nils-herrmann/clean_scirepeval huggingface dataset:
Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes

In this paper, we propose a combination of mean-shift-based tracking processes to establish migrating cell trajectories through in vitro phase-contrast video microscopy. After a recapitulation on how the mean-shift algorithm permits efficient object tracking we describe the proposed extension and apply it to the in vitro cell tracking problem. In this application, the cells are unmarked (i.e., no fluorescent probe is used) and are observed under classical phase-contrast microscopy. By introducing an adaptive combination of several kernels, we address several problems such as variations in size and shape of the tracked objects (e.g., those occurring in the case of cell membrane extensions), the presence of incomplete (or noncontrasted) object boundaries, partially overlapping objects and object splitting (in the case of cell divisions or mitoses). Comparing the tracking results automatically obtained to those generated manually by a human expert, we tested the stability of the different algorithm parameters and their effects on the tracking results. We also show how the method is resistant to a decrease in image resolution and accidental defocusing (which may occur during long experiments, e.g., dozens of hours). Finally, we applied our methodology on cancer cell tracking and showed that cytochalasin-D significantly inhibits cell motility.
Receiving right/wrong feedback: consequences for learning.

Prior work suggests that receiving feedback that one's response was correct or incorrect (right/wrong feedback) does not help learners, as compared to not receiving any feedback at all (Pashler, Cepeda, Wixted, & Rohrer, 2005). In three experiments we examined the generality of this conclusion. Right/wrong feedback did not aid error correction, regardless of whether participants learned facts embedded in prose (Experiment 1) or translations of foreign vocabulary (Experiment 2). While right/wrong feedback did not improve the overall retention of correct answers (Experiments 1 and 2), it facilitated retention of low-confidence correct answers (Experiment 3). Reviewing the original materials was very useful to learners, but this benefit was similar after receiving either right/wrong feedback or no feedback (Experiments 1 and 2). Overall, right/wrong feedback conveys some information to the learner, but is not nearly as useful as being told the correct answer or having the chance to review the to-be-learned materials.
Endodontic management of a C-shaped maxillary first molar with three independent buccal root canals by using cone-beam computed tomography

The aim of this study was to present a method for endodontic management of a maxillary first molar with unusual C-shaped morphology of the buccal root verified by cone-beam computed tomography (CBCT) images. This rare anatomical variation was confirmed using CBCT, and nonsurgical endodontic treatment was performed by meticulous evaluation of the pulpal floor. Posttreatment image revealed 3 independent canals in the buccal root obturated efficiently to the accepted lengths in all 3 canals. Our study describes a unique C-shaped variation of the root canal system in a maxillary first molar, involving the 3 buccal canals. In addition, our study highlights the usefulness of CBCT imaging for accurate diagnosis and management of this unusual canal morphology.
A Meta-Instrument for Interactive, On-the-Fly Machine Learning

Supervised learning methods have long been used to allow musical interface designers to generate new mappings by example. We propose a method for harnessing machine learning algorithms within a radically interactive paradigm, in which the designer may repeatedly generate examples, train a learner, evaluate outcomes, and modify parameters in real-time within a single software environment. We describe our meta-instrument, the Wekinator, which allows a user to engage in on-the-fly learning using arbitrary control modalities and sound synthesis environments. We provide details regarding the system implementation and discuss our experiences using the Wekinator for experimentation and performance.
Decision Trees for Uncertain Data

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives (such as mean and median), we discover that the accuracy of a decision tree classifier can be much improved if the "complete information" of a data item (taking into account the probability density function (pdf)) is utilized. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments have been conducted which show that the resulting classifiers are more accurate than those using value averages. Since processing pdfs is computationally more costly than processing single values (e.g., averages), decision tree construction on uncertain data is more CPU demanding than that for certain data. To tackle this problem, we propose a series of pruning techniques that can greatly improve construction efficiency.
SoC Implementation of VGA Driver using Spartan 3AN Series FPGA

