nils-herrmann/clean_scirepeval huggingface dataset:
Detection of gas solid flow faults of a circulating fluidized bed using pressure fluctuations in wind caps
Venue: Korean Journal of Chemical Engineering

Wind cap partial blockages and agglomeration are two of the most common gas solid flow faults that occur under the actual operations of circulating fluidized bed boilers. Using the method of measuring pressure fluctuations, for the characterization of fluid dynamics in fluidized beds, has a great advantage, due to its flexible adaptation to any operating conditions to monitor fluidization. This paper presents research into the use of measuring and analyzing pressure fluctuations in wind caps, for the analysis of the gas solid fluidization characteristics in a fluidized bed with wind cap partial blockages or agglomeration fault. Partial blockages in a wind cap near feeding side and partial blockages in another wind cap near recycling side as well as agglomeration of different extents were simulated in a cold circulating fluidized bed. Pressure fluctuations in the inlets of several wind caps were measured at different primary air velocities under different fault conditions. They were then analyzed with the methods of statistical average, standard deviation, wavelet analysis and homogeneous index. Based on the calculated characteristic parameters, the effects of gas solid flow faults on the gas solid fluidization characteristics were analyzed. Results showed that variations of characteristic parameters of pressure fluctuations were related to variations of the gas solid flow condition, which were caused by wind cap partial blockages or agglomerations. It is shown that the proposed method is practical.
Relationship between Codependency, Perceived Social Support, and Depression in Mothers of Children with Intellectual Disability

The goal of this research was to study the relationship between codependency, perceived social support and depression in mothers of children with intellectual disability (ID) The correlational method was used in this study. The research population is comprised of mothers of educable children with ID in the age range of 25 to 61 years. From among this, a sample of 251 individuals, in the multistage cluster sampling method, was selected from educational districts in Tehran, who responded to the Spann Fischer Codependency Scale (SFCDS) the Social Support Questionnaire and the Beck Depression Inventory (BDI) The findings of this study indicate that among mothers of children with ID depression has a positive and significant correlation with codependency (P<0.01, r=0.4) and a negative and significant correlation with the total score of social support (P<0.01, r= 0.34) Moreover, the results of stepwise multiple regression analysis showed that codependency is allocated a higher variance than social support in explaining depression (R2=0.023) Keywords Codependency, social support, depression, mothers of children with ID.
End to end object detection and recognition in forward looking sonar images with convolutional neural networks
Venue: 2016 IEEE/OES Autonomous Underwater Vehicles (AUV)

Object detection and recognition are typically stages that form part of the perception module of Autonomous Underwater Vehicles, used with different sensors such as Sonar and Optical imaging, but their design is usually separate and they are only combined at test time. In this work we present a convolutional neural network that does both object detection (through detection proposals) and recognition in Forward Looking Sonar images and is trained with bounding boxes and class labels only. Convolutional layers are shared and a 128 element feature vector is shared between both tasks. After training we obtain 93% correct detections and 75% accuracy, but accuracy can be improved by fine tuning the classifier sub network with the generated detection proposals. We evaluated fine tuning with a SVM classifier trained on the shared feature vector, increasing accuracy to 85% Our detection proposal method can also detect unlabeled and untrained objects, and has good generalization performance. Our unified method can be used in any kind of sonar image, does not make assumptions about an object's shadow, and learns features directly from data.
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Venue: ACM Multimedia

Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image based recognition. How to effectively use ConvNets for video based recognition is still an open problem. In this paper, we propose a compact, effective yet simple method to encode spatio temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (JTM) and ConvNets are adopted to exploit the discriminative features for real time human action recognition. The proposed method has been evaluated on three public benchmarks, i.e. MSRC 12 Kinect gesture dataset (MSRC 12) G3D dataset and UTD multimodal human action dataset (UTD MHAD) and achieved the state of the art results.
First Description of Two Sequence Type 2 Acinetobacter baumannii Isolates Carrying OXA 23 Carbapenemase in Pagellus acarne Fished from the Mediterranean Sea near Bejaia, Algeria
Venue: Antimicrobial Agents and Chemotherapy

