nils-herrmann/clean_scirepeval huggingface dataset:
Structure--activity relationships of polymyxin antibiotics.

Tony Velkov,* Philip E. Thompson, Roger L. Nation, and Jian Li* School of Medicine, Deakin University, Pigdons Road, Geelong 3217, Victoria, Australia, Medicinal Chemistry and Drug Action and Facility for Anti-infective Drug Development and Innovation, Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville 3052, Victoria,
A Case of Thanatophoric Dysplasia Type 2: A Novel Mutation

Thanatophoric dysplasia (TD) is a lethal form of skeletal dysplasia with short-limb dwarfism. Two types distinguished with their radiological characteristics have been defined clinically. The femur is curved in type 1, while it is straight in type 2. TD is known to be due to a mutation in the fibroblast growth factor receptor 3 (FGFR3) gene. We report a male patient who showed clinical findings congruent with TD type 2 and a new mutation in the FGFR3 gene, a finding which has not been reported previously.
Value of Morphometry in the Prognosis of Laryngeal Neoplasms

Laryngeal neoplasms represent 2% of all human cancers. They befall mainly the male sex, especially between 50 and 70 years of age, but exceptionally may occur in infancy or extreme old age. Their occurrence has increased considerably inclusively due to progressive population again. The present work aims at establishing a relation between this infirmity and its prognosis in patients submitted to the treatment recommended by Departament of Otolaryngology and Head Neck Surgery of the School of Medicine of São José do Rio Preto. To this effect, by means of karyometric optical microscopy, cell nuclei in the glottic region of 20 individuals, divided into groups according to their tumor stage and time of survival, were evaluated. Following comparation with a control group and statistical analsis, it became possible to verify that the lesser diameter of nuclei is of prognostic value for initial tumors in this region.
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation

In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation. However, high annotation efforts and costs are commonly needed to acquire sufficient biomedical training data for DL models. To alleviate the burden of manual annotation, in this paper, we propose a new weakly supervised DL approach for biomedical image segmentation using boxes only annotation. First, we develop a method to combine graph search (GS) and DL to generate fine object masks from box annotation, in which DL uses box annotation to compute a rough segmentation for GS and then GS is applied to locate the optimal object boundaries. During the mask generation process, we carefully utilize information from box annotation to filter out potential errors, and then use the generated masks to train an accurate DL segmentation network. Extensive experiments on gland segmentation in histology images, lymph node segmentation in ultrasound images, and fungus segmentation in electron microscopy images show that our approach attains superior performance over the best known state-of-the-art weakly supervised DL method and is able to achieve (1) nearly the same accuracy compared to fully supervised DL methods with far less annotation effort, (2) significantly better results with similar annotation time, and (3) robust performance in various applications.
Face Recognition : A Survey

Face recognition has gained a significant position among most commonly used applications of image processing furthermore availability of viable technologies in this field have contributed a great deal to it. In spite of rapid progress in this field it still has to overcome various challenges like Aging, Partial Occlusion, and Facial Expressions etc affecting the performance of the system, are covered in first part of the survey. This part also highlights the most commonly used databases, available as a standard for face recognition tests. AT & T, AR Database, FERET, ORL and Yale Database have been outlined here. While in the second part of this survey a detailed overview of some important existing methods which are used to dealing the issues of face recognition have been presented. Said methods include Eigenface, Neural Network (NN), Support Vector Machine (SVM), Gabor Wavelet and Hidden Markov Model (HMM). While in last part of the survey several applications of a face recognition system such as video surveillance, Access Control, and Pervasive Computing has been discussed.
The Rationality and Irrationality of Financing Green Start-Ups

Green start-ups contribute towards a transition to a more sustainable economy by developing sustainable and environmentally friendly innovation and bringing it to the market. Due to specific product/service characteristics, entrepreneurial motivation and company strategies that might differ from that of other start-ups, these companies might struggle even more than usual with access to finance in the early stages. This conceptual paper seeks to explain these challenges through the theoretical lenses of entrepreneurial finance and behavioural finance. While entrepreneurial finance theory contributes to a partial understanding of green start-up finance, behavioural finance is able to solve a remaining explanatory deficit produced by entrepreneurial finance theory. Although some behavioural finance theorists are suggesting that the current understanding of economic rationality underlying behavioural finance research is inadequate, most scholars have not yet challenged these assumptions, which constrict a comprehensive and realistic description of the reality of entrepreneurial finance in green start-ups. The aim of the paper is thus, first, to explore the specifics of entrepreneurial finance in green start-ups and, second, to demonstrate the need for a more up-to-date conception of rationality in behavioural finance theory in order to enable realistic empirical research in this field.
A snapshot of image pre-processing for convolutional neural networks: case study of MNIST

