Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Memory-centric Computer Architectures

Faster and cheaper computers have been constantly demanding technological and architectural improvements. However, current technology is suffering from three technology walls: leakage wall, reliability wall, and cost wall. Meanwhile, existing architecture performance is also saturating due to three well-known architecture walls: memory wall, power wall, and instruction-level parallelism (ILP) wall. Hence, a lot of novel technologies and architectures have been introduced and developed intensively. Our previous work has presented a comprehensive classification and broad overview of memory-centric computer architectures. In this article, we aim to discuss the most important classes of memory-centric architectures thoroughly and evaluate their advantages and disadvantages. Moreover, for each class, the article provides a comprehensive survey on memory-centric architectures available in the literature.
Multilinear Compressed Sensing using Tensor Least Angle Regression (T-LARS)

Multilinear compressed sensing generalizes the compressed sensing formulation to tensor signals, where the tensor signal is reconstructed using much fewer samples obtained in a sparse domain by solving a multilinear sparse coding problem. The Kronecker-OMP, a generalization of Orthogonal Matching Pursuit (OMP) solves the L0 constrained multilinear sparse least-squares problems. However, with the problem dimensions and the number of iterations, the space and computational cost of Kronecker-OMP increase in the polynomial order. Authors have previously developed a generalized least-angle regression(LARS), known as Tensor Least Angle Regression (T-LARS), with a lower asymptotic space and computational complexity than Kronecker-OMP to efficiently solve both L0 and L1 constrained multilinear sparse least-squares problems. In this paper, we used T-LARS to solve multilinear compressed sensing problems and compared the results with Kronecker-OMP, where the T-LARS is 56 times faster than Kronecker-OMP in reconstructing the 3D PET-CT images using compressed sensing samples.
Energy-efficient In-Memory Address Calculation

Computation-in-Memory (CIM) is an emerging computing paradigm to address memory bottleneck challenges in computer architecture. A CIM unit cannot fully replace a general-purpose processor. Still, it significantly reduces the amount of data transfer between a traditional memory unit and the processor by enriching the transferred information. Data transactions between processor and memory consist of memory access addresses and values. While the main focus in the field of in-memory computing is to apply computations on the content of the memory (values), the importance of CPU-CIM address transactions and calculations for generating the sequence of access addresses for data-dominated applications is generally overlooked. However, the amount of information transactions used for “address” can easily be even more than half of the total transferred bits in many applications. In this article, we propose a circuit to perform the in-memory Address Calculation Accelerator. Our simulation results showed that calculating address sequences inside the memory (instead of the CPU) can significantly reduce the CPU-CIM address transactions and therefore contribute to considerable energy saving, latency, and bus traffic. For a chosen application of guided image filtering, in-memory address calculation results in almost two orders of magnitude reduction in address transactions over the memory bus.
Comparison of imputation techniques to deal with censored responses in a bivariate experimental design

Introduction : The analysis of designs of experiments with bivariate answers can be a challenge for the researcher, especially when some data of the answers are censored. Chowdhury and Aggarwala (2007) presented a set of techniques to impute values to the censored data, in this work those techniques are compared. A method proposed by Chiao and Hamada (2001) is used to identify the optimal setting of parameters. The case with the data here is the one reported by Harper, Kosbe and Peyton (1987) about the imbalance of a plastic wheel cover component. Method : The data of the experiment are not censored originally, these are analyzed in order to have a base of comparison. Criteria are then implemented to censor 16 and 21 percent of the responses, generating two new data sets, to which the imputation techniques are applied: 1) conditional expectation after regression of the responses, 2) order statistics and 3) simulated observations. For each generated data set, the optimal setting of parameters (X opt ) is determined and the sum of squares of the error (SCE) is calculated. Results : With the censored data at 16%, the imputation techniques: conditional expectation starting with Y 1 , order statistics for Y 1 and simulated observations for Y 2 , generate values with which a X opt is obtained that agrees with the original data. With the censored data at 21%, none of the techniques obtains a X opt that matches the original data. The sum of squares of the error of the response 1 (SCE 1 ) of simulated observations for Y 2 is significantly smaller compared to that of the other methods. The difference between the SCE 2 resulting in all the techniques is not considerable. Conclusion : After comparing the X opt and the SCE resulting from the data sets imputed with the mentioned techniques it can be said that the simulated observations method with Y 2 works better to deal with censored responses of the bivariate experiment design that is being worked on here.
Efficient Cable Surface Defect Detection with Deep Learning

