nils-herrmann/clean_scirepeval huggingface dataset:
Ayahuasca, dimethyltryptamine, and psychosis: a systematic review of human studies.

Ayahuasca is a hallucinogen brew traditionally used for ritual and therapeutic purposes in Northwestern Amazon. It is rich in the tryptamine hallucinogens dimethyltryptamine (DMT), which acts as a serotonin 5-HT2A agonist. This mechanism of action is similar to other compounds such as lysergic acid diethylamide (LSD) and psilocybin. The controlled use of LSD and psilocybin in experimental settings is associated with a low incidence of psychotic episodes, and population studies corroborate these findings. Both the controlled use of DMT in experimental settings and the use of ayahuasca in experimental and ritual settings are not usually associated with psychotic episodes, but little is known regarding ayahuasca or DMT use outside these controlled contexts. Thus, we performed a systematic review of the published case reports describing psychotic episodes associated with ayahuasca and DMT intake. We found three case series and two case reports describing psychotic episodes associated with ayahuasca intake, and three case reports describing psychotic episodes associated with DMT. Several reports describe subjects with a personal and possibly a family history of psychosis (including schizophrenia, schizophreniform disorders, psychotic mania, psychotic depression), nonpsychotic mania, or concomitant use of other drugs. However, some cases also described psychotic episodes in subjects without these previous characteristics. Overall, the incidence of such episodes appears to be rare in both the ritual and the recreational/noncontrolled settings. Performance of a psychiatric screening before administration of these drugs, and other hallucinogens, in controlled settings seems to significantly reduce the possibility of adverse reactions with psychotic symptomatology. Individuals with a personal or family history of any psychotic illness or nonpsychotic mania should avoid hallucinogen intake.
Neighboring gray level dependence matrix for texture classification

A new approach, neighboring gray level dependence matrix (NGLDM), for texture classification is presented. The major properties of this approach are as follows: (a) texture features can be easily computed; (b) they are essentially invariant under spatial rotation; (c) they are invariant under linear gray level transformation and can be made insensitive to monotonic gray level transformation. These properties have enhanced the practical applications of the texture features. The accuracies of the classification are comparable with those found in the literature.
Web services and business process management

Web services based on the service-oriented architecture framework provide a suitable technical foundation for making business processes accessible within enterprises and across enterprises. But to appropriately support dynamic business processes and their management, more is needed, namely, the ability to prescribe how Web services are used to implement activities within a business process, how business processes are represented as Web services, and also which business partners perform what parts of the actual business process. In this paper, the relationship between Web services and the management of business processes is worked out and presented in a tutorial-like manner.
Performance Issues of Heterogeneous Hadoop Clusters in Cloud Computing

Nowadays most of the cloud applications process large amount of data to provide the desired results. Data volumes to be processed by cloud applications are growing much faster than computing power. This growth demands new strategies for processing and analyzing information. Dealing with large data volumes requires two things: 1) Inexpensive, reliable storagee 2) New tools for analyzing unstructured and structured data. Hadoop is a powerful open source software platform that addresses both of these problems. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Hadoop lacks performance in heterogeneous clusters where the nodes have different computing capacity. In this paper we address the issues that affect the performance of hadoop in eterogeneous clusters and also provided some guidelines on how to overcome these
MorusDB: a resource for mulberry genomics and genome biology

Mulberry is an important cultivated plant that has received the attention of biologists interested in sericulture and plant-insect interaction. Morus notabilis, a wild mulberry species with a minimal chromosome number is an ideal material for whole-genome sequencing and assembly. The genome and transcriptome of M. notabilis were sequenced and analyzed. In this article, a web-based and open-access database, the Morus Genome Database (MorusDB), was developed to enable easy-to-access and data mining. The MorusDB provides an integrated data source and an easy accession of mulberry large-scale genomic sequencing and assembly, predicted genes and functional annotations, expressed sequence tags (ESTs), transposable elements (TEs), Gene Ontology (GO) terms, horizontal gene transfers between mulberry and silkworm and ortholog and paralog groups. Transcriptome sequencing data for M. notabilis root, leaf, bark, winter bud and male flower can also be searched and downloaded. Furthermore, MorusDB provides an analytical workbench with some built-in tools and pipelines, such as BLAST, Search GO, Mulberry GO and Mulberry GBrowse, to facilitate genomic studies and comparative genomics. The MorusDB provides important genomic resources for scientists working with mulberry and other Moraceae species, which include many important fruit crops. Designed as a basic platform and accompanied by the SilkDB, MorusDB strives to be a comprehensive platform for the silkworm-mulberry interaction studies. Database URL: http://morus.swu.edu.cn/morusdb.
Learning to Detect Human-Object Interactions

