Systems and Control
Transition Path Sampling Using Koopman Operators and Exit-Time Optimal Control
Boya Hou, Shane Wang, Siddharth Ambekar et al. (+2 more)
Sampling transitions between metastable states is a central problem in dynamical systems theory and molecular dynamics in particular. A key challenge is the existence of high free-energy barriers that separate the states, making transitions extremely rare. Recent machine learning-based methods cast transition path sampling (TPS) as an optimal
stochastic control (OSC) problem over a fixed time horizon, and parameterize the drift bias via a neural network trained by simulation-in-the-loop, requiring repeated biased rollouts. To address computational and performance guarantee issues of these models, we propose a new approach for the problem based on Koopman operators. Because Koopman operators are linear, their leading eigenfunctions reveal the metastable sets and provide an estimate of the committor function with no transition path information required. Furthermore, we formulate TPS as an OSC problem up to an exit time. Our time horizon is the first hitting time of the target set, and our running cost penalizes time spent in nonreactive regions by encoding the estimated committor function. We derive the optimal controller in closed form and approximate it in a reproducing kernel
Hilbert space (RKHS). This reduces the problem of constructing the optimal controller to solving a single equality-constrained quadratic program, whose solution can be characterized by a linear Karush-Kuhn-Tucker (KKT) system. On the two-channel double well and alanine dipeptide, our controller increases the fraction of trajectories reaching the target from 0% to 99.8% within 1000 steps, and from 0% to 93% within 1ps, respectively.
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All authors Boya Hou, Shane Wang, Siddharth Ambekar, Maxim Raginsky, Olgica Milenkovic
Systems and Control · Cross-listed from cs.RO
Video-to-Model: Automatic Modeling of Deformable Linear Objects
Akshun Sharma, Kimia Forghani and Yancy Diaz-Mercado
This paper presents a
video-to-model framework for automatically modeling the motion of a deformable suture thread from an input
video. We utilize a recently developed CBF–CLF–QP numerical model that simplifies the characterization of deformable string motion through the selection of a small number of parameters. A perception module first localizes and tracks the thread in
video, producing an ordered sequence of thread nodes. The observed thread motion is then processed by a spatio-temporal CNN network that estimates the effective parameters of a structured CBF–CLF–QP model. These parameters are used to simulate the thread under a user-defined needle velocity input. Experiments using unseen thread configurations and motion demonstrate that the framework can reliably reconstruct the thread behavior from
video, automatically configure the structured model, and reproduce the expected thread motion with low tracking error. The proposed approach reduces the need for manual parameter tuning and provides a step toward automatic
video-based modeling of deformable linear objects.
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Systems and Control
Continuous-Time Critic as a Lyapunov Function: Projected Adaptation and Cart-Pole Stabilization
Pavel Osinenko
We study adaptive stabilization of known control-affine systems by treating the current parameterized critic itself as a Lyapunov candidate. Its total derivative contains a parameter-velocity term that can compensate unfavorable state
evolution. A projection onto the intersection of a bounded admissible velocity set and one decay halfspace enforces parameter invariance and Lyapunov decrease simultaneously. We give an exact pointwise feasibility test, a conditional state-stability
theorem, and a counterexample showing why a positive critic family containing a stabilizing member does not imply persistent feasibility. A convex endpoint constraint provides a distinct sampled implementation. A generic periodic
polynomial-factor critic with a local Riccati anchor is evaluated on a force-limited cart-pole without mechanical-energy features, replay training, waypoints, or controller switching. The frozen controller passes stabilization and numerical decay checks on 600 sampled initial states across two draw sets; 400 matched continuous trajectories also pass. Independent reintegration audits and a conservative local feasibility certificate are distinguished from a global or machine-arithmetic guarantee. Compared with nonlinear model-predictive control, the measured implementation uses less CPU but has higher cost, settling time, and cart travel. The results support a conditional adaptive stabilization mechanism.
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Audio and Speech Processing
LIFT-SE: Linguistic Inference Followed by Flow Transformation for Generative Speech Enhancement
Haoyin Yan, Chengwei Liu, Zheng Xue et al. (+4 more)
Generative
speech enhancement (SE) is prone to linguistic
hallucination when semantic constraint is unreliable under severe noise and reverberation. Moreover, approaches that generate discrete codec
tokens are bounded by the quantization error of the codec decoder, regardless of
token-prediction accuracy. We propose LIFT-SE, a two-stage generative framework that decouples linguistic inference from acoustic synthesis within QRes-Codec, which exposes a quantized latent and its residual-completed continuous form. The first stage predicts clean codec
tokens autoregressively, conditioned on frame-aligned features from a self-supervised front-end distilled toward clean
speech. The second stage applies conditional flow matching to transport Gaussian noise to the continuous latent conditioned on the predicted
tokens, and the frozen decoder reconstructs the enhanced waveform. Discrete generation provides naturalness, while continuous refinement restores signal fidelity. Experiments on the DNS1 and URGENT
benchmarks show that LIFT-SE attains favorable linguistic consistency under reverberant conditions together with competitive perceptual quality, and systematic ablations verify the necessity of both stages. Code will be released in the future.
