nils-herrmann/clean_scirepeval huggingface dataset:
Identifying SARS CoV 2 related coronaviruses in Malayan pangolins
Venue: Nature
Year: 2020

The ongoing outbreak of viral pneumonia in China and across the world is associated with a new coronavirus, SARS CoV 2 1 This outbreak has been tentatively associated with a seafood market in Wuhan, China, where the sale of wild animals may be the source of zoonotic infection 2 Although bats are probable reservoir hosts for SARS CoV 2, the identity of any intermediate host that may have facilitated transfer to humans is unknown. Here we report the identification of SARS CoV 2 related coronaviruses in Malayan pangolins Manis javanica seized in anti smuggling operations in southern China. Metagenomic sequencing identified pangolin associated coronaviruses that belong to two sub lineages of SARS CoV 2 related coronaviruses, including one that exhibits strong similarity in the receptor binding domain to SARS CoV 2. The discovery of multiple lineages of pangolin coronavirus and their similarity to SARS CoV 2 suggests that pangolins should be considered as possible hosts in the emergence of new coronaviruses and should be removed from wet markets to prevent zoonotic transmission. SARS CoV 2 related coronaviruses are identified in Malayan pangolins Manis javanica these pangolin associated coronaviruses belonged to two sub lineages of SARS CoV 2 related coronaviruses, including one that exhibits strong similarity in the receptor binding domain to SARS CoV 2.
Amazonian Kichwa Proper: Ethnolinguistic Domain in Pan Indian Ecuador
Year: 2012

Pan Indian organizing and language standardization are key strategies for indigenous activists advancing cultural legitimacy in the contemporary Latin American sociopolitical order. Planned ethnolinguistic unification by Kichwa activists in Ecuador is paradoxically fueling ethnogenesis among Amazonian Kichwas who see language standardization as a threat to a uniquely indigenous identity that is manifest in local Kichwa dialect. This essay examines the accomplishment of this ongoing ethnogenesis through discourses of linguistic differentiation, suggesting (1) reconsideration of reactionary ethnolinguistic boundary drawing under the new Latin American indigenous order, and (2) renewed linguistic anthropological focus on dialects as political resources for objectifying and protecting vernacular identities. [ethnic identity, Kichwa language, dialect, Amazonia, Ecuador]
Challenges and Opportunities: Cyber Physical Security in the Smart Grid
Year: 2019

In this chapter, we develop a vision to address challenges in securing the smart grid. Despite recent innovations, grid security remains a critical issue. The infrastructure is highly vulnerable due to its large scale, connectivity, and heterogeneity. Moreover, attacks on cyber physical systems and the grid have been realized, most notably the attack on the Ukraine power system in 2015. While techniques in cyber security are useful, their implementation is not sufficient to secure the smart grid. Consequently, we advocate for research in cyber physical system security, an interdisciplinary field which combines tools from both cyber security and system theory. Within this field, we argue that engineers need to develop a framework of accountability comprised of three main research thrusts: (1) the detection of attacks, (2) the attribution of attacks to particular malicious components and devices on the grid, and (3) the resilient design of systems and algorithms to ensure acceptable performance in the presence of malicious behavior. To close, we discuss the need for a unifying language and set of tools to address these problems, as we consider additional research in compositional security.
Law and the New Developmental State: The Brazilian Experience In Latin American Context
Year: 2014

1. Law, state, and the new developmentalism: an introduction David M. Trubek 2. New state activism in Brazil and the challenge for law David M. Trubek, Diogo R. Coutinho and Mario G. Schapiro Part I. New Industrial Policies: Global Insertion, Productive Transformation, Investment Strategies, and Flexible Law: 3. Understanding neo developmentalism in Latin America: new industrial policies in Brazil and Colombia Shunko Rojas 4. Rediscovering the developmental path? Development bank, law, and innovation financing in the Brazilian economy Mario Shapiro Part II.  Carving out Development Policy Space within the WTO Regime: 5. Carving out policy autonomy for developing countries in the World Trade Organization: the experience of Brazil and Mexico Alvaro Santos 6. Developmental responses to the international trade legal game: cases of intellectual property and export credit law reforms in Brazil Michelle Ratton Sanchez Badin Part III. Social Policy and Equity: Two Approaches to the Relationship between Social Policy, Law, and Development Strategy: 7. Decentralization and coordination in social law and policy: the Bolsa familia program Diogo R. Coutinho 8. Social policy and the new development state: the case of Colombia Helena Alviar Garcia.
Variational autoencoders with triplet loss for representation learning
Venue: 2018 26th Signal Processing and Communications Applications Conference (SIU)
Year: 2018

