nils-herrmann/clean_scirepeval huggingface dataset:
Convolutional Neural Networks Detect Local Infiltration of Lung Cancer Primary Lesions on Baseline FDG-PET/CT

The aim of our work is to develop an algorithm for the classification of lung cancer as T1–T2 or T3–T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. We retrospectively selected a cohort of 632 patients from patients submitted to FDG-PET/CT, over a 6-year period, for the purpose of staging a suspected lung lesion, within 60 days before biopsy or surgical procedure. Post-acquisition processing was performed to generate an adequate dataset for the convolution neural network (CNN) analyses. The input of CNNs in this study was a bounding box on both PET and CT images, cropped around the lesion centre identified by two nuclear medicine physicians. The algorithm developed and tested in the present work achieved an accuracy of 83.9%, 76.2% and 69.8% in the training, validation and test set, respectively, for the identification of T1–T2 and T3–T4 lung cancer. We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by NSCLC. Further research in this field is already being addressed by our group.
Wisdom and life satisfaction in old age.

According to previous research findings, objective life conditions such as physical health, socioeconomic status, financial situation, the physical environment, and social involvement cannot fully explain the well-being of older persons. Instead, personality characteristics and developmental influences appear to have a stronger impact. This study combines personality and individual development by introducing the ancient but neglected concept of wisdom as a predictor of life satisfaction. Using a sample of 120 elderly women and men from the 1968/69 Berkeley Guidance Study, structural equation models with latent variables show that wisdom (defined aa a composite of cognitive, reflective, and effective qualities) has a profoundly positive influence on life satisfaction independent of objective circumstances. The inclusion of wisdom as an additional predictor of subjective well-being increases the explanatory power of the model considerably. Gender differences in predictors of life satisfaction are discussed.
THE INTERPRETATION OF QUANTUM MECHANICS : MANY WORLDS OR MANY WORDS ?

As cutting-edge experiments display ever more extreme forms of non-classical behavior, the prevailing view on the interpretation of quantum mechanics appears to be gradually changing. A (highly unscientific) poll taken at the 1997 UMBC quantum mechanics workshop gave the once alldominant Copenhagen interpretation less than half of the votes. The Many Worlds interpretation (MWI) scored second, comfortably ahead of the Consistent Histories and Bohm interpretations. It is argued that since all the above-mentioned approaches to nonrelativistic quantum mechanics give identical cookbook prescriptions for how to calculate things in practice, practical-minded experimentalists, who have traditionally adopted the “shut-up-and-calculate interpretation”, typically show little interest in whether cozy classical concepts are in fact real in some untestable metaphysical sense or merely the way we subjectively perceive a mathematically simpler world where the Schrödinger equation describes everything — and that they are therefore becoming less bothered by a profusion of worlds than by a profusion of words. Common objections to the MWI are discussed. It is argued that when environment-induced decoherence is taken into account, the experimental predictions of the MWI are identical to those of the Copenhagen interpretation except for an experiment involving a Byzantine form of “quantum suicide”. This makes the choice between them purely a matter of taste, roughly equivalent to whether one believes mathematical language or human language to be more fundamental.
Adaptive Graph Convolutional Neural Networks

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for each graph data while training. To efficiently learn the graph, a distance metric learning is proposed. Extensive experiments on nine graph-structured datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy.
Discrete Neural Signatures of Basic Emotions.

Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience.
A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.
Various Approaches to Aspect-based Sentiment Analysis

The problem of aspect-based sentiment analysis deals with classifying sentiments (negative, neutral, positive) for a given aspect in a sentence. A traditional sentiment classification task involves treating the entire sentence as a text document and classifying sentiments based on all the words. Let us assume, we have a sentence such as ”the acceleration of this car is fast, but the reliability is horrible”. This can be a difficult sentence because it has two aspects with conflicting sentiments about the same entity. Considering machine learning techniques (or deep learning), how do we encode the information that we are interested in one aspect and its sentiment but not the other? Let us explore various pre-processing steps, features, and methods used to facilitate in solving this task.
Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network

Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reconstruction approaches directly predict the entire point cloud in a single stage. Although this technique can handle low-resolution point clouds, it is not a viable solution for generating dense, high-resolution outputs. In this work, we introduce DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution. Towards this end, we propose an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid. Our method generates point clouds that are accurate, uniform and dense. Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a light-weight and scalable architecture for predicting high-resolution outputs.
Discrete event simulation for performance modelling in health care: a review of the literature

