Tootfinder

Good Books are Complex Matters: Gauging Complexity Profiles Across Diverse Categories of Perceived Literary Quality
Yuri Bizzoni, Pascale Feldkamp, Ida Marie Lassen, Mia Jacobsen, Mads Rosendahl Thomsen, Kristoffer Nielbo
https://arxiv.org/abs/2404.04022

Good Books are Complex Matters: Gauging Complexity Profiles Across Diverse Categories of Perceived Literary Quality
In this study, we employ a classification approach to show that different categories of literary "quality" display unique linguistic profiles, leveraging a corpus that encompasses titles from the Norton Anthology, Penguin Classics series, and the Open Syllabus project, contrasted against contemporary bestsellers, Nobel prize winners and recipients of prestigious literary awards. Our analysis reveals that canonical and so called high-brow texts exhibit distinct textual features when compared to …

This https://arxiv.org/abs/2404.19718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Roman Domination on Graphings
We study a variant of domination, called Roman domination, where we must assign to each vertex one of the labels 0, 1, or 2 and require that every vertex with label 0 has a neighbour with label 2. We study the problem of finding a low-cost Roman dominating function on Lebesgue-measurable graphings, that is, on infinite graphs whose vertices are the points of a probability space. We provide a framework to tackle optimisation problems in the measurable combinatorial setting. In particular, we ful…

This https://arxiv.org/abs/2309.14595 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints
Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency improvement. To accelerate convergence, rule-based informed approaches sample states in an admissible ellipsoidal subset of the space determined by the current path cost. Learning-based alternatives model the topology of the free space and infer …

Ultralight Primordial Black Holes: #BlackHoles That Can't Die: https://www.universetoday.com/166866/the-universe-could-be-filled-with-ultralight-black-holes-that-cant-die/ (yes, PBHs are pretty controversial and pure speculation at this point ... but I know one eminent - is that the right word? - astrophysicist who is convinced that they'd solve numerous current problems in astrophysics all at once. And gravitational waves of black hole mergers - seeing light participants - could prove the PBH hypothesis).

Unified equations of state for cold nonaccreting neutron stars with Brussels-Montreal functionals. V. Improved parametrization of the nucleon density distributions
N. N. Shchechilin, N. Chamel, J. M. Pearson, A. I. Chugunov, A. Y. Potekhin
https://arxiv.org/abs/2404.04020

Unified equations of state for cold nonaccreting neutron stars with Brussels-Montreal functionals. V. Improved parametrization of the nucleon density distributions
We previously studied the inner crust and the pasta mantle of a neutron star within the 4th-order extended Thomas-Fermi (ETF) approach with consistent proton shell corrections added perturbatively via the Strutinsky integral (SI) theorem together with the contribution due to pairing. To speed up the computations and avoid numerical problems, we adopted parametrized nucleon density distributions. However, the errors incurred by the choice of the parametrization are expected to become more signif…

Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation
Li Wang, Min Xu, Quangui Zhang, Yunxiao Shi, Qiang Wu
https://arxiv.org/abs/2403.03578

Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation
Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. Addressing this problem is crucial, and existing methods often rely on techniques such as weight adjustment or leveraging unbiased data to eliminate this bias. However, we argue that not all biases are detrimental, i.e., some items recommended by friends may align with the user's interests. Blindly eliminating such biases could under…

Conformal prediction for multi-dimensional time series by ellipsoidal sets
Chen Xu, Hanyang Jiang, Yao Xie
https://arxiv.org/abs/2403.03850 https://…

Conformal prediction for multi-dimensional time series by ellipsoidal sets
Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound. For forecasting problems in supervised learning, most CP methods focus on building prediction intervals for univariate responses. In this work, we develop a sequential CP method called $\texttt{MultiDimSPCI}$ that builds prediction regions for a multivariate response, especially in the context of multivariate time series, which are not exch…

On the Second-Order Asymptotics of the Hoeffding Test and Other Divergence Tests
K. V. Harsha, Jithin Ravi, Tobias Koch
https://arxiv.org/abs/2403.03537 ht…

On the Second-Order Asymptotics of the Hoeffding Test and Other Divergence Tests
Consider a binary statistical hypothesis testing problem, where $n$ independent and identically distributed random variables $Z^n$ are either distributed according to the null hypothesis $P$ or the alternative hypothesis $Q$, and only $P$ is known. A well-known test that is suitable for this case is the so-called Hoeffding test, which accepts $P$ if the Kullback-Leibler (KL) divergence between the empirical distribution of $Z^n$ and $P$ is below some threshold. This work characterizes the first…

This https://arxiv.org/abs/2309.14595 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints
Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency improvement. To accelerate convergence, rule-based informed approaches sample states in an admissible ellipsoidal subset of the space determined by the current path cost. Learning-based alternatives model the topology of the free space and infer …

Conformal prediction for multi-dimensional time series by ellipsoidal sets
Chen Xu, Hanyang Jiang, Yao Xie
https://arxiv.org/abs/2403.03850 https://…

Conformal prediction for multi-dimensional time series by ellipsoidal sets
Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound. For forecasting problems in supervised learning, most CP methods focus on building prediction intervals for univariate responses. In this work, we develop a sequential CP method called $\texttt{MultiDimSPCI}$ that builds prediction regions for a multivariate response, especially in the context of multivariate time series, which are not exch…