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Interests Burn-down Diffusion Process for Personalized Collaborative Filtering
Authors:
Yifang Qin,
Zhaobin Li,
Arisa Watanabe,
Wei Ju,
Zhiping Xiao,
Ming Zhang
Abstract:
Generative methods have gained widespread attention in Collaborative Filtering (CF) tasks for their ability to produce high-quality personalized samples aligned with users' interests. Among them, diffusion generative models have raised increasing attention in recommendation field. Despite that the pioneering efforts have applied the conventional diffusion process to model diffusive user interests,…
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Generative methods have gained widespread attention in Collaborative Filtering (CF) tasks for their ability to produce high-quality personalized samples aligned with users' interests. Among them, diffusion generative models have raised increasing attention in recommendation field. Despite that the pioneering efforts have applied the conventional diffusion process to model diffusive user interests, the incongruity between the Gaussian noise and the subtle nature of user's personalized interaction behavior has led to sub-optimal results. To this end, we introduce a specifically-tailored diffusion scheme for interaction systems, namely the interests burn-down process. The interests burn-down process delineates the decay of user interests towards candidate items, complemented by its reverse burn-up process that yields personalized recommendation for users. The inherent burn-down nature of this process adeptly models the diffusive user interests, aligning seamlessly with the requirements of CF tasks. We present a novel recommendation method StageCF to illustrate the superiority of this newly proposed diffusion process. Experimental results have demonstrated the effectiveness of StageCF against existing generative and diffusion-based baseline methods. Furthermore, comprehensive studies validate the functionality of interests burn-down process, shedding light on its capacity to generate personalized interactions.
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Submitted 6 May, 2026;
originally announced May 2026.
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ProjFlow: Projection Sampling with Flow Matching for Zero-Shot Exact Spatial Motion Control
Authors:
Akihisa Watanabe,
Qing Yu,
Edgar Simo-Serra,
Kent Fujiwara
Abstract:
Generating human motion with precise spatial control is a challenging problem. Existing approaches often require task-specific training or slow optimization, and enforcing hard constraints frequently disrupts motion naturalness. Building on the observation that many animation tasks can be formulated as a linear inverse problem, we introduce ProjFlow, a training-free sampler that achieves zero-shot…
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Generating human motion with precise spatial control is a challenging problem. Existing approaches often require task-specific training or slow optimization, and enforcing hard constraints frequently disrupts motion naturalness. Building on the observation that many animation tasks can be formulated as a linear inverse problem, we introduce ProjFlow, a training-free sampler that achieves zero-shot, exact satisfaction of linear spatial constraints while preserving motion realism. Our key advance is a novel kinematics-aware metric that encodes skeletal topology. This metric allows the sampler to enforce hard constraints by distributing corrections coherently across the entire skeleton, avoiding the unnatural artifacts of naive projection. Furthermore, for sparse inputs, such as filling in long gaps between a few keyframes, we introduce a time-varying formulation using pseudo-observations that fade during sampling. Extensive experiments on representative applications, motion inpainting, and 2D-to-3D lifting, demonstrate that ProjFlow achieves exact constraint satisfaction and matches or improves realism over zero-shot baselines, while remaining competitive with training-based controllers.
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Submitted 26 February, 2026;
originally announced February 2026.
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Causal Motion Diffusion Models for Autoregressive Motion Generation
Authors:
Qing Yu,
Akihisa Watanabe,
Kent Fujiwara
Abstract:
Recent advances in motion diffusion models have substantially improved the realism of human motion synthesis. However, existing approaches either rely on full-sequence diffusion models with bidirectional generation, which limits temporal causality and real-time applicability, or autoregressive models that suffer from instability and cumulative errors. In this work, we present Causal Motion Diffusi…
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Recent advances in motion diffusion models have substantially improved the realism of human motion synthesis. However, existing approaches either rely on full-sequence diffusion models with bidirectional generation, which limits temporal causality and real-time applicability, or autoregressive models that suffer from instability and cumulative errors. In this work, we present Causal Motion Diffusion Models (CMDM), a unified framework for autoregressive motion generation based on a causal diffusion transformer that operates in a semantically aligned latent space. CMDM builds upon a Motion-Language-Aligned Causal VAE (MAC-VAE), which encodes motion sequences into temporally causal latent representations. On top of this latent representation, an autoregressive diffusion transformer is trained using causal diffusion forcing to perform temporally ordered denoising across motion frames. To achieve fast inference, we introduce a frame-wise sampling schedule with causal uncertainty, where each subsequent frame is predicted from partially denoised previous frames. The resulting framework supports high-quality text-to-motion generation, streaming synthesis, and long-horizon motion generation at interactive rates. Experiments on HumanML3D and SnapMoGen demonstrate that CMDM outperforms existing diffusion and autoregressive models in both semantic fidelity and temporal smoothness, while substantially reducing inference latency.
