nils-herrmann/clean_scirepeval huggingface dataset:
Salinity tolerance in Australian wild Oryza species varies widely and matches that observed in O. sativa
Venue: Rice
Year: 2018

BackgroundSoil salinity is widespread in rice producing areas globally, restricting both vegetative growth and grain yield. Attempts to improve the salt tolerance of Asian rice, Oryza sativa the most salt sensitive of the major cereal crops have met with limited success, due to the complexity of the trait and finite variation in salt responses among O. sativa lines. Naturally occurring variation among the more than 20 wild species of the Oryza genus has great potential to provide breeders with novel genes to improve resistance to salt. Here, through two distinct screening experiments, we investigated variation in salinity tolerance among accessions of two wild rice species endemic to Australia, O. meridionalis and O. australiensis, with O. sativa cultivars Pokkali and IR29 providing salt tolerant and sensitive controls, respectively.ResultsRice plants were grown on soil supplemented with field relevant concentrations of NaCl (0, 40, 80, and 100 mM) for 30 d, a period sufficient to reveal differences in growth and physiological traits. Two complementary screening approaches were used: destructive phenotyping and high throughput image based phenotyping. All genotypes displayed clear responses to salt treatment. In the first experiment, both salt tolerant Pokkali and an O. australiensis accession (Oa VR) showed the least reduction in biomass accumulation, SES score and chlorophyll content in response to salinity. Average shoot Na+/K+ values of these plants were the lowest among the genotypes tested. In the second experiment, plant responses to different levels of salt stress were quantified over time based on projected shoot area calculated from visible red green blue (RGB) and fluorescence images. Pokkali grew significantly faster than the other genotypes. Pokkali and Oa VR plants displayed the same absolute growth rate under 80 and 100 mM, while Oa D grew significantly slower with the same treatments. Oa VR showed substantially less inhibition of growth in response to salinity when compared with Oa D. Senescence was seen in Oa D after 30 d treatment with 40 mM NaCl, while the putatively salt tolerant Oa VR had only minor leaf damage, even at higher salt treatments, with less than a 40% increase in relative senescence at 100 mM NaCl compared to 120% for Oa VR.ConclusionThe combination of our two screening experiments uncovered striking levels of salt tolerance diversity among the Australian wild rice accessions tested and enabled analysis of their growth responses to a range of salt levels. Our results validate image based phenotyping as a valuable tool for quantitative measurement of plant responses to abiotic stresses. They also highlight the potential of exotic germplasm to provide new genetic variation for salinity tolerance in rice.
Two cases of lung metastasis originating from acquired cystic kidney disease associated carcinoma
Year: 2016

Case 1 involved a 70 year old man on hemodialysis for chronic nephritis for 19 years, who had undergone left nephrectomy for acquired cystic kidney disease (ACKD) associated renal cell carcinoma (RCC) (pT2aN0M0; stage II) 5 years earlier. An abnormal pulmonary nodule was detected on follow up chest computed tomography (CT) We performed wedge resection of the right lower lobe, and diagnosed metastatic lung tumor originating from ACKDassociated RCC histopathologically. Case 2 involved a 52 year old man on hemodialysis for diabetic nephropathy for 14 years, who had undergone bilateral nephrectomy for ACKD associated RCC (pT2aN0M0; stage II) 1 year earlier. An abnormal pulmonary nodule was detected on follow up CT. We performed wedge resection of the right lower lobe, and diagnosed metastatic lung tumor originating from ACKD associated RCC histopathologically. Although the prognosis of ACKD associated RCC is favorable and lung metastasis is rare, annual systemic screening appears important for good prognosis.
Train simultaneously, generalize better: Stability of gradient based minimax learners
Venue: ICML
Year: 2021

The success of minimax learning problems of generative adversarial networks (GANs) has been observed to depend on the minimax optimization algorithm used for their training. This dependence is commonly attributed to the convergence speed and robustness properties of the underlying optimization algorithm. In this paper, we show that the optimization algorithm also plays a key role in the generalization performance of the trained minimax model. To this end, we analyze the generalization properties of standard gradient descent ascent (GDA) and proximal point method (PPM) algorithms through the lens of algorithmic stability under both convex concave and non convex non concave minimax settings. While the GDA algorithm is not guaranteed to have a vanishing excess risk in convex concave problems, we show the PPM algorithm enjoys a bounded excess risk in the same setup. For non convex non concave problems, we compare the generalization performance of stochastic GDA and GDmax algorithms where the latter fully solves the maximization subproblem at every iteration. Our generalization analysis suggests the superiority of GDA provided that the minimization and maximization subproblems are solved simultaneously with similar learning rates. We discuss several numerical results indicating the role of optimization algorithms in the generalization of the learned minimax models.
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
Venue: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019

