Search results for: long short-term memory networks (LSTM)
9188 Optical Multicast over OBS Networks: An Approach Based on Code-Words and Tunable Decoders
Authors: Maha Sliti, Walid Abdallah, Noureddine Boudriga
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In the frame of this work, we present an optical multicasting approach based on optical code-words. Our approach associates, in the edge node, an optical code-word to a group multicast address. In the core node, a set of tunable decoders are used to send a traffic data to multiple destinations based on the received code-word. The use of code-words, which correspond to the combination of an input port and a set of output ports, allows the implementation of an optical switching matrix. At the reception of a burst, it will be delayed in an optical memory. And, the received optical code-word is split to a set of tunable optical decoders. When it matches a configured code-word, the delayed burst is switched to a set of output ports.Keywords: optical multicast, optical burst switching networks, optical code-words, tunable decoder, virtual optical memory
Procedia PDF Downloads 6039187 On the Impact of Oil Price Fluctuations on Stock Markets: A Multivariate Long-Memory GARCH Framework
Authors: Manel Youssef, Lotfi Belkacem
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This paper employs multivariate long memory GARCH models to simultaneously estimate mean and conditional variance spillover effects between oil prices and different financial markets. Since different financial assets are traded based on these market sector returns, it’s important for financial market participants to understand the volatility transmission mechanism over time and across these series in order to make optimal portfolio allocation decisions. We examine weekly returns from January 1, 2003 to November 30, 2012 and find evidence of significant transmission of shocks and volatilities between oil prices and some of the examined financial markets. The findings support the idea of cross-market hedging and sharing of common information by investors.Keywords: oil prices, stock indices returns, oil volatility, contagion, DCC-multivariate (FI) GARCH
Procedia PDF Downloads 5249186 A Virtual Grid Based Energy Efficient Data Gathering Scheme for Heterogeneous Sensor Networks
Authors: Siddhartha Chauhan, Nitin Kumar Kotania
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Traditional Wireless Sensor Networks (WSNs) generally use static sinks to collect data from the sensor nodes via multiple forwarding. Therefore, network suffers with some problems like long message relay time, bottle neck problem which reduces the performance of the network. Many approaches have been proposed to prevent this problem with the help of mobile sink to collect the data from the sensor nodes, but these approaches still suffer from the buffer overflow problem due to limited memory size of sensor nodes. This paper proposes an energy efficient scheme for data gathering which overcomes the buffer overflow problem. The proposed scheme creates virtual grid structure of heterogeneous nodes. Scheme has been designed for sensor nodes having variable sensing rate. Every node finds out its buffer overflow time and on the basis of this cluster heads are elected. A controlled traversing approach is used by the proposed scheme in order to transmit data to sink. The effectiveness of the proposed scheme is verified by simulation.Keywords: buffer overflow problem, mobile sink, virtual grid, wireless sensor networks
Procedia PDF Downloads 3799185 Learning outside the Box by Using Memory Techniques Skill: Case Study in Indonesia Memory Sports Council
Authors: Muhammad Fajar Suardi, Fathimatufzzahra, Dela Isnaini Sendra
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Learning is an activity that has been used to do, especially for a student or academics. But a handful of people have not been using and maximizing their brains work and some also do not know a good brain work time in capturing the lessons, so that knowledge is absorbed is also less than the maximum. Indonesia Memory Sports Council (IMSC) is an institution which is engaged in the performance of the brain and the development of effective learning methods by using several techniques that can be used in considering the lessons and knowledge to grasp well, including: loci method, substitution method, and chain method. This study aims to determine the techniques and benefits of using the method given in learning and memorization by applying memory techniques taught by Indonesia Memory Sports Council (IMSC) to students and the difference if not using this method. This research uses quantitative research with survey method addressed to students of Indonesian Memory Sports Council (IMSC). The results of this study indicate that learn, understand and remember the lesson using the techniques of memory which is taught in Indonesia Memory Sport Council is very effective and faster to absorb the lesson than learning without using the techniques of memory, and this affects the academic achievement of students in each educational institution.Keywords: chain method, Indonesia memory sports council, loci method, substitution method
