GENERAL DECAY OF A TRANSMISSION PROBLEM FOR KIRCHHOFF TYPE WAVE EQUATIONS WITH BOUNDARY MEMORY CONDITION

In this paper, we investigate the influence of boundary dissipation on the de-cay property of solutions for a transmission problem of Kirchhoff type wave equation with boundary memory condition. By introducing suitable energy and Lyapunov functionals, we establish a general decay estimate for the energy, which depends on the behavior of relaxation function.

GENERAL DECAY FOR A DIFFERENTIAL INCLUSION OF KIRCHHOFF TYPE WITH A MEMORY CONDITION AT THE BOUNDARY

In this article, we consider a differential inclusion of Kirchhoff type with a memory condition at the boundary. We prove the asymptotic behavior of the corresponding solutions. For a wider class of relaxation functions, we establish a more general decay result, from which the usual exponential and polynomial decay rates are only special cases.

STABILITY OF A VON KARMAN EQUATION WITH INFINITE MEMORY

In this paper, we consider a von Karman equation with infinite memory. For yon Karman equations with finite memory, there is a lot of literature concerning on existence of the solutions, decay of the energy, and existence of the attractors. However, there are few results on existence and energy decay rate of the solutions for yon Karman equations with infinite memory. The main goal of the present paper is to generalize previous results by treating infinite history instead of finite history.

Energy Decay for a Von Karman Equation of Memory Type with a Delay Term

We consider a von Karman equation of memory type with a delay term . By introducing suitable energy and Lyapunov functional, we establish a general decay estimate for the energy, which depends on the behavior of g.

A Meta-Learning Approach for Aircraft Trajectory Prediction

The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.

Stabilization and Blow-up in an Infinite Memory Wave Equation with Logarithmic Nonlinearity and Acoustic Boundary Conditions

In this paper,the authors consider the stabilization and blow up of the wave equation with infinite memory,logarithmic nonlinearity and acoustic boundary conditions.The authors discuss the existence of global solutions for the initial energy less than the depth of the potential well and investigate the energy decay estimates by introducing a Lyapunov function.Moreover,the authors establish the finite time blow up results of solutions and give the blow up time with upper bounded initial energy.

内容与自我关联性对负性情绪记忆定向遗忘的影响

本研究使用负性情绪视频片段作为实验材料,采用词表法定向遗忘范式,用2个实验分别考察了内容与自我关联性对负性情绪要义和细节记忆的定向遗忘及其权衡的影响。研究结果表明:(1)材料内容上的关联性没有导致定向遗忘的出现。在内容关联条件下,遗忘提示只增加了要义记忆没有增加细节记忆;(2)自我关联性导致了对负性情绪记忆的定向遗忘,表现为遗忘组的回忆成绩低于记住组;(3)关联性对细节和要义记忆有不同的影响,自我关联性使得被试的细节回忆率更高,但要义回忆率却更低,表现出明显的要义-细节权衡。

视空工作记忆负载对场景主旨加工的影响

本研究采用工作记忆任务和场景辨别任务相结合的双任务范式探究客体工作记忆负载(实验1)和空间工作记忆负载(实验2)对场景主旨辨别的影响。结果发现:(1)与无负载条件相比,客体负载条件下的非参辨别力指数(A’)明显下降,而空间负载条件下A’无明显变化;(2)随着场景图片呈现时间的延长,场景辨别任务的A’逐渐提高;(3)对自然场景和人工场景匹配情况下的A’明显高于对自然与自然或者人工与人工匹配情况下的A’。由此可见,场景主旨识别,特别是基本水平的场景主旨辨别,与客体工作记忆共享部分认知资源,而不与/很少与空间工作记忆共享认知资源;在识别过程中,上级水平范畴识别优先于基本水平范畴识别,进一步支持了上级水平范畴优先的主旨层级加工观点。

关联效应的实验研究

对汉语双字词的关联效应进行了实验研究，结果发现：语义关联效应显著，而语音关联效应则不存在。被试对各词再认判断确信程度和使用频率判断的模糊统计结果，在一定程度上验证了模糊痕迹理论的概括性表征、细节性表征和梗概抽取等观点，并在一定程度上验证了记忆归因理论。

情绪对连续事件定向遗忘的影响

本研究采用项目法定向遗忘范式以中性和负性连续事件的视频为实验材料,通过2个实验考察了情绪对连续事件定向遗忘的影响,并进一步探讨了情绪对细节记忆和要义记忆的定向遗忘的影响。结果发现,情绪可以消除细节记忆的定向遗忘效应,而仅有要义记忆的定向遗忘效应则主要受到事件连续性而非情绪的影响。

