arXiv subjects

Ryo Nakamura

Publications and source records attributed to Ryo Nakamura.

13 recordsLinked to original sources

arXiv2026

Host-Driven Flowlet Balancing with Segment Routing over IPv6

This paper proposes a fully host-driven method for flowlet balancing with Segment Routing over IPv6 (SRv6). In modern data center networks, load balancing plays a pivotal role in efficiently utilizing multiple paths. Flowlet balancing offers finer granularity in traffic splitting than ECMP and is therefore expected to achieve higher performance. However, deploying flowlet balancing in practice is still challenging due to the scalability issue of switches having to maintain per-flow state. In our approach, hosts detect flowlets in their outgoing traffic and steer them onto specific paths using SRv6. The switches behave as SRv6 nodes in a stateless manner. Each host distributes its flowlets as evenly as possible across paths. As a metric for this load balancing, we introduce a simple model that estimates in-flight bytes on each path. We implemented the proposed method on Linux and evaluated it on a testbed with an SRv6-capable router. The results show that, under fixed-size flows, the proposed method reduces tail latency by 15% and 33% compared with random flowlet balancing and ECMP, respectively. Furthermore, combining the method with dynamically adjusted flowlet timeouts also improves performance under two application workloads.

cs.NI↗

arXiv2026

How Helpful is LLM Assistance in Network Operations? A Case Study at a Large Demonstration Network

This paper reports on a real-world case study in which over 100 network engineers assessed how a Large Language Model (LLM) can assist in building and operating a network. The versatility of LLMs has accelerated their adoption across a wide range of domains, and assisting network operations is one such promising application. LLMs are probabilistic models, unlike deterministic protocols and configurations; therefore, clarifying their capabilities -- how and to what extent LLMs can help in network operations -- is a crucial step toward adopting LLMs. To offer practical insights into this issue, we conducted an extensive experiment on a large demonstration network built for a public exhibition, consisting of 21 racks with heterogeneous network devices. In the experiment, a total of 105 network engineers used an LLM-based chatbot while building and operating the network. The chatbot was equipped with three external functions: retrieval-augmented generation for domain-specific knowledge, CLI control of network devices running on the network, and access to a ticket system. The participants gave evaluations for the chatbot's responses on a best-effort basis. Analysis of the chat histories shows that 68.1% of the evaluations were positive, indicating a quantitative baseline of the LLM's helpfulness in network operations. Our results also demonstrate that understanding the capabilities of the chatbot is important for eliciting better responses. Moreover, we provide detailed use case analyses while sharing actual user--chatbot interactions.

cs.NI↗

arXiv2025

On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process

The double descent phenomenon, which deviates from the traditional bias-variance trade-off theory, attracts considerable research attention; however, the mechanism of its occurrence is not fully understood. On the other hand, in the study of convolutional neural networks (CNNs) for image recognition, methods are proposed to quantify the bias on shape features versus texture features in images, determining which features the CNN focuses on more. In this work, we hypothesize that there is a relationship between the shape/texture bias in the learning process of CNNs and epoch-wise double descent, and we conduct verification. As a result, we discover double descent/ascent of shape/texture bias synchronized with double descent of test error under conditions where epoch-wise double descent is observed. Quantitative evaluations confirm this correlation between the test errors and the bias values from the initial decrease to the full increase in test error. Interestingly, double descent/ascent of shape/texture bias is observed in some cases even in conditions without label noise, where double descent is thought not to occur. These experimental results are considered to contribute to the understanding of the mechanisms behind the double descent phenomenon and the learning process of CNNs in image recognition.

cs.CV↗

arXiv2024

Scaling Backwards: Minimal Synthetic Pre-training?