To determine the occurrence of carbapenem resistant Acinetobacter baumannii in fish fished from the Mediterranean Sea near the Bejaia coast (Algeria) we studied 300 gills and gut samples that had been randomly and prospectively collected during 1 year. After screening on selective agar media, using PCR arrays and whole genome sequencing, we identified for the first time two OXA 23 producing A. baumannii strains belonging to the widespread sequence type 2 (ST2)/international clone II and harboring aminoglycoside modifying enzymes [aac(6' Ib and aac(3' I genes]
PI3K in the ventromedial hypothalamic nucleus mediates estrogenic actions on energy expenditure in female mice
Venue: Scientific reports

Estrogens act in the ventromedial hypothalamic nucleus (VMH) to regulate body weight homeostasis. However, the molecular mechanisms underlying these estrogenic effects are unknown. We show that activation of estrogen receptor a (ERa) stimulates neural firing of VMH neurons expressing ERa, and these effects are blocked with intracellular application of a pharmacological inhibitor of the phosphatidyl inositol 3 kinase (PI3K) Further, we demonstrated that mice with genetic inhibition of PI3K activity in VMH neurons showed a sexual dimorphic obese phenotype, with only female mutants being affected. In addition, inhibition of VMH PI3K activity blocked effects of 17b estradiol to stimulate energy expenditure, but did not affect estrogen induced anorexia. Collectively, our results indicate that PI3K activity in VMH neurons plays a physiologically relevant role in mediating estrogenic actions on energy expenditure in females.
Medial cerebellar nucleus projects to feeding related neurons in the ventromedial hypothalamic nucleus in rats
Venue: Brain Structure and Function

The cerebellum, a hindbrain motor center, also participates in regulating nonsomatic visceral activities such as feeding control. However, the underlying neural mechanism is largely unknown. Here, we investigate whether the cerebellar medial nucleus (MN) one of the final outputs of the cerebellum, could directly project to and modulate the feeding related neurons in the ventromedial hypothalamic nucleus (VMN) which has been traditionally implicated in feeding behavior, energy balance, and body weight regulation. The retrograde tracing results show that both GABAergic and glutamatergic projection neurons in the cerebellar MN send direct projections to the VMN. Electrical stimulation of cerebellar MN elicits an inhibitory, excitatory or biphasic response of VMN neurons. Interestingly, the VMN neurons modulated by cerebellar MN afferents not only receive phasic and tonic inputs from the gastric vagal nerves, but also are sensitive to peripheral glycemia and ghrelin signals. Moreover, a summation of inputs from the cerebellar MN and gastric vagal afferents occurs on single glycemia/ghrelin sensitive neurons in the VMN, and the immunostaining result show that the axons from the cerebellar MN and the projections from the nucleus tractus solitarius, which conveys the gastric vagal inputs to hypothalamus, converge on single VMN glycemia/ghrelin sensitive neurons. These results demonstrate that the somatic information forwarded by the cerebellar MN, together with the feeding signals from periphery, converge onto single VMN neurons, suggesting that a somatic visceral integration related to feeding may occur in the VMN and the cerebellum may actively participate in the feeding regulation through the direct cerebellar MN VMN projections.
Train faster, generalize better: Stability of stochastic gradient descent
Venue: ICML

We show that parametric models trained by a stochastic gradient method (SGM) with few iterations have vanishing generalization error. We prove our results by arguing that SGM is algorithmically stable in the sense of Bousquet and Elisseeff. Our analysis only employs elementary tools from convex and continuous optimization. We derive stability bounds for both convex and non convex optimization under standard Lipschitz and smoothness assumptions. Applying our results to the convex case, we provide new insights for why multiple epochs of stochastic gradient methods generalize well in practice. In the non convex case, we give a new interpretation of common practices in neural networks, and formally show that popular techniques for training large deep models are indeed stability promoting. Our findings conceptually underscore the importance of reducing training time beyond its obvious benefit.
C2C interactions creating value in the Route of Santiago