In the last five years, deep learning methods and particularly Convolutional Neural Networks (CNNs) have exhibited excellent accuracies in many pattern classification problems. Most of the state-of-the-art models apply data-augmentation techniques at the training stage. This paper provides a brief tutorial on data preprocessing and shows its benefits by using the competitive MNIST handwritten digits classification problem. We show and analyze the impact of different preprocessing techniques on the performance of three CNNs, LeNet, Network3 and DropConnect, together with their ensembles. The analyzed transformations are, centering, elastic deformation, translation, rotation and different combinations of them. Our analysis demonstrates that data-preprocessing techniques, such as the combination of elastic deformation and rotation, together with ensembles have a high potential to further improve the state-of-the-art accuracy in MNIST classification.
User-Centered Robot Head Design: a Sensing Computing Interaction Platform for Robotics Research (SCIPRR)

We developed and evaluated a novel humanoid head, SCIPRR (Sensing, Computing, Interacting Platform for Robotics Research). SCIPRR is a head shell that was iteratively created with additive manufactur- ing. SCIPRR contains internal sca olding that allows sensors, small form computers, and a back-projection system to display an ani- mated face on a front-facing screen. SCIPRR was developed using User Centered Design principles and evaluated using three di erent methods. First, we created multiple, small-scale prototypes through additive manufacturing and performed polling and re nement of the overall head shape. Second, we performed usability evaluations of expert HRI mechanics as they swapped sensors and computers within the the SCIPRR head. Finally, we ran and analyzed an ex- periment to evaluate how much novices would like a robot with our head design to perform di erent social and traditional robot tasks. We made both major and minor changes a er each evalu- ation and iteration. Overall, expert users liked the SCIPRR head and novices wanted a robot with the SCIPRR head to perform more tasks (including social tasks) than a more traditional robot.
Visual snow syndrome: what we know so far.

We provide an overview of the neurological condition known as visual snow syndrome. Patients affected by this chronic disorder suffer with a pan-field visual disturbance described as tiny flickering dots, which resemble the static noise of an untuned television.



The term 'visual snow' has only appeared in the medical literature very recently. The clinical features of the syndrome have now been reasonably described and the pathophysiology has begun to be explored. This review focuses on what is currently known about visual snow.



Recent evidence suggests visual snow is a complex neurological syndrome characterized by debilitating visual symptoms. It is becoming better understood as it is systematically studied. Perhaps the most important unmet need for the condition is a sufficient understanding of it to generate and test hypotheses about treatment.
ALL OUR SONS: THE DEVELOPMENTAL NEUROBIOLOGY AND NEUROENDOCRINOLOGY OF BOYS AT RISK.

Why are boys at risk? To address this question, I use the perspective of regulation theory to offer a model of the deeper psychoneurobiological mechanisms that underlie the vulnerability of the developing male. The central thesis of this work dictates that significant gender differences are seen between male and female social and emotional functions in the earliest stages of development, and that these result from not only differences in sex hormones and social experiences but also in rates of male and female brain maturation, specifically in the early developing right brain. I present interdisciplinary research which indicates that the stress-regulating circuits of the male brain mature more slowly than those of the female in the prenatal, perinatal, and postnatal critical periods, and that this differential structural maturation is reflected in normal gender differences in right-brain attachment functions. Due to this maturational delay, developing males also are more vulnerable over a longer period of time to stressors in the social environment (attachment trauma) and toxins in the physical environment (endocrine disruptors) that negatively impact right-brain development. In terms of differences in gender-related psychopathology, I describe the early developmental neuroendocrinological and neurobiological mechanisms that are involved in the increased vulnerability of males to autism, early onset schizophrenia, attention deficit hyperactivity disorder, and conduct disorders as well as the epigenetic mechanisms that can account for the recent widespread increase of these disorders in U.S. culture. I also offer a clinical formulation of early assessments of boys at risk, discuss the impact of early childcare on male psychopathogenesis, and end with a neurobiological model of optimal adult male socioemotional functions.
Alzheimer's disease

