nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of a concentric coplanar capacitive sensor for nondestructive evaluation of multi-layered dielectric structures

A concentric coplanar capacitive sensor is analyzed for the quantitative characterization of material properties for multi-layered dielectrics. The sensor output signal, transcapacitance CT, is related to the thickness and dielectric constant of each layer of the material under test. Electrostatic Green's functions due to point charges over different dielectric structures are derived utilizing the Hankel transform given the cylindrical symmetry of the proposed sensor. Numerical implementations based on the Green's functions are presented. The sensor electrodes are divided into a number of circular filaments, and the sensor surface charge distribution is then calculated using the method of moments (MoM). From the sensor surface charge, CT is calculated. Numerical calculations on sensor optimization are conducted and show that normalized CT as a function of sensor configuration is determined solely by its own relative dimensions, regardless of the overall dimensions of the sensor. In addition, calculations indicate how the sensor can be optimized for sensitivity to changes in core permittivity of a three-layer test-piece such as an aircraft radome. Benchmark experiment results are provided for one, two-, and three-layer test-pieces and very good agreement with calculated CT is observed. The sensor is also applied to water ingression measurements in a sandwich structure resembling the aircraft radome, in which the water-injected area can be successfully detected from the sensor output signal.
Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector  $\ell _{p}$ -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the  $\ell _{p}$ -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.
Application of genetic algorithm–PLS for feature selection in spectral data sets

After suitable modifications, genetic algorithms can be a useful tool in the problem of wavelength selection in the case of a multivariate calibration performed by PLS. Unlike what happens with the majority of feature selection methods applied to spectral data, the variables selected by the algorithm often correspond to well-defined and characteristic spectral regions instead of being single variables scattered throughout the spectrum. This leads to a model having a better predictive ability than the full-spectrum model; furthermore, the analysis of the selected regions can be a valuable help in understanding which are the relevant parts of the spectra. After the presentation of the algorithm, several real cases are shown.
Image augmentation by blocky artifact in Deep Convolutional Neural Network for handwritten digit recognition

Deep Convolutional Neural Networks - also known as DCNN - are powerful models for different visual pattern classification problems. Many works in this field use image augmentation at the training phase to achieve better accuracy. This paper presents blocky artifact as an augmentation technique to increase the accuracy of DCNN for handwritten digit recognition, both English and Bangla digits, i.e., 0–9. This paper conducts a number of experiments on three different datasets: MNIST Dataset, CMATERDB 3.1.1 Dataset and Indian Statistical Institute (ISI) Dataset. For each dataset, DCNNs with the proposed augmentation technique give better results than those without such augmentation. Unsupervised pre-training with the blocky artifact achieves 99.56%, 99.83% and 99.35% accuracy respectively on MNIST, CMATERDDB and ISI datasets producing, in the process, so far the best accuracy rate for CMATERDB and ISI datasets.
Series Connection of IGBT ’ s with Active Voltage Balancing

This paper describes an active gate drive circuit for series-connected insulated gate bipolar transistors (IGBT’s) with voltage balancing in high-voltage applications. The gate drive circuit not only amplifies the gate signal, but also actively limits the overvoltage during switching transients, while minimizing the switching transients and losses. In order to achieve the control objective, an analog closed-loop control scheme is adopted. The closed-loop control injects current to an IGBT gate as required to limit the IGBT collector–emitter voltage to a predefined level. The performance of the gate drive circuit is examined experimentally by the series connection of three IGBT’s with conventional snubber circuits. The experimental results show the voltage balancing by an active control with wide variations in loads and imbalance conditions.
LTE in the sky: trading off propagation benefits with interference costs for aerial nodes

The popularity of unmanned aerial vehicles has exploded over the last few years, urgently demanding solutions to transfer large amounts of data from the UAV to the ground. Conversely, a control channel to the UAV is desired, in order to safely operate these vehicles remotely. This article analyzes the use of LTE for realizing this downlink data and uplink control. By means of measurements and simulations, we study the impact of interference and path loss when transmitting data to and from the UAV. Two scenarios are considered in which UAVs act as either base stations transmitting in downlink or UEs transmitting in uplink, and their impact on the respective downlink and uplink performance of an LTE ground network is analyzed. Both measurements and simulations are used to quantify such impact for a range of scenarios with varying altitude, distance from the base station, or UAV density. The measurement sets show that signal-to-interference ratio decreases up to 7 dB for UAVs at 150 m compared to ground users. Simulation results show that a UAV density of 10/km2 gives an average degradation of the signal-to-interference ratio of more than 6 dB. It is concluded that interference is going to be a major limiting factor when LTE enabled UAVs are introduced, and that strong technical solutions will have to be found.
A 0.3–6GHz broadband noise cancelling low noise amplifier