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All authors Haoyin Yan, Chengwei Liu, Zheng Xue, Xiaotao Liang, Jifa Cai, Zeyu Zhao, Jingjing Wang
Image and Video Processing
Diagnosing Diversity Collapse and Validating Mask-Conditioned Diffusion for Labeled Microtubule Microscopy
Mario Koddenbrock, Frederic Rapp, Simone Reber et al. (+1 more)
Mask-conditioned
diffusion models have become standard for generating labeled training data in biomedical imaging. But the tools used to evaluate them were built for a different problem. Standard metrics like FID and KID score images through ImageNet-trained features. Those features do not transfer to microscopy, leaving the metrics poorly calibrated for specialized domains. Even with a domain-appropriate feature space, a single blended number can still hide a real failure: a model can produce visually plausible images that still transfer poorly to downstream tasks. We identify a specific failure mode: Late in training, realism keeps improving while texture diversity collapses. The model settles onto a fixed color palette, converging to a high-similarity, low-diversity state that standard metrics do not penalize. Therefore, we propose a diversity-aware diagnostic in a domain-validated feature space. It combines a realism axis based on inter-similarity to real images with a diversity axis based on intra-similarity among generated samples. This diagnostic selects a checkpoint that domain experts cannot reliably distinguish from real recordings in a forced-choice study. It also yields useful downstream segmentation: a segmenter trained only on synthetically labeled data reaches a median skeletonised IoU comparable to one trained on real data, at lower cross-fold variance, and clearly ahead of the best available public alternative in this domain, a parametric renderer. We also reproduce a data-efficient hyperparameter-transfer experiment from prior work, tuning several foundation segmenters on a small labeled subset and evaluating on real images: transfer is stronger with our data. We release DiffuMT on HuggingFace, including the triplet
dataset, code to reproduce the downstream-utility validation, and a standalone diagnostic tool for evaluating mask-conditioned
diffusion models.
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All authors Mario Koddenbrock, Frederic Rapp, Simone Reber, Erik Rodner
Audio and Speech Processing
Toward Part-Aware Choral Transcription with singing voice assignment
Hanyu Meng, Zhanhong He, Zixun Guo et al. (+1 more)
Single-instrument automatic music transcription (AMT) has advanced substantially, yet choral applications require soprano, alto, tenor, and bass (SATB) to be transcribed as separate parts. Recent note-level choral AMT instead produces a single merged note track, limiting rehearsal, education, and score reconstruction. To address this limitation, we introduce Part-aware Choral Transcription (PawCT), to our knowledge the first end-to-end neural framework that identifies active SATB parts from choral audio and transcribes each into a separate note-level track. PawCT combines part-specific onset, offset, and frame prediction with part-presence estimation, union-level supervision, and structured training targets using a range prior (RP) based on SATB pitch ranges and its ordered-continuity (OC) extension, which adds within-part melodic continuity and cross-part pitch ordering. On YouChorale, PawCT-RP-OC achieves a macro part-aware note F1 of 0.225 at a 50-ms onset tolerance, outperforming an adapted choral baseline (0.165) by 36.4% relative and a two-stage post-hoc assignment pipeline (0.175). Its part-agnostic variant, PagCT, achieves a 50-ms onset F1 of 0.382, compared with 0.237 for the previous state-of-the-art choral AMT model. Cross-
dataset evaluations on CSD and Cantoria further assess performance under
dataset shift. These results demonstrate the benefit of jointly modeling note transcription and vocal-part assignment. Code and demos are available at https://hanyu-meng.github.io/Paw_Choral_AMT_Demo/.
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All authors Hanyu Meng, Zhanhong He, Zixun Guo, Yaolong Ju
Signal Processing
Effects of Residual Chirp Rate on DAFT-s-AFDM Uplink Performance
Henrique L. Senger, Gustavo P. Gonçalves, Bruno S. Chang et al. (+2 more)
Affine frequency division multiplexing (AFDM) is robust to doubly dispersive channels in high-mobility 6G systems but, like orthogonal frequency division multiplexing (OFDM), suffers from a high peak-to-average power ratio (PAPR). Spreading with a discrete affine
Fourier transform (DAFT) yields DAFT-s-AFDM, whose precoder and modulator chirps can be chosen independently. For a single user with zero-offset localized allocation, we show that these two chirps merge into a single residual chirp whose rate, with the other two chirp rates fixed, alone determines their effect. At the transmitter, this rate defines a circulant spreading kernel whose fourth moment quantifies the envelope fluctuation;
symmetries reduce its characterization to rates in [0, 1/4]. At a linear minimum mean-square error (MMSE) receiver, the residual chirp redistributes the mean- square error across symbols without changing its total; under a Gaussian approximation, a more uniform per-symbol error lowers the uncoded bit error rate (BER). In uncoded QPSK simulations, a zero rate yields the lowest PAPR, whereas a generic rate reduces the signal-to-noise ratio required for a BER of $10^{-3}$ by 4.4 dB at a 3.33 dB PAPR cost (exceedance probability $10^{-3}$);a rate of 1/3 captures 2.8 dB of this gain for only 0.88 dB of that cost.
