nils-herrmann/clean_scirepeval huggingface dataset:
The Henry ford production system: effective reduction of process defects and waste in surgical pathology.

By adopting a cultural transformation in its employees' approach to work and using manufacturing based continuous quality improvement methods, the surgical pathology division of Henry Ford Hospital, Detroit, MI, focused on reducing commonly encountered defects and waste in processes throughout the testing cycle. At inception, the baseline in-process defect rate was measured at nearly 1 in 3 cases (27.9%). After the year-long efforts of 77 workers implementing more than 100 process improvements, the number of cases with defects was reduced by 55% to 1 in 8 cases (12.5%), with a statistically significant reduction in the overall distribution of defects (P = .0004). Comparison with defects encountered in the pre-improvement period showed statistically significant reductions in pre-analytic (P = .0007) and analytic (P = .0002) test phase processes in the post-improvement period that included specimen receipt, specimen accessioning, grossing, histology slides, and slide recuts. We share the key improvements implemented that were responsible for the overall success in reducing waste and re-work in the broad spectrum of surgical pathology processes.
MCLUHAN AND THE " TORONTO SCHOOL OF COMMUNICATION "

How does one recognize a "school" of thought ? And why should one? These are questions that, concerning a truly distinctive and now distinguished intellectual trend originating in Toronto, I have entertained since the death of Marshall McLuhan on the last day of 1980. At the time I was impressed by the fact that Harold Innis, Eric Havelock and McLuhan, the three main scholars who taught that communication systems create definite psychological and social "states", had all been at the university of Toronto. The most significant common thread was that all three had explored different implications of ancient Greek literacy to support their theoretical approach. Even if they had not directly collaborated with each other, they had known each other's work and been inspired by common perceptions.
Low-Light Dependence of the Magnetic Field Effect on Cryptochromes: Possible Relevance to Plant Ecology

Various responses to static magnetic fields (MF) have been reported in plants, and it has been suggested that the geomagnetic field influences plant physiology. Accordingly, diverse mechanisms have been proposed to mediate MF effects in plants. The currently most probable sensor candidates are cryptochromes (Cry) which are sensitive to submillitesla MF. Here, we propose a quantitative approach of the MF effect on Cry depending on light intensity, and try to link it to a possible functional role for magnetic sensitivity in plants. Based on a theoretical evaluation and on a review of relevant data on Arabidopsis thaliana Cry 1, we point out that the MF effect on the signaling state of Cry, as well as the possible consequences of that effect on certain phenotypes (growth in particular) show parallel dependences on light intensity, being most prominent at low light levels. Based on these findings, we propose that Cry magnetosensitivity in plants could represent an ecological adaptation which regulates the amount of Cry signaling state under low light conditions. That hypothesis would preferentially be tested by studying sensitive and specific endpoints, such as the expression of clock proteins that are downregulated by Cry, but under light intensities lower than those used so far. Finally, we highlight that the low-light dependence of the MF effect described here could also apply to light-dependent functions of animal Cry, in particular magnetoreception which, from the present evaluation, would be based on the magnetic sensitivity of the photoreduction reaction, like in plants.
Inhibition of calcium-activated chloride channel ANO1 suppresses proliferation and induces apoptosis of epithelium originated cancer cells

ANO1, a calcium-activated chloride channel, has been reported to be amplified or overexpressed in tissues of several cancers. However, reports on its roles in tumor progression obtained from cancer cell lines are inconsistent, suggesting that the role of ANO1 in tumorigenesis is likely dependent on either its expression level or cell-type expressing ANO1. To investigate the biological roles of ANO1 in different tumor cells, we, in this study, selected several cancer cell lines and a normal HaCaT cell line with high expression levels of ANO1, and examined the function of ANO1 in these cells using approaches of lentiviral knockdown and pharmacological inhibition. We found that ANO1 knockdown significantly inhibited cell proliferation and induced cell apoptosis in either tumor cell lines or normal HaCaT cell line. Moreover, silencing ANO1 arrested cancer cells at G1 phase of cell cycle. Treatment with ANO1 inhibitor CaCCinh-A01 reduced cell viability in a dose-dependent manner. Furthermore, both ANO1 inhibitors CaCCinh-A01 and T16Ainh-A01 significantly suppressed cell migration. Our findings show that ANO1 overexpression promotes cancer cell proliferation and migration; and genetic or pharmacological inhibition of ANO1 induces apoptosis and cell cycle arrest at G1 phase in different types of epithelium-originated cancer cells.
Satellite Image Segmentation and Classification

