nils-herrmann/clean_scirepeval huggingface dataset:
Local Spectral Graph Convolution for Point Set Feature Learning
Venue: ECCV
Year: 2018

Feature learning on point clouds has shown great promise, with the introduction of effective and generalizable deep learning frameworks such as pointnet+ Thus far, however, point features have been abstracted in an independent and isolated manner, ignoring the relative layout of neighboring points as well as their features. In the present article, we propose to overcome this limitation by using spectral graph convolution on a local graph, combined with a novel graph pooling strategy. In our approach, graph convolution is carried out on a nearest neighbor graph constructed from a point's neighborhood, such that features are jointly learned. We replace the standard max pooling step with a recursive clustering and pooling strategy, devised to aggregate information from within clusters of nodes that are close to one another in their spectral coordinates, leading to richer overall feature descriptors. Through extensive experiments on diverse datasets, we show a consistent demonstrable advantage for the tasks of both point set classification and segmentation.
Lung inflammation promotes metastasis through neutrophil protease mediated degradation of Tsp 1
Venue: Proceedings of the National Academy of Sciences
Year: 2015

Lungs are highly susceptible to inflammation. However, molecular insights into how external inflammation enhances metastatic outgrowth in the lungs remain lacking. Clinically, approaches are required to block tumor outgrowth in secondary organs for effective treatment of metastatic cancers. We demonstrate a previously unidentified mechanism of thrombospondin 1 (Tsp 1) regulation by inflammatory neutrophil proteases in the metastatic organ. Our findings suggest the potential of using neutrophil protease inhibitors as antimetastatic therapies. Inflammation is inextricably associated with primary tumor progression. However, the contribution of inflammation to tumor outgrowth in metastatic organs has remained underexplored. Here, we show that extrinsic inflammation in the lungs leads to the recruitment of bone marrow derived neutrophils, which degranulate azurophilic granules to release the Ser proteases, elastase and cathepsin G, resulting in the proteolytic destruction of the antitumorigenic factor thrombospondin 1 (Tsp 1) Genetic ablation of these neutrophil proteases protected Tsp 1 from degradation and suppressed lung metastasis. These results provide mechanistic insights into the contribution of inflammatory neutrophils to metastasis and highlight the unique neutrophil protease Tsp 1 axis as a potential antimetastatic therapeutic target.
Monoamine oxidase A is a novel driver of stress induced premature senescence through inhibition of parkin mediated mitophagy
Venue: Aging cell
Year: 2018

Cellular senescence, the irreversible cell cycle arrest observed in somatic cells, is an important driver of age associated diseases. Mitochondria have been implicated in the process of senescence, primarily because they are both sources and targets of reactive oxygen species (ROS) In the heart, oxidative stress contributes to pathological cardiac ageing, but the mechanisms underlying ROS production are still not completely understood. The mitochondrial enzyme monoamine oxidase A (MAO A) is a relevant source of ROS in the heart through the formation of H2O2 derived from the degradation of its main substrates, norepinephrine (NE) and serotonin. However, the potential link between MAO A and senescence has not been previously investigated. Using cardiomyoblasts and primary cardiomyocytes, we demonstrate that chronic MAO A activation mediated by synthetic (tyramine) and physiological (NE) substrates induces ROS dependent DNA damage response, activation of cyclin dependent kinase inhibitors p21cip, p16ink4a, and p15ink4b and typical features of senescence such as cell flattening and SA b gal activity. Moreover, we observe that ROS produced by MAO A lead to the accumulation of p53 in the cytosol where it inhibits parkin, an important regulator of mitophagy, resulting in mitochondrial dysfunction. Additionally, we show that the mTOR kinase contributes to mitophagy dysfunction by enhancing p53 cytoplasmic accumulation. Importantly, restoration of mitophagy, either by overexpression of parkin or inhibition of mTOR, prevents mitochondrial dysfunction and induction of senescence. Altogether, our data demonstrate a novel link between MAO A and senescence in cardiomyocytes and provides mechanistic insights into the potential role of MAO dependent oxidative stress in age related pathologies.
Coupling Dynamic Covalent Bonds and Ionic Crosslinking Network to Promote Shape Memory Properties of Ethylene vinyl Acetate Copolymers
Venue: Polymers
Year: 2020

Dynamic crosslinking networks based on Diels Alder (DA) chemistry and ionic interactions were introduced to maleic anhydride modified ethylene vinyl acetate copolymer (mEVA) via in situ melt processing. The dual dynamic crosslinking networks were characterized by temperature dependent FTIR, and the effects on the shape memory properties of mEVA were evaluated with dynamic mechanical thermal analysis and cyclic tensile testing. A crosslinking density was achieved at 2.36 x 10 4 mol*cm 3 for DA crosslinked mEVA; as a result, the stress at 100% extension was increased from 3.8 to 5.6 MPa, and tensile strength and elongation at break were kept as high as 30.3 MPa and 486% respectively. The further introduction of 10 wt zinc methacrylate increased the dynamic crosslinking density to 3.74 x 10 4 mol*cm 3 and the stress at 100% extension to 9.0 MPa, while providing a tensile strength of 28.4 MPa and strain at break of 308% The combination of reversible DA covalent crosslinking and ionic network in mEVA enabled a fixing ratio of 76.4% and recovery ratio of 99.4% exhibiting an enhanced shape memory performance, especially at higher temperatures. The enhanced shape memory and mechanical performance of the dual crosslinked mEVA showed promising reprocessing and recycling abilities of the end of life products in comparison to traditional peroxide initiated covalent crosslinked counterparts.
Pseudo Conditional Random Fields: Joint Training Approach to Segmenting and Labeling Sequence Data
Venue: 2010 IEEE International Conference on Data Mining
Year: 2010