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All authors Henrique L. Senger, Gustavo P. Gonçalves, Bruno S. Chang, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu
Audio and Speech Processing
Training-Free Instruction TTS Gender Bias Calibration Using Model-Adaptive Steering
Kuan-Yu Chen, Yi-Cheng Lin, Jeng-Lin Li et al. (+1 more)
Instruction text-to-
speech (ITTS) systems systematically encode acoustic gender skews from descriptive style
prompts, such as occupations or personas, even when demographic attributes are left unspecified. Calibrating the implicit gender distribution of the synthesized voices, across heterogeneous architectures and without model retraining or overriding explicit user
prompts, is an open problem. In this work, we propose model-adaptive steering, a training-free bias calibration method that steers post-encoder conditioning representations using a group deviation vector paired with a coarse-to-fine strength search on a development set. A deterministic lexical gate bypasses intervention whenever explicit gender keywords are detected, preserving intended
prompt semantics. Evaluated on a 12,800-
prompt held-out
benchmark across four ITTS models (three architectures), the proposed method reduces aggregate calibration error from 11.5–28.1 to 0.8–5.9 percentage points (e.g., shifting Parler-TTS Mini from 78.1% and PromptTTS++ from 27.3% female to 50.8–52.4%). Output rates stay within 0.9 pp across anchor set sizes at fixed operating points, while UTMOS decreases by at most 0.08 and WER degrades by at most 3.3 pp.
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All authors Kuan-Yu Chen, Yi-Cheng Lin, Jeng-Lin Li, Jian-Jiun Ding
Signal Processing · Cross-listed from cs.LG
A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation
Lu Wang-Nöth, Hai Huang, Philipp Heiler et al. (+3 more)
Electroencephalography (
EEG) is highly susceptible to electromyographic (EMG) artifacts, whose temporal heterogeneity and spatial-spectral overlap with neural activity can leave mixed sources after blind source separation. Existing artifact-removal methods are further limited by scarce reliable component-level ground truth: expert annotations are costly and subjective, while no established method provides realistic simulation-based ground truth for EMG contamination in multichannel scalp
EEG. To address these limitations, we propose a framework combining a frequency-aware high-dimensional representation with Multi-Instance Learning. The representation unfolds separated components into frequency-resolved intra-components, creating a space in which mixed neural and muscular activity becomes more separable, while the weakly supervised learning formulation enables artifact-likelihood scores for individual intra-components to be learned from epoch-level labels without finer-grained ground truth. The resulting intra-component classifier supports fine-grained EMG artifact detection and score-guided attenuation. Experiments on held-out subjects show that the framework learns informative intra-component scores and reduces artifact-related spectral deviations most clearly for jaw tension, with moderate effects for raising eyebrows and limited effects for frowning.
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All authors Lu Wang-Nöth, Hai Huang, Philipp Heiler, Shuqiong Wu, Liyun Zhang, Helmut Mayer
Systems and Control · Cross-listed from math.OC
Sparsity in control problems: Recent trends and open challenges
Luca Ballotta, Geethu Joseph, Waseem Abbas et al. (+6 more)
As in all fields of engineering, control engineers constantly face a dichotomy between achieving high performance and the limited availability of resources for sensing, actuation, and control. This tension often translates into formalizing design problems with sparsity constraints; that is, the design involves decision variables whose number, spatial reach, temporal spread, or other physical expressions are much smaller than in the ideal scenario of unconstrained optimization of a task-related performance metric. Even though this fundamental limitation underlines a plethora of methodological approaches and application domains, the control literature is fragmented into several lines of work tailored to specific scenarios. This survey fills the gap by bringing together several lines of work on handling sparsity in relevant control problems such as actuator scheduling, minimal-energy controllability, maximum hands-off control, and online convex optimization, and weaving a red thread through theoretical foundations and
algorithms. Beyond establishing a coherent, structured narrative of problems and solutions for handling sparsity in control scenarios, we discuss open areas and relevant directions for future research, motivated by the growing complexity of control systems and their relations with key domains such as pervasive computing, distributed learning, cyberphysical security, and ecological footprint.
