nils-herrmann/clean_scirepeval huggingface dataset:
Basic local alignment search tool.

A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
Ehlers-Danlos syndromes and Marfan syndrome.

Ehlers-Danlos syndromes (EDS) and Marfan syndrome (MFS) are multisystemic disorders that primarily affect the soft connective tissues. Both disorders have benefited from recent advances in clinical and molecular characterization, allowing improvements in clinical diagnosis and management. EDS are a heterogeneous group of conditions characterized by skin hyperextensibility, atrophic scarring, joint hypermobility and generalized tissue fragility. The current classification proposes six subtypes based on clinical, biochemical and molecular characteristics. However, examples of unclassified variants and 'overlap phenotypes' are becoming more common. Mutations in genes encoding fibrillar collagens or collagen-modifying enzymes have been identified in most forms of EDS, including the classic and vascular subtypes (collagen type V and III, respectively), and the rare arthrochalasis, kyphoscoliosis and dermatosparaxis variants (type I collagen defects). To date, the genetic background of the hypermobility type of EDS remains unclear, although some new insights have been gained recently. MFS is an autosomal-dominant disorder that affects the cardiovascular, ocular and skeletal system with aortic root dilation/dissection, ectopia lentis and bone overgrowth, respectively. Advances in therapeutic, mainly surgical, techniques have improved median survival significantly, yet severe morbidity and a substantial risk for premature mortality remain associated. The disorder is caused by mutations in the FBN1 gene, encoding the microfibrillar protein fibrillin-1. Recently, new insights in the pathogenesis changed the prevailing concept of this type 1 fibrillinopathy as a structural disorder of the connective tissue into a developmental abnormality manifesting perturbed cytokine signalling. These findings have opened new and unexpected targets for aetiologically directed drug treatments.
Verbal fluency tests: Normative data for the Latin American Spanish speaking adult population.

To generate normative data for the Verbal Fluency Tests across 11 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate.



The sample consisted of 3,977 healthy adults who were recruited from Argentina, Bolivia, Chile, Cuba, El Salvador, Guatemala, Honduras, Mexico, Paraguay, Peru, and, Puerto Rico. Each subject was administered the Verbal Fluency Test as part of a larger neuropsychological battery. A standardized five-step statistical procedure was used to generate the norms.



The final multiple linear regression models for the letter F explained 8-30% of the variance, 7-32% for letter A, 8-32% for the letter S, and 16-43% for the animal category in Verbal Fluency Test scores. Although t-tests showed significant differences between men and women on the Verbal Fluency Test, they did not have an effect size larger than 0.3. As a result, gender-adjusted norms were not generated.



This is the first normative multicenter study conducted in Latin America aiming to create norms for the Verbal Fluency Test; this study will have important outcomes for the future of neuropsychology in the region.
SpatialHadoop: towards flexible and scalable spatial processing using mapreduce

Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.
A general framework for benchmarking firewall optimization techniques

Firewalls are among the most pervasive network security mechanisms, deployed extensively from the borders of networks to end systems. The complexity of modern firewall policies has raised the computational requirements for firewall implementations, potentially limiting the throughput of networks. Administrators currently rely on ad hoc solutions to firewall optimization. To address this problem, a few automatic firewall optimization techniques have been proposed, but there has been no general approach to evaluate the optimality of these techniques. In this paper we present a general framework for rule-based firewall optimization. We give a precise formulation of firewall optimization as an integer programming problem and show that our framework produces optimal reordered rule sets that are semantically equivalent to the original rule set. Our framework considers the complex interactions among the rules in firewall configurations and relies on a novel partitioning of the packet space defined by the rules themselves. For validation, we employ this framework on real firewall rule sets for a quantitative evaluation of existing heuristic approaches. Our results indicate that the framework is general and faithfully captures performance benefits of firewall optimization heuristics.
Cognitive programs: software for attention's executive

What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention.
Quasi-Dense Reconstruction from Image Sequence

This paper proposes a quasi-dense reconstruction from uncalibrated sequence. The main innovation is that all geometry is computed based on re-sampled quasi-dense correspondences rather than the standard sparse points of interest. It not only produces more accurate and robust reconstruction due to highly redundant and well spread input data, but also fills the gap of insufficiency of sparse reconstruction for visualization application. The computational engine is the quasi-dense 2-view and the quasi-dense 3-view algorithms developed in this paper. Experiments on real sequences demonstrate the superior performance of quasi-dense w.r.t. sparse reconstruction both in accuracy and robustness.
Siphon-based deadlock prevention for a class of S4PR generalized Petri nets

