nils-herrmann/clean_scirepeval huggingface dataset:
Ectopic expression of WsMBP1 from Withania somnifera in transgenic tobacco shows insecticidal activity against teak defoliator Hyblaea puera (Lepidoptera: Hyblaeidae)
Venue: Biologia
Year: 2020

An insecticidal mannose binding lectin gene of Withania somnifera, WsMBP1 was constitutively expressed in tobacco plants. Instar wise study on the response of Hyblaea puera larvae to the total protein extracted from transgenic tobacco was conducted and survivability percent was 33.33% and 55.55% in the first and the second instars, respectively. Minimum survivability of 22.22% was registered in the third instar. Further, two fold reduction was observed in mean pre pupal and pupal weight in the third instar larval populations fed with lectin protein compared to the control populations. The functional confirmation of the insecticidal activity of WsMBP1 established its potential as a novel gene resource for future transformation studies in developing teak genotypes tolerant to its leaf defoliator, H. puera.
Visceral Artery Pseudoaneurysm Rupture after Endoscopic Retrograde Cholangiopancreatography.
Venue: The Korean journal of gastroenterology Taehan Sohwagi Hakhoe chi
Year: 2020

A visceral artery pseudoaneurysm after ERCP is a rare adverse event that is potentially life threatening. Most cases reported previously originated from the peripancreatic arteries, including the splenic artery, gastroduodenal artery, or pancreaticoduodenal artery. The mechanism of the occurrence of visceral artery pseudoaneurysms after ERCP has not been elucidated until now. Recently, a pseudoaneurysm rupture originating from the superior mesenteric artery after ERCP was observed in a patient without a history of pancreatitis. This paper reports this case with a review of the relevant literature.
Learning With Average Precision: Training Image Retrieval With a Listwise Loss
Venue: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019

Image retrieval can be formulated as a ranking problem where the goal is to order database images by decreasing similarity to the query. Recent deep models for image retrieval have outperformed traditional methods by leveraging ranking tailored loss functions, but important theoretical and practical problems remain. First, rather than directly optimizing the global ranking, they minimize an upper bound on the essential loss, which does not necessarily result in an optimal mean average precision (mAP) Second, these methods require significant engineering efforts to work well, e.g. special pre training and hard negative mining. In this paper we propose instead to directly optimize the global mAP by leveraging recent advances in listwise loss formulations. Using a histogram binning approximation, the AP can be differentiated and thus employed to end to end learning. Compared to existing losses, the proposed method considers thousands of images simultaneously at each iteration and eliminates the need for ad hoc tricks. It also establishes a new state of the art on many standard retrieval benchmarks. Models and evaluation scripts have been made available at: https:/europe.naverlabs.com/Deep Image Retrieval/
Motion/Force Control of a Constrained Robot using Non Singular Terminal Sliding Mode
Year: 2014

In this paper, a hybrid motion/force control strategy is designed for a constrained robotic manipulator using non singular terminal sliding mode. At first, a reduced motion/force model is obtained according to the coordinate transformation. Then, a non singular terminal sliding mode is applied to the new dynamic model to make the motion tracking error converge to zero in finite time while the force tracking error can be reduced to arbitrarily small value. Finally, numerical simulation results for a constrained robot illustrate the expected performance.
Poster: No More Slow Messages: Programmable Packet Handling in Hard IRQ
Venue: 2020 International Symposium on Reliable Distributed Systems (SRDS)
Year: 2020

Tail latency in packet handling can easily occur in today's huge distributed systems. Serious failure can happen if a packet experiences tail latency, especially if that packet is used to manage distributed systems. For example, the delay in heartbeat packets results in unnecessary invocation of recovery procedures, and degrades the overall availability of the system. This poster presents a mechanism that handles critical management packets without any tail latency. It handles critical packets inside hard interrupt request (IRQ) contexts to avoid unexpected delay of packet handling due to resource contention. The code for handling management packets is programmable. To allow the installation of user defined code, it also provides programmable but safe execution environments. Our experimental results demonstrate our system can reduce 99.9 percentile latency up to 74.7% compared to XDP, a state of the art packet handling system.
Continuous Moving Object Clustering in Dynamic Road Network
Venue: 2020 International Conference on Inventive Computation Technologies (ICICT)
Year: 2020

