nils-herrmann/clean_scirepeval huggingface dataset:
Understanding and preventing police use of excessive force: An analysis of attitudes toward police job satisfaction and human rights laws.
Year: 2009

Although governments try to create strict policies and regulations to prevent abuses, use of excessive force is still a problem for almost every country including Turkey. This study is intended to help Turkish National Police administrators to understand and prevent police use of excessive force. Studies on police brutality categorize three factors that explain why police officers use excessive force; these are individual, situational and organizational. In addition to brutality theories, job satisfaction literature is examined in this study to understand the use of excessive force. Job satisfaction is found to be related with burnout, turnover, stress, commitment, and performance. The impact of officers' attitude toward the criminal justice system and/or laws has not been tested widely. Police officers attitudes toward human rights laws are examined in this study to measure its impact on attitude toward use of excessive force. A secondary data collected in Turkey are analyzed by structural equation modeling which provides confirmatory factor analysis, path analysis, and causal relationships between variables. It is found that police officers' attitude toward human rights laws is a significant predictor of their attitudes toward use of excessive force. Job satisfaction and education level are the other significant variables affecting attitude toward use of excessive force. Based on the analyses of findings, educational and policy implications are posed for Turkish police administrators to better understand and prevent police use of excessive force.
Effective photon mass by Super and Lorentz symmetry breaking
Year: 2017

In the context of Standard Model Extensions (SMEs) we analyse four general classes of Super Symmetry (SuSy) and Lorentz Symmetry (LoSy) breaking, leading to observable imprints at our energy scales. The photon dispersion relations show a non Maxwellian behaviour for the CPT (Charge Parity Time reversal symmetry) odd and even sectors. The group velocities exhibit also a directional dependence with respect to the breaking background vector (odd CPT) or tensor (even CPT) In the former sector, the group velocity may decay following an inverse squared frequency behaviour. Thus, we extract a massive Carroll Field Jackiw photon term in the Lagrangian and show that the effective mass is proportional to the breaking vector and moderately dependent on the direction of observation. The breaking vector absolute value is estimated by ground measurements and leads to a photon mass upper limit of 10 19eVor 2 x10 55kg, and thereby to a potentially measurable delay at low radio frequencies.
Teaching socio environmental synthesis with the case studies approach
Venue: Journal of Environmental Studies and Sciences
Year: 2014

Socio environmental synthesis (S E synthesis) is a research method and problem solving approach that integrates existing knowledge and data from natural and social sciences to advance understanding of socio environmental systems and to inform solutions to environmental problems. As science becomes more transdisciplinary and collaborative and as new opportunities presented by the information age change how we do science, the teaching of S E synthesis is increasingly important for preparing future environmental problem solvers, scientists, and citizens. S E synthesis also provides a rich context for classroom instruction that can facilitate the achievement of diverse learning objectives. Here, we discuss efforts to adapt the evidence based teaching practice of the case study method for teaching about the complex and interdisciplinary approach of S E synthesis. We provide insights about the effectiveness of this teaching approach and offer practical advice for writing and implementing S E synthesis cases. These insights are informed by experiences creating and using S E synthesis case studies, both ours and those of participants of a short course on "Teaching about Socio Environmental Synthesis with Case Studies" held at the National Socio Environmental Synthesis Center (SESYNC) Assessment of S E synthesis learning goals and the effectiveness of the case study method for teaching S E synthesis are also discussed. Full development of this approach for teaching S E synthesis will require a community effort, and our intention here is to offer a preliminary vision and qualitative assessment, to stimulate discussion, and to invite readers to join our efforts to create, use, and assess S E synthesis case studies for teaching.
Brand Influence in Business to Business (B2B) Markets Understanding the Buying Criteria of Different Organizations
Year: 2016

The brands make identification of products, services, and businesses convenient, easy, and more acceptable for the buyers. Besides, the brands help to differentiate them from competitors' offerings. The brands also communicate improved value and benefits to the business buyers. And brands also act as a measure of guarantee for quality, performance, and the source; and thus reduce the risk and complexities in buying decisions. The objective of the study was to have a deeper understanding of the various evaluative criteria used in different B2B purchase decisions, and to what extent a brand influences them and how beneficial are those decisions for the buying organizations. The study was focused around the value and importance of branding, the preference towards branded products, and the effect of factors like price, risks involved, information availability, customer support/ after sales services, and value addition on purchase decisions in organizations. The study was conducted with 110 respondents from four different industry categories purchasing and using namely, garment manufacturing machinery, photocopiers, photographic equipment, and medical diagnostic equipments with the help of a questionnaire and interview. The study revealed that more or less, industrial and organizational buyers, though very price conscious, prefer branded products because it gives a mental satisfaction that they are buying a quality product and associated services from a reliable and known source. The companies supplying branded products provide quick backup service and support, and they are more preferred because it assures quick response and solution of any bottleneck and issue with the final end product/ service produced in the buying organization.
Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018
Venue: Cell Death Differentiation
Year: 2017

