nils-herrmann/clean_scirepeval huggingface dataset:
Multiple-Group confirmatory factor analysis in R – A tutorial in measurement invariance with continuous and ordinal indicators

Multiple-group confirmatory factor analysis (MG-CFA) is among the most productive extensions of structural equation modeling. Many researchers conducting cross-cultural or longitudinal studies are interested in testing for measurement and structural invariance. The aim of the present paper is to provide a tutorial in MG-CFA using the freely available R-packages lavaan, semTools, and semPlot. The combination of these packages enable a highly efficient analysis of the measurement models both for normally distributed as well as ordinal data. Data from two freely available datasets – the first with continuous the second with ordered indicators will be used to provide a walk-through the individual steps.
On the delayed Ross-Macdonald model for malaria transmission.

The feedback dynamics from mosquito to human and back to mosquito involve considerable time delays due to the incubation periods of the parasites. In this paper, taking explicit account of the incubation periods of parasites within the human and the mosquito, we first propose a delayed Ross-Macdonald model. Then we calculate the basic reproduction number R0 and carry out some sensitivity analysis of R0 on the incubation periods, that is, to study the effect of time delays on the basic reproduction number. It is shown that the basic reproduction number is a decreasing function of both time delays. Thus, prolonging the incubation periods in either humans or mosquitos (via medicine or control measures) could reduce the prevalence of infection.
Operation CeaseFire-New Orleans: an infectious disease model for addressing community recidivism from penetrating trauma.

CeaseFire, using an infectious disease approach, addresses violence by partnering hospital resources with the community by providing violence interruption and community-based services for an area roughly composed of a single city zip code (70113). Community-based violence interrupters start in the trauma center from the moment penetrating trauma occurs, through hospital stay, and in the community after release. This study interprets statistics from this pilot program, begun May 2012. We hypothesize a decrease in penetrating trauma rates in the target area compared with others after program implementation.



This was a 3-year prospective data collection of trauma registry from May 2010 to May 2013. All intentional, target area, penetrating trauma treated at our Level I trauma center received immediate activation of CeaseFire personnel. Incidences of violent trauma and rates of change, by zip code, were compared with the same period for 2 years before implementation.



During this period, the yearly incidence of penetrating trauma in Orleans Parish increased. Four of the highest rates were found in adjacent zip codes: 70112, 70113, 70119, and 70125. Average rates per 100,000 were 722.7, 523.6, 286.4, and 248, respectively. These areas represent four of the six zip codes citywide that saw year-to-year increases in violent trauma during this period. Zip 70113 saw a lower rate of rise in trauma compared with 70112 and a higher but comparable rise compared with that of 70119 and 70125.



Hospital-based intervention programs that partner with culturally appropriate personnel and resources outside the institution walls have potential to have meaningful impact over the long term. While few conclusions of the effect of such a program can be drawn in a 12-month period, we anticipate long-term changes in the numbers of penetrating injuries in the target area and in the rest of the city as this program expands.



Therapeutic study, level IV.
BlockPy: An Open Access Data-Science Environment for Introductory Programmers

Non-computer science majors often struggle to find relevance in traditional computing curricula that tend to emphasize abstract concepts, focus on nonpractical entertainment, or rely on decontextualized settings. BlockPy, a web-based, open access Python programming environment, supports introductory programmers in a data-science context through a dual block/text programming view. The web extra at https://youtu.be/RzaOPqOpMoM illustrates BlockPy features discussed in the article.
Fault Detection and Diagnosis Methods in HVAC Building Automation Systems using Industrial Solutions

Fault Detection and Diagnosis systems offers enhanced availability and reduced risk of safety haz ards w hen comp onent failure and other unex p ected events occur in a controlled p lant. For O nline FDD an ap p rop riate method an O nline data are req uired. I t is q uite difficult to get O nline data for FDD in industrial ap p lications and solution, using O P C is suggested. T op dow n and bottomup ap p roaches to diagnostic reasoning of w hole system w ere rep resented and tw o new ap p roaches w ere suggested. S olution 1 using q ualitative data from “ similar” subsystems w as p rop osed and S olution 2 using reference subsystem w ere p rop osed.
Integrating Social and Auxiliary Semantics for Multifaceted Topic Modeling in Twitter