Location based updation and clustering is important issue in dynamic road network. In dynamic road network moving object clustering is needed because huge number of moving objects are moving in the constrained road network. In this paper introduced new clustering algorithm for grouping similar objects in dynamic road network. The proposed clustering method is called density based clustering. In this work the spatial and temporal objects are clustered separated and combined as per the requirements. The spatial distance between the moving objects are calculated with the help of Non Ecludeian Distance. The Experiment is conducted using Geo life GPS trajectory data and results are compared with K mean moving object clustering (K MCL) and moving object clustering methods (MC) The results are compared using accuracy, updation cost and speed of clustering parameters.
PerKApp: A context aware motivational system for healthier lifestyles
Venue: 2016 IEEE International Smart Cities Conference (ISC2)
Year: 2016

In this paper we present PerKApp, a context aware system for inducing the user to adopt healthier lifestyles, based on a novel combination of persuasion technologies, natural language generation techniques, and deep knowledge representation tools. In our view, personalized and tailored messages generated according to the characteristic of user, user preferences and the context are extremely useful to increase the effectiveness of persuasion efforts in terms of user acceptance of the proposed behaviors. The architecture of PerKApp is designed with the goal of ease scalability and extendibility to other domains by redefinition of the knowledge and linguistic content.
Online Learning for Statistical Machine Translation
Year: 2016

encia 1 We present online learning techniques for statistical machine translation (SMT) The availability of large training data sets that grow constantly over time is becoming more and more frequent in the field of SMT for example, in the context of translation agencies or the daily translation of government proceedings. When new knowledge is to be incorporated in the SMT models, the use of batch learning techniques require very time consuming estimation processes over the whole training set that may take days or weeks to be executed. By means of the application of online learning, new training samples can be processed individually in real time. For this purpose, we define a state of the art SMT model composed of a set of submodels, as well as a set of incremental update rules for each of these submodels. To test our techniques, we have studied two well known SMT applications that can be used in translation agencies: post editing and interactive machine translation. In both scenarios, the SMT system collaborates with the user to generate highquality translations. These user validated translations can be used to extend the SMT models by means of online learning. Empirical results in the two scenarios under consideration show the great impact of frequent updates in the system performance. The time cost of such updates was also measured, comparing the efficiency of a batch learning SMT system with that of an online learning system, showing that online learning is able to work in real time whereas the time cost of batch retraining soon becomes infeasible. Empirical results also showed that the performance of online learning is comparable to that of batch learning. Moreover, the proposed techniques were able to learn from previously estimated models or from scratch. We also propose two new measures to predict the effectiveness of online learning in SMT tasks. The translation system with online learning capabilities presented here is implemented in the open source Thot toolkit for SMT.
The Correlation between Corporate Governance Mechanisms and Earnings Quality in Listed Companies in Tehran Stock Exchange
Year: 2014

In recent years corporate governance has been discussed as one of the effective strategies for increasing the quality of financial (earnings) reporting. Corporate governance refers to a set of relationships between shareholders, directors, and accountants of the company and it contains establishing a control system in order to reserve the rights of minority shareholders and proper implementation of assembly acts and preventing any possible abuse. The current research aim is to survey the correlation between corporate governance mechanisms and earnings management in listed companies of Tehran stock exchange. From the point of aim the current research is an applied research. In this research the amount of changes in one or a few factors based on the changes of one or a few other factors through correlation coefficient is considered. Also based on the category of researches due to the collecting data method (research design) this research is a descriptive research. Regarding the imposed conditions the sampling was conducted from 492 companies listed in stock exchange up to the end of 2012 and 98 companies were chosen as the sample. Required data were collected through corporates data collecting from their financial statements and Rahavard Noven software, and in order to analyze data and extract the descriptive and inferential statistics Eviews software was used. Results showed that there is a positive and significant correlation between the size of corporate auditor, amount of non bound directors among the board of directors, and ownership percentage of institutional investors and the earnings quality, while there is a positive but insignificant correlation between the component of lack of CEO as the chairman or the vice chairman of board of directors (CEO duality of responsibilities) and block number of major shareholders and earnings quality. At the end a few practical research suggestions are provided.
Graphene Gold Au@Ag NPs PDMS Films Coated Fiber Optic for Refractive Index and Temperature Sensing
Venue: IEEE Photonics Technology Letters
Year: 2019

