nils-herrmann/clean_scirepeval huggingface dataset:
A lattice Boltzmann method for immiscible multiphase flow simulations using the level set method

We consider the lattice Boltzmann method for immiscible multiphase flow simulations. Classical lattice Boltzmann methods for this problem, e.g. the colour gradient method or the free energy approach, can only be applied when density and viscosity ratios are small. Moreover, they use additional fields defined on the whole domain to describe the different phases and model phase separation by special interactions at each node. In contrast, our approach simulates the flow using a single field and separates the fluid phases by a free moving interface. The scheme is based on the lattice Boltzmann method and uses the level set method to compute the evolution of the interface. To couple the fluid phases, we develop new boundary conditions which realise the macroscopic jump conditions at the interface and incorporate surface tension in the lattice Boltzmann framework. Various simulations are presented to validate the numerical scheme, e.g. two-phase channel flows, the Young-Laplace law for a bubble and viscous fingering in a Hele-Shaw cell. The results show that the method is feasible over a wide range of density and viscosity differences.
Compositional pattern producing networks: A novel abstraction of development

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.
Using Dempster-Shafer's Evidence Theory for Query Expansion Based on Freebase Knowledge

Query expansion is generally a useful technique in improving search performance. However, some expanded query terms obtained by traditional statistical methods (e.g., pseudo-relevance feedback) may not be relevant to the user’s information need, while some relevant terms may not be contained in the feedback documents at all. Recent studies utilize external resources to detect terms that are related to the query, and then adopt these terms in query expansion. In this paper, we present a study in the use of Freebase , which is an open source general-purpose ontology, as a source for deriving expansion terms. FreeBase provides a graphbased model of human knowledge, from which a rich and multi-step structure of instances related to the query concept can be extracted, as a complement to the traditional statistical approaches to query expansion. We propose a novel method, based on the well-principled DempsterShafer’s (D-S) evidence theory, to measure the certainty of expansion terms from the Freebase structure. The expanded query model is then combined with a state of the art statistical query expansion model – the Relevance Model (RM3). Experiments show that the proposed method achieves significant improvements over RM3.
Querying Biomedical Ontologies in Natural Language using Answer Set Programming

Recent advances in health and life sciences have led to generation of a large amount of data. To facilitate access to its desired parts, such a big mass of data has been represented in structured forms, like biomedical ontologies. On the other hand, representing ontologies in a formal language, constructing them independently from each other and storing them at different locations have brought about many challenges for answering queries about the knowledge represented in these ontologies. One of the challenges for the users is to be able represent a complex query in a natural language, and get its answers in an understandable form: Currently, such queries are answered by software systems in a formal language, however, the majority of the users lack the necessary knowledge of a formal query language to represent a query; moreover, none of these systems can provide informative explanations about the answers. Another challenge is to be able to answer complex queries that require appropriate integration of relevant knowledge stored in different places and in various forms. In this work, we address the first challenge by developing an intelligent user interface that allows users to enter biomedical queries in a natural language, and that presents the answers (possibly with explanations if requested) in a natural language. We address the second challenge by developing a rule layer over biomedical ontologies and databases, and use automated reasoners to answer queries considering relevant parts of the rule layer. The main contributions of our work can be summarized as follows:
Critical Care Management of Increased Intracranial Pressure

Increased intracranial pressure (ICP) is a pathologic state common to a variety of serious neurologic conditions, all of which are characterized by the addition of volume to the intracranial vault. Hence all ICP therapies are directed toward reducing intracranial volume. Elevated ICP can lead to brain damage or death by two principle mechanisms: (1) global hypoxic-ischemic injury, which results from reduction of cerebral perfusion pressure (CPP) and cerebral blood ̄ow, and (2) mechanical compression, displacement, and herniation of brain tissue, which results from mass effect associated with compartmentalized ICP gradients. In unmonitored patients with acute neurologic deterioration, head elevation (30 degrees), hyperventilation (pCO2 26±30 mmHg), and mannitol (1.0±1.5 g/kg) can lower ICP within minutes. Fluid-coupled ventricular catheters and intraparenchymal pressure transducers are the most accurate and reliable devices for measuring ICP in the intensive care unit (ICU) setting. In a monitored patient, treatment of critical ICP elevation (>20 mmHg) should proceed in the following steps: (1) consideration of repeat computed tomography (CT) scanning or consideration of de®nitive neurosurgical intervention, (2) intravenous sedation to attain a quiet, motionless state, (3) optimization of CPP to levels between 70 and 110 mmHg, (4) osmotherapy with mannitol or hypertonic saline, (5) hyperventilation (pCO2 26±30 mmHg), (6) high-dose pentobarbital therapy, and (7) systemic cooling to attain moderate hypothermia (32±33°C). Placement of an ICP monitor and use of a stepwise treatment algorithm are both essential for managing ICP effectively in the ICU setting. Increased intracranial pressure (ICP) can result from a number of insults to the brain, including traumatic brain injury (TBI), stroke, encephalitis, neoplasms, and abscesses (Table 1). The fundamental abnormality common to these diverse disease states is an increase in intracranial volume. Accordingly, all treatments for elevated ICP work by reducing intracranial volume. Prompt recognition and treatment of elevated ICP is essential because sustained elevated ICP can cause brain damage or be rapidly fatal.
Real Time 3D-Handwritten Character and Gesture Recognition for Smartphone

