Miracle-dz/UniFAR-FTUs huggingface dataset:
Lagrangian coherent structures in low Reynolds number swimming

This work explores the utility of the finite-time Lyapunov exponent (FTLE) field for revealing flow structures in low Reynolds number biological locomotion. Previous studies of high Reynolds number unsteady flows have demonstrated that ridges of the FTLE field coincide with transport barriers within the flow, which are not shown by a more classical quantity such as vorticity. In low Reynolds number locomotion (O(1)–O(100)), in which viscous diffusion rapidly smears the vorticity in the wake, the FTLE field has the potential to add new insight to locomotion mechanics. The target of study is an articulated two-dimensional model for jellyfish-like locomotion, with swimming Reynolds number of order 1. The self-propulsion of the model is numerically simulated with a viscous vortex particle method, using kinematics adapted from previous experimental measurements on a live medusan swimmer. The roles of the ridges of the computed forward- and backward-time FTLE fields are clarified by tracking clusters of particles both backward and forward in time. It is shown that a series of ridges in front of the jellyfish in the forward-time FTLE field transport slender fingers of fluid toward the lip of the bell orifice, which are pulled once per contraction cycle into the wake of the jellyfish, where the fluid remains partitioned. A strong ridge in the backward-time FTLE field reveals a persistent barrier between fluid inside and outside the subumbrellar cavity. The system is also analyzed in a body-fixed frame subject to a steady free stream, and the FTLE field is used to highlight differences in these frames of reference.
Network Big Data: A Literature Survey on Stream Data Mining

With the rapid development of Internet, the internet of things and other information technology, big data usually exists in cyberspace as the form of the data stream. It brings great benefits for information society. Meanwhile, it also brings crucial challenges on big data mining in the data stream. Recently, academic and industrial communities have a widespread concern on massive data mining problems and achieve some impressive results. However, due to the big data mining in data stream, those researches are not enough. In this paper, we analyzed the characteristics of stream data mining under big data environment, discussed the challenges and research issues of big data mining in data stream and summarized research achievements on data mining of massive data.
Data Load Distribution by Semi Real Time Data Warehouse

Today many organizations used data warehouse for strategic decision making. Today's real-time business stresses the potential to process increasingly volumes of data at very high speed in order to stay competitive in market. Data Warehouse is populated by data extraction, transformation and loading from different data sources by software utilities called ETL (Extraction, transformation & loading). ETL process is a time consuming process as it has to process large volume of data. ETL processes must have certain completion time window and ETL process must have to finish within this time window. In this paper we discusses a technique to distribute the volume of data to be extracted, transformed and loaded into data warehouse by merging both conventional and real-time techniques, so ETL process finishes its job within its time window by utilizing ETL idle time.
Digital Image Processing Using LabView

Digital Image processing is a topic of great relevance for practically any project, either for basic arrays of photodetectors or complex robotic systems using artificial vision. It is an interesting topic that offers to multimodal systems the capacity to see and understand their environment in order to interact in a natural and more efficient way. The development of new equipment for high speed image acquisition and with higher resolutions requires a significant effort to develop techniques that process the images in a more efficient way. Besides, medical applications use new image modalities and need algorithms for the interpretation of these images as well as for the registration and fusion of the different modalities, so that the image processing is a productive area for the development of multidisciplinary applications. The aim of this chapter is to present different digital image processing algorithms using LabView and IMAQ vision toolbox. IMAQ vision toolbox presents a complete set of digital image processing and acquisition functions that improve the efficiency of the projects and reduce the programming effort of the users obtaining better results in shorter time. Therefore, the IMAQ vision toolbox of LabView is an interesting tool to analyze in detail and through this chapter it will be presented different theories about digital image processing and different applications in the field of image acquisition, image transformations. This chapter includes in first place the image acquisition and some of the most common operations that can be locally or globally applied, the statistical information generated by the image in a histogram is commented later. Finally, the use of tools allowing to segment or filtrate the image are described making special emphasis in the algorithms of pattern recognition and matching template.
Introducing Tool Support for Managing Architectural Knowledge: An Experience Report

