Miracle-dz/UniFAR-FTUs huggingface dataset:
A scenario-based run-time task mapping algorithm for MPSoCs

The application workloads in modern MPSoC-based embedded systems are becoming increasingly dynamic. Different applications concurrently execute and contend for resources in such systems which could cause serious changes in the intensity and nature of the workload demands over time. To cope with the dynamism of application workloads at run time and improve the efficiency of the underlying system architecture, this paper presents a novel scenario-based run-time task mapping algorithm. This algorithm combines a static mapping strategy based on workload scenarios and a dynamic mapping strategy to achieve an overall improvement of system efficiency. We evaluated our algorithm using a homogeneous MPSoC system with three real applications. From the results, we found that our algorithm achieves an 11.3% performance improvement and a 13.9% energy saving compared to running the applications without using any run-time mapping algorithm. When comparing our algorithm to three other, well-known run-time mapping algorithms, it is superior to these algorithms in terms of quality of the mappings found while also reducing the overheads compared to most of these algorithms.
A Survey of RFID Authentication Protocols Based on Hash-Chain Method

Security and privacy are the inherent problems in RFID communications. There are several protocols have been proposed to overcome those problems. Hash chain is commonly employed by the protocols to improve security and privacy for RFID authentication. Although the protocols able to provide specific solution for RFID security and privacy problems, they fail to provide integrated solution. This article is a survey to closely observe those protocols in terms of its focus and limitations.
Impact of Mechanical Stress on the Full Chip Timing for Through-Silicon-Via-based 3-D ICs

In this paper, we study the impact of through-silicon-via (TSV) and shallow trench isolation (STI) stress on the timing variations of 3-D IC. We also propose the first systematic TSV-STI-stress-aware timing analysis and show how to optimize layouts for better performance. First, we generate a stress contour map with an analytical radial stress model for TSV. We also develop a stress model for STI from finite element analysis results. Then, depending on geometric relation between TSVs, STI, and transistors, the tensile and compressive stresses are converted to hole and electron mobility variations. Mobility-variation-aware cell library and netlist are generated and incorporated into an industrial engine for timing analysis of 3-D IC. We observe that TSV stress and STI stress interact with each other, and rise and fall time react differently to stress and relative locations with respect to both TSVs and STIs. Overall, TSV-STI-stress-induced timing variations can be as much as ±15% at the cell level. Thus, as an application to layout optimization, we exploit the stress-induced mobility enhancement to improve performance of 3-D ICs. We show that stress-aware layout perturbation could reduce cell delay by up to 23.37% and critical path delay by 6.67% in our test case.
An Adaptive Spatial Subdivision of the Object Space for Fast Collision Detection of Animated Rigid Bodies

Collision detection tests between objects dominate run time simulation of rigid body animation. Traditionally, hierarchical bounding box tests are used to minimize collision detection time. But the bounding boxes do not take shapes of the objects into account which results in a large number of collision detection tests. We propose an adaptive spatial subdivision of the object space based on octree structure to rectify this problem. We also present a technique for efficiently updating this structure periodically during the simulation.
A Point Cloud Contour Extraction Method based on Plane Segmentation

A method based on plane segmentation and dimensionality reduction for extracting incomplete and slow contour features of object point clouds is proposed. The method consists of two main steps: plane segmentation and contour extraction. In plane segmentation, the random sample consensus (Random Sample Consensus, RANSAC) algorithm is optimized based on principal component analysis (Principal Component Analysis, PCA); the optimized planar point cloud is then subjected to dimensionality reduction, and the contour features are extracted using gradients. Experimental results show that the method can effectively segment point clouds and extract the contours of target surfaces, and has great potential for application in industrial inspection and other fields.
The DEAD box RNA helicase family in Arabidopsis thaliana.

The numerous genomic sequences and ESTs released by the Arabidopsis thaliana Genome Initiative (AGI) have allowed a systematic and functional study of the DEAD box RNA helicase family. Sequencing and in silico analysis led to the characterization of 28 novel A. thaliana DEAD box RNA helicases forming a family of 32 members, named AtRH. Fourteen AtRH genes with an unexpected heterogeneous mosaic structure are described and compared bringing new information about the genesis of the gene family. The mapping of the AtRH genes shows their repartition on the five chromosomes without clustering and therefore AtRH s have been estimated to 60 genes per A.thaliana haploid genome. Sequence comparisons revealed a very conserved catalytic central domain flanked or not by four classes of extensions in the N- and/or C- extremities. The global amino acid composition of the extensions are tentatively correlated to specific functions such as targeting, protein interaction or RNA binding. The expression of the 32 AtRH genes has been recorded in different tissues. Separate patterns of expression and alternative polyadenylation sites have been shown. Based on the integration of all this information, we propose a classification of the AtRH proteins into subfamilies with associated functions.
The Effect of Sustainability Assurance on Asymmetry Information : Evidence Indonesia