Efficient detection of cable surface defects can prevent and reduce the potential dangers in the process of high voltage transmission. In order to achieve efficient detection of cable surface defects and solve the problem of low detection accuracy of small and unobvious defects on cable surface, we propose an efficient cable surface defect detection model with deep learning. Firstly, the lightweight backbone feature extraction network is used to extract the preliminary defect features. Secondly, the parallel convolution module and serial convolution module are designed to obtain abundant defect features and reduce the number of model parameters. Then, the feature fusion module is designed to fuse the shallow features with deep features to enhance the features of small and unobvious defects. Finally, the obtained features are put into the corresponding detection head to get the final prediction results. The experimental results on local cable dataset show that our method achieves favorable trade-off between the accuracy, speed and model size of the cable surface defect detection, which meets the requirements of high accuracy, high speed and small model in industrial application.
Development of an English Teaching Robot for Japanese Students

∗ In this paper, an English teaching robot is developed for teaching English words for Japanese students. The robot makes full use of multiple English teaching skills to make the students understand and remember the words easily, like explaining the words from its etymology, showing some synonym words and explaining the words as well as showing a related image. The robot can also detect the status of the students and adjust its teaching strategies through effective interactions. The effectiveness of our developed robot is proven by conducting new words teaching lessons.
PCube: Scaling LoRa Concurrent Transmissions with Reception Diversities

This article presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique that is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency-based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.
Cache-related Hardware Capabilities and Their Impact on Information Security

Caching is an important technique to speed-up execution, and its implementation and use cases vary. When applied specifically to the memory hierarchy, caching is used to speed up memory accesses and memory translations. Different cache implementations are considered microarchitectural secrets and oftentimes change between generations. The integration of caches in hardware greatly influences security policy enforcement in the platform since caches maintain copies of code and data and their security properties. Examples of attacks due to the existence of caches are side-channels against cryptographic software, recent speculative execution abuses to leak secret data, and usages of cache-based manipulations (e.g., forcing cache splits/incoherence) to hide from security software detection. This survey examines the security issues due to different cache usages in a microarchitecture. The survey also explains the most complicated caching features and their impact on the security of the platform in different scenarios.
Development of Multi-modal Physiological Signals Acquisition Platform and Its Application in Emotional Recognition Based on a Small Number of Samples

Emotion is a typical psychological process with obvious physiological characteristics. Physiological signals are difficult to imitate and can reflect real emotions, so the research of emotion recognition based on physiological signals is necessary. Multi-modal physiological signals are complementary, which have better performance than single-modal signals in emotion recognition. Because traditional multi-modal physiological signals acquisition device is complex or not suitable for portable products development, a multi-modal physiological signals acquisition platform with simple structure is designed in this paper to collect heart rate, ECG and body temperature. 52 features are extracted from multi-modal physiological signals in time domain and Mutual Information Feature Selection (MIFS) method is used in feature selection to obtain subset with better recognizability. Support Vector Machine (SVM) model for classifying excited state in game scene and calm state in non-game scene is trained and tested based on 60 samples. The top emotion recognition rate is 80% when 19 features are selected. The experimental results show that the multi-modal physiological signals acquisition platform built in this paper is effective and the emotions of subject can be recognized in specific scenes based on a small number of samples.
Variation of the relationships between lengths and weights applied to 123 fish species observed at Réunion Island (Indian Ocean)

Body length–weight (L–W) relationships of 123 fish species (122 Actinopterygii and 1 Elasmobranchii) were studied from 10 218 individuals caught around Réunion Island from 2000 to 2021. All species, except for 4 with a very small number of individuals and limited length range, showed a significant relationship between total length and total weight. For 52 species, the data showed the body becoming more elongated (i.e. b < 3.0) over their lifespan, while for 63 species the individuals became thicker (i.e. b > 3.0); only 4 species had a b-value equal to 3.0, signifying isometric growth. Of 72 species for which macroscopic observation allowed identification of sex, there was sexual dimorphism for only 9 species (12.5%), with a significant difference between the slopes of the length–weight relationships. Temporal effects were also investigated, using the annual component of time of capture for 75 species and the seasonal component for 73 species. A temporal effect was significant for 27 species (36.0%) when the sampling year was taken into account, and for 14 species (19.2%) when the sampling quarter (season) was taken into account. Finally, among 120 species tested, the relationship between total length and standard length was significant, and among 114 species the relationship between total weight and gutted (eviscerated) weight was significant.
Preparation and characterization of agarose-encapsulated ceramic hydroxyapatite particles for flow-through chromatography

