nils-herrmann/clean_scirepeval huggingface dataset:
A comparative study of classification methods for automatic multimodal brain tumor segmentation

This paper presents a comparative study of various classification methods in the application of automatic brain tumor segmentation. The data used in the study are 3D MRI volumes from MICCAI2016 brain tumor segmentation (BRATS) benchmark. 30 volumes are chosen randomly as a training set and 57 volumes are randomly chosen as a test set. The volumes are preprocessed and a feature vector is retrieved from each volume's four modalities (T1, T1 contrast-enhanced, T2 and Fluid-attenuated inversion recovery). The popular Dice score is used as an accuracy measure to record each classifier recognition results. All classifiers are implemented in the popular machine learning suit of algorithms, WEKA.
A Wideband Circularly Polarized Rectangular Dielectric Resonator Antenna Excited by a Lumped Resistively Loaded Monofilar-Spiral-Slot

A wideband circularly polarized (CP) rectangular dielectric resonator antenna (DRA) based on the concept of traveling-wave excitation is presented. A lumped resistively loaded monofilar-spiral-slot is used to excite the rectangular DRA. The proposed DRA is theoretically and experimentally analyzed, including design concept, design guideline, parameter study, and experimental verification. It is found that by using such an excitation, a wide 3-dB axial-ratio (AR) bandwidth of 18.7% can be achieved.
Ocular biometrics: A survey of modalities and fusion approaches

Biometrics, an integral component of Identity Science, is widely used in several large-scale-county-wide projects to provide a meaningful way of recognizing individuals. Among existing modalities, ocular biometric traits such as iris, periocular, retina, and eye movement have received significant attention in the recent past. Iris recognition is used in Unique Identification Authority of India’s Aadhaar Program and the United Arab Emirate’s border security programs, whereas the periocular recognition is used to augment the performance of face or iris when only ocular region is present in the image. This paper reviews the research progression in these modalities. The paper discusses existing algorithms and the limitations of each of the biometric traits and information fusion approaches which combine ocular modalities with other modalities. We also propose a path forward to advance the research on ocular recognition by (i) improving the sensing technology, (ii) heterogeneous recognition for addressing interoperability, (iii) utilizing advanced machine learning algorithms for better representation and classification, (iv) developing algorithms for ocular recognition at a distance, (v) using multimodal ocular biometrics for recognition, and (vi) encouraging benchmarking standards and open-source software development. ! 2015 Elsevier B.V. All rights reserved.
Pigmented oral compound nevus of retromolar area - A rare case report.

Solitary pigmented melanocytic intraoral lesions of the oral cavity are rare. Oral nevus is a congenital or acquired benign neoplasm. Oral compound nevus constitutes 5.9%-16.5% of all oral melanocytic nevi. The oral compound nevus is commonly seen on hard palate and buccal mucosa and rarely on other intraoral sites. The objective of this article is to present a rare case report of oral compound nevus in the retromolar pad region along with a review of literature. A 22 year old female reported with a solitary black pigmented papule at retromolar pad region which was surgically removed and microscopic investigation confirmed the diagnosis of oral compound nevus.
Barium titanate nanoparticles and hypergravity stimulation improve differentiation of mesenchymal stem cells into osteoblasts

Enhancement of the osteogenic potential of mesenchymal stem cells (MSCs) is highly desirable in the field of bone regeneration. This paper proposes a new approach for the improvement of osteogenesis combining hypergravity with osteoinductive nanoparticles (NPs).



In this study, we aimed to investigate the combined effects of hypergravity and barium titanate NPs (BTNPs) on the osteogenic differentiation of rat MSCs, and the hypergravity effects on NP internalization. To obtain the hypergravity condition, we used a large-diameter centrifuge in the presence of a BTNP-doped culture medium. We analyzed cell morphology and NP internalization with immunofluorescent staining and coherent anti-Stokes Raman scattering, respectively. Moreover, cell differentiation was evaluated both at the gene level with quantitative real-time reverse-transcription polymerase chain reaction and at the protein level with Western blotting.



