Multi-Scalar Multiplication is a critical operation in most pairing-based zero-knowledge proofs. In a lot of studies, memory limitations have often been reported to be the primary bottleneck preventing the calculation of larger MSMs. In this paper, we are particularly interested in the acceleration of this operation on devices with limited memory. Pippenger’s algorithm (also known as bucket method) is the most efficient and, consequently, the most widely used method to calculate Multi-Scalar Multiplications. We propose an optimization of Pippenger’s algorithm which is at least as efficient as the original, and significantly more effective when operating under limited memory. The main idea is to use an adapted number of buckets depending on the available memory instead of 2^w - 1 . We conducted tests on the curve BLS12-381 with Multi-Scalar Multiplications ranging from 2^8 to 2^14 points. The results obtained demonstrate that we have a very significant gain (up to 40% ) for very limited memories. This gain gradually decreases as more memory becomes available, until we achieve performance comparable to Pippenger’s once memory is no longer limited. For example, in a Multi-Scalar Multiplication with 2^13 points, we observe a gain of 40% with only 1 KB of memory, 20% with 15 KB, 15% with 35 KB, and so on, down to be equivalent to Pippenger’s algorithm once memory is no longer a constraint.
Natural generation allows Language Models (LMs) to produce free-form responses with rich reasoning, but the lack of guaranteed structure makes outputs difficult to parse or verify. Structured generation, or constrained decoding, addresses this drawback by producing content in standardized formats such as JSON, ensuring consistency and guaranteed-parsable outputs, but it can inadvertently restrict the model's reasoning capabilities. In this work, we propose a simple approach that combines the advantages of both natural and structured generation. By allowing LLMs to reason freely until specific trigger tokens are generated, and then switching to structured generation, our method preserves the expressive power of natural language reasoning while ensuring the reliability of structured outputs. We further evaluate our approach on several datasets, covering both classification and reasoning tasks, to demonstrate its effectiveness, achieving a substantial gain of up to 27
We present a deep neural network approach for encoding microphone array signals into Ambisonics that generalizes to arbitrary microphone array configurations with fixed microphone count but varying locations and frequency-dependent directional characteristics. Unlike previous methods that rely only on array geometry as metadata, our approach uses directional array transfer functions, enabling accurate characterization of real-world arrays. The proposed architecture employs separate encoders for audio and directional responses, combining them through cross-attention mechanisms to generate array-independent spatial audio representations. We evaluate the method on simulated data in two settings: a mobile phone with complex body scattering, and a free-field condition, both with varying numbers of sound sources in reverberant environments. Evaluations demonstrate that our approach outperforms both conventional digital signal processing-based methods and existing deep neural network solutions. Furthermore, using array transfer functions instead of geometry as metadata input improves accuracy on realistic arrays.
This paper presents a method to optimally place a limited number of hollow-core fiber (HCF) spans and high-power booster/in-line-amplifiers in optical mesh networks. Results show it effectively increases network capacity/reach while enforcing the CAPEX-related constraint.
In light of the recent advances in hollow-core fiber (HCF) design and manufacturing, wide-scale deployments of this fiber type to realize next-generation optical transport networks may become viable in the foreseeable future, with benefits in terms of lower latency and improved capacity/reach. Nevertheless, several uncertainties remain regarding the properties of HCF that can be manufactured at scale, as well as the specifications of optical amplifiers developed to leverage the negligible low linearity of this fiber type. This work evaluates the performance of HCFs considering a wide range of potential fiber and amplifier parameters and compares them with traditional standard single-mode fiber (SSMF) and pure-silica-core fiber (PSCF). The resulting analysis allows us to determine, at a system and network level, the combination of fiber and amplifier parameters that will allow HCF to become a competitive transmission medium for next-generation optical transport networks.
