Miracle-dz/UniFAR-FTUs huggingface dataset:
National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake

Abstract. Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.
Carbon dioxide removal in a global analytic climate

Net-zero climate policies foresee deployment of atmospheric carbon dioxide removal wit geological, terrestrial, or marine carbon storage. While terrestrial and geological storage would be governed under the framework of national property rights, marine storage implies that carbon is transferred from one global common, the atmosphere, to another global common, the ocean, in particular if storage exceeds beyond coastal applications. This paper investigates the option of carbon dioxide removal (CDR) and storage in different (marine) reservoir types in an analytic climate-economy model, and derives implications for optimal mitigation efforts and CDR deployment. We show that the introduction of CDR lowers net energy input and net emissions over the entire time path. Furthermore, CDR affects the Social Cost of Carbon (SCC) via changes in total economic output but leaves the analytic structure of the SCC unchanged. In the first years after CDR becomes available the SCC is lower and in later years it is higher compared to a standard climate-economy model. Carbon dioxide emissions are first higher and then lower relative to a world without CDR. The paper provides the basis for the analysis of decentralized and potentially non-cooperative CDR policies. Abstract: Net-zero climate policies foresee deployment of atmospheric carbon dioxide removal wit geological, terrestrial, or marine carbon storage. While terrestrial and geological storage would be governed under the framework of national property rights, marine storage implies that carbon is transferred from one global common, the atmosphere, to another global common, the ocean, in particular if storage exceeds beyond coastal applications. This paper investigates the option of carbon dioxide removal (CDR) and storage in diﬀerent (marine) reservoir types in an analytic climate-economy model, and derives implications for optimal mitigation eﬀorts and CDR deployment. We show that the introduction of CDR lowers net energy input and net emissions over the entire time path. Furthermore, CDR aﬀects the Social Cost of Carbon (SCC) via changes in total economic output but leaves the analytic structure of the SCC unchanged. In the ﬁrst years after CDR becomes available the SCC is lower and in later years it is higher compared to a standard climate-economy model. Carbon dioxide emissions are ﬁrst higher and then lower relative to a world without CDR. The paper provides the basis for the analysis of decentralized and potentially non-cooperative CDR policies.
Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency

Many memory models suggest self-terminating backward scanning along a memory representation. In these models, time to retrieve a particular item from memory could depend on how far in the past the item was presented or on the number of items presented since that item. To investigate which of these two types of memory representation is more likely, we designed a relative Judgment of Recency (JOR) task with variable presentation rates. The variable presentation rate de-confounded the age of memory and the number of intervening items. Our results favor the hypothesis that memory representation is temporally organized. This result is important for ad- vancing memory models and for building stronger ties between cognitive and neural models of memory.
Generating Extended and Multilingual Summaries with Pre-trained Transformers

Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended summaries of approx. 11 sentences. The dataset comprises 39,626 summaries which are news articles from Wikinews and their sources. We compare three multilingual transformer models on the extractive summarisation task and three training scenarios on which we fine-tune mT5 to perform abstractive summarisation. This results in strong baselines for both extractive and abstractive summarisation on WikinewsSum. We also show how the combination of an extractive model with an abstractive one can be used to create extended abstractive summaries from long input documents. Finally, our results show that fine-tuning mT5 on all the languages combined significantly improves the summarisation performance on low-resource languages.
A two-component model for the 40-120 mu.m emission from normal disk galaxies.

L'emission en IR lointain (40-120 μm) des galaxies normales de type avance est decrite par un modele a 2 composantes copie sur notre galaxie. La premiere composante est froide et associee aux poussieres interstellaires neutres chauffees par le champ de rayonnement ambiant. La deuxieme est chaude et associee a l'hydrogene ionise des regions H II de faible densite; elle est chauffee par les etoiles chaudes ionisantes. Ce modele ne prevoit que 75 a 80% du flux total observe dans le domaine 40-120 μm: cela est probablement du a une sous-estimation de l'emission de la composante chaude
BEAM DIAGNOSTICS FOR FLASH RT IN THE VARIAN PROBEAM SYSTEM

FLASH RT is a novel ultra-high dose rate radiation therapy technique with the potential of sparing radiation induced damages to healthy tissue while keeping tumor control unchanged. Recent studies indicate that this so-called FLASH effect occurs when applying high doses of several Grays in a fraction of a second only, and thus significantly faster than with conventionally available radiation therapy systems today. Varian’s ProBeam system has been enabled to deliver ultra-high beam currents for FLASH treatments at 250 MeV beam energy. The first clinical trial is currently conducted and all involved human patients have been successfully irradiated at FLASH dose rates, operating the system at cw cyclotron beam currents of up to 400 nA. With these modifications, treatment times could be reduced down to less than a second. First switching and
Stellar Populations as Building Blocks of Galaxies