Following a 20 g treatment, we found alterations in cytoskeleton conformation, cellular shape and morphology, as well as a significant increment of expression of osteoblastic markers both at the gene and protein levels, jointly pointing to a substantial increment of NP uptake. Taken together, our findings suggest a synergistic effect of hypergravity and BTNPs in the enhancement of the osteogenic differentiation of MSCs.



The obtained results could become useful in the design of new approaches in bone-tissue engineering, as well as for in vitro drug-delivery strategies where an increment of nanocarrier internalization could result in a higher drug uptake by cell and/or tissue constructs.
A Hydrogenated Amorphous Silicon Thin-Film Transistor Optical Pixel Sensor for Ameliorating Influences of Ambient Light and Reflected Light

This paper proposes a hydrogenated amorphous silicon thin-film transistorbased (a-Si:H TFT) optical pixel sensor. The proposed optical sensor compensates for variations of ambient light using photo TFTs that incorporate with three primary color filters, and a designed active load is utilized to release the photocurrent from the noise of reflected light. Measurement results reveal that the proposed sensor suppresses the effect of ambient light within the intensity of 12 560 lux and does not react to a blue light with an intensity of 588 lux, proving that the proposed optical sensor remains highly reliable under heavy ambient light and avoids the interference of reflected light.
New low-field extremity MRI, compacTscan: comparison with whole-body 1.5 T conventional MRI.

Low-field extremity magnetic resonance imaging (lfMRI) is currently commercially available and has been used clinically to evaluate rheumatoid arthritis (RA). However, one disadvantage of this new modality is that the field of view (FOV) is too small to assess hand and wrist joints simultaneously. Thus, we have developed a new lfMRI system, compacTscan, with a FOV that is large enough to simultaneously assess the entire wrist to proximal interphalangeal joint area. In this work, we examined its clinical value compared to conventional 1.5 tesla (T) MRI. The comparison involved evaluating three RA patients by both 0.3 T compacTscan and 1.5 T MRI on the same day. Bone erosion, bone edema, and synovitis were estimated by our new compact MRI scoring system (cMRIS) and the kappa coefficient was calculated on a joint-by-joint basis. We evaluated a total of 69 regions. Bone erosion was detected in 49 regions by compacTscan and in 48 regions by 1.5 T MRI, while the total erosion score was 77 for compacTscan and 76.5 for 1.5 T MRI. These findings point to excellent agreement between the two techniques (kappa = 0.833). Bone edema was detected in 14 regions by compacTscan and in 19 by 1.5 T MRI, and the total edema score was 36.25 by compacTscan and 47.5 by 1.5 T MRI. Pseudo-negative findings were noted in 5 regions. However, there was still good agreement between the techniques (kappa = 0.640). Total number of evaluated joints was 33. Synovitis was detected in 13 joints by compacTscan and 14 joints by 1.5 T MRI, while the total synovitis score was 30 by compacTscan and 32 by 1.5 T MRI. Thus, although 1 pseudo-positive and 2 pseudo-negative findings resulted from the joint evaluations, there was again excellent agreement between the techniques (kappa = 0.827). Overall, the data obtained by our compacTscan system showed high agreement with those obtained by conventional 1.5 T MRI with regard to diagnosis and the scoring of bone erosion, edema, and synovitis. We conclude that compacTscan is useful for diagnosis and estimation of disease activity in patients with RA.
Combat triage support using the Internet of Military Things

Triage on the battlefield is a very challenging task. Life of the wounded soldiers depends on the efficiency of this process and there is still lack of supporting solutions. This paper presents a new approach for using Internet of Military Things in combat triage. We propose an ontological approach to evaluate soldiers' health state and information framework which allows first responders and commanders to query the sensor network for needed information. Some simulation experiments were conducted, which results show that the proposed method can be applied in highly distributed and heterogeneous environment of the smart devices on the battlefield.
Three-phase LLC resonant converter with integrated magnetics

