Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel broadband omni-directional circularly polarized antenna for mobile communications

This paper presents a new broadband omni-directional circularly polarized (CP) antenna for mobile communications operating nearby 2 GHz. The new omni-directional CP antenna consists of four broadband CP rectangular loops which are bent to form a hollow cylinder. A conducting cylinder is introduced inside the hollow cylinder to improve the CP performance. A broadband balun is developed to feed the CP antenna. This antenna has a compact cylindrical configuration with a diameter of 0. 4λ. The simulation shows that this antenna has a bandwidth of 30.8% (1.7 GHz–2.32 GHz) for return loss (RL) ≥10 dB and 36.1% (1.75 GHz–2.52 GHz) for average axial ratio (AR) ≤3 dB. Good agreement between simulated and measured results is achieved.
Ambient Gestures

We present Ambient Gestures, a novel gesture-based system designed to support ubiquitous ‘in the environment’ interactions with everyday computing technology. Hand gestures and audio feedback allow users to control computer applications without reliance on a graphical user interface, and without having to switch from the context of a non-computer task to the context of the computer. The Ambient Gestures system is composed of a vision recognition software application, a set of gestures to be processed by a scripting application and a navigation and selection application that is controlled by the gestures. This system allows us to explore gestures as the primary means of interaction within a multimodal, multimedia environment. In this paper we describe the Ambient Gestures system, define the gestures and the interactions that can be achieved in this environment and present a formative study of the system. We conclude with a discussion of our findings and future applications of Ambient Gestures in ubiquitous computing.
“Theory of companions” What can theoretical models contribute to applications and understanding of human-robot interaction?

Since it becomes more and more feasible that artificial entities like robots or agents will soon be parts of our daily lives, an essential challenge is to advance the sociability of artifacts. Against this background a pivotal goal of the Sera project was to develop a theoretical framework for sociable companions as well as for human-artefact interaction. In discussing several levels of sociability from a theoretical point-of-view, we will critically reflect whether human-companion interaction has to build on basic principles of human-human interaction. Alternative approaches are presented and discussed. Additionally, a critical question is whether a theoretical framework will be able to serve as a starting point for deriving guidelines for architecture and implementation. Here, a crucial result within the Sera project demonstrates that inter-individual differences between human users determine both the interaction as well as the relationship building with an artefact. In the conclusion it is therefore summarized whether a "theory of companions" is necessary and useful and what it should be able to explain and contribute.
FlowMonitor: a network monitoring framework for the network simulator 3 (NS-3)

When networking researchers meet the task of doing simulations, there is always a need to evaluate the value of such models by measuring a set of well known network performance metrics. However, simulators in general and NS-3 in particular, require significant programming effort from the researcher in order to collect those metrics. This paper reports a contribution for NS-3 consisting of a new flow monitoring module that makes it easier to collect and save to persistent storage a common set of network performance metrics. The module automatically detects all flows passing through the network and stores in a file most of the metrics that a researcher might need to analyze about the flow, such as bitrates, duration, delays, packet sizes, and packet loss ratio. The value of this module is demonstrated using an easy to follow example. It is also validated by comparing the measurements of a simple scenario with the expected values. Finally, the performance of flow monitoring is characterized and shown to introduce small overheads.
Antibiotics resistance of Helicobacter pylori and treatment modalities in children with H. pylori infection

Pediatric infection with Helicobacter pylori may occur early in childhood and persist lifelong. Global pediatric clinical studies have reported a decreasing tendency in the overall rate of H. pylori eradication. In pediatric patients with H. pylori infection, pediatric patients with peptic ulcer, and the first-degree relatives of patients with a history of gastric cancer, it is commonly recommended that H. pylori strains be eradicated. Antibiotic drug resistance to H. pylori, which has been reported to vary widely between geographic regions, is mainly associated with treatment failure in these patients. It is therefore imperative that the antibiotic resistance rates of H. pylori in children and adolescents be meticulously monitored across countries and throughout geographic regions. This paper particularly focuses on the antibiotic drug resistance of H. pylori and the thearpy of pediatric H. pylori infection cases.
A Careful Re-Examination of Seasonality in International Stock Markets: Comment on Sentiment and Stock Returns

