Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of wavelets and radial basis function neural network for incipient fault diagnosis in induction motors

One of the most important tasks in industries is considered to be the condition monitoring of induction motors. However, most of the traditional methods employed for this purpose suffer from certain limitations in accomplishing this task. A successful implementation of condition monitoring requires the development of a simple but reliable detector of various faults. This paper investigates the performance of Discrete Wavelet Transform and Radial Basis Function based Neural Network for incipient stator fault diagnosis in induction motors. Wavelet analysis helps in the extraction of important features from the faulty signal and neural network classifies the fault type depending on the nature of fault. A mean absolute percentage error of 1.4236% between the actual values and the predicted values by the neural network show the effectiveness of the proposed approach.
Seed Dormancy and Germination

Abstract Seed dormancy allows seeds to overcome periods that are unfavourable for seedling established and is therefore important for plant ecology and agriculture. Several processes are known to be involved in the induction of dormancy and in the switch from the dormant to the germinating state. The role of plant hormones, the different tissues and genes involved, including newly identified genes in dormancy and germination are described in this chapter, as well as the use transcriptome, proteome and metabolome analyses to study these mechanistically not well understood processes.
Residual U-Net Convolutional Neural Network Architecture for Low-Dose CT Denoising

Abstract Low-dose CT has received increasing attention in the recent years and is considered a promising method to reduce the risk of cancer in patients. However, the reduction of the dosage leads to quantum noise in the raw data, which is carried on in the reconstructed images. Two different multilayer convolutional neural network (CNN) architectures for the denoising of CT images are investigated. ResFCN is based on a fully-convolutional network that consists of three blocks of 5×5 convolutions filters and a ResUNet that is trained with 10 convolutional blocks that are arranged in a multi-scale fashion. Both architectures feature a residual connection of the input image to ease learning. Training images are based on realistic simulations by using the XCAT phantom. The ResUNet approach shows the most promising results with a peak signal to noise ratio of 44.00 compared to ResFCN with 41.79.
Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual cortex

Neuronal response properties vary markedly at increasing levels of the cortical hierarchy. At present it is unclear how these variations are reflected in the organization of the intrinsic cortical circuitry. Here we analyze patterns of intrinsic horizontal connections at different hierarchical levels in the visual cortex of the macaque monkey. The connections were studied in tangential sections of flattened cortices, which were injected with the anterograde tracer biocytin. We directly compared the organization of connections in four cortical areas representing four different levels in the cortical hierarchy. The areas were visual areas 1, 2, 4 and Brodman's area 7a (V1, V2, V4 and 7a, respectively). In all areas studied, injections labeled numerous horizontally coursing axons that formed dense halos around the injection sites. Further away, the fibers tended to form separate clusters. Many fibers could be traced along the way from the injection sites to the target clusters.
Scalable core-based methodology and synthesizable core for systematic design environment in multicore SoC (MCSoC)

The strong demand for complex and high performance embedded system-on-chip requires quick turn around design methodology and high performance cores. Thus, there is a clear need for new methodologies supporting efficient and fast design of these systems on complex platforms implementing both hardware and software modules. In this paper, we describe a novel scalable core-based methodology for systematic design environment of application specific heterogeneous multicore systems-on-chip (MC-SoC). We also developed a high performance 32-bit synthesizable QueueCore (QC-2) with single precision floating point support. The core is targeted for special purpose applications within our target MCSoC system. We present the architecture description and design results in a fair amount of details
Tailoring text for automatic layouting of newspaper pages

We consider layouting news articles on a page as a cutting and packing problem with output maximization. We propose to tailor news articles by employing automatic summarization to find new solutions for the packing problem. Tailoring text items allows us to use an efficient isin-approximate greedy layouting algorithm, which scales well for larger data sets, to explore the search space. We also propose a function for adjusting the value of summarized articles. Our results show that the overall solution value as well as the individual quality of articles are notably improved, with the solution value in some cases even exceeding the optimum score achievable by an exhaustive search without summarization.
Community detection in multiplex networks using Locally Adaptive Random walks

Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms.
One Sense per N-gram

This paper presents a novel supposition, One Sense Per N-gram (N>1), which we believe is appropriate for more linguistic phenomena and can serve as a general version instead of the celebrated One Sense Per Collocation supposition, at least in Chinese language. This new supposition is based on our observation of the error detection process of annoted sense in People’s Daily that are tagged by an automatic WSD system. Our preliminary experiment on Chinese Word Sense Tagging Corpus shows that it holds with over 85.9% agreement for both nouns and verbs. Based on the supposition we build a prototype naïve Bayes WSD system and tested on Multilingual Chinese-English Lexical Sample task (MCELS) in Semeval-2007. Experimental results show our prototype system can promote the performance of baseline system by 2.7%.
Output feedback stabilisation of a tree-shaped network of vibrating strings with non-collocated observation

In this article, the authors consider the simple tree-shaped network of strings with non-collocated observation. Suppose that the network consists of three strings and the displacements of these strings are continuous at the common vertex. The sensors and actuators are required to be set at different vertices. We propose an observer-based output feedback controller to stabilise this network. The exponential stability of the closed-loop network is obtained based on the Riesz basis approach together with estimates of the spectral distribution. Finally, a numerical simulation is given to support these results.
Markov chain based method for in-domain and cross-domain sentiment classification

Sentiment classification of textual opinions in positive, negative or neutral polarity, is a method to understand people thoughts about products, services, persons, organisations, and so on. Interpreting and labelling opportunely text data polarity is a costly activity if performed by human experts. To cut this labelling cost, new cross domain approaches have been developed where the goal is to automatically classify the polarity of an unlabelled target text set of a given domain, for example movie reviews, from a labelled source text set of another domain, such as book reviews. Language heterogeneity between source and target domain is the trickiest issue in cross-domain setting so that a preliminary transfer learning phase is generally required. The best performing techniques addressing this point are generally complex and require onerous parameter tuning each time a new source-target couple is involved. This paper introduces a simpler method based on the Markov chain theory to accomplish both transfer learning and sentiment classification tasks. In fact, this straightforward technique requires a lower parameter calibration effort. Experiments on popular text sets show that our approach achieves performance comparable with other works.
RIGHT HEMISPHERIC FUNCTION IN NORMALS, AFFECTIVE DISORDER AND SCHIZOPHRENIA

The happy-sad chimeric faces test has been established as a useful test of right hemispheric function. It is known to elicit a left hemifacial bias (LHF bias) in right handed subjects. 41 normals and 19 manic, depressive and schizophrenic patients each were tested. All subjects were strictly right handed. Normals and depressives showed significant LHF bias. Monies and schizophrenics did not show significant LHF Bias. This suggests right hemispheric dysfunction in both mania and schizophrenia.
Depth Creates No Bad Local Minima

In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create non-convex loss surfaces. Then, does depth alone create bad local minima? In this paper, we prove that without nonlinearity, depth alone does not create bad local minima, although it induces non-convex loss surface. Using this insight, we greatly simplify a recently proposed proof to show that all of the local minima of feedforward deep linear neural networks are global minima. Our theoretical results generalize previous results with fewer assumptions, and this analysis provides a method to show similar results beyond square loss in deep linear models.
A High Throughput Beamforming Architecture for MIMO Systems

Quantized feedback in multiple antenna systems has potential to increase throughput or reduce probability of outage. In this paper, we present a method to generate quantization codebooks tailored for efficient implementation of beamforming- based MIMO transmissions. The proposed codebooks can reduce computational requirements significantly, making feasible an efficient architecture for a real-time transmit beamforming and receive combining MIMO orthogonal frequency division multiplexing (OFDM) system. Simulation results are used to validate the proposed codebook construction method and architecture.
The (Un)sustainability of Imagined Future Information Societies

The pathway to a sustainable society is not clear, and we need to consider different developmental possibilities. This paper describes the results of a research project in the intersection of HCI and Futures Studies as well as in the intersection between "the future information society" and sustainability. We here present parts of the body of materials that were developed in a multi-year research project with the aim of describing and evaluating the sustainability impact of possible future information societies. We also discuss some of the lessons learned and what HCI and design fiction can learn from Futures Studies in general and from this project in particular. The main stakeholders in this project have been city administrators and corporate partners, and the overarching goal has primarily been to influence planning processes at the regional (Stockholm, Sweden) level.
Fertilizer profitability Introduce the concept of elasticity Response rate Price elasticity of supply Profitability Macro level PRRA / case studies Survey based Fertilizer profitability Exercise on fertilizer production relationship National level quick analyses Problems with the method