Procedia PDF Downloads 2839184 AI-Based Techniques for Online Social Media Network Sentiment Analysis: A Methodical Review
Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha
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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text
Procedia PDF Downloads 1119183 Tactile Cues and Spatial Navigation in Mice
Authors: Rubaiyea Uddin
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The hippocampus, located in the limbic system, is most commonly known for its role in memory and spatial navigation (as cited in Brain Reward and Pathways). It maintains an especially important role in specifically episodic and declarative memory. The hippocampus has also recently been linked to dopamine, the reward pathway’s primary neurotransmitter. Since research has found that dopamine also contributes to memory consolidation and hippocampal plasticity, this neurotransmitter is potentially responsible for contributing to the hippocampus’s role in memory formation. In this experiment we tested to see the effect of tactile cues on spatial navigation for eight different mice. We used a radial arm that had one designated 'reward' arm containing sucrose. The presence or absence of bedding was our tactile cue. We attempted to see if the memory of that cue would enhance the mice’s memory of having received the reward in that arm. The results from our study showed there was no significant response from the use of tactile cues on spatial navigation on our 129 mice. Tactile cues therefore do not influence spatial navigation.Keywords: mice, radial arm maze, memory, spatial navigation, tactile cues, hippocampus, reward, sensory skills, Alzheimer’s, neurodegnerative disease
Procedia PDF Downloads 6419182 Promoting Public Participation in the Digital Memory Project: Experience from My Peking Memory Project(MPMP)
Authors: Xiaoshuang Jia, Huiling Feng, Li Niu, Wei Hai
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Led by Humanistic Beijing Studies Center in Renmin University of China, My Peking Memory Project(MPMP) is a long-time digital memory project under guarantee of public participation to enable the cultural and intellectual memory of Beijing to be collected, organized, preserved and promoted for discovery and research. Taking digital memory as a new way, MPMP is an important part of Peking Memory Project(PMP) which is aimed at using digital technologies to protect and (re)present the cultural heritage in Beijing. The key outcome of MPMP is the co-building of a total digital collection of knowledge assets about Beijing. Institutional memories are central to Beijing’s collection and consist of the official published documentary content of Beijing. These have already fall under the archival collection purview. The advances in information and communication technology and the knowledge form social memory theory have allowed us to collect more comprehensively beyond institutional collections. It is now possible to engage citizens on a large scale to collect private memories through crowdsourcing in digital formats. Private memories go beyond official published content to include personal narratives, some of which are just in people’s minds until they are captured by MPMP. One aim of MPMP is to engage individuals, communities, groups or institutions who have formed memories and content about Beijing, and would like to contribute them. The project hopes to build a culture of remembering and it believes ‘Every Memory Matters’. Digital memory contribution was achieved through the development of the MPMP. In reducing barriers to digital contribution and promoting high public Participation, MPMP has taken explored the harvesting of transcribe service for digital ingestion, mobile platform and some off-line activities like holding social forum. MPMP has also cooperated with the ‘Implementation Plan of Support Plan for Growth of Talents in Renmin University of China’ to get manpower and intellectual support. After six months of operation, now MPMP have more than 2000 memories added and 7 Special Memory Collections now online. The work of MPMP has ultimately helped to highlight the important role in safeguarding the documentary heritage and intellectual memory of Beijing.Keywords: digital memory, public participation, MPMP, cultural heritage, collection
Procedia PDF Downloads 1619181 Protective Effect of the Histamine H3 Receptor Antagonist DL77 in Behavioral Cognitive Deficits Associated with Schizophrenia
Authors: B. Sadek, N. Khan, D. Łażewska, K. Kieć-Kononowicz
