nils-herrmann/clean_scirepeval huggingface dataset:
A nanostructure voltammetric platform amplified with ionic liquid for determination of tert butylhydroxyanisole in the presence kojic acid
Venue: Journal of Food Measurement and Characterization
Year: 2019

The electrochemical behavior of tert butylhydroxyanisole antioxidant was investigated at a surface of carbon paste electrode amplified with MgO nanoparticle and n methyl 3 butylimidazolium bromide as conductive binder (CPE/MgO NPs /M3BIBr) The oxidation voltammogram of tert butylhydroxyanisole was shown an irreversible signal with a diffusion controlled system. Using pH investigation data, we suggested a possible mechanism for electro oxidation of tert butylhydroxyanisole. The oxidation current varied linearly with tert butylhydroxyanisole concentration in the range 0.1 700 uM with detection limit 0.04 uM. The CPE/MgO NPs /M3BIBr was resolved overlapping signal of tert butylhydroxyanisole and kojic acid as two important antioxidants for the first time. The CPE/MgO NPs /M3BIBr was applied to the analysis of tert butylhydroxyanisole in food samples such as edible oil and chilli sauce.
Multiple Parents/Multiple Fathers
Year: 2007

Multiple parents, especially multiple fathers, are a social reality but not a legal category. The assumption that every child has, or should have, two, but only two, parents remains a core operating assumption of family law. Yet at the same time, our knowledge of the existence of multiple fathers, whether birthfathers, stepfathers, psychological fathers or other categories, has found some reflection in cases that have granted some relational rights to fathers who do not fill the single place allotted for "legal father." In this Article, Professor Dowd proposes that it is time to think not if, but how, to recognize multiple fathers within the broader need to recognize multiple parents. Additionally, Professor Dowd explores how multiple parenthood, and particularly multiple fatherhood, might work. The author determines that the general principle of multiple fatherhood is supported by the channelling function of family law, as well as the law's role to support affirmative cultural change. Part I of this Article looks at the context of fatherhood. Part II suggests the range of possible models of multiple fatherhood, and advocates a model of shared but unequal coparenting with a core father. Part III considers some issues regarding implementation of this model. Professor Dowd concludes by returning to the law's channelling function and measuring the law's role in relation to culture.
Volatility forecasting using related markets' information for the Tokyo stock exchange
Year: 2020

Due to lack of information, volatility cannot be estimated via a high frequency approach when markets are non trading. In this paper, we focus on volatility forecasting for the Tokyo Stock Exchange (TSE) using high frequency data of related assets traded in international markets when TSE is closed. We use the heterogenous autoregressive model to identify an optimal approach of this additional information for the ten largest TSE listed stocks, TOPIX and Nikkei 225. The usefulness of harnessing global and neighbour market information in forecasting the TSE market volatility is confirmed through in depth empirical analysis. Our findings have important implications for investors and policy makers.
Learning to count: A deep learning framework for graphlet count estimation
Venue: Network Science
Year: 2020

Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time for k node graphlets in a network of size N. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real world graphs for all 3 4 and 5 node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state of the art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real world graphs with competitive accuracy.
The periaqueductal gray and primal emotional processing critical to influence complex defensive responses, fear learning and reward seeking
Venue: Neuroscience Biobehavioral Reviews
Year: 2017

HIGHLIGHTSThe PAG works as a midbrain hub to provide primal emotional processing.The PAG influences both aversive and appetite responses.The PAG influences fear cognition, complex motor patterns related to defensive responses and fear learning processes.The PAG mediates reward seeking behavior influencing both arousal and seeking motivation.  The periaqueductal gray (PAG) has been commonly recognized as a downstream site in neural networks for the expression of a variety of behaviors and is thought to provide stereotyped responses. However, a growing body of evidence suggests that the PAG may exert more complex modulation of a number of behavioral responses and work as a unique hub supplying primal emotional tone to influence prosencephalic sites mediating complex aversive and appetitive responses. Of particular relevance, we review how the PAG is involved in influencing complex forms of defensive responses, such as circa strike and risk assessment responses in animals. In addition, we discuss putative dorsal PAG ascending paths that are likely to convey information related to threatening events to cortico hippocampal amygdalar circuits involved in the processing of fear learning. Finally, we discuss the evidence supporting the role of the PAG in reward seeking and note that the lateral PAG is part of the circuitry related to goal oriented responses mediating the motivation to hunt and perhaps drug seeking behavior.
Insights into Ethanol Coupling over Hydroxyapatite using Modulation Excitation Operando Infrared Spectroscopy
Year: 2020