A broadband noise cancelling low noise amplifier (LNA) composed of a CS stage, a single-to-differential (S-to-D) converter, a voltage combiner, a negative feedback network, and a couple of differential-to-single buffers is proposed for multi-standard radio application in this paper. Different from conventional broadband feedback LNA, the proposed LNA shows improved performance parameters including gain, noise figure (NF), 1dB compression point (P1dB), and input-referred third-order intercept point (IIP3), Especially for wideband LNA design. The LNA is fabricated in TSMC 0.13-gm process. From 0.3 to 6GHz, the simulated results show a high-gain of 20 dB, a superior low noise figure of 1.6dB at 3GHz, input power at 1dB compression point (P1dB) of −19.2dBm at 2GHz, it consume 13.2mA from a 1.2V supply and occupies an area of 0.19 mm2.
Discrete Modal Decomposition: a new approach for the reflectance modeling and rendering of real surfaces

Transformation Imaging is a recent technique allowing for the measurement and the modeling of one of the most influential parameters on the appearance of a surface, namely the angular reflectance, thanks to the change in the direction of the lighting during acquisition. From these photometric stereo images (discrete data), the angular reflectance is modeled to allow both interactive and continuous relighting of the inspected surface. Two families of functions, based on polynomials and on hemispherical harmonics, are cited and used in the literature at this aim, respectively, associated to the PTM and HSH techniques. In this paper, we propose a novel method called Discrete Modal Decomposition (DMD) based on a particular and appropriate Eigen basis derived from a structural dynamic problem. The performance of the proposed method is compared with the PTM and HSH results on three real surfaces showing different reflection behaviors. Comparisons are made in terms of both visual rendering and of statistical error (local and global). The obtained results show that the DMD is more efficient in that it allows for a more accurate modeling of the angular reflectance when light–matter interaction is complex such as the presence of shadows, specularities and inter-reflections.
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.
Chapter 2 Pervasive Computing and Healthcare

A confluence of developments has led to the possibility of realizing a vision of pervasive healthcare. These include, but are not limited to, society becoming increasingly mobile, dramatic advances in various areas of technology and computer science, exponentially increasing healthcare costs coupled with workforce issues, the need to provide effective and efficient healthcare, and the change in the makeup of leading diseases most notably the increase in noncommunicable (or chronic) diseases. This is actually a very exciting time in healthcare delivery and one of the major challenges is to prudently adopt and implement appropriate pervasive healthcare solutions. To do this successfully, naturally requires a full appreciation of the key considerations in pervasive computing and healthcare; in particular, an appreciation of network healthcare operations. The objective of this chapter is to provide such a holistic perspective.
WHY SUMMARIES OF RESEARCH ON PSYCHOLOGICAL THEORIES ARE OFTEN UNINTERPRETABLE

—Null hypothesis testing of correlational predictions from weak substantive theories in soft psychology is subject to the influence of ten obfuscating factors whose effects are usually (1) sizeable, (2) opposed, (3) variable, and (4) unknown The net epistemic effect of these ten obfuscating influences is that the usual research literature review is well nigh uninterpretable Major changes in graduate education, conduct of research, and editorial policy are proposed
A Strategic Empty Container Logistics Optimization in a Major Shipping Company

