nils-herrmann/clean_scirepeval huggingface dataset:
Germline mutations of the PTEN gene in Cowden disease, an inherited breast and thyroid cancer syndrome

Cowden disease (CD) is an autosomal dominant cancer predisposition syndrome associated with an elevated risk for tumours of the breast, thyroid and skin1–2. Lhermitte-Duclos disease (LDD) cosegregates with a subset of CD families and is associated with macrocephaly, ataxia and dysplastic cerebellar gangliocytomatosis3–4. The common feature of these diseases is a predisposition to hamartomas, benign tumours containing differentiated but disorganized cells indigenous to the tissue of origin. Linkage analysis has determined that a single locus within chromosome 10q23 is likely to be responsible for both of these diseases5. A candidate tumour suppressor gene (PTEN) within this region is mutated in sporadic brain, breast and prostate cancer6. Another group has independently isolated the same gene, termed MMAC1, and also found somatic mutations throughout the gene in advanced sporadic cancers7. Mutational analysis of PTEN in CD kindreds has identified germline mutations in four of five families. We found nonsense and missense mutations that are predicted to disrupt the protein tyrosine/dual-specificity phosphatase domain of this gene. Thus, PTEN appears to behave as a tumour suppressor gene in the germline. Our data also imply that PTEN may play a role in organizing the relationship of different cell types within an organ during development.
The Impact of Project Organizational Culture on the Performance of Construction Projects

Cultural influence has recently received significant attention from academics due to its vital role in the success or failure of a project. In the construction industry, several empirical investigations have examined the influence of culture on project management. The aim of this study is to determine the impact of project organizational culture on the performance of construction projects. A total of 199 completed construction projects in Vietnam with specific data gathering through questionnaires were analyzed. The findings reveal that contractor commitment to contract agreements is the most significant cultural factor affecting project performance. Goal alignment and reliance, contractor commitment, and worker orientation (i.e., commitment to workers) contribute to improved overall performance and participant satisfaction. Contractor commitment and cooperative orientation enhance labor productivity, whereas goal alignment and trust and contractor commitment ensure learning performance (i.e., learning from experience). The findings of this study may assist construction professionals in implementing practices that can contribute to the sustainability and success of construction projects.
Crossing Minimization in Storyline Visualization

A storyline visualization is a layout that represents the temporal dynamics of social interactions along time by the convergence of chronological lines. Among the criteria oriented at improving aesthetics and legibility of a representation of this type, a small number of line crossings is the hardest to achieve. We model the crossing minimization in the storyline visualization problem as a multi-layer crossing minimization problem with tree constraints. Our algorithm can compute a layout with the minimum number of crossings of the chronological lines. Computational results demonstrate that it can solve instances with more than 100 interactions and with more than 100 chronological lines to optimality.
Towards Digital Twins Cloud Platform: Microservices and Computational Workflows to Rulea Smart Factory

The concept of "Industry 4.0" considers smart factories as data-driven and knowledge enabled enterprise intelligence. In such kind of factory, manufacturing processes and final products are accompanied by virtual models -- Digital Twins. To support Digital Twins concept, a simulation model for each process or system should be implemented as independent computational service. The only way to implement an orchestration of a set of independent services and provide scalability for simulation is to use a cloud computing platform as a provider of the computing infrastructure. In this paper, we describe a Digital Twin-as-a-Service (DTaaS) model for simulation and prediction of industrial processes using Digital Twins.
Signaling from Synapse to Nucleus: Postsynaptic CREB Phosphorylation during Multiple Forms of Hippocampal Synaptic Plasticity

Phosphorylation of the transcription factor CREB is thought to be important in processes underlying long-term memory. It is unclear whether CREB phosphorylation can carry information about the sign of changes in synaptic strength, whether CREB pathways are equally activated in neurons receiving or providing synaptic input, or how synapse-to-nucleus communication is mediated. We found that Ca(2+)-dependent nuclear CREB phosphorylation was rapidly evoked by synaptic stimuli including, but not limited to, those that induced potentiation and depression of synaptic strength. In striking contrast, high frequency action potential firing alone failed to trigger CREB phosphorylation. Activation of a submembranous Ca2+ sensor, just beneath sites of Ca2+ entry, appears critical for triggering nuclear CREB phosphorylation via calmodulin and a Ca2+/calmodulin-dependent protein kinase.
An improved Brain Storm Optimization algorithm based on graph theory

