nils-herrmann/clean_scirepeval huggingface dataset:
Number of people with glaucoma worldwide.

To estimate the prevalence of glaucoma among people worldwide.



Available published data on glaucoma prevalence were reviewed to determine the relation of open angle and angle closure glaucoma with age in people of European, African, and Asian origin. A comparison was made with estimated world population data for the year 2000.



The number of people with primary glaucoma in the world by the year 2000 is estimated at nearly 66.8 million, with 6.7 million suffering from bilateral blindness. In developed countries, fewer than 50% of those with glaucoma are aware of their disease. In the developing world, the rate of known disease is even lower.



Glaucoma is the second leading cause of vision loss in the world. Improved methods of screening and therapy for glaucoma are urgently needed.
The chemistry of graphene oxide.

The chemistry of graphene oxide is discussed in this critical review. Particular emphasis is directed toward the synthesis of graphene oxide, as well as its structure. Graphene oxide as a substrate for a variety of chemical transformations, including its reduction to graphene-like materials, is also discussed. This review will be of value to synthetic chemists interested in this emerging field of materials science, as well as those investigating applications of graphene who would find a more thorough treatment of the chemistry of graphene oxide useful in understanding the scope and limitations of current approaches which utilize this material (91 references).
Capsules for Object Segmentation

Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al. [2017], referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image classification. The success of capsule networks lies in their ability to preserve more information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for preservation of part-whole relationships in the data. This preservation of the input is demonstrated by reconstructing the input from the output capsule vectors. Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. We extend the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules. Further, we extend the masked reconstruction to reconstruct the positive input class. The proposed convolutionaldeconvolutional capsule network, called SegCaps, shows strong results for the task of object segmentation with substantial decrease in parameter space. As an example application, we applied the proposed SegCaps to segment pathological lungs from low dose CT scans and compared its accuracy and efficiency with other U-Net-based architectures. SegCaps is able to handle large image sizes (512 × 512) as opposed to baseline capsules (typically less than 32 × 32). The proposed SegCaps reduced the number of parameters of U-Net architecture by 95.4% while still providing a better segmentation accuracy.
A Multi-Stage Approach to Facial Feature Detection

We describe a novel shape constraint technique which is inco rporated into a multi-stage algorithm to automatically locate features o n the human face. The method is coarse-to-fine. First a face detector is applie d to find the approximate scale and location of the face in the image. Then in dividual feature detectors are applied and combined using a novel algorithm k nown as Pairwise Reinforcement of Feature Responses (PRFR). The points predicted by this method are then refined using a version of the Active Appe arance Model (AAM) search, which is tuned to edge and corner features. The final output of the three stage algorithm is shown to give much better resu lts than any other combination of methods. The method outperforms previ ous published results on the BIOID test set [11].
An Unequal Wilkinson Power Divider with Variable Dividing Ratio

In this paper, an unequal 1:N Wilkinson power divider with variable power dividing ratio is proposed. The proposed unequal power divider is composed of the conventional Wilkinson divider structure, rectangular-shaped defected ground structure (DGS), island in DGS, and varactor diodes of which capacitance is adjustable according to bias voltage. The high impedance value of microstrip line having DGS is going up and down by adjusting the bias voltage for varactor diodes. Output power dividing ratio (N) is adjusted from 2.59 to 10.4 for the unequal power divider with 2 diodes.
Anomaly detection in crowd scene

Anomaly detection in crowd scene is very important because of more concern with people safety in public place. This paper presents an approach to automatically detect abnormal behavior in crowd scene. For this purpose, instead of tracking every person, KLT corners are extracted as feature points to represent moving objects and tracked by optical flow technique to generate motion vectors, which are used to describe motion. We divide whole frame into small blocks, and motion pattern in each block is encoded by the distribution of motion vectors in it. Similar motion patterns are clustered into pattern model in an unsupervised way, and we classify motion pattern into normal or abnormal group according to the deviation between motion pattern and trained model. The results on abnormal events detection in real video demonstrate the effectiveness of the approach.
Contextual Reasoning

