nils-herrmann/clean_scirepeval huggingface dataset:
Real Time Physiological Measurement and Visualization Using a Synchronized Multi camera System
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Remote physiological measurement has widespread implications in healthcare and affective computing. This paper presents an efficient system for remotely measuring heart rate and heart rate variability using multiple low cost digital cameras in real time. We combine an RGB camera, monochrome camera with color filter and a thermal camera to recover the blood volume pulse (BVP) We show that using multiple cameras in synchrony yields the most accurate recovery of the BVP signal. The RGB combination is not optimal. We show that the thermal camera improves performance of measurement under dynamic ambient lighting but the thermal camera alone is not enough and accuracy can be improved by adding more spectral channels. We present a real time prototype that allows accurate physiological measurement combined with a novel user interface to visualize changes in heart rate and heart rate variability. Finally, we propose how this system might be used for applications such as patient monitoring.
Textual Affect Communication and Evocation Using Abstract Generative Visuals
Venue: IEEE Transactions on Human Machine Systems

In order to facilitate interaction in computer mediated communication and enrich user experience in general, we introduce a novel textual emotion visualization approach, grounded in generative art and evocative visuals. The approach is centered on the idea that affective computer systems should be able to relate to, communicate, and evoke human emotions. It maps emotions identified in the text to evocative abstract animation. We examined two visualizations based on our approach and two common textual emotion visualization techniques, chat emoticons and avatars, along three dimensions: emotion communication, emotion evocation, and overall user enjoyment. Our study, organized as repeated measures within subject experiment, demonstrated that in terms of emotion communication, our visualizations are comparable with emoticons and avatars. However, our main visualization based on abstract color, motion, and shape proved to be the best in evoking emotions. In addition, in terms of the overall user enjoyment, it gave results comparable with emoticons, but better than avatars.
Deep Relative Attributes
Venue: ACCV

Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an additional layer (ranking layer) that learns to rank the images based on these features. We adopt an appropriate ranking loss to train the whole network in an end to end fashion. Our proposed method outperforms the baseline and state of the art methods in relative attribute prediction on various coarse and fine grained datasets. Our qualitative results along with the visualization of the saliency maps show that the network is able to learn effective features for each specific attribute. Source code of the proposed method is available at this https URL.
End to End Localization and Ranking for Relative Attributes
Venue: ECCV

We propose an end to end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute's features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end to end framework also significantly speeds up processing and is much faster than previous methods. We show state of the art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network's ability to learn meaningful image patches.
Deep Relative Attributes
Venue: IEEE Transactions on Multimedia

Relative attribute (RA) learning aims to learn the ranking function describing the relative strength of the attribute. Most of current learning approaches learn a linear ranking function for each attribute by use of the hand crafted visual features. Different from the existing study, in this paper, we propose a novel deep relative attributes (DRA) algorithm to learn visual features and the effective nonlinear ranking function to describe the RA of image pairs in a unified framework. Here, visual features and the ranking function are learned jointly, and they can benefit each other. The proposed DRA model is comprised of five convolutional neural layers, five fully connected layers, and a relative loss function which contains the contrastive constraint and the similar constraint corresponding to the ordered image pairs and the unordered image pairs, respectively. To train the DRA model effectively, we make use of the transferred knowledge from the large scale visual recognition on ImageNet  to the RA learning task. We evaluate the proposed DRA model on three widely used datasets. Extensive experimental results demonstrate that the proposed DRA model consistently and significantly outperforms the state of the art RA learning methods. On the public OSR, PubFig, and Shoes datasets, compared with the previous RA learning results  the average ranking accuracies have been significantly improved by about 8% 9% and 14% respectively.
Learning perceptual texture similarity and relative attributes from computational features
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

