This paper presents a unified interpretation of $α$-mutual information ($α$-MI) in terms of generalized $g$-leakage. Specifically, we present a novel interpretation of $α$-MI within an extended framework for quantitative information flow based on adversarial generalized decision problems. This framework employs the Kolmogorov-Nagumo mean and the $q$-logarithm to characterize adversarial gain. Furthermore, we demonstrate that, within this framework, the parameter $α$ can be interpreted as a measure of the adversary's risk aversion.

This study presents alternating optimization (AO) algorithms for computing$\alpha$-mutual information ($\alpha$-MI) and$\alpha$capacity based on variational characterizations of$\alpha$-MI using a reverse channel. Specifically, we derive several variational characterizations of Sibson, Arimoto, Augustin-Csiszár, and LapidothPfister MI and introduce novel AO algorithms for computing$\alpha$MI and$\alpha$-capacity; their performances for computing$\alpha$-capacity are also compared. The comparison results show that the AO algorithm based on the Sibson MI's characterization has the fastest convergence speed. A full version [1] with all proofs, explanations and more discussions is accessible at: https://arxiv.org/abs/2404.10950

In this paper, we present several novel representations of$\alpha$-mutual information ($\alpha$-MI) in terms of Rényi divergence and conditional Rényi entropy. The representations are based on the variational characterizations of$\alpha$-MI using a reverse channel. Based on these representations, we provide several interpretations of the$\alpha-\text{MI}$as privacy leakage measures using generalized mean and gain functions. Further, as byproducts of the representations, we propose novel conditional Rényi entropies that satisfy the property that conditioning reduces entropy and data-processing inequality.

Court judgments reveal how legal rules have been interpreted and applied to facts, providing a foundation for understanding structured legal reasoning. However, existing automated approaches for capturing legal reasoning, including large language models, often fail to identify the relevant legal context, do not accurately trace how facts relate to legal norms, and may misrepresent the layered structure of judicial reasoning. These limitations hinder the ability to capture how courts apply the law to facts in practice. In this paper, we address these challenges by constructing a legal knowledge graph from 648 Japanese administrative court decisions. Our method extracts components of legal reasoning using prompt-based large language models, normalizes references to legal provisions, and links facts, norms, and legal applications through an ontology of legal inference. The resulting graph captures the full structure of legal reasoning as it appears in real court decisions, making implicit reasoning explicit and machine-readable. We evaluate our system using expert annotated data, and find that it achieves more accurate retrieval of relevant legal provisions from facts than large language model baselines and retrieval-augmented methods.

In the field of robotics, researches have sought to control robots capable of dealing with a variety of environments and tasks generically, through the use of foundation models. Among these, the systems for robot behavior planning utilizing video have also been proposed. The system enables the generation of robot behaviors that are not dependent on specific environments or tasks. This is achieved by generating videos based on text input, which utilizes the vast knowledge inherent in the foundation models. Also, by using a visual interface such as video, it is possible to confirm the behavioral indicators on which the robot is operating. Although, there are few examples of research on robot behavior planning utilizing video. Previous studies have emphasized the verification of behavior generation utilizing video, with simplified object manipulation for testing on simulations. This is not enough to demonstrate the usefulness of robot behavior planning utilizing video in real-world environments. In addition, the systems from previous studies are not open, and such systems have not been sufficiently discussed. This paper attempts to construct robot behavior planning utilizing video as an open system, and to verify the validity of the behavior planning using actual machines. In this paper, we first focus on using Robotis's TURTLEBOT3 Waffle Pi and Mobile Manipulator(referred to as "Waffle") to construct robot behavior planning system utilizing video. Second, we create planning videos targeting the pick-and-place motion using the proposed system, and control the arm part of Waffle in the actual machine verification. Finally, by comparing the target coordinates from the planning video with the coordinates observed from the actual machine, we can confirm whether it is possible to control Waffle as planned. Errors are calculated from the coordinate comparison, and the control is performed again. Based on the results, we verify whether the proposed system is useful for controlling robots in real-world environments.

