nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning for Medical Image Processing: Overview, Challenges and Future

Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consume huge percentage of budget. Mostly the interpretations of medical data is being done by medical expert. In terms of image interpretation by human expert, it is quite limited due to its subjectivity, complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in other real world application, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in health secotr. In this chapter, we discussed state of the art deep learning architecture and its optimization used for medical image segmentation and classification. In the last section, we have discussed the challenges deep learning based methods for medical imaging and open research issue.
Performance Comparisons Between 7-nm FinFET and Conventional Bulk CMOS Standard Cell Libraries

FinFET devices have been proposed as a promising substitute for conventional bulk CMOS-based devices at the nanoscale due to their extraordinary properties such as improved channel controllability, a high on/off current ratio, reduced short-channel effects, and relative immunity to gate line-edge roughness. This brief builds standard cell libraries for the advanced 7-nm FinFET technology, supporting multiple threshold voltages and supply voltages. The circuit synthesis results of various combinational and sequential circuits based on the presented 7-nm FinFET standard cell libraries forecast 10× and 1000× energy reductions on average in a superthreshold regime and 16× and 3000× energy reductions on average in a near-threshold regime as compared with the results of the 14-nm and 45-nm bulk CMOS technology nodes, respectively.
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency

Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex innerand cross-domain variations. To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain. We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain. Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain. To avoid semantic inconsistencies during translation that naturally appear due to the large innerand cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels. Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process. Source (GTA5) Target (BDD)  Exemplar guided image translation examples of GTA5→ BDD. Best viewed in color.
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach

We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase 'sick of' and the word 'depressed'), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive 'my' when mentioning their 'wife' or 'girlfriend' more often than females use 'my' with 'husband' or 'boyfriend'). To date, this represents the largest study, by an order of magnitude, of language and personality.
Exploring Topic Discriminating Power of Words in Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA) and its variants have bee n widely used to discover latent topics in textual documents. However, some of topics generated by L DA may be noisy with irrelevant words scattering across these topics. We name this kind of wo rds as topic-indiscriminate words, which tend to make topics more ambiguous and less interpreta ble by humans. In our work, we propose a new topic model named TWLDA, which assigns low weig hts to words with low topic discriminating power (ability). Our experimental results show that the proposed approach, which effectively reduces the number of topic-indiscriminate wo rds in discovered topics, improves the effectiveness of LDA.
APPROVAL THE ANALYSIS OF THE EFFECT OF THE EFFECTS OF LOVE VALUES IN KOREAN DRAMAS ON TAIWANESE VIEWERS-- FROM THE PERSPECTIVE OF BUSINESS . By

Korean dramas have played an influential role in Taiwanese society since they were first introduced into Taiwan. One of the most dominant themes in most Korean dramas is the theme of love. As a story topic, love accounts for about ninety percent of the themes dealt with by these dramas. By applying the theoretical idea of cultural proximity, and by using content analysis to analyze the underlying values contained in the dramas, this study examines the theme of love in these dramas. The data pool includes 10 popular Korean dramas aired between the years of 2008 and 2012. Using these 10 dramas as a sample, I examine whether contemporary feminist attitudes about women " s autonomy play a role in how Taiwanese audiences identify with stories about love in Korean dramas. Through interviews with four television station managers from companies including LTV, ETTV, ii Videoland Drama and ELTA, I also gathered information about the process of localization within Korean dramas. In addition to the above strategies, my study incorporates secondary data to analyze related reports and statistical data about Korean dramas.
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks

Neural network based architectures have recently been proposed for reconstruction of undersampled MR acquisitions. A deep network containing many free parameters is typically trained using a relatively large set of fully-sampled MRI data, and later used for on-line reconstruction of undersampled data. Ideally network performance should be optimized by drawing the training and testing data from the same domain. In practice, however, large datasets comprising hundreds of subjects scanned under a common protocol are rare. Here, we propose a transfer-learning approach to address the problem of data scarcity in training deep networks for accelerated MRI. The proposed approach trains neural networks using thousands of samples from a public dataset of natural images (ImageNet). The network is then fine-tuned using only few tens of MR images acquired in the testing domain (T1or T2weighted MRI). The ImageNet-trained network yields nearly identical reconstructions to networks trained directly in the testing domain using thousands of MR images, and it outperforms conventional compressed sensing reconstructions in terms of image quality. The proposed approach might facilitate the use of neural networks for MRI reconstruction without the need for collection of extensive imaging datasets.
ML-KNN: A lazy learning approach to multi-label learning

Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this paper, a multi-label lazy learning approach named Mlknn is presented, which is derived from the traditional k-Nearest Neighbor (kNN) algorithm. In detail, for each unseen instance, its k nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that Ml-knn achieves superior performance to some well-established multi-label learning algorithms.
Spam Email Classification using an Adaptive Ontology

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of spam emails during the past few years. As spammers always try to find a way to evade existing filters, new filters need to be developed to catch spam. Ontologies allow for machine-understandable semantics of data. It is important to share information with each other for more effective spam filtering. Thus, it is necessary to build ontology and a framework for efficient email filtering. Using ontology that is specially designed to filter spam, bunch of unsolicited bulk email could be filtered out on the system. Similar to other filters, the ontology evolves with the user requests. Hence the ontology would be customized for the user. This paper proposes to find an efficient spam email filtering method using adaptive ontology
A survey on educational process mining

Educational process mining (EPM) is an emerging field in educational data mining (EDM) aiming to make unexpressed knowledge explicit and to facilitate better understanding of the educational process. EPM uses log data gathered specifically from educational environments in order to discover, analyze, and provide a visual representation of the complete educational process. This paper introduces EPM and elaborates on some of the potential of this technology in the educational domain. It also describes some other relevant, related areas such as intentional mining, sequential pattern mining and graph mining. It highlights the components of an EPM framework and it describes the different challenges when handling event logs and other generic issues. It describes the data, tools, techniques and models used in EPM. In addition, the main work in this area is described and grouped by educational application domains.
Bifurcation and Large-Signal Stability Analysis of Three-Phase Voltage Source Converter Under Grid Voltage Dips

Three-phase voltage source converters (VSCs) are commonly used as power flow interface in ac/dc hybrid power systems. The ac power grid suffers from unpredictable short-circuit faults and power flow fluctuations, causing undesirable grid voltage dips. The voltage dips may last for a short time or a long duration, and vary the working conditions of VSCs. Due to their nonlinear characteristics, VSCs may enter abnormal operating mode in response to voltage dips. In this paper, the transient response of three-phase VSCs under practical grid voltage dips is studied and a catastrophic bifurcation phenomenon is identified in the system. The converter will exhibit an irreversible instability after the dips. The expanded magnitude of ac reactive current may cause catastrophic consequence for the system. A full-order eigenvalue analysis and a reduced-order mixed-potential-theory-based analysis are adopted to reveal the physical origin of the large-signal instability phenomenon. The key parameters of the system are identified and the boundaries of instability are located. The bifurcation phenomenon and a set of design-oriented stability boundaries in some chosen parameter space are verified by cycle-by-cycle simulations and experimental measurement on a practical grid-connected VSC prototype.
Central Pattern Generators augmented with virtual model control for quadruped rough terrain locomotion

We present a modular controller for quadruped locomotion over unperceived rough terrain. Our approach is based on a computational Central Pattern Generator (CPG) model implemented as coupled nonlinear oscillators. Stumbling correction reflex is implemented as a sensory feedback mechanism affecting the CPG. We augment the outputs of the CPG with virtual model control torques responsible for posture control. The control strategy is validated on a 3D forward dynamics simulated quadruped robot platform of about the size and weight of a cat. To demonstrate the capabilities of the proposed approach, we perform locomotion over unperceived uneven terrain and slopes, as well as situations facing external pushes.
Mitigation of Motions of Tall Buildings with Specific Examples of Recent Applications

Flexible structures may fall victim to excessive levels of vibration under the action of wind, adversely affecting serviceability and occupant comfort. To ensure the functional performance of flexible structures, various design modifications are possible, ranging from alternative structural systems to the utilization of passive and active control devices. This paper presents an overview of state-of-the-art measures to reduce structural response of buildings, including a summary of recent work in aerodynamic tailoring and a discussion of auxiliary damping devices for mitigating the wind-induced motion of structures. In addition, some discussion of the application of such devices to improve structural resistance to seismic events is also presented, concluding with detailed examples of the application of auxiliary damping devices in Australia, Canada, China, Japan, and the United States.
Graph Signal Representation of EEG for Graph Convolutional Neural Network

In this paper, we present an approach for graph signal representation of EEG toward deep learning-based modeling. In order to overcome the low dimensionality and spatial resolution of EEG, our approach divides the EEG signal into multiple frequency bands, builds an intra-band graph for each of them, and merges them with inter-band connectivity to obtain rich graph representation. The signal features on the vertices are also obtained from EEG. Finally, the graph signals are learned with graph convolutional neural networks. Experimental results on visual content identification using EEG are presented and various ways of defining intra-band and inter-band connections are examined.
Position paper: Management of men complaining of a small penis despite an actually normal size.

