nils-herrmann/clean_scirepeval huggingface dataset:
False Positive and False Negative Results in Urine Drug Screening Tests: Tampering Methods and Specimen Integrity Tests

Urine drug screening can detect cases of drug abuse, promote workplace safety, and monitor drugtherapy compliance. Compliance testing is necessary for patients taking controlled drugs. To order and interpret these tests, it is required to know of testing modalities, kinetic of drugs, and different causes of false-positive and false-negative results. Standard immunoassay testing is fast, cheap, and the preferred primarily test for urine drug screening. This method reliably detects commonly drugs of abuse such as opiates, opioids, amphetamine/methamphetamine, cocaine, cannabinoids, phencyclidine, barbiturates, and benzodiazepines. Although immunoassays are sensitive and specific to the presence of drugs/drug metabolites, false negative and positive results may be created in some cases. Unexpected positive test results should be checked with a confirmatory method such as gas chromatography/mass spectrometry. Careful attention to urine collection methods and performing the specimen integrity tests can identify some attempts by patients to produce false-negative test results.
The attention system of the human brain: 20 years after.

Here, we update our 1990 Annual Review of Neuroscience article, "The Attention System of the Human Brain." The framework presented in the original article has helped to integrate behavioral, systems, cellular, and molecular approaches to common problems in attention research. Our framework has been both elaborated and expanded in subsequent years. Research on orienting and executive functions has supported the addition of new networks of brain regions. Developmental studies have shown important changes in control systems between infancy and childhood. In some cases, evidence has supported the role of specific genetic variations, often in conjunction with experience, that account for some of the individual differences in the efficiency of attentional networks. The findings have led to increased understanding of aspects of pathology and to some new interventions.
Intelligence by Design: Principles of Modularity and Coordination for Engineerin

All intelligence relies on search for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation describes an approach, Behavior-Oriented Design (BOD) for engineering complex agents. A complex agent is one that must arbitrate between potentially conflicting goals or behaviors. Behavior-oriented design builds on work in behavior-based and hybrid architectures for agents, and the object oriented approach to software engineering. The primary contributions of this dissertation are: 1. The BOD architecture: a modular architecture with each module providing specialized representations to facilitate learning. This includes one pre-specified module and representation for action selection or behavior arbitration. The specialized representation underlying BOD action selection is Parallel-rooted, Ordered, Slip-stack Hierarchical (POSH) reactive plans. 2. The BOD development process: an iterative process that alternately scales the agent's capabilities then optimizes the agent for simplicity, exploiting tradeoffs between the component representations. This ongoing process for controlling complexity not only provides bias for the behaving agent, but also facilitates its maintenance and extendibility. The secondary contributions of this dissertation include two implementations of POSH action selection, a procedure for identifying useful idioms in agent architectures and using them to distribute knowledge across agent paradigms, several examples of applying BOD idioms to established architectures, an analysis and comparison of the attributes and design trends of a large number of agent architectures, a comparison of biological (particularly mammalian) intelligence to artificial agent architectures, a novel model of primate transitive inference, and many other examples of BOD agents and BOD development.  Lynn Andrea Stein Title: Associate Professor of Computer Science
Design of a commercial hybrid VTOL UAV system

