nils-herrmann/clean_scirepeval huggingface dataset:
Deep Multimodal Clustering for Unsupervised Audiovisual Learning
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

The seen birds twitter, the running cars accompany with noise, etc. These naturally audiovisual correspondences provide the possibilities to explore and understand the outside world. However, the mixed multiple objects and sounds make it intractable to perform efficient matching in the unconstrained environment. To settle this problem, we propose to adequately excavate audio and visual components and perform elaborate correspondence learning among them. Concretely, a novel unsupervised audiovisual learning model is proposed, named as Deep Multimodal Clustering (DMC),that synchronously performs sets of clustering with multimodal vectors of convolutional maps in different shared spaces for capturing multiple audiovisual correspondences. And such integrated multimodal clustering network can be effectively trained with max margin loss in the end to end fashion. Amounts of experiments in feature evaluation and audiovisual tasks are performed. The results demonstrate that DMC can learn effective unimodal representation, with which the classifier can even outperform human performance. Further, DMC shows noticeable performance in sound localization, multisource detection, and audiovisual understanding.
Women as Canang Sari Street Vendors in Bali
Year: 2016

Every Hindu family in Bali dedicates offerings of Canang Sari on a daily basis. The provision of the offering is not always created by them, but they buy canang sari. This has turned canang sari into a market commodity. Therefore, women have emerged as canang sari street vendors in the several towns in Bali. This study examines the reasons why women do this job, especially in Singaraja Regency. The approach of this research was qualitative study which focused on critical social theory. The results show that the reasons women do such business activities not solely because canang sari street vendors is an informal economic sector, but also it is related to the ownership of economic, social, cultural, and symbolic capitals. This reason is strengthened by the condition of Balinese who have affected by McDonaldization society so they prefer to buy canang sari rather than making it their own.
A new simulation method for single photon avalanche diode
Venue: 2015 China Semiconductor Technology International Conference
Year: 2015

Presently TCAD simulation of single photon avalanche diode (SPAD) cannot directly obtain the important performances of SPAD. In this paper, we propose a new simulation method combining TCAD simulation with the numerical computation, which can exactly predict the statistical performances of SPAD such as Quantum Detection Efficiency (QDE) Dark Count Rate (DCR) and Afterpulsing Probability (AP) This new simulation method is useful in optimizing SPAD design with higher QDE and lower DCR.
Nitrous oxide and methane fluxes vs. carbon, nitrogen and phosphorous burial in new intertidal and saltmarsh sediments.
Venue: The Science of the total environment
Year: 2012

Carbon (C) nitrogen (N) and phosphorous (P) burial rates were determined within natural saltmarsh (NSM) and 'managed realignment' (MR) sediments of the Blackwater estuary, UK. Methane (CH(4) and nitrous oxide (N(2)O) fluxes were measured along with their ability to offset a portion of the C burial to give net C sequestration. C and N densities (Cr and Nr) of NSM sediments (0.022 and 0.0019 g cm( 3) are comparable to other UK NSM sediments. Less vegetationally developed MR sediments have lower Cr and Nr (0.012 and 0.0011 g cm( 3) while the more vegetationally developed sites possess higher Cr and Nr (0.023 and 0.0030 g cm( 3) than NSM. Both NSM and MR areas were small CH(4) (0.10 0.40 g m( 2)yr( 1) and N(2)O (0.03 0.37 g m( 2) yr( 1) sources. Due to their large Global Warming Potentials, even these relatively small greenhouse gas (GHG) fluxes reduced the net C sequestration within MR marshes by as much as 49% but by only 2% from NSM. Potential MR areas within the Blackwater estuary (29.5 km(2) saltmarsh and 23.7 km(2) intertidal mudflat) could bury 5478 t C yr( 1) and 695.5 t N yr( 1) with a further 476t N yr( 1) denitrified. The saltmarsh MR would also sequester 139.4 t Pyr( 1) GHG fluxes would reduce the C burial benefit by 24% giving a C sequestration rate of 4174 t Cyr( 1) Similar areas within the Humber estuary (74.95 km(2) could bury 3597 t Cyr( 1) and 180 t N yr( 1) with a further 442 t Nyr( 1) denitrified. GHG fluxes would reduce the C burial benefit by 31% giving a C sequestration rate of 2492 t C yr( 1) Overall, MR sites provide sustainable coastal defence options with significant biogeochemical value and, despite being net sources of CH(4) and N(2)O, can sequester C and reduce estuarine nutrient loads.
The structure and vulnerability of a drug trafficking collaboration network
Venue: Soc. Networks
Year: 2017