Microblogging platforms, such as Twitter, have already played an important role in recent cultural, social and political events. Discovering latent topics from social streams is therefore important for many downstream applications, such as clustering, classification or recommendation. However, traditional topic models that rely on the bag-of-words assumption are insufficient to uncover the rich semantics and temporal aspects of topics in Twitter. In particular, microblog content is often influenced by external information sources, such as Web documents linked from Twitter posts, and often focuses on specific entities, such as people or organizations. These external sources provide useful semantics to understand microblogs and we generally refer to these semantics as auxiliary semantics. In this article, we address the mentioned issues and propose a unified framework for Multifaceted Topic Modeling from Twitter streams. We first extract social semantics from Twitter by modeling the social chatter associated with hashtags. We further extract terms and named entities from linked Web documents to serve as auxiliary semantics during topic modeling. The Multifaceted Topic Model (MfTM) is then proposed to jointly model latent semantics among the social terms from Twitter, auxiliary terms from the linked Web documents and named entities. Moreover, we capture the temporal characteristics of each topic. An efficient online inference method for MfTM is developed, which enables our model to be applied to large-scale and streaming data. Our experimental evaluation shows the effectiveness and efficiency of our model compared with state-of-the-art baselines. We evaluate each aspect of our framework and show its utility in the context of tweet clustering.
Index Modulation Techniques for Next-Generation Wireless Networks

What is index modulation (IM)? This is an interesting question that we have started to hear more and more frequently over the past few years. The aim of this paper is to answer this question in a comprehensive manner by covering not only the basic principles and emerging variants of IM, but also reviewing the most recent as well as promising advances in this field toward the application scenarios foreseen in next-generation wireless networks. More specifically, we investigate three forms of IM: spatial modulation, channel modulation and orthogonal frequency division multiplexing (OFDM) with IM, which consider the transmit antennas of a multiple-input multiple-output system, the radio frequency mirrors (parasitic elements) mounted at a transmit antenna and the subcarriers of an OFDM system for IM techniques, respectively. We present the up-to-date advances in these three promising frontiers and discuss possible future research directions for IM-based schemes toward low-complexity, spectrum- and energy-efficient next-generation wireless networks.
Real-time fingertip tracking and detection using Kinect depth sensor for a new writing-in-the air system

We propose a real-time finger writing character recognition system using depth information. This system allows user to input characters by writing freely in the air with the Kinect. During the writing process, it is reasonable to assume that the finger and hand are always holding in front of torso. Firstly, we compute the depth histogram of human body and use a switch mixture Gaussian model to characterize it. Since the hand is closer to camera, a model-based threshold can segment the hand-related region out. Then, we employ an unsupervised clustering algorithm, K-means, to classify the segmented region into two parts, the finger-hand part and hand-arm part. By identifying the arm direction, we can determine the finger-hand cluster and locate the fingertip as the farthest point from the other cluster. We collected over 8000 frames writing-in-the-air sequences including two different subjects writing numbers, strokes, pattern, English and Chinese characters from two different distances. From our experiments, the proposed algorithm can provide robust and accurate fingertip detection, and achieve encouraging character recognition result.
The physics of spreading processes in multilayer networks

Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599-3250, USA Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, OX2 6GG, UK; CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK; and Department of Mathematics, University of California, Los Angeles, California 90095, USA
CXCL1 promotes tumor growth through VEGF pathway activation and is associated with inferior survival in gastric cancer.