Learning low dimensional meaningful representations of data is an important task for classification, visualization and compression. Using autoencoders for representation learning is a successful application of deep learning. Recently, variational autoencoders have also been developed. These are more advantageous than autoencoders since these are generative and have a compact form in the latent space. In order to improve the clustering performance of variational autoencoders in the latent space, the use of variational autoencoders with triplet loss is proposed in this study.
Comparative Complete Genome Analysis of Chicken and Turkey Megriviruses (Family Picornaviridae) Long 3' Untranslated Regions with a Potential Second Open Reading Frame and Evidence for Possible Recombination
Venue: Journal of Virology
Year: 2014

Members of the family Picornaviridae consist of small positive sense single stranded RNA +ssRNA) viruses capable of infecting various vertebrate species, including birds. One of the recently identified avian picornaviruses, with a remarkably long >9,040 nucleotide) but still incompletely sequenced genome, is turkey hepatitis virus 1 (THV 1; species Melegrivirus A, genus Megrivirus) a virus associated with liver necrosis and enteritis in commercial turkeys (Meleagris gallopavo) This report presents the results of the genetic analysis of three complete genomes of megriviruses from fecal samples of chickens (chicken/B21 CHV/2012/HUN, GenBank accession no. KF961186, and chicken/CHK IV CHV/2013/HUN, GenBank accession no. KF961187) (Gallus gallus domesticus) and turkey (turkey/B407 THV/2011/HUN, GenBank accession no. KF961188) (Meleagris gallopavo) with the largest picornavirus genome (up to 9,739 nucleotides) so far described. The close phylogenetic relationship to THV 1 in the nonstructural protein coding genome region and possession of the same internal ribosomal entry site type (IVB like) suggest that the study strains belong to the genus Megrivirus. However, the genome comparisons revealed numerous unique variations (e.g. different numbers of potential 2A peptides, unusually long 3' genome parts with various lengths of a potential second open reading frame, and multiple repeating sequence motifs in the 3' untranslated region) and heterogeneous sequence relationships between the structural and nonstructural genome regions. These differences suggest the classification of chicken megrivirus like viruses into a candidate novel species in the genus Megrivirus. Based on the different phylogenetic positions of chicken megrivirus like viruses at the structural and nonstructural genome regions, the recombinant nature of these viruses is plausible.  The comparative genome analysis of turkey and novel chicken megriviruses revealed numerous unique genome features, e.g. up to four potential 2A peptides, unusually long 3' genome parts with various lengths containing a potential second open reading frame, multiple repeating sequence motifs, and heterogeneous sequence relationships (possibly due to a recombination event) between the structural and nonstructural genome regions. Our results could help us to better understand the evolution and diversity (in terms of sequence and genome layout) of picornaviruses.
Container based architecture for optimal face recognition tasks in edge computing
Venue: SEC
Year: 2019

Edge Computing has been proposed as an architecture for offering services to mobile devices or sensors. Modern multimedia applications such as face recognition, object and pose identification, and mobile augmented reality require cloud computing resources close to the mobile and sensors devices. Docker and containers have been proposed as a platform for edge computing and as a tool for efficient service handoff across edge computing servers. This paper presents a novel container based Fog computing environment to improve resource usage for the architecture using optimization algorithms.
Outlier detection approaches for wireless sensor networks: A survey
Venue: Comput. Networks
Year: 2017

Over the past few years, wireless sensor networks have gained significant attention. They have been distributed in the real world in order to collect valuable raw sensed data. Due to high density, WSNs (Wireless Sensors Networks) are exposed to faults and nasty attacks. Likewise, the sensors readings are inaccurate and unreliable, which make Wireless Sensor Networks vulnerable to outliers. Abnormal data, outliers or anomalies are usually considered to be those sensor readings that have deviated significantly from normal behavior. However, the challenge is to ensure data quality, secure monitoring and reliable detection of interesting and critical events. In this survey, we describe a comprehensive overview of existing outlier detection techniques specifically used for the wireless sensor networks. Moreover, we present a comparative table used as a guideline to select which technique is adequate for the application in terms of characteristics such as detection mode, architectural structure and correlation extraction.
The Pedagogical Shift During COVID 19 Pandemic: Online Medical Education, Barriers and Perceptions in Central Kerala
Venue: Journal of medical education and curricular development
Year: 2020