Preface 1. Model ingredients: stellar evolution models 2. Model ingredients: stellar spectral libraries 3. Initial mass function 4. Stellar population models 5. Stellar populations in the Milky Way 6. Resolved stellar populations in the Local Group 7. Stellar populations in early-type galaxies 8. Extragalactic globular cluster systems 9. Stellar populations in late-type galaxies 10. Stellar populations at higher redshifts 11. New observing facilities Author index.
An Inductive Model of Farmer Conservation Decision Making for Nitrogen Management

This study explores the drivers of and constraints to conservation decision making and practice adoption among agricultural producers in two impaired Minnesota watersheds in the Upper Mississippi River Basin. Concerns about nutrient management and non-point source nitrogen loading in the region prompted the study. Data were gathered through in-depth interviews with 30 farmers operating in the watersheds. Qualitative data analysis revealed multiple interacting drivers and constraints to conservation decision making and supported an inductive conceptual model anchored in three spheres of decision making: farming ethic, conservation practice appraisal, and conservation action. Finer descriptive analysis focused on the first two spheres revealed varying beliefs, levels of focus, and saliency involving dynamic thought processes of conservation decision making. Farming ethic encompassed multiple dimensions of farmer identity and values, social norms of farm management, and personal norms of conservation action. Appraisal dimensions were tied to resources, practice efficacy, risk and uncertainty, and autonomy. The model will enable policy makers and resource managers to better plan, design and manage conservation policies and programs. Study findings can inform nutrient management and enable better alignment with farmer decision-making processes.
Automatic Segmentation of Ethnomusicological Field Recordings

The article presents a method for segmentation of ethnomusicological field recordings. Field recordings are integral documents of folk music performances captured in the field, and typically contain performances, intertwined with interviews and commentaries. As these are live recordings, captured in non-ideal conditions, they usually contain significant background noise. We present a segmentation method that segments field recordings into individual units labelled as speech, solo singing, choir singing, and instrumentals. Classification is based on convolutional deep networks, and is augmented with a probabilistic approach for segmentation. We describe the dataset gathered for the task and the tools developed for gathering the reference annotations. We outline a deep network architecture based on residual modules for labelling short audio segments and compare it to the more standard feature based approaches, where an improvement in classification accuracy of over 10% was obtained. We also present the SeFiRe segmentation tool that incorporates the presented segmentation method.
Improved WiggleZ Dark Energy Survey Distance Measurements to z = 1 with Reconstruction of the Baryonic Acoustic Feature

We present significant improvements in cosmic distance measurements from the WiggleZ Dark Energy Survey, achieved by applying the reconstruction of the baryonic acoustic feature technique. We show using both data and simulations that the reconstruction technique can often be effective despite patchiness of the survey, significant edge effects and shot-noise. We investigate three redshift bins in the redshift range 0.2<$z$<1, and in all three find improvement after reconstruction in the detection of the baryonic acoustic feature and its usage as a standard ruler. We measure model independent distance measures $D_{\mathrm V}(r_{\mathrm s}^\mathrm{fid}/r_{\mathrm s})$ of 1716 $\pm$ 83 Mpc, 2221 $\pm$ 101 Mpc, 2516 $\pm$ 86 Mpc (68% CL) at effective redshifts z = 0.44, 0.6, 0.73, respectively, where $D_{\mathrm V}$ is the volume-average-distance, and $r_{\mathrm s}$ is the sound horizon at the end of the baryon drag epoch. These significantly improved 4.8, 4.5 and 3.4 percent accuracy measurements are equivalent to those expected from surveys with up to 2.5 times the volume of WiggleZ. These measurements are fully consistent with cosmologies allowed by the analyses of the Planck Collaboration and the Sloan Digital Sky Survey.We provide the $D_{\mathrm V}(r_{\mathrm s}^\mathrm{fid}/r_{\mathrm s})$ posterior probability distributions and their covariances. When combining these measurements with temperature fluctuations measurements of Planck, the polarization of WMAP9, and the 6dF Galaxy Survey baryonic acoustic feature, we do not detect deviations from a flat LCDM model. Assuming this model we constrain the current expansion rate to $H_0$ = 67.15 $\pm$ 0.98 kms$^{-1}$Mpc$^{-1}$. Allowing the equation of state of dark energy to vary we obtain $w_\mathrm{DE}$ = -1.080 $\pm$ 0.135. When assuming a curved LCDM model we obtain a curvature value of $\Omega_{\mathrm K}$ = -0.0043 $\pm$ 0.0047.
An Ergodic Theorem for Markov Processes and Its Application to Telephone Systems with Refusals

