Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing carbon emissions from personal road transport through the application of a Tradable Carbon Permit scheme: Empirical findings and policy implications from the UK

Climate change is an urgent issue rapidly reaching the top of the political agenda. Greenhouse gases (GHG) are an essential component of the atmosphere as without them the earth would be uninhabitable. However, anthropogenic (human produced) GHG emissions are contributing to warming of the climate beyond what would be naturally expected. Carbon Dioxide (CO2) is classified as the most significant GHG due to the quantity of release and quantity present in the atmosphere (IPCC, 2007). Based on the evidence available, the IPCC (2007) conclude with high confidence that anthropogenic warming over the last three decades has had a significant influence on many physical and biological systems. In addition, the IPCC (2007) have medium confidence regarding the anthropogenic influence on human systems. At its 33rd G8 summit in June 2007, the G8 agreed to aim to at least halve global CO2 emissions by 2050 from current levels. However, binding figures were not set as the USA would only agree to such reductions if China and India were also included in the commitment (Elliot and Wintour, 2007). Whilst the G8 agreement is a major step forward in terms of climate change policy, recent scientific evidence suggests that in order to avoid going beyond a 2 °C increase in global temperature and thus avoid the most severe and irreversible climatic change, it would be necessary to reduce CO2 emissions by 70% 90% by 2050 (Bows et al., 2006; Stern, 2006).
Evaluation of ultrasound-guided erector spinae plane block for postoperative management of video-assisted thoracoscopic surgery: a prospective, randomized, controlled clinical trial

Background Video-assisted thoracoscopic surgery (VATS) is a commonly performed minimally invasive procedure that has led to lower levels of pain, as well as procedure-related mortality and morbidity. However, VATS requires analgesia that blocks both visceral and somatic nerve fibers for more effective pain control. This randomized controlled trial evaluated the effect of erector spinae plane block (ESPB) in the postoperative analgesia management of patients undergoing VATS. Methods We performed a prospective, randomized, single-center study between December 2018 and December 2019. Fifty-four patients were recruited to two equal groups (ESPB and control group). Following exclusion, 46 patients were included in the final analysis. Patients were randomly assigned to receive preoperative ultrasound-guided ESPB with either ropivacaine or saline. The primary outcome was the numeric rating scale (NRS) score, assessed 12 hours postoperatively. Secondary outcomes were the Riker Sedation-Agitation Scale (SAS) score for emergence agitation, postoperative cumulative opioid consumption, length of post-anesthesia care unit (PACU) stay, incidence of postoperative nausea and vomiting (PONV) and dizziness, and ESPB-related adverse events. Results The NRS in the ESPB group during the postoperative period immediately after PACU admission was significantly lower than that in the control group (5.96±1.68 and 7.59±1.18, respectively; P<0.001) and remained lower until 6 hours postoperatively (P=0.001 at 1 hour and P=0.005 at 6 hours). At 12 hours postoperatively, NRS scores were not significantly different between groups (P=0.12). The median [interquartile range (IQR)] of the postoperative rescue pethidine consumption in PACU was significantly lower [25 mg (25 mg)] in the ESPB group than that in the control group [50 mg (56.2 mg); P=0.006]. The median (IQR) of PACU residual time was significantly lower [25 min (10 min)] in the ESPB group than that in the control group [30 min (15 min); P=0.034]. The median (IQR) Riker SAS was also lower in the ESPB group [4 (1.0)] than that in the control group [5 (1.25); P<0.001] in PACU. Conclusions A single preoperative injection of ESPB with ropivacaine may improve acute postoperative analgesia and emergence agitation in patients undergoing VATS.
Light variation and carbon gain in rain forest understorey palms

