Tootfinder

Attack surface reduction and entropy myths: you don't need to "gather entropy" on Linux with extra software. Your kernel RNG is doing that already and it's fine (unless your kernel is very, very old). But if you do, you may get some vulnerabilities.
https://www.openwall.com/lists/oss-securit

🇺🇦 #NowPlaying on KEXP's #SeekAndDestroy
Ossuary:
🎵 Volitional Entropy
#Ossuary
https://open.spotify.com/track/0qe1tMiH6V5fHNNnxajYHn

High-Order ADER-DG Hydrodynamics with ExaHyPE: Implementation, Validation, and Astrophysical Benchmarking
Andr\'es Mauricio Su\'arez Mantilla, Leonardo Casta\~neda Colorado
https://arxiv.org/abs/2605.17132 https://arxiv.org/pdf/2605.17132 https://arxiv.org/html/2605.17132
arXiv:2605.17132v1 Announce Type: new
Abstract: We describe a high-order ADER-DG solver for the compressible Euler equations within the ExaHyPE framework. The implementation combines a high-order ADER-DG polynomial representation, a local space-time DG predictor, adaptive mesh refinement, and an a posteriori subcell finite-volume limiter. We test the code on a deliberately mixed set of one- and two-dimensional problems: a strong-shock Sod-type problem, the Shu-Osher shock-entropy interaction, the Woodward-Colella blast wave, a contact-driven vortex sheet, and a shock-interface interaction. The one-dimensional cases recover the expected Euler wave patterns and show clear order-dependent gains in smooth and oscillatory regions. The two-dimensional cases probe a different part of the method, namely contact preservation, shear-driven roll-up, baroclinic vorticity deposition, and Richtmyer-Meshkov-type growth. In these tests the high-order update gives the expected resolution away from discontinuities, whereas the subcell limiter keeps the calculation stable near shocks and steep interfaces. The resulting code provides a reproducible ExaHyPE implementation for idealised inviscid, non-relativistic flows in which shocks, contacts, and multidimensional interfaces are the dominant features.
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Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models
Wei-Ping Huang, Chee-En Yu, Guan-Ting Lin, Hung-yi Lee
https://arxiv.org/abs/2605.08186 https://arxiv.org/pdf/2605.08186 https://arxiv.org/html/2605.08186
arXiv:2605.08186v1 Announce Type: new
Abstract: Test-Time Adaptation (TTA) via entropy minimization (EM) has proven effective for classification tasks, yet its application to generative autoregressive models remains theoretically fragmented. Existing approaches typically rely on distinct heuristics, such as teacher forcing with pseudo labels or policy-gradient-based reinforcement learning, without a unified mathematical foundation. In this work, we resolve this discrepancy by deriving a rigorous formulation of EM tailored to autoregressive models. We show that the exact objective naturally decomposes into a token-level policy gradient loss and a token-level entropy loss, and we reinterpret prior methods as partial realizations of this unified formulation. Using Whisper ASR as a testbed, we demonstrate that our approach consistently improves performance across more than 20 diverse domains, including acoustic noise, accents, and multilingual settings.
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Noise Entropy – Strange Forms
https://emerge.bandcamp.com/album/strange-forms
https://www.

On-Chip Quantum Randomness Amplification
Lang Li, Yutian Wu, Giulio Chiribella, Ravishankar Ramanathan
https://arxiv.org/abs/2606.12173 https://arxiv.org/pdf/2606.12173 https://arxiv.org/html/2606.12173
arXiv:2606.12173v1 Announce Type: new
Abstract: Randomness amplification, the task of extracting uniform private bits from biased seeds that may be partly known by a malicious third party, is of central importance in cryptography. The highest security in this task is provided by a class of quantum protocols known as device-independent, which however are challenging to integrate into scalable devices. Semi-device-independent (SDI) protocols are a promising alternative that guarantees security under few natural assumptions, such as bounds on the amount of energy used by the devices. Here, we provide the first demonstration of SDI randomness amplification on an integrated silicon photonic chip, achieving a throughput rate of 20 Mbps suitable for practical applications. This rate is achieved through a novel technique for SDI entropy certification, which delivers strictly tighter von Neumann entropy bounds compared to existing methods and remains valid even if the preparation and measurement devices share quantum correlations. Overall, the methods developed in this work enable the integration of SDI technology into portable telecom devices, opening up a new generation of quantum cryptographic hardware.
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Crosslisted article(s) found for math.SP. https://arxiv.org/list/math.SP/new
[1/1]:
- Analytic local resolution of Medvedev's Morse index conjecture for the critical hyperbolic cateno...
Alexander Pigazzini
https://arxiv.org/abs/2605.13562 https://mastoxiv.page/@arXiv_mathDG_bot/116571915012742224
- Determinantal point processes associated with the Bochner-Schr\"odinger operator
Yuri A. Kordyukov
https://arxiv.org/abs/2605.13575 https://mastoxiv.page/@arXiv_mathDG_bot/116571924843260732
- Spectral instability and non-uniqueness of mild solutions for the Keller-Segel system
Eliseo Luongo, Umberto Pappalettera
https://arxiv.org/abs/2605.13592 https://mastoxiv.page/@arXiv_mathAP_bot/116571934281016744
- Quantum Fractional Revival and Entanglement Entropy in Unitary Cayley Graphs
Duaa Abdullah
https://arxiv.org/abs/2605.13645 https://mastoxiv.page/@arXiv_mathCO_bot/116571957678938731
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Noise Entropy – Strange Forms
https://emerge.bandcamp.com/album/strange-forms
https://www.