Let $P(x,t,A)$ be the probability of transition from state $x \in \Omega $ to a measurable set A in time t of a homogeneous Markov process in the space $\Omega $. Let $P_t (A)$ be the probability that the system is in the measurable set A at time t if at $t = 0$ the distribution $P_0 $ is valid. The stationary distribution P is called ergodic if for each initial distribution $P_0 $ the variation $V(P_t - P) \to 0$ for $t \to \infty $. The following ergodic theorem is proved:Theorem 1.A Markov process homogeneous in time has a unique stationary probability distribution which is ergodic if for any$\varepsilon > \infty $there exists a measurable setC, a probability distributionRin$\Omega $, and values$t_1 > 0,k > 0,K > 0$such that 1) $kR(A) \leqq P(x,t_1 ,A)$for all points$x \in C$and measurable sets$A \subseteq C$; for any initial distribution$P_0 $there exists a$t_0 $such that for any$t \geqq t_0 $ 2) $P_t (C) \geqq 1 - \varepsilon $; 3) $P_t (A) \leqq KR(A) + \varepsilon $for any measurable set$A \subseteq.
Antimicrobial screening to molecular docking of newly synthesized ferrocenyl-substituted pyrazole

Objective: Microbial diseases are snowballing at an alarming proportion. Therefore, the intent of this study was to inspect the antimicrobial action of ferrocenyl-substituted pyrazole against various human pathogenic Gram-positive, Gram-negative, and fungal microbial strains. Pyrazoles have been recognized for over a century as a significant and bioactive class of heterocyclic compounds. The association of pyrazoles with a ferrocene moiety may give new class of compounds. The present study was designed to synthesize biological active ferrocenyl-substituted pyrazole through a novel route. Methods: The anhydride of ferrocenyl-substituted pyrazole, namely, (S)-(3-(3-(carboxyamino)-3H-pyrazol-4-yl)cyclopenta-1,3-dien-1-yl)(cyclopenta-1,3-dien-1-yl)iron was synthesized using expansion cyclocondensation. FTIR, NMR, and GC-MS were performed to analyze the structure of the synthesized ferrocenyl-substituted pyrazole. Antimicrobial, DNA photo-cleaving, and anti-angiogenic activities of ferrocenyl-substituted compounds were studied. Results: Anhydride of (S)-(3-(3-(carboxyamino)-3H-pyrazol-4-yl)cyclopenta-1,3-dien-1-yl)(cyclopenta-1,3-dien-1-yl)iron obtained with yield of 87%. Spectral analysis confirmed the formation of anhydride. The synthesized compound was found to be biological active in the range of 85–95 μg/ml. Conclusion: This study described the novel method for the synthesis of biologically active anhydride of ferrocenyl-substituted pyrazole. The study demonstrations that synthesized ferrocenyl-substituted pyrazole in today’s situation is the encouraging antimicrobial mediator against the human pathogens. In addition, it may open new doors to initiate research against drug resistance bacteria with possible biomedical applications.
A bound on certain local cohomology modules and application to ample divisors

We consider a positively graded noetherian domain R = ⊕n∈No Rn for which R0 is essentially of finite type over a perfect field K of positive characteristic and we assume that the generic fibre of the natural morphism π: Y = Proj(R) → Y 0 = Spec(R 0) is geometrically connected, geometrically normal and of dimension > 1. Then we give bounds on the “ranks” of the n-th homogeneous part H 2(R)n of the second local cohomology module of R with respect to R +:= ⊕m>0 Rm for n < 0. If Y is in addition normal, we shall see that the R 0-modules H 2 R + (R)n are torsion-free for all n < 0 and in this case our bounds on the ranks furnish a vanishing result. From these results we get bounds on the first cohomology of ample invertible sheaves in positive characteristic.
Teaching Logic with Logic Boxes.

Logic is an important but frequently neglected topic of the mathematics cur riculum. Recognizing the need for better formal reasoning skills in preparing stu dents to study mathematics and solve prob lems, An Agenda for Action (NCTM 1980, 8) recommended that "the higher-order mental processes of logical reasoning, infor mation processing, and decision-making should be considered basic to the appli cation of mathematics. Mathematics cur ricula and teachers should set as objectives the development of logical processes, con cepts, and language, including. precise use of such language as at least, at most, either-or, both-and, and if then. " The teach ing materials described in this article have been successfully used to help second-, fourth-, and seventh-grade students become skillful in the use of logic. They have also been used with selected adults. The first step in achieving the higher level mental processes of logical reasoning as used in mathematics is correct use of the terms of logic. Our preliminary research with second-, fourth-, and seventh-grade students has shown that when students learn to use logic terms correctly in sen tences, they tend to use those same logic terms correctly in inferences. For example, students commonly misuse or as a synonym for and. As a consequence, they incorrectly
Organizational Distance Also Matters

