Yasunobu Sumikawa

Multi-robot Hunting Using Mobile Agents

Multi-robot hunting problem is one of the popular issues treated with multi-robot systems. The purpose of the problem is to search and capture a target using an invisible signal, through which the multirobots can sense the distance to the target. This paper proposes a new method in which the multiple robots cooperatively search for a target using mobile agents. In the method, we employ multiple mobile software agents. The mobile agents traverse mobile robots through migrations while collecting the information of the target. Since each robot just needs to establish a connection with another robot for migration of a mobile agent, our method reduces the total communication cost of the system. Also, the mobile agents’ migration manner is restricted within the view range of the camera of a robot, and thus mainly makes robots around the target active, which contributes to suppressing moving cost. We have implemented a simulator for the mobile agents based hunting system. We show the effectiveness of our method through numerical experiments on the simulator.

Global Load Instruction Aggregation Based on Code Motion

Most modern processors have some much faster cache memories than a main memory, and therefore, it is important to effectively utilize it for the efficient execution. The cache memories work well through enhancing temporal or spatial localities in the program. Therefore, the cache efficiency can be improved by making accesses to the same array or structure continuous. We propose the new cache optimization technique improving cache efficiency based on global code motion. Our technique moves a load instruction immediately after other preceding load instructions accessing the same array or structure, and then delays it as later as possible without changing the access order. The two step code motions enable not only globally improving the cache efficiency in the entire program with any control structure, but also suppressing register pressure. We have implemented our technique in a real compiler, and evaluated it on SPEC benchmarks. The experimental results show that our technique can decrease cache misses about 94% in the best case.

Classifying Short Descriptions of Past Events

Mentions and brief descriptions of events often appear in a variety of document genres such as news articles containing references to related events, historical accounts or biographies. While event categorization has been previously studied, it was usually done on entire news articles or longer event descriptions. In this work we focus on short descriptions of historical events which are typically in the form of one or a few sentences. We categorize them into 9 general event categories using a range of diverse features and report F-measure close to 80%.

Analysis of Temporal and Web Site References in History-related Tweets

Having good knowledge and comprehension of history is believed to be important for a variety of reasons. In this paper we report the initial results of a large scale exploratory analysis of history-focused references in microblogs based on 11-months long snapshot of Twitter data. The results of this study can be used for designing content recommendation systems and can help to improve time aware search applications.

Global load instruction aggregation based on dimensions of arrays

Most modern processors have some cache memories that are much faster than main memory. These cache memories function well if temporal or spatial localities in programs are enhanced; therefore, the continuous accesses to the same array that improves the localities can enhance effective utilization of the cache memories. In addition, because a multidimensional array can be regarded as an array of lower dimensional arrays, the continuous accesses to array references with the most similar indexes can enhance the utilization of them further. We propose a new code motion algorithm called MDGLIA that improves utilization of cache memory. MDGLIA moves each array reference immediately after the preceding references accessing the same array with the most similar indexes, and then delays it as late as possible without changing the access order. These two-step code motions contribute to not only improvement of the cache efficiency in the overall program but also suppression of register pressure. We implemented MDGLIA in a real compiler and evaluated it for matrix multiplication and SPEC benchmarks. The experimental results show that our algorithm reduces the number of cache misses in most cases.

Global Load Instruction Aggregation Based on Array Dimensions

Most modern processors have some cache memoriesthat are much faster than a main memory, and it isimportant to utilize them effectively for efficient programexecution. The cache memories function well if temporal or spatial localities in the program are enhanced. Therefore, the cache efficiency can be improved by making accesses to the same array continuous. In addition, a multidimensional array can be regarded as an array of lower dimensional arrays, which means that it is more effective to continuously aggregate the array references with same indexes more in thehighest dimensions, even if they are not completely same. Wepropose a new cache optimization technique for improvingcache efficiency based on global code motion. Our techniquemoves a load instruction to immediately after the precedingload instructions accessing the same array with the most similarindexes, and then delays it as late as possible without changingthe access order. These two-step code motions contribute tonot only the improvement of the cache efficiency in the entireprogram but also the suppression of register pressure. Wehave implemented our technique in a real compiler and haveevaluated it for SPEC benchmarks. The experimental resultsshow that our technique can decrease cache misses by about99.9% in the best case.

System for Category-driven Retrieval of Historical Events

In this paper, we demonstrate an online system for historical event retrieval. Our system outputs ranked events according to an input text query, time range and category relevance. It is useful for users searching not just for important past events related to input entities but events that belong to specified subset of general categories. It can be also helpful for creating datasets of events falling into specific categories or for generating specialized timelines.

Digital History meets Microblogging: Analyzing Collective Memories in Twitter

Having good knowledge and comprehension of history is believed to be important for a variety of reasons. Microblogging platforms could offer good opportunities to study how and when people explicitly refer to the past, in which context such references appear and what purpose they serve. However, this area remains unexplored. In this paper we report the results of a large scale exploratory analysis of history-focused references in microblogs based on 11-months long snapshot of Twitter data. We are the first to analyze general historical references in Twitter based on large scale data analysis. The results of this study can be used for designing content recommendation systems and could help to improve time aware search applications.

Cooperative Control of Multi-robot System Using Mobile Agent for Multiple Source Localization

In this paper, we propose a new approach for multiple source localization tasks of multiple robots. Our approach controls robots by mobile agents that behave based on particle swarm optimization. The key process of our algorithm is to make subgroups in the population. In order to make subgroups, we indirectly transfer information using mobile agents instead of through direct communications. We have implemented our approach in a simulator, and conducted experiments to show effectiveness of our approach. Through the experimental results, we have confirmed that our approach is more efficient and less susceptible to initial placement than other approaches.

Mining Historical Social Issues

This paper presents a framework for identifying human histories that are similar to a modern social issue specified by a learner. From the historical data, the learner can study how people in history tried to resolve social issues and what results they achieved. This can help the learner consider how to resolve the modern social issue. To identify issues in history similar to a given modern issue, our framework uses the characteristics and explanation of the specified modern issue in two techniques: clustering and classification. These techniques identify the similarity between historical and the modern issues by using matrix operation and text classification. We implemented our proposed framework and evaluated it in terms of analysis time. Experimental results proved that our framework has practical usage with an analysis time of only about 0.7 s.