In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings. Given an input, potentially incomplete, 3D scene and a query location, our method predicts a probability distribution over object types that fit well in that location. Our distribution is predicted though passing learned messages in a dense graph whose nodes represent objects in the input scene and edges represent spatial and structural relationships. By weighting messages through an attention mechanism, our method learns to focus on the most relevant surrounding scene context to predict new scene objects. We found that our method significantly outperforms state of the art approaches in terms of correctly predicting objects missing in a scene based on our experiments in the SUNCG dataset. We also demonstrate other applications of our method, including context based 3D object recognition and iterative scene generation.
Two stage Algorithm for Fairness aware Machine Learning
Venue: ArXiv
Year: 2017

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off the shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two stage least squares method that is widely used in the field of economics, we propose a two stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real world datasets.
European Heart Rhythm Association (EHRA) position paper on arrhythmia management and device therapies in endocrine disorders, endorsed by Asia Pacific Heart Rhythm Society (APHRS) and Latin American Heart Rhythm Society (LAHRS)
Venue: Europace European pacing, arrhythmias, and cardiac electrophysiology journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
Year: 2018

Endocrine disorders are associated with various tachyarrhythmias, including atrial fibrillation (AF) ventricular tachycardia (VT) ventricular fibrillation (VF) and bradyarrhythmias. Along with underlying arrhythmia substrate, electrolyte disturbances, glucose, and hormone levels, accompanying endocrine disorders contribute to development of arrhythmia. Arrhythmias may be life threatening, facilitate cardiogenic shock development and increase mortality. The knowledge on the incidence of tachy and bradyarrhythmias, clinical and prognostic significance as well as their management is limited; it is represented in observational studies and mostly in case reports on management of challenging cases. It should be also emphasized, that the topic is not covered in detail in current guidelines. Therefore, cardiologists and multidisciplinary teams participating in care of such patients do need the evidence based, or in case of limited evidence expert opinion based recommendations, how to treat arrhythmias using contemporary approaches, prevent their complications and recurrence in patients with endocrine disorders. In recognizing this close relationship between endocrine disorders and arrhythmias, the European Heart Rhythm Association (EHRA) convened a Task Force, with representation from Asia Pacific Heart Rhythm Society (APHRS) and Sociedad Latinoamericana de Estimulacion Cardiaca y Electrofisiologia (SOLAECE) with the remit of comprehensively reviewing the available evidence and publishing a joint consensus document on endocrine disorders and cardiac arrhythmias, and providing up to date consensus recommendations for use in clinical practice.
Fair Channel Distribution Based Congestion Control in Vehicular Ad Hoc Networks
Year: 2019

Vehicular ad hoc network (VANET) serves as one of the significant enabling technologies in intelligent transportation system (ITS) Accurate and up to date information received by vehicle to vehicle (V2V) communication prevents road accidents. The most critical matter in IEEE 802.11p based V2V communications is channel congestion, as it results in unreliable safety applications. In this paper, two types of safety messages beacon and event driven are used to reduce hazards. Beacon is disseminated periodically, providing the necessary information about their neighbor status. Event driven is sent whenever a danger has been detected. As the number of vehicle increases, the number of safety messages disseminated by vehicle also increases, which results in congestion in the communication channel. As a countermeasure, we have proposed the most prominent decentralized congestion control (DCC) algorithm based on transmit rate. The effects of congestion on vehicular safety can be controlled by designing a DCC algorithm including priority model and transmission rate of the messages, which provides more reliable and timely reception of safety messages. DCC algorithm controls the congestion by fairly distribution of channel to each vehicle. A novel approach is proposed regarding congestion control, and performance is analyzed with respect to some indexes such as packet delivery rate (PDR) end to end (E2E) delay, throughput, and transmit frequency.
POLARIS: The Distributed SQL Engine in Azure Synapse
Venue: Proc. VLDB Endow.
Year: 2020

