In recent geospatial research, the importance of modeling and generating human mobility trajectories is rising. Whereas there are already plenty of feasible approaches applicable to geospatial sequence modeling itself, there seems to be room to improve with regard to evaluation, specifically about measuring the similarity between generated and reference trajectories. In this work, we propose a novel similarity measure, GEO-BLEU, which can be especially useful in the context of geospatial sequence modeling and generation. As the name suggests, this work is based on BLEU, one of the most popular measures used in machine translation research, while introducing spatial proximity to the idea of n-gram. We compare this measure with an established method, dynamic time warping, applying both measures to simple artificial sequences and examining differences in their characteristics.

Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places, and could be applied as essential resources to various downstream tasks including land use classification and human mobility prediction. Place embeddings with high spatial resolution are desirable for many applications, however, downscaling the spatial resolution could degrade the quality of embeddings due to data sparsity, especially in less populated areas. Our proposed method addresses this issue by leveraging the hierarchical nature of spatial information, according to the local density of observed data points. We evaluated the effectiveness of our fine grained place embeddings via next place prediction tasks using real world trajectory data from 3 cities in Japan, and compared it with non-hierarchical baseline methods. Our technique of incorporating spatial hierarchical structure can complement and reinforce various other geospatial models using place embedding generation methods.

Point Of Interest (POI) categorization is to group POIs into several categories and make them easy-to-use in geospatial applications. Previous studies mainly used geospatial features, such as check-in sequences and satellite images, to group POIs into pre-defined rough categories. However, each POI has its own "atmosphere" beyond its geospatial features, which represents what kinds of people tend to visit it and how they spend their time there. This subtle atmosphere is important for users to decide whether to visit the POI, so considering it may be critical when providing commercial services, such as a property search service. In this paper, we propose a new POI categorization method that can capture the POI atmosphere by using user behavior on a web search engine. Our key observation is that the next queries of a search query about a POI tend to contain the user's purpose for visiting it. We harness this observation to train a neural encoder that maps POIs to continuous vectors (called embeddings) via next-query prediction with a deep structured semantic model (DSSM). Experimental results indicate that our method performs well for POI atmosphere categorization of parks as a case study. We believe that our method complements the existing POI categorization methods.

Unsupervised translation has become a popular task in natural language processing (NLP) due to difficulties in collecting large scale parallel datasets. In the urban computing field, place embeddings generated using human mobility patterns via recurrent neural networks are used to understand the functionality of urban areas. Translating place embeddings across cities allow us to transfer knowledge across cities, which may be used for various downstream tasks such as planning new store locations. Despite such advances, current methods fail to translate place embeddings across domains with different scales (e.g. Tokyo to Niigata), due to the straightforward adoption of neural machine translation (NMT) methods from NLP, where vocabulary sizes are similar across languages. We refer to this issue as the domain imbalance problem in unsupervised translation tasks. We address this problem by proposing an unsupervised translation method that translates embeddings by exploiting common hierarchical structures that exist across imbalanced domains. The effectiveness of our method is tested using place embeddings generated from mobile phone data in 6 Japanese cities of heterogeneous sizes. Validation using landuse data clarify that using hierarchical anchors improves the translation accuracy across imbalanced domains. Our method is agnostic to input data type, thus could be applied to unsupervised translation tasks in various fields in addition to linguistics and urban computing.

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies have generated place representations (or embeddings) from mobility patterns in a similar manner to word embeddings to better understand the functionality of different places within a city. However, studies have been limited to generating such representations of cities in an individual manner and has lacked an inter-city perspective, which has made it difficult to transfer the insights gained from the place representations across different cities. In this study, we attempt to bridge this research gap by treating cities and languages analogously. We apply methods developed for unsupervised machine language translation tasks to translate place representations across different cities. Real world mobility data collected from mobile phone users in 2 cities in Japan are used to test our place representation translation methods. Translated place representations are validated using landuse data, and results show that our methods were able to accurately translate place representations from one city to another.

Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real time location data for calibration, which are becoming much harder to obtain due to the rising privacy concerns. Meanwhile, web search queries of anonymous users have been collected by web companies. Although such data raise less privacy concerns, they have been under-utilized for various applications. In this study, we investigate whether web search data observed prior to the disaster can be used to predict the evacuation decisions. More specifically, we utilize a session-based query encoder that learns the representations of each user's web search behavior prior to evacuation. Our proposed approach is empirically tested using web search data collected from users affected by a major flood in Japan. Results are validated using location data collected from mobile phones of the same set of users as ground truth. We show that evacuation decisions can be accurately predicted (84%) using only the users' pre-disaster web search data as input. This study proposes an alternative method for evacuation prediction that does not require highly sensitive location data, which can assist local governments to prepare effective first response strategies.

This paper proposes a design methodology in a wireless power transfer system for 3-D stacked multiple receivers. A 1:m selective power transfer system is realized by introducing a frequency/time division multiplexing system. The power transfer function is analytically formulated and an optimization methodology of multiple tuning capacitors values is proposed and compared with simulation results. By using the optimized values, power transfer efficiencies at 6.78MHz and 13.56MHz are simulated to be 80% and 84%, respectively. Crosstalk and load transient performance is also compared with 1:1 system and confirmed that its degradation is not significant.

Near real-time data processing tasks, such as multimedia streaming applications, exhibit a common fact that their deadline periods are longer than their input intervals due to buffering. Therefore, it is possible to minimize their energy consumption without deadline violations. In this work, we propose an energy efficient slack-based task scheduling algorithm for such tasks by adapting to task size variations and applying DVFS with the help of statistical analysis. We confirmed that our proposal can further reduce the energy consumption when compared to oracle frame-based scheduling.

Sensor nodes are used extensively in order to gather real-time information in the social environment and natural environment. And the production volume of sensor nodes is much increased with the development of cyber-physical systems. Therefore, it becomes important how to reduce the power consumption of huge sensor nodes. In this work, normally-off architecture of microcontroller for future low-power sensor node is proposed. To realize true low-power effects with normally-off computing technology, a co-design of hardware and software technology is much important. In this work, the power consumption of sensor nodes is possible to reduce of around 70% by using normally-off MCU architecture in sensor node.

Power consumption of Network-on-Chip (NoC) is becoming more important in many core processors. Input buffers utilized in routers consume a significant part of the total power of NoCs. In order to reduce this power consumption, a novel power efficient memory called Marching Memory Through type (MMTH) is introduced. By connecting transparent latches in tandem, MMTH achieves high speed operation with a low power consumption. MMTH, however, requires a certain overhead at read operation, and hence we propose a latency reduction scheme based on the look-ahead routing. The proposed router was designed in Renesas's 40nm process and compared with a standard router using conventional register-based FIFOs in terms of the network performance, application performance, and power consumption. The result of evaluation shows that the proposed router reduces the power consumption by 42.4% on average at 2GHz and the expense of only 0.5-2.0% performance overhead.

The M32R/D is a 32-bit microprocessor with a large-capacity on-chip DRAM. It consists of a 32-bit RISC CPU, a 32-bit/spl times/16-bit multiply and accumulator (MAC), either 1-Mbyte or 2-Mbyte DRAM, 4-Kbyte cache memory, and a memory controller. The CPU, DRAM, and cache memory are connected via a 128-bit 66.6 MHz internal bus yielding high performance and low power dissipation. The chip is capable of coping with a with range of applications and thus provides the system designer with great flexibility. For instance, a portable multimedia system can be realized by only three chips: an M32R/D chip, an I/O ASIC chip, and programming ROM. This means that a total system solution can be achieved at a lower cost with higher performance. Personal digital assistants (PDAs) and digital still cameras are such examples.

The GMICRO/100, a 32-b microprocessor based on the TRON architecture specification, is described. The GMICRO/100 uses high-level instructions, such as those in bit-map manipulation. The bit-map instructions are implemented by pipelining micro-operations, and achieve optimum use of the memory bus in the execution. The bit-map instructions are 2 to 3 times faster than repeating move instructions by the software. Microprogram development tools for the GMICRO/100 design are described.>