Cascaded approach has been used for a long time to conduct sub tasks in order to accomplish a major task. We put cascaded approach in a probabilistic framework and analyze possible reasons for cascaded errors. To reduce the occurrence of cascaded errors, we need to add a constraint when performing joint training. We suggest a pseudo Conditional Random Field (pseudo CRF) approach that models two sub tasks as two Conditional Random Fields (CRFs) We then present the formulation in the context of a linear chain CRF for solving problems on sequence data. In conducting joint training for a pseudo CRF, we reuse all existing well developed efficient inference algorithms for a linear chain CRF, which would otherwise require the use of approximate inference algorithms or simulations that involve long computational time. Our experimental results show an interesting fact that a jointly trained CRF model in a pseudo CRF may perform worse than a separately trained CRF on a sub task. However the overall system performance of a pseudo CRF would outperform that of a cascaded approach. We implement the implicit constraint in the form of a soft constraint such that users can define the penalty cost for violating the constraint. In order to work on large scale datasets, we further suggest a parallel implementation of the pseudo CRF approach, which can be implemented on a multi core CPU or GPU on a graphics card that supports multi threading. Our experimental results show that it can achieve a 12 times increase in speedup.
The Effect of Learning Strategy and Cognitive Style toward Mathematical Problem Solving Learning Outcomes
Year: 2016

This study aims to analyze and determine the effect of learning strategies (The differences between Problem Based Learning and Direct Instruction) and cognitive style on mathematical problems solving learning outcomes. This study used a quasi experimental research design (nonequivalent control group designs) The research subjects were students from state elementary school of V grade in Gading I Surabaya, east java. The totals of elementary school population in east Surabaya were 141 elementary schools. The selected schools are the schools which have some parallel classes, therefore the circumstances are relatively similar. Four classes are selected, they were from SDN Gading 1 state elementary school. These schools were randomly chosen to implement different learning strategies, they are implementing problem based learning and hands on learning. The data collection techniques were mathematical problems solving learning pre tests and post test, Group embedded figures test (GEFT) for cognitive style, assessment performance for learning achievement. Data analysis techniques in this study use ANOVA technique (analysis of variance) two lines. The results of this study showed that (1) there is a difference in learning achievement between the students' groups who are taught by PBL and direct learning; (2) there are differences in achieving learning outcomes toward students' group and different cognitive styles; (3) there are significant interactions between the use of learning strategies and cognitive style on learning outcomes.
The association between taxation increases and changes in alcohol consumption and traffic fatalities in Thailand.
Venue: Journal of public health
Year: 2016

In 2005, 2007 and 2009, alcohol taxation rates changed in Thailand. It is unknown if these changes are associated with alcohol consumption and traffic fatality rates.  Using monthly data from October 2004 to September 2011, we examined the relationship between alcohol taxation rate changes adjusted for inflation and changes in adult per capita consumption of alcohol (data obtained from the Excise Department) and the rate of traffic fatalities (data obtained from the National Police Institute)  Only the taxation increase in 2009 was significantly associated with an immediate and sustained reduction of 0.38 (95% confidence interval (CI) 0.24 0.52) fatalities per 100 000 people per 30 days. The 2005, 2007 and 2009 taxation increases were associated with immediate and sustained reductions of 0.07 (95% CI: 0.04 0.09) 0.06 (95% CI: 0.02 0.10) and 0.05 (95% CI: 0.01 0.09) litres of pure alcohol consumption per capita per 30 days, respectively.  This is the first study to examine the impact of taxation on alcohol related harms in a low to middle income country. Taxation changes were associated with reductions in the rate of traffic fatalities and in alcohol consumption; however, reductions of traffic fatalities were not always significant.
Measuring and visualizing cyber threat intelligence quality
Venue: International Journal of Information Security
Year: 2020

The very raison d'etre of cyber threat intelligence (CTI) is to provide meaningful knowledge about cyber security threats. The exchange and collaborative generation of CTI by the means of sharing platforms has proven to be an important aspect of practical application. It is evident to infer that inaccurate, incomplete, or outdated threat intelligence is a major problem as only high quality CTI can be helpful to detect and defend against cyber attacks. Additionally, while the amount of available CTI is increasing it is not warranted that quality remains unaffected. In conjunction with the increasing number of available CTI, it is thus in the best interest of every stakeholder to be aware of the quality of a CTI artifact. This allows for informed decisions and permits detailed analyses. Our work makes a twofold contribution to the challenge of assessing threat intelligence quality. We first propose a series of relevant quality dimensions and configure metrics to assess the respective dimensions in the context of CTI. In a second step, we showcase the extension of an existing CTI analysis tool to make the quality assessment transparent to security analysts. Furthermore, analysts' subjective perceptions are, where necessary, included in the quality assessment concept.
Bimodal feature based fusion for real time emotion recognition in a mobile context
Venue: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII)
Year: 2015