(1) The consequences of light variation for daily carbon gain in three understorey palm species are described. Detailed measurements of photosynthetic photon flux density (PPFD) were made in closed-canopy understorey, gap-edge and gap-centre sites to determine the range of light conditions. Based on these results and the responses of seedlings grown in full shade, computer simulations were used to compute total daily leaf carbon gain. (2) Median daily total PPFD in gap-edge habitats was 1-1.5 mol m-2, compared with 0.26-0-33 mol m-2 in adjacent understorey sites. Median daily PPFD in the gap centre increased with gap size. In the understorey, more than 80% of averages calculated for 5-min periods were below 10 ,umol m-2 S-1, whereas most readings in the centre of a 150-M2 gap were between 50-100 ,umol m-2 s-1 and along the gap edge 20-50 ,umol m-2 s(3) For Asterogyne martiana and Geonoma cuneata, two dwarf species, median daily total PPFD did not increase from ground level to 1-5 m. Fifty per cent of all PPFD measurements below 1-5 m were between 0 -18-0-39 mol m-2. At crown heights above 1.5 m, median daily total PPFD increased to 0.51 mol m-2. Within clones of G. congesta, daily total PPFD increased significantly with crown height; median PPFD incident on crowns 3-5-4-0 m tall was five times greater than PPFD at heights below 1.4 m. (4) On sunny days, the proportion of daily total PPFD contributed by sunflecks ranged from 10% to 78%. The relative proportion of daily PPFD contributed by sunflecks increased as the PPFD of background diffuse radiation decreased. (5) Photosynthesis of seedlings grown in full shade did not differ significantly among the three species. Light compensation points of full-shade-grown plants were between 3-4 ,umol m-2 s-1, and light-saturated photosynthetic rates measured as CO2 uptake were between 3 and 4 ,umol m-2 s-1. Light saturation occurred between 200 and 400,umol m-2 s(6) Carbon gain simulations of full-shade-grown seedlings indicate that a positive carbon gain over 24 h occurs at PPFD greater than 0.20 mol m-2. When most of the total daily PPFD was in the form of diffuse radiation, total daily carbon gain was linearly related to total daily PPFD. Daily carbon gain was not a simple function of daily total PPFD when sunflecks contributed more than 50% of the total quantum flux. On days with sunny periods, the proportion of total daily carbon gain during sunflecks ranged from 15% to 60%. (7) Seedlings of the three species grown in the gap edge had higher rates of leaf production, were taller and had larger leaves than seedlings grown in a closed-canopy understorey site. Measurements of growth and photosynthesis, and carbon-gain simulations suggest that light conditions along gap edges and within small gaps are ideal for these species.
A Novel Approach Of Computing With Words By Using Neutrosophic Information

—The computing with Words (CW) is a well known soft computing method to ﬁnd the solutions of many decision making problems in real life scenarios which consists of selective information used in natural language. CW is characterized by the human potentiality to do different types of physical and mental works or jobs without making any calculations or computations. Words are less accurate than numbers and words are used if exact numbers are unknown. Same word means different items to different person. There exists uncertainties associated with any word. Fuzzy set is generally used to model the words in the CW technique. The neutrosophic set is an extended version of fuzzy set. Here, we introduce a new idea of CW to model the words using neutrosophic set. In our proposed method, computation are done by words and words are translated to a mathematical model using neutrosophic set. The main objective of this paper is on CW based on neutrosophic set for taking subjective judgments. We call it as perceptual neutrosophic computing. An architecture is introduced for perceptual neutrosophic computing which we call perceptual neutrosophic computer (PNC). PNC has three components: encoder, CW engine for neutrosophic set and decoder. We have introduced a modiﬁed Kruskal’s algorithm to compute the minimum spanning tree (MST) of a neutrosophic graph. Human being describe those edge weights in real world problems using some terms, like, ’some’, ’small’, ’big’, ’large’, etc terms which do not provide any numbers. Those English terms are described here as words. A model of PNC is proposed to employ in our proposed method for solving this problem. The PNC model that is related with minimum spanning tree is deﬁned as minimum spanning tree advisor (MSTA) and we have described the design of MSTA in detail in this study. We use a numerical example to describe the efﬁciency of our proposed algorithm.
SPARTINA ALTERNIFLORA DIE-BACK IN LOUISIANA: TIME-COURSE INVESTIGATION OF SOIL WATERLOGGING EFFECTS