Previous work has shown that perceptual texture similarity and relative attributes cannot be well described by computational features. In this paper, we propose to predict human's visual perception of texture images by learning a non linear mapping from computational feature space to perceptual space. Hand crafted features and deep features, which were successfully applied in texture classification tasks, were extracted and used to train Random Forest and rankSVM models against perceptual data from psychophysical experiments. Three texture datasets were used to test our proposed method and the experiments show that the predictions of such learnt models are in high correlation with human's results.
Toward an Intersectional Approach in Developmental Science: The Role of Race, Gender, Sexual Orientation, and Immigrant Status.
Venue: Advances in child development and behavior

Developmental theory and research have often focused on a single social identity category, for example, race or sexual orientation, and examined the consequences of that category on life outcomes. Yet intersectional models of social disadvantage (eg, Cole, 2009; Crenshaw, 1995; King, 1988) suggest that social categories combine to shape the experiences and life outcomes of individuals across life domains. In this chapter, we review empirical research that offers insight into the intersectionality of social identities across three critical developmental periods, namely, middle childhood, adolescence, and emerging adulthood. We also consider the consequences of intersecting identities across several life domains, including intergroup relations and political and civic engagement. Recognizing that the body of work on social identities is expansive, we focus our review on race/ethnicity, gender, sexual orientation, and immigrant status. In each developmental stage, we discuss what we know, drawing from the limited empirical literature, and offer suggestions on where we need to go moving forward. We conclude that research that focuses on as a single category and ignores the specific domain of development provides an incomplete and inaccurate picture that will hinder efforts to develop culturally appropriate and clinically effective prevention and intervention programs to meet the needs of our diverse children and youth living in the United States.
Scaling laws predict global microbial diversity
Venue: Proceedings of the National Academy of Sciences

Ecological scaling laws are intensively studied for their predictive power and universal nature but often fail to unify biodiversity across domains of life. Using a global scale compilation of microbial and macrobial data, we uncover relationships of commonness and rarity that scale with abundance at similar rates for microorganisms and macroscopic plants and animals. We then show a unified scaling law that predicts the abundance of dominant species across 30 orders of magnitude to the scale of all microorganisms on Earth. Using this scaling law combined with the lognormal model of biodiversity, we predict that Earth is home to as many as 1 trillion (1012) microbial species. Scaling laws underpin unifying theories of biodiversity and are among the most predictively powerful relationships in biology. However, scaling laws developed for plants and animals often go untested or fail to hold for microorganisms. As a result, it is unclear whether scaling laws of biodiversity will span evolutionarily distant domains of life that encompass all modes of metabolism and scales of abundance. Using a global scale compilation of ~35,000 sites and ~5.6106 species, including the largest ever inventory of high throughput molecular data and one of the largest compilations of plant and animal community data, we show similar rates of scaling in commonness and rarity across microorganisms and macroscopic plants and animals. We document a universal dominance scaling law that holds across 30 orders of magnitude, an unprecedented expanse that predicts the abundance of dominant ocean bacteria. In combining this scaling law with the lognormal model of biodiversity, we predict that Earth is home to upward of 1 trillion (1012) microbial species. Microbial biodiversity seems greater than ever anticipated yet predictable from the smallest to the largest microbiome.
Fundamental Scaling Laws in Nanophotonics
Venue: Scientific reports

The success of information technology has clearly demonstrated that miniaturization often leads to unprecedented performance, and unanticipated applications. This hypothesis of "smaller is better" has motivated optical engineers to build various nanophotonic devices, although an understanding leading to fundamental scaling behavior for this new class of devices is missing. Here we analyze scaling laws for optoelectronic devices operating at micro and nanometer length scale. We show that optoelectronic device performance scales non monotonically with device length due to the various device tradeoffs, and analyze how both optical and electrical constrains influence device power consumption and operating speed. Specifically, we investigate the direct influence of scaling on the performance of four classes of photonic devices, namely laser sources, electro optic modulators, photodetectors, and all optical switches based on three types of optical resonators; microring, Fabry Perot cavity, and plasmonic metal nanoparticle. Results show that while microrings and Fabry Perot cavities can outperform plasmonic cavities at larger length scales, they stop working when the device length drops below 100 nanometers, due to insufficient functionality such as feedback (laser) index modulation (modulator) absorption (detector) or field density (optical switch) Our results provide a detailed understanding of the limits of nanophotonics, towards establishing an opto electronics roadmap, akin to the International Technology Roadmap for Semiconductors.
Topology dependent scaling laws for the stiffness and strength of nanoporous gold