An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease. Thus, Alzheimer's disease represents a major public health concern and has been identified as a research priority. Although there are licensed treatments that can alleviate symptoms of Alzheimer's disease, there is a pressing need to improve our understanding of pathogenesis to enable development of disease-modifying treatments. Methods for improving diagnosis are also moving forward, but a better consensus is needed for development of a panel of biological and neuroimaging biomarkers that support clinical diagnosis. There is now strong evidence of potential risk and protective factors for Alzheimer's disease, dementia, and cognitive decline, but further work is needed to understand these better and to establish whether interventions can substantially lower these risks. In this Seminar, we provide an overview of recent evidence regarding the epidemiology, pathogenesis, diagnosis, and treatment of Alzheimer's disease, and discuss potential ways to reduce the risk of developing the disease.
Energy Consumption Forecasting for Smart Meters

Earth, water, air, food, shelter and energy are essential factors required for human being to survive on the planet. Among this energy plays a key role in our day to day living including giving lighting, cooling and heating of shelter, preparation of food. Due to this interdependency, energy, specifically electricity, production and distribution became a high tech industry. Unlike other industries, the key differentiator of electricity industry is the product itself. It can be produced but cannot be stored for future; production and consumption happen almost in near real-time. This particular peculiarity of the industry is the key driver for Machine Learning and Data Science based innovations in this industry. There is always a gap between the demand and supply in the electricity market across the globe. To fill the gap and improve the service efficiency through providing necessary supply to the market, commercial as well as federal electricity companies employ forecasting techniques to predict the future demand and try to meet the demand and provide curtailment guidelines to optimise the electricity consumption/demand. In this paper the authors examine the application of Machine Learning algorithms, specifically Boosted Decision Tree Regression, to the modelling and forecasting of energy consumption for smart meters. The data used for this exercise is obtained from DECC data website. Along with this data, the methodology has been tested in Smart Meter data obtained from EMA Singapore. This paper focuses on feature engineering for time series forecasting using regression algorithms and deriving a methodology to create personalised electricity plans offers for household users based on usage history.
A novel analog 360° phase shifter design in Ku and Ka bands

This paper presents a new 360° analog phase shifter for Ku and Ka bands applications. These devices are reflection-type analog phase shifters implemented with a 90° branch-line coupler and a new load concept. Varactor diode is used as the tuning element to change capacitance with voltage. Test circuits have been optimized, in order to have 360° phase shift range with low insertion loss. The measured Ku band phase shifter presents more than 360° phase shift between 11.5 and 12.5 GHz. At 12 GHz the insertion loss is 3.3dB+/−0.5dB. For the Ka band example, the lowest insertion loss is obtained at 30.3 GHz (3.6dB+/−1.8dB) and the phase shift is 315°. In both case, the return loss is always lower than −10dB.
Dynamic Topic/Citation Influence Modeling for Chronological Citation Recommendation

With the development of academic research, the number of scientific papers has risen sharply, there is an urgent need to assist researchers in locating the candidate cited papers they are looking for. Classical relation-based and text-based approaches ignore the chronological nature of the citation recommendation task. In this study, we propose an innovative dynamic topic/citation influence model (DTCIM), which assumes user initial information need could shift while they are looking for the papers in different time slices. Specifically, we integrate text search and citation link navigation in a chronological dynamic topic model environment. Unlike previous studies, the new model characterizes user topical information need shifting and intrinsic citation time-decay. We apply this model for chronological citation recommendation, which can recommend time-series ranking lists based on users' initial textual information needs. Our experiment on the ACM sequential corpus shows that DTCIM is an effective model for chronological citation recommendation, comparing with classic models and algorithms.
What to Do with the Singularity Paradox?

The paper begins with an introduction of the Singularity Paradox, an observation that: “Superintelligent machines are feared to be too dumb to possess commonsense”. Ideas from leading researchers in the fields of philosophy, mathematics, economics, computer science and robotics regarding the ways to address said paradox are reviewed and evaluated. Suggestions are made regarding the best way to handle the Singularity Paradox.
An introductory course on Human-Computer Interaction: Programme, bibliography, practical classes and assignments

As Computer curricula have developed, Human-Computer Interaction has gradually become part of many of those curricula and the recent ACM/IEEE report on the core of Computing Science and Engineering, includes HumanComputer Interaction as one of the fundamental sub-areas that should be addressed by any such curricula. However, both technology and Human-Computer Interaction are evolving rapidly, thus a continuous effort is needed to maintain a program, bibliography and a set of practical assignments up to date and adapted to the current technology. This paper briefly presents an introductory course on Human-Computer Interaction offered to Electrical and Computer Engineering students at the University of Aveiro.
DeepAD: Alzheimer′s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