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The effects of the non-imidazole histamine H3 receptor (H3R) antagonist DL77 in passive avoidance paradigm (PAP) and novel object recognition (NOR) task in MK801-induced cognitive deficits associated with schizophrenia (CDS) in adult male rats, and applying donepezil (DOZ) as a reference drug were investigated. The results show that acute systemic administration of DL77 (2.5, 5, and 10 mg/kg, i.p.) significantly improved MK801-induced (0.1 mg/kg, i.p.) memory deficits in PAP. The ameliorating activity of DL77 (5 mg/kg, i.p.) in MK801-induced deficits was partly reversed when rats were pretreated with the centrally-acting H2R antagonist zolantidine (ZOL, 10 mg/kg, i.p.) or with the antimuscarinic antagonist scopolamine (SCO, 0.1 mg/kg, i.p.), but not with the CNS penetrant H1R antagonist pyrilamine (PYR, 10 mg/kg, i.p.). Moreover, the memory enhancing effect of DL77 (5 mg/kg, i.p.) in MK801-induced memory deficits in PAP was strongly reversed when rats were pretreated with a combination of ZOL (10 mg/kg, i.p.) and SCO (1.0 mg/kg, i.p.). Furthermore, the significant ameliorative effect of DL77 (5 mg/kg, i.p.) on MK801-induced long-term memory (LTM) impairment in NOR test was comparable to the DOZ-provided memory-enhancing effect, and was abrogated when animals were pretreated with the histamine H3R agonist R-(α)-methylhistamine (RAMH, 10 mg/kg, i.p.). However, DL77(5 mg/kg, i.p.) failed to provide procognitive effect on MK801-induced short-term memory (STM) impairment in NOR test. In addition, DL77 (5 mg/kg) did not alter anxiety levels and locomotor activity of animals naive to elevated-plus maze (EPM), demonstrating that improved performances with DL77 (5 mg/kg) in PAP or NOR are unrelated to changes in emotional responding or spontaneous locomotor activity. These results provide evidence for the potential of H3Rs for the treatment of neurodegenerative disorders related to impaired memory function, e.g. CDS.Keywords: histamine H3 receptor, antagonist, learning, memory impairment, passive avoidance paradigm, novel object recognition
Procedia PDF Downloads 1969180 A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity
Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj
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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares
Procedia PDF Downloads 679179 An Indoor Positioning System in Wireless Sensor Networks with Measurement Delay
Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang
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In the current paper, an indoor positioning system is proposed with consideration of measurement delay. Firstly, an estimation filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least square criterion, which utilizes only finite measurements on the most recent window. The proposed estimation filtering based scheme gives the filtered estimates for position, velocity and acceleration of moving target in real-time, while removing undesired noisy effects and preserving desired moving positions. Secondly, the proposed scheme is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Finally, computer simulations shows that the performance of the proposed estimation filtering based scheme can outperform to the existing infinite memory filtering based mechanism.Keywords: indoor positioning system, wireless sensor networks, measurement delay
Procedia PDF Downloads 4789178 Implementation of an Associative Memory Using a Restricted Hopfield Network
Authors: Tet H. Yeap
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An analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by directional weighted paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. By introducing hidden nodes, the proposed network can be trained to store patterns and has increased memory capacity. Training to be an associative memory, simulation results show that the associative memory performs better than a classical Hopfield network by being able to perform better memory recall when the input is noisy.Keywords: restricted Hopfield network, Lyapunov function, simultaneous perturbation stochastic approximation
Procedia PDF Downloads 1259177 WormHex: Evidence Retrieval Tool of Social Media from Volatile Memory
Authors: Norah Almubairik, Wadha Almattar, Amani Alqarni
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Social media applications are increasingly being used in our everyday communications. These applications utilise end-to-end encryption mechanisms, which make them suitable tools for criminals to exchange messages. These messages are preserved in the volatile memory until the device is restarted. Therefore, volatile forensics has become an important branch of digital forensics. In this study, the WormHex tool was developed to inspect the memory dump files of Windows and Mac-based workstations. The tool supports digital investigators to extract valuable data written in Arabic and English through web-based WhatsApp and Twitter applications. The results verify that social media applications write their data into the memory regardless of the operating system running the application, with there being no major differences between Windows and Mac.Keywords: volatile memory, REGEX, digital forensics, memory acquisition
Procedia PDF Downloads 1819176 Rheological Modeling for Shape-Memory Thermoplastic Polymers
Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev
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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model
Procedia PDF Downloads 3149175 Comparing Community Detection Algorithms in Bipartite Networks
Authors: Ehsan Khademi, Mahdi Jalili
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Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks
Procedia PDF Downloads 6169174 The Role of Planning and Memory in the Navigational Ability
Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal
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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.Keywords: memory, planning navigational ability, virtual reality
Procedia PDF Downloads 3259173 Evaluating Language Loss Effect on Autobiographical Memory by Examining Memory Phenomenology in Bilingual Speakers
Authors: Anastasia Sorokina
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Graduate language loss or attrition has been well documented in individuals who migrate and become emersed in a different language environment. This phenomenon of first language (L1) attrition is an example of non-pathological (not due to trauma) and can manifest itself in frequent pauses, search for words, or grammatical errors. While the widely experienced loss of one’s first language might seem harmless, there is convincing evidence from the disciplines of Developmental Psychology, Bilingual Studies, and even Psychotherapy that language plays a crucial role in the memory of self. In fact, we remember, store, and share personal memories with the help of language. Dual-Coding Theory suggests that language memory code deterioration could lead to forgetting. Yet, no one has investigated a possible connection between language loss and memory. The present study aims to address this research gap by examining a corpus of 1,495 memories of Russian-English bilinguals who are on a continuum of L1 (first language) attrition. Since phenomenological properties capture how well a memory is remembered, the following descriptors were selected - vividness, ease of recall, emotional valence, personal significance, and confidence in the event. A series of linear regression statistical analyses were run to examine the possible negative effects of L1 attrition on autobiographical memory. The results revealed that L1 attrition might compromise perceived vividness and confidence in the event, which is indicative of memory deterioration. These findings suggest the importance of heritage language maintenance in immigrant communities who might be forced to assimilate as language loss might negatively affect the memory of self.Keywords: L1 attrition, autobiographical memory, language loss, memory phenomenology, dual coding
Procedia PDF Downloads 1109172 Graph Neural Networks and Rotary Position Embedding for Voice Activity Detection
Authors: YingWei Tan, XueFeng Ding
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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding
Procedia PDF Downloads 599171 Analysis of Biomarkers Intractable Epileptogenic Brain Networks with Independent Component Analysis and Deep Learning Algorithms: A Comprehensive Framework for Scalable Seizure Prediction with Unimodal Neuroimaging Data in Pediatric Patients
Authors: Bliss Singhal
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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels
Procedia PDF Downloads 779170 The Relationship between the Use of Social Networks with Executive Functions and Academic Performance in High School Students in Tehran
Authors: Esmail Sadipour
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The use of social networks is increasing day by day in all societies. The purpose of this research was to know the relationship between the use of social networks (Instagram, WhatsApp, and Telegram) with executive functions and academic performance in first-year female high school students. This research was applied in terms of purpose, quantitative in terms of data type, and correlational in terms of technique. The population of this research consisted of all female high school students in the first year of district 2 of Tehran. Using Green's formula, the sample size of 150 people was determined and selected by cluster random method. In this way, from all 17 high schools in district 2 of Tehran, 5 high schools were selected by a simple random method and then one class was selected from each high school, and a total of 155 students were selected. To measure the use of social networks, a researcher-made questionnaire was used, the Barclay test (2012) was used for executive functions, and last semester's GPA was used for academic performance. Pearson's correlation coefficient and multivariate regression were used to analyze the data. The results showed that there is a negative relationship between the amount of use of social networks and self-control, self-motivation and time self-management. In other words, the more the use of social networks, the fewer executive functions of students, self-control, self-motivation, and self-management of their time. Also, with the increase in the use of social networks, the academic performance of students has decreased.Keywords: social networks, executive function, academic performance, working memory
Procedia PDF Downloads 839169 Evidences for Better Recall with Compatible Items in Episodic Memory
Authors: X. Laurent, M. A. Estevez, P. Mari-Beffa
