nils-herrmann/clean_scirepeval huggingface dataset:
Generating virtual CT from MRI using fully convolutional neural networks with improved structure quality

Estimating virtual CT(vCT) image from MRI data is in crucial need for medical application due to the relatively high dose of radiation exposure in CT scan and redundant workflow of both MR and CT. Among the existing work, the fully convolutional neural network(FCN) shows its superiority in generating vCT of high fidelity which merits further investigation. However, the most widely used evaluation metrics mean absolute error (MAE) and peak signal to noise ratio (PSNR) may not be adequate enough to reflect the structure quality of the vCT, while most of the current FCN based approaches focus more on the architectures but have little attention on the loss functions which are closely related to the final evaluation. The objective of this thesis is to apply Structure Similarity(SSIM) as loss function for predicting vCT from MRI based on FCN and see whether the prediction has improvement in terms of structure compared with conventionally used l or l loss. Inspired by the SSIM, the contextual l has been proposed to investigate the impact of introducing context information to the loss function. CT data was non-rigidly registered to MRI for training and evaluation. Patch-based 3D FCN were optimized for different loss functions to predict vCT from MRI data. Specifically for optimizing SSIM, the training data should be normalization to [0, 1] and architecture should be slightly changed by adding the ReLu layer before the output to guarantee the convexity of the SSIM during the training. The evaluation results are carried out with 7-folds cross validation of the 14 patients. MAE, PSNR and SSIM for the whole volume and tissue-wise are evaluted respectively. All optimizations successfully converged well and cl outperformed the other losses in terms of PSNR and MAE but with the worst SSIM, DSSIM works better at preserving the structures and resulting in smooth output. Yuan Zhou Delft, August 2017
Leader-member exchange and its dimensions: effects of self-effort and other's effort on relationship quality.

Two hundred thirty-two manager-subordinate dyads provided data on the effort expended toward the development of leader-member exchange (LMX) relationships, how such effort related to expectations about relationship quality, and intentions to exert future effort toward relationship development. For both managers and subordinates, higher quality LMX relationships were reported and expectations were met when the other member of the dyad put forth effort into relationship development. One's own higher effort coupled with lower effort by the other was associated with a lower quality LMX relationship. Examination of the 4 dimensions of LMX (R. C. Liden & J. M. Maslyn, 1998) revealed unique effects depending on the dimension considered. In addition, LMX and relationship tenure interacted, showing that future effort toward relationship development was greatest when individuals had been in higher quality relationships for longer periods of time.
SMS Spam Filtering Using Machine Learning Techniques: A Survey

To report a review of various machine learning and hybrid algorithms for detecting SMS spam messages and comparing them according to accuracy criterion.  Original articles written in English found in Sciencedirect.com, Google-scholar.com, Search.com, IEEE explorer, and the ACM library.  Those articles dealing with machine learning and hybrid approaches for SMS spam filtering.  Many articles extracted by searching a predefined string and the outcome was reviewed by one author and checked by the second. The primary paper was reviewed and edited by the third author.  A total of 44 articles were selected which were concerned machine learning and hybrid methods for detecting SMS spam messages. 28 methods and algorithms were extracted from these papers and studied and finally 15 algorithms among them have been compared in one table according to their accuracy, strengths, and weaknesses in detecting spam messages of the Tiago dataset of spam message. Actually, among the proposed methods DCA algorithm, the large cellular network method and graph-based KNN are three most accurate in filtering SMS spams of Tiago data set. Moreover, Hybrid methods are discussed in this paper.
Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation

Convolutive blind source separation (CBSS) that exploits the sparsity of source signals in the frequency domain is addressed in this paper. We assume the sources follow complex Laplacian-like distribution for complex random variable, in which the real part and imaginary part of complex-valued source signals are not necessarily independent. Based on the maximum a posteriori (MAP) criterion, we propose a novel natural gradient method for complex sparse representation. Moreover, a new CBSS method is further developed based on complex sparse representation. The developed CBSS algorithm works in the frequency domain. Here, we assume that the source signals are sufficiently sparse in the frequency domain. If the sources are sufficiently sparse in the frequency domain and the filter length of mixing channels is relatively small and can be estimated, we can even achieve underdetermined CBSS. We illustrate the validity and performance of the proposed learning algorithm by several simulation examples.
A scale-invariant framework for image classification with deep learning

In this paper, we propose a scale-invariant framework based on Convolutional Neural Networks (CNNs). The network exhibits robustness to scale and resolution variations in data. Previous efforts in achieving scale invariance were made on either integrating several variant-specific CNNs or data augmentation. However, these methods did not solve the fundamental problem that CNNs develop different feature representations for the variants of the same image. The topology proposed by this paper develops a uniform representation for each of the variants of the same image. The uniformity is acquired by concatenating scale-variant and scale-invariant features to enlarge the feature space so that the case when input images are of diverse variations but from the same class can be distinguished from another case when images are of different classes. Higher-order decision boundaries lead to the success of the framework. Experimental results on a challenging dataset substantiates that our framework performs better than traditional frameworks with the same number of free parameters. Our proposed framework can also achieve a higher training efficiency.
Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard

