Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-Term Growth and Policy Challenges in the Large Emerging Economies

Taken together, Brazil, China, India, Indonesia and South Africa – the “BIICS” – have been an important engine for world growth, and they account for a growing share of global output. However, further reforms will be needed to ensure catch-up to OECD GDP per capita levels over the long term. This paper uses the OECD’s Going for Growth framework, as well as other available evidence linking policies to economic performance, to identify key structural policy challenges in the BIICS for the years ahead. While such challenges vary from country to country, common areas for reform include strengthening policies in the areas of education, product market regulation and labour markets, as well as improving more basic market institutions. This Working Paper relates to the OECD’s Economic Policy Reforms: Going for Growth 2010 (www.oecd.org/goingforgrowth) and the Economic Surveys of China, India, South Africa, Indonesia, and Brazil (www.oecd.org/eco/surveys) Croissance de long terme et defis de politique economique dans les grandes economies emergentes Pris ensemble, l’Afrique du Sud, le Bresil, la Chine, l’Inde et l’Indonesie - les « BIICS » - ont largement contribue a la croissance mondiale et ils representent une part croissante de la production mondiale. Cependant, de nouvelles reformes seront necessaires pour leur permettre de rattraper, a terme, les niveaux de PIB par habitant des pays de l’OCDE. Le present chapitre utilise le cadre d’analyse mis au point par l’OCDE pour les besoins du projet Objectif croissance, ainsi que d’autres donnees etablissant un lien entre les politiques publiques et la performance economique, pour identifier les principaux enjeux de politique structurelle auxquels les BIICS vont etre confrontes dans les annees a venir. Ces enjeux different selon les pays, mais un certain nombre de reformes communes semblent necessaires, notamment pour renforcer les politiques publiques dans les domaines de l’education, de la reglementation des marches de produits et du marche du travail, ainsi que pour ameliorer certaines institutions fondamentales de l’economie de marche. Ce Document de travail se rapporte aux Reformes economiques: Objectif croissance 2010 (www.oecd.org/objectifcroissance) et aux Etudes economiques de l'OCDE de : la Chine, l’Inde, l’Afrique du Sud, l’Indonesie, et le Bresil (www.oecd.org/eco/etudes).
Computational simulation methodologies for mechanobiological modelling: a cell-centred approach to neointima development in stents

The design of medical devices could be very much improved if robust tools were available for computational simulation of tissue response to the presence of the implant. Such tools require algorithms to simulate the response of tissues to mechanical and chemical stimuli. Available methodologies include those based on the principle of mechanical homeostasis, those which use continuum models to simulate biological constituents, and the cell-centred approach, which models cells as autonomous agents. In the latter approach, cell behaviour is governed by rules based on the state of the local environment around the cell; and informed by experiment. Tissue growth and differentiation requires simulating many of these cells together. In this paper, the methodology and applications of cell-centred techniques—with particular application to mechanobiology—are reviewed, and a cell-centred model of tissue formation in the lumen of an artery in response to the deployment of a stent is presented. The method is capable of capturing some of the most important aspects of restenosis, including nonlinear lesion growth with time. The approach taken in this paper provides a framework for simulating restenosis; the next step will be to couple it with more patient-specific geometries and quantitative parameter data.
The complete mitochondrial genome of Scolopsis vosmeri and phylogenetic relationship of genus Scolopsis

Abstract The complete mitogenome genome of Scolopsis vosmeri was determined in this study, which is the first recorded for the complete mitogenome in the genus Scolopsis. The circular mtDNA molecule was 16 770 bp in size and encoded 13 protein-coding genes, 2 rRNAs, 22 tRNAs and two non-coding regions, with gene arrangement and content basically identical to those of other species of Nemipteridae. The result of phylogenetic analysis strongly supported that S. vosmeri was first clustered together with genus Nemipterus and formed a monophyly in the family Nemipteridae, and then they constituted a sister-group relationship with three families Sparidae, Lethrinidae, and Lutjanidae. It concluded that the genus Scolopsis should be classified into the family Nemipteridae. The present study also revealed the phylogenetic relationship of this genus at molecular levels.
The Gastrointestinal Microbiome