In questioning Kamstra, Kramer, and Levi’s (2003) finding of an economically and statistically significant seasonal affective disorder (SAD) effect, Kelly and Meschke (2010) make errors of commission and omission. They misrepresent their empirical results, claiming that the SAD effect arises due to a "mechanically induced" effect that is non-existent, labeling the SAD effect a "turn-of-year" effect (when in fact their models and ours separately control for turn-of-year effects), and ignoring coefficient-estimate patterns that strongly support the SAD effect. Our analysis of their data shows, even using their low-power statistical tests, there is significant international evidence supporting the SAD effect. Employing modern, panel/time-series statistical methods strengthens the case dramatically. Additionally, Kelly and Meschke represent the finance, psychology, and medical literatures in misleading ways, describing some findings as opposite to those reported by the researchers themselves, and choosing selective quotes that could easily lead readers to a distorted understanding of these findings.
Supervised learning as a tool for metagame analysis

I explore the possibility of using supervised learning as a tool for analyzing metagames. In particular I look at a data set for Natural Selection 2 build 229. The first goal of the article is to predict the outcome of individual matches based on a set of features of the round which capture the strategies being used. Using an averaged perceptron, I predict the outcome of a round with 85.42% accuracy. The second goal is to attempt to use machine learning to analyze the balance of different strategies in the metagame. Balance in this context is taken to mean a similarity in the prevalence of parallel strategies. I use a random forest of decision trees to extract the most important feature that determines the outcome of the game over a subset of all features. This exposes player total resources and player skill as the most important features, which was to be expected, and shows an imbalance in one of the strategies in the game which was largely considered to be imbalanced by the community in build 229. These methods are effective in both predicting outcomes and identifying potential balance issues, and provide a tool for the automated analysis of metagames.
Prelysosomal Compartments in the Unconventional Secretion of Amyloidogenic Seeds

A mechanistic link between neuron-to-neuron transmission of secreted amyloid and propagation of protein malconformation cytopathology and disease has recently been uncovered in animal models. An enormous interest in the unconventional secretion of amyloids from neurons has followed. Amphisomes and late endosomes are the penultimate maturation products of the autophagosomal and endosomal pathways, respectively, and normally fuse with lysosomes for degradation. However, under conditions of perturbed membrane trafficking and/or lysosomal deficiency, prelysosomal compartments may instead fuse with the plasma membrane to release any contained amyloid. After a brief introduction to the endosomal and autophagosomal pathways, we discuss the evidence for autophagosomal secretion (exophagy) of amyloids, with a comparative emphasis on Aβ1–42 and α-synuclein, as luminal and cytosolic amyloids, respectively. The ESCRT-mediated import of cytosolic amyloid into late endosomal exosomes, a known vehicle of transmission of macromolecules between cells, is also reviewed. Finally, mechanisms of lysosomal dysfunction, deficiency, and exocytosis are exemplified in the context of genetically identified risk factors, mainly for Parkinson’s disease. Exocytosis of prelysosomal or lysosomal organelles is a last resort for clearance of cytotoxic material and alleviates cytopathy. However, they also represent a vehicle for the concentration, posttranslational modification, and secretion of amyloid seeds.
Tacit knowledge and the economic geography of context, or The undefinable tacitness of being (there)

Within economic geography and industrial economics, interest in the concept of tacit knowledge has grown steadily in recent years. Nelson and Winter helped revive this interest in the work of Michael Polanyi by using the idea of tacit knowledge to inform their analysis of routines and evolutionary dynamics of technological change. More recently, the concept has received closer scrutiny. This paper offers a further contribution to this project by offering a critical analysis of the prevailing implicit and explicit economic geographies of tacit knowledge, focusing on the relationship between tacit knowledge and institutions. While much of the innovation literature focuses on a single question -- can tacit knowledge be effectively shared over long distances -- the paper argues that this issue cannot be properly addressed without considering a broader range of related questions. It highlights three tacit knowledge problems which, together, provide a more complete view of this issue. First, how is tacit knowledge produced? Second, how do firms find and appropriate tacit knowledge? Third, how is tacit knowledge reproduced or shared -- that is, how does tacit knowledge promote social learning processes, and must the participants be geographically proximate in order for effective learning to occur? The paper revisits Michael Polanyi's original conception of tacit knowledge, showing it to be limited by its experiential and cognitive emphasis, with insufficient attention devoted to the role and origins of social context. Alternatively, the paper argues that one cannot sort out the geography of tacit knowledge without inquiring into the foundations of context and culture, and the institutional underpinnings of economic activity, taking the work of another Polanyi -- Karl -- as the logical starting point. Copyright 2003, Oxford University Press.
Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.
Current Status and Future Directions for Cultural Intelligence

