Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic task and data mapping in shared memory architectures

Reducing the cost of memory accesses, both in terms of performance and energy consumption, is a major challenge in shared-memory architectures. Modern systems have deep and complex memory hierarchies with multiple cache levels and memory controllers, leading to a Non-Uniform Memory Access (NUMA) behavior. In such systems, there are two ways to improve the memory affinity: First, by mapping tasks that share data (communicate) to cores with a shared cache, cache usage and communication performance are improved. Second, by mapping memory pages to memory controllers that perform the most accesses to them and are not overloaded, the average cost of accesses is reduced. We call these two techniques task mapping and data mapping, respectively. For optimal results, task and data mapping need to be performed in an integrated way. Previous work in this area performs the mapping only separately, which limits the gains that can be achieved. Furthermore, most previous mechanisms require expensive operations, such as communication or memory access traces, to perform the mapping, require changes to the hardware or to the parallel application, or use a simple static mapping. These mechanisms can not be considered generic solutions for the mapping problem. In this thesis, we make two contributions to the mapping problem. First, we introduce a set of metrics and a methodology to analyze parallel applications in order to determine their suitability for an improved mapping and to evaluate the possible gains that can be achieved using an optimized mapping. Second, we propose two automatic mechanisms that perform task mapping and combined task/data mapping, respectively, during the execution of a parallel application. These mechanisms work on the operating system level and require no changes to the hardware, the applications themselves or their runtime libraries. An extensive evaluation with parallel applications from multiple benchmark suites as well as real scientific applications shows substantial performance and energy efficiency improvements that are significantly higher than simple mechanisms and previous work, while maintaining a low overhead.
Protective effect of estrogens on the brain of rats with essential and endocrine hypertension

Abstract Estrogen neuroprotection has been shown in pathological conditions damaging the hippocampus, such as trauma, aging, neurodegeneration, excitotoxicity, oxidative stress, hypoglycemia, amyloid-β peptide exposure and ischemia. Hypertensive encephalopathy also targets the hippocampus; therefore, hypertension seems an appropriate circumstance to evaluate steroid neuroprotection. Two experimental models of hypertension, spontaneously hypertensive rats (SHR) and deoxycorticosterone (DOCA)-salt hypertensive rats, develop hippocampal abnormalities, which include decreased neurogenesis in the dentate gyrus, astrogliosis, low expression of brain-derived neurotrophic factor (BDNF) and decreased number of neurons in the hilar region, with respect of their normotensive strains Wistar Kyoto (WKY) and Sprague-Dawley rats. After estradiol was given for 2 weeks to SHR and DOCA-treated rats, both hypertensive models normalized their faulty hippocampal parameters. Thus, estradiol treatment positively modulated neurogenesis in the dentate gyrus of the hippocampus, according to bromodeoxyuridine incorporation and doublecortin immunocytochemistry, decreased reactive astrogliosis, increased BDNF mRNA and protein expression in the dentate gyrus and increased neuronal number in the hilar region of the dentate gyrus. A role of local estrogen biosynthesis is suggested in SHR, because basal aromatase mRNA in the hippocampus and immunoreactive aromatase protein in cell processes of the dentate gyrus were highly expressed in these rats. Estradiol further stimulated aromatase-related parameters in SHR but not in WKY. These observations strongly support that a combination of exogenous estrogens to those locally synthesized might better alleviate hypertensive encephalopathy. These studies broaden estrogen neuroprotective functions to the hippocampus of hypertensive rat models.
Shear behaviour of continuous concrete beams reinforced with GFRP bars

i ABSTRACT Continuous beams represent main structural elements in most reinforced concrete (RC) structures such as parking garages and overpass bridges. Deterioration of such structures due to corrosion of steel reinforcement is common in North America. To overcome the corrosion problems, the use of fiber-reinforced polymer (FRP) bars and stirrups becomes a viable alternative to steel reinforcement. However, to date, the shear behaviour of FRP-RC continuous beams has not been explored yet. As such, the objective of this study is to investigate the shear behaviour of such beams. In this study, twenty four full-scale continuous concrete beams were constructed and tested. The test beams had rectangular cross section with 200-mm width and a height of 300, 550 or 850 mm and were continuous over two equal spans. The main investigated parameters were concrete strength, type and ratio of longitudinal reinforcement, type and ratio of transverse reinforcement and beam effective depth. Moreover, a 3-D nonlinear finite element model (FEM) was constructed to simulate the behaviour of FRP-RC continuous beams. The model was verified against the experimental results and validated against test results from previous studies. Then, the verified/validated model was used to conduct a parametric study to investigate the effect of a wide range of the parameters on the shear behaviour of GFRP-RC beams. The experimental and FEM results showed that shear-critical GFRP-RC continuous beams exhibited moment redistribution. Also, it was observed that increasing the concrete strength and the longitudinal reinforcement ratio increased the shear strength significantly. Moreover, the presence of GFRP stirrups significantly enhanced the shear strength of the tested beams. Regarding the size effect, test results showed that there was adverse or no size effect on the shear
Enabling the Participation of People with Parkinson's and their Caregivers in Co-Inquiry around Collectivist Health Technologies

