Miracle-dz/UniFAR-FTUs huggingface dataset:
Superposition Synthesis Method for 2-D Shaped-Beam Array Antenna

In this paper, a synthesis method for a shaped-beam pattern of the 2-D array antenna is proposed. The method is based on the array scanning principle and the superposition principle. The method can limit the ripple of the main beam by adjusting the sampling point interval and can easily achieve low sidelobe level (SLL). Compared to the stochastic optimization methods, this method has obvious time advantage when it is used to synthesize a large array antenna. The factors that affect the SLL, ripple, and dynamic range ratio (DRR) are discussed. Sparse array is used to reduce DRR.
Adversarial Defense by Stratified Convolutional Sparse Coding

We propose an adversarial defense method that achieves state-of-the-art performance among attack-agnostic adversarial defense methods while also maintaining robustness to input resolution, scale of adversarial perturbation, and scale of dataset size. Based on convolutional sparse coding, we construct a stratified low-dimensional quasi-natural image space that faithfully approximates the natural image space while also removing adversarial perturbations. We introduce a novel Sparse Transformation Layer (STL) in between the input image and the first layer of the neural network to efficiently project images into our quasi-natural image space. Our experiments show state-of-the-art performance of our method compared to other attack-agnostic adversarial defense methods in various adversarial settings.
Numerical Modeling of Ultrawideband Propagation Along a Wind Turbine Blade

Full-wave numerical analysis of an ultrawideband wireless link in frequency band 3–5 GHz along a 37.3 m long wind turbine blade is presented. The method used for the analysis is the well-established finite-difference time-domain (FDTD) method with staggered Yee mesh. Simulated results are compared to data obtained from measurement on a real blade, in two experiments involving antennas transmitting both from outside near the blade tip and from inside the blade. In the first experiment, when the wave is propagated along the entire blade and received near the blade root, the differences between the simulation and the measurement are found in pulse magnitudes within 3 dB and in delay within 1.5 ns. In the second experiment, the emitted waves are studied at only 10 m distance, but at higher elevations, and the error reaches 6 dB in magnitude and 1.9 ns in delay. Possible reasons for observed discrepancies between the simulations and the measurements are briefly discussed. Despite the long distances involved and challenges connected to the numerical dispersion and anisotropy, the FDTD method turns out to be a feasible choice for full-wave numerical modeling of problems of this type, albeit with slightly high sensitivity to the underlying model.
How Formal Governance Affects Multisourcing Success: A Multi-level Perspective

Multisourcing has become a common sourcing model in recent outsourcing practice. Yet, the extant and relevant IS literature has so far offered limited insight into how to stipulate both individual (i.e., individual vendor) and joint (the entire vendor network) performance while ensuring governance efficiency. Our study set about addressing this gap by examining how these three dimensions of multisourcing success can be achieved through formal governance. Specifically, we considered bilateral formal control, collective formal control and conflict arbitration (among vendors) as key formal governance elements. Results from a pan-European survey of client firms pursuing multisourcing projects show that bilateral formal control sets the stage to achieve both individual and joint performance, while conflict arbitration strengthens individual performance, and collective formal control strengthens joint performance. Governance efficiency is improved when both collective formal control and conflict arbitration are high. We also found that conflict arbitration strengthens the positive effect of collective formal control on both individual and joint performance. Our findings highlight the importance of governing inter-vendor relationships in multisourcing arrangements as opposed to relying solely on bilateral governance. Our study extends the limited literature on IS multisourcing, and assists managers in considering the strategies they wish to pursue when choosing appropriate governance mechanisms.
TextureNet: Consistent Local Parametrizations for Learning From High-Resolution Signals on Meshes

We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e.g., color texture maps). The key idea is to utilize a 4-rotational symmetric(4-RoSy) field to define a domain for convolution on a surface. Though 4-RoSy fields have several properties favor-able for convolution on surfaces (low distortion, few singularities, consistent parameterization, etc.), orientations are ambiguous up to 4-fold rotation at any sample point. So, we introduce a new convolutional operator invariant to the4-RoSy ambiguity and use it in a network to extract features from high-resolution signals on geodesic neighborhoods of a surface. In comparison to alternatives, such as PointNet-based methods which lack a notion of orientation, the coherent structure given by these neighborhoods results in significantly stronger features. As an example application, we demonstrate the benefits of our architecture for 3D semantic segmentation of textured 3D meshes. The results show that our method outperforms all existing methods on the basis of mean IoU by a significant margin in both geometry-only(6.4%) and RGB+Geometry (6.9-8.2%) settings.
Emergence of Data and Non-Data Team Networks: An Agent-Based Model

