Speed memory is a comprehensive memory training course based on recent research. As you work through methods to highly advanced systems - incresing your memory power all the time. These techniques to improve your memory form the basis of the new BBC television programme Use Your Head, devised and presnted by the author
Tony Buzan, author of the best-selling
Use Your Head and inventor of the
revolutionary Mind-Mapping technique, has acquired fame by improving the memory and learning capability of thousands of people. In this book, based on the latest research into the workings of the human brain, he presents an ingenious system for training the memory to achieve extraordinary feats. The book provides surprising, yet simple, techniques for remembering names, dates, phone numbers and appointments.
DRAM Cell ObservationDRAM Cell Observations.1T DRAM requires a sense amplifier for each bit line, due
to charge redistribution read-out.DRAM memory cells are single ended in contrast to
SRAM cells.The read-out of the 1T DRAM cell is destructive; read
and refresh operations are necessary for correct
System memory: khi ta nói đến "memory" thì có lẽ hơi mơ hồ và khó hiểu cho rất nhiều bạn, nhất
là những bạn chưa có quen biết vi cấu trúc máy tính nhiều. Thực ra từ memory trong quá khứ được
diễn tả như đại diện cho tất cả "vùng nhớ" trong computer ngoại trừ CPU.
The main purpose of a computer system is to execute programs. These programs, together with the data
they access, must be in main memory (at least partially) during execution.
To improve both the utilization of the CPU and the speed of its response to users, the computer must
keep several processes in memory. Many memory-management schemes exist, reflecting various
Khi tôi đang chơi game thì hiện lên thông báo "Windows-virtual memory minimum too low. Your system is low on virtual memory. Windows is increasing the size of your virtual memory paging file. During this process...
In the last decades, the Shape Memory Alloys, with their peculiar thermo-mechanical properties, high corrosion and extraordinary fatigue resistance, have become more popular in research and engineering applications. This book contains a number of relevant international contributions related to their properties, constitutive models and numerical simulation, medical and civil engineering applications, as well as aspects related to their processing.
Chapter 1 of our memory, mymemory, chapter 2 howitall start, chapter 3 memory and innovation, chapter 4 strength ofassociation, chapter 5 size ofassociation, chapter 6 chain ofassociation, chapter 7 the method affiliate, ... are the main contents of the ebook "You can have an Amazing Memory" invite you to consult.
For bidirectional associate memory neural networks with time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the Lyapunov functional method and linear matrix inequality (LMI) technique. A novel criterion for the stability, which give information on the delay-dependent property, is derived.
Shape memory alloys have become in the past decades a well established research subject. However, the complex relations between properties and structure have created a continuously growing interest for a deeper insight all this time. The complexity of relationships between structure and properties is mostly related to the fact that strong ?multidimensional? interactions are taking place: from the early studies focusing on the thermal and/or mechanical induced phase transformations to the more recent findings on the magnetically induced structural changes.
We present a set of algorithms that enable us to translate natural language sentences by exploiting both a translation memory and a statistical-based translation model. Our results show that an automatically derived translation memory can be used within a statistical framework to often ﬁnd translations of higher probability than those found using solely a statistical model.
Virtual address of a process does not represent the actual physical locationof an object in memory.
Each process maintains its page map
–Internal data structure used to translate virtual addresses into corresponding physical addresses
–Each time a thread references an address, the system translates the virtual address to physical address
Memory-mapped file functionality
–Map virtual memory space directlyto normal files.
–No need to perform direct file I/O
–Data structures created in memory will be saved in the file for later use
–In-memory algorithms can process file data even though the file is much larger than available physical memory
–Improvement of file processing performance
–No need to manage buffers and the file data
–Multiple processes can share memory
We address the issue of on-line detection of communication problems in spoken dialogue systems. The usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. By applying both ruleinduction and memory-based learning techniques to data obtained with a Dutch train time-table information system, the current paper demonstrates that the aforementioned features indeed lead to a method for problem detection that performs signiﬁcantly above baseline.
This paper demonstrates a novel distributed architecture to facilitate the acquisition of Language Resources. We build a factory that automates the stages involved in the acquisition, production, updating and maintenance of these resources. The factory is designed as a platform where functionalities are deployed as web services, which can be combined in complex acquisition chains using workﬂows. We show a case study, which acquires a Translation Memory for a given pair of languages and a domain using web services for crawling, sentence alignment and conversion to TMX. ...
We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are widely used by human translators in industrial settings.
This paper describes an efficient parallel system for processing Typed Feature Structures (TFSs) on shared-memory parallel machines. We call the system Parallel Substrate for TFS (PSTFS}. PSTFS is designed for parallel computing environments where a large number of agents are working and communicating with each other. Such agents use PSTFS as their low-level module for solving constraints on TFSs and sending/receiving TFSs to/from other agents in an efficient manner.
We present a general architecture for efficient and deterministic morphological analysis based on memory-based learning, and apply it to morphological analysis of Dutch. The system makes direct mappings from letters in context to rich categories that encode morphological boundaries, syntactic class labels, and spelling changes. Both precision and recall of labeled morphemes are over 84% on held-out dictionary test words and estimated to be over 93% in free text.
This paper discusses the supervised learning of morphology using stochastic transducers, trained using the ExpectationMaximization (EM) algorithm. Two approaches are presented: ﬁrst, using the transducers directly to model the process, and secondly using them to deﬁne a similarity measure, related to the Fisher kernel method (Jaakkola and Haussler, 1998), and then using a Memory-Based Learning (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic. ...
We present a new approach to disambiguating syntactically ambiguous words in context, based on Variable Memory Markov (VMM) models. In contrast to fixed-length Markov models, which predict based on fixed-length histories, variable memory Markov models dynamically adapt their history length based on the training data, and hence may use fewer parameters. In a test of a VMM based tagger on the Brown corpus, 95.81% of tokens are correctly classified.