SUMMARY (1) Transplantation of streamside Spartina alterniflora swards into the more waterlogged and less productive inland marsh caused rapid decreases in soil redox potential and increases in interstitial water sulphide and NH4 concentrations and root alcohol dehydrogenase activity. (2) One year later, standing crops in these transplanted swards were significantly reduced compared to their streamside controls. (3) Reciprocal transplantation from inland to streamside resulted in the amelioration of the detrimental conditions associated with the waterlogged inland marsh and in an increase in standing crop to levels equivalent to streamside controls. (4) Soil salinity and pH were not significant factors in causing reduced growth of S. alterniflora. (5) Sulphide toxicity, in combination with extended periods of anaerobic metabolism in the roots, appeared to be a major factor associated with reduced growth of S. alterniflora and may be a cause of dieback in these marshes.
Unsupervised MRI Reconstruction with Generative Adversarial Networks

Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic contrast enhancement (DCE), 3D cardiac cine, and 4D flow. We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks. We test the proposed method in two scenarios: retrospectively undersampled fast spin echo knee exams and prospectively undersampled abdominal DCE. The method recovers more anatomical structure compared to conventional methods.
Neural-network variational quantum algorithm for simulating many-body dynamics

We propose a neural-network variational quantum algorithm to simulate the time evolution of quantum many-body systems. Based on a modified restricted Boltzmann machine (RBM) wavefunction ansatz, the proposed algorithm can be efficiently implemented in near-term quantum computers with low measurement cost without suffering from the vanishing gradient (or barren plateau) issue. Using a qubit recycling strategy, only one ancilla qubit is required to represent all the hidden spins in an RBM architecture. The variational algorithm is extended to open quantum systems by employing a stochastic Schrodinger equation approach. Numerical simulations of spin-lattice models demonstrate that our algorithm is capable of capturing the dynamics of closed and open quantum many-body systems with high accuracy.
3D–3D correspondence from Seifert fibering operators

Using recently developed Seifert fibering operators for 3D N=2 gauge theories, we formulate the necessary ingredients for a state-integral model of the topological quantum field theory dual to a given Seifert manifold under the 3D–3D correspondence, focusing on the case of Seifert homology spheres with positive orbifold Euler characteristic. We further exhibit a set of difference operators that annihilate the wavefunctions of this TQFT on hyperbolic three-manifolds, generalizing similar constructions for lens space partition functions and holomorphic blocks. These properties offer intriguing clues as to the structure of the underlying TQFT.
Advanced Astroinformatics for Variable Star Classification

This project outlines the complete development of a variable star classification algorithm methodology. With the advent of Big-Data in astronomy, professional astronomers are left with the problem of how to manage large amounts of data, and how this deluge of information can be studied in order to improve our understanding of the universe. While our focus will be on the development of machine learning methodologies for the identification of variable star type based on light curve data and associated information, one of the goals of this work is the acknowledgment that the development of a true machine learning methodology must include not only study of what goes into the service (features, optimization methods) but a study on how we understand what comes out of the service (performance analysis). The complete development of a beginning-to-end system development strategy is presented as the following individual developments (simulation, training, feature extraction, detection, classification, and performance analysis). We propose that a complete machine learning strategy for use in the upcoming era of big data from the next generation of big telescopes, such as LSST, must consider this type of design integration.
A Meta-Learning Control Algorithm with Provable Finite-Time Guarantees

In this work we provide provable regret guarantees for an online meta-learning control algorithm in an iterative control setting, where in each iteration the system to be controlled is a linear deterministic system that is different and unknown, the cost for the controller in an iteration is a general additive cost function and the control input is required to be constrained, which if violated incurs an additional cost. We prove (i) that the algorithm achieves a regret for the controller cost and constraint violation that are $O(T^{3/4})$ for an episode of duration $T$ with respect to the best policy that satisfies the control input control constraints and (ii) that the average of the regret for the controller cost and constraint violation with respect to the same policy vary as $O((1+\log(N)/N)T^{3/4})$ with the number of iterations $N$, showing that the worst regret for the learning within an iteration continuously improves with experience of more iterations.
Virasoro conjecture for the stable pairs descendent theory of simply connected 3‐folds (with applications to the Hilbert scheme of points of a surface)