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Submitted 25 February, 2026;
originally announced February 2026.
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SimDiff: Simulator-constrained Diffusion Model for Physically Plausible Motion Generation
Authors:
Akihisa Watanabe,
Jiawei Ren,
Li Siyao,
Yichen Peng,
Erwin Wu,
Edgar Simo-Serra
Abstract:
Generating physically plausible human motion is crucial for applications such as character animation and virtual reality. Existing approaches often incorporate a simulator-based motion projection layer to the diffusion process to enforce physical plausibility. However, such methods are computationally expensive due to the sequential nature of the simulator, which prevents parallelization. We show…
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Generating physically plausible human motion is crucial for applications such as character animation and virtual reality. Existing approaches often incorporate a simulator-based motion projection layer to the diffusion process to enforce physical plausibility. However, such methods are computationally expensive due to the sequential nature of the simulator, which prevents parallelization. We show that simulator-based motion projection can be interpreted as a form of guidance, either classifier-based or classifier-free, within the diffusion process. Building on this insight, we propose SimDiff, a Simulator-constrained Diffusion Model that integrates environment parameters (e.g., gravity, wind) directly into the denoising process. By conditioning on these parameters, SimDiff generates physically plausible motions efficiently, without repeated simulator calls at inference, and also provides fine-grained control over different physical coefficients. Moreover, SimDiff successfully generalizes to unseen combinations of environmental parameters, demonstrating compositional generalization.
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Submitted 25 September, 2025;
originally announced September 2025.
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Multi-interaction TTS toward professional recording reproduction
Authors:
Hiroki Kanagawa,
Kenichi Fujita,
Aya Watanabe,
Yusuke Ijima
Abstract:
Voice directors often iteratively refine voice actors' performances by providing feedback to achieve the desired outcome. While this iterative feedback-based refinement process is important in actual recordings, it has been overlooked in text-to-speech synthesis (TTS). As a result, fine-grained style refinement after the initial synthesis is not possible, even though the synthesized speech often d…
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Voice directors often iteratively refine voice actors' performances by providing feedback to achieve the desired outcome. While this iterative feedback-based refinement process is important in actual recordings, it has been overlooked in text-to-speech synthesis (TTS). As a result, fine-grained style refinement after the initial synthesis is not possible, even though the synthesized speech often deviates from the user's intended style. To address this issue, we propose a TTS method with multi-step interaction that allows users to intuitively and rapidly refine synthesized speech. Our approach models the interaction between the TTS model and its user to emulate the relationship between voice actors and voice directors. Experiments show that the proposed model with its corresponding dataset enables iterative style refinements in accordance with users' directions, thus demonstrating its multi-interaction capability. Sample audios are available: https://ntt-hilab-gensp.github.io/ssw13multiinteractiontts/
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Submitted 2 July, 2025; v1 submitted 1 July, 2025;
originally announced July 2025.
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P2P-Insole: Human Pose Estimation Using Foot Pressure Distribution and Motion Sensors
Authors:
Atsuya Watanabe,
Ratna Aisuwarya,
Lei Jing
Abstract:
This work presents P2P-Insole, a low-cost approach for estimating and visualizing 3D human skeletal data using insole-type sensors integrated with IMUs. Each insole, fabricated with e-textile garment techniques, costs under USD 1, making it significantly cheaper than commercial alternatives and ideal for large-scale production. Our approach uses foot pressure distribution, acceleration, and rotati…
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This work presents P2P-Insole, a low-cost approach for estimating and visualizing 3D human skeletal data using insole-type sensors integrated with IMUs. Each insole, fabricated with e-textile garment techniques, costs under USD 1, making it significantly cheaper than commercial alternatives and ideal for large-scale production. Our approach uses foot pressure distribution, acceleration, and rotation data to overcome limitations, providing a lightweight, minimally intrusive, and privacy-aware solution. The system employs a Transformer model for efficient temporal feature extraction, enriched by first and second derivatives in the input stream. Including multimodal information, such as accelerometers and rotational measurements, improves the accuracy of complex motion pattern recognition. These facts are demonstrated experimentally, while error metrics show the robustness of the approach in various posture estimation tasks. This work could be the foundation for a low-cost, practical application in rehabilitation, injury prevention, and health monitoring while enabling further development through sensor optimization and expanded datasets.