Management of software architecture knowledge is vital for improving an organisation's architectural capabilities. Despite the recognition of the importance of capturing and reusing software architecture knowledge, there is currently no suitable support mechanism available. To address this issue, we have developed a conceptual framework for managing architecture design knowledge. A Web-based knowledge management tool, Process-based Architecture Knowledge Management Environment (PAKME), has been developed to support that framework. PAKME is being trialled to help systematise the architecture knowledge management and evaluation process of an industrial collaborator. This paper reports the objectives, logistics and initial findings of this project, Specifically we have deployed and used PAKME in an Australian Defence acquisition environment for evaluating architecture of a aircraft system.
Non-affine response: Jammed packings vs. spring networks

We compare the elastic response of spring networks whose contact geometry is derived from real packings of frictionless discs, to networks obtained by randomly cutting bonds in a highly connected network derived from a well-compressed packing. We find that the shear response of packing-derived networks, and both the shear and compression response of randomly cut networks, are all similar: the elastic moduli vanish linearly near jamming, and distributions characterizing the local geometry of the response scale with distance to jamming. Compression of packing-derived networks is exceptional: the elastic modulus remains constant and the geometrical distributions do not exhibit simple scaling. We conclude that the compression response of jammed packings is anomalous, rather than the shear response.
LightPS: Lightweight Content-Based Publish/Subscribe for Peer-to-Peer Systems

In this paper we present a technique for content-based publish/subscribe (pub/sub) systems that works without (distributed) explicit multicast group management. This technique, so-called LightPS, employs a rendezvous-based approach and, thus, maps subscription and event information into the peer-to-peer node Id keyspace. On the contrary to what could be expected, LightPS suits for high-dimensional pub/sub domains, requiring very low memory capacity and time to run subscription and event notification processes. We present its good performance through a formal theoretical analysis.
An Empirical Study of China's Stock Market Segmentation Based on Pooled-Panel TARCH Model

We study the market segmentation between A-shares and B-shares as well as A-shares and H-shares through the panel data of duel-listing companies. The segmentation can be reflected by the consistency of asset pricing, or by the information transferring mechanism. Therefore, we divide the close-to-close return between weeks into two parts: close-to-open return and open-to-close return, and then tests the segmentation using pooled-panel TARCH spill-over effect model. The results show that the segmentation between A, B and H shares mainly embodies the information flow relationship, and segmentation defined by asset pricing relationship is insignificant. The reasons of segmentation between A-B stocks are different with that between A-H stocks, the former reflects mainly the differences in institution factors and investors’ risk appetite, and the latter reflects mainly the difference in investors’ recognition.
To predict possible profit/loss of a movie to be launched using MLP with back-propagation learning

Film industry is the most important component of entertainment industry. Both profit and loss are very high for this business. Like every other business, business prediction system plays a vital role for this industry. Before release of a particular movie, if the Production Houses or distributors get any type of prediction that how the film will do business, then it will be very useful to reduce the risk of the investors. In this paper we have proposed a method using back propagation neural network for prediction about a given movie's profitability. Note that, a similar such method has been successfully applied in the field of Stock Market Prediction, Weather Prediction and Image Processing.
Dynamic Emotion Representation system

My motivation is to participate in the creation of Virtual Humans as described in Gratch et al, 2002. The research into Virtual Humans brings together many different fields, such as computer graphics, Artificial Intelligence, speech recognition, speech synthesis, psychology, philosophy and many more. But my interest is orientated towards emotions and personalities. Much work has been carried out to give emotions and personality to Virtual Humans or agents, as I will call them later in this report. But no one has implemented a complete emotional system, from environment appraisal to action selection and including all types of emotions. I am not proposing to solve this problem and to do all the work but instead I propose to separate the huge task in smaller ones. I will concentrate on a dynamic representation of emotion or a Dynamic Emotion Representation as the title of this report suggests. In fact, through the remainder of the paper I will explain why it is possible to dissociate the representation of emotion from the generation of emotion and from the action selection. In the first place, I will give an overview of a theory of emotions in section 2; followed by a presentation of two theories of mood in section 3. A personality model is briefly introduced in section 4. Then, I will review the implementations of different emotional and personality models in section 5. In section 6, I will present my arguments for the implementation of a Dynamics Emotion Representation system. I will expose my first attempt of this implementation and its results in section 7. Finally, section 8 will explain the possible future developments and the conclusion will be section 9.
A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles : Final Report

For this project, I implemented the genetic algorithm-based jigsaw puzzle solver described in “A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles” by Sholomon et al [1]. This method involves producing a “child” solution from two “parent” solutions by detecting, extracting, and combining correctly assembled puzzle pieces. The solver developed by the paper’s team is able to accurately solve puzzles of up to 22,834 pieces in a reasonable time. My solver is not able to quickly solve puzzles of such size, but is able to solve smaller puzzles with high accuracy.
Association Rules Mining Based on Statistical Correlation