Crosslisted article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/2]:
- Homomorphic Quantum Error Correction
Kornikar Sen, Miguel A. Martin-Delgado
https://arxiv.org/abs/2605.25692 https://mastoxiv.page/@arXiv_quantph_bot/116640053090772171
- Belief-Space Control for Personalized Cancer Treatment via Active Inference
Deniz Sargun, H. Bugra Tulay, C. Emre Koksal
https://arxiv.org/abs/2606.10376 https://mastoxiv.page/@arXiv_csAI_bot/116724820434554519
- A Geometric Profile of Semantic Information in Text: Frame-Conditional Uniqueness and a Trade-Off...
Dmitriy Kompaneets
https://arxiv.org/abs/2606.11222 https://mastoxiv.page/@arXiv_csCL_bot/116730477023664354
- An Entropy-based Framework for Hybrid Coalitions in Game Theory. Part I: Human Arbitration
Salome A. Sepulveda-Fontaine, Jose M. Amigo
https://arxiv.org/abs/2606.11288 https://mastoxiv.page/@arXiv_csGT_bot/116730374222115774
- Additive Noise, Shift Recovery, and Signed Signals in the Cumulative Distribution Transform
Harbir Antil, Ratna Khatri, Aryan Saxena
https://arxiv.org/abs/2606.11432 https://mastoxiv.page/@arXiv_eessSP_bot/116730375993496615
- A Unified Lower Bound on the Noisy Query Complexity of Boolean Functions
Yuzhou Gu, Xin Li, Yinzhan Xu
https://arxiv.org/abs/2606.11448 https://mastoxiv.page/@arXiv_csDS_bot/116730380321553481
- Optimizing Encoder Circuits of Entanglement-Assisted Quantum LDPC Codes via Beam Search
Aditya Sodhani, Pavan Kumar, Shayan Srinivasa Garani, Keshab K. Parhi
https://arxiv.org/abs/2606.11468 https://mastoxiv.page/@arXiv_quantph_bot/116730421187038523
- FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI
Mo Wang, Wenhao Ye, Junfeng Xia, Minghao Xu, Hongkai Wen, Quanying Liu
https://arxiv.org/abs/2606.11500 https://mastoxiv.page/@arXiv_eessIV_bot/116730364588705644
- Measuring language complexity from hierarchical reuse of recurring patterns
Junyi Zhou, Rui Liu, Pengyu Liu, Yu Liu
https://arxiv.org/abs/2606.11531 https://mastoxiv.page/@arXiv_csCL_bot/116730518902862163
- Superspace Concentration and Adversarial Robustness in Quantum Algorithms
Eric Yocam, Christian Yocam, Varghese Vaidyan, Yong Wang, Mahesh Kalappattil, Anthony Rizi
https://arxiv.org/abs/2606.11580 https://mastoxiv.page/@arXiv_quantph_bot/116730474074812546
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DNQ: Deep Nash Q-Network for Partially Observable n-Player Games
Qintong Xie, Edward Koh, Xavier Cadet, Peter Chin
https://arxiv.org/abs/2606.06480 https://arxiv.org/pdf/2606.06480 https://arxiv.org/html/2606.06480
arXiv:2606.06480v1 Announce Type: new
Abstract: Many real-world competitive systems require multiple decision-makers to act simultaneously under shared constraints, limited information, and repeated interaction, as in auctions, resource allocation, and security competition. We study multi-turn simultaneous bidding as a controlled testbed for such problems and propose DNQ, a solver-in-the-loop equilibrium supervision framework for training bidding agents. DNQ alternates between trajectory collection, critic-based payoff estimation, equilibrium computation, and policy imitation. At each visited state, a shared critic predicts either pairwise payoff matrices or an exact N-player payoff tensor, an external solver computes equilibrium strategies, and the agents are trained by minimizing the KL divergence between their masked policies and the solver-derived equilibrium targets. We focus on a scalable pairwise formulation that greatly reduces equilibrium-solving cost and training time compared with the exact formulation, while the shared critic amortizes payoff learning across agents and states. Experiments compare the pairwise and exact variants using critic loss, policy entropy, bidding resource usage, and training cost, showing that the pairwise method scales to larger numbers of agents, whereas the exact method becomes computationally impractical as the joint game grows. These results illustrate the trade-off between strategic fidelity and scalability in repeated competitive environments.
toXiv_bot_toot

