Related papers: In Memory of Prof. Hidekuni Takekoshi

The contribution of this paper is a generalized formulation of correctional learning using optimal transport, which is about how to optimally transport one mass distribution to another. Correctional learning is a framework developed to…

In memory of the famous physicist Bruno Pontecorvo, whereof is just occurred the centenary anniversary of the birth and the twentieth anniversary of the death, this paper traces the scientific and human adventure of the italian scientist,…

A brief remembrance of some aspects of the author's scientific interaction with Stephen Hawking. A contribution to Physics Today's March 14, 2018 web page in which Stephen Hawking is remembered by his colleagues.

The evaluation of instructors by their students has been practiced at most universities for many decades, and there has always been a great interest in a variety of aspects of the evaluations. Are students matured and knowledgeable enough…

One of the most critical challenges in deep reinforcement learning is to maintain the long-term exploration capability of the agent. To tackle this problem, it has been recently proposed to provide intrinsic rewards for the agent to…

A homage to the life and mathematics of John K. S. McKay. Obituary for the Bulletin of the London Mathematical Society.

This year, 2019 we celebrate 30 years of our friendship between Asian and European scientists at the AEW11 in Rotterdam, the Netherlands. Many of the 1989 participants are also present at the 2019 event. This year we have many participants…

Research has linked a student's affect to her epistemology (Boaler & Greeno, 2000), but those constructs often apply broadly to a discipline and/or classroom culture. Independently, an emerging line of research shows that a student in a…

Accelerators with power-law memory are proposed in the framework of the discrete time approach. To describe discrete accelerators we use the capital stock adjustment principle, which has been suggested by Matthews.The suggested discrete…

This paper presents a comparative analysis of Sudoku-solving strategies, focusing on recursive backtracking and a heuristic-based constraint propagation method. Using a dataset of 500 puzzles across five difficulty levels (Beginner to…

Transformer are widely used in various fields such as natural language processing and computer vision. However, the training time for large Transformer models can be challenging due to the Multi-Head Attention (MHA) mechanism. Especially as…

I describe parts of my joint work with S. Deser [March 19, 1931 - April 21, 2023] which started when I was working as a post-doc at Brandeis University in 2001. Our work was mostly, but not exclusively, on conserved charges of higher…

The research presented in this thesis was motivated by the need to improve introductory physics courses. Introductory physics courses are generally the first courses in which students learn to create models to solve complex problems.…

Following a previous idea, a curved geometry is proposed as being valid in accelerated systems, in Minkowski space. The curvature turns out to be generated by the source of the accelerated motion. An exponential factor depending on $\rho$…

This is the collection of the proceedings presented by the IceCube Collaboration at the TAUP2007 conference.

Part 1 of "Project X: Accelerator Reference Design, Physics Opportunities, Broader Impacts". Part 1 contains the volume Preface and a description of the conceptual design for a high-intensity proton accelerator facility being developed to…

The current status of the control system for a new high intensity proton accelerator, the JAERI-KEK Joint Project, is presented. The Phase 1 of the Joint-Project has been approved and recently started its construction at JAERI site at…

Model of active and collaborative learning applied in training specific subject makes clear advantage due to the goals of knowledge, enhanced activeness, skills that students got to develop successful future job. Studying and applying the…

The slowing of Moore's law and the increasing energy demands of machine learning present critical challenges for both the hardware and machine learning communities, and drive the development of novel computing paradigms. Of particular…

Transformer models have achieved state-of-the-art performance across a wide range of machine learning tasks. There is growing interest in training transformers on resource-constrained edge devices due to considerations such as privacy,…