Recently, inspired by the human brainstorming process, a new kind of metaheuristic algorithm, called brain storm optimization (BSO) algorithm was proposed for global optimization. Experimental results have shown its excellent performance when solving optimization problems. In order to further improve the search ability of the BSO, this paper proposes an improved BSO (IBSO) algorithm by introducing graph theory into it. In IBSO, new individuals will be generated to replace some old individuals when the BSO algorithm is in a poor status. Whether a BSO algorithm is in a poor status is determined by the length of Hamiltonian cycle, which can be obtained by transferring all the individuals into an undirected weight graph. A Hamiltonian cycle and its length will be computed according to a modified cycle algorithm. The proposed IBSO algorithm is tested on twelve benchmarks, and the experimental results illustrate its effectiveness.
Flexible Navigation: Finite state machine-based integrated navigation and control for ROS enabled robots

This paper describes the Flexible Navigation system that extends the ROS Navigation stack and compatible libraries to separate computation from decision making, and integrates the system with FlexBE — the Flexible Behavior Engine, which provides intuitive supervision with adjustable autonomy. Although the ROS Navigation plugin model offers some customization, many decisions are internal to move_base. In contrast, the Flexible Navigation system separates global planning from local planning and control, and uses a hierarchical finite state machine to coordinate behaviors. The Flexible Navigation system includes Python-based state implementations and ROS nodes derived from the move_base plugin model to provide compatibility with existing libraries as well as future extensibility. The paper concludes with complete system demonstrations in both simulation and hardware using the iRobot Create and Kobuki-based Turtlebot running under ROS Kinetic. The system supports multiple independent robots.
Gene regulatory networks in the immune system.

We describe examples of genomic control circuits that underlie developmental transitions and cellular activation states within the immune system. The architectures of simple gene regulatory networks (GRNs) are highlighted to emphasize conservation of regulatory motifs. The regulatory logic and the cell fate dynamics of each simple GRN, the latter revealed by mathematical and computational modeling, are elaborated. This framework is being expanded to enable the assembly and analysis of complex GRNs using genomic, computational, and high-throughput experimental methodologies. The paradigm will provide new insights into immune cell development and function, and into the pathophysiology of autoimmune and inflammatory disorders, as well as immune malignancies.
IoT Edge Device Based Key Frame Extraction for Face in Video Recognition

Following the development of computing and communication technologies, the idea of Internet of Things (IoT) has been realized not only at research level but also at application level. Among various IoT-related application fields, biometrics applications, especially face recognition, are widely applied in video-based surveillance, access control, law enforcement and many other scenarios. In this paper, we introduce a Face in Video Recognition (FivR) framework which performs real-time key-frame extraction on IoT edge devices, then conduct face recognition using the extracted key-frames on the Cloud back-end. With our key-frame extraction engine, we are able to reduce the data volume hence dramatically relief the processing pressure of the cloud back-end. Our experimental results show with IoT edge device acceleration, it is possible to implement face in video recognition application without introducing the middle-ware or cloud-let layer, while still achieving real-time processing speed.
Fast and viewpoint robust human detection for SAR operations

There are many advantages in using UAVs for search and rescue operations. However, detecting people from a UAV remains a challenge: the embedded detector has to be fast enough and viewpoint robust to detect people in a flexible manner from aerial views. In this paper we propose a processing pipeline to 1) reduce the search space using infrared images and to 2) detect people whatever the roll and pitch angles of the UAV's acquisition system. We tested our approach on a multimodal aerial view dataset and showed that it outperforms the Integral Channel Features (ICF) detector in this context. Moreover, this approach allows real-time compatible detection.
Learning a sparse codebook of facial and body microexpressions for emotion recognition

Obtaining a compact and discriminative representation of facial and body expressions is a difficult problem in emotion recognition. Part of the difficulty is capturing microexpressions, i.e., short, involuntary expressions that last for only a fraction of a second: at a micro-temporal scale, there are so many other subtle face and body movements that do not convey semantically meaningful information. We present a novel approach to this problem by exploiting the sparsity of the frequent micro-temporal motion patterns. Local space-time features are extracted over the face and body region for a very short time period, e.g., few milliseconds. A codebook of microexpressions is learned from the data and used to encode the features in a sparse manner. This allows us to obtain a representation that captures the most salient motion patterns of the face and body at a micro-temporal scale. Experiments performed on the AVEC 2012 dataset show our approach achieving the best published performance on the arousal dimension based solely on visual features. We also report experimental results on audio-visual emotion recognition, comparing early and late data fusion techniques.
Introducing ‘theomorphic robots’