Water stress caused by water scarcity has a negative impact on the wine industry. Several strategies have been implemented for optimizing water application in vineyards. In this regard, midday stem water potential (SWP) and thermal infrared (TIR) imaging for crop water stress index (CWSI) have been used to assess plant water stress on a vine-by-vine basis without considering the spatial variability. Unmanned Aerial Vehicle (UAV)-borne TIR images are used to assess the canopy temperature variability within vineyards that can be related to the vine water status. Nevertheless, when aerial TIR images are captured over canopy, internal shadow canopy pixels cannot be detected, leading to mixed information that negatively impacts the relationship between CWSI and SWP. This study proposes a methodology for automatic coregistration of thermal and multispectral images (ranging between 490 and 900 nm) obtained from a UAV to remove shadow canopy pixels using a modified scale invariant feature transformation (SIFT) computer vision algorithm and Kmeans++ clustering. Our results indicate that our proposed methodology improves the relationship between CWSI and SWP when shadow canopy pixels are removed from a drip-irrigated Cabernet Sauvignon vineyard. In particular, the coefficient of determination (R²) increased from 0.64 to 0.77. In addition, values of the root mean square error (RMSE) and standard error (SE) decreased from 0.2 to 0.1 MPa and 0.24 to 0.16 MPa, respectively. Finally, this study shows that the negative effect of shadow canopy pixels was higher in those vines with water stress compared with well-watered vines.
PEPsal: a Performance Enhancing Proxy designed for TCP satellite connections

Internet communications with paths that include satellite link face some peculiar challenges, due to the presence of a long propagation wireless channel. In this paper, we propose a performance enhancing proxy (PEP) solution, called PEPsal, which is, to the best of the authors' knowledge, the first open source TCP splitting solution for the GNU/Linux operating systems. PEPsal improves the performance of a TCP connection over a satellite channel making use of the TCP Hybla, a TCP enhancement for satellite networks developed by the authors. The objective of the paper is to present and evaluate the PEPsal architecture, by comparing it with end to end TCP variants (NewReno, SACK, Hybla), considering both performance and reliability issues. Performance is evaluated by making use of a testbed set up at the University of Bologna, to study advanced transport protocols and architectures for Internet satellite communications
Amino acid analysis: aqueous dimethyl sulfoxide as solvent for the ninhydrin reaction.

Methyl Cellosolve (the monomethyl ether of ethylene glycol) has been widely used as the organic solvent in ninhydrin reagents for amino acid analysis; it has, however, properties that are disadvantageous in a reagent for everyday employment. The solvent is toxic and it is difficult to keep the ether peroxide-free. A continuing effort to arrive at a chemically preferable and relatively nontoxic substitute for methyl Cellosolve has led to experiments with dimethyl s&oxide, which proves to be a better solvent for the reduced form of ninhydrin (hydrindantin) than is methyl Cellosolve. Dimethyl sulfoxide can replace the latter, volume for volume, in a ninhydrin reagent mixture that gives equal performance and has improved stability. The result is a ninhydrin-hydrindantin solution in 75% dimethyl sulfoxide25 % 4 M lithium acetate buffer at pH 5.2. This type of mixture, with appropriate hydrindantin concentrations, is recommended to replace methyl Cellosolve-containing reagents in the quantitative determination of amino acids by automatic analyzers and by the manual ninhydrin method.
Robust Ellipse Detection by Fitting Randomly Selected Edge Patches

In this paper, a method to detect multiple ellipses is presented. The technique is efficient and robust against incomplete ellipses due to partial occlusion, noise or missing edges and outliers. It is an iterative technique that finds and removes the best ellipse until no reasonable ellipse is found. At each run, the best ellipse is extracted from randomly selected edge patches, its fitness calculated and compared to a fitness threshold. RANSAC algorithm is applied as a sampling process together with the Direct Least Square fitting of ellipses (DLS) as the fitting algorithm. In our experiment, the method performs very well and is robust against noise and spurious edges on both synthetic and real-world image data. Direct Least Square Fitting, Ellipse Detection, RANSAC
Long-Term Prediction of Discharges in Manwan Reservoir Using Artificial Neural Network Models