Prior research has established that collaboration networks exhibit heavy tailed degree distributions, assortative degree mixing, and large clustering coefficients. Using court record data, we assess these properties in a collaboration network among heroin traffickers. Consistent with prior research, we find an exponential degree distribution and strong local clustering. However, the traffickers mix dissortatively by degree rather than assortatively. Using a graph sampling method, we show that a consequence of dissortative mixing is that targeted vertex removals have a greater impact on the connectivity and cohesion of the trafficking network. We also note the importance of degree mixing for characterizing and identifying topological weaknesses.
The Emergence and Rise of Industry 4.0 Viewed through the Lens of Management Fashion Theory
Venue: Administrative Sciences
Year: 2019

The Industry 4.0 (I4.0) concept is concerned with the fourth industrial revolution in manufacturing, in which technological trends such as digitalization, automation and artificial intelligence are transforming production processes. Since the concept's introduction at the Hannover Fair in Germany in 2011, I4.0 has enjoyed a meteoric rise in popularity and is currently high on the agenda of governments, politicians and business elites. In light of these observations, some commentators have asked the question of whether I4.0 is a concept that is hyped up and possibly just the latest in a long line of fashionable management concepts introduced over the course of the last few decades. Therefore, the aim of this paper is to provide a critical outside in look at the emergence and rise of I4.0. Theoretically, these processes are viewed through the lens of management fashion, a theoretical perspective well suited to examinations of evolutionary trajectories of management concepts and ideas. The findings indicate that the I4.0 concept has quickly become highly popular and is dominating much of the popular management discourse. The concept has migrated out of the specialized manufacturing discourse to become a more general concept with mainstream appeal and applicability, evidenced by a multitude of neologisms such as Work 4.0 and Innovation 4.0. The numbers 4.0 have spread in a meme like fashion, evidenced by the fact that the combination of a noun and the numbers 4.0 are used to signal and usher in discussions about the future of business and society. While there is much evidence that clearly shows that the concept has had a wide ranging impact at the discursive level, the currently available research is less clear about what impact the concept has had so far on industries and organizations worldwide.
Core Skill Development of New Media Marketers Based on Job Competency Model
Venue: 2019 International Joint Conference on Information, Media and Engineering (IJCIME)
Year: 2019

Under the new circumstances of "Internet+" new media market not only requires market segmentation and integration, but is also in urgent need of intellectuals, which poses a new challenge to college educations. According to the latest data on recruitment sites, there emerged a huge gap between supply and demand of intellectuals in the new media area. This scarcity urges colleges and universities to cultivate professional intellectuals that meet the needs of market. The author creates a "T" model for new media marketing intellectuals, providing possible training strategies for "T" model intellectual cultivation, and contributing to college curriculum reform in cultivating new media intellectuals.
The Blackwell Guide to Ethical Theory
Year: 2013

Notes on Editors and Contributors vii Introduction 1 Hugh LaFollette and Ingmar Persson Part I Metaethics and Moral Epistemology 1 Moral Realism 17 Michael Smith 2 Relativism 43 Simon Blackburn 3 Moral Agreement 59 Derek Parfit 4 Divine Command Theory 81 Philip L. Quinn 5 Moral Intuition 103 Jeff McMahan Part II Factual Background of Ethics 6 Ethics and Evolution 123 Richard Joyce 7 Psychological Egoism 148 Elliott Sober 8 The Science of Ethics 169 Ron Mallon and John M. Doris 9 The Relevance of Responsibility to Morality 197 Ingmar Persson Part III Normative Ethics 10 Act Utilitarianism 221 R.G. Frey 11 Rule Consequentialism 238 Brad Hooker 12 Nonconsequentialism 261 F.M. Kamm 13 Intuitionism 287 David McNaughton and Piers Rawling 14 Kantianism 311 Thomas E. Hill Jr 15 Contractarianism 332 Geoffrey Sayre McCord 16 Rights 354 L.W. Sumner 17 Libertarianism 373 Jan Narveson 18 Virtue Ethics 394 Michael Slote 19 Capability Ethics 412 Ingrid Robeyns 20 Feminist Ethics 433 Alison M. Jaggar 21 Continental Ethics 461 William R. Schroeder Index 487
Smart Living by Sustainable Community's Initiatives in the Historic Town of the Himalayas Shimla
Year: 2020