As robots are going to spread in human society, the study of their appearance becomes a critical matter when assessing robots performance and appropriateness for an application and for the employment in different countries, with different background cultures and religions. Robot appearance categories are commonly divided in anthropomorphic, zoomorphic and functional. In this paper, we offer a theoretical contribution by introducing a new category, called `theomorphic robots', in which robots carry the shape and the identity of a supernatural creature or object within a religion. Discussing the theory of dehumanisation and the different categories of supernatural among different religions, we hypothesise the possible advantages of the theomorphic design for different applications.
WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

1Computer Science Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia 2Computer Science Department/Computer Information Systems Department, King Abdullah II School for Information Technology (KASIT), The University of Jordan, Amman, Jordan 3Computer Science Department, College of Computation and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
Fuzzy Integer Linear Programming Mathematical Models for Examination Timetable Problem

Timetable Problem (ETP) is NP–Hard combinatorial optimization problem. It has received tremendous research attention during the past few years given its wide use in universities. ETP can be defined as assignment of courses to be examined, candidates to time periods and examination rooms while satisfying a set of constraints which may be either hard or soft. Several methods have been proposed most of which are based on heuristics like Search techniques, Evolutionary Computation etc. In this Paper, we develop three mathematical models for Netaji Subhas Open University, Kolkata, India using Fuzzy Integer Linear Programming (FILP) technique. In most real life situations, information available in is not exact, lacks precision and has an inherent degree of vagueness. To deal with this we model various allocation variables through fuzzy numbers expressing lack of precision the decision maker has. The solution to the problem is obtained using Fuzzy number ranking method. Each feasible solution has fuzzy number obtained by Fuzzy objective function. The different FILP technique performance are demonstrated by experimental data generated through extensive simulation from Netaji Subhas Open University, Kolkata, India in terms of its execution times. The proposed FILP models are compared with commonly used heuristic viz. Integer Linear Programming approach on experimental data which gives an idea about quality of heuristic. The techniques are also compared with different Artificial Intelligence based heuristics for ETP with respect to best and mean cost as well as execution time measures on Carter benchmark datasets to illustrate its effectiveness. FILP paradigm takes an appreciable amount of time to generate satisfactory solution in comparison to other heuristics. The formulation thus serves as good benchmark for other heuristics. The experimental study presented here focuses on producing a methodology that generalizes well over spectrum of techniques that generates significant results for one or more datasets. The performance of FILP model is finally compared to the best results cited in literature for Carter benchmarks to assess its potential. The problem can be further reduced by formulating with lesser number of allocation variables it without affecting optimality of solution obtained. FLIP model for ETP can also be adapted to solve other ETP as well as combinatorial optimization problems. To the best of our knowledge this is first work on ETP using FILP technique.
Ultra-Steep Cut-Off Double Mode SAW Filter and Its Application to a PCS Duplexer

This paper describes the double mode surface acoustic wave (DMS) filter design techniques for achieving the ultra-steep cut-off characteristics and low insertion loss required for the Rx filter in the personal communications services (PCS) duplexer. Simulations demonstrate that the optimal combination of the additional common ground inductance Lg and the coupling capacitance Cc between the input and output terminals of the DMS filters drastically enhances the skirt steepness and attenuation for the lower frequency side of the passband. Based on this result, we propose a novel DMS filter structure that utilizes the parasitic reactance generated in bonding wires and interdigital transducer (IDT) busbars as Lg and Cc, respectively. Because the proposed structure does not need any additional reactance component, the filter size can be small. Moreover, we propose a compact multiple-connection configuration for low insertion loss. Applying these technologies to the Rx filter, we successfully develop a PCS SAW duplexer.
Performance comparison of 2D SLAM techniques available in ROS using a differential drive robot

This document presents a performance comparison of three 2D SLAM techniques available in ROS: Gmapping, Hec-torSLAM and CRSM SLAM. These algorithms were evaluated using a Roomba 645 robotic platform with differential drive and a RGB-D Kinect sensor as an emulator of a scanner lasser. All tests were realized in static indoor environments. To improve the quality of the maps, some rosbag files were generated and used to build the maps in an off-line way.
On a New Improvement-Based Acquisition Function for Bayesian Optimization