Several artificial neural network (ANN) models with a feed-forward, back-propagation network structure and various training algorithms, are developed to forecast daily and monthly river flow discharges in Manwan Reservoir. In order to test the applicability of these models, they are compared with a conventional time series flow prediction model. Results indicate that the ANN models provide better accuracy in forecasting river flow than does the auto-regression time series model. In particular, the scaled conjugate gradient algorithm furnishes the highest correlation coefficient and the smallest root mean square error. This ANN model is finally employed in the advanced water resource project of Yunnan Power Group.
Segmentation for classification of gastroenterology images

Automatic classification of cancer lesions in tissues observed using gastroenterology imaging is a non-trivial pattern recognition task involving filtering, segmentation, feature extraction and classification. In this paper we measure the impact of a variety of segmentation algorithms (mean shift, normalized cuts, level-sets) on the automatic classification performance of gastric tissue into three classes: cancerous, pre-cancerous and normal. Classification uses a combination of color (hue-saturation histograms) and texture (local binary patterns) features, applied to two distinct imaging modalities: chromoendoscopy and narrow-band imaging. Results show that mean-shift obtains an interesting performance for both scenarios producing low classification degradations (6%), full image classification is highly inaccurate reinforcing the importance of segmentation research for Gastroenterology, and confirm that Patch Index is an interesting measure of the classification potential of small to medium segmented regions.
Mind-sets matter: a meta-analytic review of implicit theories and self-regulation.

This review builds on self-control theory (Carver & Scheier, 1998) to develop a theoretical framework for investigating associations of implicit theories with self-regulation. This framework conceptualizes self-regulation in terms of 3 crucial processes: goal setting, goal operating, and goal monitoring. In this meta-analysis, we included articles that reported a quantifiable assessment of implicit theories and at least 1 self-regulatory process or outcome. With a random effects approach used, meta-analytic results (total unique N = 28,217; k = 113) across diverse achievement domains (68% academic) and populations (age range = 5-42; 10 different nationalities; 58% from United States; 44% female) demonstrated that implicit theories predict distinct self-regulatory processes, which, in turn, predict goal achievement. Incremental theories, which, in contrast to entity theories, are characterized by the belief that human attributes are malleable rather than fixed, significantly predicted goal setting (performance goals, r = -.151; learning goals, r = .187), goal operating (helpless-oriented strategies, r = -.238; mastery-oriented strategies, r = .227), and goal monitoring (negative emotions, r = -.233; expectations, r = .157). The effects for goal setting and goal operating were stronger in the presence (vs. absence) of ego threats such as failure feedback. Discussion emphasizes how the present theoretical analysis merges an implicit theory perspective with self-control theory to advance scholarship and unlock major new directions for basic and applied research.
A New Framework for Domain Adaptation without Model Retraining

We propose a principled and effective domain adaptation framework that pursues the goal of Open Domain NLP (train once, test anywhere). Most domain adaptation frameworks adapt the models trained on the source domain data by retraining it on target domains (with a mix of labeled and unlabeled data). However, it is time consuming to retrain big models or pipeline systems, and may not even be feasible if you consider a streaming data that may not be coherent (e.g., web data). We propose an adaptation framework that does not require retraining the original model. Instead, our approach adapts the target domain input so that it is more similar to the source domain, while preserving the labeling, thus increasing the accuracy of the original model when evaluated on target data. Our experiments on the named entity recognition task in scientific domains show an absolute F1 improvement of 13% over a state-of-the-art named entity recognizer. We also show that without any retraining, the proposed method outperforms the bootstrapping based adaptation method of (Jiang and Zhai, 2007b) that requires multiple rounds of retraining on the target domain data.
SVM based Emotional Speaker Recognition using MFCC-SDC Features

Enhancing the performance of emotional speaker recognition process has witnessed an increasing interest in the last years. This paper highlights a methodology for speaker recognition under different emotional states based on the multiclass Support Vector Machine (SVM) classifier. We compare two feature extraction methods which are used to represent emotional speech utterances in order to obtain best accuracies. The first method known as traditional Mel-Frequency Cepstral Coefficients (MFCC) and the second one is MFCC combined with Shifted-Delta-Cepstra (MFCC-SDC). Experimentations are conducted on IEMOCAP database using two multiclass SVM approaches: One-Against-One (OAO) and One Against-All (OAA). Obtained results show that MFCC-SDC features outperform the conventional MFCC. Emotion; Speaker recognition; Mel Frequency Cepstral Coefficients (MFCC); Shifted-Delta-Cepstral (SDC); SVM
LabVIEW based system for PID tuning and implementation for a flow control loop

