Papers

Matrix multiplication is a fundamental computational kernel underlying a wide range of real-world applications, including machine learning, scientific computing, signal processing, and computer graphics. Its performance directly impacts the efficiency, scalability, and energy consumption of modern computing systems. This paper presents a comparative analysis of several matrix multiplication algorithms implemented in software and examined in the context of their hardware execution characteristics. Naive, NumPy, Strassen, and Winograd algorithms are evaluated based on execution time, user time, and CPU time across increasing matrix sizes. The performance metrics reveal computational bottlenecks and highlight the benefits of algorithmic optimizations. Furthermore, the study investigates the mathematical operations underlying each algorithm and analyzes how matrix dimensions influence MAC (Multiply-Accumulate) behavior and overall computational efficiency in the hardware domain. The results provide a performance benchmark and contribute to understanding how algorithmic choices interact with modern computing architectures for applications in computer architecture, data science, and real-time embedded systems.

Joint unicast and multi-group multicast transmission with RIS and RSMA is a promising enabler for 6G services. However, existing RSMA schemes for such scenarios split only unicast messages while leaving multicast messages intact, limiting the degree of freedom of interference management. To this end, we propose a joint rate splitting framework that splits both unicast and multicast information and two RSMA schemes. The common-common fusion (CCF-RSMA) scheme encodes the unicast common part into the global multicast common stream, while the private-common fusion (PCF-RSMA) scheme merges it with the group-specific multicast private part. For each scheme, we formulate energy efficiency (EE) maximization problems under both perfect and imperfect channel state information, and jointly optimize active beamforming, RIS phase shifts and rate allocation parameters. Simulation results demonstrate that the proposed schemes significantly outperform the comparative schemes in terms of EE, thereby proving the effectiveness of the proposed framework. Moreover, CCF-RSMA is more favorable in scenarios with larger groups and higher unicast QoS demands, whereas PCF-RSMA is better suited for scenarios with smaller groups and higher multicast QoS.

Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments. Existing approaches process utterances in isolation and have been evaluated primarily on English. We address these gaps along three dimensions. First, we fine-tune and evaluate six-self supervised models on a multilingual dataset of 182 children, showing that in-domain pre-training on child-centered recordings substantially outperforms models trained on adult speech. Second, we demonstrate that incorporating surrounding context substantially improves classification, with an absolute gain of 13.8% in average F1-score. Third, we evaluate our model in a realistic end-to-end pipeline, from adult speech detection to addressee classification, showing that performance drops under automatic segmentation but still consistently outperforms a rule-based baseline.

Multicast transmission in millimeter-wave (mmWave) networks is fundamentally limited by the weakest user, and blockages further exacerbate this problem. Large-scale reconfigurable intelligent surfaces (XL-RIS) offer a promising solution by providing high array gain to overcome blockages. However, the large aperture of XL-RIS significantly expands the near-field region, creating a hybrid-field scenario where some users lie in the near-field while others remain in the far-field. Existing hybrid-field studies on XL-RIS have primarily focused on channel estimation and deployment optimization, leaving multicast capacity analysis unexplored. This paper investigates the fundamental capacity limits of XL-RIS-assisted multicast communications in hybrid-field scenarios. For the fundamental two-user case consisting of one near-field and one far-field user, we derive the optimal closed-form covariance matrix and optimize the RIS phase shifts via manifold optimization. We establish that the multicast capacity scales as $\Theta(\log_2(MN))$ as the number of transmit antennas M and/or RIS elements N grow large, and prove this scaling is order-tight. Numerical results validate the bounds and show the impact of M, $N$, and distance on the multicast rate.