Bayesian optimization (BO) is a popular algorithm for solving challenging optimization tasks. It is designed for problems where the objective function is expensive to evaluate, perhaps not available in exact form, without gradient information and possibly returning noisy values. Different versions of the algorithm vary in the choice of the acquisition function, which recommends the point to query the objective at next. Initially, researchers focused on improvement-based acquisitions, while recently the attention has shifted to more computationally expensive informationtheoretical measures. In this paper we present two major contributions to the literature. First, we propose a new improvement-based acquisition function that recommends query points where the improvement is expected to be high with high confidence. The proposed algorithm is evaluated on a large set of benchmark functions from the global optimization literature, where it turns out to perform at least as well as current state-of-the-art acquisition functions, and often better. This suggests that it is a powerful default choice for BO. The novel policy is then compared to widely used global optimization solvers in order to confirm that BO methods reduce the computational costs of the optimization by keeping the number of function evaluations small. The second main contribution represents an application to precision medicine, where the interest lies in the estimation of parameters of a partial differential equations model of the human pulmonary blood circulation system. Once inferred, these parameters can help clinicians in diagnosing a patient with pulmonary hypertension without going through the standard invasive procedure of right heart catheterization, which can lead to side effects and complications (e.g. severe pain, internal bleeding, thrombosis).
Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams

Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in individual decision making in multiagent settings face the task of having to reason about other agents’ actions, which may in turn involve reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. For the purposes of this study, individual, self-interested decision making in multiagent settings is modeled using interactive dynamic influence diagrams (I-DID). These are graphical models with the benefit that they naturally offer a factored representation of the problem, allowing agents to ascribe dynamic models to others and reason about them. We demonstrate that an implication of bounded, finitely-nested reasoning by a self-interested agent is that we may not obtain optimal team solutions in cooperative settings, if it is part of a team. We address this limitation by including models at level 0 whose solutions involve reinforcement learning. We show how the learning is integrated into planning in the context of I-DIDs. This facilitates optimal teammate behavior, and we demonstrate its applicability to ad hoc teamwork on several problem domains and configurations.
Short-Range Wideband FMCW Radar for Millimetric Displacement Measurements

The frequency-modulated continuous-wave (FMCW) radar is an alternative to the pulse radar when the distance to the target is short. Typical FMCW radar implementations have a homodyne architecture transceiver which limits the performances for short-range applications: The beat frequency can be relatively small and placed in the frequency range affected by the specific homodyne issues (dc offset, self-mixing, and 1/f noise). In addition, one classical problem of an FMCW radar is that the voltage-controlled oscillator adds a certain degree of nonlinearity which can cause a dramatic resolution degradation for wideband sweeps. This paper proposes a short-range X-band FMCW radar platform which solves these two problems by using a heterodyne transceiver and a wideband nonlinearity correction algorithm based on high-order ambiguity functions and time resampling. The platform's displacement measurement capability was tested on range profiles and synthetic aperture radar images acquired for various targets. The displacements were computed from the interferometric phase, and the measurement errors were situated below 0.1 mm for metal bar targets placed at a few meters from the radar.
Calcite: Completing Code Completion for Constructors Using Crowds

Calcite is a new Eclipse plugin that helps address the difficulty of understanding and correctly using an API. Calcite finds the most popular ways to instantiate a given class or interface by using code examples. To allow the users to easily add these object instantiations to their code, Calcite adds items to the popup completion menu that will insert the appropriate code into the user’s program. Calcite also uses crowd sourcing to add to the menu instructions in the form of comments that help the user perform functions that people have identified as missing from the API. In a user study, Calcite improved users’ success rate by 40%.
Service-Dominant Strategic Sourcing: Value Creation Versus Cost Saving

The concept of strategic sourcing recognizes that procurement is not just a cost function, but supports the firm’s effort to achieve its long-term objectives. Organizations more and more expect from their chief procurement officer (CPO) to develop long-term and short-term plans in procurement. Typically, however, procurement is driven by a tactical spend management sourcing process aimed at cost saving targets, which is not able to support organizations in achieving strategic objectives like innovation, value creation, sustainable competitive advantage and long-term partnerships. A paradigm shift from a tactical way of thinking about sourcing to a more strategic way of thinking is needed by focusing on value-driven targets. To help realize the new paradigm of value-driven management in sourcing, we designed a systemic view on strategic sourcing based on Service-Dominant Logic and (service) systems thinking. We used this systemic view to develop the conceptual basis of a new modeling and analysis language that helps organizations in exploring sourcing alternatives according to value-driven management.