Nanoporous gold (np Au) differs from its macroporous counterparts through its ligament and pore length scales, its high relative density, and its very distinct mesoscale cellular architecture. When reexamining the applicability of conventional macroporous foam scaling laws to np Au, difficulties persist not only in determining the solid properties of nanoscale ligaments, but also because np Au structure is not self similar as the relative density changes. Thus, a clear distinction of the effects of relative density and structure is required. This paper aims to capture the role of topology and morphology into the scaling laws by comparing the overall mechanical response of real np Au structures with the behavior of spinodal and gyroid structures. Quantitative morphological and topological characterization of these structures has been carried out and their role on the macroscopic elastoplastic response of np Au has been studied using finite element (FE) simulations. The predicted elastic modulus of real np Au structures from FE simulations is in remarkable agreement with the nanoindentation measurements, and validates the numerical simulations. Quantitative structural analysis reveals that np Au and spinodal structures are topologically very distinct, but similar in their morphology. On the other hand, gyroids are both morphologically and topologically very distinct from np Au. The results suggest that the macroscopic stiffness and strength are highly sensitive to the topology, while being relatively much less sensitive to the morphology. The effects of structural topology are captured into modified scaling laws where the geometric pre factors for the stiffness and strength are found to vary linearly with the scaled genus.
Behavioural, emotional and social development of children who stutter.
Venue: Journal of fluency disorders

Developmental stuttering may be associated with diminished psychological well being which has been documented from late childhood onwards. It is important to establish the point at which behavioural, emotional and social problems emerge in children who stutter.  The study used data from the Millennium Cohort Study, whose initial cohort comprised 18,818 children. Analysis involved data collected when the cohort members were 3, 5 and 11 years old. The association between parent reported stuttering and performance on the Strengths and Difficulties Questionnaire was determined in regression analyses which controlled for cohort members' sex, verbal and non verbal abilities, maternal education, and family economic status.  Compared with typically developing children, those who stuttered had significantly higher Total Difficulties scores at all three ages; in addition, scores on all of the sub scales for 5 year olds who stuttered indicated poorer development than their peers, and 11 year olds who stuttered had poorer development than peers in all areas except prosocial skills. At ages 5 and 11, those who stuttered were more likely than peers to have scores indicating cause for clinical concern in almost all areas.  Children who stutter may begin to show impaired behavioural, emotional and social development as early as age 3, and these difficulties are well established in older children who stutter. Parents and practitioners need to be aware of the possibility of these difficulties and intervention needs to be provided in a timely fashion to address such difficulties in childhood and to prevent the potential development of serious mental health difficulties later in life.
Use of Mobile Technology to Calm Upset Children: Associations With Social Emotional Development.
Venue: JAMA pediatrics

4. National Center for Education Statistics; US Department of Education. Average National Assessment of Educational Progress reading scale scores and percentage distribution of students, by age, amount of reading for school and for fun, and time spent on homework and watching TV/video: selected years, 1984 through 2008. http:/nces.ed.gov/programs/digest/d12/tables/dt12_144 .asp. Accessed September 13, 2015.
A mathematical model for distribution of calcium in silicon by vacuum directional solidification