This Paper presents two different models for smartphone based 3D-handwritten character and gesture recognition. Smartphones available today are equipped with inbuilt sensors like accelerometer, gyroscope, and gravity sensor, which are able to provide data on motion of the device in 3D space. The sensor readings are easily accessible through native operating system provided by the smartphone. The accelerometer and gyroscope sensors data have been used in the models to make the systems more expressive and to disambiguate recognition. The acceleration generated by the hand motions are generated by the 3-axes accelerometer present in smartphone. Data from gyroscope is used to obtain the quaternion rotation matrix, which is used to remove the tilting and inclination offset. An automatic segmentation algorithm is implemented to identify individual gesture or character in a sequence. First model is based on training and evaluation mode, while second one is based on training-less algorithm. The recognition models as presented in this paper can be used for both user dependent and user independent 3d-character and gesture recognition. Results shows that, Model I gives an efficient recognition rate ranging from 90% to 94% obtained with minimal training sequence,
New World Tryptamine Hallucinogens and the Neuroscience of Ayahuasca.

New World indigenous peoples are noted for their sophisticated use of psychedelic plants in shamanic and ethnomedical practices. The use of psychedelic plant preparations among New World tribes is far more prevalent than in the Old World. Yet, although these preparations are botanically diverse, almost all are chemically similar in that their active principles are tryptamine derivatives, either DMT or related constituents. Part 1 of this paper provides an ethnopharmacological overview of the major tryptamine-containing New World hallucinogens. Part 2 focuses on ayahuasca and its effects on the human brain. Using complementary neurophysiological and neuroimaging techniques, we have identified brain areas involved in the cognitive effects induced by this complex botanical preparation. Initial SPECT data showed that ayahuasca modulated activity in higher order association areas of the brain. Increased blood perfusion was observed mainly in anterior brain regions encompassing the frontomedial and anterior cingulate cortices of the frontal lobes, and in the medial regions of the temporal lobes. On the other hand, applying spectral analysis and source location techniques to cortical electrical signals, we found changes in neuronal activity that predominated in more posterior sensory-selective areas of the brain. Now, using functional connectivity analysis of brain oscillations we have been able to reconcile these seemingly contradictory findings. By measuring transfer entropy, a metric based on information theory, we have shown that ayahuasca temporarily modifies the ordinary flow of information within the brain. We propose a model in which ayahuasca reduces top-down constraints and facilitates bottom-up information transfer. By simultaneously enhancing endogenous cortical excitability and reducing higher-order cognitive control, ayahuasca temporarily disrupts neural hierarchies allowing inner exploration and a new outlook on reality.
Making Counterfactual Assumptions

This paper provides an update semantics for counterfactual conditionals. It does so by giving a dynamic twist to the ‘Premise Semantics’ for counterfactuals developed in Veltman (1976) and Kratzer (1981). It also offers an alternative solution to the problems with naive Premise Semantics discussed by Angelika Kratzer in ‘Lumps of Thought’ (Kratzer, 1989). Such an alternative is called for given the triviality results presented in Kanazawa et al. (2005, this issue).
Millisecond-timescale, genetically targeted optical control of neural activity

Temporally precise, noninvasive control of activity in well-defined neuronal populations is a long-sought goal of systems neuroscience. We adapted for this purpose the naturally occurring algal protein Channelrhodopsin-2, a rapidly gated light-sensitive cation channel, by using lentiviral gene delivery in combination with high-speed optical switching to photostimulate mammalian neurons. We demonstrate reliable, millisecond-timescale control of neuronal spiking, as well as control of excitatory and inhibitory synaptic transmission. This technology allows the use of light to alter neural processing at the level of single spikes and synaptic events, yielding a widely applicable tool for neuroscientists and biomedical engineers.
Biexciton Emission from Edges and Grain Boundaries of Triangular WS₂ Monolayers.

