Miracle-dz/UniFAR-FTUs huggingface dataset:
Describing the Use of Animals in Animal-Assisted Intervention Research

ABSTRACT Currently, there has been growing attention to animal welfare in animal-assisted interventions (AAI). However, there is a lack of scrutiny regarding what steps to take to ensure animal wellbeing during AAI research. The Institutional Animal Care and Use Committee (IACUC) reviews, monitors, and approves all research involving animal subjects in order to ensure an animal’s ethical and humane treatment. However, AAI publications rarely report IACUC approval. The purpose of this critical review was to determine the frequency of IACUC approval and to report the descriptions of animal use in AAI research publications. In the 139 articles reviewed, 14 (10%) studies reported attaining IACUC approval; 4/139 (3%) reported an adverse outcome on at least one animal used in the study. The publications inconsistently reported the training or certification and veterinary care of the animals. Lacking IACUC approval in AAI studies indicates that the field underutilizes this method of monitoring animal health and welfare. The IACUC should assess, approve, and monitor research protocols involving animals prior to conducting every study to safeguard AAI animal welfare.
Ingredients of Successful Emerging Business Ecosystems: Case of Industrial IoT Adoption

An emerging business ecosystem presents a future new value creation opportunity for firms that choose to collaborate effectively in a timely manner. This paper combines innovation diffusion, systems transition, innovation systems and business ecosystem literature to identify ingredients in ecosystem dependent firms. These insights are applied to the case of Industrial Internet of Things (IoT) adoption from an ecosystem actors' perspective. Two contrasting engagement models are presented based on this guidance that possess a higher likelihood of success.
Characterization of the Akirin Gene and Its Role in the NF-κB Signaling Pathway of Sogatella furcifera

Akirin is an essential nuclear protein involved in the regulation of NF-κB signaling pathway. In most invertebrates, Akirin regulates NF-κB-related Imd and Toll pathways, however, in Drosophila, it only controls the Imd pathway, whereas its role in NF-κB signaling pathway in other insect species is unclear. In the present study, we used white-backed planthopper Sogatella furcifera as a model to investigate the functional activity of Akirin in insects. The sequence of Akirin cDNA was extracted from transcriptome database of S. furcifera; it contained a 585 bp open reading frame (ORF) encoding a putative protein of 194 amino acids. S. furcifera Akirin (SfAkirin) had a molecular weight of about 21.69 kDa and a theoretical pI of 8.66 and included a nuclear localization signal (NLS) of five amino acid residues at the N-terminal region. Evolutionary analysis showed that SfAkirin was evolutionary closer to Akirins of such relatively distant species as crustaceans than to those of some insect orders like Diptera and Hymenoptera. Tissue-specific expression analysis showed that the SfAkirin gene was expressed in all examined tissues, with the highest expression levels detected in the testis, followed by the ovary, whereas the lowest expression was found in the head. Real-time quantitative PCR analysis showed that SfAkirin mRNA was strongly induced in response to injection of heat-inactivated Escherichia coli and Bacillus subtilis, whereas SfAkirin silencing by RNA interference significantly reduced the expression of NF-κB dependent transcription factors Dorsal and Relish after B. subtilis and E. coli challenge, respectively. Our results suggest that SfAkirin may control the immune response of S. furcifera against bacterial infection via both Imd and Toll signaling pathways.
Relationship Between In-Situ Electric Field and External Magnetic Field Strength in Human Models-Rational of IEEE C95.6 Standard Revisited

IEEE C95.1 (radio frequency) and C95.6 (low frequency) standards for human protection from electromagnetic fields are being under revision. In the next revision, they will be combined into one standard. Even though C95.1 standard considers anatomical human models for deriving the relationship between internal and external field strengths, ellipse is used in C95.6 (low frequency) standard. It is worth computing their relationship and associated technical issues when using anatomical models. In this study, two different codes developed at Nagoya Institute of Technology and Aalto University were used for inter-comparison. Computational results suggested that the relationship between in-situ and external field strengths are in good agreement when considering 99th percentile of in-situ electric field; less than 10%. The classification of body parts is one of the essential factors for determining the in-situ field strength in ‘the limb’ and ‘other tissues’.
A 0.9-V 100- $\mu$ W Feedforward Adder-Less Inverter-Based MASH  $\Delta\Sigma$  Modulator With 91-dB Dynamic Range and 20-kHz Bandwidth