The excessive use of alcohol is a global problem causing many adverse pathological health effects and a significant financial health care burden. This review addresses the effect of alcohol consumption on the microbiota in the gastrointestinal tract (GIT). Although data are limited in humans, studies highlight the importance of changes in the intestinal microbiota in alcohol-related disorders. Alcohol-induced changes in the GIT microbiota composition and metabolic function may contribute to the well-established link between alcohol-induced oxidative stress, intestinal hyperpermeability to luminal bacterial products, and the subsequent development of alcoholic liver disease (ALD), as well as other diseases. In addition, clinical and preclinical data suggest that alcohol-related disorders are associated with quantitative and qualitative dysbiotic changes in the intestinal microbiota and may be associated with increased GIT inflammation, intestinal hyperpermeability resulting in endotoxemia, systemic inflammation, and tissue damage/organ pathologies including ALD. Thus, gut-directed interventions, such as probiotic and synbiotic modulation of the intestinal microbiota, should be considered and evaluated for prevention and treatment of alcohol-associated pathologies.
Spatial patterns of precipitation in England and Wales and a revised

The spatial patterns of precipitation variability over the United Kingdom are examined for the period 1861–1970 using principal components analysis. Most of the variance (around 50 per cent) is explained by the first component, which is characterized by uniform variability over the whole region. Higher-order components isolate differential variability between different regions. The first four components are essentially the same for all months, all seasons and the annual total, highlighting the intra-annual constancy of inter-regional variability and pointing to strong geographical and topographical control. A subdivision of the analysis period into two halves produces no change in the component patterns, but changes in the variance explained by the first component imply that summer precipitation has become less spatially variable since 1915. On the basis of the principal components analysis the England and Wales region is divided into five coherent subregions. A regression technique is developed to produce a homogeneous area-average precipitation series for England and Wales using the longest site precipitation records available and maintaining even spatial coverage. The method used allows changes in the reliability of the area-average due to changes in the density of station coverage to be quantified. An empirical relationship between the standard error of estimate of area-averaged precipitation and the number of recording sites is derived. Significant inhomogeneities in the Nicholas and Glasspoole England and Wales precipitation series at 1915 and 1957 are detected and quantified. The Nicholas and Glasspoole series as a whole is found to underestimate precipitation amounts noticeably in the late eighteenth and early nineteenth centuries.
The Trends of the Agrarian Foreign Trade of CR after Accession to EU, Competitiveness of Commodities

The extent of agricultural foreign trade (AFT) of the Czech Republic (CR) increased markedly after accession to EU. The first part of this article is devoted to the trends during the period of 2004 â€“ 2008. Detailed analysis of the CR AFT has been carried out for all 24 items of the basic food goods during the same period for the assessment of competitiveness of individual commodities. The Balass indicator RCA (Revealed Comparative Advantage) was used for this analysis, as well as the Michaely index that shows the specific measure of specialization for export. The method of logarithmic decomposition, which makes it possible to determine the effect of prices and quantities to net export has also been used. This paper was processed within the framework of the Research Project of MSM 6046070906 "The economics of Czech agricultural resources and their effective use in the frame of multifunctional agri-food systems".
A numerical model for the simulation of stable isotope profiles in drying soils

A numerical model was developed for the simulation of vertical profiles of water content and stable isotope concentration in a drying soil. It allows the calculation of stationary isotopic profiles occurring in steady state conditions as well as the evolution of the profiles at the first and second drying stages in transient conditions for any initial profile, atmospheric conditions, and temperature distribution. A parameterization of the kinetic fractionation in relation to surface water content is proposed to allow the simulation of its evolution during the development of a dry superficial layer in nonsteady conditions. After a qualitative study of the isotopic enrichment the model creates, it is validated by comparison with existing analytical solutions which are limited to the stationary state. A numerical application in nonsteady conditions allowed us to simulate the evolution of the isotopic profile for a drying soil and to compare it with the stationary profile. The discussion emphasizes the lack of experimental examination of the liquid-vapor isotopic equilibrium and the liquid phase isotopic homogeneity in porous media, both major assumptions in the theory of water and stable isotope transfers in soils.
How Well Can Markets for Development Rights Work? Evaluating a Farmland Preservation Program