Calcium is one of the main impurity elements in silicon. The removal of calcium strongly affects the quality of the polycrystalline silicon ingot produced by a vacuum directional solidification method. Based on the considerations of the theory of segregation, mass transfer and evaporation during vacuum directional solidification process, a mathematical model for calcium distribution in silicon was proposed and it can be used to explain the removal mechanism. In order to confirm the mathematical model, an industrial scale experiment on UMG Si with an initial purity of 99.98 wt. was performed. Since the reaction temperature strongly influences both the evaporation and segregation of calcium, the dependences of effective segregation coefficient (keff) and the evaporation coefficient (kE) on temperature were carefully investigated. The results showed that the proposed mathematical model was highly consistent with the experimental data and the calcium removal efficiency mainly relied on the evaporation step.
Exploiting Source side Monolingual Data in Neural Machine Translation
Venue: EMNLP

Neural Machine Translation (NMT) based on the encoder decoder architecture has recently become a new paradigm. Researchers have proven that the target side monolingual data can greatly enhance the decoder model of NMT. However, the source side monolingual data is not fully explored although it should be useful to strengthen the encoder model of NMT, especially when the parallel corpus is far from sufficient. In this paper, we propose two approaches to make full use of the sourceside monolingual data in NMT. The first approach employs the self learning algorithm to generate the synthetic large scale parallel data for NMT training. The second approach applies the multi task learning framework using two NMTs to predict the translation and the reordered source side monolingual sentences simultaneously. The extensive experiments demonstrate that the proposed methods obtain significant improvements over the strong attention based NMT.
Improving Neural Machine Translation Models with Monolingual Data
Venue: ACL

Neural Machine Translation (NMT) has obtained state of the art performance for several language pairs, while only using parallel data for training. Target side monolingual data plays an important role in boosting fluency for phrase based statistical machine translation, and we investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models, we note that encoder decoder NMT architectures already have the capacity to learn the same information as a language model, and we explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic back translation, we can treat it as additional parallel training data, and we obtain substantial improvements on the WMT 15 task English German +2.8 3.7 BLEU) and for the low resourced IWSLT 14 task Turkish >English +2.1 3.4 BLEU) obtaining new state of the art results. We also show that fine tuning on in domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English >German.
A review and future direction of agile, business intelligence, analytics and data science
Venue: Int. J. Inf. Manag.

We provide review of agile methodologies, business intelligence and data science.We explain the current trends and interplay between agile methodologies, business intelligence and data science.We develop lifecycle approaches for BI, analytics and data science.We discuss challenges and future directions for agile methodologies, business intelligence and data science. Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.
Cytokine release: A workshop proceedings on the state of the science, current challenges and future directions.
Venue: Cytokine

In October 2013, the International Life Sciences Institute Health and Environmental Sciences Institute Immunotoxicology Technical Committee (ILSI HESI ITC) held a one day workshop entitled, "Workshop on Cytokine Release: State of the Science, Current Challenges and Future Directions" The workshop brought together scientists from pharmaceutical, academic, health authority, and contract research organizations to discuss novel approaches and current challenges for the use of in vitro cytokine release assays (CRAs) for the identification of cytokine release syndrome (CRS) potential of novel monoclonal antibody (mAb) therapeutics. Topics presented encompassed a regulatory perspective on cytokine release and assessment, case studies regarding the translatability of preclinical cytokine data to the clinic, and the latest state of the science of CRAs, including comparisons between mAb therapeutics within one platform and across several assay platforms, a novel physiological assay platform, and assay optimization approaches such as determination of FcR expression profiles and use of statistical tests. The data and approaches presented confirmed that multiple CRA platforms are in use for identification of CRS potential and that the choice of a particular CRA platform is highly dependent on the availability of resources for individual laboratories (e.g. positive and negative controls, number of human blood donors) the assay through put required, and the mechanism of action of the therapeutic candidate to be tested. Workshop participants agreed that more data on the predictive performance of CRA platforms is needed, and current efforts to compare in vitro assay results with clinical cytokine assessments were discussed. In summary, many laboratories continue to focus research efforts on the improvement of the translatability of current CRA platforms as well explore novel approaches which may lead to more accurate, and potentially patient specific, CRS prediction in the future.