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT) driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome dependent cell death, autophagy dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.
Evidence of the syndrome of rapid onset end stage renal disease (SORO ESRD) in the acute kidney injury (AKI) literature preventable causes of AKI and SORO ESRD a call for re engineering of nephrology practice paradigms
Venue: Renal failure
Year: 2013

We described the previously unrecognized syndrome of rapid onset end stage renal disease (SORO ESRD) in 2010, in the journal Renal Failure, as distinct from the classic CKD ESRD progression of a methodical, linear, time dependent and predictable progression from CKD through CKD stages I V, ending in ESRD requiring renal replacement therapy (RRT) It remains unclear to what extent this syndrome may have been identified in the past without acknowledging its uniqueness.  We reviewed AKI reports and ascertained cases of SORO ESRD as defined by patients with a priori stable kidney function who subsequently exhibited unanticipated and irreversible ESRD requiring RRT following new AKI episodes.  Fifteen AKI reports demonstrating SORO ESRD were analyzed. The reports span most regions of the world. The 15 studies with 20 to 1095 AKI patients each, mean age 39 65 years, published between 1975 and 2010, demonstrated SORO ESRD rates from 1% to 85% of the AKI series. AKI was caused by hypovolemia/hypotension, infections/sepsis and exposure to nephrotoxics especially radiocontrast, NSAIDs, aminoglycosides and RAAS blocking agents, ACEIs and ARBs.  Irreversible ESRD following AKI, consistent with our recent description of a new and unrecognized syndrome has been sporadically reported in the AKI literature, without a clear mandate as a syndrome, potentially distinct from the classic ESRD. The contribution of SORO ESRD to the global ESRD pandemic, the impact of SORO ESRD on AV Fistula planning, any differential behavior of SORO ESRD versus classic ESRD in terms of mortality outcomes and any predisposing factors to SORO ESRD as advanced age and nephrotoxic exposure all call for serious research study.
Optimal Measurement Allocation Algorithms for Parametric Model Identification of Power Systems
Venue: IEEE Transactions on Control Systems Technology
Year: 2014

In this paper, we present an optimization algorithm that selects the optimal sets of points for placing phasor measurement units (PMUs) on the transmission lines of a multimachine power system for the purpose of identifying the best model fit for its wide area swing dynamics. Alternatively, the method can also be viewed as a way to select the optimal set of points at which phasor values should be computed using measurements available from PMUs such that these computed values, also referred to here as pseudo measurements, can generate the best estimate of the swing model, especially when the measurements are noisy. We pose the identification problem as an equivalent parameter estimation problem for the admittance of each tie line and the inertia of each machine using phasor measurements of voltage magnitude, phase angle and frequency, corrupted with high frequency measurement noise. We then formulate the Cramer Rao bounds (CRBs) for the estimates of these unknown parameters, and show that the bounds are functions of the PMU locations and of the contribution of each measurement variable in the combined output. We finally state the condition for finding the optimal PMU location that guarantees the tightest CRB, and, therefore, the most accurate swing model for the system.
Evaluacion de vulnerabilidad ante tsunami en Chile Central. Un factor para la gestion local del riesgo
Year: 2013

