Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed but Not Mixing: Enabling Agency and Collaboration among a Diverse Student Teacher Cohort to Support Culturally Responsive Teaching and Learning.

The rich diversity of pupils entering Irish primary schools has risen in recent years. This prompts initial teacher education (ITE) programme designers to be increasingly aware of how to best prepare and support student teachers. Consequently, it is important that such diversity be reflected among cohorts entering ITE. This research explores perspectives of a group of first year students on a primary ITE programme, in an Irish higher-level institution (HEI). The cohort comprised of traditional entrants (TE), who entered ITE directly from second-level education, with a smaller more diverse sub-group of mature students (MS), returning to formal education. Science education was used as a lens to exemplify markers of diversity and differentiation within the group. While the TE are a relatively homogenous group from social and cultural perspectives, the data indicated that the learning needs of both the TE and MS were highly differentiated. We present the complexities for teacher educators teaching a diverse and differentiated cohort, and the challenges of preparing large undergraduate cohorts in ITE. The results illustrate varied perceptions of the ‘other’ and assumptions about the ‘other’ between the MS and TE, which in this study were found to impede integration and collaboration across these discrete groups. This paper suggests that the diversity evident within and across the MS and TE groups offers scope for new understandings about diversity in ITE. We conclude that valuable opportunities exist for significant mutual learning and appreciation of difference, by structuring greater integration and opportunities for peer dialogue and collaboration between MS and TE cohorts.
A PRACTICAL APPROACH FOR SERVING LARGE AMOUNTS OF GEOSPATIAL DATA VIA COMPUTER NETWORKS

In this paper we describe our efforts to develop a system that generates interactive Virtual Environment at the client PCs, by rendering geo-spatial data that is stored at a remote server. The end-users of geospatial data generally demand visually appealing, upto-date and quite accurate data that covers a vast landscape. Considering these requirements we tried to implement a distributed servers/clients architecture system that significantly uses data compression, computer graphics and network technologies. Tera-bytes of geospatial data have been reorganized in a way that achieves reasonable data transfer rates even in low-bandwidth networks. Our current progress gives promising results for the future and the final system is supposed to meet the requirements of remote geospatial data users.
The Fan Effect : New Results and New Theories 1

The fan effect (Anderson, 1974) has been attributed to interference among competing associations to a concept. Recently, it has been suggested that such effects might be due to multiple mental models (Radvansky, Spieler, & Zacks, 1993) or suppression of concepts (Anderson & Spellman, 1995; Conway & Engle, 1994). We show that the ACT-R (Adaptive Control of Thought-Rational) theory, which embodies associative interference, is consistent with the Radvansky et al results and we fail to find any evidence for concept suppression in a new fan experiment. The ACT-R model provides good quantitative fits to the results from a variety of experiments. The three key concepts in these fits are (a) the associative strength between two concepts reflect the degree to which one concept predicts the other; (b) foils are rejected by retrieving mismatching facts; and (c) subjects can adjust the relative weights they give to various cues in retrieval.
Feature coverage for better homography estimation: An application to image stitching

The spatial distribution of feature points is known to affect the accuracy of homography estimation and hence the effectiveness of applications such as image stitching. This paper explores the use of spatial statistics as a metric for feature coverage, the distribution of features in an image. The coverages achieved by several feature detectors are compared and it is shown that detectors with higher coverages achieve better results in an image-stitching application. It is found that that SFOP is the most effective detector considered.
The Influence of Socioeconomic Factors on Health Parameters in Overweight and Obese Adults

The prevalence of being overweight and of obesity is increasing worldwide, and is associated with a high risk to health. Therefore, the aim of our study was to investigate whether normal weight, overweight and obese subjects of low, middle or high socioeconomic status (SES) differ with regard to their health behavior, health, quality of life, and the use of medical care. Data from the Austrian Health Interview Survey (ATHIS) 2006/07, comprising 3 groups of 1,077 individuals, each of whom were normal weight, overweight, or obese, respectively, and matched according to their age, sex and SES, were analyzed concerning health outcomes. The results show that subjects with a low SES differ significantly from those of high SES in terms of their health behavior, self-perceived health, levels of impairment, chronic conditions, quality of life, and health care. Additionally, obesity in adults is associated with sub-optimal dietary practices and worse health, poorer quality of life and medical care than normal weight and overweight individuals. A significant interaction between the weight class and SES was found concerning physical exercise, impairment due to health problems and chronic diseases. A low SES has a strong negative impact on health, especially in obese individuals. Therefore a continuous target group-oriented, non-discriminatory public health program is required, prioritizing obese subjects with low SES.
Is Attention Based on Spatial Contextual Memory Preferentially Guided by Low Spatial Frequency Signals?