Discrete Event Simulation (DES) has been widely used in modelling health-care systems for many years and a simple citation analysis shows that the number of papers published has increased markedly since 2004. Over the last 30 years several significant reviews of DES papers have been published and we build on these to focus on the most recent era, with an interest in performance modelling within hospitals. As there are few papers that propose or illustrate general approaches, we classify papers according to the areas of application evident in the literature, discussing the apparent lack of genericity. There is considerable diversity in the objectives of reported studies and in the consequent level of detail: We discuss why specificity dominates and why more generic approaches are rare. Journal of Simulation (2010) 4, 42–51.
Relationship between space-vector modulation and three-phase carrier-based PWM: a comprehensive analysis [three-phase inverters]

This paper comprehensively analyzes the relationship between space-vector modulation and three-phase carrier-based pulsewidth modualtion (PWM). The relationships involved, such as the relationship between modulation signals (including zero-sequence component and fundamental components) and space vectors, the relationship between the modulation signals and the space-vector sectors, the relationship between the switching pattern of space-vector modulation and the type of carrier, and the relationship between the distribution of zero vectors and different zero-sequence signal are systematically established. All the relationships provide a bidirectional bridge for the transformation between carrier-based PWM modulators and space-vector modulation modulators. It is shown that all the drawn conclusions are independent of the load type. Furthermore, the implementations of both space-vector modulation and carrier-based PWM in a closed-loop feedback converter are discussed.
Online Sensorless Induction Motor Temperature Monitoring

A sensorless internal temperature monitoring method for induction motors is proposed in this paper. This method can be embedded in standard motor drives, and is based on the stator windings resistance variation with temperature. A small AC signal is injected to the motor, superimposed to the power supply current, in order to measure the stator resistance online. The proposed method has the advantage of requiring a very low-level monitoring signal, hence the motor torque perturbations and additional power losses are negligible. Furthermore, temperature estimations do not depend on the knowledge of any other motor parameter, since the method is not based on a model. This makes the proposed method more robust than model-based methods. Experimental results that validate the proposal are also presented.
Targeting neurons and photons for optogenetics

Optogenetic approaches promise to revolutionize neuroscience by using light to manipulate neural activity in genetically or functionally defined neurons with millisecond precision. Harnessing the full potential of optogenetic tools, however, requires light to be targeted to the right neurons at the right time. Here we discuss some barriers and potential solutions to this problem. We review methods for targeting the expression of light-activatable molecules to specific cell types, under genetic, viral or activity-dependent control. Next we explore new ways to target light to individual neurons to allow their precise activation and inactivation. These techniques provide a precision in the temporal and spatial activation of neurons that was not achievable in previous experiments. In combination with simultaneous recording and imaging techniques, these strategies will allow us to mimic the natural activity patterns of neurons in vivo, enabling previously impossible 'dream experiments'.
Digital Applications for Cultural and Heritage Institutions

Computing in the arts, humanities and heritage sectors is becoming more pervasive, and increasingly sophisticated technologies are being developed to capture, explore, and disseminate information regarding artefacts, historical knowledge, and cultural inheritance. Conferences and symposia are central to the complex industry which has built up around using computational techniques to facilitate novel research and increased public access to cultural heritage. These are essential for fostering crossfertilisation of knowledge and expertise between “memory institutions” such as libraries, archives, museums, and archaeological and historic sites; academic disciplines such as history, archaeology, and art history; and practitioners in the computer industry. Often, proceedings of these conferences appear online, or selected papers are published in journals, but rarely is a snapshot of conference papers from across different years and disciplines brought together in a comprehensive publication to give a panorama of the diverse research explored across the “multiplicative relationship: culture x technology” (pg. 2).
A SHORT ELEMENTARY PROOF OF THE INSOLVABILITY OF THE EQUATION OF DEGREE 5

We present short elementary proofs of the well-known Ruffini-Abel-Galois theorems on unsolvability of algebraic equations in radicals. This proof is obtained from existing expositions by stripping away material not required for the proof (but presumably required elsewhere). In particular, we do not use the terms ‘Galois group’ and even ‘group’. However, our presentation is a good way to learn (or recall) the starting idea of Galois theory: to look at how the symmetry of a polynomial is decreased when a radical is extracted. So the note provides a bridge (by showing that there is no gap) between elementary mathematics and Galois theory. The note is accessible to students familiar with polynomials, complex numbers and permutations; so the note might be interesting easy reading for professional mathematicians.
Wireless body sensor networks for health-monitoring applications.