The paper proposes a new technique for deadlock control for a class of generalized Petri net (PN) with S4PR net, from the concept of control siphon. One important property of PN is to design structure, in terms of siphons, in order to the characterization of the deadlock prevent, and analytic structure of the synchronization subsystem which is needed to control place in a system. An efficient siphon method provides a solution to calculating minimal siphons, which is lead to constructing an optimal supervisor, ensuring that the system live state is preserved. The experimental results show the illustration of the proposed methods by using an S4PR net with an example is represented in FMS. The simulation result is also provided as a Petri nets the conception as well as the integration with MATLAB.
Generalized velocity obstacles

We address the problem of real-time navigation in dynamic environments for car-like robots. We present an approach to identify controls that will lead to a collision with a moving obstacle at some point in the future. Our approach generalizes the concept of velocity obstacles, which have been used for navigation among dynamic obstacles, and takes into account the constraints of a car-like robot. We use this formulation to find controls that will allow collision free navigation in dynamic environments. Finally, we demonstrate the performance of our algorithm on a simulated car-like robot among moving obstacles.
Feeling Love and Doing More for Distant Others : Specific Positive Emotions Differentially Affect Prosocial Consumption

Marketers often employ a variety of positive emotions to encourage consumption or promote a particular behavior (e.g., buying, donating, recycling) to benefit an organization or cause. The authors show that specific positive emotions do not universally increase prosocial behavior but, rather, encourage different types of prosocial behavior. Four studies show that whereas positive emotions (i.e., love, hope, pride, and compassion) all induce prosocial behavior toward close entities (relative to a neutral emotional state), only love induces prosocial behavior toward distant others and international organizations. Love’s effect is driven by a distinct form of broadening, characterized by extending feelings of social connection and the boundary of caring to be more inclusive of others regardless of relatedness. Love—as a trait and a momentary emotion— is unique among positive emotions in fostering connectedness that other positive emotions (hope and pride) do not and broadening behavior in a way that other connected emotions (compassion) do not. This research contributes to the broaden-and-build theory of positive emotion by demonstrating a distinct type of broadening for love and adds an important qualification to the general finding that positive emotions uniformly encourage prosocial behavior.
Improved lossless intra coding for H.264/MPEG-4 AVC

A new lossless intra coding method based on sample-by-sample differential pulse code modulation (DPCM) is presented as an enhancement of the H.264/MPEG-4 AVC standard. The H.264/AVC design includes a multidirectional spatial prediction method to reduce spatial redundancy by using neighboring samples as a prediction for the samples in a block of data to be encoded. In the new lossless intra coding method, the spatial prediction is performed based on samplewise DPCM instead of in the block-based manner used in the current H.264/AVC standard, while the block structure is retained for the residual difference entropy coding process. We show that the new method, based on samplewise DPCM, does not have a major complexity penalty, despite its apparent pipeline dependencies. Experiments show that the new lossless intra coding method reduces the bit rate by approximately 12% in comparison with the lossless intra coding method previously included in the H.264/AVC standard. As a result, the new method is currently being adopted into the H.264/AVC standard in a new enhancement project
Current Evidence and Opportunities for Expanding the Role of Occupational Therapy for Adults With Musculoskeletal Conditions.

Musculoskeletal conditions are the second greatest cause of disability worldwide, and chronic musculoskeletal conditions affect nearly the same percentage of the general population as chronic circulatory and respiratory conditions combined. Moreover, people with musculoskeletal conditions experience a significant decline in independence with daily activities and occupational performance, key areas targeted by occupational therapy interventions. This special issue of the American Journal of Occupational Therapy provides comprehensive summaries of evidence for the care of common musculoskeletal conditions, highlights important implications that support evidence-informed practice, and proposes ways to advance the practice of occupational therapy to improve the lives of people with musculoskeletal conditions.
POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction

Although much research has been done over the decades on the formulation of statistical regression models for road traffic relationships, they have been largely unsuitable due to the complexity of traffic characteristics. Traffic engineers have resorted to alternative methods such as neural networks, but despite some promising results, the difficulties in their design and implementation remain unresolved. In addition, the opaqueness of trained networks prevents understanding the underlying models. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy logic, thus constitute a more promising technique for modeling traffic flow. This paper describes the application of a specific class of fuzzy neural network known as the pseudo outer-product fuzzy neural network using the truth-value-restriction method (POPFNN-TVR) for short-term traffic flow prediction. The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.
Transparent operation of Kronecker product based full dimension MIMO to exploit 2D antenna array

A concept of full-dimension MIMO (FD-MIMO) adopting 2-dimensional (2D) antenna array has been proposed with intent to cope with practical challenges of massive MIMO systems. In this paper, we investigate an FD-MIMO operation that can be transparently applied with current 3GPP LTE specifications, and propose a novel method that facilitates an accurate estimation of channel quality indicator (CQI) at base station (BS). We first introduce a feedback scheme in which horizontal and vertical dimension channel information are separately reported. To further improve accuracy of modulation and coding scheme (MCS) selection to maximize system throughput, we propose an enhanced feedback scheme that is helpful to more accurate CQI estimation at the BS. In the evaluation results, we show performance improvement of the proposed scheme over the conventional MIMO scheme.
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data