Pre-training and transfer learning are an important building block of current computer vision systems. While pre-training is usually performed on large real-world image datasets, in this paper we ask whether this is truly necessary. To this end, we search for a minimal, purely synthetic pre-training dataset that allows us to achieve performance similar to the 1 million images of ImageNet-1k. We construct such a dataset from a single fractal with perturbations. With this, we contribute three main findings. (i) We show that pre-training is effective even with minimal synthetic images, with performance on par with large-scale pre-training datasets like ImageNet-1k for full fine-tuning. (ii) We investigate the single parameter with which we construct artificial categories for our dataset. We find that while the shape differences can be indistinguishable to humans, they are crucial for obtaining strong performances. (iii) Finally, we investigate the minimal requirements for successful pre-training. Surprisingly, we find that a substantial reduction of synthetic images from 1k to 1 can even lead to an increase in pre-training performance, a motivation to further investigate ''scaling backwards''. Finally, we extend our method from synthetic images to real images to see if a single real image can show similar pre-training effect through shape augmentation. We find that the use of grayscale images and affine transformations allows even real images to ''scale backwards''.

cs.CV↗

arXiv2024

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs

This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and strong Japanese LLMs, and as of this writing, more than 1,500 participants from academia and industry are working together for this purpose. This paper presents the background of the establishment of LLM-jp, summaries of its activities, and technical reports on the LLMs developed by LLM-jp. For the latest activities, visit https://llm-jp.nii.ac.jp/en/.

cs.CL↗

arXiv2024

Combined Pre-Supernova Alert System with Kamland and Super-Kamiokande

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance.

hep-ex↗

arXiv2024

Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation

The construction of 3D medical image datasets presents several issues, including requiring significant financial costs in data collection and specialized expertise for annotation, as well as strict privacy concerns for patient confidentiality compared to natural image datasets. Therefore, it has become a pressing issue in 3D medical image segmentation to enable data-efficient learning with limited 3D medical data and supervision. A promising approach is pre-training, but improving its performance in 3D medical image segmentation is difficult due to the small size of existing 3D medical image datasets. We thus present the Primitive Geometry Segment Pre-training (PrimGeoSeg) method to enable the learning of 3D semantic features by pre-training segmentation tasks using only primitive geometric objects for 3D medical image segmentation. PrimGeoSeg performs more accurate and efficient 3D medical image segmentation without manual data collection and annotation. Further, experimental results show that PrimGeoSeg on SwinUNETR improves performance over learning from scratch on BTCV, MSD (Task06), and BraTS datasets by 3.7%, 4.4%, and 0.3%, respectively. Remarkably, the performance was equal to or better than state-of-the-art self-supervised learning despite the equal number of pre-training data. From experimental results, we conclude that effective pre-training can be achieved by looking at primitive geometric objects only. Code and dataset are available at https://github.com/SUPER-TADORY/PrimGeoSeg.

cs.CV↗

arXiv2023

Traffic Incident Database with Multiple Labels Including Various Perspective Environmental Information

A large dataset of annotated traffic accidents is necessary to improve the accuracy of traffic accident recognition using deep learning models. Conventional traffic accident datasets provide annotations on traffic accidents and other teacher labels, improving traffic accident recognition performance. However, the labels annotated in conventional datasets need to be more comprehensive to describe traffic accidents in detail. Therefore, we propose V-TIDB, a large-scale traffic accident recognition dataset annotated with various environmental information as multi-labels. Our proposed dataset aims to improve the performance of traffic accident recognition by annotating ten types of environmental information as teacher labels in addition to the presence or absence of traffic accidents. V-TIDB is constructed by collecting many videos from the Internet and annotating them with appropriate environmental information. In our experiments, we compare the performance of traffic accident recognition when only labels related to the presence or absence of traffic accidents are trained and when environmental information is added as a multi-label. In the second experiment, we compare the performance of the training with only contact level, which represents the severity of the traffic accident, and the performance with environmental information added as a multi-label. The results showed that 6 out of 10 environmental information labels improved the performance of recognizing the presence or absence of traffic accidents. In the experiment on the degree of recognition of traffic accidents, the performance of recognition of car wrecks and contacts was improved for all environmental information. These experiments show that V-TIDB can be used to learn traffic accident recognition models that take environmental information into account in detail and can be used for appropriate traffic accident analysis.