It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature, see for instance [Giunchiglia 1991a, Giunchiglia & Weyhrauch 1988, Guha 1990, Guha & Lenat 1990, Shoham 1991, McCarthy 1990b]. Our intuition is that reasoning is usually performed on a subset of the global knowledge base; we never consider all we know but only a very small subset of it. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McCarthy 1990b, McCarthy 1991]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported in appendix A. Ideas are described incrementally with increasing level of detail. Thus, section 2 describes why contexts are an important notion to consider as part of our ontology. This is achieved also by comparing contexts with situations, another ontologically very important concept. Section 3 then goes more into the technical details and proposes that contexts should be formalized as particular mathematical objects, namely as logical theories. Reasoning with contexts is then formalized as a set of deductions, each deduction carried out inside a context, connected by appropriate "bridge rules". Finally, section 4 describes how an important example of common sense reasoning, reasoning about reasoning, can be formalized as multicontextual reasoning.
Encoding of direction of fingertip forces by human tactile afferents.

In most manipulations, we use our fingertips to apply time-varying forces to the target object in controlled directions. Here we used microneurography to assess how single tactile afferents encode the direction of fingertip forces at magnitudes, rates, and directions comparable to those arising in everyday manipulations. Using a flat stimulus surface, we applied forces to a standard site on the fingertip while recording impulse activity in 196 tactile afferents with receptive fields distributed over the entire terminal phalanx. Forces were applied in one of five directions: normal force and forces at a 20 degrees angle from the normal in the radial, distal, ulnar, or proximal directions. Nearly all afferents responded, and the responses in most slowly adapting (SA)-I, SA-II, and fast adapting (FA)-I afferents were broadly tuned to a preferred direction of force. Among afferents of each type, the preferred directions were distributed in all angular directions with reference to the stimulation site, but not uniformly. The SA-I population was biased for tangential force components in the distal direction, the SA-II population was biased in the proximal direction, and the FA-I population was biased in the proximal and radial directions. Anisotropic mechanical properties of the fingertip and the spatial relationship between the receptive field center of the afferent and the stimulus site appeared to influence the preferred direction in a manner dependent on afferent type. We conclude that tactile afferents from the whole terminal phalanx potentially contribute to the encoding of direction of fingertip forces similar to those that occur when subjects manipulate objects under natural conditions.
The Neural Theory of Metaphor

The neural revolution is changing our understanding of the brain and the mind in radical ways, and that is no less true in the theory of metaphor. It is 30 years since Mark Johnson and I wrote Metaphors We Live By in 1979. Though the fundamental outlines of what we discovered remain as valid today as they were then, developments in brain science and neural computation have vastly enriched our understanding of how conceptual metaphor works. This is an intermediate report, as of January 2009.
Dermoscopy of pigmented lesions of the vulva: a retrospective morphological study.

The dermoscopic patterns of pigmented skin tumors are influenced by the body site.



To evaluate the clinical and dermoscopic features associated with pigmented vulvar lesions.



Retrospective analysis of clinical and dermoscopic images of vulvar lesions. The χ² test was used to test the association between clinical data and histopathological diagnosis.



A total of 42 (32.8%) melanocytic and 86 (67.2%) nonmelanocytic vulvar lesions were analyzed. Nevi significantly prevailed in younger women compared with melanomas and melanosis and exhibited most commonly a globular/cobblestone (51.3%) and a mixed (21.6%) pattern. Dermoscopically all melanomas showed a multicomponent pattern. Melanotic macules showed clinical overlapping features with melanoma, but their dermoscopic patterns differed significantly from those observed in melanomas.



The diagnosis and management of pigmented vulvar lesions should be based on a good clinicodermoscopic correlation. Dermoscopy may be helpful in the differentiation of solitary melanotic macules from early melanoma.
A Planar Bandpass Filter Implemented With a Hybrid Structure of Substrate Integrated Waveguide and Coplanar Waveguide

In this paper, a novel planar bandpass filter is proposed, designed, and implemented with a hybrid structure of substrate integrated waveguide (SIW) and coplanar waveguide (CPW), which has the advantages of good passband and stopband performance inherited from SIW and miniaturized size accompanying with the CPW. Additional design flexibility is introduced by the hybrid structure for efficiently controlling the mixed electric and magnetic coupling, and then planar bandpass filters with controllable transmission zeros and quasi-elliptic response can be achieved. Several prototypes with single and dual SIW cavities are fabricated. The measured results verified the performance of the proposed planar bandpass filters, such as low passband insertion loss, sharp roll-off characteristics at transition band, etc.
Fast Wavenet Generation Algorithm