A novel fiber optic refractive index (RI) and temperature sensor based on a multimode single mode multimode fiber (MMF SMF MMF) structure coated with graphene metal hybrid films was proposed and investigated. The surface plasmon resonance (SPR) based RI sensitivity is improved by the addition of the graphene layer above or below the gold coating of the fiber and then further enhanced with the addition of gold silver core shell nanoparticles (Au@Ag NPs) Furthermore, the surface of graphene Au Au@Ag NPs film was modified with polydimethylsiloxane (PDMS) for temperature sensing, achieving a high sensitivity of 1.02 nm/degC with good repeatability. This new structure with graphene gold Au@Ag NPs PDMS hybrid films shows applications in many fields, including environmental protection, clean energy, and nano medicine.
Fast Transformation Invariant Component Analysis
Venue: International Journal of Computer Vision
Year: 2007

Dimensionality reduction techniques such as principal component analysis and factor analysis are used to discover a linear mapping between high dimensional data samples and points in a lower dimensional subspace. Previously, Frey and Jojic introduced transformation invariant component analysis (TCA) to learn a linear mapping, invariant to a set of known form of global transformations. However, parameter estimation in that model using the previously proposed expectation maximization (EM) algorithm required scalar operations in the order of N2 where N is the dimensionality of each training example. This is prohibitive for many applications of interest such as modeling mid to large size images, where, for instance, N may be as high as 786432 (512x512 RGB image) In this paper, we present an efficient algorithm that reduces the computational requirements to order of Nlog N. With this speedup, we show the effectiveness of transformation invariant component analysis in various applications including tracking, learning video textures, clustering, object recognition and object detection in images. Software for TCA can be downloaded from http:/www.psi.toronto.edu/fastTCA.htm.
Biodiesel from microalgae: A critical evaluation from laboratory to large scale production
Year: 2013

The economically significant production of carbon neutral biodiesel from microalgae has been hailed as the ultimate alternative to depleting resources of petro diesel due to its high cellular concentration of lipids, resources and economic sustainability and overall potential advantages over other sources of biofuels. Pertinent questions however need to be answered on the commercial viability of large scale production of biodiesel from microalgae. Vital steps need to be critically analysed at each stage. Isolation of microalgae should be based on the question of whether marine or freshwater microalgae, cultures from collections or indigenous wild types are best suited for large scale production. Furthermore, the determination of initial sampling points play a pivotal role in the determination of strain selection as well as strain viability. The screening process should identify, purify and select lipid producing strains. Are natural strains or stressed strains higher in lipid productivity? The synergistic interactions that occur naturally between algae and other microorganisms cannot be ignored. A lot of literature is available on the downstream processing of microalgae but a few reports are available on the upstream processing of microalgae for biomass and lipid production for biodiesel production. We present in this review an empirical and critical analysis on the potential of translating research findings from laboratory scale trials to full scale application. The move from laboratory to large scale microalgal cultivation requires careful planning. It is imperative to do extensive pre pilot demonstration trials and formulate a suitable trajectory for possible data extrapolation for large scale experimental designs. The pros and cons of the two widely used methods for growing microalgae by photobioreactors or open raceway ponds are discussed in detail. In addition, current methods for biomass harvesting and lipid extraction are critically evaluated. This would be novel approach to economical biodiesel production from microalgae in the near future. Globally, microalgae are largest biomass producers having higher neutral lipid content outcompeting terrestrial plants for biofuel production. However, the viscosities of microalgal oils are usually higher than that of petroleum diesel.
Coupled deformation seepage analysis of dynamic capacity tests on open ended piles in saturated sand
Year: 2017

AbstractOpen ended piles such as tubular piles or I beams are used as foundations for offshore and nearshore construction. After the pile installation a load test to estimate the bearing capacity of these open ended piles is necessary. Due to the offshore conditions and the high bearing capacity of the installed piles a static load test is not normally feasible. Therefore, dynamic load tests are carried out where the wave propagation due to an dynamic impact at the pile head is measured. The methods to estimate the bearing capacity from the measured signal of the dynamic tests were derived for solid pile profiles. It is questionable whether these evaluation techniques are applicable for open ended piles. Hence, the influence of various important system parameters as well as the differences between static and dynamic load tests on open ended piles is investigated in this paper.
ACTIVATION FUNCTIONS IN NEURAL NETWORKS
Year: 2020

