Miracle-dz/UniFAR-FTUs huggingface dataset:
The importance of inter‐individual variation in predicting species' responses to global change drivers

Abstract Inter‐individual variation in phenotypic traits has long been considered as “noise” rather than meaningful phenotypic variation, with biological studies almost exclusively generating and reporting average responses for populations and species’ average responses. Here, we compare the use of an individual approach in the investigation of extracellular acid–base regulation by the purple sea urchin Paracentrotus lividus challenged with elevated pCO2 and temperature conditions, with a more traditional approach which generates and formally compares mean values. We detected a high level of inter‐individual variation in acid–base regulation parameters both within and between treatments. Comparing individual and mean values for the first (apparent) dissociation constant of the coelomic fluid for individual sea urchins resulted in substantially different (calculated) acid–base parameters, and models with stronger statistical support. While the approach using means showed that coelomic pCO2 was influenced by seawater pCO2 and temperature combined, the individual approach indicated that it was in fact seawater temperature in isolation that had a significant effect on coelomic pCO2. On the other hand, coelomic [HCO3 −] appeared to be primarily affected by seawater pCO2, and less by seawater temperature, irrespective of the approach adopted. As a consequence, we suggest that individual variation in physiological traits needs to be considered, and where appropriate taken into account, in global change biology studies. It could be argued that an approach reliant on mean values is a “procedural error. ” It produces an artefact, that is, a population's mean phenotype. While this may allow us to conduct relatively simple statistical analyses, it will not in all cases reflect, or take into account, the degree of (physiological) diversity present in natural populations.
Master Computer Science Terrain-Adaptive PCGML in Minecraft

This work makes a ﬁrst step towards terrain-adaptive PCGML in Minecraft. We introduce an automated system and preprocessing tool to create “volume-to-volume” datasets suitable for machine learning by leveraging handwritten black-box Minecraft settlement generation algorithms. Using this system and tool, we create ten terrain-adaptive Minecraft ML datasets — including ones based on the current best-performing algorithm submitted to the Generative Design in Minecraft (GDMC) competition. Finally, we train and qualitatively evaluate various GAN-based volume-to-volume models on all ten of our datasets. Although we do not obtain good results in all cases, we demonstrate that terrain-adaptive PCGML in Minecraft is indeed feasible.
Enhanced Growth Performance,Haemato-biochemical and Immune Parameters of Asian Seabass, Lates calcarifer (Bloch,1790) Fed Dietary Supplementation with Polygonum chinense

The present study evaluated the effects of Polygonum chinense extract (PCE) on the growth performance, haemato-biochemical and innate immunity parameters of Asian seabass, Lates calcarifer (Bloch, 1790). The fish with an average weight of 9.5 ± 0.2 g were fed for 60 days using three supplementation diets of PCE at 0.2 %, 0.5 % and 1.0 %, and a non-supplemented diet as a control. Fish fed on 0.5 % PCE diet revealed significant higher body weight gain, specific growth rate, protein efficiency ratio, condition factor, and significantly lower feed conversion ratio. Dietary supplementation with 0.5 and 1.0 % showed a significant increase in red blood cell count and haemoglobin level, but the white blood cell count significantly increased only in 1.0 % diet. The alkaline phosphatase level significantly increased in fish group fed 0.2 and 0.5 % diets, but the aspartate transaminase level significantly decreases in 0.2 and 1.0 % groups. Meanwhile, all PCE supplemented diets showed significant increase in total protein, albumin, globulin, albumin: globulin ratio, total immunoglobulin, lysozyme, respiratory burst and phagocytic activities, and significantly reduced the alkaline transaminase level. These results suggest that dietary P. chinense supplementation at 0.5 % was found to be suitable to enhance the growth performance and immune status of Asian seabass.
Contrasting management regimes indicative of mesopredator release in temperate coastal fish assemblages

Abstract The absence of functional top predators has been proposed as a mechanism acting to shape fish assemblages in temperate marine ecosystems, with cascading effects on lower trophic levels. We explore this scenario by comparing the trophic and functional status of fish assemblages in Norwegian marine national parks, open to fishing, to a nearby coastal seascape that harbors a system of marine protected areas (MPAs) including a no‐take zone. Demersal fish assemblages were sampled using fyke nets over three consecutive seasons. Atlantic cod (Gadus morhua) is potentially a dominant top predator in this ecosystem, and historically, this and other gadids have been targeted by the full range of former and present fisheries. In the present study, we find that average body size of the Atlantic cod was significantly larger in the zoned seascape compared to the unprotected areas (mean ± SD: 36.6 cm ± 14.38 vs. 23.4 ± 7.50; p < .001) and that the unprotected seascape was characterized by a higher abundance of mesopredator fish species. These observations are consistent with the hypothesis that the protection of top predators within MPAs aids to control the mesopredator populations and provides empirical support to the notion that the present state of many coastal fish assemblages is driven by mesopredator release linked to functional depletion of large top predators.
A Critical Discourse Analysis of AACN’s Tool Kit of Resources for Cultural Competent Education for Baccalaureate Nurses