The objective of this research is to examine whether there is a correlation between the issuance of sustainability reporting assurance by publicly listed companies in Indonesia and a decrease in information asymmetry, thereby enhancing the trustworthiness of these reports among investors and other stakeholders. Given the limited adoption of sustainability assurance practices among Indonesian companies, this research employs multivariate analysis on a dataset consisting of 250 sustainability reports from listed companies period 2017 - 2021. The focus is on elucidating how voluntary sustainability assurance contributes to mitigating issues related to asymmetric information. Moreover, the study aims to evaluate by using bid-ask spread as an indicator.
STUDIES OF L FORMS AND PROTOPLASTS OF GROUP A STREPTOCOCCI

L forms of Group A streptococci have been isolated by the use of penicillin gradient agar plates. Osmotically fragile protoplasts of Group A streptococci have been obtained by the use of Group C phage-associated lysin which lyses Group A streptococci and their isolated cell walls. Membranes surrounding these enzymatically derived protoplasts have been isolated, and chemical and immunological studies indicate that they are free of cell wall carbohydrate and M protein. The streptococcal protoplasts reproduce as colonies which are morphologically indistinguishable from streptococcal L forms. Evidence is presented to show that these two streptococcal derivatives are serologically and physiologically related to each other as well as to the parent streptococcal strain from which they were isolated.
Extracellular Vesicle Protein Expression in Doped Bioactive Glasses: Further Insights Applying Anomaly Detection

Proteomic analysis of extracellular vesicles presents several challenges due to the unique nature of these small membrane-bound structures. Alternative analyses could reveal outcomes hidden from standard statistics to explore and develop potential new biological hypotheses that may have been overlooked during the initial evaluation of the data. An analysis sequence focusing on deviating protein expressions from donors’ primary cells was performed, leveraging machine-learning techniques to analyze small datasets, and it has been applied to evaluate extracellular vesicles’ protein content gathered from mesenchymal stem cells cultured on bioactive glass discs doped or not with metal ions. The goal was to provide additional opportunities for detecting details between experimental conditions that are not entirely revealed with classic statistical inference, offering further insights regarding the experimental design and assisting the researchers in interpreting the outcomes. The methodology extracted a set of EV-related proteins whose differences between conditions could be partially explainable with statistics, suggesting the presence of other factors involved in the bioactive glasses’ interactions with tissues. Outlier identification of extracellular vesicles’ protein expression levels related to biomaterial preparation was instrumental in improving the interpretation of the experimental outcomes.
Housing Market Dynamics : On the Contribution of Income Shocks and Credit Constraints

We propose a life-cycle model of the housing market with a property ladder and a credit constraint. We focus on equilibria which replicate the facts that credit constraints delay some households’ first home purchase and force other households to buy a home smaller than they would like. The model helps us identify a powerful driver of the housing market: the ability of young households to afford the down payment on a starter home, and in particular their income. The model also highlights a channel whereby changes in income may yield housing price overshooting, with prices of tradeup homes displaying the most volatility, and a positive correlation between housing prices and transactions. This channel relies on the capital gains or losses on starter homes incurred by credit-constrained owners. We provide empirical support for our arguments with evidence from both the U.K. and the U.S. Earlier versions of this work were circulated as discussion papers entitled “Housing Market Fluctuations in a Life-Cycle Economy with Credit Constraints. ” We thank Jean-Pascal Benassy, Jeffrey Campbell, V.V. Chari, Mark Gertler, Charles Goodhart, John Heaton, Nobu Kiyotaki, Erzo G.J. Luttmer, Christopher Mayer, David Miles, John Moore, Victor Rios-Rull, Tsur Somerville, Nancy Wallace, Ingrid Werner and Christine Whitehead for helpful discussions and suggestions. We also benefited from comments of participants at various conferences and seminars. The hospitality and support of the following institutions are gratefully acknowledged: SFB 303 and the Department of Statistics at the University of Bonn, the Center for Economic Studies at the University of Munich, the Financial Markets Group at LSE, the Wharton School, and the Institut d’Economie Industrielle at the University of Toulouse. Department of Real Estate and Urban Land Economics, School of Business, University of Wisconsin– Madison, 975 University Avenue, Madison, WI 53706, USA; email: fom@bus.wisc. edu Department of Economics, University of Munich, Kaulbachstr. 45, D-80539 Munich, Germany; email: sven.rady@lrz.uni-muenchen.de
Exploring EEG Emotion Recognition through Complex Networks: Insights from the Visibility Graph of Ordinal Patterns

