nils-herrmann/clean_scirepeval huggingface dataset:
Posverdad, mentira y falsedad historica: la logica perversa del posmodernismo
Year: 2020

El articulo analiza distintas modalidades de mentiras, tergiversaciones y falsedades presentes en el analisis historico en sus diversos niveles: la recopilacion y critica documental, la explicacion y la transmision narrativa. Se apuntan algunas continuidades y discontinuidades en estas practicas. Finalmente, se plantea como el clima intelectual potenciado por el posmodernismo, con sus principios irracionalistas, escepticos y relativistas, facilita la difusion de las imposturas y socava nuestra capacidad critica para hacerles frente.
Multi Modal Fusion with Gating Using Audio, Lexical and Disfluency Features for Alzheimer's Dementia Recognition from Spontaneous Speech
Venue: INTERSPEECH
Year: 2020

This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge, which aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data. We focus on acoustic and natural language features for cognitive impairment detection in spontaneous speech in the context of Alzheimer's Disease Diagnosis and the mini mental state examination (MMSE) score prediction. We proposed a model that obtains unimodal decisions from different LSTMs, one for each modality of text and audio, and then combines them using a gating mechanism for the final prediction. We focused on sequential modelling of text and audio and investigated whether the disfluencies present in individuals' speech relate to the extent of their cognitive impairment. Our results show that the proposed classification and regression schemes obtain very promising results on both development and test sets. This suggests Alzheimer's Disease can be detected successfully with sequence modeling of the speech data of medical sessions.
Mixed infection of Papaya leaf curl China virus and Siegesbeckia yellow vein virus in Siegesbeckia orientalis in China.
Year: 2011

During summer 2007 2010, a new disease was observed on pistachio (Pistacia vera L. cv. Mateur in several orchards of Tunisia. Symptoms on the nuts consisted of black lesions with red halos, resulting in yield losses often exceeding 20% A fungus recovered on potato dextrose agar (PDA) from surface disinfected symptomatic nuts, produced cottony colonies characterized by an abundant branched, brownish, septate mycelium which, with time, turned olive green to dark olive green due to abundant sporulation. Conidiophores were simple or branched, septate, straight or flexuous, pale olivaceous brown, and bore several conidial scars. Conidia were 100 200 mm in size, obclavate to ellipsoid, multiseptate and formed in single or, more often, branched chains. Based on these morphological characters, the fungus was identified as Alternaria sp. (Fr. Keissler (Ellis, 1971) Pathogenicity tests were carried out on detached fresh pistachio nuts inoculating the hull with 5 ml conidial suspensions prepared from actively growing colonies (105 conidia/ml sterile distilled water) All isolates induced symptoms similar to those observed in the field after 15 days. An Alternaria sp. was reisolated from symptomatic fruits. To identify the fungus at the species level, the ITS1 5.8S ITS2 region was amplified with primers ITS1 and ITS4 (White et al. 1990) and the amplicons were directly sequenced. A BLAST search of GenBank database revealed 99% homology with both A. alternata (accession No. HQ873733.1) and A. tenuissima (HQ916330.1) To our knowledge this is the first report of an Alternaria species of the alternata species group, as the causal agent of pistachio fruit blight in Tunisia.
Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection
Venue: 2018 2nd Cyber Security in Networking Conference (CSNet)
Year: 2018

With the growing usage of credit card transactions, financial fraud crimes have also been drastically increased leading to the loss of huge amounts in the finance industry. Having an efficient fraud detection method has become a necessity for all banks in order to minimize such losses. In fact, credit card fraud detection system involves a major challenge: the credit card fraud data sets are highly imbalanced since the number of fraudulent transactions is much smaller than the legitimate ones. Thus, many of traditional classifiers often fail to detect minority class objects for these skewed data sets. This paper aims first: to enhance classified performance of the minority of credit card fraud instances in the imbalanced data set, for that we propose a sampling method based on the K means clustering and the genetic algorithm. We used K means algorithm to cluster and group the minority kind of sample, and in each cluster we use the genetic algorithm to gain the new samples and construct an accurate fraud detection classifier.
Improved Sparse 3D Transform Domain Collaborative Filter for Screen Content Image Denoising
Venue: Int. J. Pattern Recognit. Artif. Intell.
Year: 2018

Screen content videos or images are widely used in applications such as screen sharing. Compressed screen content videos or images may have distortions or noises because of the quantization process. This paper proposes an improved sparse 3D transform domain collaborative filter to enhance screen content image quality by block classification and block segmentation. Experimental results show that the proposed algorithm achieves a peak signal to noise ratio increase and subjective visual quality improvements for reconstructed screen content images.
Development of Local Reinforcement and Local Metallic Foam Using FSP
Year: 2010

