nils-herrmann/clean_scirepeval huggingface dataset:
First successful stabilization of consolidated amorphous calcium phosphate (ACP) by cold sintering: toward highly resorbable reactive bioceramics.
Venue: Journal of materials chemistry. B
Year: 2019

In the field of bone regeneration, some clinical conditions require highly resorbable, reactive bone substitutes to rapidly initiate tissue neo formation. In this view, Amorphous Calcium Phosphates (ACP) appear as well suited bioceramics taking into account their high metastability. However, the metastability also leads to difficulties of sintering without transformation into crystalline compounds. In this work, various calcium phosphate samples (co)doped with carbonate (CO32 and magnesium ions were synthesized by the double decomposition method in alkaline media using ammonium and potassium hydroxide solutions. The obtained amorphous powders possess an exceptionally high carbonate content up to 18.3 wt% Spark Plasma Sintering (SPS) at very low temperature (150 degC) was then utilized to consolidate initial powders with the view to preserve their amorphous character. The influence of the introduction of different apatite growth inhibitors such as carbonate (CO32 and magnesium ions was studied. XRD and FTIR analyses revealed that sintered ceramics generally consisted in highly carbonated low crystallinity apatites, which are expected to have higher solubility than conventional apatite based systems. However, most interestingly, modulation of the doping conditions allowed us to retain, for the first time, the amorphous character of ACP powders after SPS. Such consolidated ACP compounds may now be considered as a new family of bioceramics with high metastability allowing the fast release of bioactive ions upon resorption.
Canadian Patent Agent Regulation Reform Pt. 2 Governance, Self Regulation and Canada's Patent Professional Identity Crisis
Year: 2017

Only registered Canadian patent agents may represent others before the Canadian Patent Office. To qualify as a registered Canadian patent agent, candidates must complete an apprenticeship followed by successfully passing the Canadian patent agent examination. This article analyzes the current regulatory framework for Canadian patent agents. Based on a theoretical, historical and international comparative analysis, this article demonstrates that the current Canadian regulatory model has lost sight of its purpose and is symptomatic of a greater crisis of legitimacy plaguing the Canadian patent agent profession. The article highlights the need for greater reform of the Canadian patent agent regulatory framework in order to better serve the intellectual property needs of the Canadian public. This article concludes with a series of comprehensive recommendations for reform of the current Canadian regulatory framework.
A Multi Level Formative Assessment of Autonomous College English Learning Based on Analytic Hierarchy Process
Year: 2017

Autonomous learning as a part of college English teaching system has long been a focus for the researchers. The important key to the autonomous English learning is autonomous learning ability. It is an urgent need to establish a reasonable and feasible system to evaluate the autonomous ability with some objective methods. On the basis of literature analysis and questionnaires, a multi level formative assessment scheme different from examination evaluation is designed to monitor autonomous learning ability. The weight of each evaluation index is more objectively determined though analytic hierarchy process than expert scoring method. In the teaching practice, surveys have shown that this multi level formative assessment scheme can improve the objectivity and satisfaction of the evaluation, and the autonomous learning ability is improved by multi level indexes.
Variational Recurrent Models for Solving Partially Observable Control Tasks
Venue: ICLR
Year: 2020

In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy. In this study, we propose an RL algorithm for solving PO tasks. Our method comprises two parts: a variational recurrent model (VRM) for modeling the environment, and an RL controller that has access to both the environment and the VRM. The proposed algorithm was tested in two types of PO robotic control tasks, those in which either coordinates or velocities were not observable and those that require long term memorization. Our experiments show that the proposed algorithm achieved better data efficiency and/or learned more optimal policy than other alternative approaches in tasks in which unobserved states cannot be inferred from raw observations in a simple manner.
PDE N ET 2 0 L EARNING PDE S FROM D ATA WITH A N UMERIC S YMBOLIC H YBRID D EEP N ETWORK
Year: 2018

Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers new opportunities for data driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE Net 2.0, to discover (time dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE Net 2.0 is based on our earlier work  where the original version of PDE Net was proposed. PDE Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi layer neural network for model recovery. Comparing with existing approaches, PDE Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.
Directed information measure for quantifying the information flow in the brain
Venue: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2009