In this paper, we describe the Polaris distributed SQL query engine in Azure Synapse. It is the result of a multi year project to rearchitect the query processing framework in the SQL DW parallel data warehouse service, and addresses two main goals: (i) converge data warehousing and big data workloads, and (ii) separate compute and state for cloud native execution. From a customer perspective, these goals translate into many useful features, including the ability to resize live workloads, deliver predictable performance at scale, and to efficiently handle both relational and unstructured data. Achieving these goals required many innovations, including a novel "cell" data abstraction, and flexible, fine grained, task monitoring and scheduling capable of handling partial query restarts and PB scale execution. Most importantly, while we develop a completely new scale out framework, it is fully compatible with T SQL and leverages decades of investment in the SQL Server single node runtime and query optimizer. The scalability of the system is highlighted by a 1PB scale run of all 22 TPC H queries; to our knowledge, this is the first reported run with scale larger than 100TB. PVLDB Reference Format: Josep Aguilar Saborit, Raghu Ramakrishnan et al. VLDB Conferences. PVLDB,  3216, 2020.
Ultrasonographic evaluation of vincristine induced gastric hypomotility and the prokinetic effect of mosapride in dogs.
Venue: Journal of veterinary internal medicine
Year: 2011

Vincristine induces gastrointestinal motility disorders in humans. Adverse gastrointestinal events are commonly observed in dogs receiving vincristine.  To evaluate gastric motility after vincristine administration in dogs and the prophylactic effect of a prokinetic agent, mosapride.  Five healthy Beagle dogs.  Five dogs received vincristine i.v. at a dosage of 0.75 mg/m(2) The motility index (MI) of the antral contraction was ultrasonographically evaluated 30 minutes postfeeding before administration of vincristine and for 6 days after vincristine treatment. After a 6 week washout period, the dogs received vincristine with mosapride (2 mg/kg p.o. q24h for 6 days) and the MI was re evaluated. Adverse gastrointestinal events were evaluated according to the Veterinary Co operative Group Common Terminology Criteria for Adverse Events (VCOG CTCAE)  After vincristine administration, a significant decrease (P .05) in MI was observed on days 3 (6.64 0.30) and 4 (8.02 0.94) compared with pretreatment levels (10.00 0.62) Gastrointestinal adverse events were observed in 4 dogs (grade 2 decreased appetite: 3 dogs; grade 1 vomiting: 2 dogs; and grade 1 diarrhea and grade 2 hematochezia: 1 dog) When mosapride citrate was administered with vincristine and for the next 5 days, no decrease in MI was observed. Furthermore, adverse gastrointestinal events occurred less frequently (grade 1 vomiting and grade 2 hematochezia in 1 dog each) CONCLUSIONS AND CLINICAL IMPORTANCE Vincristine (0.75 mg/m(2) induces gastric hypomotility in dogs. Preventive administration of mosapride citrate (2.0 mg/kg p.o. q24h) improves hypomotility and may decrease the adverse gastrointestinal effects of vincristine.
The Matrix Profile: Scalable Algorithms and New Primitives for Time Series Data Mining
Year: 2018

Author(s) Zhu, Yan Advisor(s) Keogh,  Primitives such as motifs, discords, shapelets, etc. are widely used in time series data mining. A versatile approach to find these primitives is through computing similarity joins for time series subsequences. The last decade has seen a significant amount of research effort on similarity joins in domains such as text and DNA, but not much progress has been made on similarity joins for time series subsequences. The lack of progress is probably due to the daunting nature of the problem: for even modest sized datasets the brute force algorithm can take months to complete. Typical speed up techniques such as indexing and lower bounding at best produce only one or two orders of magnitude speedup, and their performance can degrade significantly in the face of an unfavorable dataset.In this dissertation we introduce a suite of algorithms that significantly expand the limit of scalability for time series subsequences similarity joins. These algorithms not only provide the fastest exact solution to the discovery of time series motifs, one of the most extensively studied time series data mining primitives in the last decade, but also allow the invention of new primitives that the state of the art could not support.Specifically, we present a novel batch algorithm which, when combined with the GPU framework, can find the full set of exact motifs on a dataset two orders of magnitude larger than the literature limit in feasible time. A novel fast converging anytime algorithm further expands this scalability, allowing motif discovery for million scale datasets to be performed in interactive sessions with an off the shelf desktop. We also show how these techniques can be combined with a novel lower bound to allow fast motif discovery in the presence of missing data. Furthermore, we introduce time series chains, a new time series data mining primitive that can capture the evolution of systems and help predict the future.We demonstrate the utility of our ideas in domains as diverse as seismology, entomology, human activity monitoring, electrical power demand monitoring and medicine.
Culture, Language and Productivity in the Workplace within the BRICS Nations
Venue: Southern African Journal for Folklore Studies
Year: 2019