We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.
3 D printing : an emerging tool for novel microfluidics and lab ‐ on ‐ a ‐ chip applications

many fields such as medicine, chemistry, and biotechnologies (Barry and Ivanov 2004; Beebe et al. 2002; Dittrich and Manz 2006; Hong and Quake 2003; Stone et al. 2004; Thorsen et al. 2002; Wang et al. 2009; Weibel and Whitesides 2006; Whitesides 2006; Xu 2014; Guo et al. 2015). Fluid flow at the characteristic dimensions of microfluidic devices (MFDs) exhibits unique behavior not otherwise replicable at macroscopic scales (Karniadakis et al. 2006; Squires and Quake 2005; Hashmi et al. 2012). Currently, most microfluidic fabrication techniques are largely constrained by the complexity of real 3D structures, and limit researchers’ ability to produce complex 3D flow paths with nonstandard cross sections and of differing sizes and directions. Fortunately, the rapidly advancing 3D printing technology, also known as additive manufacturing (AM) technology, has brought us a possible route to overcome this problem. In this technology, the structure of interest is sliced into numerous 2D cross sections, and hence, the production becomes a straightforward layer-bylayer fabrication process. Such a direct approach for microfabrication is rapidly becoming established as an attractive field of interest to microfluidic engineers. The intent of this article is to introduce the state-of-the-art 3D printing technologies and its applications in fabricating microfluidics for novel experiments. We will focus on emerging applications in the past 2 years and provide our perspectives on possible future directions.
A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging.
Computational Linguistics and Deep Learning

Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pundits are predicting that the final damage will be even worse. Accompanying ICML 2015 in Lille, France, there was another, almost as big, event: the 2015 Deep Learning Workshop. The workshop ended with a panel discussion, and at it, Neil Lawrence said, “NLP is kind of like a rabbit in the headlights of the Deep Learning machine, waiting to be flattened.” Now that is a remark that the computational linguistics community has to take seriously! Is it the end of the road for us? Where are these predictions of steamrollering coming from? At the June 2015 opening of the Facebook AI Research Lab in Paris, its director Yann LeCun said: “The next big step for Deep Learning is natural language understanding, which aims to give machines the power to understand not just individual words but entire sentences and paragraphs.”1 In a November 2014 Reddit AMA (Ask Me Anything), Geoff Hinton said, “I think that the most exciting areas over the next five years will be really understanding text and videos. I will be disappointed if in five years’ time we do not have something that can watch a YouTube video and tell a story about what happened. In a few years time we will put [Deep Learning] on a chip that fits into someone’s ear and have an English-decoding chip that’s just like a real Babel fish.”2 And Yoshua Bengio, the third giant of modern Deep Learning, has also increasingly oriented his group’s research toward language, including recent exciting new developments in neural machine translation systems. It’s not just Deep Learning researchers. When leading machine learning researcher Michael Jordan was asked at a September 2014 AMA, “If you got a billion dollars to spend on a huge research project that you get to lead, what would you like to do?”, he answered: “I’d use the billion dollars to build a NASA-size program focusing on natural language processing, in all of its glory (semantics, pragmatics, etc.).” He went on: “Intellectually I think that NLP is fascinating, allowing us to focus on highly structured inference problems, on issues that go to the core of ‘what is thought’ but remain eminently practical, and on a technology
Happiness unpacked: positive emotions increase life satisfaction by building resilience.

Happiness-a composite of life satisfaction, coping resources, and positive emotions-predicts desirable life outcomes in many domains. The broaden-and-build theory suggests that this is because positive emotions help people build lasting resources. To test this hypothesis, the authors measured emotions daily for 1 month in a sample of students (N = 86) and assessed life satisfaction and trait resilience at the beginning and end of the month. Positive emotions predicted increases in both resilience and life satisfaction. Negative emotions had weak or null effects and did not interfere with the benefits of positive emotions. Positive emotions also mediated the relation between baseline and final resilience, but life satisfaction did not. This suggests that it is in-the-moment positive emotions, and not more general positive evaluations of one's life, that form the link between happiness and desirable life outcomes. Change in resilience mediated the relation between positive emotions and increased life satisfaction, suggesting that happy people become more satisfied not simply because they feel better but because they develop resources for living well.
Computing the arc length of parametric curves

Specifying constraints on motion is simpler if the curve is parameterized by arc length, but many parametric curves of practical interest cannot be parameterized by arc length. An approximate numerical reparameterization technique that improves on a previous algorithm by using a different numerical integration procedure that recursively subdivides the curve and creates a table of the subdivision points is presented. The use of the table greatly reduces the computation required for subsequent arc length calculations. After table construction, the algorithm takes nearly constant time for each arc length calculation. A linear increase in the number of control points can result in a more than linear increase in computation. Examples of this type of behavior are shown.<<ETX>>
Successfully Managing Impending Skin Necrosis following Hyaluronic Acid Filler Injection, using High-Dose Pulsed Hyaluronidase