A system is expected to facilitate coordination among multiple construction machines or robots, enabling them to adaptively perform various tasks in disaster sites and unknown environments. Prior research has generally adopted a model-based approach to designing cooperative behavior. However, it is difficult to adapt to environments and scenarios that cannot be predicted by the model. In recent years, it has been reported that a robot equipped with foundation models can adapt to unknown (open) environments and unpredictable situations. However, there has been little discussion on foundation models for multiple robot systems; a flow that cooperatively handles unexpected events does not exist. In this paper, we propose the system flow that enables multiple robots to adaptively coordinate to unforeseen scenarios based on the functional expressions of each other and environment understanding utilizing GPT-4 and GPT-4V. Through experimentation, we verify that the proposed flow is able to adapt to an unforeseen environment, particularly path obstruction via robot experiments. Furthermore, we examine the validity of the proposed flow by varying the robots’ functional expressions and sensor information for the environment.

The Sibson and Arimoto capacity, which are based on the Sibson and Arimoto mutual information (MI) of order α, respectively, are well-known generalizations of the channel capacity C. In this study, we derive novel alternating optimization algorithms for computing these capacities by providing new variational characterizations of the Sibson and Arimoto MI. Moreover, we prove that all iterative algorithms for computing these capacities are equivalent under appropriate conditions imposed on their initial distributions.

The problem of computing$\alpha$-capacity for$\alpha > 1$is equivalent to that of computing the correct decoding exponent. Various algorithms for computing them have been proposed, such as Arimoto and Jitsumatsu-Oohama algorithm. In this study, we propose a novel alternating optimization algorithm for computing the$\alpha$-capacity for$\alpha > 1$based on a variational characterization of the Augustin-Csiszár mutual information. A comparison of the convergence performance of these algorithms is demonstrated through numerical examples.

A private information retrieval (PIR) is an information retrieval scheme that allows a user to retrieve messages from information databases while keeping secret which one the user wants to retrieve. Sun et al. formulated the download rate and showed that there is an upper limit to it (PIR capacity). Previous construction methods for a capacity-achieving linear PIR (CALPIR) are ad hoc. We show a suffiecient condition of a CALPIR using its extended parity check matrix.

Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found for linear geographically weighted regression (GWR), where many extensions (e.g. multiscale GWR, scalable GWR) have been proposed. The weak development of GWPR can be attributed to the computational cost and identification problem in the underpinning Poisson regression model. This study proposes linearized GWPR (L-GWPR) by introducing a log-linear approximation into the GWPR model to overcome these bottlenecks. Because the L-GWPR model is identical to the Gaussian GWR model, it is free from the identification problem, easily implemented, computationally efficient, and offers similar potential for extension. Specifically, L-GWPR does not require a double-loop algorithm, which makes GWPR slow for large samples. Furthermore, we extended L-GWPR by introducing ridge regularization to enhance its stability (regularized L-GWPR). The results of the Monte Carlo experiments confirmed that regularized L-GWPR estimates local coefficients accurately and computationally efficiently. Finally, we compared GWPR and regularized L-GWPR through a crime analysis in Tokyo.

Although the scalable geographically weighted regression (GWR) has been developed as a fast regression approach modeling non-stationarity, its potential on spatial prediction is largely unexplored. Given that, this study applies the scalable GWR technique for large-scale spatial prediction, and compares its prediction accuracy with modern geostatistical methods including the nearest-neighbor Gaussian process, and machine learning algorithms including light gradient boosting machine. The result suggests accuracy of our scalable GWR-based prediction.

The Stratonovich’s value of information (VoI) is quantity that measures how much inferential gain is obtained from noisy data under information leakage constraint. In this paper, we introduce a generalized VoI for a general loss function and general information leakage. Then we derive an upper bound of the generalized VoI. Moreover, for a classical loss function, we provide a achievable condition of the upper bound which is weaker than that of in previous studies. Since VoI can be viewed as a formulation of a privacy-utility trade-off (PUT) problem, we provide an interpretation of the achievable condition in the PUT context.

We consider the problem of publishing data with utility and privacy guarantees in a statistical decision-theoretical framework. In this framework, we introduce a statistical decision-theoretic quantity called average gain for measuring not only privacy but also utility. We also show a relationship between the average gain and the $\alpha$-leakage, a tunable leakage measure proposed by Liao et at. Moreover, we formulate the privacyutility trade-off (PUT) problem using Stratonovich’s value of information (VoI) and present an analysis of the PUT.