Geographically distributed teams often face challenges in coordination and collaboration, lowering their productivity. Understanding the relationship between team dispersion and productivity is critical for supporting such teams. Extensive prior research has studied these relations in lab settings or using qualitative measures. This paper extends prior work by contributing an empirical case study in a real-world organization, using quantitative measures. We studied 117 new research project teams from the same discipline within an industrial research lab for 6 months. During this time, all teams shared one goal: submitting research papers to the same target conference. We analyzed these teams' dispersion-related characteristics as well as team productivity. Interestingly, we found little statistical evidence that geographic and time differences relate to team productivity. However, organizational and functional distances are predictive of the productivity of the dispersed teams we studied. We discuss the open research questions these findings revealed and their implications for future research.
CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms

Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum Algorithms (VQAs). VQAs rely upon the iterative optimization of a parameterized unitary circuit (ansatz) with respect to an objective function. Since quantum machines are noisy and expensive resources, it is imperative to classically choose the VQA ansatz initial parameters to be as close to optimal as possible to improve VQA accuracy and accelerate their convergence on today’s devices. This work tackles the problem of finding a good ansatz initialization, by proposing CAFQA, a Clifford Ansatz For Quantum Accuracy. The CAFQA ansatz is a hardware-efficient circuit built with only Clifford gates. In this ansatz, the parameters for the tunable gates are chosen by searching efficiently through the Clifford parameter space via classical simulation. The resulting initial states always equal or outperform traditional classical initialization (e.g., Hartree-Fock), and enable high-accuracy VQA estimations. CAFQA is well-suited to classical computation because: a) Clifford-only quantum circuits can be exactly simulated classically in polynomial time, and b) the discrete Clifford space is searched efficiently via Bayesian Optimization. For the Variational Quantum Eigensolver (VQE) task of molecular ground state energy estimation (up to 18 qubits), CAFQA’s Clifford Ansatz achieves a mean accuracy of nearly 99% and recovers as much as 99.99% of the molecular correlation energy that is lost in Hartree-Fock initialization. CAFQA achieves mean accuracy improvements of 6.4x and 56.8x, over the state-of-the-art, on different metrics. The scalability of the approach allows for preliminary ground state energy estimation of the challenging chromium dimer (Cr2) molecule. With CAFQA’s high-accuracy initialization, the convergence of VQAs is shown to accelerate by 2.5x, even for small molecules. Furthermore, preliminary exploration of allowing a limited number of non-Clifford (T) gates in the CAFQA framework, shows that as much as 99.9% of the correlation energy can be recovered at bond lengths for which Clifford-only CAFQA accuracy is relatively limited, while remaining classically simulable.
Limitations of Language Models in Arithmetic and Symbolic Induction

Recent work has shown that large pretrained Language Models (LMs) can not only perform remarkably well on a range of Natural Language Processing (NLP) tasks but also start improving on reasoning tasks such as arithmetic induction, symbolic manipulation, and commonsense reasoning with increasing size of models. However, it is still unclear what the underlying capabilities of these LMs are. Surprisingly, we find that these models have limitations on certain basic symbolic manipulation tasks such as copy, reverse, and addition. When the total number of symbols or repeating symbols increases, the model performance drops quickly. We investigate the potential causes behind this phenomenon and examine a set of possible methods, including explicit positional markers, fine-grained computation steps, and LMs with callable programs. Experimental results show that none of these techniques can solve the simplest addition induction problem completely. In the end, we introduce LMs with tutor, which demonstrates every single step of teaching. LMs with tutor is able to deliver 100% accuracy in situations of OOD and repeating symbols, shedding new insights on the boundary of large LMs in induction.
Inaccuracy Rates for Distributed Inference Over Random Networks With Applications to Social Learning

This paper studies probabilistic rates of convergence for consensus+innovations type of algorithms in random, generic networks. For each node, we find a lower and also a family of upper bounds on the large deviations rate function, thus enabling the computation of the exponential convergence rates for the events of interest on the iterates. Relevant applications include error exponents in distributed hypothesis testing, rates of convergence of beliefs in social learning, and inaccuracy rates in distributed estimation. The bounds on the rate function have a very particular form at each node: they are constructed as the convex envelope between the rate function of the hypothetical fusion center and the rate function corresponding to a certain topological mode of the node’s presence. We further show tightness of the discovered bounds for several cases, such as pendant nodes and regular networks, thus establishing the first proof of the large deviations principle for consensus+innovations and social learning in random networks.