Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-Free Continual Generation and Representation Learning via Dynamic Expansionable Memory Cluster

Human brains can continually acquire and learn new skills and knowledge over time from a dynamically changing environment without forgetting previously learnt information. Such a capacity can selectively transfer some important and recently seen information to the persistent knowledge regions of the brain. Inspired by this intuition, we propose a new memory-based approach for image reconstruction and generation in continual learning, consisting of a temporary and evolving memory, with two different storage strategies, corresponding to the temporary and permanent memorisation. The temporary memory aims to preserve up-to-date information while the evolving memory can dynamically increase its capacity in order to preserve permanent knowledge information. This is achieved by the proposed memory expansion mechanism that selectively transfers those data samples deemed as important from the temporary memory to new clusters defined within the evolved memory according to an information novelty criterion. Such a mechanism promotes the knowledge diversity among clusters in the evolved memory, resulting in capturing more diverse information by using a compact memory capacity. Furthermore, we propose a two-step optimization strategy for training a Variational Autoencoder (VAE) to implement generation and representation learning tasks, which updates the generator and inference models separately using two optimisation paths. This approach leads to a better trade-off between generation and reconstruction performance. We show empirically and theoretically that the proposed approach can learn meaningful latent representations while generating diverse images from different domains. The source code and supplementary material (SM) are available at https://github.com/dtuzi123/DEMC.
Flex-KV: enabling high-performance and flexible KV systems

Even within one popular sub-category of "NoSQL" solutions -- key-value (KV) storage systems -- no one existing system meets the needs of all applications. We question this poor state of affairs. In this paper, we make the case for a flexible key-value storage system (Flex-KV) that can support both DRAM and disk-based storage, can act as an unreliable cache or a durable store, and operate consistently or inconsistently. The value of such a system goes beyond ease-of-use: While exploring these dimensions of durability, consistency, and availability, we find new choices for system designs, such as a cache-consistent memcached, that offer some applications a better balance of performance and cost than was previously available.
Magnetic interaction with devices: a pilot study on mobile gaming

This work-in-progress paper presents a study of interaction techniques for mobile devices, with a focus on gaming scenarios. We introduce and explore usability and performance aspects of a novel compass-based control for tangible around-device interaction, and compare it with traditional mobile gaming controls, such as touchscreen thumbstick, swiping and tilt-based approaches.
Deep Learning Based Cervical Spine Bones Detection: A Case Study Using YOLO

Cervical spine bones detection plays a crucial role in various medical applications, such as diagnosis, surgical planning, and treatment assessment. Traditional methods for cervical spine bones detection often rely on manual identification and segmentation, which are time-consuming and prone to errors. In recent years, deep learning approaches have shown great potential in automating the detection process and achieving high accuracy. In this research paper, we propose a deep learning-based approach for detecting cervical spine bones. Our suggested approach employs the YOLOv5 architecture, a cutting-edge object identification system renowned for its effectiveness and precision. The model is trained to recognize and locate bones structures using computed tomography (CT) scans image of the cervical spine as inputs. We conduct extensive evaluations using the trained models on the cervical spine dataset. The mean average precision (mAP) scores achieved by our model are 93% at threshold (mAP _0.5) and 83% at thresholds ranging from (mAP _0.5:0.95), which demonstrate the effectiveness of our approach in accurately detecting and localizing cervical spine bones. Our deep learning-based method for detecting cervical spine bones with high mAP scores presented in this research paper has significant implications for medical applications. With accurate and reliable bones detection, medical professionals can enhance diagnosis, surgical planning, and treatment assessment processes. The achieved mAP scores showcase the performance and potential of our proposed method, contributing to the advancement of bone detection techniques in cervical spine imaging and facilitating collaboration between the medical imaging and deep learning communities.
A validated multidisciplinary study on the assessment of SMS messages as a mean to improve self-efficacy in university students

This paper describes a multidisciplinary study that included teams from the Psychology and from the Computer Science departments on the use of interactive Short Message Service messages to assess psychological changes in self-efficacy of Engineering students exposed to motivational and persuasive texts. The paper presents the definition of the platform, the configuration and setting in place of the system that handled the exchange of automatic and interactive messages with the research subjects, as well as the implementation and results of the Psychology study. The assessment of the results of this experiment, also presented, were obtained by comparing scores of self-efficacy questionnaires given, pre and post study, to both an experimental and a control group.
analysis of genetic diversity and structure of the rmplasm of faba bean ( Vicia faba L . )