The changing economic environment globally carries challenges and opportunities for business. Cross cultural environments and financial integration call for greater understanding of the workplace. The authors assess the usage and status of language and culture in workplaces within the BRICS (Brazil, Russia, India, China, South Africa) countries through a light touch survey to assist in framing further and deeper research activities. The objective is to develop a suitable research framework regarding the place of language and culture in the workplace in multilingual and multicultural contexts. The authors argue for the inclusion of a cultural dimension linked to multilingual strategies in the workplace. The inextricable link between language and culture is explored in this article. It is suggested that orality and the way we pass on information in the workplace should be considered when encouraging productivity in the multilingual, multicultural workplace in order to create a sense of integration and belonging rather than one of alienation.
Examining different recycling processes for lithium ion batteries
Venue: Nature Sustainability
Year: 2019

Finding scalable lithium ion battery recycling processes is important as gigawatt hours of batteries are deployed in electric vehicles. Governing bodies have taken notice and have begun to enact recycling targets. While several battery recycling processes exist, the greenhouse gas emissions impacts and economic prospects of these processes differ, and could vary by specific battery chemistry. Here we use an attributional life cycle analysis, and process based cost models, to examine the greenhouse gas emissions, energy inputs and costs associated with producing and recycling lithium ion cells with three common cathode chemistries: lithium nickel manganese cobalt oxide (NMC 622) lithium nickel cobalt aluminium oxide and lithium iron phosphate. We compare three recycling processes: pyrometallurgical and hydrometallurgical recycling processes, which reduce cells to elemental products, and direct cathode recycling, which recovers and reconditions ceramic powder cathode material for use in subsequent batteries retaining a substantial fraction of the energy embodied in the material from their primal manufacturing process. While pyrometallurgical and hydrometallurgical processes do not significantly reduce life cycle greenhouse gas emissions, direct cathode recycling has the potential to reduce emissions and be economically competitive. Recycling policies should incentivize battery collection and emissions reductions through energetically efficient recycling processes.Various battery recycling processes exist, but the related environmental and economic implications can vary by specific battery chemistry. This study examines the greenhouse gas emissions, energy inputs and costs associated with producing and recycling lithium ion cells with different cathode chemistries.
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Similarity Measures
Year: 2020

Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. In this paper, we propose a method for semantic relation extraction between concepts. The method relies on the definition of concept context and the semantic similarity measures to extract relations from domain corpus. In this work, we implemented algorithm for concept context construction and for similarity computation based on different semantic similarity measures. We analyze the proposed methods and evaluate their performance. The preliminary experiments showed that the best results precision of 83% are obtained with Lin measure at minimum confidence =0.50 and precision of 85% with the Cosine and Jaccard similarity measures. The main advantage is the automatic and unsupervised operation; it doesn't need any pre labeled training data. Also used effectively for relation extraction in various domains. The results show the high effectiveness of the proposed approach to extract relations for Arabic ontology construction.
Topology optimization of modulated and oriented periodic microstructures by the homogenization method
Venue: Comput. Math. Appl.
Year: 2019

This paper is concerned with the topology optimization of structures made of periodically perforated material, where the microscopic periodic cell can be macroscopically modulated and oriented. The main idea is to optimize the homogenized formulation of this problem, which is an easy task of parametric optimization, then to project the optimal microstructure at a desired lengthscale, which is a delicate issue, albeit computationally cheap. The main novelty of our work is, in a plane setting, the conformal treatment of the optimal orientation of the microstructure. In other words, although the periodicity cell has varying parameters and orientation throughout the computational domain, the angles between its members or bars are conserved. The main application of our work is the optimization of so called lattice materials which are becoming increasingly popular in the context of additive manufacturing. Several numerical examples are presented for compliance minimization in 2 d.
A STUDY ON EVALUATING EMPLOYEES STRESS IN THE SBI, ICICI AND BANK OF BARODA IN THE CHANGING SCENARIO 0F BANKING SECTOR WITH REFERENCE TO ANDHRA PRADESH
Year: 2015

This study follows a line of investigation about the existing knowledge of stress management strategies among the employees in Indian commercial banks. The study reviewed number of different issues related to occupational stress and explored the factors causing stress. To understand the factors leading to stress, an in depth study was conducted from the employees of three different banks. The study argues that stress can be managed effectively and inexpensively, if the employees learn what stress is, what are the consequences of stressors and how to control it effectively. Recognizing the implications of stress in an organisation will increase employee satisfaction and performance.