Association rules mining is an important data mining task, this paper emphatically analyzes realization skill and defects of existing algorithms. On the basis, a novel measure, named statistical correlation, which can indicate the correlation degree of multi-items in a rule, is put forward to cut association rules with independent or negative correlation, and its concept, calculating formulas and primary characteristics are defined. In order to conveniently use statistical correlation to cut redundancy rules, a fast association rules mining algorithm, called F-Fminer, is designed with two new data structures UFP-Tree and FP-Forest which use multi-trees structure to store data. F-Fminer adopts divide and conquer strategy to mine frequent itemsets for every UFP-Tree basing on depth-first searching. It can be seen from experimentation that the method in this paper has greatly enhanced mining efficiency and reduced a lot of redundant rules than other classical algorithms.
Introducing authority and hubness into graph matching

Spectral matching is an efficient approach for inexact graph matching. Many spectral matching methods boil down to power iteration which calculate the confidence vector iteratively. Inspired by the Web page ranking method Hypertext Induced Topic Search (HITS), we introduce hubness vector and authority vector to replace the traditional confidence vector, and an iterative algorithm is proposed to solve the subgraph matching problem. The incorporation of hubness and authority can help reduce the distraction caused by outliers, and provides better robustness against outliers. The performance of the proposed algorithm is evaluated on both synthetic graphs and real-world images.
Review: Advances in BTI modeling for the design of reliable ICs

A brief review of the impact of Bias-Temperature Instability (BTI) on Integrated Circuits (IC) is presented. The importance of this problem with the performance degradation on commercial IC designs, such as Physical Unclonable Functions (PUF) and Ring Oscillator (RO), which are key elements on security applications, is emphasized. A physical interpretation of this phenomenon is provided along with a brief description of the model's evolution, commonly used to simulate the degradation of transistor's parameters at high temperature and voltage supply conditions. Finally, some reported techniques are described to accomplish a better IC design, from the Process, Temperature and Voltage (PVT) variations point of view, lifetime yield optimization and weak spot detection on circuit's architecture.
Integrability for relativistic spin networks

The evaluation of relativistic spin networks plays a fundamental role in the Barrett-Crane state sum model of Lorentzian quantum gravity in 4 dimensions. A relativistic spin network is a graph labelled by unitary irreducible representations of the Lorentz group appearing in the direct integral decomposition of the space of L^2 functions on three-dimensional hyperbolic space. To `evaluate' such a spin network we must do an integral; if this integral converges we say the spin network is `integrable'. Here we show that a large class of relativistic spin networks are integrable, including any whose underlying graph is the 4-simplex (the complete graph on 5 vertices). This proves a conjecture of Barrett and Crane, whose validity is required for the convergence of their state sum model.
A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation

This paper proposes a new robotic rehabilitation training platform that is motivated by the requirement for adjusting the training strategy and intensity in a patient-specific manner. The platform is implemented for tele-rehabilitation and is comprised of a haptic device operated by therapists, a lightweight exoskeleton worn by patients and a visually shared model. Through the visually shared model, the motion of the therapist and patient are measured and mapped to the motion of the corresponding object. Thus, the force generated by the therapist can be transferred to the patient for delivering training, while real-time force feedback with high transparency can be provided to the therapist so they know the amount of force being applied to patients in real time. In particular, both assistive therapy in the early stages and resistive therapy in the later stages of stroke can be performed. The home-use exoskeleton device is specifically designed to be light-weight and compliant for safety. The patient-exoskeleton and therapist-haptic interaction performance is evaluated by observing the muscle activities and interaction force. Two volunteers were requested to imitate the process of the therapist-in-the-loop training to evaluate the proposed platform.
Methylenetetrahydrofolate Reductase (MTHFR) C677T Polymorphism and Age at Onset of Schizophrenia: No Consistent Evidence for an Association in the Nordic Population