Owing to COVID 19 pandemic, all educational institutions including medical colleges were closed by the second week of March 2020 in Kerala, India. This college started online classes using various e platforms by the third week of March. In this study, we report the barriers and perceptions of undergraduate students by an online questionnaire after 2.5 months of e classes. The study participants were 364 students who responded in a week's time. Most of the faculty used platforms like Google class room or recorded YouTube videos. The department of Physiology used the Impartus platform. Among the respondents 72.8% were using mobile data and 17.8% were using broadband facilities. Among network providers Jio was the most used. Only first year students were exposed to 3 different online platforms. Among those students, 63.6% reported in favor of Impartus, followed by YouTube and Google class room. Most of the students preferred recorded classes (69.2% over live classes (33.5% Submissions were mainly through the online platform itself (69.5% email submission to the department (17% or to the faculty (13.5% Forty seven percent of the students wanted the classes to be of 30 to 45 minutes duration and 42% felt that the classes should be short and below 30 minutes. Only 28.3% of the students favored centralized online class by the university. Providing education to students cannot be discontinued for long. In the present study students are able to follow the online classes and have good learning experience on in the Didactic part. The medical educators could rise up to the challenge of continuing to teach even in times of crisis.
Antenna coupled spintronic terahertz emitters driven by a 1550 nm femtosecond laser oscillator
Venue: Applied Physics Letters
Year: 2019

We demonstrate antenna coupled spintronic terahertz (THz) emitters excited by 1550 nm, 90 fs laser pulses. Antennas are employed to optimize THz outcoupling and frequency coverage of ferromagnetic/nonmagnetic metallic spintronic structures. We directly compare the antenna coupled devices to those without antennas. Using a 200 mm H dipole antenna and an ErAs:InGaAs photoconductive receiver, we obtain a 2.42 fold larger THz peak peak signal, a bandwidth of 4.5 THz, and an increase in the peak dynamic range (DNR) from 53 dB to 65 dB. A 25 mm slotline antenna offered 5 dB larger peak DNR and a bandwidth of 5 THz. For all measurements, we use a comparatively low laser power of 45 mW from a commercial fiber coupled system that is frequently employed in table top THz time domain systems.We demonstrate antenna coupled spintronic terahertz (THz) emitters excited by 1550 nm, 90 fs laser pulses. Antennas are employed to optimize THz outcoupling and frequency coverage of ferromagnetic/nonmagnetic metallic spintronic structures. We directly compare the antenna coupled devices to those without antennas. Using a 200 mm H dipole antenna and an ErAs:InGaAs photoconductive receiver, we obtain a 2.42 fold larger THz peak peak signal, a bandwidth of 4.5 THz, and an increase in the peak dynamic range (DNR) from 53 dB to 65 dB. A 25 mm slotline antenna offered 5 dB larger peak DNR and a bandwidth of 5 THz. For all measurements, we use a comparatively low laser power of 45 mW from a commercial fiber coupled system that is frequently employed in table top THz time domain systems.
Deep canonical correlation analysis for hyperspectral image classification
Venue: Remote Sensing
Year: 2019

Multi view learning (MVL) is a technique which utilizes multiple views of data simultaneously during training to learn more expressive representations. Multi view learning has been gaining a large amount of interest in various machine learning applications recently. In this paper, we focus on learning representations prior to classification using multi view learning via deep canonical correlation analysis (DCCA) in hyperspectral image processing. We propose a classification framework including a proposed view generation approach. The motivation of our proposed view generation approach is to fuse spatial and spectral information. The performance of our proposed view generation approach is compared with the other view generation methods in the literature; namely the uniform band slicing and correlation partition based clustering. To evaluate the effectiveness of the proposed approach, we performed experiments on two commonly used hyperspectral image datasets. Experimental results based on two hyperspectral image datasets demonstrate that the proposed classification framework provides satisfactory classification performances.
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Venue: ICLR
Year: 2020

Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph structured data. However, it is known that they do not improve (or sometimes worsen) their predictive performance as we pile up many layers and add non lineality. To tackle this problem, we investigate the expressive power of graph NNs via their asymptotic behaviors as the layer size tends to infinity. Our strategy is to generalize the forward propagation of a Graph Convolutional Network (GCN) which is a popular graph NN variant, as a specific dynamical system. In the case of a GCN, we show that when its weights satisfy the conditions determined by the spectra of the (augmented) normalized Laplacian, its output exponentially approaches the set of signals that carry information of the connected components and node degrees only for distinguishing nodes. Our theory enables us to relate the expressive power of GCNs with the topological information of the underlying graphs inherent in the graph spectra. To demonstrate this, we characterize the asymptotic behavior of GCNs on the Erdos Renyi graph. We show that when the Erdos Renyi graph is sufficiently dense and large, a broad range of GCNs on it suffers from the "information loss" in the limit of infinite layers with high probability. Based on the theory, we provide a principled guideline for weight normalization of graph NNs. We experimentally confirm that the proposed weight scaling enhances the predictive performance of GCNs in real data. Code is available at this https URL.
Use of Evolutionary Hypernetworks for Mining Prostate Cancer Data
Year: 2007

