Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Breakdown Characteristics of AIGaN/GaN HEMTs with Low- $k$ IHigh- $k$ Double Passivation Layers Paper Title

Two-dimensional analysis of off-state drain current-drain voltage characteristics in AIGaN/GaN high electron mobility transistors is performed; where two cases with a single passivation layer (SiN or high- $k$ dielectric) and double passivation layers (SiN and high- $k$ dielectric) are compared. It is shown that in the case of double passivation layers, the breakdown voltage is enhanced significantly as compared to the case of SiN single passivation layer when comparing at the same insulator thickness. This is because the electric field around the drain edge of gate is weakened. However, it is lowered remarkably as compared to the case with a high- $k$ single passivation layer even if the first SiN layer is rather thin. Also, in the case of double passivation layers, the breakdown voltage is shown to become close to the case with high- $k$ passivation layer when the relative permittivity of the second passivation layer becomes high and the SiN layer is thin.
A Multi-part Convolutional Attention Network for Fine-Grained Image Recognition

The goal of fine-grained image recognition is to recognize hundreds of sub-categories affiliating to the same basic-level category (e.g., bird species). It is a highly challenging task due to the large intra-class variance and small inter-class variance. Existing approaches deal with the subtle difference among object classes via learning and localizing discriminative parts. However, most of the part localization methods follow a step-to-step manner that first localizes larger parts and then generates smaller parts from the larger ones, which is not efficient. In this paper, we present a Multi-part Convolutional Attention Network (M-CAN), which simultaneously focuses on the discriminative image parts at multiple scales. In specific, a convolutional attention based part localization network is presented to localize multi-scale parts from different layers of the deep Convolutional Neural Networks (CNN). Importantly, our part localization network requires no part annotations but only the image labels, which avoids the heavy labor of complex part labeling. We conduct comprehensive experiments and the experimental results show that, our method outperforms the state-of-the-art approaches on three challenging fine-grained datasets, including CUB-Birds, Stanford-Dogs and Stanford-Cars.
FaceLiveNet: End-to-End Networks Combining Face Verification with Interactive Facial Expression-Based Liveness Detection

The effectiveness of the state-of-the-art face verifi-cation/recognition algorithms and the convenience of face recognition greatly boost the face-related biometric authentication applications. However, existing face verification architectures seldom integrate any liveness detection or keep such stage isolated from face verification as if it was irrelevant. This may potentially result in the system being exposed to spoof attacks between the two stages. This work introduces FaceLiveNet, a holistic end-to-end deep networks which can perform face verification and liveness detection simultaneously. An interactive scheme for facial expression recognition is proposed to perform liveness detection, providing better generalization capacity and higher security level. The proposed framework is low-cost as it relies on commodity hardware instead of costly sensors, and lightweight with much fewer parameters comparing to the other popular deep networks such as VGG16 and FaceNet. Experimental results on the benchmarks LFW, YTF, CK+, OuluCASIA, SFEW, FER2013 demonstrate that the proposed FaceLiveNet can achieve state-of-art performance or better for both face verification and facial expression recognition. We also introduce a new protocol to evaluate the global performance for face authentication with the fusion of face verification and interactive facial expression-based liveness detection.
Consensus through Blockchains: Exploring Governance across inter-organizational Settings

The blockchain technology challenges the view on established modes of governance by offering distributed authentication without the need for a central authority, which is well-exemplified by Bitcoin. While the governance of and through Bitcoin is well- accentuated in research, we spotlight impacts on governance which blockchain-based systems bring to inter-organizational settings as well as their purpose. To build our arguments, we explore those impacts on two contrasting cases from the domains of automotive and public administration and relate them to cryptocurrencies. Relying on interviews with experts from said organizations utilizing blockchain technology, and a content analysis of related grey literature, we discuss established forms of governance as well as platforms and infrastructures against the impacts which blockchain-based systems cause. After referring those to the concepts of markets, hierarchies, networks, and tribes, we critically reflect on their purpose by utilizing the notions of infrastructures and platforms, and conclude blockchain-based systems to possibly alter the way established modes of governance are enacted.
Isolation of Helicobacter pylori from Gastric Biopsy of Dyspeptic Patients in Ghana and In Vitro Preliminary Assessment of the Effect of Dissotis rotundifolia Extract on Its Growth