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Submitted 1 May, 2025;
originally announced May 2025.
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Characterization of $n$-Dimensional Toric and Burst-Error-Correcting Quantum Codes from Lattice Codes
Authors:
Cibele Cristina Trinca,
Reginaldo Palazzo Jr.,
J. Carmelo Interlando,
Ricardo Augusto Watanabe,
Clarice Dias de Albuquerque,
Edson Donizete de Carvalho,
Antonio Aparecido de Andrade
Abstract:
Quantum error correction is essential for the development of any scalable quantum computer. In this work we introduce a generalization of a quantum interleaving method for combating clusters of errors in toric quantum error-correcting codes. We present new $n$-dimensional toric quantum codes, where $n\geq 5$, which are featured by lattice codes and apply the proposed quantum interleaving method to…
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Quantum error correction is essential for the development of any scalable quantum computer. In this work we introduce a generalization of a quantum interleaving method for combating clusters of errors in toric quantum error-correcting codes. We present new $n$-dimensional toric quantum codes, where $n\geq 5$, which are featured by lattice codes and apply the proposed quantum interleaving method to such new $n$-dimensional toric quantum codes. Through the application of this method to these novel $n$-dimensional toric quantum codes we derive new $n$-dimensional quantum burst-error-correcting codes. Consequently, $n$-dimensional toric quantum codes and burst-error-correcting quantum codes are provided offering both a good code rate and a significant coding gain when it comes to toric quantum codes. Another important consequence from the presented $n$-dimensional toric quantum codes is that if the Golomb and Welch conjecture in \cite{perfcodes} regarding the Lee sphere in $n$ dimensions for the respective close packings holds true, then it follows that these $n$-dimensional toric quantum codes are the only possible ones to be obtained from lattice codes. Moreover, such a methodology can be applied for burst error correction in cases involving localized errors, quantum data storage and quantum channels with memory.
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Submitted 26 October, 2024;
originally announced October 2024.
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Who Finds This Voice Attractive? A Large-Scale Experiment Using In-the-Wild Data
Authors:
Hitoshi Suda,
Aya Watanabe,
Shinnosuke Takamichi
Abstract:
This paper introduces CocoNut-Humoresque, an open-source large-scale speech likability corpus that includes speech segments and their per-listener likability scores. Evaluating voice likability is essential to designing preferable voices for speech systems, such as dialogue or announcement systems. In this study, we let 885 listeners rate 1800 speech segments of a wide range of speakers regarding…
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This paper introduces CocoNut-Humoresque, an open-source large-scale speech likability corpus that includes speech segments and their per-listener likability scores. Evaluating voice likability is essential to designing preferable voices for speech systems, such as dialogue or announcement systems. In this study, we let 885 listeners rate 1800 speech segments of a wide range of speakers regarding their likability. When constructing the corpus, we also collected the multiple speaker attributes: genders, ages, and favorite YouTube videos. Therefore, the corpus enables the large-scale statistical analysis of voice likability regarding both speaker and listener factors. This paper describes the construction methodology and preliminary data analysis to reveal the gender and age biases in voice likability. In addition, the relationship between the likability and two acoustic features, the fundamental frequencies and the x-vectors of given utterances, is also investigated.
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Submitted 5 July, 2024;
originally announced July 2024.