Monolayer tungsten disulfides (WS2) constitute a high quantum yield two-dimensional (2D) system, and can be synthesized on a large area using chemical vapor deposition (CVD), suggesting promising nanophotonics applications. However, spatially nonuniform photoluminescence (PL) intensities and peak wavelengths observed in single WS2 grains have puzzled researchers, with the origins of variation in relative contributions of excitons, trions, and biexcitons to the PL emission not well understood. Here, we present nanoscale PL and Raman spectroscopy images of triangular CVD-grown WS2 monolayers of different sizes, with these images obtained under different temperatures and values of excitation power. Intense PL emissions were observed around the edges of individual WS2 grains and the grain boundaries between partly merged WS2 grains. The predominant origin of the main PL emission from these regions changed from neutral excitons to trions and biexcitons with increasing laser excitation power, with biexcitons completely dominating the PL emission for the high-power condition. The intense PL emission and the preferential formation of biexcitons in the edges and grain boundaries of monolayer WS2 were attributed to larger population of charge carriers caused by the excessive incorporation of growth promoters during the CVD, suggesting positive roles of excessive carriers in the PL efficiency of TMD monolayers. Our comprehensive nanoscale spectroscopic investigation sheds light on the dynamic competition between exciton complexes occurring in monolayer WS2, suggesting a rich variety of ways to engineer new nanophotonic functions using 2D transition metal dichalcogenide monolayers.
Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey

Mobile Opportunistic Network (MON) is characterized by intermittent connectivity where communication largely depends on the mobility pattern of the participating nodes. In MON, a node can take the custody of a packet for a long time and carry it until a new forwarding path has been established, unlike mobile adhoc network (MANET), where a node must drop the packet otherwise. Therefore, routing in MON depends on the repeated make-and-break of communication links, which again depends on the mobility of the nodes as they encounter and drift away from each other. MONs can simply be formed by humans carrying hand-held devices (like Personal Digital Assistant [PDAs] or cell phones) or on-board devices installed in vehicles. Therefore, with mobility playing a major role in the performance of MON, researchers have repeatedly tried to understand the nature of mobility with respect to humans, vehicles, and wild animals. To study the nature of mobility, researchers have collected mobility traces, proposed mobility models, and analyzed the performance of MON with respect to various mobility parameters. This article provides a detailed survey of different mobility models which have been proposed to date and how mobility largely determines the performance of opportunistic routing. We divide the article into four major sections. First, we provide a detailed survey of all the synthetic mobility models which have been developed to date. Second, we study the various mobility traces which have been collected and analyzed. Third, we study how mobility parameters affect the performance of MON. Finally, we highlight on some of the research areas and open challenges which yet remain unsolved.
Models and mechanisms of hyperalgesia and allodynia.

Hyperalgesia and allodynia are frequent symptoms of disease and may be useful adaptations to protect vulnerable tissues. Both may, however, also emerge as diseases in their own right. Considerable progress has been made in developing clinically relevant animal models for identifying the most significant underlying mechanisms. This review deals with experimental models that are currently used to measure (sect. II) or to induce (sect. III) hyperalgesia and allodynia in animals. Induction and expression of hyperalgesia and allodynia are context sensitive. This is discussed in section IV. Neuronal and nonneuronal cell populations have been identified that are indispensable for the induction and/or the expression of hyperalgesia and allodynia as summarized in section V. This review focuses on highly topical spinal mechanisms of hyperalgesia and allodynia including intrinsic and synaptic plasticity, the modulation of inhibitory control (sect. VI), and neuroimmune interactions (sect. VII). The scientific use of language improves also in the field of pain research. Refined definitions of some technical terms including the new definitions of hyperalgesia and allodynia by the International Association for the Study of Pain are illustrated and annotated in section I.
Research on question classification for Automatic Question Answering

Automatic Question Answering (QA) is a hot topic in both Natural Language Processing (NLP) and Information Retrieval (IR). And question classification is the key step of a successful automatic QA system. In this paper, an SVM-based approach is firstly proposed as our baseline system. Then two additional features, i.e., top-words and dependency relations, are introduced to improve the performance of our baseline system. Experiments on the UIUC corpus show that the introduced features can improve our baseline system significantly. In comparison with the state-of-the-art system, our proposed approach also achieves better performance.
EDIBLE MUSHROOMS AS A SOURCE OF VALUABLE NUTRITIVE CONSTITUENTS