Las caracteristicas tectonicas del margen continental chileno condicionan un territorio altamente expuesto a tsunamis de origen sismico. El borde costero de la region del Biobio en Chile Central se encuentra densamente poblado, en especial en las zonas planas y en los margenes de cursos fluviales, donde la amenaza de tsunami es mayor. Luego de la catastrofe del 27 de febrero de 2010, mas de 18 localidades costeras de la region del Biobio fueron impactadas por un tsunami que alcanzo alturas promedios de 15 m, afectando directamente zonas pobladas. Se evalua la vulnerabilidad socioeconomica, educativa y fisica, ante tsunami en un centro urbano caracteristico de la region del Biobio, Tirua (38deg20' S y 73deg29' W) Para realizar esta evaluacion se diseno una metodologia de trabajo en la cual se utilizaron variables representativas del ambito socioeconomico, educativo y fisico. A traves del uso de SIG se obtuvo un plano sintesis donde se zonifico el area vulnerable en las categorias de alta, media y baja vulnerabilidad. Concluyendo que un 41,6% de la poblacion del centro urbano esta expuesta a los efectos de un tsunami; de la poblacion expuesta un 75% tiene alta vulnerabilidad socioeconomica; un 57% tiene alta vulnerabilidad educativa y, finalmente un 96% de la poblacion expuesta presenta una vulnerabilidad fisica alta. Estos resultados son un primer indicador a escala nacional de los altos grados de vulnerabilidad de nuestros litorales, tan extremadamente expuestos a tsunami de origen sismico, y permiten proponer la integracion de la variable vulnerabilidad en los instrumentos de planificacion urbana pos desastre
The Relational Perspective as a Business Mindset: Managerial Implications for East and West
Year: 2011

Executive Overview Borrowing from classical Eastern philosophy and Chinese culture, this paper draws lessons in leadership, strategy, and organization for today's managers. It begins by articulating the relational philosophy, in particular the Chinese tradition of thought in which all entities are conceived to exist within the context of one another and in which integration, balance, and harmony are sought over distinction and comparison. It examines how this relational mindset shapes interpersonal relationships, communication, and temporal considerations. Lessons are then drawn for leadership, strategy, and organization for both Eastern and Western businesses. We argue that the shortcomings of many Western companies stem from too little attention to relational considerations, while those of many Eastern companies are caused by excesses and distortions of the relational perspective. The relational approach provides an ideal path toward an "ambicultural" mode of management, one that avoids dangerous extrem.
Not All Fluorescent Nanodiamonds Are Created Equal: A Comparative Study
Year: 2019

Fluorescent nanodiamonds (FNDs) are vital to many emerging nanotechnological applications, from bioimaging and sensing to quantum nanophotonics. Yet, understanding and engineering the properties of fluorescent defects in nanodiamonds remain challenging. The most comprehensive study to date is presented, of the optical and physical properties of five different nanodiamond samples, in which fluorescent nitrogen vacancy (NV) centers are created using different fabrication techniques. The FNDs' fluorescence spectra, lifetime, and spin relaxation time (T 1) are investigated via single particle confocal fluorescence microscopy and in ensemble measurements in solution (T 1 excepted) Particle sizes and shapes are determined using scanning electron microscopy and correlated with the optical results. Statistical tests are used to explore correlations between the properties of individual particles and also analyze average results to directly compare different fabrication techniques. Spectral unmixing is used to quantify the relative NV charge state (NV and NV0) contributions to the overall fluorescence. A strong variation is found and quantified in the properties of individual particles within all analyzed samples and significant differences between the different particle types. This study is an important contribution toward understanding the properties of NV centers in nanodiamonds. It motivates new approaches to the improved engineering of NV containing nanodiamonds for future applications.
Stochastic groundwater flow analysis in heterogeneous aquifer with modified neural architecture search (NAS) based physics informed neural networks using transfer learning
Venue: ArXiv
Year: 2020

In this work, a modified neural architecture search method (NAS) based physics informed deep learning model is presented to solve the groundwater flow problems in porous media. Monte Carlo method based on a randomized spectral representation is first employed to construct a stochastic model for simulation of flow through porous media. The desired hydraulic conductivity fields are assumed to be log normally distributed with exponential and Gaussian correlations. To analyze the Darcy equation with the random hydraulic conductivity in this case when its intensity of fluctuations is small, the lowest order perturbation theory is used to reduce the difficulty of calculations, by neglecting the higher order nonlinear part. To solve the governing equations for groundwater flow problem, we build a modified NAS model based on physics informed neural networks (PINNs) with transfer learning in this paper that will be able to fit different partial differential equations (PDEs) with less calculation. The performance estimation strategies adopted is constructed from an error estimation model using the method of manufactured solutions. Since the configuration selection of the neural network has a strong influence on the simulation results, we apply sensitivity analysis to obtain the prior knowledge of the PINNs model and narrow down the range of parameters for search space and use hyper parameter optimization algorithms to further determine the values of the parameters. Further the NAS based PINNs model also saves the weights and biases of the most favorable architectures, which is then used in the fine tuning process. The proposed NAS model based deep collocation method is verified to be effective and accurate through numerical examples in different dimensions using different manufactured solutions.
Weakly Supervised Learning with Multi Stream CNN LSTM HMMs to Discover Sequential Parallelism in Sign Language Videos
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2020