Friction stir welding (FSW) has been generating interest in association with friction stir processing (FSP) a new technique that employs FSW tooling. In a recent study, this author investigated the excellent fluidity of aluminum alloy with FSP for creating metal matrix composites (MMCs) by the local dispersion of aluminum oxide particulate in aluminum alloy. In this study, two novel methods of property modifications, local reinforcement and local metallic foam of 6061 aluminum alloy by FSP, are proposed. Tungsten carbide (WC) particulate and SiC whisker reinforced aluminum alloy plate are produced for local reinforcement of 6061Al by FSP. FSP can also produce local aluminum composites containing titanium hydride (TiH2) particles. These results, which indicate the feasibility of producing local metallic foam by FSP, are discussed in terms of microstructure and mechanical properties.
Phase field modeling of quasi static and dynamic crack propagation: COMSOL implementation and case studies
Venue: Adv. Eng. Softw.
Year: 2018

The phase field model (PFM) represents the crack geometry in a diffusive way without introducing sharp discontinuities. This feature enables PFM to effectively model crack propagation compared with numerical methods based on discrete crack model, especially for complex crack patterns. Due to the involvement of "phased field" phase field method can be essentially treated a multifield problem even for pure mechanical problem. Therefore, it is supposed that the implementation of PFM based on a software developer that especially supports the solution of multifield problems should be more effective, simpler and more efficient than PFM implemented on a general finite element software. In this work, the authors aim to devise a simple and efficient implementation of phase field model for the modelling of quasi static and dynamic fracture in the general purpose commercial software developer, COMSOL Multiphysics. Notably only the tensile stress induced crack is accounted for crack evolution by using the decomposition of elastic strain energy. The width of the diffusive crack is controlled by a length scale parameter. Equations that govern body motion and phase field evolution are written into different modules in COMSOL, which are then coupled to a whole system to be solved. A staggered scheme is adopted to solve the coupled system and each module is solved sequentially during one time step. A number of 2D and 3D examples are tested to investigate the performance of the present implementation. Our simulations show good agreement with previous works, indicating the feasibility and validity of the COMSOL implementation of PFM.
An Empirical Analysis of Agricultural Exports on Economic Growth in India
Venue: Economic Affairs
Year: 2019

This study has focused to analyze the influence of agricultural trade on economic growth in India obtaining annual time series data from 1990 91 to 2016 17. This study estimates the relationship between agricultural exports and economic growth in India employing the Error Correction Model. This study has used three variables such as, Gross Domestic Product (GDP) agricultural exports and non agricultural exports. According to empirical estimates, the Error Correction Model, that is to say catch can tend towards the long run relationship, has been validated. The variables are converging to equilibrium value and the change in agricultural exports and non agricultural exports are directly affecting the real GDP in India. Furthermore, this study found that there is short run uni directional causality between agriculture exports, non agriculture exports to GDP in India. The main finding of this study is that the agricultural exports and non agricultural exports are important variables to stimulate economic growth in India. This study recommends an increase effort to be directed towards policies that will expand the volume of a country's agricultural productivity and trade for the economic growth in the country.
A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2019

Remote sensing scene classification (RSSC) refers to inferring semantic labels based on the content of the remote sensing scenes. Recently, most works take the pretrained convolutional neural network (CNN) as the feature extractor to build a scene representation for RSSC. The activations in different layers of CNN (named intermediate features) contain different spatial and semantic information. Recent works demonstrate that aggregating intermediate features into a scene representation can significantly improve the classification accuracy for RSSC. However, the intermediate features are aggregated by some unsupervised feature encoding methods (e.g. Bag of Visual Words) Little attention has been paid to explore the information of semantic labels for the feature aggregation. In this paper, in order to explore the semantic label information, an end to end feature aggregation CNN (FACNN) is proposed to learn a scene representation for RSSC. In FACNN, a supervised convolutional features' encoding module and a progressive aggregation strategy are proposed to leverage the semantic label information to aggregate the intermediate features. The FACNN integrates the feature learning, feature aggregation, and classifier into a unified end to end framework for joint training. In FACNN, the scene representation is learned by considering the information of semantic labels, which can result in better performance for RSSC. Extensive experiments on AID, UC Merged, and WHU RS19 databases demonstrate that FACNN performs better than several state of the art methods.
Mechanical forces direct stem cell behaviour in development and regeneration
Venue: Nature Reviews Molecular Cell Biology
Year: 2017