While user participation is central to HCI, co-inquiry takes this further by having participants direct and control research from conceptualisation to completion. We describe a co-inquiry, conducted over 16 months with a Parkinson's support group. We explored how the participation of members might be enabled across multiple stages of a research project, from the generation of research questions to the development of a prototype. Participants directed the research into developing alternative modes of information provision, resulting in 'Parkinson's Radio' -- a collectivist health information service produced and edited by members of the support group. We reflect on how we supported participation at different stages of the project and the successes and challenges faced by the team. We contribute insights into the design of collectivist health technologies for this group, and discuss opportunities and tensions for conducting co-inquiry in HCI research.
Localizing object relatively with discrete wavelet transform feature through a similarity measure

Relativity improves localization of any object in visual aspects. Utilizing the same principle, this paper discusses about the relative localization of object of interest with DWT features and the classification of object with due reference of the background through a similarity measure. The proposed approach takes the advantage of DWT compressive features and effective distance measure making overall process very efficient and computationally inexpensive. Comparing proposed approach with four state-of-art algorithms on various video datasets, the experimental results show advancements in low center location error and visual object overlap rate.
Way adaptable D-NUCA caches

Non-uniform cache architecture (NUCA) aims to limit the wire-delay problem typical of large on-chip last level caches: by partitioning a large cache into several banks, with the latency of each one depending on its physical location and by employing a scalable on-chip network to interconnect the banks with the cache controller, the average access latency can be reduced with respect to a traditional cache. The addition of a migration mechanism to move the most frequently accessed data towards the cache controller (D-NUCA) further improves the average access latency. In this work we propose a last-level cache design, based on the D-NUCA scheme, which is able to significantly limit its static power consumption by dynamically adapting to the needs of the running application: the way adaptable D-NUCA cache. This design leads to a fast and power-efficient memory hierarchy with an average reduction by 31.2% in energy-delay product (EDP) with respect to a traditional D-NUCA. We propose and discuss a methodology for tuning the intrinsic parameters of our design and investigate the adoption of the way adaptable D-NUCA scheme as a shared L2 cache in a chip multiprocessor (CMP) system (24% reduction of EDP).
Three‐Dimensional Magnetic Assembly of Microscale Hydrogels

Directed assembly of nano and microscale particles is of great interest and has widespread applications in various fields including electronics, nanomaterials and tissue engineering. Bottom-up tissue engineering is motivated by the occurrence of repeating functional units in vivo. The bottom-up approach requires novel techniques to assemble engineered functional units as building blocks at a high speed with spatial control over three-dimensional (3D) micro-architecture. Here, we report a magnetic assembler that utilizes nanoparticles and microscale hydrogels as building blocks to create 3D complex multi-layer constructs via external magnetic fields using different concentrations of magnetic nanoparticles. This approach holds potential for 3D assembly processes that could be utilized in various tissue engineering and regenerative medicine applications.
Object Detection For Visually Impaired

In this project, we are evaluating the efficacy of utilizing deep learning and convolutional neural networks to support individuals who have visual disabilities. Our objective is to create a mobile or web application that aids people with visual impairments in their daily activities by assisting them in recognizing objects within their proximity. This application leverages recent advancements in machine learning, particularly deep learning in conjunction with transfer learning. By employing convolutional neural networks, the approach becomes less reliant on specific characteristics. The process begins by capturing an image or video through the camera. The algorithm identifies the objects present and transmits the anticipated object names to text-to-speech software. Subsequently, this software converts the names into auditory output, providing a means for visually impaired individuals to perceive the information.
Binary decompositions for high-order entropy coding of grayscale images

High-order entropy coding (HOEC) has the potential to provide higher compression ratios than the usually used zero-order entropy coding (ZOEC) approaches. However, serious implementation difficulties severely limit the practical value of HOEC for grayscale image compression. We examine the bit-plane decomposition (BPD) representation as a simple alternative that bypasses some of the implementation difficulties of HOEC. We show, however, that BPD introduces undesired coding overhead when used to represent grayscale images. We therefore propose a new binary image representation called magnitude-based binary decomposition (MBBD) which avoids any coding overhead when used to represent grayscale images. Thus, MBBD both bypasses the implementation difficulties of HOEC and does not have the drawbacks of the BPD. We present numerical experiments that verify the theoretical analysis of the BPD and MBBD representations. In addition, our experiments demonstrate that MBBD-HOEC yields better results than ZOEC for lossy image compression and is also very effective for progressive image transmission.
Vehicle sticker recognition based on multi-feature encoding and feature matrix distance