This research explores the viability of a bimodal fusion of linguistic and acoustic cues in speech to help in realtime emotion recognition in a mobile application that steers the interaction dialogue in tune with user's emotions. For capturing affect at the language level, we have utilized both, machine learning and valence assessment of the words carrying emotional connotations. The indicative values of acoustic cues in speech are of special concern in this research and an optimized feature set is proposed. We highlight the results of both independent evaluations of the underlying linguistic and acoustic processing components. We present a study and ensuing discussion on the performance metrics of a Logistic Model Tree that has outperformed the other classifiers considered for the fusion process. The results reinforce the notion that capturing the sound interplay between the diverse set of features is crucial for confronting the subtleties of affect in speech that so often elude the text or acoustic only approaches to emotion recognition.
Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care
Venue: PloS one
Year: 2018

Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables training, self efficacy, system quality and information quality on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24 item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time.
Multi Agent Systems Applications in Energy Optimization Problems: A State of the Art Review
Year: 2018

This article reviews the state of the art developments in Multi Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy efficient solutions for smart cities and intelligent buildings.
Enhancing social tagging with automated keywords from the Dewey Decimal Classification
Venue: J. Documentation
Year: 2014

The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval.  Over 11,000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings.  The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points.
Immune checkpoint inhibitor related myositis and myocarditis in patients with cancer
Venue: Neurology
Year: 2018

To report the clinicopathologic features and outcome of myositis in patients treated with immune checkpoint inhibitors (ICIs) (irMyositis)  We retrospectively analyzed patients diagnosed with irMyositis in tertiary centers in Paris, France, and Berlin, Germany, from January 2015 to July 2017. The main outcomes were clinical manifestations and muscle histology, which included major histocompatibility complex class I (MHC I) C5b 9, CD3, CD4, CD8, CD20, CD68, programmed cell death protein 1 (PD 1) programmed cell death 1 ligand 1 (PD L) 1, and programmed cell death 1 ligand 2 (PD L2)  Ten patients with metastatic cancer were included; median age was 73 (range 56 87) years. Median follow up duration was 48 (range 16 88) weeks. Six patients developed myositis during nivolumab therapy, 1 patient during pembrolizumab, 1 patient during durvalumab, and 2 patients during combined nivolumab and ipilimumab. Median delay between ICI initiation and myositis onset was 25 (range 5 87) days. Clinical manifestations were dominated by acute or subacute myalgia (8 patients) and limb girdle (7) axial (7) and oculomotor (7) weakness. Four patients had evidence of myocarditis. In all patients, creatine kinase levels were elevated (median 2,668, range 1,059 16,620 U/L) while anti acetylcholine receptor and myositis associated antibodies were negative. Electrodiagnostic studies showed myopathic process without decrement in all patients. Muscle biopsy constantly showed multifocal necrotic myofibers, sarcolemmal MHC I, and endomysial inflammation, consisting mainly of CD68+ cells expressing PD L1 and CD8+ cells expressing PD 1. ICI treatment was withdrawn in all patients; 9 patients received immunosuppressive therapy, which consistently led to marked clinical improvement. Conclusions irMyositis presents with remarkably homogeneous and unique clinicopathologic features, expanding the nosologic spectrum of inflammatory myopathies in patients with cancer. ICI withdrawal and treatment with corticosteroids improve outcome.
Bayesian network classifiers: Beyond classification accuracy
Venue: Intell. Data Anal.
Year: 2011

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed "approximate Markov Blanket" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A DMBC (Approximate DMBC) are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confirm that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.
Effects of Chamomilla recutita (L. on oral wound healing in rats.
Venue: Medicina oral, patologia oral y cirugia bucal
Year: 2011

The purpose of this study was to evaluate the effect of Chamomilla recutita on the healing of ulcers in rats.  A 5 mm wound was inflicted on the tongue of 36 rats. Treatment group animals were treated topically with 0.04 mL/day of chamomile ointment, whereas control group animals were not treated. Animals were sacrificed after 3, 7 or 10 days. Semi quantitative analysis of the degree of inflammation, fibroblast count and wound size was performed, as well as histometric analysis of re epithelialization and percentage of collagen fibers of the lesion.  Animals treated with chamomile showed the best results regarding epithelialization and percentage of collagen fibers after 10 days. As expected, time had a statistically significant effect (p 0.05) on fibroblast count, epithelialization, inflammation and wound size; animals sacrificed at 3 days showed the worst results.  Chamomile stimulated re epithelialization and the formation of collagen fibers after 10 days of treatment; it did not, however, influence inflammation or fibroblast count.