The role of interaction between customers (customer to customer; C2C) is a key factor when extracting value from a touristic experience. Several studies incorporate quantitative and qualitative aspects referring to interactions and can provide a holistic view about C2C experiences, being therefore more capable of predicting the effects of C2C interaction on customer behavior. This study analyzes C2C interactions in backpackers' experience and their effect on the cruise experience and vacation satisfaction. The study uses fuzzy set qualitative comparative analysis (fsQCA) to explore a causal relationship with dichotomous outcomes, both the presence and the absence of the variables. The study focuses on a sample of 50 pilgrims after making the Route of Santiago. The results reveal that the variables quality, intensity, valence, and influence are important conditions for the vacation experience and provoke indirect effects on vacation satisfaction. The main contribution of this study is setting the analysis of C2C interactions as a valuable tool for effective managerial intervention.
Co creating value from knowledge intensive business services in manufacturing firms: The moderating role of relationship learning in supplier customer interactions

This study seeks evidence for a positive moderating role of relationship learning in the relation between manufacturing firms' knowledge intensive business services (KIBS) i.e. product related services for developing customized solutions, and firms' customer specific sales performance. Our findings from a survey of 91 supplier customer relationships indicate that KIBS offerings do not generate performance per se; instead, supplier customer relationships must be characterized by relationship learning to co create value from the supplier's KIBS offerings. Our findings extend the literature on industrial service businesses by shedding a more nuanced light on the core activities that enable value co creation and value appropriation in the KIBS context.
An Attentive Neural Architecture for Fine grained Entity Type Classification
Venue: AKBC@NAACL HLT

In this work we propose a novel attention based neural network model for the task of fine grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts. Our model achieves state of the art performance with 74.94% loose micro F1 score on the well established FIGER dataset, a relative improvement of 2.59% We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine grained category memberships of an entity.
Label Noise Reduction in Entity Typing by Heterogeneous Partial Label Embedding
Venue: KDD

Current systems of fine grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, the type labels so obtained from knowledge bases are often noisy (i.e. incorrect for the entity mention's local context) We define a new task, Label Noise Reduction in Entity Typing (LNR) to be the automatic identification of correct type labels (type paths) for training examples, given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity between entity types pose unique challenges for solving the LNR task. We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low dimensional space where, in that space, objects whose types are semantically close have similar representations. Then we estimate the type path for each training example in a top down manner using the learned embeddings. We formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method.
Biocontrol mechanisms of Trichoderma harzianum against soybean charcoal rot caused by Macrophomina phaseolina

Throughout the world, charcoal rot, caused by Macrophomina phaseolina, is one of the most destructive and widespread dis eases of crop plants such as soybean. In this study, the biological control capability of 11 Trichoderma spp. isolates against M. phaseolina was investigated using screening tests. Among all the tested Trichoderma spp. isolates, inhibition varied from 20.22 to 58.67% in dual culture tests. Dual culture, volatile and non volatile tests revealed that two isolates of Trichoderma harzianum (including the isolates T7 and T14) best inhibited the growth of M. phaseolina in vitro. Therefore, these isolates were selected for biocontrol of M. phaseolina in vivo. The results of greenhouse experiments revealed that disease severity in the seed treatment with T. harzianum isolates was significantly lower than that of the soil treatment. In most of the cases, though, soil treatment with T. harzianum resulted in higher plant growth parameters, such as root and shoot weight. The effects of T. harzianum isolates on the activity of peroxidase enzyme and phenolic contents of the soybean root in the presence and absence of M. phaseolina were determined in greenhouse conditions. Our results suggested that a part of the inhibitory effect of T. harzianum isolates on soybean charcoal rot might be related to the indirect influence on M. phaseolina. Plant defense responses were activated as an elicitor in addition to the direct effect on the pathogen growth.
Enabling team collaboration with task management tools
Venue: OpenSym

Project and task management tools aim to support remote or face to face collaboration. Despite the growing needs for these tools, little is known about how they are utilized in practice. This paper presents the results of an exploratory study using UpWave, a task management tool, and the ways that it enables team collaboration. The group interviewees utilize UpWave for their collaborations and report on its features in terms of use, best practices, motivations and rewards for users to encourage their collaboration. This paper concludes that project and task management tools offer new possibilities for collaborations; it also makes suggestions for using such tools in teams. This study's future work will include a mixed methods approach to gain a greater understanding of the tools' effects in various collaboration settings.
Point cloud descriptors for place recognition using sparse visual information
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