For the last 40 years high - capacity Unmanned Air Vehicles have been use mostly for military services such as tracking, surveillance, engagement with active weapon or in the simplest term for data acquisition purpose. Unmanned Air Vehicles are also demanded commercially because of their advantages in comparison to manned vehicles such as their low manufacturing and operating cost, configuration flexibility depending on customer request, not risking pilot in the difficult missions. Nevertheless, they have still open issues such as integration to the manned flight air space, reliability and airworthiness. Although Civil Unmanned Air Vehicles comprise 3% of the UAV market, it is estimated that they will reach 10% level within the next 5 years. UAV systems with their useful equipment (camera, hyper spectral imager, air data sensors and with similar equipment) have been in use more and more for civil applications: Tracking and monitoring in the event of agriculture / forest / marine pollution / waste / emergency and disaster situations; Mapping for land registry and cadastre; Wildlife and ecologic monitoring; Traffic Monitoring and; Geology and mine researches. They can bring minimal risk and cost advantage to many civil applications, in which it was risky and costly to use manned air vehicles before. When the cost of Unmanned Air Vehicles designed and produced for military service is taken into account, civil market demands lower cost and original products which are suitable for civil applications. Most of civil applications which are mentioned above require UAVs that are able to take off and land on limited runway, and moreover move quickly in the operation region for mobile applications but hover for immobile measurement and tracking when necessary. This points to a hybrid unmanned vehicle concept optimally, namely the Vertical Take Off and Landing (VTOL) UAVs. At the same time, this system requires an efficient cost solution for applicability / convertibility for different civil applications. It means an Air Vehicle having easily portability of payload depending on application concept and programmability of operation (hover and cruise flight time) specific to the application. The main topic of this project is designing, producing and testing the TURAC VTOL UAV that have the following features : Vertical takeoff and landing, and hovering like helicopter ; High cruise speed and fixed-wing ; Multi-functional and designed for civil purpose ; The project involves two different variants ; The TURAC A variant is a fully electrical platform which includes 2 tilt electric motors in the front, and a fixed electric motor and ducted fan in the rear ; The TURAC B variant uses fuel cells.
Recent trends in neuromorphic engineering

Neuromorphic Engineering has emerged as an exciting research area, primarily owing to the paradigm shift from conventional computing architectures to data-driven, cognitive computing. There is a diversity of work in the literature pertaining to neuromorphic systems, devices and circuits. This review looks at recent trends in neuromorphic engineering and its sub-domains, with an attempt to identify key research directions that would assume significance in the future. We hope that this review would serve as a handy reference to both beginners and experts, and provide a glimpse into the broad spectrum of applications of neuromorphic hardware and algorithms. Our survey indicates that neuromorphic engineering holds a promising future, particularly with growing data volumes, and the imminent need for intelligent, versatile computing.
Transparent Protection of Commodity OS Kernels Using Hardware Virtualization

Kernel rootkits are among the most insidious threats to computer security to da . By employing various code injection techniques, they are able to maintain an omn ip te t presence in the compromised OS kernels. Existing preventive countermeasures typ icall employ virtualization technology as part of their solutions. However, they are still limited in either ( 1) equiring modifying the OS kernel source code for the protection or (2) leveraging so ftware-based virtualization techniques such as binary translation with a high overhead to implement a Ha rv rd architecture (which is robust to various code injection techniques used by kernel roo tkits). In this paper, we introduce hvmHarvard, a hardware virtualization-based Harvard arch itecture that transparently protects commodity OS kernels from kernel rootkit attacks and significantly re duc s the performance overhead. Our evaluation with a Xen-based prototype shows that it can tr ansparently protect legacy OS kernels with rootkit resistance while introducing < 5% performance overhead.
A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network

Datasets originating from social networks are very valuable to many fields such as sociology and psychology. However, the supports from technical perspective are far from enough, and specific approaches are urgently in need. This paper applies data mining to psychology area for detecting depressed users in social network services. Firstly, a sentiment analysis method is proposed utilizing vocabulary and man-made rules to calculate the depression inclination of each micro-blog. Secondly, a depression detection model is constructed based on the proposed method and 10 features of depressed users derived from psychological research. Then 180 users and 3 kinds of classifiers are used to verify the model, whose precisions are all around 80%. Also, the significance of each feature is analyzed. Lastly, an application is developed within the proposed model for mental health monitoring online. This study is supported by some psychologists, and facilitates them in data-centric aspect in turn.
Identifying the signs of fraudulent accounts using data mining techniques

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.
Cancer immunotherapy targeting the CD47/SIRPα axis.

