nils-herrmann/clean_scirepeval huggingface dataset:
A diffusion model account of the lexical decision task.

The diffusion model for 2-choice decisions (R. Ratcliff, 1978) was applied to data from lexical decision experiments in which word frequency, proportion of high- versus low-frequency words, and type of nonword were manipulated. The model gave a good account of all of the dependent variables--accuracy, correct and error response times, and their distributions--and provided a description of how the component processes involved in the lexical decision task were affected by experimental variables. All of the variables investigated affected the rate at which information was accumulated from the stimuli--called drift rate in the model. The different drift rates observed for the various classes of stimuli can all be explained by a 2-dimensional signal-detection representation of stimulus information. The authors discuss how this representation and the diffusion model's decision process might be integrated with current models of lexical access.
Stochastic analysis and control of real-time systems with random time delays

The paper discusses modeling and analysis of real-time systems subject to random time delays in the communication network. A new method for analysis of existing schemes is presented. The method is used to evaluate diierent suggested schemes from the literature. A new scheme for handling the random time delays is then developed and successfully compared with previous schemes. The new scheme is based on stochastic control theory and a separation property is shown to hold for the optimal controller. 1.  Many real-time systems are implemented as distributed control systems, where the control loops are closed over a communication network. There will inevitably be time delays in the communication net. As long as the sampling periods are long compared with these delays there is no need to consider the innuence of the delays. As the demand on the control system increases it will be more and more important to take the delays into account in the analysis and the design of the control system. While inaccuracies, disturbances, etc, have been extensively studied in the control literature the timing problems in real-time systems have just recently attracted attention, and in the communication literature the feedback control aspect has not been treated extensively. This is thus an area where much can be gained by combining ideas from the two elds of control and real-time systems. Several problem formulations have been suggested by previous authors. A general setup would involve a dis
Comedy to sleazy horror: No end to cinema's stigmatizing and ridiculing attitude towards mental illness and psychiatry.

Movies form a staple source of entertainment and command huge popularity worldwide. Through their motion pictures, film makers create an overwhelming dream like visual experience blending it with sound stimulus, replete with high octane drama and emotions, capturing the imaginations of and having a lasting impact on their viewers (Damjanović et al., 2009), even if it amounts to disconnecting from reality. The dissonance between the reel and the real is especially wide in reference to cinematic portrayals of physicians (Flores, 2002). Recent Hindi cinema has largely portrayed mental illnesses, psychiatrists and their treatment approaches in a stereotyped, ridiculous and/or stigmatizing manner (Banwari, 2011). The malaise however seems to be more generalized and global as there are reports of American movies (Gharaibeh, 2005; Tarsitani et al., 2006), regional Indian movies (Menon and Ranjith, 2009; Prasad et al., 2009) and Greek cinema (Fountoulakis et al., 1998) to be plagued by similar problems of depicting psychiatric illness, the psychiatric profession and patients in bad light. Pirkis et al. (2006) reported that portrayals of mental illness in fictional films and television programmes are frequent and generally negative, and have a cumulative effect on
Remyelination is extensive in a subset of multiple sclerosis patients.

Although spontaneous remyelination does occur in multiple sclerosis lesions, its extent within the global population with this disease is presently unknown. We have systematically analysed the incidence and distribution of completely remyelinated lesions (so-called shadow plaques) or partially remyelinated lesions (shadow plaque areas) in 51 autopsies of patients with different clinical courses and disease durations. The extent of remyelination was variable between cases. In 20% of the patients, the extent of remyelination was extensive with 60-96% of the global lesion area remyelinated. Extensive remyelination was found not only in patients with relapsing multiple sclerosis, but also in a subset of patients with progressive disease. Older age at death and longer disease duration were associated with significantly more remyelinated lesions or lesion areas. No correlation was found between the extent of remyelination and either gender or age at disease onset. These results suggest that the variable and patient-dependent extent of remyelination must be considered in the design of future clinical trials aimed at promoting CNS repair.
KAZE Features

In this paper, we introduce KAZE features, a novel multiscale 2D feature detection and description algorithm in nonlinear scale spaces. Previous approaches detect and describe features at different scale levels by building or approximating the Gaussian scale space of an image. However, Gaussian blurring does not respect the natural boundaries of objects and smoothes to the same degree both details and noise, reducing localization accuracy and distinctiveness. In contrast, we detect and describe 2D features in a nonlinear scale space by means of nonlinear diffusion filtering. In this way, we can make blurring locally adaptive to the image data, reducing noise but retaining object boundaries, obtaining superior localization accuracy and distinctiviness. The nonlinear scale space is built using efficient Additive Operator Splitting (AOS) techniques and variable conductance diffusion. We present an extensive evaluation on benchmark datasets and a practical matching application on deformable surfaces. Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space, but comparable to SIFT, our results reveal a step forward in performance both in detection and description against previous state-of-the-art methods.
How transferable are the datasets collected by active learners?

