大众集团

Existing time series tokenization methods predominantly encode a constant number of samples into individual tokens. This inflexible approach can generate excessive tokens for even simple patterns like extended constant values, resulting in substantial computational overhead. Inspired by the success of byte pair encoding, we propose the first pattern-centric tokenization scheme for time series analysis. Based on a discrete vocabulary of frequent motifs, our method merges samples with underlying patterns into tokens, compressing time series adaptively. Exploiting our finite set of motifs and the continuous properties of time series, we further introduce conditional decoding as a lightweight yet powerful post-hoc optimization method, which requires no gradient computation and adds no computational overhead. On recent time series foundation models, our motif-based tokenization improves forecasting performance by 36% and boosts efficiency by 1990% on average. Conditional decoding further reduces MSE by up to 44%. In an extensive analysis, we demonstrate the adaptiveness of our tokenization to diverse temporal patterns, its generalization to unseen data, and its meaningful token representations capturing distinct time series properties, including statistical moments and trends.

Safety- and security-critical systems have to be thoroughly tested against their specifications. The state of practice is to have _natural language_ specifications, from which test cases are derived manually - a process that is slow, error-prone, and difficult to scale. _Formal_ specifications, on the other hand, are well-suited for automated test generation, but are tedious to write and maintain. In this work, we propose a two-stage pipeline that uses large language models (LLMs) to bridge the gap: First, we extract _protocol elements_ from natural-language specifications; second, leveraging a protocol implementation, we synthesize and refine a formal _protocol specification_ from these elements, which we can then use to massively test further implementations. We see this two-stage approach to be superior to end-to-end LLM-based test generation, as 1. it produces an _inspectable specification_ that preserves traceability to the original text; 2. the generation of actual test cases _no longer requires an LLM_; 3. the resulting formal specs are _human-readable_, and can be reviewed, version-controlled, and incrementally refined; and 4. over time, we can build a _corpus_ of natural-language-to-formal-specification mappings that can be used to further train and refine LLMs for more automatic translations. Our prototype, AUTOSPEC, successfully demonstrated the feasibility of our approach on five widely used _internet protocols_ (SMTP, POP3, IMAP, FTP, and ManageSieve) by applying its methods on their _RFC specifications_ written in natural-language, and the recent _I/O grammar_ formalism for protocol specification and fuzzing. In its evaluation, AUTOSPEC recovers on average 92.8

We evaluate the Sample-based Krylov Quantum Diagonalization (SKQD) algorithm on one- and two-dimensional Heisenberg models, including strongly correlated regimes in which the ground state is dense. Using problem-informed initial states and magnetization sector sweeps, we investigate SKQD for problems with non-sparse ground states, where energy accuracy and sampling efficiency are theoretically anticipated to degrade. Our studies reveal that SKQD reproduces ground-state energies and field-dependent magnetization across a range of anisotropies. Benchmarks against DMRG and exact diagonalization show consistent qualitative agreement, with accuracy improving systematically in more anisotropic regimes. We further demonstrate SKQD on quantum hardware by implementing 18- and 30-qubit Heisenberg chains, obtaining magnetization curves that match theoretical expectations. Simulations on the IBM Nighthawk processor for 64-qubit two-dimensional square lattice systems further indicate that the method remains effective beyond one-dimensional geometries.

This work evaluates the morphology, mechanical performance, and environmental impact of polypropylene composites reinforced with long glass fibers and processed via conventional injection molding, MuCell and Ku-Fizz foaming technologies, combined with the Core Back cavity expansion technique. SEM and micro-CT analyses reveal the characteristic skin-core cellular structures, with fine and uniformly distributed cells ranging from 5 to 250 mu m in size and an enhanced cell density with mold expansion on the order of 106 cells & sdot;cm-3. Due to the reduction in the load-bearing area, tensile, flexural, and impact properties decrease with the density, mitigated by substantial reinforcement from long fibers. Specific properties maintain comparable to solid materials in the direction of preferential fiber alignment. The combined effect of mold expansion and fiber reinforcement in the foamed materials results in flexural stiffness up to 50 % higher than that of the solid counterparts. Complementary life cycle assessment reveals significant environmental benefits of both foaming technologies over conventional injection molding due to material savings and energy efficiency gains, with a reduction in environmental impact around 17 %. The study highlights the industrial applicability of these composite foams manufactured by advanced microcellular injection molding as promising candidates for lightweight, high-performance, and environmentally responsible industrial applications.