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Building speech corpus with diverse voice characteristics for its prompt-based representation
Authors:
Aya Watanabe,
Shinnosuke Takamichi,
Yuki Saito,
Wataru Nakata,
Detai Xin,
Hiroshi Saruwatari
Abstract:
In text-to-speech synthesis, the ability to control voice characteristics is vital for various applications. By leveraging thriving text prompt-based generation techniques, it should be possible to enhance the nuanced control of voice characteristics. While previous research has explored the prompt-based manipulation of voice characteristics, most studies have used pre-recorded speech, which limit…
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In text-to-speech synthesis, the ability to control voice characteristics is vital for various applications. By leveraging thriving text prompt-based generation techniques, it should be possible to enhance the nuanced control of voice characteristics. While previous research has explored the prompt-based manipulation of voice characteristics, most studies have used pre-recorded speech, which limits the diversity of voice characteristics available. Thus, we aim to address this gap by creating a novel corpus and developing a model for prompt-based manipulation of voice characteristics in text-to-speech synthesis, facilitating a broader range of voice characteristics. Specifically, we propose a method to build a sizable corpus pairing voice characteristics descriptions with corresponding speech samples. This involves automatically gathering voice-related speech data from the Internet, ensuring its quality, and manually annotating it using crowdsourcing. We implement this method with Japanese language data and analyze the results to validate its effectiveness. Subsequently, we propose a construction method of the model to retrieve speech from voice characteristics descriptions based on a contrastive learning method. We train the model using not only conservative contrastive learning but also feature prediction learning to predict quantitative speech features corresponding to voice characteristics. We evaluate the model performance via experiments with the corpus we constructed above.
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Submitted 20 March, 2024;
originally announced March 2024.
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Open-Domain Dialogue Quality Evaluation: Deriving Nugget-level Scores from Turn-level Scores
Authors:
Rikiya Takehi,
Akihisa Watanabe,
Tetsuya Sakai
Abstract:
Existing dialogue quality evaluation systems can return a score for a given system turn from a particular viewpoint, e.g., engagingness. However, to improve dialogue systems by locating exactly where in a system turn potential problems lie, a more fine-grained evaluation may be necessary. We therefore propose an evaluation approach where a turn is decomposed into nuggets (i.e., expressions associa…
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Existing dialogue quality evaluation systems can return a score for a given system turn from a particular viewpoint, e.g., engagingness. However, to improve dialogue systems by locating exactly where in a system turn potential problems lie, a more fine-grained evaluation may be necessary. We therefore propose an evaluation approach where a turn is decomposed into nuggets (i.e., expressions associated with a dialogue act), and nugget-level evaluation is enabled by leveraging an existing turn-level evaluation system. We demonstrate the potential effectiveness of our evaluation method through a case study.
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Submitted 30 September, 2023;
originally announced October 2023.
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Coco-Nut: Corpus of Japanese Utterance and Voice Characteristics Description for Prompt-based Control
Authors:
Aya Watanabe,
Shinnosuke Takamichi,
Yuki Saito,
Wataru Nakata,
Detai Xin,
Hiroshi Saruwatari
Abstract:
In text-to-speech, controlling voice characteristics is important in achieving various-purpose speech synthesis. Considering the success of text-conditioned generation, such as text-to-image, free-form text instruction should be useful for intuitive and complicated control of voice characteristics. A sufficiently large corpus of high-quality and diverse voice samples with corresponding free-form d…
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In text-to-speech, controlling voice characteristics is important in achieving various-purpose speech synthesis. Considering the success of text-conditioned generation, such as text-to-image, free-form text instruction should be useful for intuitive and complicated control of voice characteristics. A sufficiently large corpus of high-quality and diverse voice samples with corresponding free-form descriptions can advance such control research. However, neither an open corpus nor a scalable method is currently available. To this end, we develop Coco-Nut, a new corpus including diverse Japanese utterances, along with text transcriptions and free-form voice characteristics descriptions. Our methodology to construct this corpus consists of 1) automatic collection of voice-related audio data from the Internet, 2) quality assurance, and 3) manual annotation using crowdsourcing. Additionally, we benchmark our corpus on the prompt embedding model trained by contrastive speech-text learning.
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Submitted 23 September, 2023;
originally announced September 2023.