Active learning is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. Between rounds of training, an active learner iteratively selects examples for annotation, typically based on some measure of the model’s uncertainty, coupling the acquired dataset with the underlying model. However, owing to the high cost of annotation and the rapid pace of model development, labeled datasets may remain valuable long after a particular model is surpassed by new technology. In this paper, we investigate the transferability of datasets collected with an acquisition model A to a distinct successor model S. We seek to characterize whether the benefits of active learning persist when A and S are different models. To this end, we consider two standard NLP tasks and associated datasets: text classification and sequence tagging. We find that training S on a dataset actively acquired with a (different) model A typically yields worse performance than when S is trained with “native” data (i.e., acquired actively using S), and often performs worse than training on i.i.d. sampled data. These findings have implications for the use of active learning in practice, suggesting that it is better suited to cases where models are updated no more frequently than labeled data.
Data Mining for Direct Marketing : Problems and

Direct marketing is a process of identifying likely buyers of certain products and promoting the products accordingly. It is increasingly used by banks, insurance companies, and the retail industry. Data mining can provide an effective tool for direct marketing. During data mining, several specific problems arise. For example, the class distribution is extremely imbalanced (the response rate is about 1~), the predictive accuracy is no longer suitable for evaluating learning methods, and the number of examples can be too large. In this paper, we discuss methods of coping with these problems based on our experience on direct-marketing projects using data mining.
Shape Matching and Object Recognition

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Li, Fergus and Perona), a challenging dataset with large intraclass variation. Our approach yields a 45% correct classification rate in addition to localization. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.
3D skeleton based action recognition by video-domain translation-scale invariant mapping and multi-scale dilated CNN

In this paper, we present an image classification approach to action recognition with 3D skeleton videos. First, we propose a video domain translation-scale invariant image mapping, which transforms the 3D skeleton videos to color images, namely skeleton images. Second, a multi-scale dilated convolutional neural network (CNN) is designed for the classification of the skeleton images. Our multi-scale dilated CNN model could effectively improve the frequency adaptiveness and exploit the discriminative temporal-spatial cues for the skeleton images. Even though the skeleton images are very different from natural images, we show that the fine-tuning strategy still works well. Furthermore, we propose different kinds of data augmentation strategies to improve the generalization and robustness of our method. Experimental results on popular benchmark datasets such as NTU RGB + D, UTD-MHAD, MSRC-12 and G3D demonstrate the superiority of our approach, which outperforms the state-of-the-art methods by a large margin.
Site-D irected Mutagenesis by Gene Targeting in Mouse Embryo-Derived Stem Cells

We mutated, by gene targeting, the endogenous hypoxanthine phosphorlbosyi transferase (HPFlT) gene in mouse embryo-derived stem (ES) cells. A specialized construct of the neomycin resistance (NO’) gene was introduced into an exon of a cloned fragment of the Hprf gene and used to transfect ES cells. Among the G418’ colonies, l/l000 were also resistant to the base analog &thioguanine (&TG). The G418’, 8-TGr ceils were ail shown to be Hprtas the result of homologous recombination with the exogenous, neo’-containing, Hprf sequences. We have compared the gene-targeting efficiencies of two classes of neo’-Hprf recombinant vectors: those that replace the endogenous sequence with the exogenous sequence and those that insert the exogenous sequence into the endogenous sequence. The targeting efficiencies of both classes of vectors are strongly dependent upon the extent of homology between exogenous and endogenous sequences. The protocol described herein should be useful for targeting mutations into any gene.
A biologically inspired dual-network memory model for reduction of catastrophic forgetting