The resolution of remote sensing images increases every day, raising the level of detail and the heterogeneity of the scenes. Most of the existing geographic information systems classification tools have used the same methods for years. With these new high resolution images basic classification methods do not provide satisfactory results. In this study we developed a region-based classification method, consisting in two steps: a segmentation and a classification. The segmentation uses a Markov model to divide the image into several homogenous regions. Then follows the region-based classification performed either with the Mahalanobis distance or by the support vector machine classifier. This method was validated and a comparison between pixel-based and region-based classification was performed. We demonstrated that this method provides better results comparing to the existing remote sensing classification tools, even if some work should be done to prove its robustness. We also proved that the prior segmentation significantly improves the results of classification, both from the quantitative and qualitative points of view.
Sparse Representation-Based Open Set Recognition

We propose a generalized Sparse Representation-based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms.
Pushover Analysis of Reinforced Concrete Buildings Using Full Jacket Technics : A Case Study on an Existing Old Building in Madinah

The retrofitting of existing buildings to resist the seismic loads is very important to avoid losing lives or financial disasters. The aim at retrofitting processes is increasing total structure strength by increasing stiffness or ductility ratio. In addition, the response modification factors (R) have to satisfy the code requirements for suggested retrofitting types. In this study, two types of jackets are used, i.e. full reinforced concrete jackets and surrounding steel plate jackets. The study is carried out on an existing building in Madinah by performing static pushover analysis before and after retrofitting the columns. The selected model building represents nearly all-typical structure lacks structure built before 30 years ago in Madina City, KSA. The comparison of the results indicates a good enhancement of the structure respect to the applied seismic forces. Also, the response modification factor of the RC building is evaluated for the studied cases before and after retrofitting. The design of all vertical elements (columns) is given. The results show that the design of retrofitted columns satisfied the code's design stress requirements. However, for some retrofitting types, the ductility requirements represented by response modification factor do not satisfy KSA design code (SBC301). Concrete jackets, steel jackets, RC buildings pushover analysis, non-linear analysis.
Convention , Form , and Figurative Language Processing

There are 2 basic controversies concerning how metaphors are processed. First, are metaphoric mappings more akin to literal comparisons or to literal categorizations? And second, is metaphor comprehension indirect or direct? We believe that these controversies are more appqent than real and that a unified theoretical framework can be offered that reconciles these opposing views. The central idea is that all metaphors involve structural alignment of the target and base domains. Whether these alignments are more akin to comparisons or to categorizations, and whether they are computed directly or indirectly, depends on 2 factors: the type of representation invoked by the base term and the linguistic form of the metaphor.
Rediscovering the Maltese Temple of Borġ in-Nadur: An Archaeoastronomical Perspective