In neurophysiology, it is important to determine the causal relationships between neuronal sites. The major problem with existing methods for quantifying the causality in the brain, e.g. Granger causality, is that they assume an multi variate autoregressive signal model for the multi channel EEG signals and do not take the nonlinear dependencies between neuronal oscillations into account. In this paper, we propose to quantify the causality of the interactions based on the directed information (DI) criterion, which measures the information flow between two signals over time. The proposed measure is applied to real EEG data from control and schizophrenic subject groups. The significance of the computed DI values are verified by Fourier bootstrapping technique.
Extension of Azine Triazole Synthesis to Azole Triazoles Reduces Ligand Field, Leading to Spin Crossover in Tris L Fe(II)
Venue: Inorganic chemistry
Year: 2020

The first examples of azole triazole Rat ligands, bidentate L4NMeIm(3 (1 methyl 1H imidazol 4 yl) 5 phenyl 4 (p tolyl) 4H 1,2,4 triazole) and L4SIm (4 (5 phenyl 4 (p tolyl) 4H 1,2,4 triazol 3 yl)thiazole) have been prepared, by extension of the general synthesis used to access many examples of azine triazoles. The tris L FeII complexes of the azine triazoles are consistently low spin (LS) As intended, these new azole triazole ligands provide lower field strengths, resulting in high spin (HS) [FeII(L4NMeIm)3](BF4)2 4H2O) and spin crossover (SCO) active [FeII(L4SIm)3](BF4)2 (2*0.5H2O) Single crystal structure determinations revealed that at 100 K 1*solvents is HS whereas 2*solvents is LS. Solid state variable temperature magnetic studies of air dried crystals showed that the methylimidazole triazole complex 1*4H2O remains HS while the thiazole triazole complex 2*0.5H2O undergoes a two step gradual SCO (T1/2 approximately 275 and 350 K) Variable temperature Evans method NMR studies of 2, in five different solvents (CD3NO2, CD3CN, CD3COCD3, CD2Cl2, and CDCl3) gave T1/2 values in a relatively narrow range, 214 259 K. These T1/2 values did not correlate with the solvent polarity index P' (R2 0.25) but did correlate with the solvent basicity parameter SB (R2 0.90) Variable temperature UV vis studies on a golden yellow CH3CN solution of 2, with monitoring of the d d transition at 530 nm (e 39 L mol 1 cm 1 at 293 K) while the solution was heated from 253 to 303 K, showed that the high spin fraction increased from 0.51 to 0.77. Cyclic voltammetry studies in CH3CN revealed a Fe(III)/Fe(II) redox process that was reversible for 1 and irreversible for 2, with significant tuning of the Epa value: the methylimidazole triazole complex 1 is significantly easier to oxidize (0.46 V) than the thiazole triazole complex 2 (0.68 V; both vs 0.01 M Ag/AgNO3)
Framing by the Flock: Collective Issue Definition and Advocacy Success
Venue: Comparative political studies
Year: 2019

The framing of issues is part of the tool kit used by lobbyists in modern policy making, yet the ways in which framing works to affect lobbying success across issues remain underexplored. Analyzing a new dataset of lobbying in the news on 50 policy issues in five European countries, we demonstrate that it is not individual but collective framing that matters: Emphasis frames that enjoy collective backing from lobbying camps of like minded advocates affect an advocate's success, rather than frames being voiced by individual advocates. Crucially, it matters for advocacy success whether the advocate's camp frames its policy goals on an issue in unity with "one voice" and whether the actor's camp wins the contest of framing the issue vis a vis the opposing camp. Our results emphasize the need to consider the collective mechanisms behind the power of framing and have implications for future research on framing as an advocacy tool.
Automatic segmentation of tumor, EDEMA and healthy tissues of brain using neuro fuzzy inference system
Venue: 2014 22nd Signal Processing and Communications Applications Conference (SIU)
Year: 2014

In this study we segmented brain MR images into constituent tissues using a neuro fuzzy inference system. We segmented T1, T2 and FLAIR MR images of the patients with glial tumor into white matter, gray matter, cerebrospinal fluid and diseased tissues as edema and tumor. We retained only brain tissue in the images by stripping the skull that is beyond the scope of this study. We used statistical features obtained from the stationary wavelet transform coefficients as the input of the system. We compared the results obtained from the system with the manually segmented tissue areas and evaluated using Dice similarity index. We have shown that neuro fuzzy inference system shows quite high performance for segmenting brain MR images and can be used effectively for this purpose.
Protein Interaction Networks Define the Genetic Architecture of Preterm Birth
Venue: medRxiv
Year: 2020

