In the context of industrial factories and energy producers, unplanned outages are highly costly and difficult to service. However, existing acoustic-anomaly detection studies largely rely on generic industrial or synthetic datasets, with few focused on hydropower plants due to limited access. This paper presents a comparative analysis of acoustic-based anomaly detection methods, as a way to improve predictive maintenance in hydropower plants. We address key challenges in the acoustic preprocessing under highly noisy conditions before extracting time- and frequency-domain features. Then, we benchmark three machine learning models: LSTM AE, K-Means, and OC-SVM, which are tested on two real-world datasets from the Rodundwerk II pumped-storage plant in Austria, one with induced anomalies and one with real-world conditions. The One-Class SVM achieved the best trade-off of accuracy (ROC AUC 0.966-0.998) and minimal training time, while the LSTM autoencoder delivered strong detection (ROC AUC 0.889-0.997) at the expense of higher computational cost.

Wi-Fi-based positioning promises a scalable and privacy-preserving solution for location-based services in indoor environments such as malls, airports, and campuses. RSS-based methods are widely deployable as RSS data is available on all Wi-Fi-capable devices, but RSS is highly sensitive to multipath, channel variations, and receiver characteristics. While supervised learning methods offer improved robustness, they require large amounts of labeled data, which is often costly to obtain. We introduce a lightweight framework that solves this by automating high-resolution synchronized RSS-location data collection using a short, camera-assisted calibration phase. An overhead camera is calibrated only once with ArUco markers and then tracks a device collecting RSS data from broadcast packets of nearby access points across Wi-Fi channels. The resulting (x, y, RSS) dataset is used to automatically train mobile-deployable localization algorithms, avoiding the privacy concerns of continuous video monitoring. We quantify the accuracy limits of such vision-assisted RSS data collection under key factors such as tracking precision and label synchronization. Using the collected experimental data, we benchmark traditional and supervised learning approaches under varying signal conditions and device types, demonstrating improved accuracy and generalization, validating the utility of the proposed framework for practical use. All code, tools, and datasets are released as open source.

As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases. Extensively studied in the field of psychology, cognitive biases appear as systematic distortions commonly observed in human judgments. This paper presents a large-scale evaluation of eight well-established cognitive biases across 45 LLMs, analyzing over 2.8 million LLM responses generated through controlled prompt variations. To achieve this, we introduce a novel evaluation framework based on multiple-choice tasks, hand-curate a dataset of 220 decision scenarios targeting fundamental cognitive biases in collaboration with psychologists, and propose a scalable approach for generating diverse prompts from human-authored scenario templates. Our analysis shows that LLMs exhibit bias-consistent behavior in 17.8-57.3% of instances across a range of judgment and decision-making contexts targeting anchoring, availability, confirmation, framing, interpretation, overattribution, prospect theory, and representativeness biases. We find that both model size and prompt specificity play a significant role on bias susceptibility as follows: larger size (>32B parameters) can reduce bias in 39.5% of cases, while higher prompt detail reduces most biases by up to 14.9%, except in one case (Overattribution), which is exacerbated by up to 8.8%.

Proteomics data is essential to pathogenic understanding of a disease phenotype. In cancer, analysis of molecular signatures enables precision medicine through the identification of biological processes that drive individualized tumor progression, therapeutic resistance, and clinical heterogeneity. Recent advances in multimodal large language models (LLMs) have shown remarkable capacity to integrate and reason across heterogeneous data modalities. However, performing multi-modal language modeling for molecular understanding of patient-specific proteomics remains a significant challenge due to two barriers: (1) the lack of instruction-tuning datasets that enable clinical interpretation from proteomics data, and (2) the absence of language modeling architectures designed to capture the rich heterogeneity of molecular data. In this work, we introduce CPTAC-PROTSTRUCT, the first instruction tuning dataset for molecular understanding of oncology, comprising over 400k open-ended examples derived from individualized proteomic profiles curated from the largest national proteomics cancer study (CPTAC). Additionally, we propose KRONOS (Knowledge Representation of patient Omics Networks in Oncology via Structured tuning), a novel graph-LLM framework that leverages molecular interaction topology with proteomics to learn patient-specific graph representations for enhanced clinical reasoning. We show that KRONOS achieves competitive performance across benchmark clinical tasks, including molecular classification, temporal trajectory modeling, and tumor stage prediction from proteomics data. Ultimately, this approach empowers LLMs to understand patient-level pathogenesis, advancing precision medicine through more accurate diagnosis, prognosis, and treatment stratification.

Recently, knowledge-grounded conversations in the open domain gain great attention from researchers. Existing works on retrieval-based dialogue systems have paid tremendous efforts to utilize neural networks to build a matching model, where all of the context and knowledge contents are used to match the response candidate with various representation methods. Actually, different parts of the context and knowledge are differentially important for recognizing the proper response candidate, as many utterances are useless due to the topic shift. Those excessive useless information in the context and knowledge can influence the matching process and leads to inferior performance. To address this problem, we propose a multi-turn \textbf{R}esponse \textbf{S}election \textbf{M}odel that can \textbf{D}etect the relevant parts of the \textbf{C}ontext and \textbf{K}nowledge collection (\textbf{RSM-DCK}). Our model first uses the recent context as a query to pre-select relevant parts of the context and knowledge collection at the word-level and utterance-level semantics. Further, the response candidate interacts with the selected context and knowledge collection respectively. In the end, The fused representation of the context and response candidate is utilized to post-select the relevant parts of the knowledge collection more confidently for matching. We test our proposed model on two benchmark datasets. Evaluation results indicate that our model achieves better performance than the existing methods, and can effectively detect the relevant context and knowledge for response selection.

