nils-herrmann/clean_scirepeval huggingface dataset:
Treatment of a mandibular discontinuity defect by using a fibula free flap and an implant-supported fixed complete denture fabricated with a PEKK framework: A clinical report.

This clinical report describes the rehabilitation of a patient with a history of mandibulectomy that involved the use of a fibula free flap and an implant-supported fixed complete denture. A recently introduced material, polyetherketoneketone (PEKK), was used as the framework material for the prosthesis, and the treatment produced favorable esthetic and functional results.
Charging multiple batteries using the boost-flyback converter

A boost-flyback converter was investigated for its ability to charge separate battery stacks from a low-voltage high-current renewable energy source. A low voltage (12V) battery was connected in the boost configuration, and a high voltage (330V) battery stack was connected in the flyback configuration. This converter works extremely well for this application because it gives charging priority to the low voltage battery and dumps the reserve energy to the high voltage stack. As the low-voltage battery approaches full charge, more power is adaptively directed to the high-voltage stack, until finally the charging of the low voltage battery stops. A two-secondary flyback is also capable of this adaptive charging, but the boost-flyback does it with much higher conversion efficiency, and with a simpler (less expensive) transformer design.
Generative Adversarial Networks for Change Detection in Multispectral Imagery

Change detection can be treated as a generative learning procedure, in which the connection between bitemporal images and the desired change map can be modeled as a generative one. In this letter, we propose an unsupervised change detection method based on generative adversarial networks (GANs), which has the ability of recovering the training data distribution from noise input. Here, the joint distribution of the two images to be detected is taken as input and an initial difference image (DI), generated by traditional change detection method such as change vector analysis, is used to provide prior knowledge for sampling the training data based on Bayesian theorem and GAN’s min–max game theory. Through the continuous adversarial learning, the shared mapping function between the training data and their corresponding image patches can be built in GAN’s generator, from which a better DI can be generated. Finally, an unsupervised clustering algorithm is used to analyze the better DI to obtain the desired binary change map. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed method.
Broken rotor bar fault detection in line start permanent magnet synchronous motor using transient current signal

One of the high efficiency motors that were introduced to the industry in recent times is Line Start Permanent Magnet Synchronous Motor (LS-PMSM). Fault detection of LS-PMSM is one of interesting issues. This article presents a new technique for broken rotor bar detection based on the values of Mean and RMS features obtained from captured start-up current in the time domain. The extracted features were analyzed using analysis of variance method to predict the motor condition. Starting load condition and its interaction on detection of broken rotor bar were also investigated. The statistical evaluation of means for each feature at different conditions was performed using Tukey's method as post-hoc procedure. The result showed that the applied features could able to detect the broken rotor bar fault in LS-PMSMs.
The Effect of E-Service Quality on E-Trust and E-Satisfaction as Key Factors Influencing Creation of E-Loyalty in E-Business Context : The Moderating Role of Situational Factors

Development of loyalty in customers is a strategic goal of many firms and organizations and today, the main effort of many firms is allocated to retain customers and obtaining even more ones. Characteristics of loyal customers and method for formation of loyalty in customers in internet space are different to those in traditional one in some respects and study of them may be beneficial in improving performance of firms, organizations and shops involving in this field of business. Also it may help managers of these types of businesses to make efficient and effective decisions towards success of their organizations. Thus, present study aims to investigate the effects of e-service quality in three aspects of information, system and web-service on e-trust and e-satisfaction as key factors influencing creation of e-loyalty of Iranian customers in e-business context; Also it was tried to demonstrate moderating effect of situational factors e.g. time poverty, geographic distance, physical immobility and lack of transportation on e-loyalty level. Totally, 400 questionnaires were distributed to university students, that 382 questionnaires were used for the final analysis, which the results from analysis of them based on simple linear regression and multiple hierarchical regression show that customer loyalty to e-shops is directly influenced by e-trust in and e-satisfaction with e-shops which in turn are determined by e-service quality; also the obtained results shows that situational variables can moderate relationship between e-trust and/or e-satisfaction and e-loyalty. Therefore situational variables studied in present research can influence initiation of transaction of customer with online retailer and customer attitude importance and this in turn makes it necessary for managers to pay special attention to situational effects in examination of current attitude and behavior of customers.
Efficient multiple time-stepping algorithms of higher order