Methylenetetrahydrofolate reductase (MTHFR) is an enzyme involved in metabolic pathways of importance for nucleotide synthesis and methylation of DNA, membranes, proteins and lipids. The MTHFR gene includes a common polymorphism (rs1801133 or C677T), which is associated with enzyme activity. The T‐allele of the C677T polymorphism has been associated with earlier age at onset of schizophrenia in a Scandinavian population, although no association was found in replication attempts in other populations. Extending the study to five Nordic samples consisting of 2,198 patients with schizophrenia, including the original Scandinavian samples, there was no significant association between MTHFR C677T polymorphism and age at onset in schizophrenia. The present results do not suggest that the investigated MTHFR polymorphism has any significant influence on age at onset of schizophrenia in the Nordic population. © 2012 Wiley Periodicals, Inc.
Distributed plan reconfiguration via knowledge transfer in multi-agent systems under local LTL specifications

We propose a cooperative motion and task planning scheme for multi-agent systems where the agents have independently-assigned local tasks, specified as Linear Temporal Logic (LTL) formulas. These tasks contain hard and soft sub-specifications. A least-violating initial plan is synthesized first for the potentially infeasible task and the partially-known workspace. While the system runs, each agent updates its knowledge about the workspace via its sensing capability and shares this knowledge with its neighboring agents. Based on this update, each agent verifies and revises its plan in real time. It is ensured that the hard specification is always fulfilled and the satisfaction for the soft specification is improved gradually. The design is distributed as only local interactions are assumed. The overall framework is demonstrated by a case study.
The Relationship between Exercise Addiction, Physical Activity Level and Body Mass Index of the Students Who Are Studying at Physical Education and Sports College.

Context: The aim of the study was to identify the relationship between exercise addiction (EA) and physical activity levels (PAL) and body mass index (BMI) of students of Physical Education and Sports College (PESC). Methods: The study consists of 204 male and 101 female from Erzincan University PESC. Personal Information Form, Exercise Dependence Scale-21 and Physical Activity Survey were used. The data were analyzed in SPSS22.0 for windows. KruskalWallis-H, Mann-Whitney-U and Spearman Correlation tests were used. Results: It was detected that % 13,2 of male students of PESC, % 15.8 of female students and % 14.1 of students in general were exercise addicted, while there was no significant difference between BMI according to the EA status of male students of PESC, the PAL of exercise addicted and candidate of addiction was statistically significantly higher in comparison with non-addict, according to the EA status of female students of PESC there was no significant difference between neither MBI nor PAL, the BMI of male students of PESC is significantly higher, the PAL values of candidates of EA is significantly higher, there was no significant differences between BMI and PAL values of the students according to the family economic level, there was no significant relationship neither male nor female students between BMI and PAL according to the EA levels. Conclusion: It was extrapolated that, few of the students are EA; the students who are high in EA are also high in PAL values, the family economic, level on the students‟ EA level is not an effective factor, and also there was no significant relationship between BMI and PAL according to the EA status.
Applying Artificial Neural Network Proton - Proton Collisions at LHC

this paper shows that the use the optimal topology of an Artificial Neural Network (ANN) for a particular application is one of the difficult tasks. Neural Network is optimized by a Genetic Algorithm (GA), in a hybrid technique, to calculate the multiplicity distribution of the charged shower particles on Larger Hadron Collider (LHC). Moreover, ANN, as a machine learning technique, is usually used for modeling physical phenomena by establishing its new function. In case of modeling the p-p interactions at LHC experiments, ANN is used to simulate and predict the distribution, Pn, as a function of the number of charged particles multiplicity, n, the total center of mass energy ( s
Spectral techniques for high-resolution thermal characterization with limited sensor data

Elevated chip temperatures are true limiters to the scalability of computing systems. Excessive runtime thermal variations compromise the performance and reliability of integrated circuits. To address these thermal issues, state of the art chips have integrated thermal sensors that monitor temperatures at a few selected die locations. These temperature measurements are then used by thermal management techniques to appropriately manage chip performance. Thermal sensors and their support circuitry incur design overheads, die area, and manufacturing costs. In this paper, we propose a new direction for full thermal characterization of integrated circuits based on spectral Fourier analysis techniques. Application of these techniques to temperature sensing is based on the observation that die temperature is simply a space varying signal, and that space varying signals are treated identically to time varying signals in signal analysis. We utilize Nyquist-Shannon sampling theory to devise methods that can almost fully reconstruct the thermal status of an integrated circuit during runtime using a minimal number of thermal sensors. We propose methods that can handle uniform and non uniform thermal sensor placements. We develop an extensive experimental setup and demonstrate the effectiveness of our methods by thermally characterizing a 16-core processor. Our method produces full thermal characterization with an average absolute error of 0.6% using a limited number of sensors.