Neural networks encounter serious catastrophic forgetting when information is learned sequentially, which is unacceptable for both a model of human memory and practical engineering applications. In this study, we propose a novel biologically inspired dual-network memory model that can significantly reduce catastrophic forgetting. The proposed model consists of two distinct neural networks: hippocampal and neocortical networks. Information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network. In the hippocampal network, chaotic behavior of neurons in the CA3 region of the hippocampus and neuronal turnover in the dentate gyrus region are introduced. Chaotic recall by CA3 enables retrieval of stored information in the hippocampal network. Thereafter, information retrieved from the hippocampal network is interleaved with previously stored information and consolidated by using pseudopatterns in the neocortical network. The computer simulation results show the effectiveness of the proposed dual-network memory model. & 2014 Elsevier B.V. All rights reserved.
Mathematical Modelling of Strategic Treatments on Tumor Growth

The worldwide burden of cancer (malignant tumor) is a major health problem, with more than 8 million new cases and 5 million deaths per year. Cancer is the second leading cause of death. With growing techniques the survival rate has increased and so it becomes important to contribute even the smallest help in this field favoring the survival rate. Tumor is a mass of tissue formed as the result of abnormal, excessive, uncoordinated, autonomous and purposeless proliferation of cells.
A Visual Transactive Memory System Approach Towards Project Information Management

The Architecture Engineering Construction Facilities Management (AEC/FM) industry is currently plagued with inefficiencies in access and retrieval of relevant information across the various stakeholders and actors, because the vast amount of project related information is not only diverse but the information is also highly fragmented and distributed across different sources and actors. More often than not, even if a good part of the project and task related information may be stored in the distributed information systems, the knowledge of where what is stored, and how that information can be accessed remains a tacit knowledge stored in the minds of the people involved in the project. Consequently, navigating through this distributed and fragmented information in the current practice is heavily reliant on the knowledge and experience of the individual actors in the network, who are able to guide each other to relevant information source, and in the process answering questions such as: who knows what? What information is where? Etc. Thus, to be able to access and effectively use the distributed knowledge and information held by different actors and information systems within a project, each actor needs to know the information access path, which in turn is mediated by other actors and their knowledge of the distribution of the information. In this type of distributed-knowledge network and “actor-focused thinking” when the key actor or actors leave the project, the access path to the relevant knowledge for the associated queries may also disappear, breaking the chain of queries. Therefore, we adopt an “information-focused thinking” where all project actors are considered and represented as computational and information storage entities in a knowledge network, building on the concepts and theories of Transactive Memory Systems (TMS), which primarily deal with effective management and usage of distributed knowledge sources. We further extend the explicit representation of the information entities to visual objects such that the actors can effectively understand, construct and recognize contextual relationships among the information entities through visual management and communication. The merits and challenges of such an approach towards visual transactive memory system for project information management are discussed using a prototype information management platform, VisuaLynk, developed around graph and linked-data concepts, and currently configured for the use phase of a project.
Solving pre-processed set covering problems via cuckoo search and lévy flights

Cuckoo search is a modern bio-inspired metaheuristic that has successfully been used to solve different real world optimization problems. In particular, it has exhibited rapid convergence reaching considerable good results. In this paper, we employ this technique to solve the set covering problem, which is a well-known optimization benchmark. We illustrate interesting experimental results where the proposed algorithm is able to obtain several global optimums for different set covering instances from the OR-Library.
Investigation of multipotent postnatal stem cells from human periodontal ligament

Periodontal diseases that lead to the destruction of periodontal tissues--including periodontal ligament (PDL), cementum, and bone--are a major cause of tooth loss in adults and are a substantial public-health burden worldwide. PDL is a specialised connective tissue that connects cementum and alveolar bone to maintain and support teeth in situ and preserve tissue homoeostasis. We investigated the notion that human PDL contains stem cells that could be used to regenerate periodontal tissue.



PDL tissue was obtained from 25 surgically extracted human third molars and used to isolate PDL stem cells (PDLSCs) by single-colony selection and magnetic activated cell sorting. Immunohistochemical staining, RT-PCR, and northern and western blot analyses were used to identify putative stem-cell markers. Human PDLSCs were transplanted into immunocompromised mice (n=12) and rats (n=6) to assess capacity for tissue regeneration and periodontal repair.  PDLSCs expressed the mesenchymal stem-cell markers STRO-1 and CD146/MUC18. Under defined culture conditions, PDLSCs differentiated into cementoblast-like cells, adipocytes, and collagen-forming cells. When transplanted into immunocompromised rodents, PDLSCs showed the capacity to generate a cementum/PDL-like structure and contribute to periodontal tissue repair.