Place recognition is a core component in simultaneous localization and mapping (SLAM) limiting positional drift over space and time to unlock precise robot navigation. Determining which previously visited places belong together continues to be a highly active area of research as robotic applications demand increasingly higher accuracies. A large number of place recognition algorithms have been proposed, capable of consuming a variety of sensor data including laser, sonar and depth readings. The best performing solutions, however, have utilized visual information by either matching entire images or parts thereof. Most commonly, vision based approaches are inspired by information retrieval and utilize 3D geometry information about the observed scene as a post verification step. In this paper we propose to use the 3D scene information from sparse visual feature maps directly at the core of the place recognition pipeline. We propose a novel structural descriptor which aggregates sparse triangulated landmarks from SLAM into a compact signature. The resulting 3D features provide a discriminative fingerprint to recognize places over seasonal and viewpoint changes which are particularly challenging for approaches based on sparse visual descriptors. We evaluate our system on publicly available datasets and show how its complementary nature can provide an improvement over visual place recognition.
The Use of "Literary Fiction" to Promote Mentalizing Ability
Venue: PloS one

Empathy is a multidimensional process that incorporates both mentalizing and emotional sharing dimensions. Empathic competencies are important for creating interpersonal relationships with other people and developing adequate social behaviour. The lack of these social components also leads to isolation and exclusion in healthy populations. However, few studies have investigated how to improve these social skills. In a recent study, Kidd and Castano (2013) found that reading literary fiction increases mentalizing ability and may change how people think about other people's emotions and mental states. The aim of our study was to evaluate the effects of reading literary fiction, compared to nonfiction and science fiction, on empathic abilities. Compared to previous studies, we used a larger variety of empathy measures and utilized a pre and post test design. In all, 214 healthy participants were randomly assigned to read a book representative of one of three literary genres (literary fiction, nonfiction, science fiction) Participants were assessed before and after the reading phase using mentalizing and emotional sharing tests, according to Zaki and Ochsner' s (2012) model. Comparisons of sociodemographic, mentalizing, and emotional sharing variables across conditions were conducted using ANOVA. Our results showed that after the reading phase, the literary fiction group showed improvement in mentalizing abilities, but there was no discernible effect on emotional sharing abilities. Our study showed that the reading processes can promote mentalizing abilities. These results may set important goals for future low cost rehabilitation protocols for several disorders in which the mentalizing deficit is considered central to the disease, such as Autism Spectrum Disorders and Schizophrenia.
TOWARDS AN APPROPRIATE SEISMIC VULNERABILITY ASSESSMENT MODEL IN INDIA

Earthquakes have across history, led to heavy death tolls and monetary losses, forcing nations to deal with catastrophic repercussions. While the reasons behind these deaths and monetary losses can be attributed to a number of factors, a statistical analysis of the major cause of these fatalities worldwide, clearly points towards the collapse of buildings and other structures. The fact that the seismic hazard and exposure in a region cannot be controlled, suggests that any attempt to mitigate seismic risk should focus on minimizing the vulnerability of the building inventory. The Indian subcontinent, geographically spread over 3.287 million km of landmass, is highly non homogenous with respect to its seismogenic features. The seismic design code IS 1893 Part 1: 2002, that lays down the standards for seismic design of structures in India, recognizes four such seismic zones Zone II, Zone III, Zone IV and Zone V, indicating regions with low, moderate, high and very high levels of seismic activity respectively. Indian buildings exhibit significant heterogeneity with respect to their architectural and structural features. The performance of many building typologies in India during past earthquakes have not been satisfactory. From ill conceived design and detailing to poor quality control and construction practices, a wide spectrum of factors hinder the desirable seismic behavior of buildings. Various classes of methods exist in literature for quantifying the vulnerability of structures. Although there have been many studies in the past to assess the seismic vulnerability of individual buildings in the country, it was not until 2013 that a technical document was released by the National Disaster Management Authority (NDMA) for the territorial scale assessment of Indian building stock. Out of the fifty four typologies that have been proposed for Indian buildings, many are combined together and represented using single vulnerability function. This paper briefly describes the seismological setting of India, and the performance of various building typologies in past earthquakes, with the view of emphasizing on the need for seismic vulnerability assessment. It further focuses on the state of the art methodologies for assessment and the development of appropriate vulnerability models in the Indian context. Important issues that need to be addressed by these models form the central theme of discussion in this paper.