PID controller is widely used in industries for control applications. Tuning of PID controller is very much essential before its implementation. There are different methods of PID tuning such as Ziegler Nichols tuning method, Internal Model Control method, Cohen Coon tuning method, Tyreus-Luyben method, Chein-Hrones-Reswick method, etc. The focus of the work in this paper is to identify the system model for a flow control loop and implement PID controller in MATLAB for simulation study and in LabVIEW for real-time experimentation. Comparative study of three tuning methods viz. ZN, IMC and CC were carried out. Further the work is to appropriately tune the PID parameters. The flow control loop was interfaced to a computer via NI-DAQ card and PID was implemented using LabVIEW. The simulation and real-time results show that IMC tuning method gives better result than ZN and CC tuning methods.
Image Segmentation using Fuzzy C Means Clustering: A survey

This paper presents a latest survey of different technologies used in medical image segmentation using Fuzzy C Means (FCM).The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. To update the study of image segmentation the survey has performed. The techniques used for this survey are Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map, Robust Image Segmentation in Low Depth Of Field Images, Fuzzy C-Means Technique with Histogram Based Centroid Initialization for Brain Tissue Segmentation in MRI of Head Scans.
InfraStructs: fabricating information inside physical objects for imaging in the terahertz region

We introduce InfraStructs, material-based tags that embed information inside digitally fabricated objects for imaging in the Terahertz region. Terahertz imaging can safely penetrate many common materials, opening up new possibilities for encoding hidden information as part of the fabrication process. We outline the design, fabrication, imaging, and data processing steps to fabricate information inside physical objects. Prototype tag designs are presented for location encoding, pose estimation, object identification, data storage, and authentication. We provide detailed analysis of the constraints and performance considerations for designing InfraStruct tags. Future application scenarios range from production line inventory, to customized game accessories, to mobile robotics.
Aggregated Search Result Diversification

Search result diversification has been effectively employed to tackle query ambiguity, particularly in the context of web search. However, ambiguity can manifest differently in different search verticals, with ambiguous queries spanning, e.g., multiple place names, content genres, or time periods. In this paper, we empirically investigate the need for diversity across four different verticals of a commercial search engine, including web, image, news, and product search. As a result, we introduce the problem of aggregated search result diversification as the task of satisfying multiple information needs across multiple search verticals. Moreover, we propose a probabilistic approach to tackle this problem, as a natural extension of state-of-the-art diversification approaches. Finally, we generalise standard diversity metrics, such as ERR-IA and α-nDCG, into a framework for evaluating diversity across multiple search verticals.
Reports of repetitive penile-genital penetration often have no definitive evidence of penetration.

The goals were to evaluate the association of definitive hymenal findings with the number of reported episodes of penile-genital penetration, pain, bleeding, dysuria, and time since assault for girls presenting for nonacute, sexual assault examinations.



Charts of all girls 5 to 17 of age who provided a history of nonacute, penile-genital, penetrative abuse were reviewed. Interviews and examinations occurred over a 4-year period at a children's advocacy center. Characteristics of the histories provided by the subjects were examined for associations with definitive findings of penetrative trauma.



Five hundred six patients were included in the study. Of the 56 children with definitive examination results, 52 had no history of consensual penile-vaginal intercourse and all were > or =10 years of age. Analysis was unable to detect an association between the number of reported penile-genital penetrative events and definitive genital findings. Eighty-seven percent of victims who provided a history of >10 penetrative events had no definitive evidence of penetration. A history of bleeding with abuse was more than twice as likely for subjects with definitive findings. Children <10 years of age were twice as likely to report >10 penetrative events, although none had definitive findings on examination.



Most victims who reported repetitive penile-genital contact that involved some degree of perceived penetration had no definitive evidence of penetration on examination of the hymen. Similar results were seen for victims of repetitive assaults involving perceived penetration over long periods of time, as well as victims with a history of consensual sex.
Molecular computation: RNA solutions to chess problems.

We have expanded the field of "DNA computers" to RNA and present a general approach for the solution of satisfiability problems. As an example, we consider a variant of the "Knight problem," which asks generally what configurations of knights can one place on an n x n chess board such that no knight is attacking any other knight on the board. Using specific ribonuclease digestion to manipulate strands of a 10-bit binary RNA library, we developed a molecular algorithm and applied it to a 3 x 3 chessboard as a 9-bit instance of this problem. Here, the nine spaces on the board correspond to nine "bits" or placeholders in a combinatorial RNA library. We recovered a set of "winning" molecules that describe solutions to this problem.
A review of plants used in divination in southern Africa and their psychoactive effects

Numerous indigenous healing traditions around the world employ plants with psychoactive effects to facilitate divination and other spiritual healing rituals. Southern Africa has often been considered to have relatively few psychoactive plant species of cultural importance, and little has been published on the subject. This paper reports on 85 species of plants that are used for divination by southern Bantu-speaking people. Of these, 39 species (45 %) have other reported psychoactive uses, and a number have established hallucinogenic activity. These findings indicate that psychoactive plants have an important role in traditional healing practices in southern Africa.