The success of cancer immunotherapy has generated tremendous interest in identifying new immunotherapeutic targets. To date, the majority of therapies have focussed on stimulating the adaptive immune system to attack cancer, including agents targeting CTLA-4 and the PD-1/PD-L1 axis. However, macrophages and other myeloid immune cells offer much promise as effectors of cancer immunotherapy. The CD47/signal regulatory protein alpha (SIRPα) axis is a critical regulator of myeloid cell activation and serves a broader role as a myeloid-specific immune checkpoint. CD47 is highly expressed on many different types of cancer, and it transduces inhibitory signals through SIRPα on macrophages and other myeloid cells. In a diverse range of preclinical models, therapies that block the CD47/SIRPα axis stimulate phagocytosis of cancer cells in vitro and anti-tumour immune responses in vivo. A number of therapeutics that target the CD47/SIRPα axis are under preclinical and clinical investigation. These include anti-CD47 antibodies, engineered receptor decoys, anti-SIRPα antibodies and bispecific agents. These therapeutics differ in their pharmacodynamic, pharmacokinetic and toxicological properties. Clinical trials are underway for both solid and haematologic malignancies using anti-CD47 antibodies and recombinant SIRPα proteins. Since the CD47/SIRPα axis also limits the efficacy of tumour-opsonising antibodies, additional trials will examine their potential synergy with agents such as rituximab, cetuximab and trastuzumab. Phagocytosis in response to CD47/SIRPα-blocking agents results in antigen uptake and presentation, thereby linking the innate and adaptive immune systems. CD47/SIRPα blocking therapies may therefore synergise with immune checkpoint inhibitors that target the adaptive immune system. As a critical regulator of macrophage phagocytosis and activation, the potential applications of CD47/SIRPα blocking therapies extend beyond human cancer. They may be useful for the treatment of infectious disease, conditioning for stem cell transplant, and many other clinical indications.
Color psychology: effects of perceiving color on psychological functioning in humans.

Color is a ubiquitous perceptual stimulus that is often considered in terms of aesthetics. Here we review theoretical and empirical work that looks beyond color aesthetics to the link between color and psychological functioning in humans. We begin by setting a historical context for research in this area, particularly highlighting methodological issues that hampered earlier empirical work. We proceed to overview theoretical and methodological advances during the past decade and conduct a review of emerging empirical findings. Our empirical review focuses especially on color in achievement and affiliation/attraction contexts, but it also covers work on consumer behavior as well as food and beverage evaluation and consumption. The review clearly shows that color can carry important meaning and can have an important impact on people's affect, cognition, and behavior. The literature remains at a nascent stage of development, however, and we note that considerable work on boundary conditions, moderators, and real-world generalizability is needed before strong conceptual statements and recommendations for application are warranted. We provide suggestions for future research and conclude by emphasizing the broad promise of research in this area.
The Decision-making Model for the Stock Market under Uncertainty

21, 2017 Revised May 26, 2017 Accepted Aug 11, 2017 The main purpose of this research is developing methods and models of decision-making to assess the stock market state, and predict the possible changes in the RTS index value. This article shows that the analytical models for assessing the stock market state do not give reliable results. The absence of the reliable estimates associated with the high degree of uncertainty, random, nonlinear and non-stationary process with a significant degree of aftereffect. In this paper, to formalize the securities market parameters it’s proposed the fuzzy sets method. To assess the stock market current state and make decisions the fuzzy situational analysis model (situational model) is applied. The analytical prediction results of the stock market and graph of the RTS index expected return changes in 2014-2016 are showed. The model of calculating the fuzzy inference rules truth degree to predict the RTS index is developed. The market parameters linguistic definition is given and the expert’s rules construction to predict the RTS index growth is shown. The program in Matlab environment is designed to perform research. The study result showed that the model allows for the RTS index prediction in the condition of incomplete initial data with a confidence level about 90%.
Inclusive growth: an imperative for African agriculture

The findings, interpretations and conclusions expressed in this report are those of the authors and do not necessarily imply the expression of any opinion whatsoever on the part of the Management or the Executive Directors of the African Development Bank, nor the Governments they represent, nor of the other institutions mentioned in this study. In the preparation of this report, every effort has been made to provide the most up to date, correct and clearly expressed information as possible; however, the authors do not guarantee accuracy of the data.  Reproduction, citation and dissemination of material contained in this information product for educational and non-commercial purposes are authorized without any prior written permission from the publisher, if the source is fully acknowledged. Reproduction of material in this information product for resale or other commercial purposes is prohibited. Since 2000, Africa has been experiencing a remarkable economic growth accompanied by improving democratic environment. Real GDP growth has risen by more than twice its pace in the last decade. Telecommunications, financial services and banking, construction and private-investment inflows have also increased substantially. However, most of the benefits of the high growth rates achieved over the last few years have not reached the rural poor. For this to happen, substantial growth in the agriculture sector will need to be stimulated and sustained, as the sector is key to inclusive growth, given its proven record of contributing to more robust reduction of poverty. This is particularly important when juxtaposed with the fact that the majority of Africa's poor are engaged in agriculture, a sector which supports the livelihoods of 90 percent of Africa's population. The sector also provides employment for about 60 percent of the economically active population, and 70 percent of the continent's poorest communities. In spite of agriculture being an acknowledged leading growth driver for Africa, the potential of the sector's contribution to growth and development has been underexploited mainly due to a variety of challenges, including the widening technology divide, weak infrastructure and declining technical capacity. These challenges have been exacerbated by weak input and output marketing systems and services, slow progress in regional integration, land access and rights issues, limited access to affordable credit, challenging governance issues in some countries, conflicts, effects of climate change, and the scourge of HIV/AIDS and other diseases. Green growth is critical to Africa because of the fragility of the …
Generating Liquid Simulations with Deformation-aware Neural Networks