A popular model of visual perception states that coarse information (carried by low spatial frequencies) along the dorsal stream is rapidly transmitted to prefrontal and medial temporal areas, activating contextual information from memory, which can in turn constrain detailed input carried by high spatial frequencies arriving at a slower rate along the ventral visual stream, thus facilitating the processing of ambiguous visual stimuli. We were interested in testing whether this model contributes to memory-guided orienting of attention. In particular, we asked whether global, low-spatial frequency (LSF) inputs play a dominant role in triggering contextual memories in order to facilitate the processing of the upcoming target stimulus. We explored this question over four experiments. The first experiment replicated the LSF advantage reported in perceptual discrimination tasks by showing that participants were faster and more accurate at matching a low spatial frequency version of a scene, compared to a high spatial frequency version, to its original counterpart in a forced-choice task. The subsequent three experiments tested the relative contributions of low versus high spatial frequencies during memory-guided covert spatial attention orienting tasks. Replicating the effects of memory-guided attention, pre-exposure to scenes associated with specific spatial memories for target locations (memory cues) led to higher perceptual discrimination and faster response times to identify targets embedded in the scenes. However, either high or low spatial frequency cues were equally effective; LSF signals did not selectively or preferentially contribute to the memory-driven attention benefits to performance. Our results challenge a generalized model that LSFs activate contextual memories, which in turn bias attention and facilitate perception.
Online estimating voltage source inverter nonlinearity for PMSM by Adaline neural network

This paper investigates how to online estimate the voltage source inverter (VSI) nonlinearity by Adaline neural network (ANN) in a permanent magnet synchronous machine (PMSM) drive system. The proposed estimation includes the estimation of PMSM stator winding resistance, inductance and rotor flux linkage and can follow the VSI nonlinearity variation due to the variation of PMSM working condition. Compared with existing literatures, the proposed method is fit for id=0 control and does not need the nominal value of any PMSM parameter and will not suffer from the mismatching between actual parameter value and nominal parameter value. The estimated distorted voltage value due to VSI nonlinearity is used for compensating the drive system and the experimental result shows that it can improve the control performance more significantly compared with existing VSI nonlinearity compensation method.
Adaptive Engagement of Older Adults' Fitness through Gamification

Many older adults lead sedentary lifestyles, as the challenges of aging can complicate efforts to maintain a healthy level of physical activity. These challenges can include decreasing strength, reduced mental capacity, social isolation, and the development of chronic health conditions. My PhD research attempts to analyze the needs and challenges of older adults and review their attitudes and motivations towards physical activity (PA). Furthermore, I aim to investigate various approaches in the development of socially interactive fitness activity programs, with the goal of increasing positive lifestyle motivations and quality of life (QoL). This research defines a taxonomy of motivational and personality characteristics of older adults to engage in PA. Lastly, this dissertation proposes the development of an adaptive application that addresses fitness gamification from the motivational perspective of an older adult. This application will empower older adults to engage in PA as a means to gain freedom, mobility and social interdependence within their public spheres.
The Origin of Inference: Ediacaran Ecology and the Evolution of Bayesian Brains

The evolution of spiking neurons and nervous systems in the late Ediacaran period simultaneously with the evolution of carnivores around 550 million years ago can be explained by the need for accurately timed decisions under an imminent threat of being eaten. A simple model shows that threshold triggering devices, spiking neurons, are utility-maximizing decision-makers for the timing of escape reflexes given the sensory cues available to Ediacaran animals at the onset of carnivory. Decisions are suboptimal for very weak stimuli, providing selection pressure for secondary processing of primary spike train data. A simple network can make approximately Bayes optimal decisions given stochastic spike trains. Decisions that are arbitrarily close to Bayes optimal can be obtained by enlarging this network. A subnetwork that computes the Bayesian posterior density of the critical state variable - distance between predator and prey - emerges as a core component of the decision-making mechanism. This is a neural analog of a Bayesian particle filter with cerebellar-like architecture. The model explains fundamental properties of neurons and nervous systems in modern animals and makes testable predictions.
Improving the efficiency of adaptive routing in networks with irregular topology