In this work we present a new approach to the field of weakly supervised learning in the video domain. Our method is relevant to sequence learning problems which can be split up into sub problems that occur in parallel. Here, we experiment with sign language data. The approach exploits sequence constraints within each independent stream and combines them by explicitly imposing synchronisation points to make use of parallelism that all sub problems share. We do this with multi stream HMMs while adding intermediate synchronisation constraints among the streams. We embed powerful CNN LSTM models in each HMM stream following the hybrid approach. This allows the discovery of attributes which on their own lack sufficient discriminative power to be identified. We apply the approach to the domain of sign language recognition exploiting the sequential parallelism to learn sign language, mouth shape and hand shape classifiers. We evaluate the classifiers on three publicly available benchmark data sets featuring challenging real life sign language with over 1,000 classes, full sentence based lip reading and articulated hand shape recognition on a fine grained hand shape taxonomy featuring over 60 different hand shapes. We clearly outperform the state of the art on all data sets and observe significantly faster convergence using the parallel alignment approach.
A Flexible SDN Based Architecture for Identifying and Mitigating Low Rate DDoS Attacks Using Machine Learning
Venue: IEEE Access
Year: 2020

While there have been extensive studies of denial of service (DoS) attacks and DDoS attack mitigation, such attacks remain challenging to mitigate. For example, Low Rate DDoS (LR DDoS) attacks are known to be difficult to detect, particularly in a software defined network (SDN) Hence, in this paper we present a flexible modular architecture that allows the identification and mitigation of LR DDoS attacks in SDN settings. Specifically, we train the intrusion detection system (IDS) in our architecture using six machine learning (ML) models (i.e. J48, Random Tree, REP Tree, Random Forest, Multi Layer Perceptron (MLP) and Support Vector Machines (SVM) and evaluate their performance using the Canadian Institute of Cybersecurity (CIC) DoS dataset. The findings from the evaluation demonstrate that our approach achieves a detection rate of 95% despite the difficulty in detecting LR DoS attacks. We also remark that in our deployment, we use the open network operating system (ONOS) controller running on Mininet virtual machine in order for our simulated environment to be as close to real world production networks as possible. In our testing topology, the intrusion prevention detection system mitigates all attacks previously detected by the IDS system. This demonstrates the utility of our architecture in identifying and mitigating LR DDoS attacks.
Using App Inventor as Tool for Creating Mathematics Applications for Mobile Devices with Android OS
Year: 2019

People are programming on their personal computers since the 1980s, but today's mobile applications are making computing as "personal" as never before. Never in the history of the use of technology in education has there been a technology so widely available to citizens as mobile technology. According to this trend, we are moving into a new era of consuming information via mobile computing, one that promises greater variety in applications, highly improved usability, and networking. Our consuming information culture gives us all sorts of opportunities for entertainment, even learning using high tech devices, which are considered to be black boxes to most of us, Original Research Article Cioruta et al. AJRCOS, 2(2) 1 11, 2018; Article no.AJRCOS.44283 2 because only few people can create applications. Even though, more and more students are interested in developing their own mobile applications. In this context, we present our experience with App Inventor a user friendly platform and drag and drop block language to create Android apps. The present paper is an intro to the creation process for an applied mathematics app, started 4 years ago, corresponding to the requirements of our classroom consuming information culture.
Physics Inspired Convolutional Neural Network for Solving Full Wave Inverse Scattering Problems
Venue: IEEE Transactions on Antennas and Propagation
Year: 2019

In this paper, to bridge the gap between physical knowledge and learning approaches, we propose an induced current learning method (ICLM) by incorporating merits in traditional iterative algorithms into the architecture of convolutional neural network (CNN) The main contributions of the proposed method are threefold. First, to the best of our knowledge, it is the first time that the contrast source is learned to solve full wave inverse scattering problems (ISPs) Second, inspired by the basis expansion strategy in the traditional iterative approach for solving ISPs, a combined loss function with multiple labels is defined in a cascaded end to end CNN (CEE CNN) architecture to decrease the nonlinearity of objective function, where no additional computational cost is introduced in generating extra labels. Third, to accelerate the convergence speed and decrease the difficulties of the learning process, the proposed CEE CNN is designed to focus on learning the minor part of the induced current by introducing several skip connections and to bypass the major part of induced current to the output layers. The proposed method is compared with the state of the art of deep learning scheme and a well known iterative ISP solver, where numerical and experimental tests are conducted to verify the proposed ICLM.