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New Three and Four-Dimensional Toric and Burst-Error-Correcting Quantum Codes
Authors:
Cibele Cristina Trinca,
Reginaldo Palazzo Jr.,
Ricardo Augusto Watanabe,
Clarice Dias de Albuquerque,
José Carmelo Interlando,
Antônio Aparecido de Andrade
Abstract:
Ongoing research and experiments have enabled quantum memory to realize the storage of qubits. On the other hand, interleaving techniques are used to deal with burst of errors. Effective interleaving techniques for combating burst of errors by using classical error-correcting codes have been proposed in several articles found in the literature, however, to the best of our knowledge, little is know…
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Ongoing research and experiments have enabled quantum memory to realize the storage of qubits. On the other hand, interleaving techniques are used to deal with burst of errors. Effective interleaving techniques for combating burst of errors by using classical error-correcting codes have been proposed in several articles found in the literature, however, to the best of our knowledge, little is known regarding interleaving techniques for combating clusters of errors in topological quantum error-correcting codes. Motivated by that, in this work, we present new three and four-dimensional toric quantum codes which are featured by lattice codes and apply a quantum interleaving method to such new three and four-dimensional toric quantum codes. By applying such a method to these new codes we provide new three and four-dimensional quantum burst-error-correcting codes. As a consequence, new three and four-dimensional toric and burst-error-correcting quantum codes are obtained which have better information rates than those three and four-dimensional toric quantum codes from the literature. In addition to these proposed three and four-dimensional quantum burst-error-correcting codes improve such information rates, they can be used for burst-error-correction in errors which are located, quantum data stored and quantum channels with memory.
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Submitted 12 July, 2023;
originally announced July 2023.
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Mid-attribute speaker generation using optimal-transport-based interpolation of Gaussian mixture models
Authors:
Aya Watanabe,
Shinnosuke Takamichi,
Yuki Saito,
Detai Xin,
Hiroshi Saruwatari
Abstract:
In this paper, we propose a method for intermediating multiple speakers' attributes and diversifying their voice characteristics in ``speaker generation,'' an emerging task that aims to synthesize a nonexistent speaker's naturally sounding voice. The conventional TacoSpawn-based speaker generation method represents the distributions of speaker embeddings by Gaussian mixture models (GMMs) condition…
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In this paper, we propose a method for intermediating multiple speakers' attributes and diversifying their voice characteristics in ``speaker generation,'' an emerging task that aims to synthesize a nonexistent speaker's naturally sounding voice. The conventional TacoSpawn-based speaker generation method represents the distributions of speaker embeddings by Gaussian mixture models (GMMs) conditioned with speaker attributes. Although this method enables the sampling of various speakers from the speaker-attribute-aware GMMs, it is not yet clear whether the learned distributions can represent speakers with an intermediate attribute (i.e., mid-attribute). To this end, we propose an optimal-transport-based method that interpolates the learned GMMs to generate nonexistent speakers with mid-attribute (e.g., gender-neutral) voices. We empirically validate our method and evaluate the naturalness of synthetic speech and the controllability of two speaker attributes: gender and language fluency. The evaluation results show that our method can control the generated speakers' attributes by a continuous scalar value without statistically significant degradation of speech naturalness.
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Submitted 18 October, 2022;
originally announced October 2022.
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Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators
Authors:
Akino Watanabe,
Sara Ketabi,
Khashayar,
Namdar,
Farzad Khalvati
Abstract:
As deep learning is widely used in the radiology field, the explainability of such models is increasingly becoming essential to gain clinicians' trust when using the models for diagnosis. In this research, three experiment sets were conducted with a U-Net architecture to improve the classification performance while enhancing the heatmaps corresponding to the model's focus through incorporating hea…
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As deep learning is widely used in the radiology field, the explainability of such models is increasingly becoming essential to gain clinicians' trust when using the models for diagnosis. In this research, three experiment sets were conducted with a U-Net architecture to improve the classification performance while enhancing the heatmaps corresponding to the model's focus through incorporating heatmap generators during training. All of the experiments used the dataset that contained chest radiographs, associated labels from one of the three conditions ("normal", "congestive heart failure (CHF)", and "pneumonia"), and numerical information regarding a radiologist's eye-gaze coordinates on the images. The paper (A. Karargyris and Moradi, 2021) that introduced this dataset developed a U-Net model, which was treated as the baseline model for this research, to show how the eye-gaze data can be used in multi-modal training for explainability improvement. To compare the classification performances, the 95% confidence intervals (CI) of the area under the receiver operating characteristic curve (AUC) were measured. The best method achieved an AUC of 0.913 (CI: 0.860-0.966). The greatest improvements were for the "pneumonia" and "CHF" classes, which the baseline model struggled most to classify, resulting in AUCs of 0.859 (CI: 0.732-0.957) and 0.962 (CI: 0.933-0.989), respectively. The proposed method's decoder was also able to produce probability masks that highlight the determining image parts in model classifications, similarly as the radiologist's eye-gaze data. Hence, this work showed that incorporating heatmap generators and eye-gaze information into training can simultaneously improve disease classification and provide explainable visuals that align well with how the radiologist viewed the chest radiographs when making diagnosis.
