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People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow. This fact is taken to suggest that yellowness is somehow a typical feature of bananas, and blueness is exceptional. Similar to how a yellow color is typical of bananas, there may also be genders that are typical of occupations. In this work, we explore this question using information theoretic techniques coupled with corpus statistic analysis. In two distinct large corpora, we do not find strong evidence that occupations and gender display the same patterns of mentioning as do bananas and color. Instead, we find that gender mentioning is correlated with femaleness of occupation in particular, suggesting perhaps that woman-dominated occupations are seen as somehow ``more gendered'' than male-dominated ones, and thereby they encourage more gender mentioning overall.
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The stability of incompressible flow past a circular cylinder under collinear steady and oscillatory forcing is investigated within a two-dimensional Floquet framework. The flow is parameterised by the Keulegan-Carpenter number $KC \in [4,12]$, the steady-to-oscillatory velocity ratio $m \in [0,1]$, and the oscillatory Reynolds number $Re_m \in [20,100]$. The loci of the leading Floquet multipliers, and hence case-specific bifurcation modes, are examined by progressively reducing $Re_m$ to subcritical values for prescribed $m$. A steady current with $m > 0.5$ gives rise to a period-doubling subharmonic bifurcation that does not occur in purely oscillatory flow, where only synchronous and quasi-periodic modes arise. For $Re_m = 100$, three key features are discernible. First, the neutral stability curve in $(KC,m)$ space is strongly non-monotonic in $m$, separating intrinsically stable regions from those with single unstable modes; a sub-region of striking mode re-stabilisation appears beyond $m \approx 0.9$, where the flow recovers a $Z_2$-symmetric state at peak Reynolds number $\approx 190$, despite the steady and oscillatory components each being individually unstable. Second
Construction industry scholars have advocated increasing digitalization as a harbinger of manifold improvements, from safe training to efficient waste management. Small construction enterprises, which often face greater difficulties in embracing such a paradigm, are frequently overlooked in investigations of stakeholders' views on technical innovation. This study aims to start filling this gap by investigating the views of small construction enterprises on technical innovation. We report the themes that emerged from the qualitative analysis of interviews with construction artisans in North-East Italy in 2018-2019, including installers, restorers, carpenters, painters, and upholsterers (N=25). We asked what makes new technical devices acceptable to them and conducted inductive and deductive thematic analyses to identify recurrent arguments supporting their positions. The analysis identified fifteen premises underlying the interviewees' positions on technical innovation, grouped around four issues: Is technological innovation part of my job? Is the financial cost of innovating worthwhile? Will the new technology be practical? What part of my business is served by communication device
We show that a smooth $d$-manifold $M$ is diffeomorphic to $\mathbb R^d$ if it admits a Lyapunov-Reeb function, i.e., a smooth map $f:M\to\mathbb R$ that is proper, lower-bounded, and has a unique critical point. By constructing such functions, we prove that the moduli spaces of self-avoiding polygonal linkages and configurations are diffeomorphic to Euclidean spaces. This resolves the Refined Carpenter's Rule Problem and confirms a conjecture proposed by González and Sedano-Mendoza. Furthermore, we describe foliation structures of these moduli spaces via level sets of Lyapunov-Reeb functions and develop algorithms for related problems.
Probabilistic programming methods have revolutionised Bayesian inference, making it easier than ever for practitioners to perform Markov-chain-Monte-Carlo sampling from non-conjugate posterior distributions. Here we focus on Stan, arguably the most used probabilistic programming tool for Bayesian inference (Carpenter et al., 2017), and its interface with R via the brms (Burkner, 2017) and rstanarm (Goodrich et al., 2024) packages. Although easy to implement, these tools can become computationally prohibitive when applied to datasets with many observations or models with numerous parameters. While the use of sufficient statistics is well-established in theory, it has been surprisingly overlooked in state-of-the-art Stan software. We show that when the likelihood can be written in terms of sufficient statistics, considerable computational improvements can be made to current implementations. We demonstrate how this approach provides accurate inference at a fraction of the time than state-of-the-art implementations for Gaussian linear regression models with non-conjugate priors, hierarchical random effects models, and factor analysis models. Our results also show that moderate computat
Low-storage Runge-Kutta schemes of Williamson's type, so-called 2N-storage schemes, are further examined as a follow-up to the recent work. It is found that the augmented Butcher tableau factorizes into a product of matrices with special properties. Those properties reveal that the 2N-storage methods of the order of global accuracy less than five possess a symmetry, called c-reflection symmetry, i.e. most methods exist in pairs. A transformation that relates the Butcher tableaux of the pairs is found and the fact that the c-reflected method satisfies the same order conditions as the original one is proven. Numerical evidence that validates the analytic results is presented. Branches of solutions for (5,4) methods, first explored by Carpenter and Kennedy, are constructed numerically. Four new (5,4) schemes with coefficients expressed in radicals and one with rational coefficients are examined for illustration. Eight new (6,4) schemes, some of which can be expressed in rationals or radicals, and one (8,4) scheme, are studied to understand the practical implications of the c-reflection symmetry for methods with higher number of stages. In the absence of closed-form analytic solutions
Drug-target interaction (DTI) prediction is crucial for identifying new therapeutics and detecting mechanisms of action. While structure-based methods accurately model physical interactions between a drug and its protein target, cell-based assays such as Cell Painting can better capture complex DTI interactions. This paper introduces MOTIVE, a Morphological cOmpound Target Interaction Graph dataset comprising Cell Painting features for 11,000 genes and 3,600 compounds, along with their relationships extracted from seven publicly available databases. We provide random, cold-source (new drugs), and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Our benchmark results show that graph neural networks that use Cell Painting features consistently outperform those that learn from graph structure alone, feature-based models, and topological heuristics. MOTIVE accelerates both graph ML research and drug discovery by promoting the development of more reliable DTI prediction models. MOTIVE resources are available at https://github.com/carpenter-singh-lab/motive.
