Ultrafast emergence of ferromagnetism in antiferromagnetic FeRh in high magnetic fields by I. A. Dolgikh, T. G. H. Blank, A. G. Buzdakov, G. Li, K. H. Prabhakara, S. K. K. Patel, R. Medapalli, E. E. Fullerton, O. V. Koplak, J. H. Mentink, K. A. Zvezdin, A. K. Zvezdin, P. C. M. Christianen, A. V. Kimel

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Beneficial Effect of Multidomain Cognitive Training on the Neuropsychological Performance of Patients with Early-Stage Alzheimer’s Disease by Anastasia Nousia, Vasileios Siokas, Eleni Aretouli, Lambros Messinis, Athina-Maria Aloizou, Maria Martzoukou, Maria Karala, Charalampos Koumpoulis, Grigorios Nasios, Efthimios Dardiotis

“…Objective measures of episodic memory, delayed memory, word recognition, attention, executive function, processing speed, semantic fluency, and naming were assessed at baseline and after the completion of the program in both groups. …”
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Effects of Treadmill Exercise on Hippocampal Synaptic Plasticity and Expression of Related Proteins in Aging Mice by WANG Sinuo, CAO Yajun, XIANG Qing, YAN Jiamin, LIN Yanting, ZHUANG Yueyang, GUO Xiaoqin, LIU Weilin

“…The memory function of mice in each group after intervention was detected by Barnes maze test, T maze test and new object recognition test. …”
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Au fil de nos souvenirs : le 17 octobre 1961, emblème des violences policières by Clotilde Lebas

“…It shows how a traumatic event can constitute a memorial landmark which generates substitutions and successive transformations. …”
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Human activity recognition algorithm based on the spatial feature for WBAN by Chi JIN, Zhijun LI, Dayang SUN, Fengye HU

“…Traditional image-based activity recognition algorithms have some problems,such as high computational cost,numerous blind spots and easy privacy leakage.To solve the problem above,the CCLA (convolution-convolutional long short-term memory-attention) activity recognition algorithm based on the acceleration and gyroscope data was proposed.The convolutional neural network was used to extract spatial features of activity data and got the hidden time series information from the convolutional long short-term memory network.Simulating human brain selecting attention mechanism,attention-encoder was constructed to extract the spatial and temporal features at a higher level.The CCLA algorithm was tested on UCI-HAPT (university of California Irvine-smartphone-based recognition of human activities and postural transitions) public data set,and realized the classification of 12 types of activity with the accuracy of 93.27%.…”
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Multi-core helper thread prefetching for irregular data intensive applications by Jian-xun ZHANG, Zhi-min GU, Xiao-han HU, Min CAI

“…Big data analysis applications often use sparse graph traversal algorithm which characterized by irregular data intensive memory access. For improving performance of memory access in sparse graph traversal algorithm, helper thread prefetching could convert discontinuous locality into continuous-instant spatial-temporal locality effectively by using the shared last level cache of chip multi-processor platforms. …”
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Fast and live virtual machine migration in data center networks by Teng MA, Yuxiang HU

“…In data center networks,virtual machine live migration causes large volume of data transfer on core links of the network,which leads to performance degradation of the baring application and other background services.Based on inheriting the existing memory page de-duplication technique,a new search structure was presented composed of a counting Bloom filter with each bucket pointing to a link list,which overcame the problem of false positive of memory page lookup.Furthermore,a pruned algorithm was proposed to reduce the length of each link list and accelerate the lookup as much as possible to accomplish live and fast virtual machine migration in rack level.Experiment results show that the proposed method transfers less data on core links,takes less time to finish migration,and decreases the degradation of network applications caused by migration.…”
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Real time interpolation algorithm based on Lanczos kernel by Ying GUO, Lun LI, Peng WANG

“…In order to improve the timeliness and reliability of color image scaling algorithms,a high effective real-time color image scaling algorithm was proposed,which first generated the kernel look up table that could be used for whole target image based on Lanczos kernel,and interpolation of all target image pixels had only fixed point operation which had low computational complexity.Meanwhile,an optimized method for rasterizing pixels of target image was proposed,which needed little memory space but embraced high efficiency of memory utilization.The experiment results show that the proposed algorithm achieves good image quality,and the computational complexity is far more less than classic image scaling algorithms.So the proposed algorithm can effectively implement real-time resizing of color images and color videos in any scaling factors.…”
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COORDINATED CONTROL ALGORITHM FOR SEMI-ACTIVE SUSPENSION IN VARIOUS ROAD CONDITIONS BASED ON NAVIGATION by YAO JiaLing, SHI WenKu, CHEN ZhiYong, SUN Yu, Saied Taheri

“…A preliminary study is conducted to detect and memory the road conditions by which semi-active suspension switches control strategies or certain damping from one to another. …”
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Characteristics Analysis and Testing of SMA Spring Actuator by Jianzuo Ma, Haolei Huang, Jin Huang

“…The biasing form two-way shape memory alloy (SMA) actuator composed of SMA spring and steel spring is analyzed. …”
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Long Term Neuropsychological Follow-Up in Patients With Herpes Simplex Encephalitis and Predominantly Left-Sided Lesions by B. Laurent, R. F. Allegri, C. Thomas-Anterion, N. Foyatier, B. Naegele-Faure, J. Pellat