Networks of workstations are emerging as a cost-effective alternative to parallel computers. The interconnection between workstations usually relies on switch-based networks with irregular topologies. This irregularity makes routing and deadlock avoidance quite complicated. Current proposals avoid deadlock by removing cyclic dependencies between channels and therefore, many messages are routed along non-minimal paths, increasing latency and wasting resources. We propose a general methodology for the design of adaptive routing algorithms for networks with irregular topology that improves a previously proposed one by reducing the probability of routing over non-minimal paths. The resulting routing algorithms allow messages to follow minimal paths in most cases, reducing message latency and increasing network throughput. As an example of application, we propose an improved adaptive routing algorithm for Autonet.
Pricing Of Asian Exchange Rate Options Under Stochastic Interest Rates As A Sum Of Delayed Payment O

The aim of the paper is to develop pricing formulas for European type Asian options written on the exchange rate in a two currency economy. The exchange rate as well as the foreign and domestic zero coupon bond prices are assumed to follow geometric Brownian motions. As a special case of a discrete Asian option we analyse the delayed payment currency option and develop closed form pricing and hedging formulas. The main emphasis is devoted to the discretely sampled Asian option. It is shown how the value of this option can be approximated as the sum of Black-Scholes options. The formula is obtained under the application of results developed by Rogers and Shi (1995) and Jamshidian (1991). In addition bounds for the pricing error are determined. Date. September 1997. This Version: March 5, 1999. JEL Classi cation. G13.
A framework for simulating hybrid MTJ/CMOS circuits: Atoms to system approach

A simulation framework that can comprehend the impact of material changes at the device level to the system level design can be of great value, especially to evaluate the impact of emerging devices on various applications. To that effect, we have developed a SPICE-based hybrid MTJ/CMOS (magnetic tunnel junction) simulator, which can be used to explore new opportunities in large scale system design. In the proposed simulation framework, MTJ modeling is based on Landau-Lifshitz-Gilbert (LLG) equation, incorporating both spin-torque and external magnetic field(s). LLG along with heat diffusion equation, thermal variations, and electron transport are implemented using SPICE-in built voltage dependent current sources and capacitors. The proposed simulation framework is flexible since the device dimensions such as MgO thickness and area, are user defined parameters. Furthermore, we have benchmarked this model with experiments in terms of switching current density (JC), switching time (TSWITCH) and tunneling magneto-resistance (TMR). Finally, we used our framework to simulate STT-MRAMs and magnetic flip-flops (MFF).
Understanding discretion in modern policing

Discretion is a ubiquitous and legitimate aspect of modern policing, though its scope and limits are poorly understood. In this article, the authors seek to refine our understanding of discretion in modern policing by examining the historical evolution of the concept, and the modern challenges facing individual officers and police organisations in reconciling, on the one hand, the duty to enforce the law fairly and impartially, and on the other hand, the need to temper strict law enforcement for sound policy and operational reasons. The article reviews recent case law in Canada exploring these tensions within policing and the proper limits of police discretion.
Support Vector Machine Learning for Region‐Based Image Retrieval with Relevance Feedback

We present a relevance feedback approach based on multi‐class support vector machine (SVM) learning and cluster‐merging which can significantly improve the retrieval performance in region‐based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low‐level features and high‐level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re‐clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two‐class SVM and multi‐class relevance feedback methods.
Security through Elliptic Curves for Wireless Network in Mobile Devices

The basic principle is "A function is easy to evaluate but its invert is infeasible unless a secret key is known". It is mathematically proved that security of cryptographic does not imply its implementation security of system against Side-channel Attacks. The security of system lies in the difficulty of extracting k from P and Q. It is essential to secure the implementation of cryptosystems in embedded devices against side-channel attacks. These attacks monitor the power consumption or the Electromagnetic emanations of a device Ex smart cards or mobile devices. The attacker's goal is to retrieve partial or full information about a long-term key that is employed in several ECSM executions. We are implementing a secret key avoid to retrieving the valuable information by the attacker through Simple Power Analysis Attacks.
Affinity-aware DMA buffer management for reducing off-chip memory access

It is well recognized that moving I/O data in/out memory has become critical for high bandwidth devices. Specifically, the embedded system, with limited cache size and simple architecture, consumes a large amount of CPU cycles for off-chip memory access. The work presented in this paper addresses this problem through an Affinity-aware DMA Buffer management strategy, called ADB, requiring no change to underlying hardware. We introduce the concept of buffer affinity describes the data location of the recently released DMA buffer in the memory hierarchy. The more data in cache, the higher affinity the buffer has. Based on the character of the embedded system, we can identify buffer affinity at runtime. Using this online profiling, ADB allocates buffer with different affinity. For output processes, ADB allocates the high affinity buffer to reduce off-chip memory access when OS copies data from the user buffer to the kernel buffer. For input processes, ADB allocates the low affinity buffer to skip part of cache invalidation operations for maintaining I/O coherence. Measurements show that ADB, implemented in the Linux-2.6. 32 kernel and running on a 1GHz UniCore-2 processor, improves the performance of network related programs from 5.2% to 8.8%.
Rapid and sensitive detection of glucose based on radio frequency resonator fabricated using integrated passive device technology