Abstract The concept of cultural intelligence was formulated to ease and understand multicultural interactions in business organizations. Based on the theory of multiple intelligence; it is the cross-cultural facet of intelligence that assists adaptive cultural adjustment. Due to its implications for the contemporary world witnessing increased cultural contact and clashes; it has attracted multidisciplinary scholarly interest. This article reviews the development, validation and major approaches to the construct, along with a focus on its critical analysis. Significance of the construct in promoting intercultural discourse to encourage pacific multicultural existence is implicated. Furthermore, suggestions are made to extend the scope of cultural intelligence research and integrate it in diverse fields encompassing intercultural contact and communication.
Composite Panel Damage Detection using Ultrasonic Testing and Neural Networks

For modern aircraft, wind turbines, and other aerospace structures composites are replacing metal components. These aerospace structures will be designed and expected to perform well for many years ahead. Therefore, structural health monitoring (SHM) systems are being designed to monitor these structures to improve maintenance, repairing or replacing composites components when the need arises. The focus of this research is on creating an SHM system, using ultrasonic testing methods to monitor damage in a composite panel. The ultrasonic testing consists of either actively scanning the panel by introducing controlled strain waves or passively listening to noises created by damage, which is a technique called acoustic emission. Through acoustic emission, composite coupons with damage introduced in the form of fiber breakage, delamination, and matrix cracking are examined by introducing tensile loading, since these forms of damage are present in aircraft and wind turbines through normal use of the systems. Artificial neural networks are used to analyze the signals received from the ultrasonic testing method. These networks allow for fast processing of the strain waves even with some extra noise present. Through this study, a system, using ultrasonic testing techniques, was shown to be a plausible SHM system for aerospace systems of the future.
One loop corrections to quantum hadrodynamics with vector mesons

The renormalized elastic \ensuremath{\pi}\ensuremath{\pi} scattering amplitude to one loop is calculated in the chiral limit in the \ensuremath{\sigma} model and in a quantum hadrodynamic model (QHD-III) with vector mesons. It is argued that QHD-III reduces to the linear \ensuremath{\sigma} model in the limit that the vector meson masses become large. The pion decay constant is also calculated to 1-loop in the \ensuremath{\sigma} model, and at tree level in QHD-III; it is shown that the coefficient of the tree level term in the scattering amplitude equals ${F}_{\ensuremath{\pi}}^{\ensuremath{-}2}. $ The 1-loop correction of ${F}_{\ensuremath{\pi}}$ in QHD-III violates strong isospin current conservation. Thus, it is concluded that QHD-III can, at best, only describe the strongly interacting nuclear sector.
BAYESIAN CLUSTERING ANALYSIS FOR PERSONAL INCOME DYNAMICS OF USA

In recent years, time series clustering has provided promising results in a variety of applications. This paper uses a Bayesian clustering method to categorize the personal income data set, which is a collection of 25 time series representing the per capital personal income during 1929-1999 in 25 states of the USA. The method considers each time series in a cluster as the same auto-regressive model and adopts an agglomerative clustering procedure to determine the number of clusters automatically and discover the most probable set of clusters describing the essential dynamics of these time series. The case analysis results show the Bayesian clustering method compare favorably with other methods proposed previously by others for similar time series clustering tasks.
CS 2523 OMR Final Project Report : Human Body Pose Estimation

H UMAN Body Pose Estimation (HBPE) is the process of identifying how a human body and/or its individual limbs are configured in a given scene [9]. The importance of such task has been identified by the computer vision community which has dedicated a large amount of effort towards finding an effective solution. A wide variety of techniques have been proposed in an attempt of finding the one which performs better in a wide variety of scenarios. However, the ultimate solution is yet to come and research in this area is quite active these days. It is not surprising to find such a great interest and dedication towards the solution of this problem, not only because of the extensive number of applications (medical diagnosis and treatment, sports training, surveillance, study of human behavior, motion capture, among many others), but also because it presents huge difficulty, as people can describe a large (and perhaps unpredictable) set of poses, on top of the natural variability that comes from humans themselves. The initial goal of this project was to build a system to (partially) solve the problem of HBPE based on [19]. However, it rapidly became clear that neither the simple implementation of a system would allow me to explore the richness of the existing solutions in this area, nor I would be able to come up with mature ideas to incorporate to the implementation without a comprehensive literature review. At the end, the this review became a significant portion of the work of this project and this report will summarize important concepts I learnt from both the implementation and the literature review. The remaining of this report is organized as follows: A summary of the literature review is presented in Section 2, followed by the proposed methodology on Section 3. Section 4 shows some experimental results and finally Section 5 presents some conclusions.
Improving Hatching Strategies for Powder Bed Based Additive Manufacturing with an Electron Beam by 3D Simulations