Natural language generation plays a critical role in any spoken dialogue system. We present a new approach to natural language generation using recurrent neural networks in an encoderdecoder framework. In contrast with previous work, our model uses both lexicalized and delexicalized versions of slot-value pairs for each dialogue act. This allows our model to learn from all available data, rather than being restricted to learning only from delexicalized slot-value pairs. We show that this helps our model generate more natural sentences with better grammar. We further improve our model’s performance by initializing its weights from a pretrained language model. Human evaluation of our best-performing model indicates that it generates sentences which users find more natural and appealing.
SMART LIBRARY MANAGEMENT ROBOT USING LAB VIEW

The objective of this project is to develop smart library management system using robot. This scheme facilitates fast issuing, reissuing and returning of book with the help of robotic arm. It provides the book information and library associate information to the library management system and does not require the manual typing. In case of any difficulty faced by the robot when it does book searching process, the robot halts and sends an alarm. Typically we need librarian to pick the book and give up it to the person to whom the books are being issued. This is not easy task in case the library floor area is large. Humans take a lot of time for searching the book. To overcome this problem we are developing automation in library to fast diction of books using robotic arm with degrees of freedom. Robotic arm will pick and place book at desired location according to sensor data. To implement above mentioned tasks we are using national instruments myRIO model which has wide variety of sensors, actuators, and displays.  Labview, Myrio ,RFID, Robot, ,End-Effector.
Fostering Intrinsic Motivation in Early Childhood Classrooms

Young children are born with an innate curiosity to learn about their world. This intrinsically instigated learning is often called mastery motivation. Patterns of motivation are established at an early age. The early childhood years are crucial for establishing robust intrinsic motivational orientations which will last a lifetime. By the time many children reach school, much of their motivation has been lost or replaced with extrinsically motivated learning strategies. Preschools and elementary schools have been criticized for contributing to such negative motivational patterns in children. This can be changed. Early child care situations and preschools can instead be instrumental in the strengthening of children's motivation. The goal of this paper is to show that through an understanding of the beginnings of motivation, we can begin to find ways to build strong motivational patterns in children that can carry on to later years of learning.
Dynamical Models and Tracking Regret in Online Convex Programming

This paper describes a new online convex optimization method which incorporates a family of candidate dynamical models and establishes novel tracking regret bounds that scale with the comparator’s deviation from the best dynamical model in this family. Previous online optimization methods are designed to have a total accumulated loss comparable to that of the best comparator sequence, and existing tracking or shifting regret bounds scale with the overall variation of the comparator sequence. In many practical scenarios, however, the environment is nonstationary and comparator sequences with small variation are quite weak, resulting in large losses. The proposed Dynamic Mirror Descent method, in contrast, can yield low regret relative to highly variable comparator sequences by both tracking the best dynamical model and forming predictions based on that model. This concept is demonstrated empirically in the context of sequential compressive observations of a dynamic scene and tracking a dynamic social network.
A multi-scale target detection method for optical remote sensing images

Faster RCNN is a region proposal based object detection approach. It integrates the region proposal stage and classification stage into a single pipeline, which has both rapid speed and high detection accuracy. However, when the model is applied to the target detection of remote sensing imagery, faced with multi-scale targets, its performance is degraded. We analyze the influences of pooling operation and target size on region proposal, then a modified solution for region proposal is introduced to improve recall rate of multi-scale targets. To speed up the convergence of the region proposal networks, an improved generation strategy of foreground samples is proposed, which could suppresses the generation of non-effective foreground samples. Extensive evaluations on the remote sensing image dataset show that the proposed model can obviously improve detection accuracy for multi-scale targets, moreover the training of the model is rapid and high-efficient.
Design and optimisation of a voice coil motor (VCM) with a rotary actuator for an ultrasound scanner. IEEE Transactions on Industrial Electronics

This paper proposes a new application for the rotary VCM. In developing a low cost ultrasound scanner for the developing world an oscillating transducer is required to sweep over the skin. The ultrasound scanner must operate from a USB power supply in remote locations. The application requires a 3.3N force on the coils of the motor to overcome the inertia of the skin. A proof of concept prototype motor with electronics has been designed, simulated and tested. The VCM optimisation is discussed in detail with the unique separation of the magnets being critical to reduce the axial bearing forces for this application.
Detection of and compensation for shadows in colored urban aerial images

A new method for shadow detection in colored urban aerial images is proposed. First, a new imaging model of shadows is presented, which indicates that hue values of shadowed image areas are larger than those of these areas non-shadowed. Based on this model, a thresholding technique is employed to detect shadowed areas. After detection, the Retinex technique is applied to shadowed and non-shadowed areas individually to compensate for shadows. Experiment results show the effectiveness of the proposed method.