This paper concerns the recent Virasoro conjecture for the theory of stable pairs on a 3‐fold proposed by Oblomkov, Okounkov, Pandharipande, and the author. Here we extend the conjecture to 3‐folds with non‐ (p,p)$(p,p)$ ‐cohomology and we prove it in two specializations. For the first specialization, we let S$S$ be a surface with H1(S)=0$H^1(S)=0$ and consider the moduli space Pn(S×P1,n[P1])$P_n(S\times \mathbb {P}^1, n[\mathbb {P}^1])$ , which happens to be isomorphic to the Hilbert scheme S[n]$S^{[n]}$ of n$n$ points on S$S$ . The Virasoro constraints for stable pairs, in this case, can be formulated entirely in terms of descendents in the Hilbert scheme of points. The two main ingredients of the proof are the toric case and the existence of universal formulas for integrals of descendents on S[n]$S^{[n]}$ . The second specialization consists in taking the 3‐fold X$X$ to be a cubic and the curve class β$\beta$ to be the line class. In this case we compute the full theory of stable pairs using the geometry of the Fano variety of lines.
Beyond variance reduction: Understanding the true impact of baselines on policy optimization

Policy gradients methods are a popular and effective choice to train reinforcement learning agents in complex environments. The variance of the stochastic policy gradient is often seen as a key quantity to determine the effectiveness of the algorithm. Baselines are a common addition to reduce the variance of the gradient, but previous works have hardly ever considered other effects baselines may have on the optimization process. Using simple examples, we find that baselines modify the optimization dynamics even when the variance is the same. In certain cases, a baseline with lower variance may even be worse than another with higher variance. Furthermore, we find that the choice of baseline can affect the convergence of natural policy gradient, where certain baselines may lead to convergence to a suboptimal policy for any stepsize. Such behaviour emerges when sampling is constrained to be done using the current policy and we show how decoupling the sampling policy from the current policy guarantees convergence for a much wider range of baselines. More broadly, this work suggests that a more careful treatment of stochasticity in the updates---beyond the immediate variance---is necessary to understand the optimization process of policy gradient algorithms.
Langevin Cooling for Domain Translation

Domain translation is the task of finding correspondence between two domains. Several Deep Neural Network (DNN) models, e.g., CycleGAN and cross-lingual language models, have shown remarkable successes on this task under the unsupervised setting---the mappings between the domains are learned from two independent sets of training data in both domains (without paired samples). However, those methods typically do not perform well on a significant proportion of test samples. In this paper, we hypothesize that many of such unsuccessful samples lie at the fringe---relatively low-density areas---of data distribution, where the DNN was not trained very well, and propose to perform Langevin dynamics to bring such fringe samples towards high density areas. We demonstrate qualitatively and quantitatively that our strategy, called Langevin Cooling (L-Cool), enhances state-of-the-art methods in image translation and language translation tasks.
Effect of Protein Level and Feeding Frequency on Growth and Body Composition of Cage-Reared Channel Catfish

Abstract Channel catfish (Ictalurus punctatus) fingerlings were stocked in eight 1.25-m3 floating cages at densities of 320 fish/m3 and fed to satiety once or twice daily for 105 d with diets containing either 34 or 38% protein. Experimental diets were formulated by a commercial feed mill with recommended levels of essential amino acids, vitamins, and minerals. Fish fed a diet containing 38% protein had significantly (P 0.05) were found when body composition was analyzed in relation to dietary protein level, feeding frequency, and their interaction. These data indicate that channel catfish reared in cages may require a diet with a hi.
How does the structure embedded in learning policy affect learning quadruped locomotion?