Transferable development rights (TDRs) can be used as a local planning tool to preserve land for particular uses. TDRs separate ownership of the right to develop land from ownership of the land itself, creating a market in which the development rights can be bought and sold. Landowners who sell TDRs permanently preserve their land in an undeveloped state; those TDRs are then used to increase the density of development elsewhere. In this paper, we evaluate a TDR program for preserving farmland in Calvert County, Maryland. We evaluate the performance of the TDR market over the 23-year life of the program by looking at the number of transactions and TDRs sold and the level and dispersion of prices over time. We also look closely at the influence of the county government as a participant in the market. We locate the properties that have been preserved in the county as well as the subdivisions that have used TDRs to increase the density of development. We find that the program is achieving Calvert’s farmland preservation goals and the TDR market appears to have operated efficiently, at least since 1993 when the county increased its role in the TDR market. At that time, the county began purchasing a small number of development rights each year at a fixed and known price and also began publishing a newsletter providing information about the program. These actions stabilized prices and appear to have bolstered participants’ faith in the longevity of the program. Most of the agricultural properties preserved in the program are in areas less profitable for development. The demand for TDRs to increase density is greatest in subdivisions in the northern part of the county, closer to the major urban cities, and interestingly, in relatively rural areas with lowdensity zoning. There appears to be little demand for TDRs and the associated higher density in town centers or areas zoned with residential zoning.
Multi-Swap k-Means++

The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular $k$-means clustering objective and is known to give an $O(\log k)$-approximation in expectation. To obtain higher quality solutions, Lattanzi and Sohler (ICML 2019) proposed augmenting $k$-means++ with $O(k \log \log k)$ local search steps obtained through the $k$-means++ sampling distribution to yield a $c$-approximation to the $k$-means clustering problem, where $c$ is a large absolute constant. Here we generalize and extend their local search algorithm by considering larger and more sophisticated local search neighborhoods hence allowing to swap multiple centers at the same time. Our algorithm achieves a $9 + \varepsilon$ approximation ratio, which is the best possible for local search. Importantly we show that our approach yields substantial practical improvements, we show significant quality improvements over the approach of Lattanzi and Sohler (ICML 2019) on several datasets.
HTC-DC Net: Monocular Height Estimation From Single Remote Sensing Images

Three-dimensional geoinformation is of great significance for understanding the living environment; however, 3-D perception from remote sensing data, especially on a large scale, is restricted, mainly due to the high costs of 3-D sensors such as light detection and ranging (LiDAR). To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data. As an ill-posed problem, monocular height estimation requires well-designed networks for enhanced representations to improve the performance. Moreover, the distribution of height values is long-tailed with the low-height pixels, e.g., the background (BG), as the head, and thus, trained networks are usually biased and tend to underestimate building heights. To solve the problems, instead of formalizing the problem as a regression task, we propose HTC-DC Net following the classification–regression paradigm, with the head-tail cut (HTC) and the distribution-based constraints (DCs) as the main contributions. HTC-DC Net is composed of the backbone network as the feature extractor, the HTC-AdaBins module, and the hybrid regression process. The HTC-AdaBins module serves as the classification phase to determine bins adaptive to each input image. It is equipped with a vision transformer (ViT) encoder to incorporate local context with holistic information and involves an HTC to address the long-tailed problem in monocular height estimation for balancing the performances of foreground (FG) and BG pixels. The hybrid regression process does the regression via the smoothing of bins from the classification phase, which is trained via DCs. The proposed network is tested on three datasets of different resolutions, namely ISPRS Vaihingen (0.09 m), Data Fusion Contest 19 (DFC19) (1.3 m), and Global Building Height (GBH) (3 m). The experimental results show the superiority of the proposed network over existing methods by large margins. Extensive ablation studies demonstrate the effectiveness of each design component. The codes and trained models are published at https://github.com/zhu-xlab/HTC-DC-Net.
Estimating Volume, Biomass, and Potential Emissions of Hand-Piled Fuels

Dimensions, volume, and biomass were measured for 121 hand-constructed piles composed primarily of coniferous (n = 63) and shrub/hardwood (n = 58) material at sites in Washington and California. Equations using pile dimensions, shape, and type allow users to accurately estimate the biomass of hand piles. Equations for estimating true pile volume from simple geometric shapes and measurements of pile dimensions were also developed for users who require estimates of pile volume for regulatory reporting. Biomass and volume estimation equations were developed to allow users to estimate either value from pile dimensions. Hand pile biomass estimates can be used to predict fuel consumption and smoke emissions by applying proportional consumption estimates and emission factors. Equations to estimate pile volume, pile biomass, fuel consumption, and pollutant emissions from pile shape, dimensions, and quantity are programmed into a Web-based calculator for use by the management and regulatory communities.
Neuro-Inspired Hierarchical Multimodal Learning

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Distinct from most traditional fusion models that aim to incorporate all modalities as input, our model designates the prime modality as input, while the remaining modalities act as detectors in the information pathway. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of downstream tasks. Experimental evaluations on both the MUStARD and CMU-MOSI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks.
SU(d)-Symmetric Random Unitaries: Quantum Scrambling, Error Correction, and Machine Learning

