Miracle-dz/UniFAR-FTUs huggingface dataset:
A metatheoretical framework of diversity in teams

In the last 22 years, research on diversity in teams has been propelled by information processing and social categorization theories, and more recently, by theories of disparity/(in)justice and access to external networks. These theories stress different diversity processes, treating team diversity respectively as variety of information, as separation, as disparity, and as variety of access. We appraise this literature by identifying major problems in the way these four foundational theories are used either alone or in combination, arguing that the related theoretical models are inherently incomplete and static. In an attempt to resolve these problems, we introduce a metatheoretical framework that relates these four foundational theories according to the metadimensions of group boundary and diversity mindset. We also propose a metatheoretical model that identifies interactions among the four diversity processes and specifies diversity response patterns to team success or failure over time. Our metatheoretical approach resolves significant omissions in the literature and penetrates into the dynamic nature of team diversity in more complex, temporally sensitive and synthetic ways.
Contactless fever measurement based on thermal imagery analysis

Fever is a very common symptom mostly in infections. As it is a reaction of the immune system, fever is supporting the healing process. However, if fever is heavily rising, it can cause serious damages to the human body. By considering the aging society user group, it is sometimes difficult to detect high fever, either due to false fever measurements, or due to the absence of a second person, in cases of single person households. Therefore, in this paper, the authors propose the design and implementation of a contactless fever measurement system, based on thermal imagery analysis. A FLIR thermal camera was used for the thermal image acquisition, interfaced with a BeagleBone Black (BBB) board, running the proposed algorithm. A platform independent solution is proposed, based on open source libraries (OpenCV, Tesseract OCR), to allow robustness in contrast to standardized branded SDKs. The proposed approach enables a compact, lightweight, hygienic and real-time fever measurement solution tool for ambient assisted living environments.
Analyzing Service Order Data using Association Mining

Association mining was originally developed and successfully applied to analyze the sale transactions of retail goods. According to common practices of association mining, each item is treated equally. But in a service order transaction especially in a telecom setup, an item (service) can be in several states. We propose a simple solution to analyze service order data using association mining by tagging items with corresponding state identifiers. We successfully apply our result to an association mining based product recommender system. Results from random and näıve product recommender systems are also reported for the comparison purposes.
Supplementary information for Protein corona composition of PEGylated nanoparticles correlates strongly with amino acid composition of protein surface

Molecular dynamics simulations All the simulations were carried out using the program NAMD1 and the charmm27 force field2,3 with the extension for PEG4. Tip3p5 was used as model for the explicit treatment of water. An integration time step of 1 fs was used across the simulations. Simulations were carried out using periodic boundary conditions. Pressure and temperature were maintained constant at 1atm and 300K, respectively, during the simulations using the Langevin piston algorithm and Langevin thermostat6,7. A cutoff of 1.2 nm was used for the non-bonded interactions with a switch function. Long range electrostatic interactions were treated using the smooth particle mesh Ewald (PME) method8 with a grid spacing of about 0.1 nm. To prepare the PEG-water mixture, 64 PEG molecules (H-[O-CH2-CH2]n-OH, with either 4 or 7) were placed on n
Validity of LIDAS (LIfetime Depression Assessment Self-report): a self-report online assessment of lifetime major depressive disorder

Background There is a paucity of valid, brief instruments for the assessment of lifetime major depressive disorder (MDD) that can be used in, for example, large-scale genomics, imaging or biomarker studies on depression. We developed the LIfetime Depression Assessment Self-report (LIDAS), which assesses lifetime MDD diagnosis according to DSM criteria, and is largely based on the widely used Composite International Diagnostic Interview (CIDI). Here, we tested the feasibility and determined the sensitivity and specificity for measuring lifetime MDD with this new questionnaire, with a regular CIDI as reference. Method Sensitivity and specificity analyses of the online lifetime MDD questionnaire were performed in adults with (n = 177) and without (n = 87) lifetime MDD according to regular index CIDIs, selected from the Netherlands Study of Depression and Anxiety (NESDA) and Netherlands Twin Register (NTR). Feasibility was tested in an additional non-selective, population-based sample of NTR participants (n = 245). Results Of the 753 invited persons, 509 (68%) completed the LIDAS, of which 419 (82%) did this online. User-friendliness of the instrument was rated high. Median completion time was 6.2 min. Sensitivity and specificity for lifetime MDD were 85% [95% confidence interval (CI) 80–91%] and 80% (95% CI 72–89%), respectively. This LIDAS instrument gave a lifetime MDD prevalence of 20.8% in the population-based sample. Conclusions Measuring lifetime MDD with an online instrument was feasible. Sensitivity and specificity were adequate. The instrument gave a prevalence of lifetime MDD in line with reported population prevalences. LIDAS is a promising tool for rapid determination of lifetime MDD status in large samples, such as needed for genomics studies.
A white paper on the benefits of chipkill-correct ecc for pc server main memory