In this paper we use farmers' actual experiences with changes in rainfall levels and their responses to these changes to assess if patterns of fertilizer use are responsive to changes in rainfall patterns. Using plot and farm level panel data from the central Highlands of Ethiopia matched with corresponding village level rainfall data; results show that both the current year’s decision to adopt and the intensity of fertilizer adoption is positively associated with higher rainfall levels experienced in the previous year. Furthermore, we find a concave relationship between previous season rainfall levels and fertilizer adoption, indicating that too much rainfall discourages adoption. Abundant rainfall in the previous year could depict relaxed liquidity constraints and increased affordability of fertilizer, which makes rainfall availability critical in severely credit constrained environments. In light of similar existing literature, the major contribution of the study is its use of plot level panel data, which permits us to investigate the importance of plot characteristics in fertilizer adoption decisions.
A Robust Approach for Singular Point Extraction Based on Complex Polynomial Model

This paper focuses on a general framework for singular point extraction from vector field. We design a new index of singular point based on complex polynomial model. We test our method in the publicly available benchmark dataset of the singular point detection competition (SPD2010). Our algorithm gets the best results and produces large margins compared to the top five algorithms which took part in the public competition. We also compare our algorithm with the state-of-the-art singular point detection algorithm (called "ZPM" method) with the benchmark. The performance of our method is much better than that of the state-of-the-art method.
Advances in Scanning Thermal Microscopy Measurements for Thin Films

The authors acknowledge the financial support of the Intramural INFANTE, and MAT2017- 86450-C4-3-R, as well as the service from the MiNa Laboratory at IMN, and funding from CM (project SpaceTec, S2013/ICE2822), MINECO (project CSIC13-4E-1794), and EU (FEDER, FSE). O.C. C. acknowledges the funding of the Ramon y Cajal program (MINECO).
Development of a Highly Sensitive and Specific Method for Detection of Circulating Tumor Cells Harboring Somatic Mutations in Non-Small-Cell Lung Cancer Patients

Background Oncogenic mutations are powerful predictive biomarkers for molecularly targeted cancer therapies. For mutation detection patients have to undergo invasive tumor biopsies. Alternatively, archival samples are used which may no longer reflect the actual tumor status. Circulating tumor cells (CTC) could serve as an alternative platform to detect somatic mutations in cancer patients. We sought to develop a sensitive and specific assay to detect mutations in the EGFR gene in CTC from lung cancer patients. Methods We developed a novel assay based on real-time polymerase chain reaction (PCR) and melting curve analysis to detect activating EGFR mutations in blood cell fractions enriched in CTC. Non-small-cell lung cancer (NSCLC) was chosen as disease model with reportedly very low CTC counts. The assay was prospectively validated in samples from patients with EGFR-mutant and EGFR-wild type NSCLC treated within a randomized clinical trial. Sequential analyses were conducted to monitor CTC signals during therapy and correlate mutation detection in CTC with treatment outcome. Results Assay sensitivity was optimized to enable detection of a single EGFR-mutant CTC/mL peripheral blood. CTC were detected in pretreatment blood samples from all 8 EGFR-mutant lung cancer patients studied. Loss of EGFR-mutant CTC signals correlated with treatment response, and its reoccurrence preceded relapse. Conclusions Despite low abundance of CTC in NSCLC oncogenic mutations can be reproducibly detected by applying an unbiased CTC enrichment strategy and highly sensitive PCR and melting curve analysis. This strategy may enable non-invasive, specific biomarker diagnostics and monitoring in patients undergoing targeted cancer therapies.
Visual servo trajectory tracking for a four rotor VTOL aerial vehicle

An image-based visual servo control design is proposed for a four rotor vertical take-off and landing (VTOL) craft, known as an X4-flyer, accomplishing a trajectory tracking task. The approach taken is an image-based visual servo design that is applicable to under-actuated dynamic systems. The work is an extension of the authors earlier work (2000) to the trajectory tracking problem. Semiglobal stability of the closed loop system is proved for bounded trajectories.
Standard-based engineering and distributed execution framework for intelligent fault management for FREEDM system

This paper proposes standards' based software and communication platform for implementation of distributed grid intelligence in FREEDM system. This enables system level design for distributed application and consistent mapping to the interfacing hardware. The system becomes scalable, reconfigurable and easily maintainable. Applying IEC 61499 standard for distributed and embedded applications and IEC 61850 standard for communication in substation, multi-agent design can be systematically introduced to power system automation. With this approach distributed grid intelligence nodes become intelligent, independent and interactive agents, with vendor independent software and unified communication. This concept is illustrated on an example of distributed intelligent fault management system, and verified through simulation.
Collision Resolution in Slotted ALOHA with Multi-User Physical-Layer Network Coding