This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet. Compared to a naı̈ve implementation that has complexity O(2) (L denotes the number of layers in the network), our proposed approach removes redundant convolution operations by caching previous calculations, thereby reducing the complexity to O(L) time. Timing experiments show significant advantages of our fast implementation over a naı̈ve one. While this method is presented for Wavenet, the same scheme can be applied anytime one wants to perform autoregressive generation or online prediction using a model with dilated convolution layers. The code for our method is publicly available.
Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to these existing approaches, while also supporting weaker supervision scenarios. Importantly, it can be trained purely from 2D images, without ground-truth pose annotations, and with a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach on synthetic data in various settings, showing that (i) it learns to disentangle shape from pose; (ii) using shading in the loss improves performance; (iii) our model is comparable or superior to state-of-the-art voxel-based approaches on quantitative metrics, while producing results that are visually more pleasing; (iv) it still performs well when given supervision weaker than in prior works.
Experiences in Building a Real-World Eating Recogniser

In this paper, we describe the progressive design of the gesture recognition module of an automated food journaling system -- Annapurna. Annapurna runs on a smartwatch and utilises data from the inertial sensors to first identify eating gestures, and then captures food images which are presented to the user in the form of a food journal. We detail the lessons we learnt from multiple in-the-wild studies, and show how eating recognizer is refined to tackle challenges such as (i) high gestural diversity, and (ii) non-eating activities with similar gestural signatures. Annapurna is finally robust (identifying eating across a wide diversity in food content, eating styles and environments) and accurate (false-positive and false-negative rates of 6.5% and 3.3% respectively)
Fingerprint Based Attendance System Using LabVIEW and GSM

Attendance is one of the work ethics which is valued by most employers. In educational institutions also, attendance and academic success are directly related. Therefore, proper attendance management systems must be in place. Most of the educational institutions and government organizations in developing countries still use paper based attendance method to monitor the attendance. There is a need to replace these traditional methods of attendance recording with a more secure and robust system. Fingerprint based automated identification system based are gaining popularity due to unique nature of fingerprints. In this paper, a novel approach for fingerprint based attendance system using LabVIEW and GSM technology is proposed. Optical fingerprint module is used for capturing and processing fingerprints. Features such as recording of attendance in a text file along with the date and time of attendance are also incorporated in the system. GSM technology is used to intimate the parents about student’s attendance. The proposed system is implemented in the university and its performance is evaluated based upon user friendliness, accuracy, speed, security and cost.
Probabilistic Neural Network with Complex Exponential Activation Functions in Image Recognition using Deep Learning Framework

If the training dataset is not very large, image recognition is usually implemented with the transfer learning methods. In these methods the features are extracted using a deep convolutional neural network, which was preliminarily trained with an external very-large dataset. In this paper we consider the nonparametric classification of extracted feature vectors with the probabilistic neural network (PNN). The number of neurons at the pattern layer of the PNN is equal to the database size, which causes the low recognition performance and high memory space complexity of this network. We propose to overcome these drawbacks by replacing the exponential activation function in the Gaussian Parzen kernel to the complex exponential functions in the Fej\'er kernel. We demonstrate that in this case it is possible to implement the network with the number of neurons in the pattern layer proportional to the cubic root of the database size. Thus, the proposed modification of the PNN makes it possible to significantly decrease runtime and memory complexities without loosing its main advantages, namely, extremely fast training procedure and the convergence to the optimal Bayesian decision. An experimental study in visual object category classification and unconstrained face recognition with contemporary deep neural networks have shown, that our approach obtains very efficient and rather accurate decisions for the small training sample in comparison with the well-known classifiers.
A Reconfigurable Differential CMOS RF Energy Scavenger With 60% Peak Efficiency and -21 dBm Sensitivity

A differential RF-DC CMOS converter for RF energy scavenging based on a reconfigurable voltage rectifier topology is presented. The converter efficiency and sensitivity are optimized thanks to the proposed reconfigurable architecture. Prototypes, realized in 130 nm, provide a regulated output voltage of ~2 V when working at 868 MHz, with a -21 dBm sensitivity. The circuit efficiency peaks at 60%, remaining above the 40% for a 18 dB input power range.
UNIVERSALITY AND COMPLEXITY IN CELLULAR AUTOMATA