cs.CV↗

arXiv2023

Pre-training Vision Transformers with Very Limited Synthesized Images

Formula-driven supervised learning (FDSL) is a pre-training method that relies on synthetic images generated from mathematical formulae such as fractals. Prior work on FDSL has shown that pre-training vision transformers on such synthetic datasets can yield competitive accuracy on a wide range of downstream tasks. These synthetic images are categorized according to the parameters in the mathematical formula that generate them. In the present work, we hypothesize that the process for generating different instances for the same category in FDSL, can be viewed as a form of data augmentation. We validate this hypothesis by replacing the instances with data augmentation, which means we only need a single image per category. Our experiments shows that this one-instance fractal database (OFDB) performs better than the original dataset where instances were explicitly generated. We further scale up OFDB to 21,000 categories and show that it matches, or even surpasses, the model pre-trained on ImageNet-21k in ImageNet-1k fine-tuning. The number of images in OFDB is 21k, whereas ImageNet-21k has 14M. This opens new possibilities for pre-training vision transformers with much smaller datasets.

cs.CV↗

arXiv2022

mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an the overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.

cs.LG↗

arXiv2021

Classifying DNS Servers based on Response Message Matrix using Machine Learning

Improperly configured domain name system (DNS) servers are sometimes used as packet reflectors as part of a DoS or DDoS attack. Detecting packets created as a result of this activity is logically possible by monitoring the DNS request and response traffic. Any response that does not have a corresponding request can be considered a reflected message; checking and tracking every DNS packet, however, is a non-trivial operation. In this paper, we propose a detection mechanism for DNS servers used as reflectors by using a DNS server feature matrix built from a small number of packets and a machine learning algorithm. The F1 score of bad DNS server detection was more than 0.9 when the test and training data are generated within the same day, and more than 0.7 for the data not used for the training and testing phase of the same day.

cs.NI↗

arXiv2018

Another Diversity-Promoting Objective Function for Neural Dialogue Generation

Although generation-based dialogue systems have been widely researched, the response generations by most existing systems have very low diversities. The most likely reason for this problem is Maximum Likelihood Estimation (MLE) with Softmax Cross-Entropy (SCE) loss. MLE trains models to generate the most frequent responses from enormous generation candidates, although in actual dialogues there are various responses based on the context. In this paper, we propose a new objective function called Inverse Token Frequency (ITF) loss, which individually scales smaller loss for frequent token classes and larger loss for rare token classes. This function encourages the model to generate rare tokens rather than frequent tokens. It does not complicate the model and its training is stable because we only replace the objective function. On the OpenSubtitles dialogue dataset, our loss model establishes a state-of-the-art DIST-1 of 7.56, which is the unigram diversity score, while maintaining a good BLEU-1 score. On a Japanese Twitter replies dataset, our loss model achieves a DIST-1 score comparable to the ground truth.

cs.CL↗

arXiv2015

The Si/CdTe semiconductor Compton camera of the ASTRO-H Soft Gamma-ray Detector (SGD)

The Soft Gamma-ray Detector (SGD) is one of the instrument payloads onboard ASTRO-H, and will cover a wide energy band (60--600 keV) at a background level 10 times better than instruments currently in orbit. The SGD achieves low background by combining a Compton camera scheme with a narrow field-of-view active shield. The Compton camera in the SGD is realized as a hybrid semiconductor detector system which consists of silicon and cadmium telluride (CdTe) sensors. The design of the SGD Compton camera has been finalized and the final prototype, which has the same configuration as the flight model, has been fabricated for performance evaluation. The Compton camera has overall dimensions of 12 cm x 12 cm x 12 cm, consisting of 32 layers of Si pixel sensors and 8 layers of CdTe pixel sensors surrounded by 2 layers of CdTe pixel sensors. The detection efficiency of the Compton camera reaches about 15% and 3% for 100 keV and 511 keV gamma rays, respectively. The pixel pitch of the Si and CdTe sensors is 3.2 mm, and the signals from all 13312 pixels are processed by 208 ASICs developed for the SGD. Good energy resolution is afforded by semiconductor sensors and low noise ASICs, and the obtained energy resolutions with the prototype Si and CdTe pixel sensors are 1.0--2.0 keV (FWHM) at 60 keV and 1.6--2.5 keV (FWHM) at 122 keV, respectively. This results in good background rejection capability due to better constraints on Compton kinematics. Compton camera energy resolutions achieved with the final prototype are 6.3 keV (FWHM) at 356 keV and 10.5 keV (FWHM) at 662 keV, respectively, which satisfy the instrument requirements for the SGD Compton camera (better than 2%). Moreover, a low intrinsic background has been confirmed by the background measurement with the final prototype.

astro-ph.IM↗