Assessing the diversity and genetic structure of faba bean (Vicia faba L.) germplasm is essential to improve the quality and yield of this economically important crop. In this study, simple sequence repeats (SSRs) were utilized to evaluate the diversity and structure of 35 faba bean genotypes originating from three different geographical regions (Northern Africa, Eastern Africa, and Near East). All 15 SSR loci generated a total of 100 alleles. The allele number per locus varied from 4 to 11, with a mean of 6.67. The expected heterozygosity (He) of SSR loci ranged between 0.51 and 0.81, with a mean of 0.63. The PIC value also varied from 0.44 to 0.78, with an average of 0.58. The expected heterozygosity of 22 faba bean genotypes was higher than the observed one. Interestingly, AMOVA analysis showed that much of variability resided within accessions (79.2%). A highly significant difference among regions was also evidenced, and represented 5.3% of the total variation. Moreover, cluster analysis divided the 35 faba bean genotypes into two main clusters. The first main cluster comprised all faba bean genotypes originating from the Near East region, whereas the second main cluster comprised all the genotypes originating from the Northern and Eastern Africa regions, indicating that the Northern and Eastern African faba bean genotypes were more closely related to each other than to the Near East genotypes. Structure analysis also revealed that the 35 faba bean genotypes might be assigned to two populations, in complete accordance with cluster analysis data. In conclusion, this study showed high levels of diversity in the analysed genotypes of faba bean, and could be utilized in future breeding programmes to develop new cultivars of high yield. C 2017 Académie des sciences. Published by Elsevier Masson SAS. All rights reserved. Botany Department, Faculty of Science, Tanta University, Tanta,
Biometric Authentication from sEMG: Assessing the Potential of Adaptive Chirp Mode Decomposition

Surface electromyography (sEMG)-based biometrics authentication has drawn a significant attention in recent years as a promising methodology. This technique takes advantage of the distinctive electrical activity patterns that muscles produce when performing various gestures. Individual differences can be observed during training for multiple users which led the researchers towards biometric investigations using sEMG. This paper proposes the biometric authentication using sEMG signals based on adaptive chirp mode decomposition (ACMD). The recorded sEMG signals are decomposed into its modes using ACMD and further each mode is segmented using windowing approach. The time domain (TD) and auto regressive (AR) model based features are extracted from each segment. The authentication performance is investigated for normal test scenario. Results show the performance of the selected 4-channel configuration with average equal error rate (EER) of 2.402%. The comparative authentication performance claims that proposed method authenticate with better values of performance parameters than the existing method.
Chloroplast «cryptic» promoter can be activated upon their transfer to plant nuclear genome

It is shown that a DNA fragment containing the previously described exon 2 sequence of the chloroplast gene for ribosomal protein S12 can determine expression of the reporter npt-II gene in transgenic plant nuclear genome. Transcription start points in the transgenic plant were localized withintherpS12 DNA coding sequences. After5'-rpSJ2-CAT-nos-3' gene construction has been introduced to tobacco protoplasts by PEG-treatment, chloramphenicol acetyltransferase (CAT) enzyme activity was detectable by transient assays. These facts indicate that «cryptic» promoter-like sequences exist in chloroplast genome which can be activated as a result of their artifical or natural transfer to the nuclei
Cross-display object movement in multi-display environments

Many types of multi-display environments (MDEs) are emerging that allow users to better interact with computers. In these environments, being able to move visual objects (such as window icons or the cursor) from one display to another is a fundamental activity. This dissertation focuses on understanding how human performance of cross-display actions is affected by the design of cross-display object movement interaction techniques. Three main aspects of cross-display actions are studied: how displays are referred to by the system and the users, how spatial actions are planned, and how actions are executed. Each of these three aspects is analyzed through laboratory experiments that provide empirical evidence on how different characteristics of interaction techniques affect performance. The results further our understanding of cross-display interaction and can be used by designers of new MDEs to create more efficient multi-display interfaces.
Water Body Information Extraction from Remote Sensing Images based on PSPNet

Remote sensing image has the characteristics of real-time, periodicity and wide monitoring range. It can quickly and accurately obtain water area, distribution and other information, which is of great significance to the utilization and development of water resources, agricultural irrigation, flood disaster assessment and so on. Since traditional water information extraction methods only use part of image band information, the accuracy of water information extraction is low and has certain limitations. In recent years, convolutional neural network technology has developed rapidly and achieved good results in water information extraction from remote sensing images. Therefore, in this paper, Pyramid Scene Parsing Neural Network (PSPNet) was used to extract water information from Ziyuan-3 multispectral remote sensing images, to make sample sets of water, and train the convolutional neural network model. Compared with the traditional normalized difference water index (NDWI) and support vector machine (SVM), the results show that PSPNet has the highest accuracy and the lowest misclassification rate.
A Multispectral Image Creating Method for a New Airborne Four-Camera System with Different Bandpass Filters