A 0.9-V <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> modulator integrated into a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology for digitizing signals in low-power devices is presented in this paper. To do so, a cascade (multistage noise shaping) architecture based on an <italic>adder-less</italic> feedforward structure is proposed. The proposed modulator has a unity signal transfer function in both stages of the modulator in order to reduce the integrator’s output swings. To mitigate the failure of slow process corner in the weak inversion as well as to further diminish the power consumption of the presented modulator, a fully differential self- and bulk-biased inverter-based operational transconductance amplifier is proposed. Experimental results are shown to demonstrate the efficiency of the proposed <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> converter, showing state-of-the-art performance, by featuring 88.7-dB signal-to-noise ratio, 86.4-dB signal-to-noise plus distortion ratio, and 91-dB dynamic range within a signal bandwidth of 20 kHz, with a power dissipation of <inline-formula> <tex-math notation="LaTeX">$103.4~\mu \text{W}$ </tex-math></inline-formula> when the circuit is clocked at 5.12 MHz.
Machinability of Thermo-Plastic Carbon Fiber Reinforced Plastic in Inclined Planetary Motion Milling

The study deals with an improved method of milling thermo-plastic CFRP with a radius end mill. The authors use inclined planetary milling to carry out a fine CFRP boring technique. The inclined planetary motion milling consists of the two independent spindle motions of tool rotation and revolution. The eccentricity of the tool rotation axis is realized by a few degrees of inclination from the revolution axis. The movement of eccentric mechanism can be reduced by comparing it with that of orbital drilling. The inclined planetary motion milling reduces inertial vibration and decreases cutting force. Owing to the geometrical cutting principle, material delamination and burrs can be decreased. Thermo-plastic CFRP has recently been under development as an alternative structural material for the next generation of automobiles and in response to demands for bored fastening holes. The shape of the cutting edge of the ball end mill is suitable for the inclined planetary milling, as revealed by results of past experiments done on thermo-set CFRP. However, the ball end mill has left burrs and melted matrix on the exit side in the case of thermo-plastic CFRP. The radius end mill has the advantage over the ball end mill in terms of finishing fine boring. Based on the consideration of the schematic model and experiments using the Taguchi method, the improved milling conditions are examined.
Plant Species Identification from Occluded Leaf Images

We present an approach to identify the plant species from the contour information from occluded leaf image using a database of full plant leaves. Although contour based 2D shape matching has been studied extensively in the last couple of decades, matching occluded leaves with full leaf databases is an open and little worked on problem. Classifying occluded plant leaves is even more challenging than full leaf matching because of large variations and complexity of leaf structures. Matching an occluded contour with all the full contours in a database is an NP-hard problem, so our algorithm is necessarily suboptimal. First, we represent the 2D contour points as a <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math><alternatives><mml:math><mml:mi>β</mml:mi></mml:math><inline-graphic xlink:href="chaudhury-ieq1-2873611.gif"/></alternatives></inline-formula>-Spline curve. Then, we extract interest points on these curves via the Discrete Contour Evolution (DCE) algorithm. We use subgraph matching using the DCE points as graph nodes, which produces a number of open curves for each closed leaf contour. Next, we compute the similarity transformation parameters (translation, rotation, and uniform scaling) for each open curve. We then “overlay” each open curve with the inverse similarity transformed occluded curve and use the Fréchet distance metric to measure the quality of the match, retaining the best <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math><alternatives><mml:math><mml:mi>η</mml:mi></mml:math><inline-graphic xlink:href="chaudhury-ieq2-2873611.gif"/></alternatives></inline-formula> matched curves. Since the Fréchet metric is cheap to compute but not perfectly correlated with the quality of the match, we formulate an energy functional that is well correlated with the quality of the match, but is considerably more expensive to compute. The functional uses local and global curvature, Shape Context descriptors, and String Cut features. We minimize this energy functional using a convex-concave relaxation framework. The curve among these best <inline-formula><tex-math notation="LaTeX">$\eta$</tex-math><alternatives><mml:math><mml:mi>η</mml:mi></mml:math><inline-graphic xlink:href="chaudhury-ieq3-2873611.gif"/></alternatives></inline-formula> curves, that has the minimum energy, is considered to be the best overall match with the occluded leaf. Experiments on three publicly available leaf image database shows that our method is both effective and efficient, outperforming other current state-of-the-art methods. Occlusion is measured as the percentage of the overall contour (and not leaf area) that is missing. We show that our algorithm can, even for leaves with a high amounts of occlusion (say 50 percent occlusion), still identify the best full leaf match from the databases.
Multi-label Learning via Supervised Autoencoder

Multi-label learning has been applied in various areas. One crucial research issue in multi-label learning is how to find a complex nonlinear mapping between instances and multi-label such that the instances with unknown labels can be predicted with the mapping. In this paper, we propose a multi-label learning method based on supervised deep autoencoder, and study the effects of the various combinations of the constraints of hidden layers and output layers. In the output layer, the sum-square error of the true labels and estimating labels is minimized through backpropagation-through-time learning algorithm. Extensive experiments conducted on eight real-world datasets demonstrate the effectiveness of our proposed method compared with several state-of-art baseline methods.
Benefits of combining prevalent and incident cohorts: An application to myotonic dystrophy