Specificity- and Calibration-Aware Breast Ultrasound Segmentation via Entropy-Guided Boundary Supervision
Manar Alsaid, Mandip Shrestha, Mohammad Abbas
https://arxiv.org/abs/2606.22308

Today I used an enterprise site that disallowed “fuck” in the password. Not “shit” though. Not sure reducing entropy is the right play here guys

Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models
Wei-Ping Huang, Chee-En Yu, Guan-Ting Lin, Hung-yi Lee
https://arxiv.org/abs/2605.08186 https:/…

Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
Jeongho Bang, Kyoungho Cho, Jeongwoo Jae
https://arxiv.org/abs/2606.12211 https://arxiv.org/pdf/2606.12211 https://arxiv.org/html/2606.12211
arXiv:2606.12211v1 Announce Type: new
Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically meaningful only insofar as it can be learned from finitely many copies of an unknown quantum state. In this work, we develop an information-theoretic Occam theory for quantum data generated by finite-size quantum circuits. For the class $S_{n,G}$ of $n$-qubit pure states preparable with at most $G$ two-qubit gates, a metric-entropy argument gives the realizable sample law $\widetilde{\Theta}(G/\epsilon^2)$ in the circuit-limited regime. For an arbitrary source $\hat{\rho}$, we introduce the best $G$-gate approximation error $d_G(\hat{\rho})$ and the approximate circuit complexity $C_\eta(\hat{\rho})$. We prove an agnostic quantum Occam theorem: with $M$ copies, one can learn up to the best $G$-gate approximation error plus a statistical penalty $\widetilde{O}(\sqrt{G/M})$. We then remove the need to know $G$ in advance through an adaptive model-selection theorem whose oracle inequality selects the circuit complexity justified by the data. Matching lower bounds yield a sample-supported expressibility law: at trace-distance accuracy $\epsilon$, $M$ samples can support only $G_{\rm supported} \simeq M\epsilon^2$ gates, up to logarithmic factors and tomography saturation at $2^n$. Thus, the circuit complexity becomes an adaptive statistical resource rather than a static promise. Our framework turns bounded circuit complexity into a model-selection principle for quantum machine learning.
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Quantum ergodicity and semiclassical measures: mathematical results
St\'ephane Nonnenmacher
https://arxiv.org/abs/2606.12098 https://arxiv.org/pdf/2606.12098 https://arxiv.org/html/2606.12098
arXiv:2606.12098v1 Announce Type: new
Abstract: In this chapter we review some results describing the high-frequency eigenmodes of the Laplacian on compact manifolds, or Euclidean domains, for which the geodesic flow is chaotic. We focus on the macroscopic distribution of these eigenmodes, which is described by the concept of semiclassical measure. The main result on the question is the Quantum Ergodicity theorem, originally due to Schnirelman. We provide the detailed proof of this theorem, including the adjustments necessary to treat the case of manifolds with boundary. We also discuss the Quantum Unique Ergodicity conjecture, and some progress towards this conjecture for strongly chaotic (Anosov) systems. In particular, we describe the constraints on admissible semiclassical measures, in terms of their Kolmogorov-Sinai entropy, as well as more recent delocalization results.
toXiv_bot_toot

Replaced article(s) found for math.KT. https://arxiv.org/list/math.KT/new
[1/1]:
- On higher Du Bois singularities and $K$-regularity
Wanchun Shen
https://arxiv.org/abs/2504.12402 https://mastoxiv.page/@arXiv_mathAG_bot/114357763878794264
- Higher Koszul duality and $n$-affineness
James Pascaleff, Emanuele Pavia, Nicol\`o Sibilla
https://arxiv.org/abs/2504.16935 https://mastoxiv.page/@arXiv_mathAG_bot/114397398626428739
- Growth in noncommutative algebras and entropy in derived categories
Dmitri Piontkovski
https://arxiv.org/abs/2604.13373 https://mastoxiv.page/@arXiv_mathRA_bot/116413312400294881
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🇺🇦 #NowPlaying on KEXP's #AstralPlane
The Claypool Lennon Delirium:
🎵 Melody of Entropy
#TheClaypoolLennonDelirium
#newRelease 🆕 single
https://open.spotify.com/track/04Zys1Xo7L0OaUL4OSp0Yn