For near-field communications, it is a hardware-efficient means to form an extremely large-scale array (XL-array) by concatenating multiple modular arrays (also referred to as subarrays). In this letter, we aim to investigate the effect of time synchronization errors among transmissions of different subarrays on the beam-focusing performance. To this end, we first characterize the beam pattern function when the transmit beamforming is designed based on maximum ratio transmission (MRT) under the premise of perfect time synchronization. As this function is highly difficult for analysis, we then consider a typical case with two subarrays. Interestingly, we show that for this case, the beam-focusing effect still persists even in the presence of time synchronization errors, while the focused location is deviated from the user location with an angle offset upper-bounded by 1/M, where M denotes the number of antennas in each subarray. Subsequently, for the general case with multiple subarrays, despite analytical intractability, we numerically show that time synchronization errors give rise to an imbricated (instead of focused) beam pattern. This may significantly degrade multi-user communication performance in practice due to the reduced desired signal power and increased inter-user interference.

While End-to-End (E2E) Speech-Large Language Models (Speech-LLMs) are rapidly evolving, their evaluation methodologies remain limited to the era of simple transcription. Existing benchmarks suffer from three critical limitations: a pronounced bias towards high-resource languages, a focus on low-level recognition (ASR) rather than semantic reasoning, and a neglect of regional dialects. To bridge this gap, we introduce PolySpeech-100, a massive-scale benchmark designed to assess `native-level' speech comprehension across 110 linguistic variants. We employ a novel hybrid construction pipeline that augments gold-standard human recordings with instruction-driven synthetic speech, allowing us to cover 19 distinct Chinese dialects and over 80 low-resource languages. Extensive evaluation of 22 state-of-the-art models (including Gemini-3, GPT-Audio, and Qwen2.5-Omni) yields pivotal insights. First, we demonstrate that open-source E2E models outperform Cascade (ASR+LLM) systems on heavy dialects, proving that direct audio processing preserves critical paralinguistic cues and prosodic features (e.g., intonation, stress) that are often lost in standard transcription. Second, we reveal a significant performance gap: while commercial models maintain robustness, open-source models suffer catastrophic degradation on low-resource languages. Finally, counter-intuitively, we observe that under standard zero-shot settings, Chain-of-Thought prompting frequently degrades speech understanding performance for most evaluated models, revealing a potential modality alignment gap in current architectures. PolySpeech-100 establishes a rigorous standard for the next generation of inclusive, omni-capable Speech-LLMs. The data, demo, and code are publicly available at https://github.com/YoungSeng/PolySpeech-100.

The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundation of real-time, intelligent, and context-aware systems. AI enables perception and reasoning, IoT provides scalable sensing and communication, and robotics delivers embodied actuation. Despite significant progress in pairwise combinations such as AIoT and the Internet of Robotic Things (IoRT), there remains a lack of unified design frameworks that fully integrate all three. This survey synthesizes the state-of-the-art across these domains, emphasizing the emerging role of Small Language Models (SLMs) at the edge and Large Language Models (LLMs) in the cloud for distributed cognition and autonomous decision-making. We propose a modular system architecture that aligns with these trends, analyze persistent gaps in interoperability and feedback control, and classify existing work by integration depth. Our review highlights how hybrid SLM-LLM systems, when coupled with IoT infrastructure and robotic agents, can address challenges in real-time adaptation, scalability, and reliability. This work offers a conceptual and technical roadmap for designing next-generation AI-IoT-Robotic ecosystems that are modular, interpretable, and capable of learning within dynamic environments, paving the way for the emerging paradigm of Connected Robotics and Physical AI.

Digital twin (DT) is envisioned as a key enabler of sixth-generation (6G) communication systems, evolving from offline descriptive replicas for monitoring and analysis to inthe-loop agents within digital twin networks (DTNs) that couple physical and digital worlds. Recent advances in integrated sensing and communication (ISAC)-driven electromagnetic (EM) scattering methods enable environment twinning by linking channel behaviors to EM properties of the scatterers, supporting interpretable DT states and EM-grounded optimization. However, existing studies primarily focus on DT construction and lack mechanisms for closed-loop control in wireless systems. Moreover, array-geometry mismatch can bias DT reconstruction and degrade control performance, while prior works assume known arrays. To address these gaps, we propose an EM-ISACbased closed-loop DTN framework with a hierarchical design integrating environment twinning, prior injection, and control decision into an end-to-end loop. Leveraging ISAC measurements, the proposed framework jointly reconstructs scatterer information and array-dependent forward operator and employs a low-complexity Bayesian message-passing algorithm to perform contrast inference and array calibration. The reconstructed DT guides codebook preselection to reduce training overhead and narrow candidate beams. Subsequently, downlink beamforming (BF) is performed based on DT-predicted channels, enabling latency-bounded closed-loop control. Simulation results demonstrate improved robustness and control performance under array mismatch.