Errors in land cover classification are often spatially heterogeneous even though a soft classification model such as spectral unmixing is implemented to mitigate a mixed pixel problem. The estimated land covers are fractions of targeted classes with the restriction of the sum to one and being non-negative. To assess the classification with considering a spatial heterogeneity, we propose a geographically weighted total composite error analysis. By using the USGS global reference database, we assessed errors of spectral unmixing classification of ALOS AVNIR-2 data into 4 land cover classes. Results yield a spatial surface of local errors by the Aitchison distance and address that the error magnitude across space is associated with the complexity of land covers.

This study attempts to simulate evaluation flow after the flooding in the Kanto-Tohoku heavy rain in September 2015, using a mobile GPS data. A simple Monte Carlo approach is introduced to simulate micro-scale people flow from spatially sparse GPS information. The simulation result shows that GPS data effectively recovers people flow after flooding even if the simulation approach is very simple.

Urban heatwave is increasingly severe as the global warming advances. Even worse, in Tokyo, an increasing proportion of residences are vulnerable against heats under the aging society. Today, heatwave monitoring is an emergent task toward climate adaptive urban development. Our final goal is developing a system to monitor real-time and micro-scale heatwave risk in Tokyo. To achieve it, we performed the following observation experiments: airborne monitoring, monitoring from the Tokyo Sky Tree, and micro-scale monitoring censoring inside and outside comforts. A method to combine these multi-scale information is developed to estimate micro-scale spatiotemporal behavior on heatwave risks. Based on the result, it is discussed how we can achieve the real-time and micro-scale heatwave monitoring, and make Tokyo more risk adaptive.

Urban heatwave is increasingly severe as the global warming advances. Even worse, especially in Japan, an increasing proportion of residences are vulnerable against heats under the aging society. Today, heatwave monitoring is an emergent task toward climate adaptive urban development. Our final goal is developing a smart navigation system to monitor real-time and micro-scale heatwave risk in Tokyo. To achieve it, we performed the following observation experiments: collection people location data with age information and people sentiment data by twitter, ground surface temperature monitoring by airborne and from the Tokyo Sky Tree. A method to combine these multi-scale information is developed to estimate micro-scale spatiotemporal behavior on heatwave risks. Based on the result, it is discussed how we can achieve the real-time and micro-scale heatwave monitoring, and make Tokyo more heat risk mitigative.

This study attempts to simulate people flow before and after the flooding in the West Japan heavy rain on July 2018, using a mobile GPS (global positioning system) data. We focus on Mabi-Cho, Kurashiki-City, which is one of the most damaged area on the flood disaster. A Monte Carlo simulation approach is introduced to generate micro-scale people's evacuation flows from spatially sparse GPS information. The result shows that GPS data effectively simulate people flow after flooding even if the simulation approach is very simple, and how people flow changed before and after the disaster.

We consider a distributed learning problem in which the computation is carried out on a system consisting of a master node and multiple worker nodes. In such systems, the existence of slow-running machines called stragglers will cause a significant decrease in performance. Recently, coding theoretic framework, which is named Gradient Coding (GC), for mitigating stragglers in distributed learning has been established by Tandon et al. Most studies on GC are aiming at recovering the gradient information completely assuming that the Gradient Descent (GD) algorithm is used as a learning algorithm. On the other hand, if the Stochastic Gradient Descent (SGD) algorithm is used, it is not necessary to completely recover the gradient information, and its unbiased estimator is sufficient for the learning. In this paper, we propose a distributed SGD scheme using Low Density Generator Matrix (LDGM) codes. In the proposed system, it may take longer time than existing GC methods to recover the gradient information completely, however, it enables the master node to obtain a high-quality unbiased estimator of the gradient at low computational cost and it leads to overall performance improvement.