Cellular automata are discrete dynamical systems with simple construction but complex self-organizing behaviour. Evidence is presented that all one-dimensional cellular automata fall into four distinct universality classes. Characterizations of the structures generated in these classes are discussed. Three classes exhibit behaviour analogous to limit points, limit cycles and chaotic attractors. The fourth class is probably capable of universal computation, so that properties of its infinite time behaviour are undeeidable.
Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks

IP traffic classification has been a vitally important topic that attracts persistent interest in the networking and machine learning communities for past decades. While there exist quite a number of works applying machine learning techniques to realize IP traffic classification, most works suffer from limitations like either heavily depending on handcrafted features or be only able to handle offline traffic classification. To get rid of the aforementioned weakness, in this paper, we propose our online Convolutional Neural Networks (CNNs) based traffic classification framework named Seq2Img. The basic idea is to employ a compact nonparametric kernel embedding based method to convert early flow sequences into images which fully capture the static and dynamic behaviors of different applications and avoid using handcrafted features that might cause loss of information. A CNN is then applied on the generated images to obtain traffic classification results. Experiments on real network traffic are conducted and encouraging results justify the efficacy of our proposed approach.
The Impact of Online Recommendations and Consumer Feedback on Sales

Quality uncertainty and high search costs for identifying relevant information from an ocean of information may prevent customers from making purchases. Recognizing potential negative impacts of this search cost for quality information and relevant information, firms began to invest in creating a virtual community that enables consumers to share their opinions and experiences to reduce quality uncertainty, and in developing recommendation systems that help customers identify goods in which they might have an interest. However, not much is known regarding the effectiveness of these efforts. In this paper, we empirically investigate the impacts of recommendations and consumer feedbacks on sales based on data gathered from Amazon.com. Our results indicate that more recommendations indeed improve sales at Amazon.com; however, consumer ratings are not found to be related to sales. On the other hand, number of consumer reviews is positively associated with sales. We also find that recommendations work better for less-popular books than for more-popular books. This is consistent with the search cost argument: a consumer’s search cost for less-popular books may be higher, and thus they may rely more on recommendations to locate a product of interest.
Humans can learn new information during sleep

During sleep, humans can strengthen previously acquired memories, but whether they can acquire entirely new information remains unknown. The nonverbal nature of the olfactory sniff response, in which pleasant odors drive stronger sniffs and unpleasant odors drive weaker sniffs, allowed us to test learning in humans during sleep. Using partial-reinforcement trace conditioning, we paired pleasant and unpleasant odors with different tones during sleep and then measured the sniff response to tones alone during the same nights' sleep and during ensuing wake. We found that sleeping subjects learned novel associations between tones and odors such that they then sniffed in response to tones alone. Moreover, these newly learned tone-induced sniffs differed according to the odor pleasantness that was previously associated with the tone during sleep. This acquired behavior persisted throughout the night and into ensuing wake, without later awareness of the learning process. Thus, humans learned new information during sleep.
Mobile phone addiction levels and negative emotions among Chinese young adults: The mediating role of interpersonal problems

This study evaluated the mediating role of interpersonal problems in the link between mobile phone addiction levels and negative emotions among mobile phone addicts and possible-mobile phone addicts respectively. The purpose of this study was to address two gaps in research. First, previous research has revealed that a positive association existed between mobile phone addiction levels and negative emotions, especially among those with interpersonal problems. The present study extended research by exploring the mechanisms underlying these associations. Second, Mobile phone addicts as a particular group has received much attention, however, research concerning the possible mobile phone addicts as another particular group is limited. A total of 1089 young adults in China were surveyed by using cluster sampling. Results showed that compared with nonaddicts, mobile phone addicts or possible mobile phone addicts spent more money and time on mobile phone use and were more vulnerable to have negative emotions. The effects of the possible mobile phone addiction on negative emotions were significant but weaker than those of the mobile phone addiction. Further, interpersonal problem was a significant mediator between mobile phone addiction levels and negative emotions. Findings and their implications are discussed.