This paper describes an airborne high resolution four-camera multispectral system which mainly consists of four identical monochrome cameras equipped with four interchangeable bandpass filters. For this multispectral system, an automatic multispectral data composing method was proposed. The homography registration model was chosen, and the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) were used to generate matching points. For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system. Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels.
CANSim: When to Utilize Synchronous and Asynchronous Routers in Large and Complex NoCs

Asynchronous routers offer benefits in latency, energy, and area over synchronous routers, but their large system design adoption is limited due to inadequate tool support for performance quantification. We introduce CANSim, a fast and accurate simulator for complex asynchronous and synchronous NoCs. Verified against synthesis models, CANSim models any graph-representable topology, asymmetric links, data-dependent delays, meta-stability, and supports synthetic and real-world traffic. It also accommodates power gating, hierarchical networks, and GALS-like networks. Our comparisons reveal that asynchronous NoCs provide lower packet latency at no-load conditions, but synchronous NoCs may outperform near saturation. On average, asynchronous NoCs offer up to 36% latency and 52% power benefits, with power-gating potentially reducing static power by 90%.
Conditional Forecasting of Bitcoin Prices Using Exogenous Variables

Bitcoin is known for its high volatility, which makes it challenging to accurately predict future prices. In this study, we aim to forecast Bitcoin prices for a month by incorporating exogenous variables, specifically the interest rate and recession probability. Our primary objective is to explore whether these variables have a positive impact on the prediction of Bitcoin prices. We used two popular time series forecasting models: Long Short-Term Memory (LSTM) and Facebook Prophet. Our approach involves exploring the impact of these exogenous variables on the performance of the models and comparing their results through plots and cross-validation. We trained the models using historical Bitcoin price data along with exogenous variables and evaluated their performance on a test dataset. Our results indicate that LSTM outperforms Facebook Prophet in terms of Bitcoin price prediction accuracy. This is because, while Facebook Prophet is optimized for statistical forecasting modeling, LSTM has the capability to learn intricate patterns and relationships given the right architecture with sufficient neurons. Importantly, we demonstrate that incorporating interest rates and recession probabilities significantly enhances the predictive capability of our models. Our findings suggest that changes in interest rates and recession probabilities have an impact on Bitcoin prices, and our models perform better when equipped with this valuable information.
Data Sharing System Based on Blockchain to Enhance The Visible of Meta Data Information in Privacy Computing

Blockchain and privacy computing, as two hot technologies in the field of privacy protection today, have similar goals and different technical characteristics. How to better leverage data value while ensuring data privacy has become a concern for many blockchain and privacy computing vendors. In order to achieve this goal, many scholars deeply integrates blockchain technology with privacy computing technology through techno-logical innovation, combining the decentralized, tamper-proof of block chain with the trustworthy characteristics of privacy computing data. However, a data sharing system based on blockchain and privacy computing implements the security and privacy of data in computing, but it is unable to obtain the data of structure and the data of description. We propose a data sharing system called ChainMaker Privacy Computing (CPC) system to solve this problem. It provides a series of capabilities such as reliable data sources, reliable transaction authorization, reliable computing scheduling, reliable computing processes, reliable computing results, and end-to-end traceability and possess the function of decentralized, traceable, low tamper-resistant modification and the security and privacy of actual data. We design resource catalog to provide the trusted environment of data usage in privacy computing system and the visible for members in blockchain. Based on Cipher-Policy Attribute based Encryption (ABE), we design CPC-CP-ABE algorithm to implement attribute based encryption for fine-grained access control. The experiments result shows that the CPC system can finish the trusted data sharing to all organizations on blockchain.
Anger and stroke: a potential association that deserves serious consideration