This article addresses recent advances in artificial intelligence, which have set off an astounding race among technology frontiers to build large data centers. It provides insights into impacts of large data centers on the planning and operation of the power grid.

The comparison of two binary images is formulated in terms of mathematical morphology. A new operator, the dilated symmetric difference, is introduced. It is shown that the dilated symmetric difference effectively detects differences between binary images, provided that the residual alignment error is within specified bounds.

We introduce Echo-POSED, a self-supervised framework for real-time transthoracic echocardiography (TTE) guidance that recommends probe adjustments directly from 2D ultrasound images, without the need for expert-labelled views or tracked probe trajectories. Instead, it trains on 2D views sliced from routinely acquired 3D echocardiography volumes, enforcing equivariance to probe motions while remaining invariant to cardiac phase, yielding a pose representation on $\mathrm{SO}(3)\times\mathrm{SO}(3)$. Across a held-out split and public external 3D–TTE datasets (including vendor shift), Echo-POSED maintains geometric consistency under virtual perturbations and enables intra- and inter-patient guidance simulations, achieving a combined mean angular error of 8.2 degrees between the guided and target views in intra-patient simulations with cardiac motion.

This paper studies whether audio, images, and video can share a common wavelet token schema rather than relying on separate modality-specific latent grids. It introduces a preliminary continuous-token model built around a one-level Haar DWT/IDWT frontend, a shared coefficient-token layout, optional structural metadata, lightweight modality value adapters, and a shared token-wise encoder-decoder trunk. On Speech Commands, EuroSAT RGB, and DAVIS 2017 data, a dense shared model reaches 39.92 dB audio, 29.37 dB image, and 23.93 dB video PSNR. A matched-rate sweep under continuous latent scalar budgets indicates that the visual gains are not explained solely by latent capacity, while also showing that additive metadata embeddings are not a universal source of improvement. Finally, fixed-rate energy selection provides a strong non-parametric baseline: energy_global improves average PSNR over uniform selection by 16.73 dB for audio, 16.90 dB for images, and 15.86 dB for video under compressed keep ratios. Masked sparse training reaches 34.45 dB video PSNR with 50% of dense tokens. The results support a unified wavelet token schema and sparse token interface, while stopping short of establishing a universal discrete vocabulary.

Stacked intelligent metasurfaces (SIMs) are emerging as a promising architecture for sixth-generation (6G) and beyond wireless systems, enabling richer electromagnetic-wave manipulation than conventional single-layer metasurfaces. However, strong inter-layer coupling and multilinear parameter interactions make accurate, scalable channel estimation challenging. This paper proposes TenSIM, a tensor-based channel-estimation framework for SIM-assisted multiple-input multiple-output (MIMO) systems. By exploiting a structured SIM training protocol, TenSIM derives two parity-dependent observation models: a PARAllel FACtor (PARAFAC) model for odd-layer SIMs and a Tucker model for even-layer SIMs. These formulations decouple the transmitter-SIM and SIM-receiver channel factors while accounting for inter-layer wave coupling. Based on these tensor models, we develop alternating least squares estimators, establish rank-based identifiability conditions using the associated design matrices, and provide practical sufficient conditions for full-column-rank training designs, including scaling ambiguities. Numerical results reveal the main trade-offs. Both TenSIM-PARAFAC and TenSIM-Tucker improve with signal-to-noise ratio and training diversity, outperforming unstructured least-squares baselines by exploiting the tensor structure of the SIM cascade. TenSIM-PARAFAC offers better scalability, lower complexity, and stronger robustness to inter-layer spacing, whereas TenSIM-Tucker can achieve more accurate channel reconstruction when sufficient training and strong layer coupling are available. The framework also remains effective under imperfect or blind SIM training with additional pilot diversity. Overall, TenSIM offers a unified, physically interpretable approach to channel estimation in SIM-assisted MIMO systems, with explicit identifiability, complexity, and performance trade-offs.