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

God Is a Porcupine—Brain, Consciousness and Spacetime Physics

Whereas the human body requires a vast numbers of atoms to maintain its intricate anatomical functions, we assert that the human brain requires “something extra” to carry out its higher mental and emotional functions. Recently, neuroscientists are beginning to suspect brain cells are not fast enough, or intricate enough, to correlate complex spatiotemporal information into cognitive understanding. They conclude that spacetime fields may be necessary to assist the brain during neurological processing—in much the same way magnetic and electric fields are essential for the propagation of light. This “something extra,” we argue, is spacetime itself—where structures in the brain, called facilitators (somewhat like Descartes pineal gland), have evolved biologically in such a way, so as to be able to store and retrieve spacetime quanta for the formation and generation of consciousness and memory. In this way, cognition is not a thing complete. Rather it is emergent, and accumulates as discretized spacetime quanta in the brain so rapidly, we perceive our own awareness to be continuous, events spontaneous. In this paper, we consider spacetime to be a field (like all quantum fields), which can be excited into quanta particles called gravitons. We then apply this quanta excitation to help explain the brain’s cognitive processes. If the brain has indeed evolved to interact with discretized spacetime, then with the advent of improved functional imaging equipment, we might be able to map detailed correlations between neural processes, conscious experience and spacetime. In so doing, it might be possible to learn more about the fundamental workings of spacetime itself.

Erratum to “Is Alzheimer’s Disease an Adaptability Disorder? What Role Does Happiness Have in Treatment, Management and Prevention” [World Journal of Neuroscience 5 (2015) 180-188]

A case presentation indicating the importance of “happiness” in childhood causing memory block until the patent presented with probable mixed vascular and neurodegenerative memory loss at 60 years of age is presented to highlight the role of emotional factors in causing the disease. The question of whether Alzheimer’s disease is an adaptability disorder is raised, given the patient blocked out her memory of her childhood experience. The importance of “happiness” as a treatment goal raises issues of advocacy and Guardianship as well as capacity, which is addressed by actual case reference and court action in defence of the patient’s rights to have their wishes respected and observed. Functional mental capacity assessment, using the Functional Mental State Measure (FMSM) gives a greater indication of neuronal reserve than standard cognitive testing, as it helps to unravel the dilemma associated with pure cognitive assessment in Alzheimer’s Disease as well as vascular dementia patients and patients who, despite retained and intact functional capacity and ability to express their wishes, i.e. “best interest”, are “wrongly” placed under Guardianship. A General Systems approach, which recognises functional interaction as optimal and withdrawal or inadequate and/or inappropriate response as not, provides further understanding of the relationship between emotional factors, memory and neurodegenerative (Alzheimer’s) disease.

Is Alzheimer’s Disease an Adaptability Disorder? What Role Does Happiness Have in Treatment, Management and Prevention

A case presentation indicating the importance of “happiness” in childhood causing memory block until the patent presented with probable mixed vascular and neurodegenerative memory loss at 60 years of age is presented to highlight the role of emotional factors in causing the disease. The question of whether Alzheimer’s disease is an adaptability disorder is raised, given the patient blocked out her memory of her childhood experience. The importance of “happiness” as a treatment goal raises issues of advocacy and Guardianship as well as capacity, which is addressed by actual case reference and court action in defence of the patient’s rights to have their wishes respected and observed. Functional mental capacity assessment, using the Functional Mental State Measure (FMSM) gives a greater indication of neuronal reserve than standard cognitive testing, as it helps to unravel the dilemma associated with pure cognitive assessment in Alzheimer’s Disease as well as vascular dementia patients and patients who, despite retained and intact functional capacity and ability to express their wishes, i.e. “best interest”, are “wrongly” placed under Guardianship. Maladaptive responses, to control the change in external environment that are sensed or perceived, and which lead to disorder or to susceptibility to disease, exemplify a General Systems Theory approach, in which appropriate and adequate responses to environmental change, in behavioural terms, by a person, whether independently, or as the recipient or giver, or both, leads to functional interaction and happiness.