The coupling of biomass derived ethanol to n butanol is a topic of contemporary interest. Indeed, n butanol can not only be used as a higher energy density fuel additive, it is also a key component in perfumes and serves as a solvent for paints and dyes. Hydroxyapatite (HAP) emerged in the literature as a promising catalysts for this transformation, with n butanol selectivity reaching ~75 at 10 ethanol conversion. However, the molecular level mechanism for this reaction is still unclear and several mechanistic questions remain unanswered. Here, we use diffuse reflectance infrared Fourier Transform spectroscopy, coupled with mass spectrometry following a modulation excitation approach (ME DRIFTS MS) that enables us to better understand the dynamic processes involved. Our approach allows for a vibrational characterization of the active surface species and the formulation of a consistent mechanism. Based on our experimental observations, Ca2+/OH can be put forward as the main active site for the aldol condensation. POH/OH acid base pair is proposed as the active site for the Meerwein Ponndorf Verley (MPV) direct hydrogen transfer of the aldol condensation product, crotonaldehyde.
A Programmable Heterogeneous Microprocessor Based on Bit Scalable In Memory Computing
Venue: IEEE Journal of Solid State Circuits
Year: 2020

In memory computing (IMC) addresses the cost of accessing data from memory in a manner that introduces a tradeoff between energy/throughput and computation signal to noise ratio (SNR) However, low SNR posed a primary restriction to integrating IMC in larger, heterogeneous architectures required for practical workloads due to the challenges with creating robust abstractions necessary for the hardware and software stack. This work exploits recent progress in high SNR IMC to achieve a programmable heterogeneous microprocessor architecture implemented in 65 nm CMOS and corresponding interfaces to the software that enables mapping of application workloads. The architecture consists of a 590 Kb IMC accelerator, configurable digital near memory computing (NMC) accelerator, RISC V CPU, and other peripherals. To enable programmability, microarchitectural design of the IMC accelerator provides the integration in the standard processor memory space, area and energy efficient analog to digital conversion for interfacing to NMC, bit scalable computation (1 8 b) and input vector sparsity proportional energy consumption. The IMC accelerator demonstrates excellent matching between computed outputs and idealized software modeled outputs, at 1b TOPS/W of 192|400 and 1b TOPS/mm2 of 0.60|0.24 for MAC hardware, at $V_{DD} of 1.2|0.85 V, both of which scale directly with the bit precision of the input vector and matrix elements. Software libraries developed for application mapping are used to demonstrate CIFAR 10 image classification with a ten layer CNN, achieving accuracy, throughput, and energy of 89.3%|92.4% 176|23 images/s, and $5.31\mid 105.2~\mu \text{J} /image, for 1|4 b quantization levels.
THE CHALLENGES OF THE DIGITAL ECONOMY
Year: 2020

The problem of transforming the economy into a digital one is discussed. The main challenges the digital economy poses to society, the state and business in the technological field are given. The characteristics of the digitalization of the economy are presented, including the rapid accumulation of digital data, the creation of a global information infrastructure, the formation of a new information sphere of people's lives, the exponential increase in the rate of introduction of digital technologies and the emergence of new scientific and technical directions, the radical change in social relations, the transformation of old models of activity, wide geoinformatization. Examples of digital economy projects of developed countries Germany, USA, Great Britain, Japan are given. The conclusion is drawn about the main emphasis of the digital economy programs adopted by the developed countries. The stages of industrial revolutions, showing a change in technological waves from the advent of a steam engine in Industry 1, the automotive industry and electrification in Industry 2, the development of cybernetics in Industry 3, to the advent of digital production and the merging of the virtual and real worlds in Industry 4.0, are presented. The end to end technologies defined in the "Digital Economy of the Russian Federation" national program are listed. Four groups of new scientific and technical areas serving as the basis for the planned transformation of the economy into the digital one (digitalization, geoinformatization, datification and materialization) are identified. Digitalization helps create a digital environment to support all activities. Geoinformatization allows building more effective models of activity due to the addition of spatial positioning technologies to the functional areas of information systems. Datification allows the introduction of new adoption models based on big data analysis. Materialization implements digital production of objects without direct human participation and traditional material technologies.
Interference in Language Processing Reflects Direct Access Memory Retrieval: Evidence from Drift Diffusion Modeling
Venue: CogSci
Year: 2019