Common smoothness indicators used in Weighted Essentially Non\--Os\-cil\-la\-to\-ry (WENO) reconstructions [Jiang, G.S., Shu, C.W.: Efficient implementation of {Weighted} {ENO} schemes, J.\ Comput.\ Phys. \textbf{126}, 202--228 (1996)] have quadratic cost with respect to the order. A set of novel smoothness indicators with linear cost of computation with respect to the order is presented. These smoothness indicators can be used in the context of schemes of the type introduced by Yamaleev and Carpenter [Yamaleev, N.K., Carpenter, M.H.: A systematic methodology to for constructing high-order energy stable WENO schemes. J. Comput. Phys. \textbf{228}(11), 4248-4272 (2009)]. The accuracy properties of the resulting non-linear weights are the same as those arising from using the traditional Jiang-Shu smoothness indicators in Yamaleev-Carpenter-type reconstructions. The increase of the efficiency and ease of implementation are shown.
Vortex-induced vibration (VIV) test of a tensioned flexible pipe in oscillatory sheared flow was performed in an ocean basin. The model was 28.41 mm in diameter and 3.88 m in length. The test was performed on a rotating test rig to simulate oscillatory sheared flow conditions. One end of the test pipe is fixed, and one end is forced to harmonically oscillate to simulate oscillatory sheared flows with various combinations of amplitudes and periods, Keulegan-Carpenter ($KC$) numbers from $25$ to $160$ and five kinds of reduced velocities $Vr$ from $6$ to $14$. Fiber Bragg Grating (FBG) strain sensors were arranged along the test pipe to measure bending strains, and the modal analysis approach was used to determine the VIV response. The VIV response in the cross flow (CF) direction is investigated. The results show that VIV under oscillatory sheared flow exhibit amplitude modulation and hysteresis phenomena. Compared with oscillatory uniform flow-induced VIV, the Strouhal number is smaller in oscillatory sheared flow-induced VIVs. The VIV developing process in oscillatory sheared flow is analyzed, and critical $KC$ is proposed to describe the occurrence of modulated VIV under oscillat
Liquid Crystalline Elastomers (LCEs) are active materials that are of interest due to their programmable response to various external stimuli such as light and heat. When exposed to these stimuli, the anisotropy in the response of the material is governed by the nematic director, which is a continuum parameter that is defined as the average local orientation of the mesogens in the liquid crystal phase. This nematic director can be programmed to be heterogeneous in space, creating a vast design space that is useful for applications ranging from artificial ligaments to deployable structures to self-assembling mechanisms. Even when specialized to long and thin strips of LCEs -- the focus of this work -- the vast design space has required the use of numerical simulations to aid in experimental discovery. To mitigate the computational expense of full 3-d numerical simulations, several dimensionally-reduced rod and ribbon models have been developed for LCE strips, but these have not accounted for the possibility of initial transverse curvature, like carpenter's tape spring. Motivated by recent experiments showing that transversely-curved LCE strips display a rich variety of configuration
The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt. In this work, we leverage one popular generative language model, GPT-3, with the goal of writing unbiased and realistic job advertisements. We first assess the bias and realism of zero-shot generated advertisements and compare them to real-world advertisements. We then evaluate prompt-engineering and fine-tuning as debiasing methods. We find that prompt-engineering with diversity-encouraging prompts gives no significant improvement to bias, nor realism. Conversely, fine-tuning, especially on unbiased real advertisements, can improve realism and reduce bias.
This is the preface to a special issue of the Journal of Physical Organic Chemistry dedicated to an outstanding physical organic chemist, mentor and friend, Barry Carpenter on the occasion of his official retirement.
A quantum problem once described as impossible for classical computers has now been solved using relatively modest hardware。 Researchers used tensor networks to compress the overwhelming wave function created by hundreds of entangled qubits, allowing some calculations to run on a laptop。 Their results matched both theoretical predictions and simula
Expect bumps with X Money rollout as major US financial markets are excluded
The asteroid that wiped out the dinosaurs was likely an exceptionally rare CO chondrite from a distant region of the solar system。 Its unusual chemistry suggests that planet-cooling dust and debris, rather than sulfur inside the asteroid, may have delivered the deadliest blow
Google's new API relies on parents to set age ranges in Family Link
A new theoretical study offers a possible explanation for how the Universe can grow more complex without violating the second law of thermodynamics。 Using a quantum gravity framework called Gravity from Entropy, mathematician Ginestra Bianconi found that the Universe’s total entropy may rise as space expands, even while entropy within each unit of
Lab students were supposed to ID a mild germ。 They all identified a deadly pathogen
Researchers are applying evolutionary theory to cancer by changing treatments before tumors have time to develop resistance。 Mathematical models suggest that rapid, carefully timed switches between multiple therapies could improve cure rates