Facial fillers are becoming increasingly popular as aesthetic procedures to temporarily reduce the depth of wrinkles or to contour faces. However, even in the hands of very experienced injectors, there is always a small possibility of vascular complications like intra-arterial injection of filler substance. We present a case report of a patient who developed features of vascular obstruction in right infraorbital artery and tell-tale signs of impending skin necrosis, after hyaluronic acid filler injection by an experienced injector. The diagnosis of a vascular complication was made quickly with the help of clinical features like blanching, livedo reticularis, and poor capillary refill. Patient was treated promptly with "high-dose pulsed hyaluronidase protocol" comprising three 1,000-unit pulses of hyaluronidase, administered hourly. There was no further increase in size of the involved area after the first dose of hyaluronidase. All of the involved area, along with 1 cm overlapping in uninvolved skin area, was injected during each injection pulse, using a combination of cannula and needle. Complete reperfusion and good capillary filling were achieved after completion of 3 pulses, and these were taken as the end-point of high-dose pulsed hyaluronidase treatment. Immediate skin changes after filler injections, as well as after hyaluronidase injections and during the 3-week recovery period, were documented with photographs and clinical notes. Involved skin was found to have been fully recovered from this vascular episode, thus indicating that complete recovery of the ischemic skin changes secondary to possible intra-arterial injection could be achieved using high-dose pulsed hyaluronidase protocol.
Health Monitoring and Assistance to Support Aging in Place

To many people, home is a sanctuary. For those people who need special medical care, they may need to be pulled out of their home to meet their medical needs. As the population ages, the percentage of people in this group is increasing and the effects are expensive as well as unsatisfying. We hypothesize that many people with disabilities can lead independent lives in their own homes with the aid of athome automated assistance and health monitoring. In order to accomplish this, robust methods must be developed to collect relevant data and process it dynamically and adaptively to detect and/or predict threatening long-term trends or immediate crises. The main objective of this paper is to investigate techniques for using agent-based smart home technologies to provide this at-home health monitoring and assistance. To this end, we have developed novel inhabitant modeling and automation algorithms that provide remote health monitoring for caregivers. Specifically, we address the following technological challenges: 1) identifying lifestyle trends, 2) detecting anomalies in current data, and 3) designing a reminder assistance system. Our solution approaches are being tested in simulation and with volunteers at the UTA’s MavHome site, an agent-based
Distributed Packet Inspection By Using PIL-2 Language

Packet Inspection language PIL-2 incorporates directly support for distributed processing into the language.PIL-2 supports generic header filter, payload scanning, packet replication and traffic flow in network. By distributing PIL-2 based on the possibility created for heterogeneous network of processing node, which gives monitoring architecture facilities with high speed packet processing. The result shows that perform complex processing at terra bit speeds. This proposed frame work can be used to execute distinct task as load balancing, traffic monitoring, firewalls and IDS at high bandwidth links. PIL-2,Load balancing, IDS, traffic monitoring.
Static hand gesture recognition using neural networks

This paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using backpropagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.
Measurement of Driver's Consciousness by Image Processing -A Method for Presuming Driver's Drowsiness by Eye-Blinks coping with Individual Differences -

Nowadays, driving support systems, such as car navigation systems, are getting common, and they support drivers in several aspects. It is important for driving support systems to detect status of driver's consciousness. Particularly, detecting driver's drowsiness could prevent drivers from collisions caused by drowsy driving. In this paper, we discuss a system and a method for detecting driver's drowsiness, which use a property of blinks waveforms that driver's eye-closing time is strongly connected to their drowsiness. Moreover, our system detects the drowsiness robustly for individual differences by using three factors extracted from the individual blink waveform.
Cover your ACKs: pitfalls of covert channel censorship circumvention

In response to increasingly sophisticated methods of blocking access to censorship circumvention schemes such as Tor, recently proposed systems such as Skypemorph, FreeWave, and CensorSpoofer have used voice and video conferencing protocols as "cover channels" to hide proxy connections. We demonstrate that even with perfect emulation of the cover channel, these systems can be vulnerable to attacks that detect or disrupt the covert communications while having no effect on legitimate cover traffic. Our attacks stem from differences in the channel requirements for the cover protocols, which are peer-to-peer and loss tolerant, and the covert traffic, which is client-proxy and loss intolerant. These differences represent significant limitations and suggest that such protocols are a poor choice of cover channel for general censorship circumvention schemes.
Assessing SMS and PJD Schemes of Anti-Islanding with Varying Quality Factor

When a particular zone of the distribution network containing an embedded generator is disconnected from the main supply grid, yet the generator continues to operate with normal voltage and frequency to feed power to the isolated section, islanding is said to occur. In a distributed power system, islanding may occur at different possible zones consisting of distribution feeders, substations and voltage levels as long as the isolated zone is found to operate independently of the main grid but remained energized by the distributed generation unit. To ensure safety and reliability, it is necessary to detect islanding condition as quickly as possible by using anti-islanding algorithms. Among the available islanding prevention methods, slip mode frequency shift (SMS) and phase jump detection (PJD) schemes are very common and have numerous advantages over others. This paper presents detailed discussion and comparison of the characteristics of the various anti-islanding schemes. Both SMS and PJD techniques are examined in detail through design and simulation. In the islanding situation with quality factor range (0.1les Qf les10), load voltage waveforms are analyzed and plotted. Both the methods are assessed in terms of detection times with different Qf's. The results are complying with IEEE standard specifications and show that the two developed algorithms could prevent islanding more consistently.