It is frequently of interest to estimate the time that individuals survive with a disease, that is, to estimate the time between disease onset and occurrence of a clinical endpoint such as death. Epidemiologic survival data are commonly collected from either an incident cohort, whose members' disease onset occurs after the study baseline date, or from a cohort with prevalent disease that is followed forward in time. Incident cohort survival data are limited by study termination, while prevalent cohort data provide biased (left-truncated) survival data. In this article, we investigate the advantages of a study design featuring simultaneous follow-up of prevalent and incident cohorts to the estimation of the survivor function. Our analyses are supported by simulations and illustrated using data on survival after myotonic dystrophy diagnosis from the United Kingdom Clinical Practice Research Datalink (CPRD). We demonstrate that the NPMLE using combined incident and prevalent cohort data estimates the true survivor function very well, even for moderate sample sizes, and ameliorates the disadvantages of using a purely incident or prevalent cohort.
Trust in Entrepreneur — Venture Capitalist Relationships: A Bilateral Perspective

For new ventures, acquiring venture capital (VC) investments is crucial in order to grow successfully. Using a two-study mixed-methods design, we detail and empirically test a conceptual framework about the development of cognition- and affect-based trust between ventures and VC-investors, and the influence of trust on resource transfer and new venture growth. Grounded in exchange-theory and based on interviews with 18 VC-investors and venture-founders in Germany (Study 1), our framework posits that informational and interpersonal signals, send out by the entrepreneur during first negotiations, help overcome initial information asymmetries and build trust. Trust then lays the foundation for the transfer of the financial and non-financial resources required for venture growth. We test this framework using structural equation modeling with empirical data on 119 out of 500 (23.8%) German technology-based ventures (Study 2). Our data indicates that entrepreneur interpersonal signaling is decisive in building initial affect-based trust. This leap of faith, however, then needs to be substantiated by the ventures performance, underscoring the development of cognition-based trust. In sum, both dimensions of trust are important preconditions for resource transfer, whereby we find that new venture growth is most significantly driven by financial support. Implications of our findings for theory and practice are discussed.
A Review of Fault Diagnosis for Traction Induction Motor

With the rapid development of traction motor, the mechanical health monitoring and fault diagnosis field have entered the era of big data. Definite harmonic signals of the line current are located by a popular method known as motor current signature analysis. Different faults of an induction motor such as rotor, stator, bearing, vibration, air gap eccentricity and their different diagnosis techniques are also explored. In fact, the actual fault detection also has a deep development in the artificial intelligence. It is truly evident that the scope of this area is vast. Hence, acknowledging the need for future research, this review paper presents a birds eye view on different types of traction induction faults and their diagnostics schemes.
Edit Distance between Merge Trees

Topological structures such as the merge tree provide an abstract and succinct representation of scalar fields. They facilitate effective visualization and interactive exploration of feature-rich data. A merge tree captures the topology of sub-level and super-level sets in a scalar field. Estimating the similarity between merge trees is an important problem with applications to feature-directed visualization of time-varying data. We present an approach based on tree edit distance to compare merge trees. The comparison measure satisfies metric properties, it can be computed efficiently, and the cost model for the edit operations is both intuitive and captures well-known properties of merge trees. Experimental results on time-varying scalar fields, 3D cryo electron microscopy data, shape data, and various synthetic datasets show the utility of the edit distance towards a feature-driven analysis of scalar fields.
Motion Correlation Discovery for Visual Tracking

Motion information plays an important role in identifying moving objects, which has not been well utilized in state-of-the-art tracking algorithms. In this letter, we propose a unified framework integrating two tracking problems, i.e., pixel-level foreground probabilistic inference and motion parameter estimation. Our model employs motion fields to propagate probability forward, and discovers motion patterns in the spatial domain to distinguish targets from the background. It takes advantage of continuity and inertia of both target and camera motion, and provides reliable evidence to resolve confusion caused by appearance similarity between targets and the background. Target localization is effectively achieved from the pixel-level foreground probabilistic map. Experimental results demonstrate that the proposed method significantly improves our baseline method, and achieves performance comparable to state-of-the-art tracking methods with more complex features.
Research on Imaging Model and Unwrapping Algorithm of Catadioptrio-mnidirectional Vision System

This paper discuss some key techniques of the single view point (SVP) catadioptric-omnidirectional Vision System: the system composition, the optical imaging principle, the unified-sphere imaging model and the image unwrapping methods. The unified imaging model based on spherical projection is analyzed and deduced. The comparison experimental results of the panoramic image unwrapping methods based on different projection models shows that the cylindrical expansion algorithm based on the unified-sphere imaging model can obtain the undistorted images quickly and easily, which is more suitable for real-time tasks.
An Evolutionary Learning Approach to Play Othello Using XCS