Helicobacter pylori (H. pylori) is a gram-negative bacterium that colonizes the human stomach. Infection with this microaerophilic bacterium causes gastric and duodenal ulcer. This study sought to isolate H. pylori, from gastric biopsy samples of dyspeptic patients in Ghana using a 2,3,5-triphenyltetrazolium chloride (TTC) dye incorporated medium method. This TTC dye method was further used in an antimicrobial susceptibility assay involving Dissotis rotundifolia extract (DRE). H. pylori were successfully isolated from gastric biopsy of dyspeptic patients. Pure cultures of H. pylori in 2,3,5-triphenyltetrazolium chloride (TTC) dye incorporated medium were seen as sparkling colonies. Isolates, identified as H. pylori, were gram-negative and urease, catalase, and oxidase positive and showed characteristic morphology as spiral-shaped bacteria under the microscope. The organisms were found to be susceptible to cephalothin and resistant to nalidixic acid. Above all, the observation that H. pylori grew only at 37°C and not 25°C or 42°C affirms that the bacterium is neither Helicobacter cinaedi nor Helicobacter fenneliae. The anti-H. pylori study depicts a statistically lower zone of inhibition for DRE compared to standard drugs [amoxicillin and clarithromycin] (p<0.05), whereas metronidazole showed no zone of inhibition. This study reports the first successful isolation and culturing of H. pylori in Ghana using TTC dye. It also shows that DRE possess an in vitro anti-H. pylori activity and that DRE has some therapeutic potential against H. pylori infection.
Group B Streptococcus Serotypes in Pregnant Women From the Western Cape Region of South Africa

Background: Maternal colonization of Group B streptococci (GBS) during pregnancy is an important risk factor for neonatal morbidity and mortality. The aim of this study was to determine the prevalence and serotype distribution of GBS isolated from a cohort of pregnant women in the Western Cape, South Africa. Methods: Two ano-vaginal swabs were collected from 301 women at 28–37 weeks of gestation. Participants were recruited from four different antenatal clinics in the Western Cape, South Africa. GBS were detected by culture and PCR and serotypes confirmed by latex agglutination tests. Antibiotic sensitivity was performed using disc diffusion. Results: The GBS colonization rate was 16.6%. Serotype distribution revealed serotype V as the predominant serotype (66.67%) followed by serotype III (21.05%). Serotypes Ia, II, IV, and IX constituted 1.75% each and 3 GBS isolates were non-typeable. Serotype V demonstrated resistance to most of the antibiotics tested, while serotype III demonstrated better susceptibility, except for tetracycline. No significant differences were observed for GBS colonization or serotype distribution according to HIV status. Conclusion: Predominating serotypes differed from those previously reported from other regions in South Africa. Global surveillance of serotype distribution plays an important role in informing vaccine development and antibiotic prophylaxis.
Interpretable Deep Learning under Fire

Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. A plethora of interpretation models have been proposed to help users understand the inner workings of DNNs: how does a DNN arrive at a specific decision for a given input? The improved interpretability is believed to offer a sense of security by involving human in the decision-making process. Yet, due to its data-driven nature, the interpretability itself is potentially susceptible to malicious manipulations, about which little is known thus far. Here we bridge this gap by conducting the first systematic study on the security of interpretable deep learning systems (IDLSes). We show that existing \imlses are highly vulnerable to adversarial manipulations. Specifically, we present ADV^2, a new class of attacks that generate adversarial inputs not only misleading target DNNs but also deceiving their coupled interpretation models. Through empirical evaluation against four major types of IDLSes on benchmark datasets and in security-critical applications (e.g., skin cancer diagnosis), we demonstrate that with ADV^2 the adversary is able to arbitrarily designate an input's prediction and interpretation. Further, with both analytical and empirical evidence, we identify the prediction-interpretation gap as one root cause of this vulnerability -- a DNN and its interpretation model are often misaligned, resulting in the possibility of exploiting both models simultaneously. Finally, we explore potential countermeasures against ADV^2, including leveraging its low transferability and incorporating it in an adversarial training framework. Our findings shed light on designing and operating IDLSes in a more secure and informative fashion, leading to several promising research directions.
Facial Pain Expression Recognition in Real-Time Videos

Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%–55% and an improved processing time of 20%–25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%–100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC‐McMaster Shoulder Pain Database as well.
Loss Calculation and Thermal Analysis for Nine-Phase Flux Switching Permanent Magnet Machine

This paper investigates the accurate loss calculation and thermal analysis for a nine-phase flux switching permanent magnet (FSPM) machine. First, the 3 D finite element method (FEM) model is developed to calculate the PM eddy current loss, in which magnet axial segmentation is taken into account. In order to calculate the iron loss quickly and precisely, the magnetic flux leakage coefficient is proposed in this paper to modify the 2 D calculation model of iron loss. Both the dc-biased magnetic induction and the rotational magnetization are taken into account. Then, the key parameters for thermal modeling, such as the convective heat-transfer coefficient between external frame and ambient, and the coefficient of convection heat transfer in the end region, are derived, based on which a dissymmetric 3 D FEM are used for thermal calculation of nine-phase FSPM machine. Finally, experiments on a prototype machine at the rated generation operation condition are carried out to verify the effectiveness of the developed model.
State/Disturbance Observer and Controller Synthesis for the T–S Fuzzy System With an Enlarged Class of Disturbances