Stem cells and their local microenvironment, or niche, communicate through mechanical cues to regulate cell fate and cell behaviour and to guide developmental processes. During embryonic development, mechanical forces are involved in patterning and organogenesis. The physical environment of pluripotent stem cells regulates their self renewal and differentiation. Mechanical and physical cues are also important in adult tissues, where adult stem cells require physical interactions with the extracellular matrix to maintain their potency. In vitro, synthetic models of the stem cell niche can be used to precisely control and manipulate the biophysical and biochemical properties of the stem cell microenvironment and to examine how the mode and magnitude of mechanical cues, such as matrix stiffness or applied forces, direct stem cell differentiation and function. Fundamental insights into the mechanobiology of stem cells also inform the design of artificial niches to support stem cells for regenerative therapies.
Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer.
Venue: The New England journal of medicine
Year: 2006

A regimen of epirubicin, cisplatin, and infused fluorouracil (ECF) improves survival among patients with incurable locally advanced or metastatic gastric adenocarcinoma. We assessed whether the addition of a perioperative regimen of ECF to surgery improves outcomes among patients with potentially curable gastric cancer.  We randomly assigned patients with resectable adenocarcinoma of the stomach, esophagogastric junction, or lower esophagus to either perioperative chemotherapy and surgery (250 patients) or surgery alone (253 patients) Chemotherapy consisted of three preoperative and three postoperative cycles of intravenous epirubicin (50 mg per square meter of body surface area) and cisplatin (60 mg per square meter) on day 1, and a continuous intravenous infusion of fluorouracil (200 mg per square meter per day) for 21 days. The primary end point was overall survival.  ECF related adverse effects were similar to those previously reported among patients with advanced gastric cancer. Rates of postoperative complications were similar in the perioperative chemotherapy group and the surgery group (46 percent and 45 percent, respectively) as were the numbers of deaths within 30 days after surgery. The resected tumors were significantly smaller and less advanced in the perioperative chemotherapy group. With a median follow up of four years, 149 patients in the perioperative chemotherapy group and 170 in the surgery group had died. As compared with the surgery group, the perioperative chemotherapy group had a higher likelihood of overall survival (hazard ratio for death, 0.75; 95 percent confidence interval, 0.60 to 0.93; P=0.009; five year survival rate, 36 percent vs. 23 percent) and of progression free survival (hazard ratio for progression, 0.66; 95 percent confidence interval, 0.53 to 0.81; P<0.001)  In patients with operable gastric or lower esophageal adenocarcinomas, a perioperative regimen of ECF decreased tumor size and stage and significantly improved progression free and overall survival.
Catalytic reduction of nitrate in reverse osmosis concentrate by using Pd Cu/activated carbon felt
Year: 2020

The reverse osmosis concentrate generated during the water reuse process contains a high concentration of nitrate but a low amount of biodegradable organic carbon for heterotrophic denitrification. Catalytic reduction of nitrates using Pd Cu is one of the most promising technologies to achieve complete removal of nitrate; however, the effect of a range of experimental factors on the nitrate removal rate and N2 selectivity is still an ongoing concern. Two kinds of supporting materials, alumina and activated carbon felt, were used to immobilize the Pd Cu catalyst. The alumina based catalyst was used to establish reference conditions for further experiments, and the effect of pH control was evaluated for both supporting materials. It was observed that pH has a direct influence on the nitrate reduction rate as well as the N2 selectivity. Nitrate reduction efficiency was low at acidic conditions while the highest N2 selectivity was obtained at the acidic conditions. The optimal pH condition for Pd Cu/activated carbon felt was determined as pH 4, showing the highest total nitrogen removal as N2 gas. Finally, the feasibility of catalytic nitrate reduction for reverse osmosis concentrate was evaluated by investigating the effects of organic and inorganic components commonly present in reverse osmosis concentrate. The organic and inorganic components did not show a significant inhibitory effect on catalytic nitrate reduction, while a high concentration of salt significantly decreased the nitrate reduction rate as well as the N2 selectivity. The filter type morphology of the Pd Cu/activated carbon felt would be beneficial for field application compared to the conventional catalyst with powder form.
The Impact of a Six Year Climate Anomaly on the "Spanish Flu" Pandemic and WWI
Venue: GeoHealth
Year: 2020

The H1N1 "Spanish influenza" pandemic of 1918 1919 caused the highest known number of deaths recorded for a single pandemic in human history. Several theories have been offered to explain the virulence and spread of the disease, but the environmental context remains underexamined. In this study, we present a new environmental record from a European, Alpine ice core, showing a significant climate anomaly that affected the continent from 1914 to 1919. Incessant torrential rain and declining temperatures increased casualties in the battlefields of World War I (WWI) setting the stage for the spread of the pandemic at the end of the conflict. Multiple independent records of temperature, precipitation, and mortality corroborate these findings.
On conservation laws in quantum mechanics
Venue: Proceedings of the National Academy of Sciences
Year: 2020