Hypernetwork models have been proposed as a random graph model of learning and memory inspired by biomolecular networks in the cell. The hypergraph structure of the hypernetworks turned out to be very useful for discovering the building blocks of higher order interaction of multiple variables. Here we use the hypernetwork model for the analysis of microarray data for cancer diagnosis. Tested on a prostate cancer problem consisting of 102 training examples and 34 test examples of 225 features, the hypernetwork models outperformed the accuracy of multilayer perceptrons and decision trees. We also analyze the modules discovered by the hypernetwork models as potential macro biomarkers for molecular diagnosis.
Reorganizing Complex Network to Improve Large Scale Multiagent Teamwork
Year: 2014

Large scale multiagent teamwork has been popular in various domains. Similar to human society infrastructure, agents only coordinate with some of the others, with a peer to peer complex network structure. Their organization has been proven as a key factor to influence their performance. To expedite team performance, we have analyzed that there are three key factors. First, complex network effects may be able to promote team performance. Second, coordination interactions coming from their sources are always trying to be routed to capable agents. Although they could be transferred across the network via different paths, their sources and sinks depend on the intrinsic nature of the team which is irrelevant to the network connections. In addition, the agents involved in the same plan often form a subteam and communicate with each other more frequently. Therefore, if the interactions between agents can be statistically recorded, we are able to set up an integrated network adjustment algorithm by combining the three key factors. Based on our abstracted teamwork simulations and the coordination statistics, we implemented the adaptive reorganization algorithm. The experimental results briefly support our design that the reorganized network is more capable of coordinating heterogeneous agents.
Coreference Resolution in Full Text Articles with BERT and Syntax based Mention Filtering
Venue: BioNLP OST@EMNLP IJNCLP
Year: 2019

This paper describes our system developed for the coreference resolution task of the CRAFT Shared Tasks 2019. The CRAFT corpus is more challenging than other existing corpora because it contains full text articles. We have employed an existing span based state of theart neural coreference resolution system as a baseline system. We enhance the system with two different techniques to capture longdistance coreferent pairs. Firstly, we filter noisy mentions based on parse trees with increasing the number of antecedent candidates. Secondly, instead of relying on the LSTMs, we integrate the highly expressive language model BERT into our model. Experimental results show that our proposed systems significantly outperform the baseline. The best performing system obtained F scores of 44% 48% 39% 49% 40% and 57% on the test set with B3, BLANC, CEAFE, CEAFM, LEA, and MUC metrics, respectively. Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.
Real world experience using combination therapy with riociguat and risk assessment using REVEAL Lite 2.0 in patients with pulmonary arterial hypertension
Venue: Pulmonary circulation
Year: 2020

Pulmonary arterial hypertension is a progressive disease that can lead to right sided ventricular failure and premature death. Tailoring therapy to individual patient's needs, along with regular risk assessment, is integral for optimal outcomes in patients with pulmonary arterial hypertension. Results from the AMBITION trial support the use of upfront combination of tadalafil and ambrisentan. In a recent analysis of risk assessment in pulmonary arterial hypertension, abridged versions of the REVEAL 2.0 risk score were shown to be comparable to the full tools. In this report, we present a case series of the use of riociguat in upfront combination or sequentially, and the impact on risk scores as determined by the abridged REVEAL Lite 2.0 approach.
Emerging market mutual fund performance: evidence for China
Year: 2017

The purpose of this paper is to find whether Chinese equity funds outperform the market and do Chinese fund managers possess positive market timing ability. This study also aims to investigate whether well performing (worst) funds of last year continue to perform well (worst) in the following year.  Capital Asset Pricing Model and Carhart four factor model are used for performance analysis, whereas for analyzing market timing ability, the Treynor and Mazuy (1966) and Henriksson and Merton (1981) models are applied. To investigate persistence in the performance of Chinese equity funds, all equity funds are divided, on the basis of performance in the past 12 months, into three equally weighted groups (high, middle and low) and then observed for next 12 months. After that, groups are again rebalanced according to their performance. This study uses a panel regression model for analysis.  Chinese equity funds are successful in providing higher than market returns, and fund managers possess positive market timing ability. The authors find that Chinese equity funds do not show persistence in performance as witnessed in developed markets. Well performing funds (worst funds) of last year do not continue to provide higher (lower) return in the following year. Moreover, the authors detect positive relationship of fund size, age and expense ratio with the fund's performance. Overall results suggest that emerging market equity funds show better performance than that of developed markets.  Investors are better off if they invest in equity funds instead of index funds, as results illustrate that equity funds outperformed the market. Further, the strategy of buying well performing funds of last year and selling poorly performing funds of last year does not look very attractive in China. This study helps investors to understand the Chinese managed funds industry, and such an understanding is also helpful for fund managers and asset management companies who use performance information in marketing strategies.  This is the first study to investigate the performance persistence in Chinese equity funds and also contributes to the literature about the performance and market timing ability of equity funds. The study takes the sample of 520 equity funds for the period from 2004 to 2014, which includes a period of financial crisis of 2008.