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All authors Luca Ballotta, Geethu Joseph, Waseem Abbas, Sophie M. Fosson, Chandra R. Murthy, Masaaki Nagahara, Alex Olshevsky, Kyle Poe, René Vidal
Audio and Speech Processing
A Strength-Monotonic Law for Domain Alignment in Frozen-Embedding Bioacoustic Classification
Yucheng Gong, Rui Zhou, Binbin Zeng et al. (+2 more)
When does distribution
alignment help a frozen foundation-model
embedding generalize across acoustic domains? For cross-domain mosquito-species classification we report a strength-monotonic law: the stronger an encoder is on the target task, the more its unseen-domain generalization relies on a distribution-
alignment (MMD) term, and the more it is harmed by domain-rebalanced sampling. Across four encoder families and a within-encoder HuBERT layer sweep (n=8), the rebalancing leg orders exactly with encoder strength (Spearman -1.000), while the MMD-benefit leg is monotonic within each stream and -0.857 pooled; fixing architecture and varying only representation strength flips the rebalancing effect from benefit to collapse. The law is actionable: a single MMD term is the sole lever on a strong encoder, so we reduce the field's default recipe to a frozen Perch 2.0
embedding, a lightweight probe, cross-
entropy, one MMD, and input augmentation. The reduced recipe stays within seed noise of the full composite (BA_unseen 0.299+/-0.006 vs. 0.307+/-0.014). As boundary conditions of the same law, three community defaults (backbone fine-tuning, multi-modal fusion, and domain rebalancing) each hurt unseen-domain accuracy under a leave-domain protocol, shown with single-variable, multi-seed evidence. We present a mechanism and the recipe it explains, not a leaderboard entry.
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All authors Yucheng Gong, Rui Zhou, Binbin Zeng, Qiang Ren, Hongjin Hui
Audio and Speech Processing
Direction-preserving Spatial Audio Signal Enhancement in The Spherical Harmonic Domain
Huawei Zhang, Jihui Aimee Zhang, Huiyuan June Sun et al. (+1 more)
Spatial audio signal enhancement (SASE) aims to separate a desired soundfield from interference and noise while preserving its spatial cues. However, existing
algorithms pay limited
attention to preserving the spatial cues of the residual interference, which reduces their effectiveness in practical applications. In this paper, we propose a direction-preserving SASE
algorithm based on multi-output beamforming. The proposed
algorithm extracts the desired soundfield while preserving the spatial cues of both the desired soundfield and the residual interference soundfield. The simulation study shows that the proposed
algorithm achieves performance comparable to the existing SASE
algorithm in terms of estimation error, while it better preserves the spatial cues of the residual interference within a region of interest, in a noisy and reverberant environment.
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All authors Huawei Zhang, Jihui Aimee Zhang, Huiyuan June Sun, Prasanga Samarasinghe
Signal Processing
Self-Supervised Deconvolution of In-Air Sonar Images Using Sensor Ego-Motion
Jan Steckel
Broadband in-air sonar sensors with a sparse microphone array form images through delay-and-sum beamforming, which leaves high sidelobes and an angular resolution that degrades strongly towards the sides of the field of view. Deconvolving these images requires access to the point-spread function of the complete imaging chain, including the emitter spectrum and directivity and the array imperfections. In practice, these aspects are hard to calibrate for. In this letter, we propose a self-supervised method that learns a deconvolution network together with a physics-structured model of the point-spread function, requiring only the sonar data itself and the odometry of a moving
robot on which the sensor is mounted. Using acoustic flow warping, the internal representation of the model is warped and subsequently rendered through the learned point-spread function. This is then compared to the measured images under a speckle likelihood in accordance with the statistical model of image formation. In simulation, the learned model recovers the unknown emitter directivity, and the deconvolved images reach a constant angular resolution of 5 to 6 degrees over the full field of view, against 12 to 30 degrees for delay-and-sum and 7 to 23 degrees for delay-multiply-and-sum with coherence factor. The method outperforms classical deconvolution with the nominal point-spread function, and even outperforms CLEAN with the true one.
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Audio and Speech Processing
A Lightweight Hybrid Framework for Acoustic Echo Cancellation and Suppression via Constrained Recursive Training
Huawei Zhang, Rilin Chen, Hao Zhang et al. (+2 more)
We propose a hybrid framework that combines deep learning with adaptive filtering for acoustic echo cancellation (AEC) and suppression. It integrates a multi-output neural network to jointly update filter parameters and perform post-processing. A learnable variant of the normalized least mean squares (NLMS) filter is introduced, incorporating a variable step size and a variable transition factor that are recursively updated for dynamic adaptation. In addition, an enhanced near-end
speech estimate is generated from the filter output to suppress the residual echoes. To ensure training stability, we propose a constrained recursive training strategy that aligns with the adaptive nature of the filter, where an upper-bound constraint is introduced on the filter output. For efficiency, the framework adopts a lightweight implementation with only 253 k parameters. Experimental results show that the framework outperforms the baselines in terms of both signal quality and
speech intelligibility.