Current wireless technologies, such as wireless body area networks and wireless personal area networks, provide promising applications in medical monitoring systems to measure specified physiological data and also provide location-based information, if required. With the increasing sophistication of wearable and implantable medical devices and their integration with wireless sensors, an ever-expanding range of therapeutic and diagnostic applications is being pursued by research and commercial organizations. This paper aims to provide a comprehensive review of recent developments in wireless sensor technology for monitoring behaviour related to human physiological responses. It presents background information on the use of wireless technology and sensors to develop a wireless physiological measurement system. A generic miniature platform and other available technologies for wireless sensors have been studied in terms of hardware and software structural requirements for a low-cost, low-power, non-invasive and unobtrusive system.
End-to-End Adversarial Retinal Image Synthesis

In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.
Reframing Professional Development Through Understanding Authentic Professional Learning

Continuing to learn is universally accepted and expected by professionals and other stakeholders across all professions. However, despite changes in response to research findings about how professionals learn, many professional development practices still focus on delivering content rather than enhancing learning. In exploring reasons for the continuation of didactic practices in professional development, this article critiques the usual conceptualization of professional development through a review of recent literature across professions. An alternative conceptualization is proposed, based on philosophical assumptions congruent with evidence about professional learning from seminal educational research of the past two decades. An argument is presented for a shift in discourse and focus from delivering and evaluating professional development programs to understanding and supporting authentic professional learning.
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set

Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. As a consequence, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity. In this paper we present a clustering validity procedure, which evaluates the results of clustering algorithms on data sets. We define a validity index, S_Dbw, based on well-defined clustering criteria enabling the selection of the optimal input parameters’ values for a clustering algorithm that result in the best partitioning of a data set. We evaluate the reliability of our index both theoretically and experimentally, considering three representative clustering algorithms ran on synthetic and real data sets. Also, we carried out an evaluation study to compare S_Dbw performance with other known validity indices. Our approach performed favorably in all cases, even in those that other indices failed to indicate the correct partitions in a data set.
Privacy and analytics: it's a DELICATE issue a checklist for trusted learning analytics

The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution's learning support by implementing data and analytics with a view on improving student success. In this paper, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
Effects of hip exercises for chronic low-back pain patients with lumbar
instability

The purpose of this study was to compare hip range of motion between a lumbar stability group and a lumbar instability group, and to evaluate the effectiveness of hip exercises for low-back pain patients with lumbar instability.  Seventy-eight patients with chronic low-back pain were the subjects.  The patients were divided into two groups: a lumbar stability group (n=45) and a lumbar instability group (n=33). They were assessed using the Korean version of the Oswestry Disability Index (KODI) to determine the level of disability of the patients with low-back pain. A 100 mm visual analog scale (VAS) was used to assess low-back pain.  The limitation of hip range of motion of the lumbar instability group was significantly greater than that of the lumbar stability group. Comparisons among four groups at three weeks and six weeks after the start of hip exercises revealed that the VAS score of each group had significantly decreased. Comparisons among four groups at three weeks and at six weeks after the start of hip exercises revealed that the KODI score of each group had significantly decreased.  These findings suggest that the performance of hip exercises by chronic low-back pain patients with lumbar instability is more effective than conventional therapy at reducing low-back pain and levels of disability.
Visualizing Information in Digital Forensics

The evolution of modern electronic devices is outpacing the scalability and effectiveness of the tools used to analyze digital evidence recovered from them. Indeed, current digital forensic techniques and tools are unable to handle large datasets in an efficient manner. As a result, the time and effort required to conduct digital forensic investigations are increasing. This paper describes a promising digital forensic visualization framework that displays digital evidence in a simple and intuitive manner, enhancing decision making and facilitating the explanation of phenomena in evidentiary data.
Inkjet printing for flexible electronics : Materials , processes and equipments

Inkjet printing, known as digital writing technique, can directly deposit functional materials to form pattern onto substrate. This paper provides an overview of inkjet printing technologies for flexible electronics. Firstly, we highlight materials challenges in implementing flexible devices into practical application, especially for inkjet printing process. Then the micro/nano-patterning technologies of inkjet printing are discussed, including conventional inkjet printing techniques and electrohydrodynamic printing techniques. Thirdly, the related equipments on inkjet printing are shown. Finally, challenges for its future development are also discussed. The main purpose of the work is to condense the basic knowledge and highlight the challenges associated with the burgeoning and exciting field of inkjet printing for flexible electronics.