ABSTRACT Composite adsorbents were prepared by encapsulating ceramic hydroxyapatite particles (CHT Type I) within inert agarose beads using an emulsion method as stationary phases for the removal of small protein and nucleic acid impurities from large proteins and bioparticles in a flow-through mode. The composite particles were prepared with 6% and 10% agarose and contain 35% and 25% (v/v) CHT, respectively, with effective pore radii of 15.1 and 8.8 nm. The functional properties were studied using yeast RNA, bovine serum albumin (BSA), thyroglobulin (Tg), IgM, and 30 and 50 nm silica nanoparticles (NP) as models. The RNA and BSA adsorption capacities are smaller than for unencapsulated CHT on a bead volume basis but comparable when normalized by the volume of CHT particles in the composite beads attaining values between 30 and 40 mg/mL. Confocal laser scanning microscopy shows that, while RNA and BSA have complete access to the encapsulated CHT particles, Tg, IgM, and the NPs are largely excluded. RNA and BSA breakthrough experiments in laboratory-scale columns at a 5-min residence time show RNA and BSA binding capacities comparable to those measured in a batch mode with either single component feeds or with feed mixtures containing 30 nm NPs.
The social scaffolding of machine intelligence

The Internet provides access to a global space of information assets and computational services. It also, however, serves as a platform for social interaction (e.g., Facebook) and participatory involvement in all manner of online tasks and activities (e.g., Wikipedia). There is a sense, therefore, that the Internet provides an unprecedented form of access to the human social environment. It provides insight into the dynamics of human behavior (both individual and collective), and it additionally provides access to the digital products of human cognitive labor (again, both individual and collective). This is important, for the human social environment looks to be of crucial importance when it comes to the evolutionary and developmental origins of the human mind. In the present paper, we combine these ideas to develop a theoretical account that sees the Internet as providing opportunities for online intelligent systems to function as socially-situated agents. The result is a vision of machine intelligence in which advanced forms of cognitive competence are seen to arise from the creation of a new kind of digital socio-ecological niche. The present paper attempts to detail this vision with respect to the notion of socially-scaffolded cognition. It also describes some of the forms of machine learning that may be required to enable online systems to press maximal cognitive benefit from their new-found contact with the human social world.
Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor

Abstract Slurry polymerization processes using Zeigler–Natta catalysts are most widely used for the production of polyethylene due to their several advantages over other processes. Optimal operating conditions are required to obtain the maximum productivity of the polymer at minimal cost while ensuring operational safety in the slurry phase ethylene polymerization reactors. The main focus of this multi-objective optimization study is to obtain the optimal operating conditions corresponding to the maximization of productivity and yield at a minimal operating cost. The tuned reactor model has been optimized. The single objective optimization (SOO) and multi-objective optimization (MOO) problems are solved using non-dominating sorting genetic algorithm-II (NSGA-II). A complete range of Pareto optimal solutions are obtained to obtain the maximum productivity and polymer yield at different input costs.
Spontaneous pain as a challenge of research and management in chronic pain

Abstract Spontaneous pain occurring without apparent external stimuli, is a significant complaint of individuals with chronic pain whose mechanisms, somewhat surprisingly, remain poorly understood. Over the past decades, neuroimaging studies start to reveal brain activities accompanying spontaneous pain. Meanwhile, a variety of animal models and behavioral tests have been established, including non-reflexive tests and free-choice tests, which have been shown to be effective in assessing spontaneous pain. For the spontaneous pain mechanisms, multiple lines of research mainly focus on three aspects: (1) sensitization of peripheral nociceptor receptors and ion channels, (2) spontaneous neuronal firing and abnormal activity patterns at the dorsal root ganglion and spinal cord level, (3) functional and structural alterations in the brain, particularly the limbic system and the medial pain pathway. Despite accumulating evidence revealing distinct neuronal mechanisms from evoked pain, we are still far from full understanding of spontaneous pain, leaving a big gap between bench and bedside for chronic pain treatment. A better understanding of the neural processes in chronic pain, with specific linkage as to which anatomical structures and molecules related to spontaneous pain perception and comorbidities, will greatly improve our ability to develop novel therapeutics.
Design of a web laboratory interface for ECG signal analysis using MATLAB builder NE