American institutions of nursing education have integrated cultural competence as a pillar approach to addressing health disparities. The theoretical frameworks, priorities, and solutions that national organizations pursue and endorse have far-reaching implications. The American Association of Colleges of Nursing (AACN) is one such organization. The purpose of this project was to critically analyze the AACN’s Tool Kit of Resources for Cultural Competent Education for Baccalaureate Nurses to excavate dynamics related to language, power, and inequality. Findings of this critical discourse analysis indicate: (1) the centrality of the AACN’s assumed authority and lack of relationality with readers, (2) nursing insularity and narrow theorization of culture and power, and (3) the harm of whiteness and colonialism as pedagogy. Accountability and repair include transparency, taking note of resources and viewpoints available and endorsed on organization websites, and updating (or refuting) outdated and harmful approaches.
A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications [version 1; peer review: 3 approved]

Background: The University of Southampton, in collaboration with the University Hospital Southampton (UHS) NHS Foundation Trust and industrial partners, has been at the forefront of developing three-dimensional (3D) imaging workflows using X-ray microfocus computed tomography (μCT) -based technology. This article presents the outcomes of these endeavours and highlights the distinctive characteristics of a μCT facility specifically tailored for 3D X-ray Histology, with primary focus on applications in biomedical research and preclinical
Camouflage efficiency in a colour‐polymorphic predator is dependent on environmental variation and snow presence in the wild

Abstract Colour polymorphism can be maintained by colour morph‐specific benefits across environmental conditions. Currently, the amount and the duration of snow cover during winter decrease especially in northern latitudes, which can alter the potential for camouflage of animals with light and dark morphs. Tawny owls, Strix aluco, are colour‐polymorphic avian predators with dark (brown) and light (grey) colour morphs, where the grey morph is presumed to enjoy camouflage benefits under snowy conditions. We studied the camouflage potential of morphs in two tawny owls potential using passerines' probability to mob in the wild during winter with and without snow. For comparison with other seasons, we also repeated the experiment during spring and autumn. We found that grey tawny owls have a lower probability of being mobbed than the brown tawny owls only during snowy winters. The two colour morphs therefore experience differential benefits across snow conditions, which may help to maintain colour morphs in the population, although further warming of winter climate will reduce the potential for camouflage for grey tawny owls in northern latitudes.
A Unified Particle-Based Solver for Non-Newtonian Behaviors Simulation

In this article, we present a unified framework to simulate non-Newtonian behaviors. We combine viscous and elasto-plastic stress into a unified particle solver to achieve various non-Newtonian behaviors ranging from fluid-like to solid-like. Our constitutive model is based on a Generalized Maxwell model, which incorporates viscosity, elasticity and plasticity in one non-linear framework by a unified way. On the one hand, taking advantage of the viscous term, we construct a series of strain-rate dependent models for classical non-Newtonian behaviors such as shear-thickening, shear-thinning, Bingham plastic, etc. On the other hand, benefiting from the elasto-plastic model, we empower our framework with the ability to simulate solid-like non-Newtonian behaviors, i.e., visco-elasticity/plasticity. In addition, we enrich our method with a heat diffusion model to make our method flexible in simulating phase change. Through sufficient experiments, we demonstrate a wide range of non-Newtonian behaviors ranging from viscous fluid to deformable objects. We believe this non-Newtonian model will enhance the realism of physically-based animation, which has great potential for computer graphics.
Optimal Routes to Ultrafast Polarization Reversal in Ferroelectric LiNbO3

We use the frozen phonon method to calculate the anharmonic potential energy surface and to model the ultrafast ferroelectric polarization reversal in LiNbO3 driven by intense pulses of THz light. Before stable switching of the polarization occurs, there exists a region of excitation field-strengths where transient switching can occur, as observed experimentally [Physical Review Letters 118, 197601 (2017)]. By varying the excitation frequency from 4 to 20 THz, our modeling suggests that more efficient, permanent polarization switching can occur by directly exciting the soft mode at 7 THz, compared to nonlinear phononic-induced switching driven by exciting a high frequency mode at 18 THz. We also show that neglecting anharmonic coupling pathways in the modeled experiment can lead to significant differences in the modeled switching field strengths.
Parametrically enhancing sensor sensitivity at an exceptional point

