Bilingual’s Advantages of not Using Mutual Exclusivity

It is a known fact that monolingual children will take advantage of the principle of mutual exclusivity(ME)in the process of early word learning,i.e.,the names of two different objects are mutually exclusive(one label for one referent).With the help of ME,they can expand their vocabulary effectively with a rapid speed.However,for bilingual children,it seems this principle is not that friendly to them,since they are exposed to two languages at the same time,so there could be at least two labels for the same referent.Hence bilingual children may be confused and encounter difficulties in learning words,which will slower their word learning process.This paper tries to,based on earlier research,probe into the question that how bilingual children acquire words without the help of ME,and explore whether there are advantages of not using ME in word learning for bilingual children.

Ad hoc distributed mutual exclusion algorithm based on token-asking

The solution of distributed mutual exclusion is difficult in Ad hoc networks owing to dynamic topologies and mobility. Based on the analysis of the properties of Ad hoc networks and the disadvantages of the traditional algorithms, an improved Ad hoc system model was given and a novel algorithm was presented as AHDME （Ad Hoc Distributed Mutual Exclusion）; it was based on the token-asking algorithms. It utilized broadcast to search for the token and to decrease the message complexity of multi-hop Ad hoc networks. Lamport＇s timestamp was improved to ensure the time sequence and to prevent nodes from starvation. When compared to traditional algorithms, AHDME does not require the fixed size of request queues and the global system information, which adapts itself to the frequent arrival/departures and the limited computing capability of nodes in Ad hoc networks. Performance analysis and simulation results show that the AHDME algorithm has low message complexity, small space complexity, and short response delay.

Improved design of online fault diagnoser for partially observed Petri nets with generalized mutual exclusion constraints

This paper investigates the fault detection problem for discrete event systems (DESs) which can be modeled by partially observed Petri nets (POPNs). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, an improved online fault diagnosis algorithm that integrates generalized mutual exclusion constraints (GMECs) and integer linear programming (ILP) is proposed. Assume that the POPN structure and its initial markings are known, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. GMEC is used for elementary diagnosis of the system behavior, then the ILP problem of POPN is solved for further diagnosis. Finally, an example of a real DES to test the new fault diagnoser is analyzed. The proposed algorithm increases the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP is verified.

Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

Affinity propagation(AP)is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which limits its ability to perform effective aggregation.This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters,without changing the similarity matrix or customizing preference parameters,as done in existing enhanced approaches.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm is proposed,which combines a dependable partitioning clustering approach with AP to achieve this purpose.The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge.Based on these findings,mutually exclusive exemplar detection was conducted on the current AP exemplars,and a pair of unsuitable exemplars for coexistence is recommended.The recommendation is then mapped as a novel constraint,designated mutual exclusion and aggregation.To address this limitation,a modified AP clustering model is derived and the clustering is restarted,which can result in exemplar number reduction,exemplar selection adjustment,and other data point redistribution.The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected.Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison,and many internal and external clustering evaluation indexes are used to measure the clustering performance.The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.

Query and Improvement of the Project Appraise Method Promoted by the State Planning Commission

This paper expounds the existent problems in the method and parameter of economic evaluation on construction project, which distributed by the State Planning Commission. By using technical economics principles, the author analyzes the problems and finds out the reasonable, simple, practical methods to solve the mutually exclusive schemes problems.

Are the Methods of the Net Present Value Rate and Net Annual Value Rate the Effective Scheme Comparison Methods

As for the dispute about the best-choosing method of mutually-exclusive scheme and index selection, this text proves that there is an error to make a point that using net present value rate or net annual value rate, the most scientific and effective method is chain-system differential analysis and the most short-cutting method is the method of net annual value, to select the NA V maximum scheme.

Is the Increment Principle Really Wrong ？

In view of the problems in the selection of alternatives which are mutually exclusive and the criticism against the increment principle, this article reveals the cause of the mistake and demonstrates that only the increment principle and the incremental analysis method is the principle and method that we should follow and adopt in schematizing the economic policy-making.

Are China and Russia Axis or Partners?

Grounded in an analysis of the unique advantages of China-Russia relations, the paper argues that the two countries have established a high level of political trust and will continue to work in concert on all fronts. It also looks back at how China entered into alliances throughout its history and the lessons it has learned from those experiences. It emphasizes that China does not deem the establishment of exclusive alliances or political blocs as a foreign policy option.

Verifying Mutual Exclusion and Liveness Properties with Split Preconditions

This work is focused on presenting a split precondition approach for the modeling and proving the correctness of distributed algorithms. Formal specification and precise analysis of Peterson's distributed mutual exclusion algorithm for two process has been considered. The proof of properties like, mutual exclusion, liveness, and lockout-freedom have also been presented. Keywords distributed algorithms - state transition rule - mutual exclusion - weakest self-precondition - weakest co-operation - correctness Regular PaperAwadhesh Kumar Singh received the B.E. degree in computer science & engineering from Gorakhpur University, Gorakhpur, India in 1988. He received the M.E. and Ph.D. (Engg) degrees in the same area from Jadavpur University, Kolkata, India. He is a faculty member in Computer Engineering Department, National Institute of Technology, Kurukshetra, India. His present research interest is distributed systems.Anup Kumar Bandyopadhyay received the B.E. (Tel.E.), M.E. (Tel.E.), and Ph.D. (Engg) degrees from Jadavpur University, Calcutta, India in 1968, 1970 and 1983, respectively. From 1970 to 1972 he worked with the Microwave Antenna System Engineering Group of the Indian Space Research Organization. In 1972 he joined the Department of Electronics and Telecommuication Engineering, Jadavpur University, where he is currently a professor. His research interests include computer communication networks and distributed systems.

Lower Bound Estimation of Hardware Resources for Scheduling in High—Level Synthesis

In high-level synthesis of VLSI circuits, good lower bound prediction canefficiently narrow down the large space of possible designs. Previous approaches predict the lowerbound by relaxing or even ignoring the precedence constraints of the data flow graph (DFG), andresult in inaccuracy of the lower bound. The loop folding and conditional branch were also notconsidered. In this paper, a new stepwise refinement algorithm is proposed, which takesconsideration of precedence constraints of the DFG to estimate the lower bound of hardware resourcesunder time constraints. Processing techniques to handle multi-cycle, chaining, pipelining, as wellas loop folding and mutual exclusion among conditional branches are also incorporated in thealgorithm. Experimental results show that the algorithm can produce a very tight and close tooptimal lower bound in reasonable computation time.