As the network-centric model of computing continues to mature, customers are constantly trying to evaluate what the correct price/performance point is for their business. For the growing number of businesses that choose a PC Server for a departmental, workgroup, or application server function, one of the key parameters is the reliability of the server. This paper addresses one area of concern in the RAS (Reliability, Availability, and Serviceability) arena of PC Servers that has been addressed thoroughly at the mainframe and midrange class of machines, but not at the lower end of the server spectrum: error recovery when an entire DRAM chip fails.
No SQL in Practice: A Write-Heavy Enterprise Application

The continuous information growth in current organizations has created a need for adaptation and innovation in the field of data storage. Alternative technologies such as NoSQL have been heralded as the solution to the ever-growing data requirements of the corporate world, but these claims have not been backed by many real world studies. Current benchmarks evaluate database performance by executing specific queries over mostly synthetic data. These artificial scenarios, then, prevent us from easily drawing conclusions for the real world and appropriately characterize the performance of databases in a real system. To counter this, we used a real world enterprise system with real corporate data to evaluate the performance characteristics of popular NoSQL databases and compare them to SQL counterparts. In particular, we present one of the first write-heavy evaluations using enterprise software and big data. We tested Cassandra, Mongo DB, Couchbase Server and MS SQL Server and compared their performance while handling demanding and large write requests from a real company with an electrical measurement enterprise system.
Establishing profiles on the use of number sense

Number sense includes the ability to use numbers and operations in a flexible and reasonable way. This work presents a case study on the use of number sense by eighth grade students (13-14 years old). We analyse individual interviews of eleven students that include number tasks that can be solved using strategies associated with number sense. By studying the strategies used by the students to answer the questions, we establish four profiles based on their preference for using number sense strategies, rules or algorithms, or on their lack of knowledge of basic number concepts.
Weighted-Position and Abstract Ranking Algorithm Based on User Profile for Metasearch Engine

To improve search precision, a model of personalized meta-search engine is presented. In this model, a query result ranking algorithm called Weighted-Position & Abstract Ranking based-on User Profile (WPARUP) is proposed, which integrates these threes factors: global similarity between query result's abstract and the query, the position information of query results, the relevance between member search engine and the user’s search topic. The results illustrate that the ranking algorithm can perfectly rank high relevancy results on query in the front of the list, thus improve the search precision.
Peer to Peer File Sharing Systems : What Matters to the End Users ?

Peer-to-peer systems have been received much attention recently. However, few studies have examined what makes them successful from the user point of view. For example, how important is the interface for the success of a peer-to-peer system? How serious is the free-loading problem for the end user? This article reports a study examining end user perception of the features in peer-to-peer file sharing systems. First, it discusses the motivation for the study. Section 2 then describes the details of the study including the data collection and the analysis methods used. In particular, it identifies twenty-six features of peer-to-peer file sharing systems and examines how these features are perceived by the end user in similarity and in importance. Section 3 presents the results, interpretations, and an overall picture relating the system features to the traditional software requirement categories. The final section explores potential implications.
ROBUST STABILIZATION OF A CLASS OF MARKOVIAN JUMP SYSTEMS

Abstract. This paper is concerned with the exponential mean-square stabilization problem via dynamic output-feedback controller for Markovian jump systems with nonlinear perturbations and time delays which are time-varying in an interval and depend on the system mode. Sufficient delay-range-dependent stabilization conditions for the existence of robust output-feedback controller are proposed in terms of linear matrix inequalities (LMIs) with equality constraints. Since the obtained conditions are not strict LMI conditions, a globally convergent algorithm involving convex optimization based on the sequential linear programming matrix is suggested to solve the matrix inequalities characterizing the output-feedback controller solutions. It is shown that the maximum allowable bound on the nonlinearity could be computed thorough solving a constraint optimization problem. Finally, an illustrative example is proposed to show the effectiveness of the proposed approach.
Line extraction in central hyper-catadioptric systems

