nils-herrmann/clean_scirepeval huggingface dataset:
Characterisation of Aeromonas spp. isolated from frozen fish intended for human consumption in Mexico.

A total of 82 strains of presumptive Aeromonas spp. were identified biochemically and genetically (16S rDNA-RFLP). The strains were isolated from 250 samples of frozen fish (Tilapia, Oreochromis niloticus niloticus) purchased in local markets in Mexico City. In the present study, we detected the presence of several genes encoding for putative virulence factors and phenotypic activities that may play an important role in bacterial infection. In addition, we studied the antimicrobial patterns of those strains. Molecular identification demonstrated that the prevalent species in frozen fish were Aeromonas salmonicida (67.5%) and Aeromonas bestiarum (20.9%), accounting for 88.3% of the isolates, while the other strains belonged to the species Aeromonas veronii (5.2%), Aeromonas encheleia (3.9%) and Aeromonas hydrophila (2.6%). Detection by polymerase chain reaction (PCR) of genes encoding putative virulence factors common in Aeromonas, such as aerolysin/hemolysin, lipases including the glycerophospholipid-cholesterol acyltransferase (GCAT), serine protease and DNases, revealed that they were all common in these strains. Our results showed that first generation quinolones and second and third generation cephalosporins were the drugs with the best antimicrobial effect against Aeromonas spp. In Mexico, there have been few studies on Aeromonas and its putative virulence factors. The present work therefore highlights an important incidence of Aeromonas spp., with virulence potential and antimicrobial resistance, isolated from frozen fish intended for human consumption in Mexico City.
Firm value , risk , and growth opportunities

We show that Tobin's q, as proxied by the ratio of the firm's market value to its book value, increases with the firm's systematic equity risk and falls with the firm's unsystematic equity risk. Further, an increase in the firm's total equity risk is associated with a fall in q. The negative relation between the change in total risk and the change in q is robust through time for the whole sample, but it does not hold for the largest firms.
ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latent fingerprint reconstruction model based on conditional generative adversarial networks (cGANs). Two modifications are applied to the cGAN to adapt it for the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual ID preservation approach is developed to force the generator to preserve the ID information during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. We achieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50 accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.
SEVEN KOHLBERG ' S STAGES OF

Kohlberg, who was born in 1927, grew up in Bronxville, New York, and attended the Andover Academy in Massachusetts, a private high school for bright and usually wealthy students. He did not go immediately to college, but instead went to help the Israeli cause, in which he was made the Second Engineer on an old freighter carrying refugees from parts of Europe to Israel. After this, in 1948, he enrolled at the University of Chicago, where he scored so high on admission tests that he had to take only a few courses to earn his bachelor's degree. This he did in one year. He stayed on at Chicago for graduate work in psychology, at first thinking he would become a clinical psychologist. However, he soon became interested in Piaget and began interviewing children and adolescents on moral issues. The result was his doctoral dissertation (1958a), the first rendition of his new stage theory.
Dimensional ridge alterations following tooth extraction. An experimental study in the dog.

To study dimensional alterations of the alveolar ridge that occurred following tooth extraction as well as processes of bone modelling and remodelling associated with such change.



Twelve mongrel dogs were included in the study. In both quadrants of the mandible incisions were made in the crevice region of the 3rd and 4th premolars. Minute buccal and lingual full thickness flaps were elevated. The four premolars were hemi-sected. The distal roots were removed. The extraction sites were covered with the mobilized gingival tissue. The extractions of the roots and the sacrifice of the dogs were staggered in such a manner that all dogs contributed with sockets representing 1, 2, 4 and 8 weeks of healing. The animals were sacrificed and tissue blocks containing the extraction socket were dissected, decalcified in EDTA, embedded in paraffin and cut in the buccal-lingual plane. The sections were stained in haematoxyline-eosine and examined in the microscope.



It was demonstrated that marked dimensional alterations occurred during the first 8 weeks following the extraction of mandibular premolars. Thus, in this interval there was a marked osteoclastic activity resulting in resorption of the crestal region of both the buccal and the lingual bone wall. The reduction of the height of the walls was more pronounced at the buccal than at the lingual aspect of the extraction socket. The height reduction was accompanied by a "horizontal" bone loss that was caused by osteoclasts present in lacunae on the surface of both the buccal and the lingual bone wall.



The resorption of the buccal/lingual walls of the extraction site occurred in two overlapping phases. During phase 1, the bundle bone was resorbed and replaced with woven bone. Since the crest of the buccal bone wall was comprised solely of bundle this modelling resulted in substantial vertical reduction of the buccal crest. Phase 2 included resorption that occurred from the outer surfaces of both bone walls. The reason for this additional bone loss is presently not understood.
Three-Dimensional Printing of Multifunctional Nanocomposites: Manufacturing Techniques and Applications.