Recently, Electric Vehicles (EVs) have required high power density and high efficiency systems in order to save energy and costs. Specifically, in the DC-DC converter that feeds the non-propulsive loads in these vehicles, where the output voltage is much lower than the one of the energy storage unit. Therefore, the output current becomes quite high, and the efficiency and power density are reduced due to the high current ratings. Furthermore, magnetic components usually are the biggest contributors to the mass and volume in these converters. This paper proposes a Three-phase LLC resonant converter with one integrated transformer where all the windings of the three independent transformers are installed into only one core. Using this technique, a high reduction in the core size and thereby an increment in the power density and a reduction of the production cost are obtained. In addition, this integrated transformer is intended to be applied in the novel Three-phase LLC resonant converter with Star connection that is expected to offer reduction of the imbalanced output current, which is produced by tolerances between the phase components. Finally, the proposed converter with the novel integrated transformer is discussed and evaluated from the experimental point of view. As a result, a 70% reduction in the mass of the magnetic cores was achieved.
Text and non-text separation in offline document images: a survey

Separation of text and non-text is an essential processing step for any document analysis system. Therefore, it is important to have a clear understanding of the state-of-the-art of text/non-text separation in order to facilitate the development of efficient document processing systems. This paper first summarizes the technical challenges of performing text/non-text separation. It then categorizes offline document images into different classes according to the nature of the challenges one faces, in an attempt to provide insight into various techniques presented in the literature. The pros and cons of various techniques are explained wherever possible. Along with the evaluation protocols, benchmark databases, this paper also presents a performance comparison of different methods. Finally, this article highlights the future research challenges and directions in this domain.
Queue Mining - Predicting Delays in Service Processes

Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Recently, work on process mining showed how management of these processes, and engineering of supporting systems, can be guided by models extracted from the event logs that are recorded during process operation. In this work, we establish a queueing perspective in operational process mining. We propose to consider queues as first-class citizens and use queueing theory as a basis for queue mining techniques. To demonstrate the value of queue mining, we revisit the specific operational problem of online delay prediction: using event data, we show that queue mining yields accurate online predictions of case delay.
An RLS-Based Lattice-Form Complex Adaptive Notch Filter

This letter presents a new lattice-form complex adaptive IIR notch filter to estimate and track the frequency of a complex sinusoid signal. The IIR filter is a cascade of a direct-form all-pole prefilter and an adaptive lattice-form all-zero filter. A complex domain exponentially weighted recursive least square algorithm is adopted instead of the widely used least mean square algorithm to increase the convergence rate. The convergence property of this algorithm is investigated, and an expression for the steady-state asymptotic bias is derived. Analysis results indicate that the frequency estimate for a single complex sinusoid is unbiased. Simulation results demonstrate that the proposed method achieves faster convergence and better tracking performance than all traditional algorithms.
The validity and reliability of a brief measure of cocaine craving.

Although craving plays an important role in relapse, there are few brief, valid and reliable instruments to measure the desire to use cocaine in routine clinical practice. The 45-item Cocaine Craving Questionnaire-Now (CCQ-Now) is widely used in research, but its length makes its use in everyday clinical work relatively impractical. This study sought to determine the psychometric properties of the CCQ-Brief, a measure composed of 10 items from the CCQ-Now, in treatment-seeking cocaine abusers.



Subjects with cocaine abuse or dependence (n=247) completed the CCQ-Brief, the CCQ-Now, the Voris Cocaine Craving Scale, the Beck Depression Inventory-II, the Beck Anxiety Inventory, and the Addiction Severity Index.



The CCQ-Brief was significantly correlated with the CCQ-Now (r=.85, p<.01), the CCQ-Now with the items in common with the CCQ-Brief removed (r=.78, p<.01), all four subscales of the VCCS (craving intensity: r=.47, p<.01; mood: r=.27, p<.01; energy: r=.30, p<.01; sick feelings: r=.28, p<.01), the BDI-II (r=.39, p<.01), the BAI (r=.35, p<.01) and recent drug use (r=.26, p<.01). The internal consistency of the CCQ-Brief was strong (alpha=.90).