In central hyper-catadioptric systems the 3D lines are projected into conics in the image plane. The general representation of conics considers five parameters. These conics can be represented by two parameters if the hyper-catadioptric camera calibration is known. In this paper we present a new approach to extract these projected lines that we name catadioptric image lines (CILs). We propose an approximation to the geometric distance from point to conic that combined with the two point representation inside a RANSAC approach allows us to extract the CILs present in a hyper-catadioptric image. We also perform an exhaustive analysis on the elements that can affect the CILs extraction accuracy. Particularly we analyze the effect of calibration errors of the omnidirectional system and the effect of the distribution of the image points on the projected line, i. e. the conic in the image and its length. We present simulations with different hyper-catadioptric systems, and experiments with real images.
Exploring the Leading Authors and Journals in Major Topics by Citation Sentences and Topic Modeling

Citation plays an important role in understanding the knowledge sharing among scholars. Citation sentences embed useful contents that signify the influence of cited authors on shared ideas, and express own opinion of citing authors to others' articles. The purpose of the study is to provide a new lens to analyze the topical relationship embedded in the citation sentences in an integrated manner. To this end, we extract citation sentences from full-text articles in the field of Oncology. In addition, we adopt Author-Journal-Topic (AJT) model to take both authors and journals into consideration of topic analysis. For the study, we collect the 6,360 full-text articles from PubMed Central and select the top 15 journals on Oncology. By applying AJT model, we identify what the major topics are shared among researchers in Oncology and which authors and journal lead the idea exchange in sub-disciplines of Oncology.
Statistical Learning in Optimization: Gaussian Modeling for Population Search

Population search algorithms for optimization problems such as Genetic algorithm is an e ective way to nd an optimal value, especially when we have little information about the objective function. Baluja has proposed e ective algorithms modeling the distribution of elites explicitly by some statistical model. We propose such an algorithm based on Gaussian modeling of elites, and analyze the convergence property of the algorithm by de ning the objective function as a stochastic model. We point out that the algorithms based on the explicit modeling of the elites' distribution tend to converge to unpreferable local optima, and we modify the algorithm to conquer the defect.
Neural Substrates of Visual Spatial Coding and Visual Feedback Control for Hand Movements in Allocentric and Target-Directed Tasks

Neuropsychological evidence suggests that different brain areas may be involved in movements that are directed at visual targets (e.g., pointing or reaching), and movements that are based on allocentric visual information (e.g., drawing or copying). Here we used fMRI to investigate the neural correlates of these two types of movements in healthy volunteers. Subjects (n = 14) performed right hand movements in either a target-directed task (moving a cursor to a target dot) or an allocentric task (moving a cursor to reproduce the distance and direction between two distal target dots) with or without visual feedback about their hand movement. Movements were monitored with an MR compatible touch panel. A whole brain analysis revealed that movements in allocentric conditions led to an increase in activity in the fundus of the left intra-parietal sulcus (IPS), in posterior IPS, in bilateral dorsal premotor cortex (PMd), and in the lateral occipital complex (LOC). Visual feedback in both target-directed and allocentric conditions led to an increase in activity in area MT+, superior parietal–occipital cortex (SPOC), and posterior IPS (all bilateral). In addition, we found that visual feedback affected brain activity differently in target-directed as compared to allocentric conditions, particularly in the pre-supplementary motor area, PMd, IPS, and parieto-occipital cortex. Our results, in combination with previous findings, suggest that the LOC is essential for allocentric visual coding and that SPOC is involved in visual feedback control. The differences in brain activity between target-directed and allocentric visual feedback conditions may be related to behavioral differences in visual feedback control. Our results advance the understanding of the visual coordinate frame used by the LOC. In addition, because of the nature of the allocentric task, our results have relevance for the understanding of neural substrates of magnitude estimation and vector coding of movements.
sCOLA: The N-body COLA Method Extended to the Spatial Domain