Reinforcement learning (RL) is a popular data-driven method that has demonstrated great success in robotics. Previous works usually focus on learning an end-to-end (direct) policy to directly output joint torques. While the direct policy seems convenient, the resultant performance may not meet our expectations. To improve its performance, more sophisticated reward functions or more structured policies can be utilized. This paper focuses on the latter because the structured policy is more intuitive and can inherit insights from previous model-based controllers. It is unsurprising that the structure, such as a better choice of the action space and constraints of motion trajectory, may benefit the training process and the final performance of the policy at the cost of generality, but the quantitative effect is still unclear. To analyze the effect of the structure quantitatively, this paper investigates three policies with different levels of structure in learning quadruped locomotion: a direct policy, a structured policy, and a highly structured policy. The structured policy is trained to learn a task-space impedance controller and the highly structured policy learns a controller tailored for trot running, which we adopt from previous work. To evaluate trained policies, we design a simulation experiment to track different desired velocities under force disturbances. Simulation results show that structured policy and highly structured policy require 1/3 and 3/4 fewer training steps than the direct policy to achieve a similar level of cumulative reward, and seem more robust and efficient than the direct policy. We highlight that the structure embedded in the policies significantly affects the overall performance of learning a complicated task when complex dynamics are involved, such as legged locomotion.
The turbulent bubble break-up cascade. Part 1. Theoretical developments

Abstract Breaking waves entrain gas beneath the surface. The wave-breaking process energizes turbulent fluctuations that break bubbles in quick succession to generate a wide range of bubble sizes. Understanding this generation mechanism paves the way towards the development of predictive models for large-scale maritime and climate simulations. Garrett et al. (J. Phys. Oceanogr., vol. 30, 2000, pp. 2163–2171) suggested that super-Hinze-scale turbulent break-up transfers entrained gas from large- to small-bubble sizes in the manner of a cascade. We provide a theoretical basis for this bubble-mass cascade by appealing to how energy is transferred from large to small scales in the energy cascade central to single-phase turbulence theories. A bubble break-up cascade requires that break-up events predominantly transfer bubble mass from a certain bubble size to a slightly smaller size on average. This property is called locality. In this paper, we analytically quantify locality by extending the population balance equation in conservative form to derive the bubble-mass-transfer rate from large to small sizes. Using our proposed measures of locality, we show that scalings relevant to turbulent bubbly flows, including those postulated by Garrett et al. (J. Phys. Oceanogr., vol. 30, 2000, pp. 2163–2171) and observed in breaking-wave experiments and simulations, are consistent with a strongly local transfer rate, where the influence of non-local contributions decays in a power-law fashion. These theoretical predictions are confirmed using numerical simulations in Part 2 (Chan et al., J. Fluid. Mech. vol. 912, 2021, A43), revealing key physical aspects of the bubble break-up cascade phenomenology. Locality supports the universality of turbulent small-bubble break-up, which simplifies the development of cascade-based subgrid-scale models to predict oceanic small-bubble statistics of practical importance.
Nucleosynthesis of an 11.8 M ⊙ Supernova with 3D Simulation of the Inner Ejecta: Overall Yields and Implications for Short-lived Radionuclides in the Early Solar System

Based on a 3D supernova simulation of an 11.8 M ⊙ progenitor model with an initial solar composition, we study the nucleosynthesis using tracers covering the innermost 0.1 M ⊙ of the ejecta. These ejecta are mostly proton-rich and contribute significant amounts of 45Sc and 64Zn. The production of heavier isotopes is sensitive to the electron fraction and hence the neutrino emission from the proto-neutron star. The yields of these isotopes are rather uncertain due to the approximate neutrino transport used in the simulation. In order to obtain the total yields for the whole supernova, we combine the results from the tracers with those for the outer layers from a suitable 1D model. Using the yields of short-lived radionuclides (SLRs), we explore the possibility that an 11.8 M ⊙ supernova might have triggered the formation of the solar system and provided some of the SLRs measured in meteorites. In particular, we discuss two new scenarios that can account for at least the data on 41Ca, 53Mn, and 60Fe without exceeding those on the other SLRs.