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Submitted 28 June, 2022;
originally announced July 2022.
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On the Construction of New Toric Quantum Codes and Quantum Burst-Error Correcting Codes
Authors:
Cibele Cristina Trinca,
J. Carmelo Interlando,
Reginaldo Palazzo Jr.,
Antonio Aparecido de Andrade,
Ricardo Augusto Watanabe
Abstract:
A toric quantum error-correcting code construction procedure is presented in this work. A new class of an infinite family of toric quantum codes is provided by constructing a classical cyclic code on the square lattice $\mathbb{Z}_{q}\times \mathbb{Z}_{q}$ for all odd integers $q\geq 5$ and, consequently, new toric quantum codes are constructed on such square lattices regardless of whether $q$ can…
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A toric quantum error-correcting code construction procedure is presented in this work. A new class of an infinite family of toric quantum codes is provided by constructing a classical cyclic code on the square lattice $\mathbb{Z}_{q}\times \mathbb{Z}_{q}$ for all odd integers $q\geq 5$ and, consequently, new toric quantum codes are constructed on such square lattices regardless of whether $q$ can be represented as a sum of two squares. Furthermore this work supplies for each $q$ the polyomino shapes that tessellate the corresponding square lattices and, consequently, tile the lattice $\mathbb{Z}^{2}$. The channel without memory to be considered for these constructed toric quantum codes is symmetric, since the $\mathbb{Z}^{2}$-lattice is autodual. Moreover, we propose a quantum interleaving technique by using the constructed toric quantum codes which shows that the code rate and the coding gain of the interleaved toric quantum codes are better than the code rate and the coding gain of Kitaev's toric quantum codes for $q=2n+1$, where $n\geq 2$, and of an infinite class of Bombin and Martin-Delgado's toric quantum codes. In addition to the proposed quantum interleaving technique improves such parameters, it can be used for burst-error correction in errors which are located, quantum data stored and quantum channels with memory.
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Submitted 26 May, 2022;
originally announced May 2022.
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A Novel Approach to Analyze Fashion Digital Archive from Humanities
Authors:
Satoshi Takahashi,
Keiko Yamaguchi,
Asuka Watanabe
Abstract:
Fashion styles adopted every day are an important aspect of culture, and style trend analysis helps provide a deeper understanding of our societies and cultures. To analyze everyday fashion trends from the humanities perspective, we need a digital archive that includes images of what people wore in their daily lives over an extended period. In fashion research, building digital fashion image archi…
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Fashion styles adopted every day are an important aspect of culture, and style trend analysis helps provide a deeper understanding of our societies and cultures. To analyze everyday fashion trends from the humanities perspective, we need a digital archive that includes images of what people wore in their daily lives over an extended period. In fashion research, building digital fashion image archives has attracted significant attention. However, the existing archives are not suitable for retrieving everyday fashion trends. In addition, to interpret how the trends emerge, we need non-fashion data sources relevant to why and how people choose fashion. In this study, we created a new fashion image archive called Chronicle Archive of Tokyo Street Fashion (CAT STREET) based on a review of the limitations in the existing digital fashion archives. CAT STREET includes images showing the clothing people wore in their daily lives during the period 1970--2017, which contain timestamps and street location annotations. We applied machine learning to CAT STREET and found two types of fashion trend patterns. Then, we demonstrated how magazine archives help us interpret how trend patterns emerge. These empirical analyses show our approach's potential to discover new perspectives to promote an understanding of our societies and cultures through fashion embedded in consumers' daily lives.
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Submitted 10 September, 2021; v1 submitted 17 July, 2021;
originally announced July 2021.
