Journal of Systems Architecture (JSA), March 2026 Chi-Wei Chu, Ding-Yong Hong, Jan-Jan Wu In deep learning frameworks, weight pruning is a widely used technique for improving computational efficiency by reducing the size of large models. This is especially critical for convolutional operators, which often act as performance bottlenecks in convolutional neural networks (CNNs). However, the effectiveness of pruning heavily depends on how it is implemented, as different methods can significantly impact both computational performance and memory footprint. In this work, we propose a column-wise N:M pruning strategy applied at the tile level and modify XNNPACK to enable efficient execution of pruned models on the RISC-V vector architecture. Additionally, we propose fusing the operations of im2col and data packing to minimize redundant memory accesses and memory overhead. To further optimize performance, we incorporate AITemplate’s profiling technique to identify the optimal implementation for each convolutional operator. Our proposed approach effectively increases ResNet inference throughput by as much as 4×, and preserves ImageNet top-1 accuracy within 2.1% of the dense baseline. IEEE Transactions on Cognitive and Developmental Systems, December 2025 Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, and Hsin-Min Wang The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. The 40th Annual AAAI Conference on Artificial Intelligence (AAAI), January 2026 Cheng-Chang Tsai, Kevin Cheng, and Chun-Shien Lu Federated learning (FL) has shown success in collaboratively
training a model among decentralized data resources without
directly sharing privacy-sensitive training data. Despite
recent advances, non-IID (non-independent and identically
distributed) data poses an inevitable challenge that hinders
the use of FL. In this work, we address the issue of non-IID
histopathological images with feature distribution shifts from
an intuitive perspective that has only received limited attention.
Specifically, we address this issue from the perspective
of data distribution by solely adjusting the data distributions
of all clients. Building on the success of diffusion models
in fitting data distributions and leveraging stain separation
to extract the pivotal features that are closely related to the
non-IID properties of histopathological images, we propose
a Federated Stain Distribution Alignment (FedSDA) method.
FedSDA aligns the stain distribution of each client with a target
distribution in an FL framework to mitigate distribution
shifts among clients. Furthermore, considering that training
diffusion models on raw data in FL has been shown to be susceptible
to privacy leakage risks, we circumvent this problem
while still effectively achieving alignment. Extensive experimental
results show that FedSDA is not only effective in improving
baselines that focus on mitigating disparities across
clients’ model updates but also outperforms baselines that address
the non-IID data issues from the perspective of data distribution.
We show that FedSDA provides valuable and practical
insights for the computational pathology community. GigaScience, November 2025 Yu-Hsin Chen, Chien-Fu Liu, Jun-Yi Leu*, and Huai-Kuang Tsai* Co-fractionation coupled with mass spectrometry (CF-MS) is a powerful strategy for mapping protein-protein interactions (PPIs) under near-physiological conditions. Despite recent progress, existing analysis pipelines remain constrained by reliance on handcrafted features, sensitivity to experimental noise, and an inherent focus on pairwise interactions, which limit their scalability and generalizability. To address these difficulties, we introduce FREEPII (Feature Representation Enhancement End-to-End Protein Interaction Inference), a unified deep learning framework that integrates CF-MS data with sequence-derived features to learn biologically meaningful protein-level representations for accurate and efficient inference of PPIs and protein complexes. FREEPII employs a convolutional neural network (CNN) architecture to learn protein-level representations directly from raw data, enabling feature sharing across interaction pairs and reducing computational complexity. To enhance robustness against CF-MS noise, protein sequences are introduced as auxiliary input to enrich the feature space with complementary biological cues. The supervised protein embeddings further encode network-level context derived from complex annotations, allowing the model to capture higher-order interactions and enhance the expressive power of protein representations. Extensive benchmarking demonstrates that FREEPII consistently outperforms state-of-the-art CF-MS analysis tools, capturing more biologically coherent and discriminative protein features. Cross-dataset evaluations further reveal that integrating multi-modal data from diverse experimental contexts substantially improves the generalization and sensitivity of data-driven models, offering a scalable, cross-species strategy for reliable protein interaction inference. IEEE Transactions on Information Forensics and Security , November 2025 Hanrui Wang, Ching-Chun Chang, Chun-Shien Lu, Christopher Leckie, and Isao Echizen Deep neural networks are highly vulnerable to
adversarial examples, which are inputs with small, carefully
crafted perturbations that cause misclassification—making
adversarial attacks a critical tool for evaluating robustness.