Shimla often referred to as the summer capital of British, is a unique historic town that demonstrates smart living for the context. The local communities are key for the sustainable initiatives taken up by them over the years, strongly underpinned by religion. Shimla has the challenge of being environmentally sensitive, rich in biodiversity and steep land gradient. The city took shape with British establishing Shimla, as the summer capital; earlier it was a cantonment base with few villages. With altitude, the gradual changes in the environment are reflected by the local communities a unique feature of this historic town. Shimla developed as administration capital, continues to nurture the socio cultural strength of the local communities and that is strongly depicted in their lifestyles and cultural spaces. The chapter enumerates changing nature and role of the local communities catering to the needs of rapid urbanisation and development especially in the recent past and how that has got translated in the ecology of the place integrating the conservation and planning perspectives in the context of sustainable development in the Himalayas; drawing upon interdisciplinary approaches linking with the approach to smart living at large.
How live streaming influences purchase intentions in social commerce: An IT affordance perspective
Venue: Electron. Commer. Res. Appl.
Year: 2019

As a new form of social commerce, live streaming shopping is becoming increasingly popular among Chinese consumers, which has aroused great interest among practitioners and researchers. Building a theoretical model from the perspective of IT affordance, our study examines how live streaming influences social commerce customers' purchase intentions in China. We empirically measure the model by surveying customers who have shopped via live streaming shopping platforms including Taobao.com JD.com Mogujie.com and Sina Microblog. Our results show that visibility affordance, metavoicing affordance, and guidance shopping affordance can influence customer purchase intention through live streaming engagement. Our study highlights the importance of understanding the implications of IT affordance for social commerce.
Hog1 signalling in the emerging multidrug resistant fungal pathogen Candida auris
Venue: Access Microbiology
Year: 2019

The emerging fungal pathogen Candida auris is responsible for recent global outbreaks caused by genetically divergent clades that, alarmingly, display multi drug resistance to currently available antifungals. Although not much is known about the pathobiology of this emerging pathogen, we recently found that the Hog1 stress activated protein kinase (SAPK) in C. auris promotes stress resistance and virulence. SAPKs are evolutionary conserved pathways employed by fungi to promote survival in hostile environments. The aim of this project is to characterise the upstream signalling proteins that relay stress signals to C. auris Hog1. In all eukaryotes, SAPK pathways are comprised of three tiers of kinases with upstream MAPKKK and MAPKKs relaying signals to the terminal SAPK. However, unique to fungi and plants, is an upstream two component related signalling cascade that transmits signals to the SAPK module, comprising of a histidine kinase, a phosphorelay protein and a response regulator. In C. auris, the proteins that regulate Hog1 activation are unknown. However, bioinformatics analyses of the C. auris genome has allowed for the identification of putative homologues of key Hog1 regulatory proteins including the Pbs2 MAPKK, the Ssk2 MAPKKK, the Ssk1 response regulator, the Ypd1 phosphorelay protein and the Sln1 and Nik1 histidine kinases. Mutational analyses is currently underway to dissect the role of these proteins in Hog1 regulation. Understanding how the Hog1 SAPK is regulated in C. auris may facilitate the identification of fungal specific anti fungal targets that can be exploited to tackle the threat of this emerging drug resistant pathogen.
Age and Sex Differences in Self Stigma and Public Stigma Concerning Depression and Suicide in Men
Venue: Stigma and Health
Year: 2019

Men are more likely to take their own lives, but less likely to seek professional help when they are depressed, especially as they age. Important determinants of help seeking are perceived public and self stigma concerning depression and suicide. Yet little research has explored sex and age differences in public stigma of men's depression and suicide, and in self stigma of depression. Our study examined this neglected topic with a nationally representative survey of 452 male and 449 female (N 901) Canadian adults ranging in age from 18 83. We examined age (18 34; 35 49; 50 64; 65+ and sex differences in public stigma of men's depression, public stigma of men's suicide, and self stigma. Results demonstrated that, as expected, men were more likely to endorse most aspects of public stigma of men's depression and suicide. Our data also showed that public stigma was highest in younger adults, with young men demonstrating especially high levels. Our hypotheses that self stigma would be largely independent of sex, and higher in younger adults, were also confirmed. In conclusion, although young men endorsed the highest levels of public stigma, overall levels of public stigma were low. This suggests that antistigma efforts to increase the use of mental health services might be most effective with a focus on self stigma in young men and women.
Feature Whitening via Gradient Transformation for Improved Convergence
Venue: ArXiv
Year: 2020