The integration of nanotechnology into three-dimensional printing (3DP) offers huge potential and opportunities for the manufacturing of 3D engineered materials exhibiting optimized properties and multifunctionality. The literature relating to different 3DP techniques used to fabricate 3D structures at the macro- and microscale made of nanocomposite materials is reviewed here. The current state-of-the-art fabrication methods, their main characteristics (e.g., resolutions, advantages, limitations), the process parameters, and materials requirements are discussed. A comprehensive review is carried out on the use of metal- and carbon-based nanomaterials incorporated into polymers or hydrogels for the manufacturing of 3D structures, mostly at the microscale, using different 3D-printing techniques. Several methods, including but not limited to micro-stereolithography, extrusion-based direct-write technologies, inkjet-printing techniques, and popular powder-bed technology, are discussed. Various examples of 3D nanocomposite macro- and microstructures manufactured using different 3D-printing technologies for a wide range of domains such as microelectromechanical systems (MEMS), lab-on-a-chip, microfluidics, engineered materials and composites, microelectronics, tissue engineering, and biosystems are reviewed. Parallel advances on materials and techniques are still required in order to employ the full potential of 3D printing of multifunctional nanocomposites.
BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.
Low-cost synthesis of flowerlike α-Fe2O3 nanostructures for heavy metal ion removal: adsorption property and mechanism.

Flowerlike α-Fe(2)O(3) nanostructures were synthesized via a template-free microwave-assisted solvothermal method. All chemicals used were low-cost compounds and environmentally benign. These flowerlike α-Fe(2)O(3) nanostructures had high surface area and abundant hydroxyl on their surface. When tested as an adsorbent for arsenic and chromium removal, the flowerlike α-Fe(2)O(3) nanostructures showed excellent adsorption properties. The adsorption mechanism for As(V) and Cr(VI) onto flowerlike α-Fe(2)O(3) nanostructures was elucidated by X-ray photoelectron spectroscopy and synchrotron-based X-ray absorption near edge structure analysis. The results suggested that ion exchange between surface hydroxyl groups and As(V) or Cr(VI) species was accounted for by the adsorption. With maximum capacities of 51 and 30 mg g(-1) for As(V) and Cr(VI), respectively, these low-cost flowerlike α-Fe(2)O(3) nanostructures are an attractive adsorbent for the removal of As(V) and Cr(VI) from water.
The Triple Aim : Care , Health , And Cost The remaining barriers to integrated care are not technical ; they are political

Improving the U.S. health care system requires simultaneous pursuit of three aims: improving the experience of care, improving the health of populations, and reducing per capita costs of health care. Preconditions for this include the enrollment of an identified population, a commitment to universality for its members, and the existence of an organization (an “integrator”) that accepts responsibility for all three aims for that population. The integrator’s role includes at least five components: partnership with individuals and families, redesign of primary care, population health management, financial management, and macro system integration.
Compressing Word Embeddings

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using large-scale unlabled text analysis. However, these representations typically consist of dense vectors that require a great deal of storage and cause the internal structure of the vector space to be opaque. A more ‘idealized’ representation of a vocabulary would be both compact and readily interpretable. With this goal, this paper first shows that Lloyd’s algorithm can compress the standard dense vector representation by a factor of 10 without much loss in performance. Then, using that compressed size as a ‘storage budget’, we describe a new GPU-friendly factorization procedure to obtain a representation which gains interpretability as a side-effect of being sparse and non-negative in each encoding dimension. Word similarity and word-analogy tests are used to demonstrate the effectiveness of the compressed representations obtained.
A Novel Image Steganographic Approach for Hiding Text in Color Images using HSI Color Model

Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both subjective and Objective Analysis. Experimentally it is found that the proposed method have larger Peak Signal-to Noise Ratio (PSNR) values, good imperceptibility and multiple security levels which shows its superiority as compared to several existing methods.
Computer Vision – ECCV 2018

Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems—BN’s error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN’s usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN’s computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code.
A Supervised Approach to Extractive Summarisation of Scientific Papers

Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.
Self-Efficacy: An Essential Motive to Learn.

During the past two decades, self-efficacy has emerged as a highly effective predictor of students' motivation and learning. As a performance-based measure of perceived capability, self-efficacy differs conceptually and psychometrically from related motivational constructs, such as outcome expectations, self-concept, or locus of control. Researchers have succeeded in verifying its discriminant validity as well as convergent validity in predicting common motivational outcomes, such as students' activity choices, effort, persistence, and emotional reactions. Self-efficacy beliefs have been found to be sensitive to subtle changes in students' performance context, to interact with self-regulated learning processes, and to mediate students' academic achievement.
Tensegrity: the architectural basis of cellular mechanotransduction.