Existing black-box methods typically entail a trade-o between
precision and flexibility: pixel-sparse attacks (e.g., single- or fewpixel
attacks) provide fine-grained control but lack adaptability,
whereas patch- or frequency-based attacks improve eciency or
transferability, but at the cost of producing larger and less precise
perturbations. We present GreedyPixel, a fine-grained black-box
attack method that performs brute-force-style, per-pixel greedy
optimization guided by a surrogate-derived priority map and
refined by means of query feedback. It evaluates each coordinate
directly without any gradient information, guaranteeing
monotonic loss reduction and convergence to a coordinate-wise
optimum, while also yielding near white-box-level precision and
pixel-wise sparsity and perceptual quality. On the CIFAR-10
and ImageNet datasets, spanning convolutional neural networks
(CNNs) and Transformer models, GreedyPixel achieved state-ofthe-
art success rates with visually imperceptible perturbations,
e ectively bridging the gap between black-box practicality and
white-box performance. The implementation is available at
https://github.com/azrealwang/greedypixel IEEE Transactions on Human-Machine System, December 2025 Sahibzada Adil Shahzad, Ammarah Hashmi, Yan-Tsung Peng, Yu Tsao, and Hsin-Min Wang Multimodal manipulations (also known as audio-visual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely detection is crucial. The damage to either modality (i.e., visual or audio) can only be discovered through multimodal models that can exploit both pieces of information simultaneously. However, previous methods mainly adopt unimodal video forensics and use supervised pretraining for forgery detection. This study proposes a new method based on a multimodal self-supervised-learning (SSL) feature extractor to exploit inconsistency between audio and visual modalities for multimodal video forgery detection. We use the transformer-based SSL pretrained Audio-Visual HuBERT (AV-HuBERT) model as a visual and acoustic feature extractor and a multiscale temporal convolutional neural network to capture the temporal correlation between the audio and visual modalities. Since AV-HuBERT only extracts visual features from the lip region, we also adopt another transformer-based video model to exploit facial features and capture spatial and temporal artifacts caused during the deepfake generation process. Experimental results show that our model outperforms all existing models and achieves new state-of-the-art performance on the FakeAVCeleb and DeepfakeTIMIT datasets. the Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), December 2025 Jian-Ting Guo, Yu-Cheng Chen, Ping-Chun Hsieh, Kuo-Hao Ho, Po-Wei Huang, Ti-Rong Wu, I-Chen Wu Human-like agents have long been one of the goals in pursuing artificial intelligence. Although reinforcement learning (RL) has achieved superhuman performance in many domains, relatively little attention has been focused on designing human-like RL agents. As a result, many reward-driven RL agents often exhibit unnatural behaviors compared to humans, raising concerns for both interpretability and trustworthiness. To achieve human-like behavior in RL, this paper first formulates human-likeness as trajectory optimization, where the objective is to find an action sequence that closely aligns with human behavior while also maximizing rewards, and adapts the classic receding-horizon control to human-like learning as a tractable and efficient implementation. To achieve this, we introduce Macro Action Quantization (MAQ), a human-like RL framework that distills human demonstrations into macro actions via Vector-Quantized VAE. Experiments on D4RL Adroit benchmarks show that MAQ significantly improves human-likeness, increasing trajectory similarity scores, and achieving the highest human-likeness rankings among all RL agents in the human evaluation study. Our results also demonstrate that MAQ can be easily integrated into various off-the-shelf RL algorithms, opening a promising direction for learning human-like RL agents. Our code is available at https://rlg.iis.sinica.edu.tw/papers/MAQ. Annual Conference on Neural Information Processing Systems (NeurIPS), December 2025 Scott Cheng, Meng-Yu Tsai, Ding-Yong Hong, Mahmut Kandemir AlphaZero has achieved remarkable success in complex decision-making problems through self-play and neural network training. However, its self-play process remains inefficient due to limited exploration of high-uncertainty positions, the overlooked runner-up decisions in Monte Carlo Tree Search (MCTS), and high variance in value labels. To address these challenges, we propose and evaluate uncertainty-guided exploration by branching from high-uncertainty positions using our proposed Label Change Rate (LCR) metric, which is further refined by a Bayesian inference framework. Our proposed approach leverages runner-up MCTS decisions to create multiple variations, and ensembles value labels across these variations to reduce variance. We investigate three key design parameters for our branching strategy: where to branch, how many variations to branch, and which move to play in the new branch. Our empirical findings indicate that branching with 10 variations per game provides the best performance-exploration balance. Overall, our end-to-end results show an improved sample efficiency over the baseline by 58.5% on 9x9 Go in the early stage of training and by 47.3% on 19x19 Go in the late stage of training. IEEE International Conference on Computers, Software, and Applications (COMPSAC), July 2025 Cai-Feng Lin, Ding-Yong Hong, Tzu-Hsien Tsai, Pangfeng Liu, Jan-Jan Wu Graph Neural Network (GNN) is an important tool in deep learning to handle structured data, where graphs with nodes and edges represent entities and their relationships. Various
challenges arise when GNN is tree-shaped, with irregular connectivity patterns and varying depth. It is difficult to distribute and process the dynamic structure for parallel execution on multiple GPUs. In addition, tree data dependency demands the processing of parent nodes before their children, severely limiting execution parallelism.