To extract patterns from neuroimaging data, various techniques, including statistical methods and machine learning algorithms, have been explored to ultimately aid in Alzheimer's disease diagnosis of older adults in both clinical and research applications. However, identifying the distinctions between Alzheimer's brain data and healthy brain data in older adults (age > 75) is challenging due to highly similar brain patterns and image intensities. Recently, cutting-edge deep learning technologies have been rapidly expanding into numerous fields, including medical image analysis. This work outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer's magnetic resonance imaging (MRI) and functional MRI data from normal healthy control data for the same age group. Using these pipelines, which were executed on a GPU-based high performance computing platform, the data were strictly and carefully preprocessed. Next, scale and shift invariant lowto high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, functional MRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer's disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output when compared to other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively.
A compact Ka-band waveguide orthomode transducer fabricated by 3-D printing

This paper presents an orthomode transducer (OMT) fabricated using 3-D printing. The OMT is designed to operate at two frequency bands, with each band passing an orthogonal polarization. For one polarization the band is 17–22 GHz and the other 27–31 GHz. Both polarizations are combined into a common port with square waveguide cross section. To facilitate the measurement, two tapered waveguide transitions are designed to convert the square waveguide port to standard WR-42 and WR-28 waveguides. The OMT and the waveguide transitions are made from polymer using stereolithography based 3-D printing and a 5 μm thick copper layer is deposited on them. The experimental results of the OMT are compared with simulations and discussed.
Heterogeneous Distribution of Computations While Solving Linear Algebra Problems on Networks of Heterogeneous Computers

This paper presents and analyzes two different strategies of heterogeneous distribution of computations solving dense linear algebra problems on heterogeneous networks of computers. The first strategy is based on heterogeneous distribution of processes over processors and homogeneous block cyclic distribution of data over the processes. The second is based on homogeneous distribution of processes over processors and heterogeneous block cyclic distribution of data over the processes. Both strategies were implemented in the mpC language a dedicated parallel extension of ANSI C for efficient and portable programming of heterogeneous networks of computers. The first strategy was implemented using calls to ScaLAPACK; the second strategy was implemented with calls to LAPACK and BLAS. Cholesky factorization on a heterogeneous network of workstations is used to demonstrate that the heterogeneous distributions have an advantage over the traditional homogeneous distribution.
Ageing populations: the challenges ahead

If the pace of increase in life expectancy in developed countries over the past two centuries continues through the 21st century, most babies born since 2000 in France, Germany, Italy, the UK, the USA, Canada, Japan, and other countries with long life expectancies will celebrate their 100th birthdays. Although trends differ between countries, populations of nearly all such countries are ageing as a result of low fertility, low immigration, and long lives. A key question is: are increases in life expectancy accompanied by a concurrent postponement of functional limitations and disability? The answer is still open, but research suggests that ageing processes are modifiable and that people are living longer without severe disability. This finding, together with technological and medical development and redistribution of work, will be important for our chances to meet the challenges of ageing populations.
The use of zygomatic implants for the retention of nasal prosthesis following rhinectomy: the Morriston experience.

Reconstruction and rehabilitation following rhinectomy remains controversial and presents a complex problem. Although reconstruction with local and microvascular flaps is a valid option, the aesthetic results may not always be satisfactory. The aesthetic results achieved with a nasal prosthesis are excellent; however patient acceptance relies on a secure method of retention. The technique used and results obtained in a large series of patients undergoing rhinectomy and receiving zygomatic implants for the retention of a nasal prosthesis are described here. A total of 56 zygomatic implants (28 patients) were placed, providing excellent retention and durability with the loss of only one implant in 15 years.
“RF-DNA” Fingerprinting for Antenna Classification

Device classification is important for many applications such as industrial quality controls, through-wall imaging, and network security. A novel approach to detection is proposed using a random noise radar (RNR), coupled with Radio Frequency “Distinct Native Attribute (RF-DNA)” fingerprinting processing algorithms to non-destructively interrogate microwave devices. RF-DNA has previously demonstrated “serial number” discrimination of passive Radio Frequency (RF) emissions such as Orthogonal Frequency Division Multiplexed (OFDM) signals, Worldwide Interoperability for Microwave Access (WiMAX) signals and others with classification accuracies above 80% using a Multiple Discriminant Analysis/Maximum Likelihood (MDAML) classifier. This approach proposes to couple the classification successes of the RF-DNA fingerprint processing with a non-destructive active interrogation waveform. An Ultra Wideband (UWB) noise waveform is uniquely suitable as an active interrogation method since it will not cause damage to sensitive microwave components and multiple RNRs can operate simultaneously in close proximity, allowing for significant parallelization of detection systems.