The chemokine (C-X-C motif) ligand 1 (CXCL1) regulates tumor-stromal interactions and tumor invasion. However, the precise role of CXCL1 on gastric tumor growth and patient survival remains unclear. In the current study, protein expressions of CXCL1, vascular endothelial growth factor (VEGF) and phospho-signal transducer and activator of transcription 3 (p-STAT3) in primary tumor tissues from 98 gastric cancer patients were measured by immunohistochemistry (IHC). CXCL1 overexpressed cell lines were constructed using Lipofectamine 2000 reagent or lentiviral vectors. Effects of CXCL1 on VEGF expression and local tumor growth were evaluated in vitro and in vivo. CXCL1 was positively expressed in 41.4% of patients and correlated with VEGF and p-STAT3 expression. Higher CXCL1 expression was associated with advanced tumor stage and poorer prognosis. In vitro studies in AGS and SGC-7901 cells revealed that CXCL1 increased cell migration but had little effect on cell proliferation. CXCL1 activated VEGF signaling in gastric cancer (GC) cells, which was inhibited by STAT3 or chemokine (C-X-C motif) receptor 2 (CXCR2) blockade. CXCL1 also increased p-STAT3 expression in GC cells. In vivo, CXCL1 increased xenograft local tumor growth, phospho-Janus kinase 2 (p-JAK2), p-STAT3 levels, VEGF expression and microvessel density. These results suggested that CXCL1 increased local tumor growth through activation of VEGF signaling which may have mechanistic implications for the observed inferior GC survival. The CXCL1/CXCR2 pathway might be potent to improve anti-angiogenic therapy for gastric cancer.
Whose Idea Was This, and Why Does it Matter? Attributing Scientific Work to Citations

Scientific papers revolve around citations, and for many discourse level tasks one needs to know whose work is being talked about at any point in the discourse. In this paper, we introduce the scientific attribution task, which links different linguistic expressions to citations. We discuss the suitability of different evaluation metrics and evaluate our classification approach to deciding attribution both intrinsically and in an extrinsic evaluation where information about scientific attribution is shown to improve performance on Argumentative Zoning, a rhetorical classification task.
Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes: a systematic review.

Patient portals tied to provider electronic health record (EHR) systems are increasingly popular.



To systematically review the literature reporting the effect of patient portals on clinical care.



PubMed and Web of Science searches from 1 January 1990 to 24 January 2013.



Hypothesis-testing or quantitative studies of patient portals tethered to a provider EHR that addressed patient outcomes, satisfaction, adherence, efficiency, utilization, attitudes, and patient characteristics, as well as qualitative studies of barriers or facilitators, were included.



Two reviewers independently extracted data and addressed discrepancies through consensus discussion.



From 6508 titles, 14 randomized, controlled trials; 21 observational, hypothesis-testing studies; 5 quantitative, descriptive studies; and 6 qualitative studies were included. Evidence is mixed about the effect of portals on patient outcomes and satisfaction, although they may be more effective when used with case management. The effect of portals on utilization and efficiency is unclear, although patient race and ethnicity, education level or literacy, and degree of comorbid conditions may influence use.



Limited data for most outcomes and an absence of reporting on organizational and provider context and implementation processes.



Evidence that patient portals improve health outcomes, cost, or utilization is insufficient. Patient attitudes are generally positive, but more widespread use may require efforts to overcome racial, ethnic, and literacy barriers. Portals represent a new technology with benefits that are still unclear. Better understanding requires studies that include details about context, implementation factors, and cost.
Carbon quantum dots and their applications.

Fluorescent carbon nanoparticles or carbon quantum dots (CQDs) are a new class of carbon nanomaterials that have emerged recently and have garnered much interest as potential competitors to conventional semiconductor quantum dots. In addition to their comparable optical properties, CQDs have the desired advantages of low toxicity, environmental friendliness low cost and simple synthetic routes. Moreover, surface passivation and functionalization of CQDs allow for the control of their physicochemical properties. Since their discovery, CQDs have found many applications in the fields of chemical sensing, biosensing, bioimaging, nanomedicine, photocatalysis and electrocatalysis. This article reviews the progress in the research and development of CQDs with an emphasis on their synthesis, functionalization and technical applications along with some discussion on challenges and perspectives in this exciting and promising field.
Explorer Modeling Local Coherence : An Entity-Based Approach