We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields. Our method specifically targets the space-time representation of physical surfaces from liquid simulations. Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as different initial conditions. Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface. Key for successful training runs in this setting is a suitable loss function that encodes the effect of the deformations, and a robust calculation of the corresponding gradients. To demonstrate the effectiveness of our approach, we showcase our method with several complex examples of flowing liquids with topology changes. Our representation makes it possible to rapidly generate the desired implicit surfaces. We have implemented a mobile application to demonstrate that real-time interactions with complex liquid effects are possible with our approach.
Stroke prediction using artificial intelligence

A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. In this work, we compare different methods with our approach for stroke prediction on the Cardiovascular Health Study (CHS) dataset. Here, decision tree algorithm is used for feature selection process, principle component analysis algorithm is used for reducing the dimension and adopted back propagation neural network classification algorithm, to construct a classification model. After analyzing and comparing classification efficiencies with different methods and variation models accuracy, our work has the optimum predictive model for the stroke disease with 97.7% accuracy.
A wheel-track-Leg hybrid Locomotion Mechanism based on transformable rims

This paper presents a novel wheel-track-Leg hybrid Locomotion Mechanism that has a compact structure. Compared to most robot wheels that have a rigid round rim, the transformable wheel with a flexible rim can switch to track mode for higher efficiency locomotion on swampy terrain or leg mode for better over-obstacle capability on rugged road. In detail, the wheel rim of this robot is cut into four end-to-end circles to make it capable of transforming between a round circle with a flat ring (just like “O” and “∞”) to change the contact type between transformable wheels with the ground. The transformation principle and constraint conditions between different locomotion modes are explained. The driving methods and locomotion strategies on various terrains of the robot are analyzed. Meanwhile, an initial experiment is conducted to verify the design.
The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour.

Species extinctions pose serious threats to the functioning of ecological communities worldwide. We used two qualitative and quantitative pollination networks to simulate extinction patterns following three removal scenarios: random removal and systematic removal of the strongest and weakest interactors. We accounted for pollinator behaviour by including potential links into temporal snapshots (12 consecutive 2-week networks) to reflect mutualists' ability to 'switch' interaction partners (re-wiring). Qualitative data suggested a linear or slower than linear secondary extinction while quantitative data showed sigmoidal decline of plant interaction strength upon removal of the strongest interactor. Temporal snapshots indicated greater stability of re-wired networks over static systems. Tolerance of generalized networks to species extinctions was high in the random removal scenario, with an increase in network stability if species formed new interactions. Anthropogenic disturbance, however, that promote the extinction of the strongest interactors might induce a sudden collapse of pollination networks.
Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems

Unsupervised word embeddings provide rich linguistic and conceptual information about words. However, they may provide weak information about domain specific semantic relations for certain tasks such as semantic parsing of natural language queries, where such information about words can be valuable. To encode the prior knowledge about the semantic word relations, we present new method as follows: we extend the neural network based lexical word embedding objective function (Mikolov et al. 2013) by incorporating the information about relationship between entities that we extract from knowledge bases. Our model can jointly learn lexical word representations from free text enriched by the relational word embeddings from relational data (e.g., Freebase) for each type of entity relations. We empirically show on the task of semantic tagging of natural language queries that our enriched embeddings can provide information about not only short-range syntactic dependencies but also long-range semantic dependencies between words. Using the enriched embeddings, we obtain an average of 2% improvement in F-score compared to the previous baselines.