In this paper, we present a system that Compañía Sud Americana de Vapores (CSAV), one of the world’s largest shipping companies, developed to support its decisions for repositioning and stocking empty containers. CSAV’s main business is shipping cargo in containers to clients worldwide. It uses a fleet of about 700,000 TEU containers of different types, which are carried by both CSAV-owned and third-party ships. Managing the container fleet is complex; CSAV must make thousands of decisions each day. In particular, imbalances exist among the regions. For example, China often has a deficit of empty containers and is a net importer; Saudi Arabia often has a surplus and is a net exporter. CSAV and researchers from the University of Chile developed the Empty Container Logistics Optimization System (ECO) to manage this imbalance. ECO’s multicommodity, multiperiod model manages the repositioning problem, whereas an inventory model determines the safety stock required at each location. CSAV uses safety stock to ensure high service levels despite uncertainties, particularly in the demand for containers. A hybrid forecasting system supports both the inventory and the multicommodity network flow model. Major improvements in data gathering, real-time communications, and automation of data handling were needed as input to the models. A collaborative Web-based optimization framework allows agents from different zones to interact in decision making. The use of ECO led to direct savings of $81 million for CSAV, a reduction in inventory stock of 50 percent, and an increase in container turnover of 60 percent. Moreover, the system helped CSAV to become more efficient and to overcome the 2008 economic crisis.
Noise radar using random phase and frequency modulation

Pulse compression radar is used in a great number of applications. Excellent range resolution and high electronic counter-countermeasures performance is achieved by wideband long pulses, which spread out the transmitted energy in frequency and time. By using a random noise waveform, the range ambiguity is suppressed as well. In most applications, the random signal is transmitted directly from a noise-generating microwave source. A sine wave, which is phase or frequency modulated by random noise, is an alternative, and in this paper, the ambiguity function and the statistical characteristics of the correlation output for the latter configuration are further analyzed. Range resolution is then improved because the noise bandwidth of the modulated carrier is wider than that of the modulating signal, and the range sidelobes are also further suppressed. Random biphase modulation gives a 4-dB (/spl pi//sup 2//4) improvement, but much higher sidelobe suppression could be achieved using continuous phase/frequency modulation. Due to the randomness of the waveform, the output correlation integral is accompanied by a noise floor, which limits the possible sidelobe suppression as determined by the time-bandwidth product. In synthetic aperture radar (SAR) applications with distributed targets, this product should be large compared with the number of resolution elements inside the antenna main beam. The advantages of low range sidelobes and enhanced range resolution make frequency/phase-modulated noise radar attractive for many applications, including SAR mapping, surveillance, altimetry, and scatterometry. Computer algorithms for reference signal delay and compression are discussed as replacements for the classical delay line implementation.
Baluns , and Detuning Elements in MRI RF coils

T/R switches, baluns and detuning elements are three major devices to reduce RF/EM couplings among receive RF coils, transmit RF coils and MRI system environment around RF coils in MRI system. Inadequate designs on these parts often lead to unnecessary high transmit power consumption, noisy NMR signal and the RF heating issues. In this paper we discuss the basic functions, theories and commonly used types of these three devices. Figure 1 shows a diagram of typical transmit/receive coil with these devices.
On the Logic and Purpose of Significance Testing

There has been much recent attention given to the problems involved with the traditional approach to null hypothesis significance testing (NHST). Many have suggested that, perhaps, NHST should be abandoned altogether in favor of other bases for conclusions such as confidence intervals and effect size estimates (e.g., Schmidt, 1996). The purposes of this article are to (a) review the function that data analysis is supposed to serve in the social sciences, (b) examine the ways in which these functions are performed by NHST, (c) examine the case against NHST, and (d) evaluate interval-based estimation as an alternative to NHST.
Pharmacological considerations for predicting PK/PD at the site of action for therapeutic proteins.

For therapeutic proteins whose sites of action are distal to the systemic circulation, both drug and target concentrations at the tissue sites are not necessarily proportional to those in systemic circulation, highlighting the importance of understanding pharmacokinetic/pharmacodynamic (PK/PD) relationship at the sites of action. This review summarizes the pharmacological considerations for predicting local PK/PD and the importance of measuring PK and PD at site of action. Three case examples are presented to show how mechanistic and physiologically based PK/PD (PBPK/PD) models which incorporated the PK and PD at the tissue site can be used to facilitate understanding the exposure-response relationship for therapeutic proteins.
Spaceborne bi-and multistatic SAR : potential and challenges

Biand multistatic synthetic aperture radar (SAR) operates with distinct transmit and receive antennas that are mounted on separate platforms. Such a spatial separation has several operational advantages, which will increase the capability, reliability and flexibility of future SAR missions. Various spaceborne biand multistatic SAR configurations are introduced, and their potential for different applications such as frequent monitoring, wide-swath imaging, scene classification, single pass cross-track interferometry and resolution enhancement is compared. Furthermore, some major challenges such as phase and time synchronisation, biand multistatic SAR processing, satellite orbit selection and relative position sensing are addressed.
A Next-Generation Secure Cloud-Based Deep Learning License Plate Recognition for Smart Cities