Rather than pathogenic variants in single genes, the likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks and pathways sufficient to give rise to a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify subgroups of patients with shared networks and pathways associated with preterm birth (PTB) We previously identified genes, gene sets and haplotype blocks that were highly associated with preterm birth. We performed targeted sequencing on these genes and genomic regions on highly phenotyped patients with 2 or 3 generations of preterm birth, and term controls with no family history of preterm birth. We performed a genotype test for differential abundance of variants between cases and controls. We used the genotype association statistics for ranking purposes in order to analyze the data using a multi sample, protein protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared interaction networks of proteins among 45 preterm cases in two statistically significant clusters, p<0.001. We also found two small control dominated clusters. For replication, we compared our data to an independent, large birth cohort. Sequence data on 60 cases and 321 controls identified 34 preterm cases with shared networks of proteins distributed in two significant clusters. Analysis of the layered PPI networks of these clusters showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B cell signaling, These results provide insights into the genetics of PTB and support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.
Risk management framework for outsourcing in the defence sector: a case from India
Venue: Int. J. Prod. Res.
Year: 2019

A nation automatically becomes strong if it has self sufficiency to fulfil its defence requirements. India is moving in the direction to attain self reliance in the defence sector. The aim of this paper is to analyse the critical issues faced in outsourcing by an organisation in Indian Defence Sector and extend easy, quick, complete and practically implementable risk mitigation strategies for enabling better decision making. In this research, a list of 20 risk criteria is finalised on the basis of the literature survey and expert's opinion. Analysis in this research shows that, three criteria which can seriously affect the outsourcing are Material shortage risk, Quality Risk and On time delivery risk. The paper employs an integrated framework which includes 'Grey Theory, Failure Mode and Effect Analysis (FMEA) and Risk Management Matrices' for a comprehensive risk management in the defence sector. The paper delivers a 10 point risk management action plan to counter the various problems faced during outsourcing in the defence sector, especially in the Indian context. In brief, this paper investigates the various important issues related to outsourcing, which are faced by organisations working in Indian Defence Sector and extends a practically implementable methodology to derive a practically amicable solution for the case organisation.
Partial Occlusion Modulates Contour Based Shape Encoding in Primate Area V4
Venue: The Journal of Neuroscience
Year: 2011

Past studies of shape coding in visual cortical area V4 have demonstrated that neurons can accurately represent isolated shapes in terms of their component contour features. However, rich natural scenes contain many partially occluded objects, which have "accidental" contours at the junction between the occluded and occluding objects. These contours do not represent the true shape of the occluded object and are known to be perceptually discounted. To discover whether V4 neurons differentially encode accidental contours, we studied the responses of single neurons in fixating monkeys to complex shapes and contextual stimuli presented either in isolation or adjoining each other to provide a percept of partial occlusion. Responses to preferred contours were suppressed when the adjoining context rendered those contours accidental. The observed suppression was reversed when the partial occlusion percept was compromised by introducing a small gap between the component stimuli. Control experiments demonstrated that these results likely depend on contour geometry at T junctions and cannot be attributed to mechanisms based solely on local color/luminance contrast, spatial proximity of stimuli, or the spatial frequency content of images. Our findings provide novel insights into how occluded objects, which are fundamental to complex visual scenes, are encoded in area V4. They also raise the possibility that the weakened encoding of accidental contours at the junction between objects could mark the first step of image segmentation along the ventral visual pathway.
The impact of relationship quality and supplier development on green supply chain integration: A mediation and moderation analysis
Year: 2018

This study aims to understand how relationship quality (top management support and relational governance) influences upstream Green Supply Chain Integration (GSCI) through supplier development. It also examines whether green drivers (customer and cost drivers) moderate the relationship between supplier development and upstream GSCI. The data used in this study are part of the international survey of high performance manufacturing. Structural equation modeling is applied to 285 samples collected from ten countries. The results show that supplier development fully mediates the relationship between top management support and upstream GSCI and partially mediates the relationship between relational governance and upstream GSCI. Additionally, both cost and customer drivers are found to significantly moderate the relationship between supplier development and upstream GSCI. This study contributes to theory and practice by combining and balancing the interests of suppliers in green integration, examining the enablers of upstream GSCI, and testing the contingent effect of supplier development on upstream GSCI.
Language Anxiety Levels in Urban, Suburban and Rural Secondary Grammar School Students
Year: 2012