The growing penetration of grid-connected inverters renders Transient Stability Analysis (TSA) increasingly challenging in modern power systems. Existing TSA methodologies encounter an intrinsic trade-off between accuracy and scalability when dealing with these networked inverter-based resources (IBRs). To bridge this gap, this paper proposes a Lipschitz-enforced machine learning framework that leverages Lipschitz continuity to restructure the transient stability certification mechanism. By replacing computationally intensive verification procedures with a deterministic and efficient algebraic check, the proposed method enables rigorous stability guarantees for complex multi-inverter systems, effectively bypassing the complexity limits of traditional analytical approximations. Validated on networked Grid-Forming (GFM) inverter systems, the proposed framework accelerates the training process by over 5 times compared to existing methods. Notably, the proposed framework substantially outperforms traditional transient stability analysis approaches (e.g., Linear Matrix Inequality and Sum-of-Squares methods) by capturing up to 30% larger Regions of Attraction (ROA), effectively shattering the conservativeness bottleneck that has long constrained traditional analytical tools. This advancement provides a scalable and theoretically rigorous solution for the TSA of networked IBRs in modern power grids.

Recent publications have suggested using the Shap- ley value for sensor anomaly/attack localization. We study the performance of such an approach by using mathematically de- fined optimum binary classifiers in the Shapley value calculation. To judge localization performance, we study the ability of the Shapley value of a given sensor observation to determine if that observation is anomalous. First, we prove that for cases with independent sensor observations, an optimized anomaly test using the Shapley value is equivalent to an optimized lower-complexity anomaly test using a single term in the Shapley value calculation, yielding the exact same probability of error. For some popular dependent observation cases involving two sensors, including correlated bivariate Gaussian/Laplacian probability density functions and constant/Gaussian at- tacks/anomalies, we prove that these two tests are fundamentally different, yielding different decision regions and error probabil- ities. Further, we prove that the Shapley value test is sometimes strictly inferior to the other (single term in Shapley calculation) test in certain statistically dependent bivariate Gaussian scenarios with large correlation magnitude and additive attacks/anomalies, while it is strictly superior in others, depending on the sign of the correlation. One can combine these two approaches to obtain a strictly better approach in these cases. These results, which provide the first theoretical statistical analysis of Shapley-based localization, seem very interesting based on the wide acceptance of the Shapley value by many researchers and should encourage further research on this topic. Numerical results are provided which illustrate our findings.

Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues. To address this, we introduce Sympatheia, a speech-to-speech dialogue framework conditioned on affect inferred from the user's speech and, when available, explicit affect specifications provided as a continuous valence–arousal (VA) control signal by a multimodal sensing module or user interface. To train our model, we construct Sympatheia-18k, an emotion-conditioned synthetic spoken dialogue corpus with 12 emotion anchors. This dataset includes an emotional split for learning affective speech behavior, and a neutral split that pairs emotionally neutral queries with multiple emotion-conditioned responses to isolate explicit emotion control in emotionally ambiguous cases. Empirical results show that Sympatheia outperforms speech conversational baselines in generating responses whose semantic content and spoken delivery are both emotionally appropriate. We further show that the same VA interface can integrate emotion estimates from diverse sensing modules, including facial expression, biosignals, and textual affect descriptions, improving response alignment when speech alone provides limited emotional evidence. These results suggest that continuous affect conditioning is an effective practical step for building emotionally adaptive voice assistants.

