 4 km grid spacing to better understand the limits of predictability of short term (12 h) quantitative precipitation forecasts (QPFs) that might be used in hydrology models. Radar data assimilation was performed to reduce spinup problems. Three grid to grid verification methods, as well as two spatial techniques, neighborhood and object based, were used to compare the QPFs from the high resolution runs with coarser operational GFS and NAM QPFs to verify QPFs for various precipitation accumulation intervals and on two grid configurations with different resolutions. In general, NAM had the worst performance not only for model skill but also for spatial feature attributes as a result of the existence of large dry bias and location errors. The finer resolution of NAM did not offer any advantage in predicting small scale storms compared to the coarser GFS. WRF had a large advantage for high precipitation thresholds. A greater i.
Heterogeneity of Human CD4( T Cells Against Microbes.
Venue: Annual review of immunology

CD4( T helper (Th) cells play a central role in the adaptive immune response by providing help to B cells and cytotoxic T cells and by releasing different types of cytokines in tissues to mediate protection against a wide range of pathogenic microorganisms. These functions are performed by different types of Th cells endowed with distinct migratory capacities and effector functions. Here we discuss how studies of the human T cell response to microbes have advanced our understanding of Th cell functional heterogeneity, in particular with the discovery of a distinct Th1 subset involved in the response to Mycobacteria and the characterization of two types of Th17 cells specific for extracellular bacteria or fungi. We also review new approaches to dissect at the clonal level the human CD4( T cell response induced by pathogens or vaccines that have revealed an unexpected degree of intraclonal diversification and propose a progressive and selective model of CD4( T cell differentiation.
Molecular mechanisms and clinical implications of bacterial persistence.
Venue: Drug resistance updates reviews and commentaries in antimicrobial and anticancer chemotherapy

Any bacterial population harbors a small number of phenotypic variants that survive exposure to high concentrations of antibiotic. Importantly, these so called 'persister cells' compromise successful antibiotic therapy of bacterial infections and are thought to contribute to the development of antibiotic resistance. Intriguingly, drug tolerant persisters have also been identified as a factor underlying failure of chemotherapy in tumor cell populations. Recent studies have begun to unravel the complex molecular mechanisms underlying persister formation and revolve around stress responses and toxin antitoxin modules. Additionally, in vitro evolution experiments are revealing insights into the evolutionary and adaptive aspects of this phenotype. Furthermore, ever improving experimental techniques are stimulating efforts to investigate persisters in their natural, infection associated, in vivo environment. This review summarizes recent insights into the molecular mechanisms of persister formation, explains how persisters complicate antibiotic treatment of infections, and outlines emerging strategies to combat these tolerant cells.