This paper reports a highly sensitive mediator-free glucose biosensor based on miniaturized radio frequency resonator fabricated on Gallium Arsenide substrate using integrated passive device technology. The level of electromagnetic coupling between coupled stepped-impedance resonators, which significantly determines the resonator characteristics, is perturbed enough by a glucose sample. As a result, the proposed glucose biosensor exhibits linear detection range of 1 to 5 mg/mL with ultrahigh sensitivity and ultralow detection limit of 0.37 GHz/mgmL-1 and 0.0948 μM, respectively. Additionally, the quantities, such as propagation constant (γ), impedance (Z), resistance (R), inductance (L), conductance (G) and capacitance (C), estimated from the measured S-parameters, enable the effective multi-dimensional detection of glucose.
Expression of microRNA-122 contributes to apoptosis in H9C2 myocytes

Objectives The microRNAs (miRNAs) can post-transcriptionally regulate gene expression and heart development. Pax-8 gene knockout mice have apparent heart abnormalities. This study investigated the role of miRNAs in regulation of cardiac apoptosis and development in the knockout mice. Methods microRNA microarrays demonstrated differential expression of microRNAs between Pax-8−/− and Pax-8+/− mice, confirmed by real-time PCR. Results miR-122 was up-regulated by 1.92 folds in Pax-8−/− mice. There were ventricular septum defects in Pax-8−/− mice, and increased numbers of apoptotic cells in the left ventricular wall and interventricular septum in Pax-8−/− mice. In H9C2 myocytes, treatment with miR-122 mimics or miR-122 inhibitor affect the expression of CCK-8 and activity of Caspase-3. Conclusions The miR-122 is upregulated in the myocytes of Pax-8−/− mice and may participate in the apoptotic gene expression and pathogenesis of heart development defect.
Two-Scale limit of the solution to a Robin Problem in Perforated Media

The two scale convergence of the solution to a Robin's type-like problem of a stationary diffusion problem in a periodically perforated domain is investigated. It is shown that the Robin's problem converges to a problem associated to a new operator which is the sum of a standard homogenized operator plus an extra first order "strange" term; its appearance is due to the non-symmetry of the diffusion matrix and to the non rescaled resistivity.
Is High Performance Contagious among Knowledge Workers?

This study investigates the peer effects of worker productivity among knowledge workers who interact through digital communication channels. We draw on social and developmental network research to examine the performance of 248 knowledge workers and their 1,027 email contacts. The average performance of contacts in a digital communication network relates positively to employee performance: Interacting with high performing coworkers benefits the employee’s performance, though no evidence indicates that interacting with low performers harms it. Consistent with preferential attachment and direct reciprocity explanations, high performing employees benefit most from the performance of employees in their network. Social network researchers should consider both structure and resources to determine the effects of networks.
Performance of the Improvements of the CAESAR Robot

Robots are able to enter concealed and unstable environments inaccessible to rescuers. Previous Urban Search And Rescue (USAR) robots have experienced problems with malfunction of communication systems, traction systems, control and telemetry. These problems were accessed and improved in developing a prototype robot called CAESAR, which is an acronym for Contractible Arms Elevating Search And Rescue. Problems encountered with previous USAR robots are discussed. The mechanical, sensory and communication systems that were used on CAESAR are briefly explained. Each system was separately tested by performed experiments. Results of field tests and the robot performance experienced during a disaster scenario that was created are discussed. The capabilities of CAESAR are explained in these tests to determine if some of the problems experienced previously are solved.
Automatic Derivation, Integration and Verification of Synchronization Aspects in Object-Oriented Design Methods

In this project, which we call SaVES (Specification, Synthesis and Verification of Embedded Systems), we investigate solutions to the problem of implementing robust, efficient global synchronization policies in multithreaded, distributed and object-oriented embedded software systems. Synchronization is an important aspect in the design of complex embedded systems. Object-oriented analysis and design techniques have been used successfully to design and maintain large software systems, but multiple shortcomings in their treatment of synchronization aspects limit their effectiveness in many applications. We propose to advance the use of object-oriented methodologies to design high-assurance embedded systems by addressing the following challenges: