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Smaller and More Flexible Cuckoo Filters
Announce Type: replace Abstract: Cuckoo filters are space-efficient approximate set membership data structures with a controllable false positive rate (FPR) and zero false negatives, similar to Bloom filters. In contrast to Bloom filters, Cuckoo filters store multi-bit fingerprints of keys in a hash table using variants of Cuckoo hashing, allowing each fingerprint to be stored at a small number of possible locations. Existing Cuckoo filters use fingerprints of $(k+3)$ bits per key and an...
Deformable Wiener Filter for Future Video Coding
Announce Type: new Abstract: In-loop filters have attracted increasing attention due to the remarkable noise-reduction capability in the hybrid video coding framework. However, the existing in-loop filters in Versatile Video Coding (VVC) mainly take advantage of the image local similarity. Although some non-local based in-loop filters can make up for this shortcoming, the widely-used unsupervised parameter estimation method by non-local filters limits the performance.
19 GHz Single-Ended-to-Balanced Modified Ladder-Lattice Filters Realized Using Periodically Polarized AlScN BAW Resonators
Announce Type: new Abstract: We present a single-ended to balanced modified ladder-lattice filter topology, realized with periodically polarized piezoelectric (P3F) AlScN resonators, for single-ended to differential filtering applications. This filter topology eliminates the need for baluns or other passive components and can interface, for instance, directly between differential antennas and single-ended power and low noise amplifiers. Two filter designs operating at 19 GHz were implemented.
Tunable Real-Time Safety Filters via Set-Based Control Barrier Functions
arXiv:2507.07805v3 Announce Type: replace Abstract: Safety filters for industrial constrained systems are required to combine certified constraint satisfaction, predictable online computation, and a transparent tuning interface. Existing set-based filters are based on a well-established control invariant set design that scales favorably with state and input constraints, but typically intervene only at the set boundary. Control barrier function (CBF)-based filters, by contrast, provide...
Epistemic Injustice in Language Models: An Audit of Pretraining Filters and Guardrails
arXiv:2606.05936v1 Announce Type: new Abstract: Modern language models rely on pretraining filters to remove undesirable content from training corpora and inference-time guardrails to suppress undesirable outputs during deployment. In this paper, we examine how these filtering and moderation decisions produce forms of epistemic erasure and reveal tensions both across automated systems and between these systems and human judgment. We audit four pretraining filters and three inference-time...
FW-NKF: Frequency-Weighted Neural Kalman Filters
arXiv:2606.02251v1 Announce Type: new Abstract: Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch such as sensor vibrations, electromagnetic interference, and periodic noise. Although Deep Kalman Filter (DKF) variants extend the Extended Kalman Filtering (EKF) framework by learning latent transitions, they lack explicit mechanisms to suppress band-limited noise components that typically...
Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
Announce Type: new Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate. Safety filters are a popular approach for ensuring safety in interactive robotics, since their modular design separates safety from performance, allowing robots to operate safely around people with minimal impact on task efficiency. While traditional safety filters typically...
Practical Algebraic Stepping with Scoped Filters
arXiv:2605.31517v1 Announce Type: new Abstract: Algebraic steppers help students learn functional programming by displaying evaluation as a sequence of small-step reductions, but even simple programs produce long traces in which key ideas are buried under mundane reductions. This paper presents the filtered stepper calculus, a formal framework that gives users scoped, pattern-based control over which reduction steps are shown or hidden. Users annotate programs with lightweight filter...
An Asynchronous Two-Speed Kalman Filter for Real-Time UUV Cooperative Navigation Under Acoustic Delays
arXiv:2604.02878v2 Announce Type: replace Abstract: In Global Navigation Satellite System (GNSS)-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded dead-reckoning drift, making collaborative navigation (CN) crucial for accurate state estimation. However, the severe communication delay inherent in underwater acoustic channels poses serious challenges to real-time state estimation. Traditional filters, such as Extended Kalman Filters (EKFs) or...
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
arXiv:2606.03946v1 Announce Type: new Abstract: Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, they unveil new data management challenges. Concretely, traditional data skipping techniques for integer and string data fail to be applicable to the new filter type.