Conservation laws are one of the most important aspects of nature. As such, they have been intensively studied and extensively applied, and are considered to be perfectly well established. We, however, raise fundamental question about the very meaning of conservation laws in quantum mechanics. We argue that, although the standard way in which conservation laws are defined in quantum mechanics is perfectly valid as far as it goes, it misses essential features of nature and has to be revisited and extended. We raise fundamental questions about the very meaning of conservation laws in quantum mechanics, and we argue that the standard way of defining conservation laws, while perfectly valid as far as it goes, misses essential features of nature and has to be revisited and extended.
Episodic Memory in Lifelong Language Learning
Venue: NeurIPS
Year: 2019

We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier. We propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay and local adaptation to allow the model to continuously learn from new datasets. We also show that the space complexity of the episodic memory module can be reduced significantly ~50 90% by randomly choosing which examples to store in memory with a minimal decrease in performance. We consider an episodic memory component as a crucial building block of general linguistic intelligence and see our model as a first step in that direction.
Research and Realization of Internet Public Opinion Analysis Based on Improved TF IDF Algorithm
Venue: 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)
Year: 2017

At present, the main methods of network public opinion analysis include data acquisition, information extraction, spam filtering, similarity clustering, emotion analysis, positive and negative judgment. The extraction of data information based on text characteristic extraction is a key step. In this paper, the traditional TF IDF method is improved by introducing the part of speech weight coefficient and the position weight (span weight) of the characteristic word. The experimental results show that the improved method can effectively improve the clustering effect of the characteristic words, and is better able to reflect the textual characteristics. Applying it to the public opinion analysis system has achieved good results.
General lemmas for Berge Turan hypergraph problems
Venue: Eur. J. Comb.
Year: 2020

For a graph $F$ a hypergraph \mathcal{H} is a Berge copy of $F$ (or a Berge $F$ in short) if there is a bijection $f E(F) \rightarrow E(\mathcal{H} such that for each $e \in E(F) we have $e \subset f(e) A hypergraph is Berge $F$ free if it does not contain a Berge copy of $F$ We denote the maximum number of hyperedges in an $n$ vertex $r$ uniform Berge $F$ free hypergraph by \mathrm{ex}_r(n,\textrm{Berge }F) In this paper we prove two general lemmas concerning the maximum size of a Berge $F$ free hypergraph and use them to establish new results and improve several old results. In particular, we give bounds on \mathrm{ex}_r(n,\textrm{Berge }F) when $F$ is a path (reproving a result of Gyori, Katona and Lemons) a cycle (extending a result of Furedi and Ozkahya) a theta graph (improving a result of He and Tait) or a $K_{2,t} (extending a result of Gerbner, Methuku and Vizer) We also establish new bounds when $F$ is a clique (which implies extensions of results by Maherani and Shahsiah and by Gyarfas) and when $F$ is a general tree.
Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2007

In this paper, a new two step approach (detecting and pruning) for automatic extraction of road networks from aerial images is presented. The road detection step is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the footprint of the pixel. This step involves detecting road footprints, tracking roads, and growing a road tree. We use a spoke wheel operator to obtain the road footprint. We propose an automatic road seeding method based on rectangular approximations to road footprints and a toe finding algorithm to classify footprints for growing a road tree. The road tree pruning step makes use of a Bayes decision model based on the area to perimeter ratio (the A/P ratio) of the footprint to prune the paths that leak into the surroundings. We introduce a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree and present an automatic method to estimate the parameters that are related to the Bayes decision model. Results are presented for various aerial images. Evaluation of the extracted road networks using representative aerial images shows that the completeness of our road tracker ranges from 84% to 94% correctness is above 81% and quality is from 82% to 92%
Transferability of Natural Language Inference to Biomedical Question Answering
Venue: CLEF
Year: 2020

Biomedical question answering (QA) is a challenging task due to the scarcity of data and the requirement of domain expertise. Pre trained language models have been used to address these issues. Recently, learning relationships between sentence pairs has been proved to improve performance in general QA. In this paper, we focus on applying BioBERT to transfer the knowledge of natural language inference (NLI) to biomedical QA. We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No +5.59% Factoid +0.53% List type +13.58% questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B) We present a sequential transfer learning method that significantly performed well in the 8th BioASQ Challenge (Phase B) In sequential transfer learning, the order in which tasks are fine tuned is important. We measure an unanswerable rate of the extractive QA setting when the formats of factoid and list type questions are converted to the format of the Stanford Question Answering Dataset (SQuAD)