Abstract An electrocardiogram (ECG) is a noninvasive test, determining any defect in the heart rate or rhythm or changes in the shape of the QRS complex is very significant to detect cardiac arrhythmia. In this study, novel web-ECG simulation tools were proposed using MATLAB Builder NE with WebFigure and ASP.NET platform. The proposed web-ECG simulation tools consisted of two components. First, involved the analyses of normal real ECG signals by calculating the P, Q, R, S, and T values and detecting heart rate, while the second part related to extracting the futures of several types of abnormality real ECG. For calculating the PQRST values, simple and new mathematical equations are proposed in the current study using MATLAB. The Web ECG is capable to plot normal ECG signals and five arrhythmia cases, so the users are able to calculate PQRST easily using the proposed simple method. ECG simulation tools have been tested for validity and educational contributions with 62 undergraduate and graduate students at the Al-Nahrain University-Biomedical Engineering Department, Iraq. The proposed ECG simulation tools have been designed for academic learning to be run easily by a student using only any web browsers without the need for installing MATLAB or any extra programs. The proposed tools could provide a laboratory course for ECG signal analysis using a few buttons, as well as increase and develop the educational skills of students and researchers.
Group B Streptococcus adaptation promotes survival in a hyperinflammatory diabetic wound environment

Diabetic wounds have poor healing outcomes due to the presence of numerous pathogens and a dysregulated immune response. Group B Streptococcus (GBS) is commonly isolated from diabetic wound infections, but the mechanisms of GBS virulence during these infections have not been investigated. Here, we develop a murine model of GBS diabetic wound infection, and using dual RNA-sequencing, demonstrate that GBS infection triggers an inflammatory response. GBS adapts to this hyperinflammatory environment by upregulating virulence factors including those known to be regulated by the two-component system covRS, such as the surface protein pbsP, and the cyl operon which is responsible for hemolysin/pigmentation production. We recover hyperpigmented/hemolytic GBS colonies from the murine diabetic wound which we determined encode mutations in covR. We further demonstrate that GBS mutants in cylE and pbsP are attenuated in the diabetic wound. This foundational study provides insight into the pathogenesis of GBS diabetic wound infections. Teaser The Fight for Survival by the Bacterium Group B Streptococcus in the Diabetic Wound.
Cellular aging impedes stress-activation of a crucial JDP-Hsp70 protein disaggregase

Affiliations 1Australian Regenerative Medicine Institute, Monash University, Clayton, Victoria, Australia. 2Department of Biomedical Sciences of Cells & Systems, University Medical Center 15 Groningen (UMCG) and University of Groningen (RuG), Groningen, The Netherlands. 3Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia. 4Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, Victoria, Australia. 20 5Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany. 6Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V. , Dortmund, Germany. 7Monash Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia. 25 8Core facility for Mass Spectrometry and Proteomics, Zentrum für Molekulare Biologie der Universität Heidelberg, Heidelberg, Germany. 9European Molecular Biological Laboratory Australia (EMBL Australia), Monash University, Melbourne, Victoria, Australia 10Australian Research Council Centre of Excellence for Advanced Molecular Imaging, 30 Monash University, Clayton, Victoria, Australia 11Centre for Dementia and Brain Repair at the Australian Regenerative Medicine Institute, Monash University, Melbourne, Victoria, Australia
Linking ecological specialization to its macroevolutionary consequences: An example with passerine nest type

A long-standing hypothesis in evolutionary biology is that the evolution of resource specialization can lead to an evolutionary dead end, where specialists have low diversification rates and limited ability to evolve into generalists. In recent years, advances in comparative methods investigating trait-based differences associated with diversification have enabled more robust tests of this idea and have found mixed support. We test the evolutionary dead end hypothesis by estimating net diversification rate differences associated with nest type specialization among 3,224 species of passerine birds. In particular, we test whether the adoption of hole-nesting, a nest type specialization that decreases predation, results in reduced diversification rates relative to nesting outside of holes. Further, we examine whether evolutionary transitions to the specialist hole-nesting state have been more frequent than transitions out of hole-nesting. Using diversification models that accounted for background rate heterogeneity and different extinction rate scenarios, we found that hole-nesting specialization was not associated with diversification rate differences. Furthermore, contrary to the assumption that specialists rarely evolve into generalists, we found that transitions out of hole-nesting occur more frequently than transitions into hole-nesting. These results suggest that interspecific competition may limit adoption of hole-nesting, but that such competition does not result in limited diversification of hole-nesters. In conjunction with other recent studies using robust comparative methods, our results add to growing evidence that evolutionary dead ends are not a typical outcome of resource specialization.