The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, we propose a novel method for constructing complex networks from EEG signals for emotion recognition, which begins with phase space reconstruction to obtain ordinal patterns and subsequently forms a graph network representation from the sequence of ordinal patterns based on the visibility graph method, named ComNet-PSR-VG. For the proposed ComNet-PSR-VG, the initial step involves mapping EEG signals into a series of ordinal partitions using phase space reconstruction, generating a sequence of ordinal patterns. These ordinal patterns are then quantified to form a symbolized new sequence. Subsequently, the resulting symbolized sequence of ordinal patterns is transformed into a graph network using the visibility graph method. Two types of network node measures, average node degree (AND) and node degree entropy (NDE), are extracted from the graph networks as the inputs of machine learning for EEG emotion recognition. To evaluate the effectiveness of the proposed construction method of complex networks based on the visibility graph of ordinal patterns, comparative experiments are conducted using two types of simulated signals (random and Lorenz signals). Subsequently, EEG emotion recognition is performed on the SEED EEG emotion dataset. The experimental results show that, with AND as the feature, our proposed method is 4.88% higher than the existing visibility graph method and 12.23% higher than the phase space reconstruction method. These findings indicate that our proposed novel method for constructing complex networks from EEG signals not only achieves effective emotional EEG pattern recognition but also exhibits the potential for extension to other EEG pattern learning tasks, suggesting broad adaptability and application potential for our method.
Restoration of Mature Etiolated Cucumber Hypocotyl Cell Wall Susceptibility to Expansin by Pretreatment with Fungal Pectinases and EGTA in Vitro1

Mature plant cell walls lose their ability to expand and become unresponsive to expansin. This phenomenon is believed to be due to cross-linking of hemicellulose, pectin, or phenolic groups in the wall. By screening various hydrolytic enzymes, we found that pretreatment of nongrowing, heat-inactivated, basal cucumber (Cucumis sativus) hypocotyls with pectin lyase (Pel1) from Aspergillus japonicus could restore reconstituted exogenous expansin-induced extension in mature cell walls in vitro. Recombinant pectate lyase A (PelA) and polygalacturonase (PG) from Aspergillus spp. exhibited similar capacity to Pel1. Pel1, PelA, and PG also enhanced the reconstituted expansin-induced extension of the apical (elongating) segments of cucumber hypocotyls. However, the effective concentrations of PelA and PG for enhancing the reconstituted expansin-induced extension were greater in the apical segments than in the basal segments, whereas Pel1 behaved in the opposite manner. These data are consistent with distribution of more methyl-esterified pectin in cell walls of the apical segments and less esterified pectin in the basal segments. Associated with the degree of esterification of pectin, more calcium was found in cell walls of basal segments compared to apical segments. Pretreatment of the calcium chelator EGTA could also restore mature cell walls' susceptibility to expansin by removing calcium from mature cell walls. Because recombinant pectinases do not hydrolyze other wall polysaccharides, and endoglucanase, xylanase, and protease cannot restore the mature wall's extensibility, we can conclude that the pectin network, especially calcium-pectate bridges, may be the primary factor that determines cucumber hypocotyl mature cell walls' unresponsiveness to expansin.
Comparing neural network with linear Regression for stock market prediction

There are both gains and losses possible in stock market investing. Brokerage firms' stock investments carry a higher risk of loss since their stock prices are not being tracked or analyzed, which might be problematic for businesses seeking investors or individuals. Thanks to progress in information and communication technologies, investors may now easily collect and analyze stock market data to determine whether to buy or sell. Implementing machine learning algorithms in data mining to obtain information close to the truth from the desired objective will make it easier for an individual or group of investors to make stock trades. In this study, we test hypotheses on the performance of a financial services firm's stock using various machine learning and regression techniques. The relative error for the neural network method is only 0.72 percentage points, while it is 0.78 percentage points for the Linear Regression. More training cycles must be applied to the Algortima neural network to achieve more accurate results.
Cross-Dressing by Donor Dendritic Cells after Allogeneic Bone Marrow Transplantation Contributes to Formation of the Immunological Synapse and Maximizes Responses to Indirectly Presented Antigen