Many studies on memory retrieval in language processing have identified similarity based interference as a key determinant of comprehension. The broad consensus is that similarity based interference reflects erroneous retrieval of a non target item that matches some of the retrieval cues. However, the mechanisms responsible for such effects remain debated. Activation based models of retrieval (e.g. Lewis Vasishth, 2005) claim that any differences in processing difficulty due to interference in standard RT measures and judgments reflect differences in the speed of retrieval (i.e. the amount of time it takes to retrieve a memory item) But this claim is inconsistent with empirical data showing that retrieval time is constant due to the use of a direct access procedure (e.g. McElree, 2000, 2006) According to direct access accounts, differences in judgments or RTs due to interference arise from differences in the quality or availability of the candidate memory representations, rather than differences in retrieval speed. To adjudicate between these accounts, we employed a novel methodology that combined a high powered (N 200) twoalternative forced choice study on interference effects with drift diffusion modeling to disassociate the effects of retrieval speed and representation quality. Results showed that the presence of a distractor that matched some of the retrieval cues lowered asymptotic accuracy, reflecting an effect of representation quality, but did not affect retrieval speed, consistent with a direct access procedure. These results suggest that the differences observed in RTs and judgment studies reflect differences in the ease of integrating the retrieved item back into the current processing stream, rather than differences in retrieval speed.
Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks
Venue: 2017 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2017

Wireless mesh network is prevalent for providing a decentralized access for users. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep belief network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low pass component of network traffic that describes the long range dependence of itself. Then a prediction model is built by learning a deep belief network from the extracted low pass component. Otherwise, for the rest high pass component that expresses the gusty and irregular fluctuations of network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high pass component by the built model. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
Discovering Hypernymy in Text Rich Heterogeneous Information Network by Exploiting Context Granularity
Venue: CIKM
Year: 2019

Text rich heterogeneous information networks (text rich HINs) are ubiquitous in real world applications. Hypernymy, also known as is a relation or subclass of relation, lays in the core of many knowledge graphs and benefits many downstream applications. Existing methods of hypernymy discovery either leverage textual patterns to extract explicitly mentioned hypernym hyponym pairs, or learn a distributional representation for each term of interest based its context. These approaches rely on statistical signals from the textual corpus, and their effectiveness would therefore be hindered when the signals from the corpus are not sufficient for all terms of interest. In this work, we propose to discover hypernymy in text rich HINs, which can introduce additional high quality signals. We develop a new framework, named HyperMine, that exploits multi granular contexts and combines signals from both text and network without human labeled data. HyperMine extends the definition of "context" to the scenario of text rich HIN. For example, we can define typed nodes and communities as contexts. These contexts encode signals of different granularities and we feed them into a hypernymy inference model. HyperMine learns this model using weak supervision acquired based on high precision textual patterns. Extensive experiments on two large real world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity. We further show a case study that a high quality taxonomy can be generated solely based on the hypernymy discovered by HyperMine.
Improving visual speech synthesis using Decision Tree Models
Year: 2016

Improving Visual Speech Synthesis using Decision Tree Models C.F. Rademan Department of Electrical and Electronic Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa. Thesis: MScEng (Elec) March 2016 Visual speech synthesis is essential for believable virtual character interaction. Traditionally, animation artists recreate the oral motions expected from speech utterances. In response, we present decision tree based clustering techniques which are employed in automating visual speech animation. This is achieved using a small dataset of phoneticallyannotated audiovisual speech. Our work focuses on extending existing tree based clustering algorithms by improving on the modelling of coarticulation effects. This is accomplished by capturing the motion of natural speech segments, referred to as dynamic visemes, and conserving their parameters during clustering and speech synthesis. Dynamic visemes are defined as the trajectories of oral features segmented by triphone boundaries. By applying simple search and concatenation criteria, our visual speech synthesis system uses decision trees to better predict which dynamic visemes to use. Experimentation guided all design decisions, suggesting which oral features were of greatest importance, identifying an appropriate dynamic viseme length and finding an effective interpolation method for conserving coarticulation. We evaluate the performance of our visual speech synthesis models by computing squared error differences between synthesised and measured feature trajectories. Perceptual tests also asked participants to compare virtual characters animated by model outputs. Both measured and perceptual tests show that our approaches lead to a clear improvement over a comparable baseline. Through our research, we intended on making speech synthesis more accessible. Therefore, the conversational agents are based on the freely available MakeHuman and Blender software components. The customised oral feature motion capture system is also easily reproduced and requires only consumer grade recording equipment. ii Stellenbosch University https:/scholar.sun.ac.za
Revision of failed hemiarthroplasty and anatomic total shoulder arthroplasty to reverse total shoulder arthroplasty.
Venue: Journal of shoulder and elbow surgery
Year: 2019