Lung cancer remains the leading cause of cancerrelated deaths globally. Accurate delineation of internal gross tumor volume (IGTV) in PET/CT imaging is pivotal for optimal radiation therapy in mobile tumors such as lung cancer to account for tumor motion, yet is hindered by the limited availability of annotated IGTV datasets and attenuated PET signal intensity at tumor boundaries. In this study, we present a transfer learningbased methodology utilizing a multimodal interactive perception network with MAMBA, pre-trained on extensive gross tumor volume (GTV) datasets and subsequently fine-tuned on a private IGTV cohort. This cohort constitutes the PET/CT subset of the Lung-cancer Unified Cross-modal Imaging Dataset (LUCID). To further address the challenge of weak PET intensities in IGTV peripheral slices, we introduce a slice interaction module (SIM) within a 2.5D segmentation framework to effectively model inter-slice relationships. Our proposed module integrates channel and spatial attention branches with depthwise convolutions, enabling more robust learning of slice-to-slice dependencies and thereby improving overall segmentation performance. A comprehensive experimental evaluation demonstrates that our approach achieves a Dice of 0.609 on the private IGTV dataset, substantially surpassing the conventional baseline score of 0.385. This work highlights the potential of transfer learning, coupled with advanced multimodal techniques and a SIM to enhance the reliability and clinical relevance of IGTV segmentation for lung cancer radiation therapy planning.

Explainability in time series forecasting is essential for improving model transparency and supporting informed decision-making. In this work, we present CrossScaleNet, an innovative architecture that combines a patch-based cross-attention mechanism with multi-scale processing to achieve both high performance and enhanced temporal explainability. By embedding attention mechanisms into the training process, our model provides intrinsic explainability for temporal saliency, making its decision-making process more transparent. Traditional post-hoc methods for temporal saliency detection are computationally expensive, particularly when compared to feature importance detection. While ablation techniques may suffice for datasets with fewer features, identifying temporal saliency poses greater challenges due to its complexity. We validate CrossScaleNet on synthetic datasets with known saliency ground truth and on established public benchmarks, demonstrating the robustness of our method in identifying temporal saliency. Experiments on real-world datasets for forecasting task show that our approach consistently outperforms most transformer-based models, offering better explainability without sacrificing predictive accuracy. Our evaluations demonstrate superior performance in both temporal saliency detection and forecasting accuracy. Moreover, we highlight that existing models claiming explainability often fail to maintain strong performance on standard benchmarks. CrossScaleNet addresses this gap, offering a balanced approach that captures temporal saliency effectively while delivering state-of-the-art forecasting performance across datasets of varying complexity.

Speaker identification has become a crucial component in various applications, including security systems, virtual assistants, and personalized user experiences. In this paper, we investigate the effectiveness of CosFace Loss and ArcFace Loss for text-independent speaker identification using a Convolutional Neural Network architecture based on the VGG16 model, modified to accommodate mel spectrogram inputs of variable sizes generated from the Voxceleb1 dataset. Our approach involves implementing both loss functions to analyze their effects on model accuracy and robustness, where the Softmax loss function was employed as a comparative baseline. Additionally, we examine how the sizes of mel spectrograms and their varying time lengths influence model performance. The experimental results demonstrate superior identification accuracy compared to traditional Softmax loss methods. Furthermore, we discuss the implications of these findings for future research.

Abstract--Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). T o address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval-Augmented Generation (RAG). By enriching design decisions with domain-specific optical standards and systematically incorporating symbolic reasoning and verification techniques, our pipeline generates explainable, verifiable, and trustworthy optical network designs. Intent-Based Networking (IBN) simplifies network management by allowing users to express high-level objectives--such as connectivity, performance, or security--without specifying implementation details [1], [2]. Standardization bodies like TM Forum and the Internet Engineering Task Force define intent as a declarative statement of desired outcomes, delegating the detailed configuration and implementation tasks to automated systems. By abstracting away low-level complexities, IBN significantly reduces operational overhead, human error, and management complexity [2]. Existing research predominantly explores intent translation into configurations or incremental topology adjustments [3], [4], but largely overlooks the initial phase of comprehensive network design, particularly for optical networks. Poor initial design decisions can lead to significant performance degradation or expensive reconfigurations throughout the operational lifecycle [5], [6].

Accurate parameter identification of a subject-specific human musculoskeletal model is crucial to the development of safe and reliable physically collaborative robotic systems, for instance, assistive exoskeletons. Electromyography (EMG)-based parameter identification methods have demonstrated promising performance for personalized musculoskeletal modeling, whereas their applicability is limited by the difficulty of measuring deep muscle EMGs invasively. Although several strategies have been proposed to reconstruct deep muscle EMGs or activations for parameter identification, their reliability and robustness are limited by assumptions about the deep muscle behavior. In this work, we proposed an approach to simultaneously identify the bone and superficial muscle parameters of a human arm musculoskeletal model without reconstructing the deep muscle EMGs. This is achieved by only using the least-squares solution of the deep muscle forces to calculate a loss gradient with respect to the model parameters for identifying them in a framework of differentiable optimization. The results of extensive comparative simulations manifested that our proposed method can achieve comparable estimation accuracy compared to a similar method, but with all the muscle EMGs available.