Multiple time-stepping (MTS) algorithms allow to efficiently integrate large systems of ordinary differential equations, where a few stiff terms restrict the timestep of an otherwise non-stiff system. In this work, we discuss a flexible class of MTS techniques, based on multistep methods. Our approach contains several popular methods as special cases and it allows for the easy construction of novel and efficient higher-order MTS schemes. In addition, we demonstrate how to adapt the stability contour of the non-stiff time-integration to the physical system at hand. This allows significantly larger timesteps when compared to previously known multistep MTS approaches. As an example, we derive novel predictor-corrector (PCMTS) schemes specifically optimized for the time-integration of damped wave equations on locally refined meshes. In a set of numerical experiments, we demonstrate the performance of our scheme on discontinuous Galerkin time-domain (DGTD) simulations of Maxwell’s equations.
Liposomes in drug delivery: Progress and limitations

Liposomes are microparticulate lipoidal vesicles which are under extensive investigation as drug carriers for improving the delivery of therapeutic agents. Due to new developments in liposome technology, several liposomebased drug formulations are currently in clinical trial, and recently some of them have been approved for clinical use. Reformulation of drugs in liposomes has provided an opportunity to enhance the therapeutic indices of various agents mainly through alteration in their biodistribution. This review discusses the potential applications of liposomes in drug delivery with examples of formulations approved for clinical use, and the problems associated with further exploitation of this drug delivery system.
Optimal Space and Time Complexity Analysis on the Lattice of Cuboids Using Galois Connections for Data Warehousing

In this paper, an optimal aggregation and counter-aggregation (drill-down) methodology is proposed on multidimensional data cube. The main idea is to aggregate on smaller cuboids after partitioning those depending on the cardinality of the individual dimensions. Based on the operations to make these partitions, a Galois Connection is identified for formal analysis that allow to guarantee the soundness of optimizations of storage space and time complexity for the abstraction and concretization functions defined on the lattice structure. Our contribution can be seen as an application to OLAP operations on multidimensional data model in the Abstract Interpretation framework.
Learning a Generative Model for Validity in Complex Discrete Structures

Deep generative models have been successfully used to learn representations for high-dimensional discrete spaces by representing discrete objects as sequences and employing powerful sequence-based deep models. Unfortunately, these sequencebased models often produce invalid sequences: sequences which do not represent any underlying discrete structure; invalid sequences hinder the utility of such models. As a step towards solving this problem, we propose to learn a deep recurrent validator model, which can estimate whether a partial sequence can function as the beginning of a full, valid sequence. This validator provides insight as to how individual sequence elements influence the validity of the overall sequence, and can be used to constrain sequence based models to generate valid sequences – and thus faithfully model discrete objects. Our approach is inspired by reinforcement learning, where an oracle which can evaluate validity of complete sequences provides a sparse reward signal. We demonstrate its effectiveness as a generative model of Python 3 source code for mathematical expressions, and in improving the ability of a variational autoencoder trained on SMILES strings to decode valid molecular structures.
Model for the Findings about Hologram Generating Properties of DNA

A TGD inspired model for the strange replica structures observed when DNA sample is radiated by red, IR, and UV light using two methods by Peter Gariaev and collaborators. The first method produces what is tentatively interpreted as replica images of either DNA sample or of five red lamps used to irradiate the sample. Second method produce replica image of environment with replication in horizontal direction but only at the right hand side of the apparatus. Also a white phantom variant of the replica trajectory observed in the first experiment is observed and has in vertical direction the size scale of the apparatus. A model is developed in order to explain the characteristic features of the replica patterns. The basic notions are magnetic body, massless extremal (topological light ray), the existence of Bose-Einstein condensates of Cooper pairs at magnetic flux tubes, and dark photons with large value of Planck constant for which macroscopic quantum coherence is possible. The hypothesis is that the first method makes part of the magnetic body of DNA sample visible whereas method II would produce replica hologram of environment using dark photons and produce also a phantom image of the magnetic tubes becoming visible by method I. Replicas would result as mirror hall effect in the sense that the dark photons would move back and forth between the part of magnetic body becoming visible by method I and serving as a mirror and the objects of environment serving also as mirrors. What is however required is that not only the outer boundaries of objects visible via ordinary reflection act as mirrors but also the parts of the outer boundary not usually visible perform mirror function so that an essentially 3-D vision providing information about the geometry of the entire object would be in question. Many-sheeted space-time allows this. The presence of the hologram image for method II requires the self-sustainment of the reference beam only whereas the presence of phantom DNA image for method I requires the self-sustainment of both beams. Non-linear dynamics for the energy feed from DNA to the magnetic body could make possible self-sustainment for both beams simultaneously. Nonlinear dynamics for beams themselves could allow for the self-sustainment of reference beam and/or reflected beam. The latter option is favored by data.
Subspace Pursuit for Compressive Sensing Signal Reconstruction

We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same order as that of linear programming (LP) optimization methods. The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter. In the noisy setting and in the case that the signal is not exactly sparse, it can be shown that the mean-squared error of the reconstruction is upper-bounded by constant multiples of the measurement and signal perturbation energies.
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates, and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. An algorithm for intra-patient lesion matching is proposed and validated with experiments.
A Low-Power Robust Easily Cascaded PentaMTJ-Based Combinational and Sequential Circuits

Advanced computing systems embed spintronic devices to improve the leakage performance of conventional CMOS systems. High speed, low power, and infinite endurance are important properties of magnetic tunnel junction (MTJ), a spintronic device, which assures its use in memories and logic circuits. This paper presents a PentaMTJ-based logic gate, which provides easy cascading, self-referencing, less voltage headroom problem in precharge sense amplifier and low area overhead contrary to existing MTJ-based gates. PentaMTJ is used here because it provides guaranteed disturbance free reading and increased tolerance to process variations along with compatibility with CMOS process. The logic gate is validated by simulation at the 45-nm technology node using a VerilogA model of the PentaMTJ.
Thrombus formation in vivo.

To examine thrombus formation in a living mouse, new technologies involving intravital videomicroscopy have been applied to the analysis of vascular windows to directly visualize arterioles and venules. After vessel wall injury in the microcirculation, thrombus development can be imaged in real time. These systems have been used to explore the role of platelets, blood coagulation proteins, endothelium, and the vessel wall during thrombus formation. The study of biochemistry and cell biology in a living animal offers new understanding of physiology and pathology in complex biologic systems.
Moving camera background-subtraction for obstacle detection on railway tracks

We propose a method for detecting obstacles by comparing input and reference train frontal view camera images. In the field of obstacle detection, most methods employ a machine learning approach, so they can only detect pre-trained classes, such as pedestrian, bicycle, etc. This means that obstacles of unknown classes cannot be detected. To overcome this problem, we propose a background subtraction method that can be applied to moving cameras. First, the proposed method computes frame-by-frame correspondences between the current and the reference (database) image sequences. Then, obstacles are detected by applying image subtraction to corresponding frames. To confirm the effectiveness of the proposed method, we conducted an experiment using several image sequences captured on an experimental track. Its results showed that the proposed method could detect various obstacles accurately and effectively.
BatchDB: Efficient Isolated Execution of Hybrid OLTP+OLAP Workloads for Interactive Applications

In this paper we present BatchDB, an in-memory database engine designed for hybrid OLTP and OLAP workloads. BatchDB achieves good performance, provides a high level of data freshness, and minimizes load interaction between the transactional and analytical engines, thus enabling real time analysis over fresh data under tight SLAs for both OLTP and OLAP workloads.
 BatchDB relies on primary-secondary replication with dedicated replicas, each optimized for a particular workload type (OLTP, OLAP), and a light-weight propagation of transactional updates. The evaluation shows that for standard TPC-C and TPC-H benchmarks, BatchDB can achieve competitive performance to specialized engines for the corresponding transactional and analytical workloads, while providing a level of performance isolation and predictable runtime for hybrid workload mixes (OLTP+OLAP) otherwise unmet by existing solutions.
Stacked Square Microstrip Antenna for 4G System

A single line feed stacked microstrip antenna for 4G system is presented. The proposed antenna with two properly square patches are stacked. The top patch can perform as a driven element is design on 2.44 GHz and lower patch is also design on 2.44 GHz. The performance of proposed antenna for 4G band frequency (2400-2500 MHz). Also gating the improvement of bandwidth (15%) and antenna efficiency (95%) are very high compared to conventional antenna. Key word — Microstrip patch antenna; stacked, 4G, Antenna efficiency.
Sex differences in cooperation: a meta-analytic review of social dilemmas.