With the worldwide increase in penile augmentation procedures and claims of devices designed to elongate the penis, it becomes crucial to study the scientific basis of such procedures or devices, as well as the management of a complaint of a small penis in men with a normal penile size.



The aim of this work is to study the scientific basis of opting to penile augmentation procedures and to develop guidelines based on the best available evidence for the management of men complaining of a small penis despite an actually normal size.



We reviewed the literature and evaluated the evidence about what the normal penile size is, what patients complaining of a small penis usually suffer from, benefits vs. complications of surgery, penile stretching or traction devices, and outcome with patient education and counseling. Repeated presentation and detailed discussions within the Standard Committee of the International Society for Sexual Medicine were performed.



Recommendations are based on the evaluation of evidence-based medical literature, widespread standards committee discussion, public presentation, and debate.



We propose a practical approach for evaluating and counseling patients complaining of a small-sized penis.



Based on the current status of science, penile lengthening procedure surgery is still considered experimental and should only be limited to special circumstances within research or university institutions with supervising ethics committees.
Low-Cost Subpixel Rendering for Diverse Displays

Subpixel rendering increases the apparent display resolution by taking into account the subpixel structure of a given display. In essence, each subpixel is addressed individually, allowing the underlying signal to be sampled more densely. Unfortunately, naïve subpixel sampling introduces color aliasing, as each subpixel only displays a specific color (usually R, G, and B subpixels are used). As previous work has shown, chromatic aliasing can be reduced significantly by taking the sensitivity of the human visual system into account. In this work, we find optimal filters for subpixel rendering for a diverse set of 1D and 2D subpixel layout patterns. We demonstrate that these optimal filters can be approximated well with analytical functions. We incorporate our filters into GPU-based multisample antialiasing to yield subpixel rendering at a very low cost (1-2ms filtering time at HD resolution). We also show that texture filtering can be adapted to perform efficient subpixel rendering. Finally, we analyze the findings of a user study we performed, which underpins the increased visual fidelity that can be achieved for diverse display layouts, by using our optimal filters.
A tutorial survey of architectures, algorithms, and applications for deep learning

In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference  are expanded and updated to include more recent developments in deep learning. The previous and the updatedmaterials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higherlevel concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.
Genetic programs constructed from layered logic gates in single cells

Genetic programs function to integrate environmental sensors, implement signal processing algorithms and control expression dynamics. These programs consist of integrated genetic circuits that individually implement operations ranging from digital logic to dynamic circuits, and they have been used in various cellular engineering applications, including the implementation of process control in metabolic networks and the coordination of spatial differentiation in artificial tissues. A key limitation is that the circuits are based on biochemical interactions occurring in the confined volume of the cell, so the size of programs has been limited to a few circuits. Here we apply part mining and directed evolution to build a set of transcriptional AND gates in Escherichia coli. Each AND gate integrates two promoter inputs and controls one promoter output. This allows the gates to be layered by having the output promoter of an upstream circuit serve as the input promoter for a downstream circuit. Each gate consists of a transcription factor that requires a second chaperone protein to activate the output promoter. Multiple activator–chaperone pairs are identified from type III secretion pathways in different strains of bacteria. Directed evolution is applied to increase the dynamic range and orthogonality of the circuits. These gates are connected in different permutations to form programs, the largest of which is a 4-input AND gate that consists of 3 circuits that integrate 4 inducible systems, thus requiring 11 regulatory proteins. Measuring the performance of individual gates is sufficient to capture the behaviour of the complete program. Errors in the output due to delays (faults), a common problem for layered circuits, are not observed. This work demonstrates the successful layering of orthogonal logic gates, a design strategy that could enable the construction of large, integrated circuits in single cells.