Artificial Neural Networks are inspired from the human brain and the network of neurons present in the brain. The information is processed and passed on from one neuron to another through neuro synaptic junctions. Similarly, in artificial neural networks there are different layers of cells arranged and connected to each other. The output/information from the inner layers of the neural network are passed on to the next layers and finally to the outermost layer which gives the output. The input to the outer layer is provided nonlinearity to inner layers' output so that it can be further processed. In an Artificial Neural Network, activation functions are very important as they help in learning and making sense of non linear and complicated mappings between the inputs and corresponding outputs.
The future of IMF studies with the ELT and MICADO
Year: 2020

In this work we aim to estimate the lowest stellar mass that MICADO at the ELT will be able to reliably detect given a stellar density and distance. We also show that instrumental effects that will play a critical role, and report the number of young clusters that will be accessible for IMF studies in the local Universe with the ELT.  We used SimCADO, the instrument simulator package for the MICADO camera, to generate observations of 56 dense stellar regions with densities similar to the cores of young stellar clusters. We placed the cluster fields at distances between 8 kpc and 5 Mpc from the Earth, implying core densities from 10^2 to 10^5 stars arcsec^ 2, and determined the lowest reliably observable mass for each stellar field through point spread function (PSF) fitting photometry.  Our results show that stellar densities of 2 Msun will be resolved in fields of <10^4 stars arcsec^ 2 allowing the high mass end of the IMF to be studied in all galaxies out to and including NGC300.
State of art review of traffic signal control methods: challenges and opportunities
Year: 2020

Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope up with the prevailing traffic scenarios and to meet the ever increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network.  Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field.  In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper.  Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.
Simultaneous task allocation and planning for temporal logic goals in heterogeneous multi robot systems
Venue: Int. J. Robotics Res.
Year: 2018

This paper describes a framework for automatically generating optimal action level behavior for a team of robots based on temporal logic mission specifications under resource constraints. The proposed approach optimally allocates separable tasks to available robots, without requiring a priori an explicit representation of the tasks or the computation of all task execution costs. Instead, we propose an approach for identifying sub tasks in an automaton representation of the mission specification and for simultaneously allocating the tasks and planning their execution. The proposed framework avoids the need to compute a combinatorial number of possible assignment costs, where each computation itself requires solving a complex planning problem. This can improve computational efficiency compared with classical assignment solutions, in particular for on demand missions where task costs are unknown in advance. We demonstrate the applicability of the approach with multiple robots in an existing office environment and evaluate its performance in several case study scenarios.
London Zoo and the Victorians, 1848 1859. By Takashi Ito. (Suffolk, England: Boydell Press, 2014. Pp. 204. $90.00.
Year: 2016

defined his position over the Suez Canal in opposition to that of Chamberlain, thereby precluding negotiation. The same could be said of Thatcher (the Falklands) Major (the First Gulf War) Blair (the Second Gulf War) and Cameron (Libya/Syria) Each position of strength distances itself from the perception of "weakness" that Chamberlain is argued to have demonstrated. The memory of Chamberlain's failure (and, indeed, Churchill's subsequent success) Hughes suggests, acted as a driver for foreign policy in the postwar period. It was without a doubt a powerful rhetorical position to adopt given that it enabled postwar prime ministers to associate themselves with Churchill's position, thereby demonstrating strength and courage in the face of an enemy. It must be noted that taking appeasement as simply a single act of substantial failure belies a more nuanced understanding of Chamberlain's position and attitude towards Hitler. However, given the subsequent rhetorical use of appeasement as something against which stronger leaders could define their position does not prevent Hughes (or other scholars) from using this understanding of appeasement. Put simply, regardless of the realities of Chamberlain's position, the memory of substantive failure is sufficient for modern day politicians to exploit in justifying subsequent actions in Suez, the Falklands, and the Gulf. Hughes successfully explores these dimensions of British foreign policy with a wide breadth of scholarly evidence. Indeed, evidence of his scholarship has a commanding representation within the book, occupying a significant part of the pages. He presents the argument in a manner likely to retain the interest of the reader whilst resisting the temptations towards over simplification. It is a highly interesting book that would be of interest to scholars and students alike.