As the microelectronic industry grows there has been an exponential increase in the importance of FPGAs in modern digital circuit design. FPGAs not only provided an advantage of being reconfigurable but also come in extremely small packaging. In this paper we use the Xilinx Spartan 3AN series FPGA and propose its use as part of a System on Chip controller for a Video Graphics Array (VGA), that serves as one of the most popular display units today.
Experimental Assessment of Moringa oleifera Leaf and Fruit for Its Antistress, Antioxidant, and Scavenging Potential Using In Vitro and In Vivo Assays

We have investigated effect of Moringa oleifera leaf and fruit extracts on markers of oxidative stress, its toxicity evaluation, and correlation with antioxidant properties using in vitro and in vitro assays. The aqueous extract of leaf was able to increase the GSH and reduce MDA level in a concentration-dependent manner. The ethanolic extract of fruit showed highest phenolic content, strong reducing power and free radical scavenging capacity. The antioxidant capacity of ethanolic extract of both fruit and leaf was higher in the in vitro assay compared to aqueous extract which showed higher potential in vivo. Safety evaluation studies showed no toxicity of the extracts up to a dose of 100 mg/kg body weight. Our results support the potent antioxidant activity of aqueous and ethanolic extract of Moringa oleifera which adds one more positive attribute to its known pharmacological importance.
Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton

Robot-aided gait training is an emerging clinical tool for gait rehabilitation of neurological patients. This paper deals with a novel method of offering gait assistance, using an impedance controlled exoskeleton (LOPES). The provided assistance is based on a recent finding that, in the control of walking, different modules can be discerned that are associated with different subtasks. In this study, a Virtual Model Controller (VMC) for supporting one of these subtasks, namely the foot clearance, is presented and evaluated. The developed VMC provides virtual support at the ankle, to increase foot clearance. Therefore, we first developed a new method to derive reference trajectories of the ankle position. These trajectories consist of splines between key events, which are dependent on walking speed and body height. Subsequently, the VMC was evaluated in twelve healthy subjects and six chronic stroke survivors. The impedance levels, of the support, were altered between trials to investigate whether the controller allowed gradual and selective support. Additionally, an adaptive algorithm was tested, that automatically shaped the amount of support to the subjects’ needs. Catch trials were introduced to determine whether the subjects tended to rely on the support. We also assessed the additional value of providing visual feedback. With the VMC, the step height could be selectively and gradually influenced. The adaptive algorithm clearly shaped the support level to the specific needs of every stroke survivor. The provided support did not result in reliance on the support for both groups. All healthy subjects and most patients were able to utilize the visual feedback to increase their active participation. The presented approach can provide selective control on one of the essential subtasks of walking. This module is the first in a set of modules to control all subtasks. This enables the therapist to focus the support on the subtasks that are impaired, and leave the other subtasks up to the patient, encouraging him to participate more actively in the training. Additionally, the speed-dependent reference patterns provide the therapist with the tools to easily adapt the treadmill speed to the capabilities and progress of the patient.
Mesoscopic representation of texts as complex networks

Texts are complex structures emerging from an intricate system consisting of syntactical constraints and semantical relationships. While the complete modeling of such structures is impractical owing to the high level of complexity inherent to linguistic constructions, under a limited domain, certain tasks can still be performed. Recently, statistical techniques aiming at analysis of texts, referred to as text analytics, have departed from the use of simple word count statistics towards a new paradigm. Text mining now hinges on a more sophisticate set of methods, including the representation of texts as complex networks. In this perspective, networks represent a set of textual elements, typically words; and links are established via adjacency relationships. While current word-adjacency (co-occurrence) methods successfully grasp syntactical and stylistic features of written texts, they are unable to represent important aspects of textual data, such as its topical structure. As a consequence, the mesoscopic structure of texts is often overlooked by current methodologies. In order to grasp mesoscopic characteristics of semantical content in written texts, we devised a network approach which is able to analyze documents in a multi-scale, mesoscopic fashion. In the proposed model, a limited amount of adjacent paragraphs are represented as nodes, which are connected whenever they share a minimum semantical content. To illustrate the capabilities of our model, we present, as a use case, a qualitative analysis of “Alice’s Adventures in Wonderland”, a novel by Lewis Carroll. We show that the mesoscopic structure of documents modeled as networks reveals many semantic traits of texts, a feature that could be explored in a myriad of semantic-based applications.
Perception of premenstrual syndrome and attitude of evaluations of work performance among incoming university female students.