The CCQ-Brief is a valid and reliable instrument that can be easily administered as a measure of current cocaine craving.
The energy allocation function of sleep: A unifying theory of sleep, torpor, and continuous wakefulness

The energy allocation (EA) model defines behavioral strategies that optimize the temporal utilization of energy to maximize reproductive success. This model proposes that all species of the animal kingdom share a universal sleep function that shunts waking energy utilization toward sleep-dependent biological investment. For endotherms, REM sleep evolved to enhance energy appropriation for somatic and CNS-related processes by eliminating thermoregulatory defenses and skeletal muscle tone. Alternating REM with NREM sleep conserves energy by decreasing the need for core body temperature defense. Three EA phenotypes are proposed: sleep-wake cycling, torpor, and continuous (or predominant) wakefulness. Each phenotype carries inherent costs and benefits. Sleep-wake cycling downregulates specific biological processes in waking and upregulates them in sleep, thereby decreasing energy demands imposed by wakefulness, reducing cellular infrastructure requirements, and resulting in overall energy conservation. Torpor achieves the greatest energy savings, but critical biological operations are compromised. Continuous wakefulness maximizes niche exploitation, but endures the greatest energy demands. The EA model advances a new construct for understanding sleep-wake organization in ontogenetic and phylogenetic domains.
Generating topologically consistent triangle meshes from large scale Kinect Fusion

Generating polygonal maps from RGB-D data is an active field of research in robotic mapping. Kinect Fusion and related algorithms provide means to generate reconstructions of large environments. However, most available implementations generate topological artifacts like redundant vertices and triangles. In this paper we present a novel data structure that allows to generate topologically consistent triangle meshes from RGB-D data without additional filtering.
Fuzzy set approach for automatic tagging in evolving software

Software tagging has been shown to be an efficient, lightweight social computing mechanism to improve different social and technical aspects of software development. Despite the importance of tags, there exists limited support for automatic tagging for software artifacts, especially during the evolutionary process of software development. We conducted an empirical study on IBM Jazz's repository and found that there are several missing tags in artifacts and more precise tags are desirable. This paper introduces a novel, accurate, automatic tagging recommendation tool that is able to take into account users' feedbacks on tags, and is very efficient in coping with software evolution. The core technique is an automatic tagging algorithm that is based on fuzzy set theory. Our empirical evaluation on the real-world IBM Jazz project shows the usefulness and accuracy of our approach and tool.
Fault Direction Estimation in Radial Distribution System Using Phase Change in Sequence Current

When a remotely sited wind farm is connected to the utility power system through a distribution line, the overcurrent relay at the common coupling point needs a directional feature. This paper presents a method for estimating the direction of fault in such radial distribution systems using phase change in current. The difference in phase angle between the positive-sequence component of the current during fault and prefault conditions has been found to be a good indicator of the fault direction in a three-phase system. A rule base formed for the purpose decides the location of fault with respect to the relay in a distribution system. Such a strategy reduces the cost of the voltage sensor and/or connection for a protection scheme which is of relevance in emerging distributed-generation systems. The algorithm has been tested through simulation for different radial distribution systems.
Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.
Light load efficiency improvement for PFC

With the fast growing information technologies, high efficiency AC-DC front-end power supplies are becoming more and more desired in all kinds of distributed power system applications due to the energy conservation consideration. For the power factor correction (PFC) stage, the conventional constant frequency average current mode control has very low efficiency at light load due to high switching frequency related loss. The constant on-time control for PFC features the automatic reduction of switching frequency at light load, resulting improved light load efficiency. However, lower heavy load efficiency of the constant on-time control is observed because of very high frequency at Continuous Conduction Mode (CCM). By carefully comparing the on-time and frequency profiles between constant on-time and constant frequency control, a novel adaptive on-time control is proposed to improve the light load efficiency without sacrificing the heavy load efficiency. The performance of the adaptive on-time control is verified by experiment.
Moo : A Batteryless Computational RFID and Sensing Platform

The UMass Moo is a passively powered computational RFID that harvests RFID reader energy from the UHF band, communicates with an RFID reader, and processes data from its onboard sensors. Its function can be extended via its general-purpose I/Os, serial buses, and 12-bit ADC/DAC ports. Based on the Intel DL WISP (revision 4.1), the Moo provides an RFID-scale, reprogrammable, batteryless sensing platform. This report compares the Moo to its ancestor, documents our design decisions, and details the Moo’s compatibility with other devices. It is meant to be a companion document for the open-source release of code and specifications for the Moo (revision 1.x). We made an initial batch of Moo 1.1 hardware available to other researchers in June 2011.