东芝

Nonlinear dynamical systems with continuous variables can be used for solving combinatorial optimization problems with discrete variables. Numerical simulations of them are also useful as heuristic algorithms with a desirable property, namely, parallelizability, which allows us to execute them in a massively parallel manner, leading to ultrafast performance. However, the dynamical-system approaches with continuous variables are usually less accurate than conventional approaches with discrete variables such as simulated annealing. To improve the solution accuracy of a quantum-inspired algorithm called simulated bifurcation (SB), which was found from classical simulation of a quantum nonlinear oscillator network exhibiting quantum bifurcation, here we generalize it by introducing nonlinear control of individual bifurcation parameters and show that the generalized SB (GSB) can achieve surprisingly high performance, namely, almost 100% success probabilities for some large-scale problems. As a result, the time to solution for a 2000-variable problem is shortened to 10 ms by a GSB-based machine, which is 2 orders of magnitude shorter than the best known value, 1.3 s, previously obtained by an SB-based machine. To examine the reason for the ultrahigh performance, we investigated chaos in the GSB changing the nonlinear-control strength and found that the dramatic increase of success probabilities happens near the edge of chaos. That is, the GSB can find a solution with high probability by harnessing the edge of chaos. This finding suggests that dynamical-system approaches to combinatorial optimization will be enhanced by harnessing the edge of chaos, opening a broad possibility for physics-inspired approaches to combinatorial optimization.

Contrastive Language-Audio Pretraining (CLAP) learns aligned text and audio representations in a shared embedding space. However, independent encoding of each modality limits its ability to model cross-modal semantics in complex audio understanding and retrieval tasks. To address this limitation, this paper proposes Text-Prompted CLAP (TP-CLAP), a parameter-efficient extension of CLAP that introduces a cross-attention-based fusion module to incorporate textual prompts into audio features. TP-CLAP is trained using an audio multiple-choice question answering (AMCQA) framework, where it learns to align text-conditioned audio representations with text embeddings of correct answer choices via contrastive learning. Experiments demonstrate that TP-CLAP performs competitively with substantially larger audio-LLMs on audio question answering, while also improving the base CLAP model on conventional audio-text retrieval and zero-shot classification benchmarks. The learned representations are further fine-tuned for attribute-focused audio-to-audio retrieval, showing that TP-CLAP consistently outperforms the standard CLAP baseline in music retrieval tasks.

Many real-time systems require the optimization of discrete variables. Black-box optimization (BBO) algorithms and multi-armed bandit (MAB) algorithms perform optimization by repeatedly taking actions and observing the corresponding immediate rewards without any prior knowledge. Recently, a BBO method using an Ising machine has been proposed to find the best action represented by a combination of discrete values and maximize the immediate reward in static environments. By contrast, real-time systems operate in dynamic environments and necessitate MAB algorithms that maximize the average reward over repeated trials. Due to the enormous number of actions resulting from the combinatorial nature of discrete optimization, conventional MAB algorithms cannot effectively optimize actions for dynamic, discrete environments. Here, we show a heuristic method to maximize the average reward for dynamic discrete environments by extending the BBO method, in which an Ising machine efficiently explores actions while accounting for interactions between variables and environmental changes. We demonstrate the adaptability to dynamic environments of the proposed method in a wireless communication system with moving users.

This article proposes a warm-start design-optimization method based on potential-shaped latent representations learned by a variational autoencoder (VAE). The latent space is structured so that the objective-function values correspond to a quadratic potential, concentrating high-performance designs near the origin without explicit latent-space optimization. Candidate designs are generated by sampling the latent space around the origin and subsequently refined using level-set-based optimization. The effectiveness of the proposed method is demonstrated through rotor-flux-barrier shape optimization of a synchronous reluctance motor (SynRM).

We introduce latest power electronics technologies for adjustable-speed pumped-storage hydropower systems based on the converter renewal of Unit 2 at the Electric Power Development Co., Ltd. Okukiyotsu No. 2 Hydropower Station. The system includes a doubly-fed induction machine and a secondary excitation converter. Controlling the excitation converter provides fast and flexible system output/input in generation and pump operations. While pre-renewal excitation converter employed Gate-Turn-Off thyristor as the switching device, the renewed one employed Injection Enhanced Gate Transistors. This change reduced the loss and the volume of the excitation converter by approximately 26% and 37%, respectively, compared to the pre-renewal converter. Moreover, the total self-startup time of the doubly-fed induction machine was reduced by approximately 18%.