In modern radio networks, nodes frequently access multiple communication interfaces such as WiFi, cellular, LoRa, and Zigbee. Optimal utilization of such heterogeneous networks (HetNets) at link and network levels is essential for ensuring efficient and secure communication. Some applications require a high level of security, requiring the signal to be completely undetectable. Previous works have considered such covertness, but it often results in limited achievable rates. Physical layer analysis shows that friendly jamming can significantly improve covert data rates, motivating its incorporation into HetNets. Here, we analyze a scenario where a jammer assists communication in a HetNet in the presence of an adversary attempting signal detection. We first optimize the physical layer (PHY) for a single link and then incorporate those results into an optimal routing and link configuration approach that accounts for an adversary observing the aggregate signals from all links. Numerical results demonstrate significant performance gains when compared to alternative approaches. In fact, the rate observed for the proposed approach is high enough to question the optimality of the low rate design approach employed; we address this concern through revised algorithms and characterize their performance.

We present a framework for planning trajectories that avoid obstacles and satisfy logical precedence constraints expressed with a fragment of signal temporal logic (STL). Our approach models environments containing obstacles, keys, and doors, where collecting a key unlocks its associated door and potentially opens shorter paths to a goal. Based on an exact convex partitioning of the free space that encodes connectivity among convex free space, key, and door regions, we construct an augmented graph of convex sets (GCS) whose layered structure exactly encodes the key-door precedence logic. A shortest path in the augmented GCS simultaneously selects an optimal key collection sequence and computes an optimal continuous trajectory, providing an exact solution up to a finite Bézier curve parameterization.

The main focus of this talk is to present mathematical fundamentals, state-of-the-art, technical challenges and open problems in control of flight for atmospheric vehicles, such as aircraft and other aerial platforms. Reduced order modeling and flight simulation key features for control applications will be discussed. The emphasis is on the theoretical and engineering aspects of creating and transitioning to practice guidance and flight control systems with guarantees of closed-loop stability, robustness and performance.

In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tools for the analysis of signals defined on nodes while accounting for their relationships represented by edges. These tools have been successfully applied to various settings, including statistical hypothesis testing. In particular, non-parametric approaches based on surrogate generation have been proposed for signals on undirected graphs. However, they are yet to be extended to directed graphs. In this work, we first revisit the notion of stationary graph signals on directed graphs. Specifically, and through the eigendecomposition of the graph shift operator, we define directed graph wide-sense stationary signals. Then, we propose a new framework to generate surrogate graph signals that preserve covariance structure under stationarity assumptions. Null distributions of the test metric can then be constructed from these surrogates and serve as a reference for the empirical data. Finally, we provide guiding examples and an application on real data, in which we compare the performance of our framework with existing techniques for undirected graphs or based on naive permutation, demonstrating feasibility and superiority of the proposed approach.

Low-Earth orbit (LEO) satellite beam-hopping (BH) technology is emerging as a promising approach to meet the ever-increasing global connectivity demands, enabling agile, on-demand coverage. LEO satellite BH can address the spatio-temporal non-uniformity of ground user traffic by dynamically allocating capacity and optimizing network performance. Cooperative multi-satellite BH enables joint transmission and interference avoidance to improve received signal quality. This article provides a comprehensive paradigm of BH, detailing its key dimensions, strategies, and architectures. Through exploration of key challenges, including beam pattern design, on-demand scheduling, and interference management, this paper identifies the potential applications of BH, ranging from adaptive capacity allocation for hotspot areas, low-power Internet-of-Things (IoT), delay-sensitive services, to massive connectivity support. Furthermore, a system-level analysis is presented, including key metrics, models of inter-beam and inter-satellite interference, and cooperative joint transmission, and a case study is provided to demonstrate the performance benefits of BH with cooperative transmission. Several promising future research directions are discussed to guide the future development of LEO satellite BH networks.

Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive controllers, schedulers, and observers are often not closed-form and must re-solve a numerical optimization per instance, with the operator re-supplied each time. Physics-informed learning moves this cost to training, but uses a single encoder pathway whose latent geometry de-learns under fine-tuning and admits no quantitative transfer guarantee. We propose an asymmetric two-pathway architecture that resolves both issues. A teacher encoder consumes privileged dense states from a high-fidelity simulator and represents the system through operator-polynomial features stable under spectral perturbation; a student encoder learns the same latent geometry from sparse field data and operator descriptors. At deployment the teacher is discarded, and the frozen student runs in a single forward pass with a transfer certificate. The design connects to privileged-information learning, knowledge distillation, and cross-modal distillation, but targets cross-instance transfer rather than fixed-instance prediction: topology and operator may change, while the latent task does not. We establish sufficient and near-necessary transfer conditions via Wasserstein proximity between latent laws, yielding a zero-shot error bound, and develop a finite-sample certification protocol with active expansion when coverage is incomplete. The framework applies wherever a system admits an operator with reportable spectrum. On power-system estimation, it achieves zero-shot transfer to 100 unseen topologies, a 95% certificate pass rate, accuracy competitive with topology-aware Newton–Raphson, and sub-millisecond inference. These results suggest asymmetric pathways plus operator-anchored latent geometry provide a foundation for certified zero-shot inference and control.

Narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is a key enabler for massive Internet of Things (IoT) in 6G, but in low Earth orbit (LEO) scenarios, large and time-varying Doppler shifts generate carrier frequency offset (CFO) beyond the correction range of standard user equipment (UE), making initial downlink synchronization a major bottleneck. This paper analyzes Doppler characteristics in realistic NB-IoT LEO scenarios, reviews Doppler mitigation strategies, and proposes a standard-compliant, low-overhead search-space optimization method for downlink acquisition. Results under realistic LEO conditions with real-time measurements show reduced acquisition overhead while maintaining synchronization reliability, supporting NB-IoT adaptation to 6G NTN deployment.

Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance consideration, economy consideration, society consideration. However, besides all such aspects of consideration, one aspect that is most essential to the control system is methodology consideration in mathematical sense, knowledge on which is what we refer to as control theory. Besides its importance from the mathematical perspective, control theory is even more charming as it is deeply rooted in practical applications. Charms of control theory consist in both know-why and know-how and it is the fusion of control theory and practical applications that highlights such charms. Control theory for practical applications, especially when somewhat with so-called “advanced” flavour, involves several fundamental aspects. This article introduces the State-Space Modelling and Analysis aspect of Advanced Control Theory for Practical Applications [1,2].

We address the problem of out-of-distribution (OOD) detection for target observations embedded in a subspace of the high dimensional data space. Using continuous normalizing flows (CNFs), we propose a Lagrangian sub-flow (LSF) framework designed to isolate and estimate the density for the relevant components in the representation and using the remaining components as context. Through experimentation with models for speech synthesis, we show that CNFs, similarly to other deep generative models (DGMs), are susceptible to the "likelihood paradox", where high likelihood is erroneously assigned to OOD samples. This is attributed to the inductive bias of DGMs that prioritize low-level structural details over high-level semantic coherence. To mitigate this phenomenon, we propose a number of geometric diagnostic signals based on the velocity field over the sub-flow trajectory. Based on these signals, we design metrics for the challenging task of zero-shot phoneme-level mispronunciation detection. Finally, we demonstrate the superiority of these metrics compared to likelihood-based methods on a real-world mispronunciation detection benchmark.