Premenstrual syndrome (PMS) is a common condition, and for 5% of women, the influence is so severe as to interfere with their mental health, interpersonal relationships, or studies. Severe PMS may result in decreased occupational productivity. The aim of this study was to investigate the influence of perception of PMS on evaluation of work performance.



A total of 1971 incoming female university students were recruited in September 2009. A simulated clinical scenario was used, with a test battery including measurement of psychological symptoms and the Chinese Premenstrual Symptom Questionnaire.



When evaluating employee performance in the simulated scenario, 1565 (79.4%) students neglected the impact of PMS, while 136 (6.9%) students considered it. Multivariate logistic regression showed that perception of daily function impairment due to PMS and frequency of measuring body weight were significantly associated with consideration of the influence of PMS on evaluation of work performance.



It is important to increase the awareness of functional impairments related to severe PMS.
OSA: An Optical Switching Architecture for Data Center Networks With Unprecedented Flexibility

A detailed examination of evolving traffic characteristics, operator requirements, and network technology trends suggests a move away from nonblocking interconnects in data center networks (DCNs). As a result, recent efforts have advocated oversubscribed networks with the capability to adapt to traffic requirements on-demand. In this paper, we present the design, implementation, and evaluation of OSA, a novel Optical Switching Architecture for DCNs. Leveraging runtime reconfigurable optical devices, OSA dynamically changes its topology and link capacities, thereby achieving unprecedented flexibility to adapt to dynamic traffic patterns. Extensive analytical simulations using both real and synthetic traffic patterns demonstrate that OSA can deliver high bisection bandwidth (60%-100% of the nonblocking architecture). Implementation and evaluation of a small-scale functional prototype further demonstrate the feasibility of OSA.
An image analysis toolbox for high-throughput C. elegans assays

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.
Decoding Sentiment from Distributed Representations of Sentences

Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no ’one-sizefits-all’ representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass state-of-art architectures such as bi-directional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.
Design and Analysis of UHF Micropower CMOS DTMOST Rectifiers

Design and analysis of ultrahigh-frequency (UHF) micropower rectifiers based on a diode-connected dynamic threshold MOSFET (DTMOST) is discussed. An analytical design model for DTMOST rectifiers is derived based on curve-fitted diode equation parameters. Several DTMOST six-stage charge-pump rectifiers were designed and fabricated using a CMOS 0.18-mum process with deep n-well isolation. Measured results verified the design model with average accuracy of 10.85% for an input power level between -4 and 0 dBm. At the same time, three other rectifiers based on various types of transistors were fabricated on the same chip. The measured results are compared with a Schottky diode solution.
Neural Paraphrase Generation with Stacked Residual LSTM Networks

In this paper, we propose a novel neural approach for paraphrase generation. Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We experiment with our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based and bi-directional LSTM models on BLEU, METEOR, TER and an embedding-based sentence similarity metric.
Center frequency and bandwidth tunable waveguide bandpass filter with transmission zeros

In this paper, a novel center frequency and bandwidth tunable, cross-coupled waveguide resonator filter is presented. The coupling between adjacent resonators can be adjusted using non-resonating coupling resonators. The negative sign for the cross coupling, which is required to generate transmission zeros, is enforced by choosing an appropriate resonant frequency for the cross-coupling resonator. The coupling iris design itself is identical regardless of the sign of the coupling. The design equations for the novel coupling elements are given in this paper. A four pole filter breadboard with two transmission zeros (elliptic filter function) has been built up and measured at various bandwidth and center frequency settings. It operates at Ka-band frequencies and can be tuned to bandwidths from 36 to 72 MHz in the frequency range 19.7-20.2 GHz.
1-to-4 double-side slotted waveguide power divider/combiner for Ka-band power amplifiers