We present sCOLA -- an extension of the N-body COmoving Lagrangian Acceleration (COLA) method to the spatial domain. Similar to the original temporal-domain COLA, sCOLA is an N-body method for solving for large-scale structure in a frame that is comoving with observers following trajectories calculated in Lagrangian Perturbation Theory. Incorporating the sCOLA method in an N-body code allows one to gain computational speed by capturing the gravitational potential from the far field using perturbative techniques, while letting the N-body code solve only for the near field. The far and near fields are completely decoupled, effectively localizing gravity for the N-body side of the code. Thus, running an N-body code for a small simulation volume using sCOLA can reproduce the results of a standard N-body run for the same small volume embedded inside a much larger simulation. We demonstrate that sCOLA can be safely combined with the original temporal-domain COLA. sCOLA can be used as a method for performing zoom-in simulations. It also allows N-body codes to be made embarrassingly parallel, thus allowing for efficiently tiling a volume of interest using grid computing. Moreover, sCOLA can be useful for cheaply generating large ensembles of accurate mock halo catalogs required to study galaxy clustering. Surveys that will benefit the most are ones with large aspect ratios, such as pencil-beam surveys, where sCOLA can easily capture the effects of large-scale transverse modes without the need to substantially increase the simulated volume. As an illustration of the method, we present proof-of-concept zoom-in simulations using a freely available sCOLA-based N-body code.
Topic Segmentation with an Ordering-Based Topic Model

Documents from the same domain usually discuss similar topics in a similar order. However, the number of topics and the exact topics discussed in each individual document can vary. In this paper we present a simple topic model that uses generalised Mallows models and incomplete topic orderings to incorporate this ordering regularity into the probabilistic generative process of the new model. We show how to reparameterise the new model so that a point-wise sampling algorithm from the Bayesian word segmentation literature can be used for inference. This algorithm jointly samples not only the topic orders and the topic assignments but also topic segmentations of documents. Experimental results show that our model performs significantly better than the other ordering-based topic models on nearly all the corpora that we used, and competitively with other state-of-the-art topic segmentation models on corpora that have a strong ordering regularity.
Stochastic effects in the representation of stratocumulus - topped mixed layers

Stratocumulus clouds are an integral component of the climate system. They affect both its mean state and variability, and are central to many questions related to climate change. They are also very difficult to represent with fidelity in modern forecast systems and climate models. Their importance is associated with what one may call long-term climatic robustness, which is in marked contrast to the physical sensitivity they exhibit at any particular time, and which is one of the main reasons they prove so difficult to represent.
Open Source Business Intelligence for Business Economic Development

Pervasive technology has long influenced us to create a ubiquitous lifestyle. However, use of inexpensive technology in a cogent business environment has long been the dream of many entrepreneurs. Capital investment for an innovative business idea has been a perplex issue. This article, sheds light on some of the marked down business intelligence systems available in the free market today. The article aims to present diversified tools and its uses in a startup company in variegated industries. Diversified tools which aid to build an expedient and innovative way to strategize therein abetting a scrupulous enterprise.
Sleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device.

STUDY OBJECTIVES To assess the sleep detection and staging validity of a non-contact, commercially available bedside bio-motion sensing device (S+, ResMed) and evaluate the impact of algorithm updates. METHODS Polysomnography data from 27 healthy adult participants was compared epoch-by-epoch to synchronized data that were recorded and staged by actigraphy and S+. An update to the S+ algorithm (common in the rapidly evolving commercial sleep tracker industry) permitted comparison of the original (S+V1) and updated (S+V2) versions. RESULTS Sleep detection accuracy by S+V1 (93.3%), S+V2 (93.8%), and actigraphy (96.0%) was high; wake detection accuracy by each (69.6%, 73.1%, and 47.9%, respectively) was low. Higher overall S+ specificity, compared to actigraphy, was driven by higher accuracy in detecting wake before sleep onset (WBSO), which differed between S+V2 (90.4%) and actigraphy (46.5%). Stage detection accuracy by the S+ did not exceed 67.6% (for stage N2 sleep, by S+V2) for any stage. Performance is compared to previously established variance in polysomnography scored by humans: a performance standard which commercial devices should ideally strive to reach. CONCLUSIONS Similar limitations in detecting wake after sleep onset (WASO) were found for the S+ as have been previously reported for actigraphy and other commercial sleep tracking devices. S+ WBSO detection was higher than actigraphy, and S+V2 algorithm further improved WASO accuracy. Researchers and clinicians should remain aware of the potential for algorithm updates to impact validity. COMMENTARY A commentary on this article appears in this issue on page 935.