“…Verbal amnesia was the main deficit but amnesic aphasia sometimes associated with impairment of remote memory also occurred. Semantic and episodic memory deficits were also explored in one case and the role of the right cerebral hemisphere in facilitating recovery of learning is discussed.…”
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Software and hardware co-design of lightweight authenticated ciphers ASCON for the internet of things by Jing WANG, Lesheng HE, Zhonghong LI, Luchi LI, Hang YANG

“…ASCON was the most promising algorithm to become an international standard in the 2021 NIST lightweight authenticated encryption call for proposals.The algorithm was designed to achieve the best performance in IoT resource-constrained environments, and there was no hardware IP core implementation based on this algorithm in the open literature.A software-hardware collaborative implementation method of ASCON was proposed, which improved the speed and reduced the memory footprint of ASCON in IoT security authentication applications through hardware means such as S-box optimization, prior calculation and advanced pipeline design.As a comparison, ASCON has been transplanted on the common IoT embedded processor platform.The results showed that the described method was more than 7.9 times faster, while the memory footprint was reduced by at least 90%.The schemes can be used for the design and implementation of IoT security application-specific integrated circuits or SoCs.…”
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Faster implicit motor sequence learning of new sequences compatible in terms of movement transitions by Susanne Dyck, Christian Klaes

“…Abstract New information that is compatible with pre-existing knowledge can be learned faster. Such schema memory effect has been reported in declarative memory and in explicit motor sequence learning (MSL). …”
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Discours, agro-industrie et le silenciement des femmes dans la production de café au Brésil by Mónica Graciela Zoppi-Fontana, Sheilla Maria Resende

“…The theoretical approach for this research is that of Discourse Analysis, in specific the research of Michel Pêcheux (1975; 1990) and Eni Orlandi (2000), through the mobilization of the following theoretical-analytical categories: silencing, silence, form-subject, memory. The results provide a description of the syntactic and semantic operations that make it possible to actualize and put into operation a patriarchal memory of agricultural practice, which silences both the role of women in the coffee production chain and family agriculture.…”
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PREDICTING STOCK PRICE DIRECTION OF EUROZONE BANKS: CAN DEEP LEARNING TECHNIQUES OUTPERFORM TRADITIONAL MODELS? by Bogdan Ionuț ANGHEL

“…This study compares the predictive performance of Bidirectional Long Short-Term Memory (BiLSTM) and Long Short Term Memory (LSTM) with traditional models - Extreme Gradient Boosting (XGBoost) and Logistic Regression - in predicting the daily stock price direction of the ten largest Eurozone banks by market capitalization. …”
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Fast Single Pbase Algoritbm for Utility Mining in Big Data by Junqiang Liu, Qingfeng Zhou, Wenhui Wang, Lei Shi

“…Most of the latest works on utility mining generates a huge number of candidates in dealing with big data,which suffers from the scalability issue.Some work does not generate candidates,but suffers from the efficiency issue due to lack of strong pruning and high computation overhead.A novel algorithm that finds high utility patterns in a single phase without generating candidates was proposed.The novelties lie in a prefix growth strategy with strong pruning,and a sparse matrix based representation of transactions with pseudo projection.The proposed algorithm works in a depth first manner and does not materialize high utility patterns in memory,which further improves the scalability.Extensive experiments on synthetic and rea1-world data show that the proposed algorithm outperforms the latest works in terms of running time,memory overhead,and scalability.…”
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Géohistoire environnementale : contours sémantiques et conceptuels. Discussions sur les héritages et patrimoines reconnus aux zones humides fluviales by Delphine Gramond

“…This article proposes a thought on the use and the effectiveness of the concepts and methods of the environmental geohistory use to identify and to highlight the « memory(ies) » of wetlands, here in fluvial contexts. …”
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Secure data collection method of WSN based on mobile Sink by Chunyu MIAO, Yuan FAN, Hui LI, Kaiqiang GE, Xiaomeng ZHANG

“…At present, WSN data collection method based on mobile Sink has some problems, such as low detection rate of network attack and large memory cost, which makes the network vulnerable to network attack and difficult to be applied in practice.To solve this problem, a secure data collection WSN method of WSN based on mobile Sink was proposed, which used convex hull algorithm of energy perception to identify data collection points, used elliptic encryption algorithm (ECC) to generate key for all nodes in the network, used ElGamal algorithm to realize node identity and message authentication, and used support vector machine (SVM) to identify network attack types.The simulation results show that the proposed secure data collection method has good performance in attack detection rate, memory overhead and packet delivery rate.…”
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Carved in stone by Ana Catarina Basílio

“…Ultimately, this study examines these agglomerations as ideological tools that reshaped collective memory during the transition between the Chalcolithic and Bronze Age, shedding light on the more intangible behaviours of past communities. …”
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Neural network and Markov based combination prediction algorithm of video popularity by Xuesen MA, Shuyou CHEN, Xiangdong XU, Zhaokun CHU

“…Caching popular video into user-side in advance improves the user experience and reduces operator costs, which is a common practice in the industry.How to effectively predict the popularity of videos has become a hot issue in the industry.On account of the shortcomings of traditional prediction algorithms such as poor nonlinear mapping ability, low prediction accuracy and weak adaptability, a video popularity prediction algorithm based on a neural network and Markov combined model (Mar-BiLSTM) was proposed.Information dependencies were preserved by constructing bidirectional memory network model (bi-directional long short-term memory, BiLSTM), the prediction accuracy of the model was further improved by using Markov properties while avoiding the increase of the complexity of the model caused by the introduction of external variables.Experimental results show that compared with traditional time series and classic neural network algorithms, the proposed algorithm improves predicting accuracy, effectiveness and reduces the amount of calculation.…”
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