In this paper, a Ka-band power divider/combiner structure based on a double-side slotted waveguide technology is proposed. The novel structure uses slots on both sides of the waveguide to divide/combine the power thus reducing half the size compared to previous power dividers/combiners structures, based on similar technology. The 4-way combiner design is the first step toward the design/development of a 40-W Ka-band spatial power amplifier, aimed to operate at the 29-31 GHz band. The validation of this design is performed by means of simulations using a commercially available Full-Wave Electromagnetic (FWEM) simulator; e.g., the CST CAD Tool.
A 22nm ±0.95V CMOS OTA-C front-end with 50/60 Hz notch for biomedical signal acquisition

This paper presents a 22 nm CMOS technology analog front-end (AFE) for biomedical applications. The circuit is designed for low power and small size implementations, especially for battery-powered implantable devices, and is capable of reading out biomedical signals in the range of 0.01 Hz to 300 Hz in frequency, while rejecting power-line frequency of 50/60Hz. It employs Operational Transconductance Amplifiers (OTAs) in an OTA-C structure to realize a notch filter. The OTA designed has a very low transconductance, which is programmable from 1.069 nA/V to 2.114 nA/V. The notch at power-line frequency (50/60 Hz) achieves an attenuation of 20 dB. The power consumption of the entire AFE was found to be 11.34 nW at ±0.95V supply.
Fe-Doped CoP Nanoarray: A Monolithic Multifunctional Catalyst for Highly Efficient Hydrogen Generation.

An Fe-doped CoP nanoarray behaves as a robust 3D monolithic multifunctional catalyst for electrolytic and hydrolytic hydrogen evolution with high activity. Its two-electrode electrolyzer needs a cell voltage of only 1.60 V for 10 mA cm-2 water-splitting current. It also catalyzes effectively NaBH4 hydrolysis with a low activation energy of ≈39.6 kJ mol-1 and a hydrogen generation rate of 6.06 L min-1 g-1 .
Craniofacial characteristics in children with Angle Class II div. 2 malocclusion combined with extreme deep bite.

Craniofacial characteristics in two groups of children were compared. In one group (n = 22) the children had Angle Class II division 2 malocclusion combined with extreme deep bite. The other group (n = 25) was composed of children with ideal occlusion. The mean ages of the children were 12.8 and 12.9 years respectively. In the Class II-2 group the distance between gonion and B-point was underdeveloped, causing B-point to have a retruded position in relation to both A-point and cranial base. The Class II-2 children also had a retroclination of the symphysis, which gave the B-point a retruded position in relation to pogonion. As for vertical dimensions, Class II-2 children had a smaller anterior lower facial height than normal. Furthermore, Class II-2 had a discrepancy between the maxillary incisal and molar heights, i.e. a slightly larger incisal height and a slightly smaller molar height. Finally, children with Class II-2 had a high lip line and a very large interincisal angle. Three variables--the sagittal distance between points A and B, the inclination of the symphysis, and the relationship between the maxillary incisal and molar heights--in combination, differentiated nearly 100% correctly between Class II-2 and normal occlusion.
A Graphical User Interface (GUI) Testing Methodology

Software testing in general and graphical user interface (GUI) testing in particular is one of the major challenges in the lifecycle of any software system. GUI testing is inherently more difficult than the traditional and command-line interface testing. Some of the factors that make GUI testing different from the traditional software testing and significantly more difficult are: a large number of objects, different look and feel of objects, many parameters associated with each object, progressive disclosure, complex inputs from multiple sources, and graphical outputs. The existing testing techniques for the creation and management of test suites need to be adapted/enhanced for GUIs, and new testing techniques are desired to make the creation and management of test suites more efficient and effective. In this article, a methodology is proposed to create test suites for a GUI. The proposed methodology organizes the testing activity into various levels. The tests created at a particular level can be reused at higher levels. This methodology extends the notion of modularity and reusability to the testing phase. The organization and management of the created test suites resembles closely to the structure of the GUI under test.