Research on the organizational impact of business analytics (BA) systems has mainly focused on the identification of analytics resources and capabilities in isolation and linking them directly or indirectly to business value. In contrast, achieving value from data assets in real organizational settings requires data teams to form connections with other organizational (non-data) teams so that the teams can work together and utilize complementary and shared resources. This paper focuses on the embeddedness of data and non-data teams within an organizational network, and it examines how the two teams connect and collectively influence organizational performance. For that, we built an agent-based computational model in which data and non-data teams solve organizational problems under dynamic organizational structures and team-level learning. In our model, data and non-data teams (i) simultaneously search for satisfying solutions over a complex space (i.e. an NK landscape), (ii) are initially connected to each other through a given network configuration, (iii) are endowed with learning capabilities (through a reinforcement learning algorithm), and (iv) update their links to other agents (i.e. create new connections or disconnect existing ones) according to their learning capabilities. Results reveal conditions under which performance differences are obtained, considering variations in the number of agents, space complexity, agents' screening capabilities and reinforcement learning.
A Wide Beamwidth and Wideband Magnetoelectric Dipole Antenna

In this paper, a wideband magnetoelectric (ME) dipole antenna with wide beamwidth both in the E-plane and H-plane is investigated. The proposed antenna, which is a differential one, has a simple structure and can realize wide beamwidth in the E-plane and H-plane in whole operating bandwidth. This antenna is easy to manufacture and low cost. The antenna consists of a pair of <inline-formula> <tex-math notation="LaTeX">$\Gamma$ </tex-math></inline-formula>-shaped metal plates, a <inline-formula> <tex-math notation="LaTeX">$\Gamma$ </tex-math></inline-formula>-shaped fed line, and six metal columns. The <inline-formula> <tex-math notation="LaTeX">$\Gamma$ </tex-math></inline-formula>-shaped metal plates and metal columns change the current distribution which changes the radiation performance of the proposed antenna. Some parameters, which effect on the performances of the antenna, are studied. The proposed antenna achieves the impedance bandwidth (≤−10 dB) of about 81.1% (3.3–7.8 GHz). The half-power beamwidth (HPBW) of the proposed antenna is obviously broadened. The HPBW is 215° in the E-plane and 186° in the H-plane at 5.5 GHz. Besides, the cross polarization of the antenna is very low. The antenna is fabricated and measured to verify the design. The measured results are in good agreements with the simulated one. Good radiation characteristics of the new ME dipole antenna have been obtained in the whole frequency band.
Plug-and-play Commissionable Models for Water Networks with Multiple Inlets*

Water scarcity is an increasing problem world-wide, yet at the same time huge amounts of water are lost through leakages in the distribution network. Fortunately, improved pressure control can lower the leakage problems. In this work, water networks with multiple pressure actuators and several consumers are considered. We show that a simple model mapping from the actuator pressures and flows to measured pressures inside the network can be derived, when the actuation is restricted to a well-defined subspace. This is shown under mild assumptions on the consumption pattern and hydraulic resistances of pipes. Numerical results of a real-life case-study comparing the output of an EPANETl model with the output of the proposed reduced network model support the theoretical results.
Does One Size Fit All? Theorizing Governance Configurations for Digital Innovation

Organizations from nearly every industry face critical challenges of organizing for successful digital innovation. While the notion of digital innovation management has received increasing interest, prior IS research has offered limited insights into the effectiveness of specific organizing structures and mechanisms, such as the allocation of decision rights or the coordination between IS and business units. In this study, we combine an IT governance perspective with configurational theory to explore digital innovation governance configurations that achieve high performance. We employ fuzzyset qualitative comparative analysis (fsQCA), a method that accommodates mutual causality, discontinuity and equifinality, to analyze survey data from a sample of U.S. IT managers. The analysis reveals six different configurations that produce high digital innovation performance. We use these results to derive theoretical insights as configurational propositions for further research, and discuss their practical implications.
Two-Segment Three-Phase PMSM Drive With Carrier Phase-Shift PWM for Torque Ripple and Vibration Reduction

This paper presents a two-segment motor system powered by two inverters with phase-shift pulsewidth modulation (PWM) to fulfill the purpose of low-torque ripple and vibration noise. Different from conventional multi-inverter structure, the inverters for this type of machine are electrically isolated between each other. First, the concept of multisegment machine is presented and the basic operation principle is analyzed with finite-element analysis (FEA) method. Then vibration caused by the switching device in machine is investigated and the control model of a two-segment motor is developed. Further, the principle of carrier phase-shift PWM is illustrated with the vibration analysis. Carrier phase shift can generate interleaved torque and force on the rotor for the two groups of windings in the machine and cancel each other for certain harmonics. Then simulation of control strategy for this type of machine has been done in MATLAB. Finally, based on the concept and principles mentioned before, a two-segment motor with its drive system is fabricated. Both the test of torque ripple and the vibration of the motor have been significantly improved. Compared with a normal three-phase motor driven by a paralleled inverter with interleaving, the proposed approach is with comparable vibration reduction but without using extra coupled inductors.
Neuroprotective Mechanisms of Resveratrol in Alzheimer's Disease: Role of SIRT1