Reinventing Memory System Design for Many-Accelerator Architecture

The many-accelerator architecture, mostly composed of general-purpose cores and accelerator-like function units （FUs）, becomes a great alternative to homogeneous chip multiprocessors （CMPs） for its superior power-efficiency. However, the emerging many-accelerator processor shows a much more complicated memory accessing pattern than general purpose processors （GPPs） because the abundant on-chip FUs tend to generate highly-concurrent memory streams with distinct locality and bandwidth demand. The disordered memory streams issued by diverse accelerators exhibit a mutual- interference behavior and cannot be efficiently handled by the orthodox main memory interface that provides an inflexible data fetching mode. Unlike the traditional DRAM memory, our proposed Aggregation Memory System （AMS） can function adaptively to the characterized memory streams from different FUs, because it provides the FUs with different data fetching sizes and protects their locality in memory access by intelligently interleaving their data to memory devices through sub-rank binding. Moreover, AMS can batch the requests without sub-rank conflict into a read burst with our optimized memory scheduling policy. Experimental results from trace-based simulation show both conspicuous performance boost and energy saving brought by AMS.

Optimizing non-coalesced memory access for irregular applications with GPU computing

General purpose graphics processing units(GPGPUs)can be used to improve computing performance considerably for regular applications.However,irregular memory access exists in many applications,and the benefits of graphics processing units(GPUs)are less substantial for irregular applications.In recent years,several studies have presented some solutions to remove static irregular memory access.However,eliminating dynamic irregular memory access with software remains a serious challenge.A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access,especially for indirect memory access.Data reordering and index redirection are suggested to reduce the number of memory transactions,thereby improving the performance of GPU kernels.To improve the efficiency of data reordering,an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data.Through concurrently executing the compute unified device architecture(CUDA)streams of data reordering and the data processing kernel,the overhead of data reordering can be reduced.After these optimizations,the volume of memory transactions can be reduced by 16.7%-50%compared with CUSPARSE-based benchmarks,and the performance of irregular kernels can be improved by 9.64%-34.9%using an NVIDIA Tesla P4 GPU.

The adaptive characteristics of memory: A perspective from the life-span development of associative memory illusion

The reconstructive process in memory may cause distortions or errors.Associative memory illusion is one of the most common types of false memories.It acts as a reflection of the adaptive characteristic of memory by suggesting its ability to adaptively use semantic gist information even with limited cognitive resources.Meanwhile, the adaptive characteristic of memory is also manifested by inhibiting false memories with certain strategies (e.g., encoding item-specific information) to ensure memory accuracy according to the demands of specific situations.This article looks into the life-span developmental features of the adaptive characteristic of memory by summarizing the age features of two types of abilities: employing gist representation and inhibiting associative memory illusion.In the future, further efforts in this field are proposed to explore the specific adaptive nature of memory from the perspective of evolution.

情绪性事件的社会分享对分享者情绪变化与记忆的影响

目的:探讨在中性和消极情绪条件下,社会分享和单独分享对分享者情绪和记忆的影响。方法:将被试随机分配到中性组和消极组,观看相应的情绪视频,并且将自己所观看的视频内容与倾听者进行社会分享或单独分享。结果:(1)相对于单独分享,社会分享后分享者对中性事件的情绪强度上升,而对于负性事件的情绪强度显著降低。(2)消极组的细节回忆率显著高于中性组,而消极组要义回忆率显著低于中性组,单独分享时的细节回忆率显著高于社会分享。结论:相比较单独分享,社会分享能够降低分享者的负面情绪,而且降低对消极事件细节的记忆。

Research progress on Drosophila visual cognition in China

Visual cognition,as one of the fundamental aspects of cognitive neuroscience,is generally associated with high-order brain functions in animals and human.Drosophila,as a model organism,shares certain features of visual cognition in common with mammals at the genetic,molecular,cellular,and even higher behavioral levels.From learning and memory to decision making,Drosophila covers a broad spectrum of higher cognitive behaviors beyond what we had expected.Armed with powerful tools of genetic manipulation in Drosophila,an increasing number of studies have been conducted in order to elucidate the neural circuit mechanisms underlying these cognitive behaviors from a genes-brain-behavior perspective.The goal of this review is to integrate the most important studies on visual cognition in Drosophila carried out in China's Mainland during the last decade into a body of knowledge encompassing both the basic neural operations and circuitry of higher brain function in Drosophila.Here,we consider a series of the higher cognitive behaviors beyond learning and memory,such as visual pattern recognition,feature and context generalization,different feature memory traces,salience-based decision,attention-like behavior,and cross-modal leaning and memory.We discuss the possible general gain-gating mechanism implementing by dopamine-mushroom body circuit in fly's visual cognition.We hope that our brief review on this aspect will inspire further study on visual cognition in flies,or even beyond.