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All authors Huawei Zhang, Rilin Chen, Hao Zhang, Meng Yu, Dong Yu
Audio and Speech Processing · Cross-listed from cs.SD
Tracing Inputs, Verifying Outputs: Validating Attribution in Music Generation
Taejun Kim, Wonil Kim, Jongmin Jung et al. (+10 more)
How can we verify whose music contributed to an AI-generated output? This paper demonstrates how input-based attribution can provide verifiable evidence of which audio sources were used in a generation and whether they shaped the output. To do so, we condition the generation solely on audio without any text input, then trace the inputs behind each output, and establish their musical effect. In
prompt adherence tests and controlled input swaps, the stems generated by our generator, MixAudio, follow the
prompt audio in timbre and the context audio in harmony. Yet these outputs may still reproduce training data not supplied as inputs. We therefore audit memorization with our musical version identification model, musicDNA, and find few reproductions outside the input records. On human-judged cases within the flagged pool, it achieves higher precision and recall than the other tested memorization detectors. The two evaluations suggest that input records and output analysis provide complementary evidence for attribution, on which rights-holder reporting and compensation can draw as the AI music economy takes shape. Audio examples are available at https://neutune.github.io/attr2027demo/
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All authors Taejun Kim, Wonil Kim, Jongmin Jung, Hyeongseok Wi, Sangeun Kum, Keunhyoung Luke Kim, Taehyoung Kim, Dongjoo Moon, Seungsoon Park, Taewan Kim, Virginie Berger, Juhan Nam, Jongpil Lee
Systems and Control
Safe Control of Semi-Explicit Differential-Algebraic Systems: Control Barrier Functions with SOS Verification
Mohamad H. Kazma, Hongchao Zhang, Abdallah A. Albustami et al. (+4 more)
Differential-algebraic equation (DAE) systems arise in power networks, multibody mechanics, and chemical processes, where algebraic constraints couple dynamic and algebraic states. Standard control barrier function (CBF) methods, designed for ordinary differential equations, fail for DAE systems because the algebraic constraints impose compatibility requirements that may render safety filters infeasible. This paper develops a safety-critical control framework for semi-explicit DAE systems using CBFs that consider compatibility with the algebraic constraints, termed DAE-aware CBFs. We construct projected vector fields on the constraint
manifold to account for the hidden dynamics, while ensuring compatibility conditions that keep the algebraic constraints satisfied. We then present sum of squares (SOS) verification conditions that certify both correctness and feasibility of the resulting DAE-aware CBF. The framework is validated on power system case studies.
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All authors Mohamad H. Kazma, Hongchao Zhang, Abdallah A. Albustami, Ben Wooding, Meiyi Ma, Taylor T. Johnson, Ahmad F. Taha
Signal Processing
Second-order optimization of variable projection SVM models and road abnormality detection
Andrea Angino, Matthias Voigt, Rolf Krause et al. (+1 more)
We introduce a novel second-order optimization framework for minimizing so-called variable projection functionals. We demonstrate that the proposed framework is especially usefulfor the training of variable projection based kernel methods. In particular, the problem of efficiently training variable projection support vector machines (VP-SVMs) is considered. We show the effectiveness of the proposed training methodology in a real-world application, namely we demonstrate how second-order trust region
algorithms can be used to train VPSVM models to recognize road surface abnormalities based on 1D signals obtained from a tire sensor.
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All authors Andrea Angino, Matthias Voigt, Rolf Krause, Tamás Dózsa
Audio and Speech Processing
Mitigating Accent-Language Confusion in Self-Supervised Speech Representations for Language Identification
Minu Kim, Jihwan Lee, David R. Mortensen et al. (+1 more)
Spoken
language identification (LID) aims to recognize the target
language regardless of accent. In practice, however, LID models fine-tuned from self-supervised
speech representations frequently confuse accents with
languages, misclassifying non-native (L2)
speech as the speaker's first
language (L1). We show that non-native
speech representations lie between native target-
language and native L1 poles, causing systematic misclassification. To address this, we introduce a
geometric projection that estimates an L1-bias direction solely from native
speech and removes it before the frozen LID head. Across five MMS-LID models and non-native corpora, this projection substantially improves target
language identification for L2-accented
speech while preserving predictions for native
speech. These results show that accent-induced L1 bias can be corrected directly within the representation space without L2 training data or model adaptation.