This paper proposes a novel approach for designing the observer and controller for Takagi–Sugeno (T–S) fuzzy systems with an enlarged class of disturbances. The class of the disturbances in this study is significantly enlarged because they do not need to satisfy the constraints such as bounded, generated from an exogenous system, or finite derivative is equal to zero. First, the state/disturbance observer is proposed to estimate the unmeasurable states and disturbances, simultaneously. Then, the controller incorporating with the observer is synthesized to attenuate the disturbances completely and stabilize the T–S fuzzy systems. On the basis of the Lyapunov function and linear matrix inequality technique, the sufficient conditions for the synthesis of the observer and controller are derived in the main theorems. Finally, the effectiveness of the proposed method is verified via two examples.
Cation and anion substitutions in hybrid perovskites: solubility limits and phase stabilizing effects

Organic or inorganic (A) metal (M) halide (X) perovskites (AMX}}$_{\mathbf{3}}$\textbf{) are semiconductor materials setting the basis for the development of highly efficient, low-cost and multijunction solar energy conversion devices. The best efficiencies nowadays are obtained with mixed compositions containing methylammonium, formamidinium, Cs and Rb as well as iodine, bromine and chlorine as anions. The understanding of fundamental properties such as crystal structure and its effect on the band gap, as well as their phase stability is essential. In this systematic study X-ray diffraction and photoluminescense spectroscopy were applied to evaluate structural and optoelectronic properties of hybrid perovskites with mixed compositions.
Impact of Business Analytics on Organizational Outcomes: Role of Analytical Insights and Usage

Even though industry practitioners are making heavy investments in business analytics for informed decision making, research in developing models and theories to explain the value of analytics is still in its nascent stage. Drawing support from the information processing, contingency, resource-based theories, and the analytics process, this study has conceptualized a model to investigate the impact of business analytics on organizational output variables. The model provides an understanding of the value of business analytics through two novel constructs namely ‘analytical insights generation’ and ‘analytical insights usage’. This study plans to develop a comprehensive measurement scale for the two constructs. In this attempt, exploratory factor analysis (EFA) will be conducted to establish the constructs. Later, the empirical validity of the research model will be tested through confirmatory factor analysis. The study expects to contribute to existing literature in terms of theory development and provide practical implications for establishing the value of business analytics.
A Novel Selective JAK2 Inhibitor Identified Using Pharmacological Interactions

The JAK2/STAT signaling pathway mediates cytokine receptor signals that are involved in cell growth, survival and homeostasis. JAK2 is a member of the Janus kinase (JAK) family and aberrant JAK2/STAT is involved with various diseases, making the pathway a therapeutic target. The similarity between the ATP binding site of protein kinases has made development of specific inhibitors difficult. Current JAK2 inhibitors are not selective and produce unwanted side effects. It is thought that increasing selectivity of kinase inhibitors may reduce the side effects seen with current treatment options. Thus, there is a great need for a selective JAK inhibitor. In this study, we identified a JAK2 specific inhibitor. We first identified key pharmacological interactions in the JAK2 binding site by analyzing known JAK2 inhibitors. Then, we performed structure-based virtual screening and filtered compounds based on their pharmacological interactions and identified compound NSC13626 as a potential JAK2 inhibitor. Results of enzymatic assays revealed that against a panel of kinases, compound NSC13626 is a JAK2 inhibitor and has high selectivity toward the JAK2 and JAK3 isozymes. Our cellular assays revealed that compound NSC13626 inhibits colorectal cancer cell (CRC) growth by downregulating phosphorylation of STAT3 and arresting the cell cycle in the S phase. Thus, we believe that compound NSC13626 has potential to be further optimized as a selective JAK2 drug.
Despeckling CNN with Ensembles of Classical Outputs

Ultrasound (US) image despeckling is a problem of high clinical importance. Machine learning solutions to the problem are considered impractical due to the unavailability of speckle-free US image dataset. On the other hand, the classical approaches, which are able to provide the desired outputs, have limitations like input dependent parameter tuning. In this work, a convolutional neural network (CNN) is developed which learns to remove speckle from US images using the outputs of these classical approaches. It is observed that the existing approaches can be combined in a complementary manner to generate an output better than their individual outputs. Thus, the CNN is trained using the individual outputs as well as the output ensembles. It eliminates the cumbersome process of parameter tuning required by the existing approaches for every new input. Further, the proposed CNN is able to outperform the state-of-the-art despeckling approaches and produces the outputs even better than the ensembles for certain images.
Graph Node-Feature Convolution for Representation Learning