Alzheimer's disease (AD) is a progressive and neurodegenerative disorder of the cortex and hippocampus, which eventually leads to cognitive impairment. Although the etiology of AD remains unclear, the presence of β-amyloid (Aβ) peptides in these learning and memory regions is a hallmark of AD. Therefore, the inhibition of Aβ peptide aggregation has been considered the primary therapeutic strategy for AD treatment. Many studies have shown that resveratrol has antioxidant, anti-inflammatory, and neuroprotective properties and can decrease the toxicity and aggregation of Aβ peptides in the hippocampus of AD patients, promote neurogenesis, and prevent hippocampal damage. In addition, the antioxidant activity of resveratrol plays an important role in neuronal differentiation through the activation of silent information regulator-1 (SIRT1). SIRT1 plays a vital role in the growth and differentiation of neurons and prevents the apoptotic death of these neurons by deacetylating and repressing p53 activity; however, the exact mechanisms remain unclear. Resveratrol also has anti-inflammatory effects as it suppresses M1 microglia activation, which is involved in the initiation of neurodegeneration, and promotes Th2 responses by increasing anti-inflammatory cytokines and SIRT1 expression. This review will focus on the antioxidant and anti-inflammatory neuroprotective effects of resveratrol, specifically on its role in SIRT1 and the association with AD pathophysiology.
Geochemistry of Noble Gases and CO2 in Fluid Inclusions From Lithospheric Mantle Beneath Wilcza Góra (Lower Silesia, Southwest Poland)

Knowledge of the products originating from the subcontinental lithospheric mantle (SCLM) is crucial for constraining the geochemical features and evolution of the mantle. This study investigated the chemistry and isotope composition (noble gases and CO2) of fluid inclusions (FI) from selected mantle xenoliths originating from Wilcza Gora (Lower Silesia, southwest Poland), with the aim of integrating their petrography and mineral chemistry. Mantle xenoliths are mostly harzburgites and sometimes bear amphiboles, and are brought to the surface by intraplate alkaline basalts that erupted outside the north-easternmost part of the Eger (Ohře) Rift in Lower Silesia. Olivine (Ol) is classified into two groups based on its forsterite content: (1) Fo88.9–91.5, which accounts for a fertile-to-residual mantle, and (2) Fo85.5–88.1, which indicates large interactions with circulating (basic) melts. This dichotomy is also related to orthopyroxene (Opx) and clinopyroxene (Cpx), which show two ranges of Mg# values (87–90 and 91–93, respectively) and clear evidence of recrystallization. CO2 predominates within FI, followed by N2. The δ13C of mantle CO2 varies between –4.7‰ and –3.1‰, which mostly spans the MORB range (−8‰ < δ13C < −4‰). The 3He/4He ratio is 6.7–6.9 Ra in Cpx, 6.3–6.8 Ra in Opx, and 5.9–6.2 Ra in Ol. These values are within the range proposed for European SCLM (6.3±0.3 Ra). The decrease in 3He/4He from Cpx to Ol is decoupled from the He concentration, and excludes any diffusive fractionation from FI. The chemistry of FI entrapped in Ol indicates that the mantle is depleted by variable extents of partial melting, while that of Opx and Cpx suggests the overprinting of at least one metasomatic event. According to Matusiak-Malek et al. (2017), Cpx, Opx, and amphiboles were added to the original harzburgite by carbonated hydrous silicate melt related to Cenozoic volcanism. This process resulted in entrapment of CO2-rich inclusions whose chemical and isotope composition resembles that of metasomatizing fluids. We argue that FI data reflect a mixing between two endmembers: (1) the residual mantle, resulting from partial melting of European SCLM, and (2) the metasomatic agent, which is strongly He-depleted and characterized by MORB-like features.
A Shortly and Densely Connected Convolutional Neural Network for Vehicle Re-identification

In this paper, we propose a shortly and densely connected convolutional neural network (SDC-CNN) for vehicle re-identification. The proposed SDC-CNN mainly consists of short and dense units (SDUs), necessary pooling and normalization layers. The main contribution lies at the design of short and dense connection mechanism, which would effectively improve the feature learning ability. Specifically, in the proposed short and dense connection mechanism, each SDU contains a short list of densely connected convolutional layers and each convolutional layer is of the same appropriate channels. Consequently, the number of connections and the input channel of each convolutional layer are limited in each SDU, and the architecture of SDC-CNN is simple. Extensive experiments on both VeRi and VehicleID datasets show that the proposed SDC-CNN is obviously superior to multiple state-of-the-art vehicle re-identification methods.
Building a Tensor Framework for the Analysis and Classification of Steady-State Visual Evoked Potentials in Children