A novel algorithm to use vehicle sticker (or tag) features and encode the features is proposed. It can make the representation more precise and recognition more accurate. In vehicle recognition or searching, traditional algorithms will be limited because they focus only on the features extracted from colors, logos or sub-types that are not enough to identify a vehicle. Furthermore, the license plate (LP) can be forged easily so the LP is not reliable to identify a specified vehicle. Our algorithm solves this problem by sticker multi-feature encoding. Most vehicles have printed permission labels or certification symbols named vehicle stickers or tags mounted on the frontal glass. These stickers are a kind of special fingerprint features to identify a unique vehicle. Every driver has his own habit to paste different stickers. In this meaning these stickers form specified multi-feature including color, shape, position and amount. Our algorithm encodes the sticker multi-feature to construct structured feature presentation, i.e. the sticker code. In recognition stage, with the matrix distance of the multi-feature encoding, the detailed sticker code can be utilized to distinguish the vehicle types and colors reliably, and can recognize the tiny difference among vehicles with the same colors, logos and even sub-types. Our algorithm decreases the amount of vehicle candidates effectively by accurate feature coding. In our experiments, we coped with 10000 vehicle images taken by public traffic surveillance system to verify the effectiveness of this algorithm in vehicle sticker multi-feature encoding recognition.
Off-Line Sensing: Memorizing Interactions in Passive 3D-Printed Objects

Embedding sensors into objects allow them to recognize various interactions. However, sensing usually requires active electronics that are often costly, need time to be assembled, and constantly draw power. Thus, we propose off-line sensing: passive 3D-printed sensors that detect one-time interactions, such as accelerating or flipping, but neither require active electronics nor power at the time of the interaction. They memorize a pre-defined interaction via an embedded structure filled with a conductive medium (e.g., a liquid). Whether a sensor was exposed to the interaction can be read-out via a capacitive touchscreen. Sensors are printed in a single pass on a consumer-level 3D printer. Through a series of experiments, we show the feasibility of off-line sensing.
Dissecting the role of the γ-subunit in the rotary–chemical coupling and torque generation of F1-ATPase

Significance Understanding the molecular basis of energy conversion in F1-ATPase is of fundamental importance and requires a clear structure-based description of the system with particular emphasis on the conversion of ATP chemical energy to mechanical torque. Here we used our coarse-grained model of F1-ATPase and generated a structure-based rotary–chemical landscape that reproduced the observed torque for the wild-type enzyme without using phenomenological parameters. In doing so, we discovered the principles that determine whether free energy surfaces of specific shapes can lead to torque generation. Furthermore, by calculating the torque in several types of γ-deletion systems, we gained major insights into the molecular nature of the F1-ATPase rotary motor and established that electrostatics are important in generating the rotary–chemical coupling. Unraveling the molecular nature of the conversion of chemical energy (ATP hydrolysis in the α/β-subunits) to mechanical energy and torque (rotation of the γ-subunit) in F1-ATPase is very challenging. A major part of the challenge involves understanding the rotary–chemical coupling by a nonphenomenological structure–energy description, while accounting for the observed torque generated on the γ-subunit and its change due to mutation of this unit. Here we extend our previous study that used a coarse-grained model of the F1-ATPase to generate a structure-based free energy landscape of the rotary–chemical process. Our quantitative analysis of the landscape reproduced the observed torque for the wild-type enzyme. In doing so, we found that there are several possibilities of torque generation from landscapes with various shapes and demonstrated that a downhill slope along the chemical coordinate could still result in negligible torque, due to ineffective coupling of the chemistry to the γ-subunit rotation. We then explored the relationship between the functionality and the underlying sequence through systematic examination of the effect of various parts of the γ-subunit on free energy surfaces of F1-ATPase. Furthermore, by constructing several types of γ-deletion systems and calculating the corresponding torque generation, we gained previously unknown insights into the molecular nature of the F1-ATPase rotary motor. Significantly, our results are in excellent agreement with recent experimental findings and indicate that the rotary–chemical coupling is primarily established through electrostatic effects, although specific contacts through γ-ionizable residue side chains are not essential for establishing the basic features of the coupling.
Using Media While Interacting Face-to-Face Is Associated With Psychosocial Well-Being and Personality Traits