Although it is commonly believed that women are kinder and more cooperative than men, there is conflicting evidence for this assertion. Current theories of sex differences in social behavior suggest that it may be useful to examine in what situations men and women are likely to differ in cooperation. Here, we derive predictions from both sociocultural and evolutionary perspectives on context-specific sex differences in cooperation, and we conduct a unique meta-analytic study of 272 effect sizes-sampled across 50 years of research-on social dilemmas to examine several potential moderators. The overall average effect size is not statistically different from zero (d = -0.05), suggesting that men and women do not differ in their overall amounts of cooperation. However, the association between sex and cooperation is moderated by several key features of the social context: Male-male interactions are more cooperative than female-female interactions (d = 0.16), yet women cooperate more than men in mixed-sex interactions (d = -0.22). In repeated interactions, men are more cooperative than women. Women were more cooperative than men in larger groups and in more recent studies, but these differences disappeared after statistically controlling for several study characteristics. We discuss these results in the context of both sociocultural and evolutionary theories of sex differences, stress the need for an integrated biosocial approach, and outline directions for future research.
DYNAMICS OF LAND-USE AND LANDCOVER CHANGE IN TROPICAL REGIONS

■ Abstract We highlight the complexity of land-use/cover change and propose a framework for a more general understanding of the issue, with emphasis on tropical regions. The review summarizes recent estimates on changes in cropland, agricultural intensification, tropical deforestation, pasture expansion, and urbanization and identifies the still unmeasured land-cover changes. Climate-driven land-cover modifications interact with land-use changes. Land-use change is driven by synergetic factor combinations of resource scarcity leading to an increase in the pressure of production on resources, changing opportunities created by markets, outside policy intervention, loss of adaptive capacity, and changes in social organization and attitudes. The changes in ecosystem goods and services that result from land-use change feed back on the drivers of land-use change. A restricted set of dominant pathways of land-use change is identified. Land-use change can be understood using the concepts of complex adaptive systems and transitions. Integrated, place-based research on land-use/land-cover change requires a combination of the agent-based systems and narrative perspectives of understanding. We argue in this paper that a systematic analysis of local-scale land-use change studies, conducted over a range of timescales, helps to uncover general principles that provide an explanation and prediction of new land-use changes.
Probabilistic data association for semantic SLAM

Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Furthermore, loop closure recognition based on low-level features is often viewpoint-dependent and subject to failure in ambiguous or repetitive environments. On the other hand, object recognition methods can infer landmark classes and scales, resulting in a small set of easily recognizable landmarks, ideal for view-independent unambiguous loop closure. In a map with several objects of the same class, however, a crucial data association problem exists. While data association and recognition are discrete problems usually solved using discrete inference, classical SLAM is a continuous optimization over metric information. In this paper, we formulate an optimization problem over sensor states and semantic landmark positions that integrates metric information, semantic information, and data associations, and decompose it into two interconnected problems: an estimation of discrete data association and landmark class probabilities, and a continuous optimization over the metric states. The estimated landmark and robot poses affect the association and class distributions, which in turn affect the robot-landmark pose optimization. The performance of our algorithm is demonstrated on indoor and outdoor datasets.
Classifying Conversation in Digital Communication

Many studies of digital communication, in particular of Twitter, use natural language processing (NLP) to find topics, assess sentiment, and describe user behaviour. In finding topics often the relationships between users who participate in the topic are neglected. We propose a novel method of describing and classifying online conversations using only the structure of the underlying temporal network and not the content of individual messages. This method utilises all available information in the temporal network (no aggregation), combining both topological and temporal structure using temporal motifs and inter-event times. This allows us create an embedding of the temporal network in order to describe the behaviour of individuals and collectives over time and examine the structure of conversation over multiple timescales.