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A focus of recent research is to understand the role of our own response goals in the selection of information that will be encoded in episodic memory. For example, if we respond to a target in the presence of distractors, an important aspect under study is whether the distractor and the target share a common response (compatible) or not (incompatible). Some studies have found that compatible objects tend to be groups together and stored in episodic memory, whereas others found that targets in the presence of incompatible distractors are remembered better. Our current research seems to support both views. We used a Tulving-based definition of episodic memory to differentiate memory from episodic and non-episodic traces. In this task, participants first had to classify a blue object as human or animal (target) which appeared in the presence of a green one (distractor) that could belong to the same category of the target (compatible), to the opposite (incompatible) or to an irrelevant one (neutral). Later they had to report the identity (What), location (Where) and time (When) of both target objects (which had been previously responded to) and distractors (which had been ignored). Episodic memory was inferred when the three scene properties (identity, location and time) were correct. The measure of non-episodic memory consisted of those trials in which the identity was correctly remembered, but not the location or time. Our results showed that episodic memory for compatible stimuli is significantly superior to incompatible ones. In sharp contrast, non-episodic measures found superior memory for targets in the presence of incompatible distractors. Our results demonstrate that response compatibility affects the encoding of episodic and non-episodic memory traces in different ways.Keywords: episodic memory, action systems, compatible response, what-where-when task
Procedia PDF Downloads 1659168 Resistive Switching in TaN/AlNx/TiN Cell
Authors: Hsin-Ping Huang, Shyankay Jou
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Resistive switching of aluminum nitride (AlNx) thin film was demonstrated in a TaN/AlNx/TiN memory cell that was prepared by sputter deposition techniques. The memory cell showed bipolar switching of resistance between +3.5 V and –3.5 V. The resistance ratio of high resistance state (HRS) to low resistance state (HRS), RHRS/RLRS, was about 2 over 100 cycles of endurance test. Both the LRS and HRS of the memory cell exhibited ohmic conduction at low voltages and Poole-Frenkel emission at high voltages. The electrical conduction in the TaN/AlNx/TiN memory cell was possibly attributed to the interactions between charges and defects in the AlNx film.Keywords: aluminum nitride, nonvolatile memory, resistive switching, thin films
Procedia PDF Downloads 3959167 Molecular Dynamics Simulation on Nanoelectromechanical Graphene Nanoflake Shuttle Device
Authors: Eunae Lee, Oh-Kuen Kwon, Ki-Sub Kim, Jeong Won Kang
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We investigated the dynamic properties of graphene-nanoribbon (GNR) memory encapsulating graphene-nanoflake (GNF) shuttle in the potential to be applicable as a non-volatile random access memory via molecular dynamics simulations. This work explicitly demonstrates that the GNR encapsulating the GNF shuttle can be applied to nonvolatile memory. The potential well was originated by the increase of the attractive vdW energy between the GNRs when the GNF approached the edges of the GNRs. So the bistable positions were located near the edges of the GNRs. Such a nanoelectromechanical non-volatile memory based on graphene is also applicable to the development of switches, sensors, and quantum computing.Keywords: graphene nanoribbon, graphene nanoflake, shuttle memory, molecular dynamics
Procedia PDF Downloads 4509166 Exposure to Tactile Cues Does Not Influence Spatial Navigation in 129 S1/SvLm Mice
Authors: Rubaiyea Uddin, Rebecca Taylor, Emily Levesque
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The hippocampus, located in the limbic system, is most commonly known for its role in memory and spatial navigation (as cited in Brain Reward and Pathways). It maintains an especially important role in specifically episodic and declarative memory. The hippocampus has also recently been linked to dopamine, the reward pathway’s primary neurotransmitter. Since research has found that dopamine also contributes to memory consolidation and hippocampal plasticity, this neurotransmitter is potentially responsible for contributing to the hippocampus’s role in memory formation. In this experiment we tested to see the effect of tactile cues on spatial navigation for eight different mice. We used a radial arm that had one designated “reward” arm containing sucrose. The presence or absence of bedding was our tactile cue. We attempted to see if the memory of that cue would enhance the mice’s memory of having received the reward in that arm. The results from our study showed there was no significant response from the use of tactile cues on spatial navigation on our 129 mice. Tactile cues therefore do not influence spatial navigation.Keywords: mice, radial arm maze, memory, spatial navigation, tactile cues, hippocampus, reward, sensory skills, Alzheimer's, neuro-degenerative diseases
Procedia PDF Downloads 6839165 A Lifetime-Enhancing Monitoring Node Distribution Using Minimum Spanning Tree in Mobile Ad Hoc Networks
Authors: Sungchul Ha, Hyunwoo Kim