Steady-state visual evoked potentials (SSVEP) are one of several underlying signals used in various electroencephalography (EEG) based applications, including braincomputer interface (BCI) technology. Through oscillating visual stimulus at distinct frequencies, an SSVEP can be detected by EEG at occipital electrodes on the scalp, with distinct visual stimuli representing distinct choices. Rapid, accurate detection and classification of these signals is crucial for realtime analysis in SSVEP-based applications. However, signal analysis and interpretation of SSVEP events may be hindered in children due to the significant variability in electrophysiological signals throughout development. Recently, multi-way tensors have been shown capable of exploiting higher-order interactions present in the naturally multi-dimensional EEG data. Using tensors as tools to identify latent structures between varying maturational signals thus may provide a potential solution for rapid classification of SSVEP signals in children at different developmental stages. The presented methodology builds upon previous tensor-based SSVEP analysis and extends it for the first time to developing paediatric populations. Results from a binary SSVEP classification task of $n$ = 40 children age 8–11 are reported to be significantly greater than chance, at 67-74% accuracy across multiple training and testing blocks. The findings support that tensor decomposition could provide flexible advantages capable of accommodating developmental differences across children and lay groundwork for future tensor analysis in SSVEP-based applications, like BCIs.
Tangiblizing Your Service: The Role of Visual Cues in Online Group Buying

While past studies have acknowledged that both tangible and intangible features reside in all goods and services, a persistent challenge for online service retailing resides in the difficultly of portraying services online due to the latter’s abstract nature. Drawing on visual rhetoric theory, we advance human facial cues and verbal anchoring as visual cues that can bolster the appeal of tangible elements embedded in the portal image of a given service, which in turn culminates in increased sales. We further examine how the effects of facial cue and verbal anchoring are moderated by consumers’ reliance on the tangible cues of the service. By employing computer vision and deep learning techniques to parse out portal images of over 299,000 local service offerings on a popular Chinese group buying site, we discovered that the presence of facial cues and the richness of verbal anchoring embedded in the portal image of a service offering significantly increase sales. Our results further illustrate that these effects are reinforced by consumers’ perceived importance of service tangibility.
Anomalous Sound Event Detection Based on WaveNet

This paper proposes a new method of anomalous sound event detection for use in public spaces. The proposed method utilizes WaveNet, a generative model based on a convolutional neural network, to model in the time domain the various acoustic patterns which occur in public spaces. When the model detects unknown acoustic patterns, they are identified as anomalous sound events. WaveNet has been used to precisely model a waveform signal and to directly generate it using random sampling in generation tasks, such as speech synthesis. On the other hand, our proposed method uses WaveNet as a predictor rather than a generator to detect waveform segments causing large prediction errors as unknown acoustic patterns. Because WaveNet is capable of modeling detailed temporal structures, such as phase information, of the waveform signals, the proposed method is expected to detect anomalous sound events more accurately than conventional methods based on reconstruction errors of acoustic features. To evaluate the performance of the proposed method, we conduct an experimental evaluation using a real-world dataset recorded in a subway station. We compare the proposed method with the conventional feature-based methods such as an auto-encoder and a long short-term memory network. Experimental results demonstrate that the proposed method outperforms the conventional methods and that the prediction errors of WaveNet can be effectively used as a good metric for unsupervised anomalous detection.
Antecedents and Outcomes of Health Information Sharing: Role of EMR Sourcing

We examine the impact of electronic medical records (EMR) sourcing strategy and vendor selection, as well as participation in health information exchanges (HIE), on the extent of health information sharing. Drawing on seven years of data from a national sample of US hospitals, we find that hospitals which use enterprise and best-of-breed EMR solutions exhibit greater levels of information sharing, compared to hospitals which use self-developed EMR systems. We also observe that HIE participation and regional EMR similarity are positively associated with the level of information sharing. Further, we find that health information sharing improves hospital care outcomes in terms of emergency department efficiency, health expenditures, and quality of care. Our results highlight the role of EMR sourcing and selection in promoting information sharing across healthcare providers, and empirically delineate the underlying mechanisms through which the benefits of health IT on outcomes are realized.
Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach

In this paper we demonstrate the effectiveness of a deep learning approach for object detection and classification using a mono-vision feedback of a NAO humanoid robot for assessing the child’s behavior during a free play with standardized toys. The free play is one of the tasks contained in the standard ADOS-2 autism spectrum disorder diagnostic protocol used by clinicians. In order to make an accurate, robust and fast object detector, a new data set for learning and testing has been created to enable a reliable assessment of the child’s behavior while playing with the toys. This has also led to the development of algorithms and mechanism to assess child’s attention based on the toys that the child is playing with. This paper concludes with the discussion about the challenges encountered and their solutions, as well as about the prospective development goals focused on achieving more robust and accurate child attention analyzer.