Cooperation in International Procedural Conflicts: Prospects and Benefits

The need for international integration of civil procedure has been strongly felt all over the world, particularly in the countries of Asia, North America and Europe. The birth of an international treaty will be good news for all those involved in international civil disputes

Civil Procedure Reform in Japan

Delay in court has been a problem common in all eras, both ancient and modern, and to all systems of law, Western and Eastern alike. In Japan, however, the problem is arguably more acute. The average delay between filing and judgment for cases that require at least a minimum level of proof-taking or an evidentiary hearing is 27 months. This deplorable reality has recently led to renewed efforts to tackle the problem of delay in Japan. Two groups that have been particularly important in this effort are two local bar associations and the Tokyo and Osaka district courts. The First Tokyo Bar Association and the Second Tokyo Bar Association have each separately published their own recommendations as to how to remedy the situation. The First Tokyo Bar Association\u27s publication, entitled A Tentative Draft of a New Civil Procedure Law, is a radical proposal, while the Second Tokyo Bar Association\u27s proposal is more or less similar to the more moderate proposals of the Tokyo and Osaka district courts. These proposals are particularly noteworthy because they represent a change of heart by the two bar associations. Like some of the other bar associations in Japan, they were not supportive of attempts at procedural reform in the past. This article will describe and evaluate recent proposals for reform and conclude that the legal community should experiment with the suggested procedural changes. The constitutional, procedural and social implications of the innovative methods will also be considered

Comparative Study of Judicial Administration

Description of an international project directed by Takeshi Kojima (Chuo University) with the assistance of Professor Frederick H. Zemans (Osgoode Hall Law School)

Multivalued Discrete Tomography Using Dynamical System That Describes Competition

Multivalued discrete tomography involves reconstructing images composed of three or more gray levels from projections. We propose a method based on the continuous-time optimization approach with a nonlinear dynamical system that effectively utilizes competition dynamics to solve the problem of multivalued discrete tomography. We perform theoretical analysis to understand how the system obtains the desired multivalued reconstructed image. Numerical experiments illustrate that the proposed method also works well when the number of pixels is comparatively high even if the exact labels are unknown

Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text

While Large Language Models (LLMs) have achieved remarkable performance in many tasks, much about their inner workings remains unclear. In this study, we present novel experimental insights into the resilience of LLMs, particularly GPT-4, when subjected to extensive character-level permutations. To investigate this, we first propose the Scrambled Bench, a suite designed to measure the capacity of LLMs to handle scrambled input, in terms of both recovering scrambled sentences and answering questions given scrambled context. The experimental results indicate that most powerful LLMs demonstrate the capability akin to typoglycemia, a phenomenon where humans can understand the meaning of words even when the letters within those words are scrambled, as long as the first and last letters remain in place. More surprisingly, we found that only GPT-4 nearly flawlessly processes inputs with unnatural errors, even under the extreme condition, a task that poses significant challenges for other LLMs and often even for humans. Specifically, GPT-4 can almost perfectly reconstruct the original sentences from scrambled ones, decreasing the edit distance by 95%, even when all letters within each word are entirely scrambled. It is counter-intuitive that LLMs can exhibit such resilience despite severe disruption to input tokenization caused by scrambled text.Comment: EMNLP 2023 (with an additional analysis section in appendix

Tomographic Image Reconstruction Based on Minimization of Symmetrized Kullback-Leibler Divergence

Iterative reconstruction (IR) algorithms based on the principle of optimization are known for producing better reconstructed images in computed tomography. In this paper, we present an IR algorithm based on minimizing a symmetrized Kullback-Leibler divergence (SKLD) that is called Jeffreys’ J-divergence. The SKLD with iterative steps is guaranteed to decrease in convergence monotonically using a continuous dynamical method for consistent inverse problems. Specifically, we construct an autonomous differential equation for which the proposed iterative formula gives a first-order numerical discretization and demonstrate the stability of a desired solution using Lyapunov’s theorem. We describe a hybrid Euler method combined with additive and multiplicative calculus for constructing an effective and robust discretization method, thereby enabling us to obtain an approximate solution to the differential equation.We performed experiments and found that the IR algorithm derived from the hybrid discretization achieved high performance

Hemodynamic change in occipital lobe during visual search: Visual attention allocation measured with NIRS

金沢大学人間社会研究域人間科学系We examined the changes in regional cerebral blood volume (rCBV) around visual cortex using Near Infrared Spectroscopy (NIRS) when observers attended to visual scenes. The oxygenated and deoxygenated hemoglobin (Oxy-Hb and Deoxy-Hb) concentration changes at occipital lobe were monitored during a dual task. Observers were asked to name a digit superimposed on a scenery picture, while in parallel, they had to detect an on-and-off flickering object in a Change Blindness paradigm. Results showed the typical activation patterns in and around the visual cortex with increases in Oxy-Hb and decreases in Deoxy-Hb. The Oxy-Hb increase doubled when observers could not find the target, as opposed to trials in which they could. The results strongly suggest that active attention to a visual scene enhances Oxy-Hb change much stronger than passive watching, and that attention and Oxy-Hb increases are possibly correlated. © 2009 Elsevier Ltd. All rights reserved

Large Language Models are Zero-Shot Reasoners

Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding "Let's think step by step" before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled Objects), without any hand-crafted few-shot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with 175B parameter InstructGPT model, as well as similar magnitudes of improvements with another off-the-shelf large model, 540B parameter PaLM. The versatility of this single prompt across very diverse reasoning tasks hints at untapped and understudied fundamental zero-shot capabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilities may be extracted by simple prompting. We hope our work not only serves as the minimal strongest zero-shot baseline for the challenging reasoning benchmarks, but also highlights the importance of carefully exploring and analyzing the enormous zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or few-shot exemplars.Comment: Accepted to NeurIPS2022. Our code is available at https://github.com/kojima-takeshi188/zero_shot_co

Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures

The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters

Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure

Iterative reconstruction of density pixel images from measured projections in computed tomography has attracted considerable attention. The ordered-subsets algorithm is an acceleration scheme that uses subsets of projections in a previously decided order. Several methods have been proposed to improve the convergence rate by permuting the order of the projections. However, they do not incorporate object information, such as shape, into the selection process. We propose a block-iterative reconstruction from sparse projection views with the dynamic selection of subsets based on an estimating function constructed by an extended power-divergence measure for decreasing the objective function as much as possible. We give a unified proposition for the inequality related to the difference between objective functions caused by one iteration as the theoretical basis of the proposed optimization strategy. Through the theory and numerical experiments, we show that nonuniform and sparse use of projection views leads to a reconstruction of higher-quality images and that an ordered subset is not the most effective for block-iterative reconstruction. The two-parameter class of extended power-divergence measures is the key to estimating an effective decrease in the objective function and plays a significant role in constructing a robust algorithm against noise