The main purpose of this study is to investigate language anxiety levels in Polish secondary grammar school students from urban (N 223) suburban (N 48) and rural areas (N 122) The results show that rural students suffer from significantly higher levels of anxiety over the length of their secondary school education when compared to their urban and suburban peers. These results confirm the findings in the literature of the field, further demonstrating that social and educational deprivation of rural adolescents continues to prevail. However, this study also shows that the language anxiety levels of all the study participants significantly decrease towards the end of their secondary school education, irrespective of their residential location.
Data Driven Bottom up Asynchronous Federated Learning
Year: 2019

Federated learning (FL) is a distributed machine learning approach that can train a model on decentralized sets of data without the data ever leaving their host devices. The predominant FL model is the Google FL protocol (GFL) which is a topdown, synchronous and time division resource allocation system. It faces several issues, such as increased bias in selecting data and inability to scale beyond a small number of devices. In this paper, we design and investigate an alternative FL architecture to address the challenges of GFL. Our system is a bottom up, data driven, asynchronous resource allocation system, that maximizes key resources consisting of samples, computation, and bandwidth. The performance of our system is evaluated with the MNIST dataset on a networked prototype system. The experimental results show that our system improves fairness and scalability without compromising convergence efficiency.
A crypto safe haven against Bitcoin
Year: 2020

The design of Bitcoin is closely related to gold which has led to the idea that Bitcoin has gold like features such as being a store of value and a safe haven. However, given Bitcoin's extreme volatility investors may rather need a safe haven against Bitcoin. We hypothesize that stablecoins provide such a safe haven and analyze high frequency price changes of the largest stablecoins. We find that they act as safe havens, and Tether showing the strongest effect. The results demonstrate that Bitcoin investors seek out stablecoins when Bitcoin experiences extreme negative prices changes. Since stablecoins react to such price changes they are not stable at all times.
The Labour Market Effects of Immigration and Emigration in OECD Countries
Year: 2014

In this study, we quantify the labour market effects of migration flows in OECD countries during the 1990s based on a new global database on the bilateral stock of migrants, by education level. We simulate various outcomes using an aggregate model of labour markets, parameterised by a range of estimates from the literature. We find that immigration had a positive effect on the wages of less educated natives and it increased or left unchanged the average native wages. Emigration, instead, had a negative effect on the wages of less educated native workers and increased inequality within countries.
Geographies of vulnerability: a research note on human system adaptations to climate change in the Caribbean
Year: 2020

Caribbean small island communities are particularly vulnerable to the impacts of climate change. While climate policy is often formulated at the national level, climate impacts are first felt in local communities. This research note, which is part of a larger research project on climate change adaptation in Caribbean coastal communities, reviews the recent literature on human system adaptations to climate change at the local level in the Caribbean. It seeks to answer two questions: (1) How are human systems at the local level adapting to climate change in the Caribbean? (2) What challenges exist for local level climate adaptations in human systems that seek to protect vulnerable communities in the Caribbean? The studies reviewed highlight the importance of considering the vulnerability of sub populations, protecting their livelihoods, and pursuing institutional cooperation at the local and national levels. In interrogating whether the recommendations for community adaptations in the literature are transferrable to other geographies, this note further emphasizes that understanding the challenges that communities face in protecting human systems against climate change can help policy makers develop more effective adaptation strategies that centre climate justice.
A Unifying View on SMT Based Software Verification
Venue: Journal of Automated Reasoning
Year: 2017

After many years of successful development of new approaches for software verification, there is a need to consolidate the knowledge about the different abstract domains and algorithms. The goal of this paper is to provide a compact and accessible presentation of four SMT based verification approaches in order to study them in theory and in practice. We present and compare the following different "schools of thought" of software verification: bounded model checking, k induction, predicate abstraction, and lazy abstraction with interpolants. Those approaches are well known and successful in software verification and have in common that they are based on SMT solving as the back end technology. We reformulate all four approaches in the unifying theoretical framework of configurable program analysis and implement them in the verification framework CPAchecker. Based on this, we can present an evaluation that thoroughly compares the different approaches, where the core differences are expressed in configuration parameters and all other variables are kept constant (such as parser front end, SMT solver, used theory in SMT formulas) We evaluate the effectiveness and the efficiency of the approaches on a large set of verification tasks and discuss the conclusions.