Feature whitening is a known technique for speeding up training of DNN. Under certain assumptions, whitening the activations reduces the Fisher information matrix to a simple identity matrix, in which case stochastic gradient descent is equivalent to the faster natural gradient descent. Due to the additional complexity resulting from transforming the layer inputs and their corresponding gradients in the forward and backward propagation, and from repeatedly computing the Eigenvalue decomposition (EVD) this method is not commonly used to date. In this work, we address the complexity drawbacks of feature whitening. Our contribution is twofold. First, we derive an equivalent method, which replaces the sample transformations by a transformation to the weight gradients, applied to every batch of B samples. The complexity is reduced by a factor of S=(2B) where S denotes the feature dimension of the layer output. As the batch size increases with distributed training, the benefit of using the proposed method becomes more compelling. Second, motivated by the theoretical relation between the condition number of the sample covariance matrix and the convergence speed, we derive an alternative sub optimal algorithm which recursively reduces the condition number of the latter matrix. Compared to EVD, complexity is reduced by a factor of the input feature dimension M. We exemplify the proposed algorithms with ResNet based networks for image classification demonstrated on the CIFAR and Imagenet datasets. Parallelizing the proposed algorithms is straightforward and we implement a distributed version thereof. Improved convergence, in terms of speed and attained accuracy, can be observed in our experiments.
Mobility Management With Transferable Reinforcement Learning Trajectory Prediction
Venue: IEEE Transactions on Network and Service Management
Year: 2020

Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve network performance. In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction algorithm by combining the Long Short Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model training procedure. We further develop a group user trajectory predictor to reduce prediction calculation overheads of users with similar movement patterns. To validate the impact of the proposed mobility management approach, we present a virtual reality (VR) service migration scheme built on the top of the proactive handover mechanism that benefits from trajectory predictions. Experiment results validate our predictor's outstanding accuracy and its impacts on enhancing handover and service migration performance to provide quality of service assurance.
Global food losses and food waste: extent, causes and prevention
Year: 2011

This publication is based on studies carried out from August 2010 to January 2011 by The Swedish Institute for Food and Biotechnology (SIK) on request from the Food and Agriculture Organization of the United Nations (FAO) The two studies on global food losses (one for high/medium income countries and one for low income countries) have been carried out to serve as a basis for the international congress Save Food! 16 17 May 2011, at the international packaging industry fair Interpack2011 in Dusseldorf, Germany. Save Food! has been co organized by Interpack2011 and FAO. Save Food! aims at awareness raising on global food losses and waste, and on the impact of these on poverty and hunger in the world, as well as on climate change and on the use of natural resources.
Video denoising by online 3D sparsifying transform learning
Venue: 2015 IEEE International Conference on Image Processing (ICIP)
Year: 2015

Exploiting the sparsity of signals in an adaptive dictionary or transform domain benefits various applications in image/video processing. As opposed to synthesis dictionary learning, transform learning allows for cheap computations, and has been demonstrated to perform well in applications such as image denoising. Very recently, we proposed methods for online sparsifying transform learning, which are particularly useful for processing large scale or streaming data. Online transform learning has good convergence guarantees and enjoys a much lower computational cost than online synthesis dictionary learning. In this work, we present a video denoising framework based on online 3D spatio temporal sparsifying transform learning. The proposed scheme has low computational and memory costs, and can potentially handle streaming video. Our numerical experiments show promising performance for the proposed video denoising method compared to popular prior or state of the art methods.
NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug target interactions
Venue: Bioinform.
Year: 2019

Accurately predicting drug target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.  Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks to mine large scale graph data and greatly improve the performance of many network related prediction tasks, we develop a new nonlinear end to end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state of the art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g. compound protein binding affinity data) All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning. Availability and implementation: The source code and data used in NeoDTI are available at: https:/github.com/FangpingWan/NeoDTI.
A data driven robust optimization approach to scenario based stochastic model predictive control
Venue: Journal of Process Control
Year: 2019

Stochastic model predictive control (SMPC) has been a promising solution to complex control problems under uncertain disturbances. However, traditional SMPC approaches either require exact knowledge of probabilistic distributions, or rely on massive scenarios that are generated to represent uncertainties. In this paper, a novel scenario based SMPC approach is proposed by actively learning a data driven uncertainty set from available data with machine learning techniques. A systematical procedure is then proposed to further calibrate the uncertainty set, which gives appropriate probabilistic guarantee. The resulting data driven uncertainty set is more compact than traditional norm based sets, and can help reducing conservatism of control actions. Meanwhile, the proposed method requires less data samples than traditional scenario based SMPC approaches, thereby enhancing the practicability of SMPC. Finally the optimal control problem is cast as a single stage robust optimization problem, which can be solved efficiently by deriving the robust counterpart problem. The feasibility and stability issue is also discussed in detail. The efficacy of the proposed approach is demonstrated through a two mass spring system and a building energy control problem under uncertain disturbances.