Hybrid beamforming for single carrier mmWave MIMO systems

Hybrid analog and digital beamforming (HBF) has been recognized as an attractive technique offering a tradeoff between hardware implementation limitation and system performance for future broadband millimeter wave (mmWave) communications. In contrast to most current works focusing on the HBF design for orthogonal frequency division multiplexing based mmWave systems, this paper investigates the HBF design for single carrier (SC) systems due to the advantage of low peak-to-average power ratio in transmissions. By applying the alternating minimization method, we propose an efficient HBF scheme based on the minimum mean square error criterion. Simulation results show that the proposed scheme outperforms the conventional HBF scheme for SC systems.Comment: IEEE GlobalSIP2018, Feb. 201

効果的な河川生息場の再生のための土砂還元に伴う生態-水文-河床地形的効果に関する研究

京都大学新制・課程博士博士(工学)甲第24593号工博第5099号新制||工||1976(附属図書館)京都大学大学院工学研究科都市社会工学専攻(主査)教授 角 哲也, 准教授 竹門 康弘, 准教授 Kantoush Sameh学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability

The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, this deep neural network has not aroused much attention in anomaly detection through predictively process monitoring. Furthermore, the traditional statistic models work on assumptions and hypothesis tests, while neural network (NN) models do not need that many assumptions. This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice. This paper explores the ability of the recurrent neural network structure to monitor processes and proposes a control chart based on long short-term memory (LSTM) prediction intervals for data with time-varying variability. The simulation studies provide empirical evidence that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied to time series sensor data, which confirms that the proposed method is an effective technique for detecting abnormalities.Comment: 19 pages, 9 figures, 6 table

Alphabet of one-loop Feynman integrals

In this paper, we present the universal structure of the alphabet of one-loop Feynman integrals. The letters in the alphabet are calculated using the Baikov representation with cuts. We consider both convergent and divergent cut integrals and observe that letters in the divergent cases can be easily obtained from convergent cases by applying certain limits. The letters are written as simple expressions in terms of various Gram determinants. The knowledge of the alphabet enables us to easily construct the canonical differential equations of the $d\log$ form and aids in bootstrapping the symbols of the solutions.Comment: 13 pages, 2 figures; v3: published version in Chinese physics

Intersection theory rules symbology

We propose a novel method to determine the structure of symbols for a family of polylogarithmic Feynman integrals. Using the d log-bases and simple formulas for the first- and second-order contributions to the intersection numbers, we give a streamlined procedure to compute the entries in the coefficient matrices of canonical differential equations, including the symbol letters and the rational coefficients. We also provide a selection rule to decide whether a given matrix element must be zero. The symbol letters are deeply related with the poles of the integrands, and also have interesting connections to the geometry of Newton polytopes. Our method will have important applications in cutting-edge multi-loop calculations. The simplicity of our results also hints at possible underlying structure in perturbative quantum field theories.Comment: 7 pages, 1 figur

ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet often constrained by the scarcity of high-quality image-text data. To address this bottleneck, we introduce the ShareGPT4V dataset, a pioneering large-scale resource featuring 1.2 million highly descriptive captions, which surpasses existing datasets in diversity and information content, covering world knowledge, object properties, spatial relationships, and aesthetic evaluations. Specifically, ShareGPT4V originates from a curated 100K high-quality captions collected from advanced GPT4-Vision and has been expanded to 1.2M with a superb caption model trained on this subset. ShareGPT4V first demonstrates its effectiveness for the Supervised Fine-Tuning (SFT) phase, by substituting an equivalent quantity of detailed captions in existing SFT datasets with a subset of our high-quality captions, significantly enhancing the LMMs like LLaVA-7B, LLaVA-1.5-13B, and Qwen-VL-Chat-7B on the MME and MMBench benchmarks, with respective gains of 222.8/22.0/22.3 and 2.7/1.3/1.5. We further incorporate ShareGPT4V data into both the pre-training and SFT phases, obtaining ShareGPT4V-7B, a superior LMM based on a simple architecture that has remarkable performance across a majority of the multi-modal benchmarks. This project is available at https://ShareGPT4V.github.io to serve as a pivotal resource for advancing the LMMs community.Comment: Project: https://ShareGPT4V.github.i