An overview of Polish and foreign literature concerning the chemical composition of edible mushrooms both cultivated and harvested in natural sites in Poland and abroad is presented. 100 g of fresh mushrooms contains 5.3-14.8 g dry matter, 1.5-6.7 g of carbohydrates, 1.5-3.0 g of protein and 0.3-0.4 g of fat. Mushrooms are a high valued source of mineral constituents, particularly potassium, phosphorus and magnesium and of vitamins of the B group, chiefly vitamins B2 and B3 and also vitamin D. The aroma of the discussed raw materials is based on about 150 aromatic compounds. The mushrooms can be a source of heavy metals and radioactive substances. They are also characterized by the occurrence of numerous enzymes.
Modelling maintenance in railway infrastructure management

Conventional railway track, of the type seen throughout the majority of the UK rail network, is made up of rails that are fixed to sleepers (ties), which, in turn, are supported by ballast. The ballast comprises crushed, hard stone and its main purpose is to distribute loads from the sleepers as rail traffic passes along the track. Over time, the stones in the ballast deteriorate, leading the track to settle and the geometry of the rails to change. Changes in geometry must be addressed in order that the track remains in a safe condition. Track inspections are carried out by measurement trains, which use sensors to precisely measure the track geometry. Network operators aim to carry out maintenance before the track geometry degrades to such an extent that speed restrictions or line closures are required. However, despite the fact that it restores the track geometry, the maintenance also worsens the general condition of the ballast, meaning that the rate of track geometry deterioration tends to increase as the amount of maintenance performed to the ballast increases. This paper considers the degradation, inspection and maintenance of a single one eighth of a mile section of railway track. A Markov model of such a section is produced. Track degradation data from the UK rail network has been analysed to produce degradation distributions which are used to define transition rates within the Markov model. The model considers the changing deterioration rate of the track section following maintenance and is used to analyse the effects of changing the level of track geometry degradation at which maintenance is requested for the section. The results are also used to show the effects of unrevealed levels of degradation. A model such as the one presented can be used to form an integral part of an asset management strategy and maintenance decision making process for railway track.
AFRAID: Fraud detection via active inference in time-evolving social networks

Fraud is a social process that occurs over time. We introduce a new approach, called AFRAID, which utilizes active inference to better detect fraud in time-varying social networks. That is, classify nodes as fraudulent vs. non-fraudulent. In active inference on social networks, a set of unlabeled nodes is given to an oracle (in our case one or more fraud inspectors) to label. These labels are used to seed the inference process on previously trained classifier(s). The challenge in active inference is to select a small set of unlabeled nodes that would lead to the highest classification performance. Since fraud is highly adaptive and dynamic, selecting such nodes is even more challenging than in other settings. We apply our approach to a real-life fraud data set obtained from the Belgian Social Security Institution to detect social security fraud. In this setting, fraud is defined as the intentional failing of companies to pay tax contributions to the government. Thus, the social network is composed of companies and the links between companies indicate shared resources. Our approach, AFRAID, outperforms the approaches that do not utilize active inference by up to 15% in terms of precision.
Efficient Data Encoding for Convolutional Neural Network application

This article presents an approximate data encoding scheme called Significant Position Encoding (SPE). The encoding allows efficient implementation of the recall phase (forward propagation pass) of Convolutional Neural Networks (CNN)—a typical Feed-Forward Neural Network. This implementation uses only 7 bits data representation and achieves almost the same classification performance compared with the initial network: on MNIST handwriting recognition task, using this data encoding scheme losses only 0.03% in terms of recognition rate (99.27% vs. 99.3%). In terms of storage, we achieve a 12.5% gain compared with an 8 bits fixed-point implementation of the same CNN. Moreover, this data encoding allows efficient implementation of processing unit thanks to the simplicity of scalar product operation—the principal operation in a Feed-Forward Neural Network.
Iterative Hard Thresholding for Compressed Sensing

Compressed sensing is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper we present a theoretical analysis of the iterative hard thresholding algorithm when applied to the compressed sensing recovery problem. We show that the algorithm has the following properties (made more precise in the main text of the paper) • It gives near-optimal error guarantees. • It is robust to observation noise. • It succeeds with a minimum number of observations. • It can be used with any sampling operator for which the operator and its adjoint can be computed. • The memory requirement is linear in the problem size.  • Its computational complexity per iteration is of the same order as the application of the measurement operator or its adjoint. • It requires a fixed number of iterations depending only on the logarithm of a form of signal to noise ratio of the signal. • Its performance guarantees are uniform in that they only depend on properties of the sampling operator and signal sparsity.