Our Work

For a pair of strings (x, y) and a time bound t, the time-bounded Zurek defect σ_t(x, y) = [pK^t(y) + pK^t(x | y)] − [pK^t(x) + pK^t(y | x)] compares the two orders of describing the pair …

For a pair of strings (x, y) and a time bound t, the time-bounded Zurek defect σ_t(x, y) = [pK^t(y) + pK^t(x | y)] − [pK^t(x) + pK^t(y | x)] compares the two orders of describing the pair within time t, where pK^t is probabilistic time-bounded Kolmogorov complexity. Say that a polynomial-time samplable distribution on pairs has bounded exponential moments if E[2^{σ_t}] and E[2^{−σ_t}] are both polynomially bounded once t is a large enough polynomial, and write BEM for the statement that every such distribution does. It follows from results of Hirahara, Ilango, Lu, Nanashima and Oliveira, with the data-processing bound of Vadhan and Zheng, that NP ⊆ BPP ⇒ BEM ⇒ there are no infinitely-often one-way functions. We show that the second implication does not reverse relative to an oracle: relative to a random oracle from the construction of Hirahara and Nanashima (CCC 2022), with probability 1 there are no infinitely-often one-way functions, yet an explicit samplable distribution on pairs of m-bit strings has E[2^{σ_t}] ≥ 2^{m/4} at the time bound t = m^k, for every constant k ≥ 3 and all large even m. So no relativizing argument derives bounded exponential moments from the non-existence of one-way functions. The moment is carried by a slice of pairs of negligible mass, which a world without one-way functions permits. Along the way we record that the mean one-sided production log D(x, y) + pK^t(y) + pK^t(x | y) equals, up to O(log n) and a polynomial change of time bound, the KL-hardness for sampling of Vadhan and Zheng measured against the universal time-bounded sampler. This is a direct consequence of the equivalence between pK^t and the time-bounded universal distribution recorded by Hirahara and Nanashima, one half of which is the coding theorem of Lu, Oliveira and Zimand; we state it because it connects the time-bounded Kolmogorov and the computational-entropy formulations of the duality between one-way functions and hardness of sampling. Relation to 10.5281/zenodo.22947444 ("One-way functions are computational entropy production"): this note strengthens Proposition 10 of that paper (probability 1 over the oracle, and the absence of one-way functions proved directly) and credits background the paper did not cite (Vadhan–Zheng 2012; Agrawal–Chen–Horel–Vadhan 2019; Hirahara–Nanashima 2023, Appendix B; Lu–Oliveira–Zimand 2022). It supersedes that paper for the results stated here. Not peer-reviewed.

Un estudio científico con fundamentos en la Historia, Geografía y Antropología.

Verification and reproducibility code for the revised version of the paper "Closed-form moment propagation through fractional-power response surfaces via the Mellin transform". Reproduces the exact re…

Verification and reproducibility code for the revised version of the paper "Closed-form moment propagation through fractional-power response surfaces via the Mellin transform". Reproduces the exact region-of-validity analysis with the mixed-multi-index counterexample, the Mellin transform of the truncated normal, the confluent (log-power) treatment of the degenerate point alpha = 1/2, the 50-digit re-verification of the manufactured-solution benchmark and the separable 2-D propagation, the balanced benchmark over two response classes, the heavy-tail accuracy benchmark, the engineering example (convective cooling under Weibull wind) and the out-of-sample real-data case study on CWRU bearing records 105 and 106 with a moving-block bootstrap. The scripts of the submitted version are kept. Includes the Lean 4 / Mathlib formalization of the four supporting lemmas.

Batch upload of files from directory 'pterois_volitans_FSCR'.

Cell-Free (CF) massive Multiple-Input Multiple Output (MIMO) networks are a key enabler for future sixth generation wireless systems, as they allow distributed Radio Units (RUs) to cooperatively serve…

Cell-Free (CF) massive Multiple-Input Multiple Output (MIMO) networks are a key enabler for future sixth generation wireless systems, as they allow distributed Radio Units (RUs) to cooperatively serve users and mitigate inter-user interference. However, under dense deployments and high spatial loading, conventional linear precoding schemes such as Maximum Ratio Transmission (MRT), Zero Forcing (ZF), and Minimum Mean-Square Error (MMSE) suffer from channel ill-conditioning, spatial correlation, and noise amplification, which limit their achievable spectral efficiency (SE). Non-linear Vector Perturbation (VP) precoding can overcome these limitations by adding an integer perturbation vector before transmission, thereby reducing transmit energy while preserving the original information symbols through a modulo operation at the receiver. Despite its strong performance, optimal VP precoding requires an exhaustive integer search whose complexity grows exponentially with the number of users, making it unsuitable for real-time Open Radio Access Network (O-RAN) operation. This work proposes VP-Proximal Policy Optimization (PPO) , a real-time learning-based non-linear precoding framework for O-RAN-compliant CF massive MIMO networks. The proposed method is implemented as an intelligent xApp within the near-real time RAN Intelligent Controller (near-RT RIC), where it replaces the exhaustive VP perturbation search with a single neural network inference. A hybrid PPO agent is designed to jointly determine the discrete perturbation vector and the continuous transmit power level. The state representation includes the MMSE precoding matrix, transmitted symbols, effective channel informa tion, signal and interference features, channel condition number, and spatial loading ratio. The hybrid action space combines categorical distributions for user-wise perturbation selection with a continuous power-control head. The reward function jointly maximizes spectral efficiency, reduces transmit power, and min imizes the number of occupied Resource Blocks (RBs), enabling an efficient trade-off between throughput, energy consumption, and radio-resource utilization. Simulation results using 3GPP CDL-D channel conditions demonstrate that VP-PPO provides a practical balance between non-linear precoding performance and real-time feasibility. For nine users, VP-PPO achieves 28.69 bits/s/Hz, corresponding to a 53% spectral-efficiency improvement over MMSE precoding, while reducing transmit power by approximately 8% compared with the fixed 1 W power budget used by the baseline methods. In addition, VP-PPO reduces RB consumption compared with linear precoders and closely approaches the exhaustive VP oracle. Most importantly, the proposed method achieves millisecond scale inference latency, satisfying the 10–100 ms near-RT RIC control-loop requirement, whereas exhaustive VP search becomes computationally infeasible. These results show that reinforcement learning-assisted xApps can enable practical deployment of ad vanced physical-layer techniques in future O-RAN CF networks.

What's Changed Switch from yarn to npm to (hopefully) fix publication pipeline by @FlorianK13 in https://github.com/open-pv/simshady/pull/143 Full Changelog: https://github.com/open-pv/simshady/comp…

What's Changed Switch from yarn to npm to (hopefully) fix publication pipeline by @FlorianK13 in https://github.com/open-pv/simshady/pull/143 Full Changelog: https://github.com/open-pv/simshady/compare/v0.2.5...v0.2.6

This record publishes the public key I use to sign research data and documents. It also contains proof that I held the matching private key on the date above. Files I sign carry a detached signature (…

This record publishes the public key I use to sign research data and documents. It also contains proof that I held the matching private key on the date above. Files I sign carry a detached signature (`<file>.sig`) made with this key, using the SSH signature format and the namespace `file`.

This dataset constitutes simulated scMetaG data of a 10-bacterial mock community, ATCC MSA-2003, prepared to be used with Zorn/Bascet