The impending burden of revision shoulder arthroplasty has increased interest in outcomes of revision procedures. Revision of failed anatomic arthroplasty to reverse total shoulder arthroplasty has shown promise alongside concerning complication rates.  Patients who underwent revision shoulder arthroplasty during a 7 year period at a tertiary care health system were identified. Presurgical and operative data were analyzed for 110 patients who met inclusion and exclusion criteria. Patients were contacted at a mean follow up of 57 26 months (range, 23 113 months) from revision surgery for functional outcomes scores, reoperations, and implant survival.  Implant survival was 92% at 2 years and 74% at 5 years. Mean American Shoulder and Elbow Surgeons score, Single Assessment Numerical Evaluation score, and visual analog scale pain scores were 63 24 (range, 5 97) 60 25 (range, 0 100) and 2.9 2.9 (range, 0 10) respectively. Seventy percent of patients were "very satisfied" or "satisfied with their outcome. Complications occurred in 18 patients (20% and 10 patients (11% underwent reoperation.  Modest patient results and satisfaction can be achieved with revision of a failed anatomic arthroplasty to a reverse total shoulder arthroplasty. As is typical of revision surgery, complications are common and can compromise results. Further study is needed to identify factors that may contribute to successful outcomes.
Spin scattering and noncollinear spin structure induced intrinsic anomalous Hall effect in antiferromagnetic topological insulator MnBi2Te4
Venue: Physical Review Research
Year: 2019

\mathrm{MnBi_2Te_4} has recently been established as an intrinsic antiferromagnetic (AFM) topological insulator and predicted to be an ideal platform to realize quantum anomalous Hall (QAH) insulator and axion insulator states. We performed comprehensive studies on the structure, nontrivial surface state and magnetotransport properties of this material. Our results reveal an intrinsic anomalous Hall effect arising from a non collinear spin structure for the magnetic field parallel to the $c$ axis. We also observed remarkable negative magnetoresistance under arbitrary field orientation below and above the Neel temperature (T$_N$ providing clear evidence for strong spin fluctuation driven spin scattering in both the AFM and paramagnetic states. Further, we found that the nontrivial surface state opens a large gap ~85 meV) even at temperatures far above T$_N$ 25K. These findings demonstrate that the bulk band structure of \mathrm{MnBi_2Te_4} is strongly coupled to the magnetic structure and that a net Berry curvature in momentum space can be created in a canted AFM state. In addition, our results imply that the gap opening in the surface states is intrinsic, likely caused by the strong spin fluctuations near the surface layers.
Influentials, Imitables, or Susceptibles? Virality and Word of Mouth Conversations in Online Social Networks
Year: 2016

Motivated by the rise of social media platforms that achieve a fusion of content and community, we consider the role of word of mouth communications (WOM) structured through a network. Using a data set from YouTube, we examine how cascades of WOM interactions enhance the popularity of videos. We first estimate the impact of channel influence and other network parameters in initiating WOM communications. The probit estimation considers the selection effect in videos that are likely to be associated with a greater propensity to trigger WOM. We find that factors related to a channel's ability to be a connector and a translator is most likely to result in the incidence of WOM. We then examine how cascades of WOM conversations have persistent impacts on subsequent video popularity. Empirically, the main issue here is heterogeneity in the epidemic potential of a video. Since the threshold might vary across videos, we use a finite mixture model. We also conduct a simultaneous estimation using latent instrumental variables to address endogeneity from unobservables. Our research has implications for researchers and practitioners by highlighting how WOM travels through networks of influence and susceptibility in disseminating awareness, and holds insights in regard to designing social recommendation systems and identifying trending topics in social media.