Two new schemes are proposed for collision resolution in slotted ALOHA networks based on multi-user physical-layer network coding (MU PHY NC). In the proposed random access schemes, a collision of a generic number of packets can be recovered decoding the XOR of the original messages, such that the signal resulting from the collision is exploited rather than being discarded. Two different schemes that differ in terms of the amount of control information that needs to be transmitted from the access point, are studied.
Response to the Reviews on Bargas-Avila et al. (2009) 'Intranet Satisfaction Questionnaire: Development and Validation of a Questionnaire to Measure User Satisfaction with the Intranet'

This article contains the response to the reviews regarding the development and validation of the Intranet Satisfaction Questionnaire (ISQ), which measures user satisfaction with the Intranet. Where appropriate additional data analysis and interpretation is provided, the data show further evidence for the good validity, reliability and sensitivity of this tool. In addition, we provide a short preview of a follow-up publication and show that the ISQ can differentiate effectively between bad and good Intranets.
Changes in monosaccharides, organic acids and amino acids during Cabernet Sauvignon wine ageing based on a simultaneous analysis using gas chromatography-mass spectrometry.

BACKGROUND Monosaccharides, organic acids and amino acids are the important flavour-related components in wines. The aim of this article is to develop and validate a method that could simultaneously analyse these compounds in wine based on silylation derivatisation and gas chromatography-mass spectrometry (GC-MS), and apply this method to the investigation of the changes of these compounds and speculate upon their related influences on Cabernet Sauvignon wine flavour during wine ageing. This work presented a new approach for wine analysis and provided more information concerning red wine ageing. RESULTS This method could simultaneously quantitatively analyse 2 monosaccharides, 8 organic acids and 13 amino acids in wine. A validation experiment showed good linearity, sensitivity, reproducibility and recovery. Multiple derivatives of five amino acids have been found but their effects on quantitative analysis were negligible, except for methionine. The evolution pattern of each category was different, and we speculated that the corresponding mechanisms involving microorganism activities, physical interactions and chemical reactions had a great correlation with red wine flavours during ageing. CONCLUSION Simultaneously quantitative analysis of monosaccharides, organic acids and amino acids in wine was feasible and reliable and this method has extensive application prospects. © 2017 Society of Chemical Industry.
Object-Oriented Analysis through a Knowledge-Based System

This research is an attempt to provide some basis for a systematic and problem independent object-oriented analysis approach. It presents an object oriented requirements analysis strategy based on a knowledge representation scheme. An abstract model and a strategy for object-oriented requirements analysis (OORA) are described. The proposed OORA strategy is based on a semantic-net represen-tational scheme that provides a set of constructs to organize the requirements model in a structured form. Moreover, the graphical notation of this knowledge representation formalism enhances the under-standability of the requirements model and facilitates the mapping of the objects speciied in the user's requirements. To demonstrate the viability of this approach a prototype knowledge-based system was implemented. The implemented knowledge-based system is capable of performing diierent analyses on an initial requirements model to identify: (1) common patterns among classes; (2) message-connections; and (3) groups of cohesive classes. The ability of the system to identify such properties can be of great beneet to the software engineer.
Host cell autophagy contributes to Plasmodium liver development

Autophagy plays an important role in the defence against intracellular pathogens. However, some microorganisms can manipulate this host cell pathway to their advantage. In this study, we addressed the role of host cell autophagy during Plasmodium berghei liver infection. We show that vesicles containing the autophagic marker LC3 surround parasites from early time‐points after invasion and throughout infection and colocalize with the parasitophorous vacuole membrane. Moreover, we show that the LC3‐positive vesicles that surround Plasmodium parasites are amphisomes that converge from the endocytic and autophagic pathways, because they contain markers of both pathways. When the host autophagic pathway was inhibited by silencing several of its key regulators such as LC3, Beclin1, Vps34 or Atg5, we observed a reduction in parasite size. We also found that LC3 surrounds parasites in vivo and that parasite load is diminished in a mouse model deficient for autophagy. Together, these results show the importance of the host autophagic pathway for parasite development during the liver stage of Plasmodium infection.