The Maltese island have megalithic temples of extraordinary interest for archaeoastronomy. In literature we find different works that involve most of its archaeological sites. The temple of Borġ in-Nadur, set on the top of a hill by the Marsaxlokk Bay in southern Malta, is less well known than the rest of the others, even though it started off as a major attraction for grand tourists and travellers in the Early Modern and Colonial periods. It was explored in the second half of the 1920s by a team of British archaeologists, led by Margaret Murray, who gradually uncovered the ruins of typical Maltese megalithic temple dated to the 3rd millennium BC. In 2011 the efforts of an international team of scholars brought to the publication of a general reassessment of the evidences about the temple of Borġ in-Nadur and the artefacts collected during its exploration, emphasizing its importance for the Maltese Temple Period. The new picture that emerged has reactivate the research around the Borġ in-Nadur temple attracting for the first time the interest of scholars in archaeoastronomy. The archaeoastronomical study of the Borġ in-Nadur’s archaeological site is the first of its kind, as the archaeological remains were put into evidence in 2010, and pays particular attention to the temple. At the moment it is difficult to determine the exact plan of the entire temple, and therefore its axes, but the apsidal building and the main entrance are quite intact. Although this research is at its early stage of development, with regards to archaeoastronomical issues and features related with the temple of Borġ inNadur some preliminary considerations can be put forth.
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we present FINN, a framework for building fast and flexible FPGA accelerators using a flexible heterogeneous streaming architecture. By utilizing a novel set of optimizations that enable efficient mapping of binarized neural networks to hardware, we implement fully connected, convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements. On a ZC706 embedded FPGA platform drawing less than 25 W total system power, we demonstrate up to 12.3 million image classifications per second with 0.31 μs latency on the MNIST dataset with 95.8% accuracy, and 21906 image classifications per second with 283 μs latency on the CIFAR-10 and SVHN datasets with respectively 80.1% and 94.9% accuracy. To the best of our knowledge, ours are the fastest classification rates reported to date on these benchmarks.
Safe Visual Navigation via Deep Learning and Novelty Detection

Robots that use learned perceptual models in the real world must be able to safely handle cases where they are forced to make decisions in scenarios that are unlike any of their training examples. However, state-of-the-art deep learning methods are known to produce erratic or unsafe predictions when faced with novel inputs. Furthermore, recent ensemble, bootstrap and dropout methods for quantifying neural network uncertainty may not efficiently provide accurate uncertainty estimates when queried with inputs that are very different from their training data. Rather than unconditionally trusting the predictions of a neural network for unpredictable real-world data, we use an autoencoder to recognize when a query is novel, and revert to a safe prior behavior. With this capability, we can deploy an autonomous deep learning system in arbitrary environments, without concern for whether it has received the appropriate training. We demonstrate our method with a vision-guided robot that can leverage its deep neural network to navigate 50% faster than a safe baseline policy in familiar types of environments, while reverting to the prior behavior in novel environments so that it can safely collect additional training data and continually improve. A video illustrating our approach is available at: http://groups.csail.mit.edu/rrg/videos/safe visual navigation.
ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.
Linguistic communication and the semantics/pragmatics distinction

Most people working on linguistic meaning or communication assume that semantics and pragmatics are distinct domains, yet there is still little consensus on how the distinction is to be drawn. The position defended in this paper is that the semantics/pragmatics distinction holds between (context-invariant) encoded linguistic meaning and speaker meaning. Two other ‘minimalist’ positions on semantics are explored and found wanting: Kent Bach’s view that there is a narrow semantic notion of context which is responsible for providing semantic values for a small number of indexicals, and Herman Cappelen and Ernie Lepore’s view that semantics includes the provision of values for all indexicals, even though these depend on the speaker’s communicative intentions. Finally, some implications are considered for the favoured semantics/pragmatics distinction of the fact that there are linguistic elements (lexical and syntactic) which do not contribute to truth-conditional content but rather provide guidance on pragmatic inference.
Sensory Integration Therapy : Affect or Ef ect ( research , practice , developmental disabilities , learning disabilities )

The results of studies examining the effectiveness of sensory integration therapy were reviewed, uszng recently developed quantitative methods that treat the literature review process as a unique type of research. Forty-nine studies were located initially. Eight of these studies met the following criteria: (a) they investigated the effect of sensory integration therapy; (b) they included dependent measures of academic achievement, motor or reflex performance, and! or language function; (c) they Included a comparison between at least two groups; and (d) they reported quantitative results of the effect of sensory integration therapy. The 8 studies contained a total of 47 statistical hypothesis tests that evaluated the effectiveness of sensory zntegration therapy. An analySIS of these tests, using quantitative reviewing methods, revealed that subjects participating in sensory integration therapy performed significantly better than members in the control groups who did not receIVe sensory integration therapy. The application of sensory integration therapy to various client populations is discussed in relation to the existing empirical support revealed In the studies reviewed. The advantages of quantitative reviewing procedures are discussed, and use of the procedures with the developing occupational therapy research literature is recommended. Sensory integration therapy is popular with occupational therapists and has been applied to various clinical populations. Price (l) observed that "sensory integrati ve thera py has been extensi vel y applied to patients with neuromuscular dysfunction, as well as learning disability, retardation, and sensory impairment. Though most visible in pediatrics, it has a firm base and wide application in acq uired brain dysfunction, specifically cerebrovascular accidents, usually at the upper end of the age continuum.. . Even more promising and challenging is the increasing role of the sensory integration approach in the treatment of psychiatric disorders, as preventive therapy with children, as well as remedial therapy with children, adolescents, and adults." (I, pp 287-288) This widespread application of sensory integration therapy has not gone uncriticized. For example, Lerer (2) stated that "there is no valid, convincing proof in the limited studies conducted thus far
Extracting and composing robust features with denoising autoencoders

Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite.
Fully Convolutional Multi-Class Multiple Instance Learning

Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network. In this setting, we seek to learn a semantic segmentation model from just weak image-level labels. The model is trained endto-end to jointly optimize the representation while disambiguating the pixel-image label assignment. Fully convolutional training accepts inputs of any size, does not need object proposal pre-processing, and offers a pixelwise loss map for selecting latent instances. Our multi-class MIL loss exploits the further supervision given by images with multiple labels. We evaluate this approach through preliminary experiments on the PASCAL VOC segmentation challenge.
Knowledge Management Strategies in Public Sector — Case Study

Knowledge management (KM) as an emerging discipline is becoming increasingly important to organizations seeking to improve their efficiency and competitive abilities. This research aims to investigate knowledge management strategies (KMS) from different fields of knowledge and to identify what are critical factors for effective KMS in public sector and the challenges it faces for the future. This research is possibly the first attempt to investigate empirically the compatibility in one of the most important Saudi public organizations of KMS. To investigate KMS, the research focuses on KM as practiced in the Institute of Public Administration (IPA). The research focuses on factors that may critically influence the development of KMS in public sector in Saudi Arabia. The main question research is: What are the success factors for effective KMS at IPA? The research design was employed with quantitative data collection methods. Questionnaires were distributed to 238 employees in all IPA organizations. The resulting data is analyzed at descriptive and explanatory levels. The research identified 13 critical factors that must be carefully considered to ensure KMS success. The study divided these critical factors into four groups from different perspectives point views to KMS, namely: KM resources, KM technology, KM learning and innovation, and KM beneficiaries. By integrating the insights from organizational knowledge, information systems, customer-based knowledge, and organizational learning literatures, this study has demonstrated the need to implement complementary strategies.
Cost-Optimal Learning of Causal Graphs

We consider the problem of learning a causal graph over a set of variables with interventions. We study the cost-optimal causal graph learning problem: For a given skeleton (undirected version of the causal graph), design the set of interventions with minimum total cost, that can uniquely identify any causal graph with the given skeleton. We show that this problem is solvable in polynomial time. Later, we consider the case when the number of interventions is limited. For this case, we provide polynomial time algorithms when the skeleton is a tree or a clique tree. For a general chordal skeleton, we develop an efficient greedy algorithm, which can be improved when the causal graph skeleton is an interval graph.
VFH+: Reliable Obstacle Avoidance for Fast Mobile Robots

This paper presents further developments of the earlier Vector Field Histogram (VFH) method for realtime mobile robot obstacle avoidance. The enhanced method, called VFH+, offers several improvements that result in smoother robot trajectories and greater reliability. VFH+ reduces some of the parameter tuning of the original VFH method by explicitly compensating for the robot width. Also added in VFH+ is a better approximation of the mobile robot trajectory, which results in higher reliability.
Towards relation based Argumentation Mining

We advocate a relation based approach to Argumentation Mining. Our focus lies on the extraction of argumentative relations instead of the identification of arguments, themselves. By classifying pairs of sentences according to the relation that holds between them we are able to identify sentences that may be factual when considered in isolation, but carry argumentative meaning when read in context. We describe scenarios in which this is useful, as well as a corpus of annotated sentence pairs we are developing to provide a testbed for this approach.