Distinct Huffman codes, i.e., distinct codeword sets obtained by Huffman's algorithm are constructed for the n-th degree extended binary memoryless sources whose alphabet is {0, 1}nif (n, p) varies, where p ≥ 1/2 denotes the probability that symbol 0 occurs. For a fixed n ≥ 2, sufficient conditions with respect to p constructing a part of all distinct Huffman codes have been shown. Necessary conditions with respect to p, constructing such Huffman codes have also been shown. However, sufficient conditions corresponding to some but not all such distinct Huffman codes are equivalent to necessary conditions. In this work, we tighten necessary conditions and discuss whether or not sufficient conditions are equivalent to necessary conditions corresponding to other distinct Huffman codes. In addition, we present examples in which sufficient conditions are or are not equivalent to necessary conditions.

The impression of a video changes depending on the audio and visual content of the video. Adding appropriate background music to a video is an important process for making the video more impressive. In this paper, we present a system called OtoPittan which recommends background music for video based on the valence and arousal model. As input for the system, first a user registers a video with no background music, and then inputs a desired impression by setting the valence and the arousal level. The system recommends music clips as candidates of background music for the video. Recommended music clips are determined by minimizing the difference between the desired impression and the predicted impression calculated from the audio features of each candidate music and the visual features of the registered video. We have confirmed that with the proposed system users can quickly create videos giving desired impressions.

We propose a new application of stochastic point-based rendering, which was recently proposed for implicit surfaces, to large-scale laser-scanned 3D point data. Specifically, we propose a scheme to apply the rendering to transparent and fused visualization of recent large and complex laser-scanned data from cultural assets. Our scheme uses 3D points that are directly acquired using a laser scanner as the rendering primitives. For laser-scanned data that consist of more than 107 or 108 3D points, the pre-processing stage takes only a few minutes, and the rendering stage is executable at interactive frame rates. We do not encounter rendering artifacts originating from the indefiniteness of depth-sorted orders of rendering primitives. Fused visualization with various visual assistants is also possible. We demonstrate the effectiveness of our scheme by visualizing a campus building and a culturally important festival float.

This paper examines numerical algorithms for factorization of a low-rank matrix with missing components. We first propose a new method that incorporates a damping factor into the Wiberg method to solve the problem. The new method is characterized by the way it constrains the ambiguity of the matrix factorization, which helps improve both the global convergence ability and the local convergence speed. We then present experimental comparisons with the latest methods used to solve the problem. No comprehensive comparison of the methods that have been proposed recently has yet been reported in literature. In our experiments, we prioritize the assessment of the global convergence performance of each method, that is, how often and how fast the method can reach the global optimum starting from random initial values. Our conclusion is that top performance is achieved by a group of methods based on Newton-family minimization with damping factor that reduce the problem by eliminating either of the two factored matrices. Our method, which belongs to this group, consistently shows a 100% global convergence rate for different types of affine structure from motion data with a very high population of missing components.

In recent years, biometrics authentication has been widely used to realize high security in airports, office buildings and so on. In public area, surveillance cameras, which are combined with a high efficient coding recorder such as MPEG2 or MPEG4 recorder, are set to take face images of pedestrians and car license plates. However, high compression for efficient recording leads to degradation of details in face and characters on car license plates. Consequently, it is important that region of interest (ROI) is detected correctly and recorded with high quality image. Although there are a number of proposals [1, 2 ] concerning the detection of face areas and license plates, the relationship between ROI masks and face recognition rate or character recognition rate is not cleared. In this work, we investigate the recognition rates for face authentication with compressed face images under limited storage size. Several kinds of ROI mask are compared, and face recognition rates with respect to ROIs are shown.

Recently, not only still pictures but also moving pictures are displayed on the PDAs or cellular phones. When we take a look such pictures, the picture quality seems to be better than those on the CRTs or the large LCDs. In order to clarify the reason, we have investigated the relationship between the subjective quality of different size of pictures with keeping the viewing distance 6H. After three kinds of pictures (Girl, Mandrill, Milkdrop) are subjectively tested, the MOS using the small sized pictures with several distortions is better than that of the middle and large sized pictures by the psychophysical factors. In this paper, after discussing the effect of picture size on the MOS, we show the experimental results obtained by subjective evaluation with 20 observers. And, the relationship between the MOS and the Weighted SNR compensated by the subjective spatial frequency are given.