AbstractThis paper extends 3D simulation results of layer hatching process strategies for additive manufacturing by electronbeam melting (EBM) applications to exploit the future energy potential of electron guns with higher beam power. Thephysical model, discretized by a three dimensional thermal lattice Boltzmann method, is briey presented. The numericalimplementation is validated on the basis of an experimental process window up to 1.2kW beam power of hatching acuboid with a basic, state-of-the-art process strategy, whereby the numerical results are classi ed into three categories:‘porous’, ‘good’ and ‘swelling’, depending on the part density and quality of the surface. In this paper we extend andimprove the basic process strategy, to use higher beam powers. First, the numerical process window is extended, todetermine the limits of this technique. Then we introduce di erent process strategies of the electron beam to build thepart as fast as possible under the restriction of a dense part with a good dimensional accuracy. These process strategiescan be used to improve the quality of the parts, reduce the build time and costs, maximize the beam power usage and,therefore, use the full potential of high power electron guns.Keywords: Powder bed based additive manufacturing, electron beam melting, process strategies, hatching strategies,numerical simulation, thermal lattice Boltzmann method1. IntroductionElectron Beam Melting (EBM) is a powder bed basedAdditive Manufacturing (AM) method used to producemetallic structures (Heinl et al., 2007). The great advan-tage of EBM manufacturing is the construction of strongand exible structures with a high geometric complexity,which opens new opportunities in many di erent applica-tions, ranging from aerospace (Rawal et al., 2013) to medi-cal implant (Murr et al., 2011) manufacturing. In contrastto other AM methods, the EBM process produces partswith a sucient surface and dimensional quality and ma-terial properties at relatively low build times (Vayre et al.,2012). However, until today the build is still not su-ciently fast to make it economically viable for less speci c,mass production applications. In order to accelerate thistechnology and to ensure the nal quality, numerical simu-lations are used to improve state-of-the-art process strate-gies.We discretize the EBM process with a 3D thermal lat-tice Boltzmann method (LBM), including physical phe-nomena, like hydrodynamic ow, capillarity, wetting, aswell as beam absorption and phase transformations, butexcluding viscous heat dissipation and compressibility and
ANALYSIS OF OPTIMIZATION TECHNIQUES IN CHI- SQUARED AUTOMATIC INTERACTION DETECTION

In the context of pattern classification, one of the major issues discussed by most of researchers is ‘curse of dimensionality’ problem which occurs in data classification because the data processed in most of the application is high-dimensional feature space.So, the essential consideration here is that irrelevant features should be identified which causes less classification accuracy and the main motto is to find a minimum set of attributes from the initial set of data helping to make the patterns easier to understand along with improved classification accuracy and reduced learning time. Therefore, the selection of feature set is the process to search for an optimal feature subset from the initial data set without compromising the classification performance and efficiency in generating classification model. In this paper, we develop a hybrid classifier by combining CHAID and genetic algorithm. Initially, the genetic algorithm with ABC operator will extract the best attributes and based on the extracted attributes the CHAID will generate the decision tree. We analyze the performance with different datasets and compare the analysis with the existing technique.
Toward Network-based Keyword Extraction from Multitopic Web Documents

In this paper we analyse the selectivity measure calculated from the complex network in the task of the automatic keyword extraction. Texts, collected from different web sources (portals, forums), are represented as directed and weighted co-occurrence complex networks of words. Words are nodes and links are established between two nodes if they are directly co-occurring within the sentence. We test different centrality measures for ranking nodes - keyword candidates. The promising results are achieved using the selectivity measure. Then we propose an approach which enables extracting word pairs according to the values of the in/out selectivity and weight measures combined with filtering.