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All authors Minu Kim, Jihwan Lee, David R. Mortensen, Shrikanth Narayanan
Systems and Control · Cross-listed from cs.RO
Precise SE(3) End-Effector Tracking in Whole-Body Humanoid Control
Joohwan Seo, Xiaofeng Guo, Jinkun Cao et al. (+4 more)
Precise end-effector tracking during humanoid whole-body motion is challenging due to floating-base oscillations,
gravity, dynamic coupling, and locomotion-induced disturbances. We propose ResGAC, a whole-body humanoid controller for precise end-effector pose tracking that combines
geometric admittance control (GAC) with residual reinforcement learning. GAC provides structured $\SE$ task-space feedback and generates nominal arm joint-position targets, while residual RL compensates for unmodeled dynamics and coordinates locomotion and balance in the shared joint-position action space. The left-invariant
geometric formulation allows the same GAC law to be used across manipulation reference frames. This enables the use of a ground-attached heading frame that preserves planar locomotion while removing pelvis roll, pitch, and heave from the manipulation reference, thereby reducing reference-induced end-effector motion during locomotion. ResGAC is validated on a real Unitree G1 humanoid. Across four standing end-effector tracking
benchmarks, ResGAC consistently outperforms representative baselines, including SONIC, achieving lower translational and rotational errors. Real-world experiments further demonstrate reduced propagation of pelvis motion to the desired end-effector pose using the proposed ground-attached heading frame. ResGAC achieves $90\%$ success in a standing peg-in-hole task compared with $50\%$ for SONIC, and accurate world-frame $\SE$ end-effector pose tracking during lower-body motion. Experimental
videos are included in the supplementary material and are also available on the project website: https://resgac.github.io/ResGAC-website/.
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All authors Joohwan Seo, Xiaofeng Guo, Jinkun Cao, Roberto Horowitz, Rocky Duan, Guanya Shi, Koushil Sreenath
Audio and Speech Processing
Boundary-Free Contextual Biasing: Depth-Adaptive Gating and Reading-Space Matching for Unsegmented Languages
Muhammad Huzaifah, Yu Pan, Zachary Yeo et al. (+2 more)
Contextual biasing supplies an ASR system with a list of expected words at inference time, but existing methods rely on word boundaries that Japanese and Chinese do not provide. We present a boundary-free biasing decoder for frozen public CTC models, built on a character-level Aho-Corasick automaton, with no training and no second pass. Two evidence-based mechanisms replace the boundary: a depth-adaptive gate that sets how hard to push from match depth, and reading-space matching for when the audio is right but the characters are wrong. On Aishell-1 NE's hard R1 subset we reach 66.5% recall, above the trained CLAS baseline (64%), transferring to WenetSpeech and to a second architecture without retuning. We release the first open Japanese contextual-biasing
benchmark, where biasing lifts rare-word recall by 25 points at precision above 97%, and still by 19 and 22 points against 1,000-word lists.
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All authors Muhammad Huzaifah, Yu Pan, Zachary Yeo, Ningjie Bai, Guangzhao Yang
Systems and Control · Cross-listed from cs.AI
LLM-Enabled UAV Dispatch: A System-Level Survey and Taxonomy
Xiao Han, Aoyang Quan, Xiangyu Zhao et al. (+2 more)
Unmanned aerial vehicle (
UAV) dispatch is beginning to move beyond isolated path planning and optimization-driven resource allocation toward system-level coordination supported by semantic
reasoning and LLM-based interfaces. This survey provides a unified characterization of LLM-enabled
UAV dispatch systems that bridges semantic intent, symbolic decision-making, and physical
UAV execution. Rather than treating LLMs as standalone add-ons, we conceptualize them as a cross-layer semantic orchestration layer connecting human instructions, external solvers, and distributed control modules. We organize the literature into four representative dispatch paradigms: pipeline dispatch, global assignment dispatch, decentralized agentic dispatch, and divide-and-conquer dispatch. For each paradigm, we analyze its decision logic, system structure, control flow, representative methods, and potential LLM roles. We further examine how LLMs support semantic parsing,
retrieval-grounded planning, solver orchestration, local
agent reasoning, multi-
agent coordination, safety assessment, and human-facing explanation. We discuss the implications of these paradigms for scalability, robustness, coordination burden, and verification requirements, and identify open challenges including latency-aware
reasoning, grounding reliability, physical feasibility guarantees, edge deployment,
privacy protection, and distributed consistency. This survey provides a system-level taxonomy and design perspective for integrating LLMs into safety-critical
UAV dispatch systems.
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All authors Xiao Han, Aoyang Quan, Xiangyu Zhao, Xiangjie Kong, Guojiang Shen
Audio and Speech Processing
Beyond Token Revision: Investigating Mask-and-Replace Diffusion for Zero-Shot Text-to-Speech
Hounsu Kim, Joonyong Park, Yuki Saito et al. (+2 more)
Unlike autoregressive models, discrete
diffusion-based models for zero-shot text-to-
speech generate
speech tokens in parallel and can revisit earlier predictions. Mask-and-replace training extends mask-only training by randomly replacing some
tokens, and its gains are commonly attributed to self-correction, the ability to revise previously generated
tokens. However, exposure to randomly perturbed context during training may itself improve generation, raising the question of whether these gains require inference-time
token revision. To investigate this question, we use DeMaR, which combines mask-and-replace training with confidence-ranked mask-only sampling while preserving the total training corruption probability. Trained from scratch on LibriTTS, DeMaR achieves lower word error rates (WER) than autoregressive and mask-only
diffusion baselines using the same
speech tokenizer. This advantage persists when each
token remains unchanged after first being unmasked. Matched training conditions on two heterogeneous
speech tokenizers show that both noisy-context augmentation and replacement supervision improve WER under this restriction. These findings demonstrate training-side benefits of replacement beyond enabling inference-time
token revision.