Physical forces of gravity, hemodynamic stresses, and movement play a critical role in tissue development. Yet, little is known about how cells convert these mechanical signals into a chemical response. This review attempts to place the potential molecular mediators of mechanotransduction (e.g. stretch-sensitive ion channels, signaling molecules, cytoskeleton, integrins) within the context of the structural complexity of living cells. The model presented relies on recent experimental findings, which suggests that cells use tensegrity architecture for their organization. Tensegrity predicts that cells are hard-wired to respond immediately to mechanical stresses transmitted over cell surface receptors that physically couple the cytoskeleton to extracellular matrix (e.g. integrins) or to other cells (cadherins, selectins, CAMs). Many signal transducing molecules that are activated by cell binding to growth factors and extracellular matrix associate with cytoskeletal scaffolds within focal adhesion complexes. Mechanical signals, therefore, may be integrated with other environmental signals and transduced into a biochemical response through force-dependent changes in scaffold geometry or molecular mechanics. Tensegrity also provides a mechanism to focus mechanical energy on molecular transducers and to orchestrate and tune the cellular response.
The Inherent Dangers of Unidirectional Emotional Bonds between Humans and Social Robots

The early 21st century is witnessing a rapid advance in social robots. From vacuum cleaning robots (like the Roomba), to entertainment robots (like the Pleo), to robot pets (like KittyCat), to robot dolls (like Baby Alive), to therapy robots (like Paro), and many others, social robots are rapidly finding applications in households and elder care settings. In 2006, the number of service robots world-wide alone outnum-bered industrial robots by a factor of four and this gap is expected to widen to a factor of six by 2010, only fueled by ambitious goals like those of South Korea to put one robot into each household by the year 2013 or by the Japanese expectation that the robot industry will be worth ten times the present value in 2025 (Gates, 2007). From these expectations alone, it should be clear that social robots will soon become an integral part of human societies, very much like computers and the Internet in the last decade. In fact, using computer technology as an analogy, it seems likely that social robotics will follow a similar trajectory: once social robots have been fully embraced by societies, life without them will become inconceivable. As a consequence of this societal penetration, social robots will also enter our personal lives, and that fact alone requires us to reflect on what exactly happens in our interactions with these machines. For social robots are specifically designed for personal interactions that will involve human emotions and feelings: “A sociable robot is able to communicate and interact with us, understand and even relate to us, in a personal way. It is a robot that is socially intelligent in a human-like way.” (Breazeal, 2002) And while social robots can have benefits for humans (e.g., health benefits as demonstrated with Paro (Shibata..., 2005)), it is also possible that they could inflict harm, emotional harm, that is. And exactly herein lies the hitherto underestimated danger: the potential for humans’ emotional dependence on social robots. As we will see shortly, such emotional dependence on social robots is different from other human dependencies on technology (e.g., different both in kind and quality from depending on one’s cell phone, wrist watch, or PDA). To be able to understand the difference and the potential ramifications of building complex social robots that are freely deployed in human societies, we have to understand how social robots are different from other related technologies and how they, as a result, can affect humans at a very basic level.
The Experts in the Crowd : The Role of Reputable Investors in a Crowdfunding Market

This paper examines the role of experts in an online crowdfunding market. Using a novel data set on individual investments in a crowdfunding market for mobile applications, we investigate whether early investments serve as signals of quality for later investors, and if the value of these signals differs depending on the identity of early investors. We find that while early investors have a significant influence on later investors, not all early investors are equally influential. Specifically, we find that among the early investors, two categories of experts — app developer investors and experienced investors—have a significant influence on the later investors the crowd. More interestingly, the specifics of their expertise determine their influence. App developer investors who have a better knowledge of the product are found to be more influential for “concept apps” (apps in the pre-release stage), while experienced investors – investors with a better knowledge of market performance are found to be more influential for “live apps” (apps that are already being sold in the market). Our findings show that the majority of investors in this market – the crowd – although inexperienced, are rather sophisticated in their ability to identify and exploit nuanced differences in the informational content of the investments made by these different experts. In examining the ex-post performance of apps, we find that successful funding in the market is positively associated with ex-post app sales and that the quality signals provided by the experts’ investment choices are indeed credible. Contrary to popular perceptions of crowdfunding markets as means for democratizing expertise and as substitutes for traditional expert-dominated mechanisms, our findings indicate that despite the freedom of choice provided by these crowd-based markets, the crowd’s decisions are highly influenced by experts participating in these markets.
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping

We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We assess the performance of deep learning algorithms and compare them with classical NLP approaches.  We compare convolutional neural networks (CNNs), n-gram models, and approaches based on cTAKES that extract pre-defined medical concepts from clinical notes and use them to predict patient phenotypes. The performance is tested on 10 different phenotyping tasks using 1,610 discharge summaries extracted from the MIMIC-III database.  CNNs outperform other phenotyping algorithms in all 10 tasks. The average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our model having an F1-score up to 37 points higher than alternative approaches. We additionally assess the interpretability of our model by presenting a method that extracts the most salient phrases for a particular prediction.  We show that NLP methods based on deep learning improve the performance of patient phenotyping. Our CNN-based algorithm automatically learns the phrases associated with each patient phenotype. As such, it reduces the annotation complexity for clinical domain experts, who are normally required to develop task-specific annotation rules and identify relevant phrases. Our method performs well in terms of both performance and interpretability, which indicates that deep learning is an effective approach to patient phenotyping based on clinicians’ notes. gehrmann@seas.harvard.edu