Curved beams, that is, beams that are able to propagate on nonlinear trajectories, are often envisioned as ideal candidates for blockage avoidance in future wireless connectivity. Owing to this unique feature, they are considered as ideal beams for bending around and behind corners to reach users beyond the line-of-sight (LoS), thus offering unprecedented connectivity. In this work, we explain the various mechanisms of beam propagation beyond the LoS, and we demonstrate that beam bending behind corners results from an interplay between wavefront engineering and edge diffraction, with distinct characteristics that depend on the extent of blockage and the beam formation efficiency. We identify three distinct regimes of operation, namely the unblocked, the partially blocked, and the fully blocked regime, and we show that beam bending through wavefront engineering dominates in the unblocked and partially blocked regimes, while edge diffraction dominates in the fully blocked regime; as a result, curved beams cannot really bend behind the corner, unless there is some LoS between the user and the transmitter. Based on our findings, we compare curved beams with focused beams, and we demonstrate that they perform similarly in the partially blocked regime, while focused beams outperform curved beams in the unblocked and fully blocked regimes.

Semantic communications which can significantly reduce spectrum consumption in wireless networks, have recently become a popular research area. When combined with wireless power transfer (WPT), semantic communications can help achieve high spectral efficiency for energy-limited devices in wireless communications. In energy-constrained and link budget-limited scenarios such as UAV networks, the integration of semantic communications and WPT enables highly energyefficient transmission mechanisms. In this paper, we investigate semantic communications in UAV-enabled WPT networks. To achieve adaptability to varying signal-to-noise ratio (SNR) and task requirements, we introduce a multi-layer hybrid bit and semantic communication framework. We adopt a semantic communication efficiency metric and aim to maximize it by jointly optimizing UAV trajectory, energy harvesting base station (EHBS) selection, user association, semantic mode selection, and energy harvesting time allocation. To address this complex longterm optimization problem, we introduce the distributional soft actor-critic (DSAC) algorithm and introduce a decision assistant to further enhance the convergence performance of DSAC. Simulation results validate the effectiveness of the proposed method and framework and demonstrate that our algorithm can achieve superior long-term optimization performance in dynamic network environments.

This paper proposes a recursive algorithm, rARX-DIPCA, for identifying errors-in-variables autoregressive models with exogenous input (EIV-ARX), for tracking time-varying SISO processes. Building on a recently developed recursive iterative PCA method, the proposed algorithm recursively updates model parameters and noise variances as new measurements arrive, without storing historical data beyond a specified lag window. The method enables real-time adaptation to sensor degradation, and changes in model coefficients. The algorithm simultaneously identifies process order, time delay, and noise variances while maintaining computational efficiency through online covariance updates. Simulation studies on benchmark systems demonstrate effective tracking performance and practical applicability.

Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance consideration, economy consideration, society consideration. However, besides all such aspects of consideration, one aspect that is most essential to the control system is methodology consideration in mathematical sense, knowledge on which is what we refer to as control theory. Besides its importance from the mathematical perspective, control theory is even more charming as it is deeply rooted in practical applications. Charms of control theory consist in both know-why and know-how and it is the fusion of control theory and practical applications that highlights such charms. Control theory for practical applications, especially when somewhat with so-called “advanced” flavour, involves several fundamental aspects. This article introduces the Handling Control System Optimality aspect of Advanced Control Theory for Practical Applications [1,2].

Sound design workflows frequently oscillate between time-consuming library searches and the complexity of procedural synthesis, with practitioners typically relying on disconnected tools to address each challenge separately. This paper introduces Quality Audio Prototyping (QuAP), a working prototype that unifies content-based audio retrieval and procedural sound generation within a single interface, reducing the procedural distance between a narrative concept and its sonic realisation. QuAP integrates a similarity-based retrieval engine with real-time procedural audio models, complemented by a rule-based assistant that provides perceptually informed parameter guidance, offering definitions and recommendations derived from empirical optimisation rather than requiring prior synthesis knowledge. Preliminary evaluation confirms the viability of this approach: subjective assessment demonstrated statistically significant quality improvements in five of six embedded synthesis models, and an encoder ablation study established the preferred retrieval architecture on a sound effect dataset. A user evaluation with 16 practitioners confirmed the tool's workflow utility, with all participants agreeing that the parameter assistant preserved creative agency while lowering the barrier to procedural interaction.