The stimulation of naive donor T cells by recipient alloantigen is central to the pathogenesis of graft-versus-host disease after bone marrow transplantation (BMT). Using mouse models of transplantation, we have observed that donor cells become “cross-dressed” in very high levels of recipient hematopoietic cell–derived MHC class I and II molecules following BMT. Recipient-type MHC is transiently present on donor dendritic cells (DCs) after BMT in the setting of myeloablative conditioning but is persistent after nonmyeloablative conditioning, in which recipient hematopoietic cells remain in high numbers. Despite the high level of recipient-derived alloantigen present on the surface of donor DCs, donor T cell proliferative responses are generated only in response to processed recipient alloantigen presented via the indirect pathway and not in response to cross-dressed MHC. Assays in which exogenous peptide is added to cross-dressed MHC in the presence of naive TCR transgenic T cells specific to the MHC class II–peptide combination confirm that cross-dressed APC cannot induce T cell proliferation in isolation. Despite failure to induce T cell proliferation, cross-dressing by donor DCs contributes to generation of the immunological synapse between DCs and CD4 T cells, and this is required for maximal responses induced by classical indirectly presented alloantigen. We conclude that the process of cross-dressing by donor DCs serves as an efficient alternative pathway for the acquisition of recipient alloantigen and that once acquired, this cross-dressed MHC can assist in immune synapse formation prior to the induction of full T cell proliferative responses by concurrent indirect Ag presentation.
Learning Optimal Adaptation Strategies in Unpredictable Motor Tasks

Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.
Pallidotomy increases activity of motor association cortex in parkinson's disease: A positron emission tomographic study

Stereotactic posteroventral pallidotomy can improve motor performance in Parkinson's disease. Interruption of inhibitory pallidal projections to ventrolateral thalamus, components of a cortical‐basal ganglia motor loop allows for this clinical benefit. We hypothesized that pallidotomy would lead to increased movement related activity in motor cortical areas receiving projections from ventrolateral thalamus. This was tested in 6 Parkinson's disease patients who underwent stereotactic posteroventral pallidotomy. Each patient was imaged with positron emission tomography (PET) measures of regional cerebral blood flow (rCBF) during performance of a simple prehension task and at rest. Scans were acquired before and 17 weeks after surgery. After pallidotomy, movement‐related changes of rCBF increased significantly in both the supplementary motor area (SMA) and premotor cortex but not in primary motor cortex. The results demonstrate the importance of pallidothalamic circuitry for regulating volitional movements and confirm that disruption of inhibitory input to the ventrolateral thalamus can augment movement‐related activity in motor association areas.
Back-End-of-Line Defect Analysis for Rnv8T Nonvolatile SRAM

Rnv8T nonvolatile SRAM combines conventional SRAM and resistive RAM to provide both fast access speed and data retention. Traditional test methods for conventional SRAM or resistive RAM are not suitable for nonvolatile SRAM. This paper analyzes the defective behavior of the Rnv8T nonvolatile SRAM based on defect injection and simulation. Simulation results showed that the inject defects caused stuck-at faults and transition faults which escaped from conventional March tests. Based-on the defective behavior and circuit operations, a straight forward test algorithm is proposed to detect the escaped faults.
Undergraduates’ experience and satisfaction of massive open online course in Malaysia

While massive open online course (MOOC) is gaining popularity, studies have shown how negative user experience of the learning platform can lead to issues related to poor learning performance. OpenLearning, as the official national coordinated MOOCs program for higher education institutions in Malaysia, is still considerably gaining momentum in this country, thus much can still be observed in the usability aspect of this platform from the student’s perspective. This study aimed to evaluate the aspects of user experience and learning satisfaction of MOOC OpenLearning involving undergraduate students in a public university in Malaysia. Data were gathered through the online survey method which adapted questions from the user experience questionnaire (UEQ) and the usefulness, satisfaction, and ease of use (USE) questionnaire. Descriptive analyses revealed moderate positive levels of user experience and learning satisfaction towards MOOC OpenLearning among the respondents. Furthermore, there was a significant positive correlation between both factors, suggesting the importance of considering user experience as the key factor in enhancing students’ satisfaction with MOOC. Recommendations arising from the students’ perceptions and concerns on the usability aspect contribute towards developing strategic guidelines for enhancing students’ learning experiences, especially during circumstances that necessitate effective online learning strategies.
IDW‐Plus: An ArcGIS Toolset for Calculating Spatially Explicit Watershed Attributes for Survey Sites

Watershed characteristics such as land‐use and land‐cover affect stream condition at multiple scales, but it is widely accepted that conditions in close proximity to the stream or survey site tend to have a stronger influence. Although spatially weighted watershed metrics have existed for years, nonspatial lumped landscape metrics (i.e., areal mean or percentage) are still widely used because relatively few technical skills are needed to implement them. The Inverse Distance Weighted Percent Land Use for Streams (IDW‐Plus) custom ArcGIS toolset provides the functionality to efficiently calculate six spatially explicit watershed metrics which account for the Euclidean or flow length distance to the stream or outlet, as well as the probability for overland runoff. These include four distance‐weighted metrics, those being inverse Euclidean distance to the stream or outlet, and the inverse flow length to the stream or outlet. Two tools are also included to generate hydrologically active (i.e., runoff potential), inverse flow length to the stream or outlet metrics. We demonstrate the tools using real data from Southeast Queensland, Australia. We also provide detailed instructions, so readers can recreate the examples themselves before applying the tools to their own data.