Positive face-to-face human interactions are known to benefit well-being. Drawing upon previous work regarding the interference of media (via technological devices or print) in social interaction, the aim of this study was to identify whether using media during face-to-face interaction could potentially limit the positive effect of interaction on well-being. Participants were 437 university students who completed an online survey which assessed media multitasking behaviors, well-being (trait depression, trait anxiety, social anxiety, empathy, and psychological well-being), and personality traits (Big-5 and narcissism). Face-to-face interaction was positively associated with well-being. However, when media use during face-to-face interaction was considered, there was a negative relationship with well-being (more depression, more anxiety, and less psychological well-being). Those who used certain media types, such as phone or video chatting, listening to music, and gaming, while interacting with others, also had lower scores on measures of empathy. Regression analyses showed significant contributions by these media types to empathy levels, even after controlling for age, gender, and personality traits. Face-to-face media multitasking was related to higher levels of narcissism and neuroticism, and lower levels of agreeableness, conscientiousness, and openness. This study provides insight into the possible role of media multitasking during face-to-face interaction on psychosocial outcomes.
The Roll Angle Consistency Constraint

A new method is presented for reasoning with constraints on the three orientation parameters of an object which arise from the identification of features discovered in a single image. The method uses a geometrical model of the object and a tetrahedral representation of view-direction. The final angle parameter, the roll angle, is computed for each view direction. This method is subject to errors from the data and the assumptions inherent in the use of view directions. These errors are taken into account by considering upper and lower bounds on the roll angle. A method is presented for using constraints on the orientation parameters to prune the search for a consistent set of labelled image lines and to guide an iterative inversion of the view perspective. One of the major problems with the recognition of a three dimensional object from a single view is that the geometry of the image of the object can change radically as the object rotates with respect to the camera. This
How Stress Affects Functional Near-Infrared Spectroscopy (fNIRS) Measurements of Mental Workload

Recent work has demonstrated that functional Near-Infrared Spectroscopy has the potential to measure changes in Mental Workload with increasing ecological validity. It is not clear, however, whether these measurements are affected by anxiety and stress of the workload, where our informal observations see some participants enjoying the workload and succeeding in tasks, while others worry and struggle with the tasks. This research evaluated the effects of stress on fNIRS measurements and performance, using the Montreal Imaging Stress Task to manipulate the experience of stress. While our results largely support this hypothesis, our conclusions were undermined by data from the Rest condition, which indicated that Mental Workload and Stress were often higher than during tasks. We hypothesize that participants were experiencing anxiety in anticipation of subsequent stress tasks. We discuss this hypothesis and present a revised study designed to better control for this result.
A Physics Based Method for Combining Multiple Anatomy Models with Application to Medical Simulation

We present a physics based approach to the construction of anatomy models by combining components from different sources; different image modalities, protocols, and patients. Given an initial anatomy, a mass-spring model is generated which mimics the physical properties of the solid anatomy components. This helps maintain valid spatial relationships between the components, as well as the validity of their shapes. Combination can be either replacing/modifying an existing component, or inserting a new component. The external forces that deform the model components to fit the new shape are estimated from Gradient Vector Flow and Distance Transform maps. We demonstrate the applicability and validity of the described approach in the area of medical simulation, by showing the processes of non-rigid surface alignment, component replacement, and component insertion.
Utilizing Narrative Grounding to Design Storytelling Gamesfor Creative Foreign Language Production

Foreign language students must learn to use language creatively to overcome knowledge gaps and keep readers or listeners interested. However, few tools exist to support practicing this skill. Therefore, we set out to explore design of storytelling games for practicing creative language use. Through an iterative design process, we identified narrative grounding (establishing common ground for collaborative narrative) as key to student engagement and learning. However, designing games for narrative grounding while keeping the game flexible enough to easily accommodate teacher goals is challenging. Considering this challenge, we designed a collaborative storytelling game where students help scaffold the narrative and teachers can easily integrate language goals with "language cards". In an in-classroom evaluation with 36 students, we show the importance of narrative grounding for learning. Qualitative evidence also suggests narrative grounding makes the game more engaging for players. We conclude with discussion of design implications for digital language learning tools.
An integrated approach for the selection of software requirements using fuzzy AHP and fuzzy TOPSIS method

Software requirements selection (SRS) is an important research issue of the software requirements elicitation process. Different methods have been developed for the selection of software requirements by using the ant colony optimization, teaching learning based optimization, “analytic hierarchy process” (AHP), “technique for order preference by similarity to ideal solution” (TOPSIS), etc. Based on our literature review, we identify that existing methods for SRS do not support the integrated approach based on fuzzy AHP and fuzzy TOPSIS for the selection of software requirements from the set of requirements. Therefore, to address this issue in this paper we propose an “ integrated fuzzy AHP and fuzzy TOPSIS method for the selection of software requirements”. Finally, the explanation of the proposed method is demonstrated with the help of a case study.