We propose a scheme to enhance the sensitivity of Non-Hermitian optomechanical mass-sensors. The benchmark system consists of two coupled optomechanical systems where the mechanical resonators are mechanically coupled. The optical cavities are driven either by a blue or red detuned laser to produce gain and loss, respectively. Moreover, the mechanical resonators are parametrically driven through the modulation of their spring constant. For a specific strength of the optical driving field and without parametric driving, the system features an Exceptional Point (EP). Any perturbation to the mechanical frequency (dissipation) induces a splitting (shifting) of the EP, which scales as the square root of the perturbation strength, resulting in a sensitivity-factor enhancement compared with conventional optomechanical sensors. The sensitivity enhancement induced by the shifting scenario is weak as compared to the one based on the splitting phenomenon. By switching on parametric driving, the sensitivity of both sensing schemes is greatly improved, yielding to a better performance of the sensor. We have also confirmed these results through an analysis of the output spectra and the transmissions of the optical cavities. In addition to enhancing EP sensitivity, our scheme also reveals nonlinear effects on sensing under splitting and shifting scenarii. This work sheds light on new mechanisms of enhancing the sensitivity of Non-Hermitian mass sensors, paving a way to improve sensors performance for better nanoparticles or pollutants detection, and for water treatment.
The Formal Theory of Green's Function in non-Hermitian Systems

In this paper, we employ the generalized Bloch theory to rediscover the generalized Brillouin zone theory and follow this way to obtain Green's function of the non-Hermitian system. We focus on a classical chiral model and give the exact expression of the Green function for a finite-size system and the formal expression of the Green function suitable for infinite size. Based on these results, we further derive the correlation matrix and validate it numerically against direct calculations for a system of size 40. The numerical results show the accuracy of our exact expression and the high fidelity of our formal expression.
Enhanced quantum sensing mediated by a cavity in open systems

We simulate the dynamics of systems with $N$ = 1-20 qubits coupled to a cavity in order to assess their potential for quantum metrology of a parameter in the open systems limit. The qubits and the cavity are both allowed to have losses and the system is studied under various coupling strength regimes. The focus is primarily on the coupling between the qubits using the quantum Fisher information as the measured parameter. Some results on estimating the qubit-cavity detuning parameter are also presented. We investigate the scaling of the uncertainty in the estimate of the qubit-cavity coupling with the number of qubits and for different initial states of the qubits that act as the quantum probe. As initial probe states, we consider Dicke states with varying excitation numbers, the GHZ state, and separable X-polarized states. It is shown that in the strong coupling regime, i.e., when the coupling between the qubits and the cavity is greater than the decay parameters of both the qubits and the cavity, Dicke states with a large excitation number can achieve the Heisenberg limit, with the precision scaling improving as the excitation number increases. A particularly intriguing finding of our study is that in the weak coupling regime, as well as in situations where either the qubit or cavity decay parameters exceed the coupling, the separable $X$-polarized state is the best in terms of scaling and is even able to achieve the Heisenberg limit in these lossy regimes for the range of $N$ considered.
Efficient Parallel Reinforcement Learning Framework using the Reactor Model

Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster generation of samples, estimation of values, and policy improvement. These computational paradigms require a seamless integration of training, serving, and simulation workloads. Existing frameworks, such as Ray, are not managing this orchestration efficiently, especially in RL tasks that demand intensive input/output and synchronization between actors on a single node. In this study, we have proposed a solution implementing the reactor model, which enforces a set of actors to have a fixed communication pattern. This allows the scheduler to eliminate work needed for synchronization, such as acquiring and releasing locks for each actor or sending and processing coordination-related messages. Our framework, Lingua Franca (LF), a coordination language based on the reactor model, also supports true parallelism in Python and provides a unified interface that allows users to automatically generate dataflow graphs for RL tasks. In comparison to Ray on a single-node multi-core compute platform, LF achieves 1.21x and 11.62x higher simulation throughput in OpenAI Gym and Atari environments, reduces the average training time of synchronized parallel Q-learning by 31.2%, and accelerates multi-agent RL inference by 5.12x.
Exploratory Simulation of Thrombosis in a Temporary LVAD Catheter Pump within a Virtual In-vivo Left Heart Environment

Percutaneous catheter pumps are intraventricular temporary mechanical circulatory support (MCS) devices that are positioned across the aortic valve into the left ventricle (LV) and provide continuous antegrade blood flow from the LV into the ascending aorta (AA). MCS devices are most often computationally evaluated as isolated devices subject to idealized steady-state blood flow conditions. In clinical practice, MCS devices operate connected to or within diseased pulsatile native hearts and are often complicated by hemocompatibility related adverse events such as stroke, bleeding, and thrombosis. Whereas aspects of the human circulation are increasingly being simulated via computational methods, the precise interplay of pulsatile LV hemodynamics with MCS pump hemocompatibility remains mostly unknown and not well characterized. Technologies are rapidly converging such that next-generation MCS devices will soon be evaluated in virtual physiological environments that increasingly mimic clinical settings. The purpose of this brief communication is to report results and lessons learned from an exploratory CFD simulation of hemodynamics and thrombosis for a catheter pump situated within a virtual in-vivo left heart environment.
A Modified Cosmic Brane Proposal for Holographic Renyi Entropy

We propose a new formula for computing holographic Renyi entropies in the presence of multiple extremal surfaces. Our proposal is based on computing the wave function in the basis of fixed-area states and assuming a diagonal approximation for the Renyi entropy. For Renyi index $n\geq1$, our proposal agrees with the existing cosmic brane proposal for holographic Renyi entropy. For $n<1$, however, our proposal predicts a new phase with leading order (in Newton's constant $G$) corrections to the cosmic brane proposal, even far from entanglement phase transitions and when bulk quantum corrections are unimportant. Recast in terms of optimization over fixed-area states, the difference between the two proposals can be understood to come from the order of optimization: for $n<1$, the cosmic brane proposal is a minimax prescription whereas our proposal is a maximin prescription. We demonstrate the presence of such leading order corrections using illustrative examples. In particular, our proposal reproduces existing results in the literature for the PSSY model and high-energy eigenstates, providing a universal explanation for previously found leading order corrections to the $n<1$ Renyi entropies.
Luck, skill, and depth of competition in games and social hierarchies

Patterns of wins and losses in pairwise contests, such as occur in sports and games, consumer research and paired comparison studies, and human and animal social hierarchies, are commonly analyzed using probabilistic models that allow one to quantify the strength of competitors or predict the outcome of future contests. Here we generalize this approach to incorporate two additional features: an element of randomness or luck that leads to upset wins, and a"depth of competition"variable that measures the complexity of a game or hierarchy. Fitting the resulting model to a large collection of data sets we estimate depth and luck in a range of games, sports, and social situations. In general, we find that social competition tends to be"deep,"meaning it has a pronounced hierarchy with many distinct levels, but also that there is often a nonzero chance of an upset victory, meaning that dominance challenges can be won even by significant underdogs. Competition in sports and games, by contrast, tends to be shallow and in most cases there is little evidence of upset wins, beyond those already implied by the shallowness of the hierarchy.
Robust Elastic Net Estimators for High Dimensional Generalized Linear Models

Robust estimators for Generalized Linear Models (GLMs) are not easy to develop because of the nature of the distributions involved. Recently, there has been an increasing interest in this topic, especially in the presence of a possibly large number of explanatory variables. Transformed M-estimators (MT) are a natural way to extend the methodology of M-estimators to the class of GLMs and to obtain robust methods. We introduce a penalized version of MT-estimators in order to deal with high-dimensional data. We prove, under appropriate assumptions, consistency and asymptotic normality of this new class of estimators. The theory is developed for redescending $\rho$-functions and Elastic Net penalization. An iterative re-weighted least squares algorithm is given, together with a procedure to initialize it. The latter is of particular importance, since the estimating equations might have multiple roots. We illustrate the performance of this new method for the Poisson family under several type of contaminations in a Monte Carlo experiment and in an example based on a real dataset.
LLM4TDD: Best Practices for Test Driven Development Using Large Language Models

In today's society, we are becoming increasingly dependent on software systems. However, we also constantly witness the negative impacts of buggy software. Program synthesis aims to improve software correctness by automatically generating the program given an outline of the expected behavior. For decades, program synthesis has been an active research field, with recent approaches looking to incorporate Large Language Models to help generate code. This paper explores the concept of LLM4TDD, where we guide Large Language Models to generate code iteratively using a test-driven development methodology. We conduct an empirical evaluation using ChatGPT and coding problems from LeetCode to investigate the impact of different test, prompt and problem attributes on the efficacy of LLM4TDD.
Analytical model and dynamical phase-field simulation of terahertz transmission across ferroelectrics

We theoretically investigate the steady-state transmission of continuous terahertz (THz) wave across a freestanding ferroelectric slab. Based on the Landau-Ginzburg-Devonshire theory of ferroelectrics and the coupled equations of motion for polarization and electromagnetic (EM) waves, we derive the analytical expressions of the frequency- and thickness-dependent dielectric susceptibility and transmission coefficient at the thin slab limit in the harmonic excitation regime. When the slab thickness is much smaller than the THz wavelength in the ferroelectric, the analytical predictions agree well with the numerical simulations from a dynamical phase-field model that incorporates the coupled dynamics of strain, polarization, and EM wave in multiphase systems. At larger thicknesses, the transmission is mainly determined by the frequency-dependent attenuation of THz waves in the ferroelectric and the formation of a standing polarization/THz wave. Our results advance the understanding of the interaction between THz wave and ferroelectrics and suggest the potential of exploiting ferroelectrics to achieve low-heat-dissipation, nonvolatile voltage modulation of THz transmission for high-data-rate wireless communication.