Due to the multifarious challenges that emerge when developing an artificial intelligent (AI) agent that can compete with human players, the classic game of Othello has received a lot of attention from the Computational-Intelligence community. This paper proposes an AI agent that learns a winning strategy for the game of Othello using the eXtended Classifier System (XCS) algorithm which is a popular variant of the Learning Classifier System (LCS) algorithm. Othello has been a favourite in the study of AI due to its simple set of rules, low branching factor and well defined strategic concepts. The LCS system consists of a rule-set which is made to evolve using a combination of Reinforcement Learning (RL) and Genetic Algorithm (GA) such that the evolved rule-set learns an optimal action for each board state. A 6×6 Othello board will be used for this experiment in order to evaluate the applicability of the proposed agent in learning a winning game-playing strategy. The performance of the proposed agent was evaluated against three categories of agents: minimax, human and random agent. The XCS agent was able to outperform the above-mentioned agents showing the effectiveness of rule-based evolutionary learning in Othello. This work demonstrates the possibility of using the XCS algorithm in other strategy-based combinatorial games.
A Survey of Genetic Algorithms for Multi-Label Classification

In recent years, multi-label classification (MLC) has become an emerging research topic in big data analytics and machine learning. In this problem, each object of a dataset may belong to multiple class labels and the goal is to learn a classification model that can infer the correct labels of new, previously unseen, objects. This paper presents a survey of genetic algorithms (GAs) designed for MLC tasks. The study is organized in three parts. First, we propose a new taxonomy focused on GAs for MLC. In the second part, we provide an up-to-date overview of the work in this area, categorizing the approaches identified in the literature with respect to the taxonomy. In the third and last part, we discuss some new ideas for combining GAs with MLC.
Multi-Stream Convolutional Neural Network for SAR Automatic Target Recognition

Despite the fact that automatic target recognition (ATR) in Synthetic aperture radar (SAR) images has been extensively researched due to its practical use in both military and civil applications, it remains an unsolved problem. The major challenges of ATR in SAR stem from severe data scarcity and great variation of SAR images. Recent work started to adopt convolutional neural networks (CNNs), which, however, remain unable to handle the aforementioned challenges due to their high dependency on large quantities of data. In this paper, we propose a novel deep convolutional learning architecture, called Multi-Stream CNN (MS-CNN), for ATR in SAR by leveraging SAR images from multiple views. Specifically, we deploy a multi-input architecture that fuses information from multiple views of the same target in different aspects; therefore, the elaborated multi-view design of MS-CNN enables it to make full use of limited SAR image data to improve recognition performance. We design a Fourier feature fusion framework derived from kernel approximation based on random Fourier features which allows us to unravel the highly nonlinear relationship between images and classes. More importantly, MS-CNN is qualified with the desired characteristic of easy and quick manoeuvrability in real SAR ATR scenarios, because it only needs to acquire real-time GPS information from airborne SAR to calculate aspect differences used for constructing testing samples. The effectiveness and generalization ability of MS-CNN have been demonstrated by extensive experiments under both the Standard Operating Condition (SOC) and Extended Operating Condition (EOC) on the MSTAR dataset. Experimental results have shown that our proposed MS-CNN can achieve high recognition rates and outperform other state-of-the-art ATR methods.
Overview of Cellular LPWAN Technologies for IoT Deployment: Sigfox, LoRaWAN, and NB-IoT

LPWAN are actually the most popular low cost, long battery lifetime, and long range communication technology for IoT applications. This paper presents a comprehensive and comparative study on three actually leading LPWAN technologies called Sigfox, LoRaWAN, and NB-IoT. We show that Sigfox and LoRaWAN excel on network capacity, devices lifetime, and cost. Whereas, NB-IoT excels on quality of service and latency. In addition, we consider application scenarios and explain which technology fits best to guide future researchers and industrials.
Improving Network Positions through M&As: The Case of the Japanese Pharmaceutical Industry's International Expansion

In this paper, we propose a novel strategic motivation for M&As. We argue that beside traditional strategies, such as geographic extension, vertical integration, extension of the product line, and developing R&D capabilities, M&As can also be undertaken and analysed with a field or network perspective. M&A strategy can be thought of as a particular tool to improve an organization's position within an organizational field and to build specific network positions. Thus, a number of strategic M&As undertaken by a company can help it move towards a more beneficial position and gain access to vital resources embedded in the network through social capital. These positions can also provide new collaborative opportunities. For our investigations, we looked at top firms in the Japanese life science industry and attempted to understand how these firms were able to become more central in the international network, where they had only occupied quite peripheral positions. We also looked at one particular case through a qualitative lens, the case of Takeda Pharmaceutical, to see how the process takes place in more detail and found that M&As lead to new collaborations and acquisitions that the company would probably not have taken without the help of their previously acquired companies.