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Abstract We propose a generic compiler that can convert any zero-knowledge (ZK) proof for SIMD circuits to general circuits efficiently, and an extension that can preserve the space complexity of the proof systems. Our compiler can immediately produce new results improving upon state of the art.By plugging in our compiler to Antman, an interactive sublinear-communication protocol, we improve the overall communication complexity for general circuits from$$\mathcal {O}(C^{3/4})$$ $O\left(C^{3/4}\right)$to$$\mathcal {O}(C^{1/2})$$ $O\left(C^{1/2}\right)$. Our implementation shows that for a circuit of size$$2^{27}$$ $2^{27}$, it achieves up to$$83.6\times $$ $83.6\times$improvement on communication compared to the state-of-the-art implementation. Its end-to-end running time is at least$$70\%$$ $70\%$faster in a 10Mbps network.Using the recent results on compressed$$\varSigma $$ $\Sigma$-protocol theory, we obtain a discrete-log-based constant-round zero-knowledge argument with$$\mathcal {O}(C^{1/2})$$ $O\left(C^{1/2}\right)$communication and common random string length, improving over the state of the art that has linear-size common random string and requires heavier computation.We improve the communication of a designatedn-verifier zero-knowledge proof from$$\mathcal {O}(nC/B+n^2B^2)$$ $O(nC/B+n^2{B}^2)$to$$\mathcal {O}(nC/B+n^2)$$ $O(nC/B+n^2)$.To demonstrate the scalability of our compilers, we were able to extract a commit-and-prove SIMD ZK from Ligero and cast it in our framework. We also give one instantiation derived from LegoSNARK, demonstrating that the idea of CP-SNARK also fits in our methodology. 

Abstract Physical processes behind flow‐topography interactions and turbulent transitions are essential for parameterization in numerical models. We examine how the Kuroshio cascades energy into turbulence upon passing over a seamount, employing a combination of shipboard measurements, tow‐yo microstructure profiling, and high‐resolution mooring. The seamount, spanning 5 km horizontally with two summits, interacts with the Kuroshio, whose flow speed ranges from 1 to 2 m s⁻¹, modulated by tides. The forward energy cascade process is commenced by forming a train of 2–3 nonlinear lee waves behind the summit with a wavelength of 0.5–1 km and an amplitude of 50–100 m. A train of Kelvin‐Helmholtz (KH) billows develops immediately below the lee waves and extends downstream, leading to enhanced turbulence. The turbulent kinetic energy dissipation rate isO(10⁻⁷–10⁻⁴) W kg⁻¹, varying in phase with the upstream flow speed modulated by tides. KH billows occur primarily at the lee wave's trailing edge, where the combined strong downstream shear and low‐stratification recirculation trigger the shear instability,Ri < 1/4. The recirculation also creates an overturn susceptible to gravitational instability. This scenario resembles the rotor, commonly found in atmospheric mountain waves but rarely observed in the ocean. A linear stability analysis further suggests that critical levels, where the KH instability extracts energy from the mean flow, are located predominantly at the strong shear layer of the lee wave's upwelling portion, coinciding with the upper boundary of the rotor. These novel observations may provide insights into flow‐topography interactions and improve physics‐based turbulence parameterization. 

Emerging wearable devices would benefit from integrating ductile photovoltaic light-harvesting power sources. In this work, we report a small-molecule acceptor (SMA), also known as a non–fullerene acceptor (NFA), designed for stretchable organic solar cell (s-OSC) blends with large mechanical compliance and performance. Blends of the organosilane-functionalized SMA BTP-Si4 with the polymer donor PNTB6-Cl achieved a power conversion efficiency (PCE) of >16% and ultimate strain (ε_u) of >95%. Typical SMAs suppress OSC blend ductility, but the addition of BTP-Si4 enhances it. Although BTP-Si4 is less crystalline than other SMAs, it retains considerable electron mobility and is highly miscible with PNTB6-Cl and is essential for enhancing ε_u. Thus,s-OSCs with PCE > 14% and operating normally under various deformations (>80% PCE retention under an 80% strain) were demonstrated. Analysis of several SMA-polymer blends revealed general molecular structure–miscibility–stretchability relationships for designing ductile blends. 

We introduce our Maximum-Entropy Rewarded Reinforcement Learning (MERRL) framework that selects training data for more accurate Natural Language Processing (NLP). Because conventional data selection methods select training samples based on the test domain knowledge and not on real life data, they frequently fail in unknown domains like patent and Twitter. Our approach selects training samples that maximize information uncertainty measured by entropy, including observation entropy like empirical Shannon entropy, Min-entropy, R\'enyi entropy, and prediction entropy using mutual information, to cover more possible queries that may appear in unknown worlds. Our MERRL using regularized A2C and SAC achieves up to -99.7 perplexity decrease (-43.4\% relatively) in language modeling, +25.0 accuracy increase (+40.0\% relatively) in sentiment analysis, and +5.0 F1 score increase (+30.8\% relatively) in named entity recognition over various domains, demonstrating strong generalization power on unknown test sets.