General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. This article proposes a novel framework for representing and measuring local coherence. Central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. The algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential information about discourse entities. We re-conceptualize coherence assessment as a learning task and show that our entity-based representation is well-suited for ranking-based generation and text classification tasks. Using the proposed representation, we achieve good performance on text ordering, summary coherence evaluation, and readability assessment.
Central and autonomic nervous system integration in emotion

Emotions involve physiological responses that are regulated by the brain. The present paper reviews the empirical literature on central nervous system (CNS) and autonomic nervous system (ANS) concomitants of emotional states, with a focus on studies that simultaneously assessed CNS and ANS activity. The reviewed data support two primary conclusions: (1) numerous cortical and subcortical regions show co-occurring activity with ANS responses in emotion, and (2) there may be reversed asymmetries on cortical and subcortical levels with respect to CNS/ANS interrelations. These observations are interpreted in terms of a model of neurovisceral integration in emotion, and directions for future research are presented.
CommunitySourcing: engaging local crowds to perform expert work via physical kiosks

Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80% of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2% higher accuracy (at the same price) or at 33% lower cost (at equivalent accuracy) than traditional single-expert grading.  Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities.
Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems

Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT), due to the large-scale nature of the system and its susceptibility to man-in-the-middle and data-injection attacks. In this paper, a novel watermarking algorithm is proposed for dynamic authentication of IoT signals to detect cyber-attacks. The proposed watermarking algorithm, based on a deep learning long short-term memory structure, enables the IoT devices (IoTDs) to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT gateway, which collects signals from the IoTDs, to effectively authenticate the reliability of the signals. Moreover, in massive IoT scenarios, since the gateway cannot authenticate all of the IoTDs simultaneously due to computational limitations, a game-theoretic framework is proposed to improve the gateway’s decision making process by predicting vulnerable IoTDs. The mixed-strategy Nash equilibrium (MSNE) for this game is derived, and the uniqueness of the expected utility at the equilibrium is proven. In the massive IoT system, due to the large set of available actions for the gateway, the MSNE is shown to be analytically challenging to derive, and thus, a learning algorithm that converges to the MSNE is proposed. Moreover, in order to handle incomplete information scenarios, in which the gateway cannot access the state of the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed to dynamically predict the state of unauthenticated IoTDs and allow the gateway to decide on which IoTDs to authenticate. Simulation results show that with an attack detection delay of under 1 s, the messages can be transmitted from IoTDs with an almost 100% reliability. The results also show that by optimally predicting the set of vulnerable IoTDs, the proposed deep reinforcement learning algorithm reduces the number of compromised IoTDs by up to 30%, compared to an equal probability baseline.
Nested Sequential Monte Carlo Methods

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.
Clustering under approximation stability

A common approach to clustering data is to view data objects as points in a metric space, and then to optimize a natural distance-based objective such as the k-median, k-means, or min-sum score. For applications such as clustering proteins by function or clustering images by subject, the implicit hope in taking this approach is that the optimal solution for the chosen objective will closely match the desired “target” clustering (e.g., a correct clustering of proteins by function or of images by who is in them). However, most distance-based objectives, including those mentioned here, are NP-hard to optimize. So, this assumption by itself is not sufficient, assuming P ≠ NP, to achieve clusterings of low-error via polynomial time algorithms.
 In this article, we show that we can bypass this barrier if we slightly extend this assumption to ask that for some small constant c, not only the optimal solution, but also all c-approximations to the optimal solution, differ from the target on at most some ε fraction of points—we call this (c,ε)-approximation-stability. We show that under this condition, it is possible to efficiently obtain low-error clusterings even if the property holds only for values c for which the objective is known to be NP-hard to approximate. Specifically, for any constant c > 1, (c,ε)-approximation-stability of k-median or k-means objectives can be used to efficiently produce a clustering of error O(ε) with respect to the target clustering, as can stability of the min-sum objective if the target clusters are sufficiently large. Thus, we can perform nearly as well in terms of agreement with the target clustering as if we could approximate these objectives to this NP-hard value.