In this study, we propose MoDeMIT (Mobility-Derived Multi-Insight Transfer), a human mobility prediction method that transfers various patterns shared across cities, extracted from mobility histories. Existing studies on human mobility prediction have primarily focused on learning the mobility patterns of users in the target city, without considering applications in cities with limited data. Moreover, relying solely on mobility patterns poses inherent limitations on prediction accuracy. The proposed MoDeMIT addresses these issues by defining and transferring multiple patterns shared across cities, such as lifestyle patterns and large-scale mobility patterns derived from mobility histories, as well as mobility patterns. This approach enables improvements in prediction accuracy compared to existing methods. We validate the effectiveness of MoDeMIT using real-world human mobility datasets. Furthermore, in the HuMob Challenge 2025 (GISCUP), MoDeMIT achieved a GEOBLEU score of [Average: 0.1632, CityA: 0.1504, CityB: 0.1471, CityC: 0.1801, CityD: 0.1753] and ranked within the top five teams.

Clustering people by their life patterns is valuable in government and business fields. Existing studies often rely on semantic data such as Point of Interest or stay purpose. However, they have the problem that obtaining large datasets is difficult due to the need for annotation work. Some studies try to use only location data. However, they do not reveal the semantics of the area where visitors stay because they only label visited areas by significance according to duration and frequency of stay. In this paper, we propose a framework, LPSeL, for clustering people's Life Patterns at a Semantic Level using only raw GPS location data. LPSeL is based on the idea that analyzing human mobility first requires understanding urban space. Therefore, it begins with area modeling, which models areas in a city based on people's activities. Then, treating human mobility as a sequence of area representations makes it possible to model individuals by semantic-level characteristics of their life patterns. We showed that LPSeL is capable of estimating people's attributes from their life patterns using a real-world dataset consisting of GPS data collected from tens of thousands of smartphone users.

Understanding the characteristics of various urban areas is crucial for applications such as urban planning, tourism policies, market analysis, and infection control. Techniques for embedding areas as vectors in a latent space based on human mobility patterns are actively researched. Many of these area embedding methods define areas as points, grids, or polygons on a geospatial plane and then embed them. However, existing methods do not allow for mutual transformation between these forms and sizes after the initial embedding. Additionally, if the characteristics of an area change due to events such as the opening of new buildings, re-embedding is necessary. Meanwhile, the Word2Vec technique, a representative word embedding method, has a property called additive compositionality. This property allows for the arithmetic operation of word meanings through the arithmetic operations of word embeddings. In this paper, we propose a method to apply this property to existing area embedding techniques, leveraging it for practical tasks such as area shape transformation and searching for areas with trends change.

This paper focuses on on-demand wireless sensor networks (WSNs) where a wake-up receiver is installed into each node. In on-demand WSNs, each node sends a wake-up signal including a wake-up ID assigned to a specific destination node in order to remotely activate its main radio interface. This wake-up control helps each node to reduce energy consumed during idle periods, however, the wake-up signal transmitted before every data transmission results in overhead causing degradation of communication quality and increase of energy consumption at each sender node. In order to reduce the overhead for wake-up control, a scheme called Double Modulation (DM) has been proposed, where each node embeds a sensing data to be transmitted into the payload field of a wake-up signal. However, the efficiency of DM scheme becomes smaller as the size of sensing data is increased. Therefore, in this paper, we propose a scheme called Overhearing (OH) that can alleviate the negative impact of wake-up overhead for any size of sensing data. In OH scheme, each node observes frames transmitted by a destination node and suppresses the transmission of wake-up signal when detecting the active state of their destination. Furthermore, we propose a hybrid scheme that combines OH and DM schemes. Our simulation results show that the proposed hybrid scheme can effectively reduce the negative impact of wake-up overhead.

We propose a novel post-production facial performance relighting system for human actors. Our system uses just a dataset of view-dependent facial appearances with a neutral expression, captured for a static subject using a Light Stage apparatus. For the actual performance, however, a potentially different actor is captured under known, but static, illumination. During post-production, the reflectance field of the reference dataset actor is transferred onto the dynamic performance, enabling image-based relighting of the entire sequence. Our approach makes post-production relighting more practical and could easily be incorporated in a traditional production pipeline since it does not require additional hardware during principal photography. Additionally, we show that our system is suitable for real-time post-production illumination editing.

Abstract Environment illumination, which is a complex and distant lighting environment represented by images, is often applied to create photo‐realistic images. However, creating photo‐realistic animations under environment illumination is exceedingly compute intensive. The Precomputed Radiance Transfer (PRT) methods achieve real‐time rendering under environment illumination; however, these methods only have a limited application in animation because the objects in the scene cannot be moved or rotated. In this paper, we propose a method for rendering photo‐realistic animations of dynamic scenes under environment illumination in real time. We notice the fact that when objects are moved or rotated, changes of radiances occur mainly in the regions of shadows cast by other objects. Our method makes a distinction between self‐shadow and shadows cast by other objects and computes these two kinds of shadows efficiently. Copyright © 2005 John Wiley & Sons, Ltd.

The authors propose a method for recording and analyzing actual design processes, which is called the problem-product-knowledge (PPK) method. This method consists of two parts: a method for recording design activities and a framework for organizing the record. The recording method allows designers to record their activities easily, and the framework allows analysts to organize a record into a design process description and provides two different views of a design process: a problem solving process and a product generating process. The PPK model can be considered as a tool which analysts can use to construct a tree (or dag) of the designer's activity in a bottom-up manner. This tree/dag includes abstraction of both the problem-solving process and the product generating process. Such an abstraction greatly enhances the understandability of the design.>