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All authors Hounsu Kim, Joonyong Park, Yuki Saito, Satoru Fukayama, Juhan Nam
Systems and Control
Restorative Control and the Limits of Safe Model Discrimination
Micheal Myers Jr
Restorative control seeks lasting recovery through an intervention that ends in finite time. We formulate this objective as a reach-avoid problem in which the input must bring the system safely into a desired basin of attraction before withdrawal. Two candidates share bistable baseline dynamics but accumulate irreversible damage through different allocations of drive-generated exposure. We derive a lower bound on the exposure required for basin transfer and a sufficient construction for safe restoration. When the lower bound exceeds the summed tolerances, every common open-loop input that restores either model causes failure under at least one. With sufficient tolerance, proportional allocation restores both without identification. For feedback from output samples with Gaussian noise, we bound safe-restoration probability in terms of failure risk under the alternative model. A numerical example illustrates the certificates under two sensing regimes.
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Signal Processing
Predictive Beamforming via Echo-Beam Modality Separation with Cross-Attention for ISAC Systems
Seokjun Park, Jiwhan Jang and Jinseok Choi
In this paper, we propose a deep learning-based predictive beamforming
algorithm for integrated sensing and communications in vehicle-to-infrastructure networks, where echo signals are leveraged to enhance communication performance. We formulate a sum spectral efficiency maximization problem under a sensing signal-to-interference-plus-noise ratio (SINR) constraint and construct a penalty-based unconstrained surrogate objective for network training. Based on this formulation, the beamforming
matrix is predicted using sensing echoes and historical beamformers from previous time slots. To this end, we develop a predictive beamforming network that processes sensing echoes and beamforming inputs through separate encoders to preserve their modality-specific characteristics. This separate design enables each modality to learn rich and complementary representations which are more effectively exploited through a cross-
attention mechanism compared to naive input fusion. The resulting features are fused via cross-
attention, while gated recurrent units capture temporal dependencies across time slots. Simulation results validate the performance of the proposed method.
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Systems and Control
Hierarchical Defense in Leader-Defender-Attacker Games: Nested Equilibria and Receding-Horizon Control
Honglei Guo, Hanfang Zhang, Guochuan Zhang et al. (+1 more)
We formulate a discrete-time leader-defender-attacker game that combines hierarchical interaction within a protective group with competition against an external attacker. The leader seeks to reach a prescribed demand point while avoiding capture, whereas the defender seeks to intercept the attacker while remaining near the leader. To capture their distinct objectives and asymmetric interactions, we introduce a nested
equilibrium concept combining a leader-defender Stackelberg relationship with a Nash-type best-response condition for the attacker. For linear dynamics and quadratic objectives, we derive affine response laws and a backward recursion, together with sufficient conditions for unique stagewise solutions. We further express arrival, capture, and interception conditions as quadratic inequalities in the initial joint state, characterizing winning regions under the resulting policy. A receding-horizon
algorithm implements the computed control laws using updated state information. Numerical experiments over randomized initial configurations examine the influence of the prediction horizon and compare the proposed method with an independent-control baseline, illustrating differences in game outcomes and the
agents' realized costs.
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All authors Honglei Guo, Hanfang Zhang, Guochuan Zhang, Yuhan Zhao
Systems and Control
Distributionally Robust Frequency Response Estimation Using Non-Steady-State Frequency-Domain Data
Stefan Damjanovic, John W. Simpson-Porco and Klaske van Heusden
This paper develops a distributionally robust optimization (DRO) framework for identifying the frequency response function (FRF) of an unknown linear time-invariant system from repeated noisy non-steady-state input-output measurements. Building on a recent frequency-domain extension of Willems' fundamental lemma, we formulate FRF estimation as a DRO problem, wherein robustness is desired with respect to the unknown probability distribution generating the noisy data. The proposed
estimator computes FRF estimates by minimizing a convex upper bound on the DRO cost and allows for frequency-dependent calibration of the Wasserstein metric; the tractable upper bound consists of an empirical fit and a regularizer that penalizes the largest weighted sensitivity to spectral perturbations. Under suitable assumptions, we prove that the FRF estimation error is upper bounded by the optimal value of the DRO problem. The method is validated via a simple numerical study, which demonstrates improved performance under severely heavy-tailed noise.