Our findings suggest that PDL contains stem cells that have the potential to generate cementum/PDL-like tissue in vivo. Transplantation of these cells, which can be obtained from an easily accessible tissue resource and expanded ex vivo, might hold promise as a therapeutic approach for reconstruction of tissues destroyed by periodontal diseases.
Moral accountability and integrity in nursing practice.

The therapeutic nature of the nurse-patient relationship is grounded in an ethic of caring. Florence Nightingale envisioned nursing as an art and a science...a blending of humanistic, caring presence with evidence-based knowledge and exquisite skill. In this article, the author explores the caring practice of nursing as a framework for understanding moral accountability and integrity in practice. Being morally accountable and responsible for one's judgment and actions is central to the nurse's role as a moral agent. Nurses who practice with moral integrity possess a strong sense of themselves and act in ways consistent with what they understand is the right thing to do. A review of the literature related to caring theory, the concepts of moral accountability and integrity, and the documents that speak of these values and concepts in professional practice (eg, Code of Ethics for Nurses with Interpretive Statements, Nursing's Social Policy Statement) are presented in this article.
Sequence Discriminative Training for Offline Handwriting Recognition by an Interpolated CTC and Lattice-Free MMI Objective Function

We study two sequence discriminative training criteria, i.e., Lattice-Free Maximum Mutual Information (LFMMI) and Connectionist Temporal Classification (CTC), for end-to-end training of Deep Bidirectional Long Short-Term Memory (DBLSTM) based character models of two offline English handwriting recognition systems with an input feature vector sequence extracted by Principal Component Analysis (PCA) and Convolutional Neural Network (CNN), respectively. We observe that refining CTC-trained PCA-DBLSTM model with an interpolated CTC and LFMMI objective function ("CTC+LFMMI") for several additional iterations achieves a relative Word Error Rate (WER) reduction of 24.6% and 13.9% on the public IAM test set and an in-house E2E test set, respectively. For a much better CTC-trained CNN-DBLSTM system, the proposed "CTC+LFMMI" method achieves a relative WER reduction of 19.6% and 8.3% on the above two test sets, respectively.
Applications of Graph Theory in Computer Science

Graphs are among the most ubiquitous models of both natural and human-made structures. They can be used to model many types of relations and process dynamics in computer science, physical, biological and social systems. Many problems of practical interest can be represented by graphs. In general graphs theory has a wide range of applications in diverse fields. This paper explores different elements involved in graph theory including graph representations using computer systems and graph-theoretic data structures such as list structure and matrix structure. The emphasis of this paper is on graph applications in computer science. To demonstrate the importance of graph theory in computer science, this article addresses most common applications for graph theory in computer science. These applications are presented especially to project the idea of graph theory and to demonstrate its importance in computer science.
The prevalence of antibiotic resistance genes among Aeromonas species in aquatic environments

The global rise in antimicrobial resistance (AMR) among bacteria causing infectious diseases is well documented, and the associated risks for human health are well known. There is much less research on AMR with regard to environmental strains, both opportunistic and pathogenic ones. The genus Aeromonas is widely distributed in the environment and causes many variable diseases in fish and humans. Infections in humans are predominantly caused by Aeromonas veronii, A. hydrophila and A. caviae (A. punctata) in a form of bacteremia, gastroenteritis or even septicaemia in immunocompetent and immunocompromised individuals. Different groups of antibiotics are used in the treatment, but studies indicate that fluoroquinolones and cefotaxime are the most efficient. A disturbing consequence of antibiotic overuse is an increasing number of detection of various antibiotic resistance genes (ARG) within this genus. The water environment is one of the major modes of transmission of resistant bacteria from animals to humans, and, thus, the dissemination of antibiotic resistance genes, particularly those located in mobile genetic elements (MGE) occurs in such as plasmids and transposons. This review summarizes recently published information on the type, distribution, and transmission of ARG by MGE, widespread in Aeromonas strains living in various aquatic environments, including wastewater, natural water, aquaculture and urban drinking water. The data available indicate that the opportunistic pathogens like Aeromonas spp. might serve as important vectors of ARG for clinically relevant pathogens present in such bodies of water .