License Plate Recognition System (LPRS) plays a vital role in smart city initiatives such as traffic control, smart parking, toll management and security. In this article, a cloud-based LPRS is addressed in the context of efficiency where accuracy and speed of processing plays a critical role towards its success. Signature-based features technique as a deep convolutional neural network in a cloud platform is proposed for plate localization, character detection and segmentation. Extracting significant features makes the LPRS to adequately recognize the license plate in a challenging situation such as i) congested traffic with multiple plates in the image ii) plate orientation towards brightness, iii) extra information on the plate, iv) distortion due to wear and tear and v) distortion about captured images in bad weather like as hazy images. Furthermore, the deep learning algorithm computed using bare-metal cloud servers with kernels optimized for NVIDIA GPUs, which speed up the training phase of the CNN LPDS algorithm. The experiments and results show the superiority of the performance in both recall and precision and accuracy in comparison with traditional LP detecting systems.
Periodontal diseases.

The periodontal diseases are highly prevalent and can affect up to 90% of the worldwide population. Gingivitis, the mildest form of periodontal disease, is caused by the bacterial biofilm (dental plaque) that accumulates on teeth adjacent to the gingiva (gums). However, gingivitis does not affect the underlying supporting structures of the teeth and is reversible. Periodontitis results in loss of connective tissue and bone support and is a major cause of tooth loss in adults. In addition to pathogenic microorganisms in the biofilm, genetic and environmental factors, especially tobacco use, contribute to the cause of these diseases. Genetic, dermatological, haematological, granulomatous, immunosuppressive, and neoplastic disorders can also have periodontal manifestations. Common forms of periodontal disease have been associated with adverse pregnancy outcomes, cardiovascular disease, stroke, pulmonary disease, and diabetes, but the causal relations have not been established. Prevention and treatment are aimed at controlling the bacterial biofilm and other risk factors, arresting progressive disease, and restoring lost tooth support.
T-KVM : A Trusted architecture for KVM ARM v 7 and v 8 Virtual Machines Securing Virtual Machines by means of KVM , TrustZone , TEE and SELinux

The first market release of Advanced RISC Machines (ARM) v8 System on Chips (SoCs) has created big expectations from smart devices, servers and network equipment vendors, who see compelling advantages in integrating them into their systems. As a consequence software stack deployments for ARMv8 platforms translate market requirements to support OpenStack, Network Functions Virtualization (NFV), Mobile Edge Computing (MEC), In-Vehicle Infotainment (IVI) automotive functions. At the same time, ARMv8 will empower Internet of Things (IoT), Cyber Physical Systems (CPS) and user convergence devices. In this context, virtualization is a key feature to enable the cloud delivery model, to implement multitenancy, to isolate different execution environments and to improve hardware/software standardization and consolidation. Since guaranteeing a strict ownership of both the data and the code executed in Virtual Machines (VMs), which belong to governments, companies, telecom operators and private users, counts more than ever, the security of the hypervisor and its guests has become dramatically important. In this paper, Trusted Kernel-based Virtual Machine (T-KVM), a novel security architecture for the KVM-on-ARM hypervisor, is proposed to satisfy the current market trend. T-KVM integrates software and hardware components to secure guest Operating Systems (OSes) and enable Trusted Computing in ARM virtual machines. The proposed architecture combines four isolation layers: ARM Virtualization and Security Extensions (also known as ARM VE and TrustZone), GlobalPlatform Trusted Execution Environment (TEE) APIs and SELinux Mandatory Access Control (MAC) security policy. The T-KVM architecture can be implemented on platforms based on ARM v7 and v8 architectures, without requiring additional custom hardware extensions, since, starting from Cortex-A15 (ARM v7 architecture) released in 2012, both the ARM VE and TrustZone are made available. In this paper the T-KVM architecture is described in details, as well as its key implementation challenges and system security considerations. Lastly, a performance evaluation of the proposed solution is presented.  TrustZone, ARM Virtualization, SELinux, TEE, ARM VMs Security.