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Systems and Control
Trajectory Sensitivity-Based Dynamic Assessment of an AI Data Center Under Contingencies
Arnav Bagga, Saskia A. Putri, Abhishek Banerjee et al. (+1 more)
Artificial Intelligence (AI) data centers are emerging as converter-dominated, parameter-rich electrical systems whose dynamic response depends on fast workload-driven demand variations, tightly regulated data-hall converters, and coordinated on-site sources. Assessing such systems by repeatedly solving nonlinear time-domain models for every outage, load realization, and parameter perturbation is computationally expensive and provides limited insight into response sensitivity. This paper presents a trajectory sensitivity (TS)-based dynamic assessment framework for a self-contained AI data center supplied by a 2 kV DC microgrid with a grid-tied interface. The model includes time-varying data-hall load profiles, a voltage source converter (VSC), battery energy storage systems (BESS) units, supercapacitors, and synchronous-generation-side sources. For each disturbance, firstly, TS are computed along the nonlinear disturbance trajectory, capturing the local effect of source trips, data-hall outages, converter controls, and load ramps. Later, the computed TS data is used to estimate system trajectories under simultaneous parameter perturbations. Validation against full nonlinear ODE simulations with $\pm 15\%$ perturbations shows close agreement. In addition, the TS-based approach reduces computational time of dynamic-response evaluations from seconds to milliseconds, demonstrating its potential as a scalable screening tool for AI data center dynamics.
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All authors Arnav Bagga, Saskia A. Putri, Abhishek Banerjee, Xiaofan Wu
Signal Processing
Finite-Aperture Modeling of Pattern-Reconfigurable Antennas for Multiuser Beamforming
Kangjian Chen, Han Qing Yang and Hang Wong
This paper establishes a finite-aperture model for pattern-reconfigurable
antennas (PRAs) and exploits its aperture-constrained
Fourier representation to develop an efficient multiuser beamforming framework. First, we develop an ideal model by representing the complex radiation pattern using a
Fourier series. By applying the Jacobi-Anger expansion, we relate the
antenna aperture to the effective
Fourier order and derive aperture-dependent power bounds for individual
Fourier coefficients. Second, we employ a practical multiport network model and design three representative reconfigurable pixel
antennas to verify the predicted effective
Fourier orders and per-order power constraints. We then formulate weighted sum-rate maximization for multiuser downlink transmission under both models. Using bounded
Fourier coefficients as auxiliary variables, we develop a coefficient-bound penalty-based weighted minimum mean-square error
algorithm that separates continuous beamforming from discrete switch-state optimization. We further propose a one-bit neighborhood search that exploits the Sherman-Morrison identity to accelerate the discrete optimization. Simulation results validate the physical relevance of the ideal model and demonstrate multiuser sum-rate gains and reduced optimization runtime for densely switched
antennas. The proposed model provides a physically grounded basis for PRA-enabled communication analysis and design, while the framework accommodates different beamforming objectives and PRA implementations.
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Systems and Control · Cross-listed from math.DS
Unified Relative Orbital Element Framework for Eccentric Formation Flying
Yuji Takubo and Simone D'Amico
Quasi-nonsingular relative orbital elements (qnsROE) are among the most widely used state representations for formation flying and rendezvous in near-circular inclined orbits. They admit closed-form perturbed state transition
matrices (STMs) and a direct
geometric mapping, which underpins eccentricity/inclination (E/I)-vector separation for passive safety. Existing eccentric extensions of the qnsROE resolve this geometry in the radial–tangential–normal frame, where a residual semimajor-axis difference makes the E/I separation margin phase dependent. This paper introduces eccentric ROE that reduce to the qnsROE as \(e\to0\) and whose first-order geometry is resolved in the velocity–normal–binormal (VNB) frame, where the secular drift is confined to the velocity direction. Their first-order position and velocity maps yield a closed-form, phase-independent binormal–normal separation bound with an explicit margin for semimajor-axis residuals. Closed-form Keplerian and secular-\(J_2\) STMs and a decoupled impulsive control input
matrix are derived for the same elements, and the elements are recovered as the physically normalized integration constants of the Cartesian equations of relative motion in the VNB frame. Together, these results provide a unified ROE framework for formation flying in general elliptic orbits.
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Systems and Control
Learning Stability of Replay-Based Co-Optimization for Transmission Expansion under Strategic Bidding
Tomonari Kanazawa, Hikaru Hoshino and Eiko Furutani
This paper investigates the behavior of learning-based co-optimization for transmission expansion under strategic bidding in electricity
markets. In this framework, transmission capacities are updated while
market participants simultaneously learn their bidding strategies through deep reinforcement learning, resulting in coupled and non-stationary learning dynamics. We show that transient policy degradation of bidding
agents can generate inconsistent cost-capacity samples, which bias the transmission-capacity update and prevent the co-optimization process from converging to the desired solution. To mitigate this, replay-based capacity updates with nearest-neighbor filtering are introduced to exclude inconsistent samples from the update data. We then analyze a new oscillatory behavior that appears when the replay
memory is enlarged. Numerical results on the IEEE 30-bus system demonstrate that enlarged replay
memories improve robustness against transient policy degradation but can introduce a temporal lag, leading to oscillations unless the capacity-update learning rate is appropriately reduced. These results reveal the trade-off between replay-
memory size and learning rate and provide practical guidelines for stable co-optimization.
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