Objective To assess the relationship between states of anger and stroke. Methods Systematic review of the literature. Results In total, 21 papers were selected for the systematic review of data published on the subject of anger and stroke. A state of anger may be a risk factor for stroke, as well as a consequence of brain lesions affecting specific areas that are caused by a stroke. Scales to assess anger varied among authors. There was no consensus regarding the area of brain lesions that might lead to a state of anger. Although some authors agreed that lesions on the right side led to angrier behaviour, others found that lesions on the left side were more relevant to anger. Likewise, there was no consensus regarding the prevalence of anger pre or post-stroke. Some authors did not even find that these two conditions were related. Conclusion Although most authors have accepted that there is a relationship between anger and stroke, studies with uniform methodology need to be conducted if this association is to be properly evaluated and understood.
MoV-SLAM: Using Motion Vectors for Real-Time Single-CPU Visual SLAM

The 6 degrees-of-freedom movement of micro-aerial vehicles (MAVs) combined with their lightweight, low-power computing makes accurate, real-time (RT) visual simultaneous localization and mapping (vSLAM) for MAVs considerably challenging. Existing classical and learning-based SLAM approaches require high computing capabilities to achieve RT performance, which prevents long-range deployment on lightweight, lowpowered, and low-resource devices. We present MoV-SLAM, an approach that obtains long-range robust correspondence tracks for vSLAM using motion vectors that link macroblocks decoded from H.264 video coders. We contribute efficient algorithms to detect and match distinctive blocks for fast, accurate, and reliable vSLAM on low-resource, single-processor systems in MAVs. Our approach outperforms the existing state-of-the-art (SOTA) for single-processor vSLAM reconstruction when evaluated on multiple off-the-shelf datasets. Project page: https://github.com/Terascale-All-sensing-Research-Studio/MOV-SLAM
Automatic alignment and error correction of human generated transcripts for long speech recordings

In this paper we examine the issues of aligning and correcting approximate human generated transcripts for long audio files. Accurate time-aligned transcriptions help provide easier access to audio materials by aiding downstream applications such as the indexing, summarizing and retrieving of audio segments. Accurate time alignments are also necessary when incorporating audio data into the training data for a speech recognizer’s acoustic model. We provide some initial analysis of manual transcriptions which show that there can be significant differences between the “approximate” manual transcripts generated by typical commercial transcription services and what was actually spoken in the recording. We then present a new alignment approach for approximate transcriptions of long audio files which is designed to discover and correct errors in the manual transcription during the alignment process.
Late-onset inner retinal dysfunction in mice lacking sigma receptor 1 (σR1).

PURPOSE Sigma receptor 1 (σR1) is expressed abundantly in the eye, and several reports suggest that this putative molecular chaperone plays a role in lens cell survival, control of intraocular pressure (IOP), and retinal neuroprotection. The present study examined the consequence of the absence of σR1 on ocular development, structure, and function. METHODS Wild-type (σR1⁺/⁺), heterozygous (σR1⁺/⁻), and homozygous (σR1⁻/⁻, knockout) mice aged 5 to 59 weeks were subjected to comprehensive electrophysiological testing and IOP measurement. The eyes were examined by light and electron microscopy and subjected to morphometric examination and detection of apoptosis. RESULTS Cornea and lens of σR1⁻/⁻ mice were similar to wild-type mice in morphologic appearance at all ages examined, and IOP was within normal limits. Comprehensive ERG and morphometric analyses initially yielded normal findings in the σR1⁻/⁻ mice compared with those in the wild-type. By 12 months, however, significantly decreased ERG b-wave amplitudes and diminished negative scotopic threshold responses, consistent with inner retinal dysfunction, were detected in σR1⁻/⁻ mice. Concomitant with these late-onset changes were increased TUNEL- and active caspase 3-positive cells in the inner retina and significant loss of cells in the ganglion cell layer, particularly in the central retina. Before these functional and structural abnormalities, there was ultrastructural evidence of axonal disruption in the optic nerve head of σR1⁻/⁻ mice as early as 6 months of age, although there were no alterations observed in retinal vascularization in σR1⁻/⁻ mice. CONCLUSIONS These data suggest that lack of σR1 leads to development of late-onset retinal dysfunction with similarities to optic neuropathy.
Analysis of association at single nucleotide polymorphisms in the APOE region.

The discussion of the prospects of using a dense map of single nucleotide polymorphisms (SNPs) to identify disease genes with association analysis has been extensive. However, there is little empiric evidence to support this strategy. To begin to examine the practical issues surrounding this methodology, we identified 10 SNPs in the region immediately surrounding the apolipoprotein E locus (APOE), an established susceptibility gene for Alzheimer disease. Our goal was to examine patterns of allelic association to begin to investigate the question of whether APOE could have been identified using SNPs. Our strongest evidence of association was at the 2 SNPs immediately flanking APOE.