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In mobile ad hoc networks, all nodes in a network only have limited resources and calculation ability. Therefore communication topology which have long lifetime is good for all nodes in mobile ad hoc networks. There are a variety of researches on security problems in wireless ad hoc networks. The existing many researches try to make efficient security schemes to reduce network power consumption and enhance network lifetime. Because a new node can join the network at any time, the wireless ad hoc networks are exposed to various threats and can be destroyed by attacks. Resource consumption is absolutely necessary to secure networks, but more resource consumption can be a critical problem to network lifetime. This paper focuses on efficient monitoring node distribution to enhance network lifetime in wireless ad hoc networks. Since the wireless ad hoc networks cannot use centralized infrastructure and security systems of wired networks, a new special IDS scheme is necessary. The scheme should not only cover all nodes in a network but also enhance the network lifetime. In this paper, we propose an efficient IDS node distribution scheme using minimum spanning tree (MST) method. The simulation results show that the proposed algorithm has superior performance in comparison with existing algorithms.Keywords: MANETs, IDS, power control, minimum spanning tree
Procedia PDF Downloads 3639164 Data Collection Techniques for Robotics to Identify the Facial Expressions of Traumatic Brain Injured Patients
Authors: Chaudhary Muhammad Aqdus Ilyas, Matthias Rehm, Kamal Nasrollahi, Thomas B. Moeslund
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This paper presents the investigation of data collection procedures, associated with robots when placed with traumatic brain injured (TBI) patients for rehabilitation purposes through facial expression and mood analysis. Rehabilitation after TBI is very crucial due to nature of injury and variation in recovery time. It is advantageous to analyze these emotional signals in a contactless manner, due to the non-supportive behavior of patients, limited muscle movements and increase in negative emotional expressions. This work aims at the development of framework where robots can recognize TBI emotions through facial expressions to perform rehabilitation tasks by physical, cognitive or interactive activities. The result of these studies shows that with customized data collection strategies, proposed framework identify facial and emotional expressions more accurately that can be utilized in enhancing recovery treatment and social interaction in robotic context.Keywords: computer vision, convolution neural network- long short term memory network (CNN-LSTM), facial expression and mood recognition, multimodal (RGB-thermal) analysis, rehabilitation, robots, traumatic brain injured patients
Procedia PDF Downloads 1429163 Memory and Narratives Rereading before and after One Week
Authors: Abigail M. Csik, Gabriel A. Radvansky
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As people read through event-based narratives, they construct an event model that captures information about the characters, goals, location, time, and causality. For many reasons, memory for such narratives is represented at different levels, namely, the surface form, textbase, and event model levels. Rereading has been shown to decrease surface form memory, while, at the same time, increasing textbase and event model memories. More generally, distributed practice has consistently shown memory benefits over massed practice for different types of materials, including texts. However, little research has investigated distributed practice of narratives at different inter-study intervals and these effects on these three levels of memory. Recent work in our lab has indicated that there may be dramatic changes in patterns of forgetting around one week, which may affect the three levels of memory. The present experiment aimed to determine the effects of rereading on the three levels of memory as a factor of whether the texts were reread before versus after one week. Participants (N = 42) read a set of stories, re-read them either before or after one week (with an inter-study interval of three days, seven days, or fourteen days), and then took a recognition test, from which the three levels of representation were derived. Signal detection results from this study reveal that differential patterns at the three levels as a factor of whether the narratives were re-read prior to one week or after one week. In particular, an ANOVA revealed that surface form memory was lower (p = .08) while textbase (p = .02) and event model memory (p = .04) were greater if narratives were re-read 14 days later compared to memory when narratives were re-read 3 days later. These results have implications for what type of memory benefits from distributed practice at various inter-study intervals.Keywords: memory, event cognition, distributed practice, consolidation
Procedia PDF Downloads 2169162 Formal Verification of Cache System Using a Novel Cache Memory Model
Authors: Guowei Hou, Lixin Yu, Wei Zhuang, Hui Qin, Xue Yang