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Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
Authors:
Akihisa Watanabe,
Michiya Kuramata,
Kaito Majima,
Haruka Kiyohara,
Kensho Kondo,
Kazuhide Nakata
Abstract:
In recent years, machine learning and AI have been introduced in many industrial fields. In fields such as finance, medicine, and autonomous driving, where the inference results of a model may have serious consequences, high interpretability as well as prediction accuracy is required. In this study, we propose CGA2M+, which is based on the Generalized Additive 2 Model (GA2M) and differs from it in…
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In recent years, machine learning and AI have been introduced in many industrial fields. In fields such as finance, medicine, and autonomous driving, where the inference results of a model may have serious consequences, high interpretability as well as prediction accuracy is required. In this study, we propose CGA2M+, which is based on the Generalized Additive 2 Model (GA2M) and differs from it in two major ways. The first is the introduction of monotonicity. Imposing monotonicity on some functions based on an analyst's knowledge is expected to improve not only interpretability but also generalization performance. The second is the introduction of a higher-order term: given that GA2M considers only second-order interactions, we aim to balance interpretability and prediction accuracy by introducing a higher-order term that can capture higher-order interactions. In this way, we can improve prediction performance without compromising interpretability by applying learning innovation. Numerical experiments showed that the proposed model has high predictive performance and interpretability. Furthermore, we confirmed that generalization performance is improved by introducing monotonicity.
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Submitted 22 November, 2021; v1 submitted 5 June, 2021;
originally announced June 2021.
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CAT STREET: Chronicle Archive of Tokyo Street-fashion
Authors:
Satoshi Takahashi,
Keiko Yamaguchi,
Asuka Watanabe
Abstract:
The analysis of daily-life fashion trends can provide us a profound understanding of our societies and cultures. However, no appropriate digital archive exists that includes images illustrating what people wore in their daily lives over an extended period. In this study, we propose a new fashion image archive, Chronicle Archive of Tokyo Street-fashion (CAT STREET), to shed light on daily-life fash…
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The analysis of daily-life fashion trends can provide us a profound understanding of our societies and cultures. However, no appropriate digital archive exists that includes images illustrating what people wore in their daily lives over an extended period. In this study, we propose a new fashion image archive, Chronicle Archive of Tokyo Street-fashion (CAT STREET), to shed light on daily-life fashion trends. CAT STREET includes images showing what people wore in their daily lives during 1970--2017, and these images contain timestamps and street location annotations. This novel database combined with machine learning enables us to observe daily-life fashion trends over a long term and analyze them quantitatively. To evaluate the potential of our proposed approach with the novel database, we corroborated the rules-of-thumb of two fashion trend phenomena that have been observed and discussed qualitatively in previous studies. Through these empirical analyses, we verified that our approach to quantify fashion trends can help in exploring unsolved research questions. We also demonstrate CAT STREET's potential to find new standpoints to promote the understanding of societies and cultures through fashion embedded in consumers' daily lives.
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Submitted 29 April, 2021; v1 submitted 28 September, 2020;
originally announced September 2020.
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Recovery command generation towards automatic recovery in ICT systems by Seq2Seq learning
Authors:
Hiroki Ikeuchi,
Akio Watanabe,
Tsutomu Hirao,
Makoto Morishita,
Masaaki Nishino,
Yoichi Matsuo,
Keishiro Watanabe
Abstract:
With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures. Although it has become possible to automatically detect anomalies and analyze root causes of failures with current methods, making decisions on what commands should be executed to recover from failures still depends on manual operation, which is quite time-consuming. To…
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With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures. Although it has become possible to automatically detect anomalies and analyze root causes of failures with current methods, making decisions on what commands should be executed to recover from failures still depends on manual operation, which is quite time-consuming. Toward automatic recovery, we propose a method of estimating recovery commands by using Seq2Seq, a neural network model. This model learns complex relationships between logs obtained from equipment and recovery commands that operators executed in the past. When a new failure occurs, our method estimates plausible commands that recover from the failure on the basis of collected logs. We conducted experiments using a synthetic dataset and realistic OpenStack dataset, demonstrating that our method can estimate recovery commands with high accuracy.
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Submitted 24 March, 2020;
originally announced March 2020.