Graph convolutional network (GCN) is an emerging neural network approach. It learns new representation of a node by aggregating feature vectors of all neighbors in the aggregation process without considering whether the neighbors or features are useful or not. Recent methods have improved solutions by sampling a fixed size set of neighbors, or assigning different weights to different neighbors in the aggregation process, but features within a feature vector are still treated equally in the aggregation process. In this paper, we introduce a new convolution operation on regular size feature maps constructed from features of a fixed node bandwidth via sampling to get the first-level node representation, which is then passed to a standard GCN to learn the second-level node representation. Experiments show that our method outperforms competing methods in semi-supervised node classification tasks. Furthermore, our method opens new doors for exploring new GCN architectures, particularly deeper GCN models.
Chitosan, a Biopolymer With Triple Action on Postharvest Decay of Fruit and Vegetables: Eliciting, Antimicrobial and Film-Forming Properties

Chitosan is a natural biopolymer from crab shells that is known for its biocompatibility, biodegradability, and bioactivity. In human medicine, chitosan is used as a stabilizer for active ingredients in tablets, and is popular in slimming diets. Due to its low toxicity, it was the first basic substance approved by the European Union for plant protection (Reg. EU 2014/563), for both organic agriculture and integrated pest management. When applied to plants, chitosan shows triple activity: (i) elicitation of host defenses; (ii) antimicrobial activity; and (iii) film formation on the treated surface. The eliciting activity of chitosan has been studied since the 1990’s, which started with monitoring of enzyme activities linked to defense mechanisms (e.g., chitinase, β-1,3 glucanase, phenylalanine ammonia-lyase) in different fruit (e.g., strawberry, other berries, citrus fruit, table grapes). This continued with investigations with qRT-PCR (Quantitative Real-Time Polymerase Chain Reaction), and more recently, with RNA-Seq. The antimicrobial activity of chitosan against a wide range of plant pathogens has been confirmed through many in-vitro and in-vivo studies. Once applied to a plant surface (e.g., dipping, spraying), chitosan forms an edible coating, the properties of which (e.g., thickness, viscosity, gas and water permeability) depend on the acid in which it is dissolved. Based on data in literature, we propose that overall, the eliciting represents 30 to 40% of the chitosan activity, its antimicrobial activity 35 to 45%, and its film-forming activity 20 to 30%, in terms of its effectiveness in the control of postharvest decay of fresh fruit. As well as being used alone, chitosan can be applied together with many other alternatives to synthetic fungicides, to boost its eliciting, antimicrobial and film-forming properties, with additive, and at times synergistic, interactions. Several commercial chitosan formulations are available as biopesticides, with their effectiveness due to the integrated combination of these three mechanisms of action of chitosan.
The Magic of Cinemagraphs: Investigation of Different Image Formats in Online Product Presentation

A cinemagraph is a special type of animation characterized by the contrast and coexistence of motion and stillness due to artificial manipulation. Cinemagraphs have attracted visual artists and marketers and have been widely used in online marketing campaigns. However, the effects of cinemagraphs in online product presentations require further examination. This study plans to examine the potential effects of cinemagraphs in online product presentations by comparing them with photographs and short animations. In addition, we aimed to examine the visual processing of cinemagraphs using eye-tracking technology. Drawing on the competition for attention theory, the attention-guiding principle, and incongruity theories of curiosity, we hypothesize that these different image formats have different effects on human visual attention and product interest. We then propose a laboratory experiment to test our hypotheses. Finally, potential theoretical contributions and practical implications are discussed.
An Accurate and Efficient 3-D Shooting-and- Bouncing-Polygon Ray Tracer for Radio Propagation Modeling

This paper describes two important improvements to a 3-D shooting-and-bouncing-polygon (SBP) ray tracer for radio propagation modeling. It introduces a way to trace diffracted–reflected (D–R) rays in the SBP framework and improves SBP accuracy when the D–R rays play an important role. It also introduces a better space partitioning scheme for the use with SBP and improves the SBP’s computation efficiency by more than <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> in some applications. The numerical results are compared against published measurements and 3-D full-wave solutions. They show that the improved SBP ray tracer is a good multipurpose 3-D ray tracer which can be used to simulate urban street canyon (without double over-rooftop diffraction), indoor, and tunnel propagation environments.