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Formal verification is proposed to ensure the correctness of the design and make functional verification more efficient. As cache plays a vital role in the design of System on Chip (SoC), and cache with Memory Management Unit (MMU) and cache memory unit makes the state space too large for simulation to verify, then a formal verification is presented for such system design. In the paper, a formal model checking verification flow is suggested and a new cache memory model which is called “exhaustive search model” is proposed. Instead of using large size ram to denote the whole cache memory, exhaustive search model employs just two cache blocks. For cache system contains data cache (Dcache) and instruction cache (Icache), Dcache memory model and Icache memory model are established separately using the same mechanism. At last, the novel model is employed to the verification of a cache which is module of a custom-built SoC system that has been applied in practical, and the result shows that the cache system is verified correctly using the exhaustive search model, and it makes the verification much more manageable and flexible.Keywords: cache system, formal verification, novel model, system on chip (SoC)
Procedia PDF Downloads 4889161 Sustainable Renovation and Restoration of the Rural — Based on the View Point of Psychology
Authors: Luo Jin China, Jin Fang
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Countryside has been generally recognized and regarded as a characteristic symbol which presents in human memory for a long time. As a result of the change of times, because of it’s failure to meet the growing needs of the growing life and mental decline, the vast rural area began to decline. But their history feature image which accumulated by the ancient tradition provides people with the origins of existence on the spiritual level, such as "identity" and "belonging", makes people closer to the others in the spiritual and psychological aspects of a common experience about the past, thus the sense of a lack of culture caused by the losing of memory symbols is weakened. So, in the modernization process, how to repair its vitality and transform and planning it in a sustainable way has become a hot topics in architectural and urban planning. This paper aims to break the constraints of disciplines, from the perspective of interdiscipline, using the research methods of systems science to analyze and discuss the theories and methods of rural form factors, which based on the viewpoint of memory in psychology. So, we can find a right way to transform the Rural to give full play to the role of the countryside in the actual use and the shape of history spirits.Keywords: rural, sustainable renovation, restoration, psychology, memory
Procedia PDF Downloads 5699160 The Ameliorative Effects of the Histamine H3 Receptor Antagonist/Inverse Agonist DL77 on MK801-Induced Memory Deficits in Rats
Authors: B. Sadek, N. Khan, Shreesh K. Ojha, Adel Sadeq, D. Lazewska, K. Kiec-Kononowicz
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The involvement of Histamine H3 receptors (H3Rs) in memory and the potential role of H3R antagonists in pharmacological control of neurodegenerative disorders, e.g., Alzheimer disease (AD) is well established. Therefore, the memory-enhancing effects of the H3R antagonist DL77 on MK801-induced cognitive deficits were evaluated in passive avoidance paradigm (PAP) and novel object recognition (NOR) tasks in adult male rats, applying donepezil (DOZ) as a reference drug. Animals pretreated with acute systemic administration of DL77 (2.5, 5, and 10 mg/kg, i.p.) were significantly ameliorated in regard to MK801-induced memory deficits in PAP. The ameliorative effect of most effective dose of DL77 (5 mg/kg, i.p.) was abrogated when animals were pretreated with a co-injection with the H3R agonist R-(α)-methylhistamine (RAMH, 10 mg/kg, i.p.). Moreover, and in the NOR paradigm, DL77 (5 mg/kg, i.p.) reversed MK801-induced deficits long-term memory (LTM), and the DL77-provided procognitive effect was comparable to that of reference drug DOZ, and was reversed when animals were co-injected with RAMH (10 mg/kg, i.p.). However, DL77(5 mg/kg, i.p.) failed to alter short-term memory (STM) impairment in NOR test. Furthermore, DL77 (5 mg/kg) failed to induce any alterations of anxiety and locomotor behaviors of animals naive to elevated-plus maze (EPM), indicating that the ameliorative effects observed in PAP or NOR tests were not associated to alterations in emotions or in natural locomotion of tested animals. These results reveal the potential contribution of H3Rs in modulating CNS neurotransmission systems associated with neurodegenerative disorders, e.g., AD.Keywords: histamine H3 receptor, antagonist, learning and memory, Alzheimer's disease, neurodegeneration, passive avoidance paradigm, novel object recognition, behavioral research
Procedia PDF Downloads 1509159 Transformers in Gene Expression-Based Classification
Authors: Babak Forouraghi
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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.Keywords: transformers, generative ai, gene expression design, classification
Procedia PDF Downloads 52