This research aims to improve the training speed of treeshaped GNN on multi-GPU systems. First, we introduce a cost model that estimates the running time of the training across
multiple GPUs. Then, we demonstrate that finding an optimal way to distribute tree-structured data across GPUs is an NP-complete problem on this cost model. We then propose a practical
heuristic method for distributing data that improves efficiency while maintaining training quality. The heuristic method first assigns data to batches based on our cost model and then assigns
data in each batch to the devices. We also show that our device assigning algorithm is a 4-approximation algorithm. That is, it guarantees that its cost is four times the optimal running time in each training batch, ensuring that it performs effectively in practice.
We implement the algorithm and conduct the experiments. The results show that our algorithm achieves a significant increase in training time. The speedup is up to 1.86 for two GPUs, 3.43 for
four GPUs, and 7.25 for eight GPUs.
the thirty-fourth International Joint Conference on Artificial Intelligence (IJCAI), August 2025 Yan-Ru Ju, Tai-Lin Wu, Chung-Chin Shih, Ti-Rong Wu Although AlphaZero has achieved superhuman performance in board games, recent studies reveal its limitations in handling scenarios requiring a comprehensive understanding of the entire board, such as recognizing long-sequence patterns in Go. To address this challenge, we propose ResTNet, a network that interleaves residual and Transformer blocks to bridge local and global knowledge. ResTNet improves playing strength across multiple board games, increasing win rate from 54.6% to 60.8% in 9x9 Go, 53.6% to 60.9% in 19x19 Go, and 50.4% to 58.0% in 19x19 Hex. In addition, ResTNet effectively processes global information and tackles two long-sequence patterns in 19x19 Go, including circular pattern and ladder pattern. It reduces the mean square error for circular pattern recognition from 2.58 to 1.07 and lowers the attack probability against an adversary program from 70.44% to 23.91%. ResTNet also improves ladder pattern recognition accuracy from 59.15% to 80.01%. By visualizing attention maps, we demonstrate that ResTNet captures critical game concepts in both Go and Hex, offering insights into AlphaZero's decision-making process. Overall, ResTNet shows a promising approach to integrating local and global knowledge, paving the way for more effective AlphaZero-based algorithms in board games. Our code is available at https://rlg.iis.sinica.edu.tw/papers/restnet. IEEE Transactions on Artificial Intelligence, August 2025 Shafique Ahmed, Ryandhimas E. Zezario, Hui-Guan Yuan, Amir Hussain, Hsin-Min Wang, Wei-Ho Chung, and Yu Tsao The prevalence of hearing aids is increasing. However, optimizing their amplification remains challenging due to the complexity of integrating multiple components in traditional methods. To address this, we present NeuroAMP, a novel deep neural network for end-to-end, personalized amplification in hearing aids. NeuroAMP leverages spectral features and the listener’s audiogram as inputs, and we explore four architectures: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Convolutional Recurrent Neural Network (CRNN), and Transformer. We also introduce Denoising NeuroAMP, an extension that integrates noise reduction with amplification for improved real-world performance. To enhance generalization, we employed a comprehensive data augmentation strategy during training on diverse speech (TIMIT, TMHINT) and music (Cadenza Challenge MUSIC) datasets. Evaluation using the Hearing Aid Speech Perception Index (HASPI), Hearing Aid Speech Quality Index (HASQI), and Hearing Aid Audio Quality Index (HAAQI) shows that the Transformer-based NeuroAMP achieves the best performance, with SRCC scores of 0.9927 (HASQI) and 0.9905 (HASPI) on TIMIT, and 0.9738 (HAAQI) on Cadenza dataset. Notably, the augmentation strategy maintains robust performance on unseen datasets (e.g., VoiceBank-DEMAND, MUSDB18-HQ). Furthermore, Denoising NeuroAMP outperforms both the conventional NAL-R+WDRC method and a two-stage baseline on the VoiceBank-DEMAND dataset, achieving HASPI of 0.90 and HASQI of 0.59. These results highlight the strong potential of NeuroAMP and Denoising NeuroAMP to provide a novel and effective framework for personalized hearing aid amplification.Efficient Column-Wise N:M Pruning on RISC-V CPU
Abstract
AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection
Abstract
FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification
Abstract
Complete end-to-end learning from protein feature representation to protein interactome inference
Abstract
GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm
Abstract
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Deepfake Detection of Frontal Face Videos
Abstract
Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization
Abstract
Uncertainty-Guided Exploration for Efficient AlphaZero Training
Abstract
A Grouping Algorithm for Training Tree-Shaped Models on Multiple GPUs with High Efficiency
Abstract
Bridging Local and Global Knowledge via Transformer in Board Games
Abstract
NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids
Abstract