Quantum information processing in the presence of continuous symmetry is of wide importance and exhibits many novel physical and mathematical phenomena. SU(d) is a continuous symmetry group of particular interest since it represents a fundamental type of non-Abelian symmetry and also plays a vital role in quantum computation. Here, we explicate the applications of SU(d)-symmetric random unitaries in three different contexts ranging from physics to quantum computing: information scrambling with non-Abelian conserved quantities, covariant quantum error correcting random codes, and geometric quantum machine learning. First, we show that, in the presence of SU(d) symmetry, the local conserved quantities would exhibit residual values even at $t \rightarrow \infty$ which decays as $\Omega(1/n^{3/2})$ under local Pauli basis for qubits and $\Omega(1/n^{(d+2)^2/2})$ under local symmetric basis for general qudits with respect to the system size, in contrast to $O(1/n)$ decay for U(1) case and the exponential decay for no-symmetry case in the sense of out-of-time ordered correlator (OTOC). Second, we show that SU(d)-symmetric unitaries can be used to construct asymptotically optimal (in the sense of saturating the fundamental limits on the code error that have been called the approximate Eastin-Knill theorems) SU(d)-covariant codes that encodes any constant $k$ logical qudits, extending [Kong \&Liu; PRXQ 3, 020314 (2022)]. Finally, we derive an overpartameterization threshold via the quantum neural tangent kernel (QNTK) required for exponential convergence guarantee of generic ansatz for geometric quantum machine learning, which reveals that the number of parameters required scales only with the dimension of desired subspaces rather than that of the entire Hilbert space. We expect that our work invites further research on quantum information with continuous symmetries.
Assessing the uncertainty associated with intermittent rainfall fields

In many practical situations where spatial rainfall estimates are needed, rainfall occurs as a spatially intermittent phenomenon. An efficient geostatistical method for rainfall estimation in the case of intermittency has previously been published and comprises the estimation of two independent components: a binary random function for modeling the intermittency and a continuous random function that models the rainfall inside the rainy areas. The final rainfall estimates are obtained as the product of the estimates of these two random functions. However the published approach does not contain a method for estimation of uncertainties. The contribution of this paper is the presentation of the indicator maximum likelihood estimator from which the local conditional distribution of the rainfall value at any location may be derived using an ensemble approach. From the conditional distribution, representations of uncertainty such as the estimation variance and confidence intervals can be obtained. An approximation to the variance can be calculated more simply by assuming rainfall intensity is independent of location within the rainy area. The methodology has been validated using simulated and real rainfall data sets. The results of these case studies show good agreement between predicted uncertainties and measured errors obtained from the validation data.
A Comprehensive Survey of Document-level Relation Extraction (2016-2023)

Document-level relation extraction (DocRE) is an active area of research in natural language processing (NLP) concerned with identifying and extracting relationships between entities beyond sentence boundaries. Compared to the more traditional sentence-level relation extraction, DocRE provides a broader context for analysis and is more challenging because it involves identifying relationships that may span multiple sentences or paragraphs. This task has gained increased interest as a viable solution to build and populate knowledge bases automatically from unstructured large-scale documents (e.g., scientific papers, legal contracts, or news articles), in order to have a better understanding of relationships between entities. This paper aims to provide a comprehensive overview of recent advances in this field, highlighting its different applications in comparison to sentence-level relation extraction.
Interannual Variability of Chlorofluorocarbons, pCFC Ages and Ideal Ages in the North Pacific from 1958-2000 as Simulated by an Ocean General Circulation Model

Chlorofluorocarbon (CFCs) concentrations and ideal age are simulated in a global integration of the Parallel Ocean Program (POP) with realistic surface forcing derived from the NCEP/NCAR reanalysis (1958-2000). The present study focuses on the subtropical and subpolar thermocline of the North Pacific due to the relatively good observational coverage and the well documented decadal timescale variability of the circulation for that region. Tracer ventilation ages are computed from the simulation using the CFC partial pressure (pCFC). Biases arise in pCFC ages due to the interaction of mixing with the non-linear CFC atmospheric time-histories, and the model CFC ages significantly underestimate ideal age in the midto lower-thermocline and in the shadow zone of the eastern tropics and subtropics. Variability in pCFC ages is well correlated with variability of ideal age on interannual timescales, though the amplitude of the pCFC variability may be substantially less due to the offsets between the tracer and ideal ages and their spatial gradients. Over much of the subtropical gyre, age variability is also well correlated with that of potential vorticity. The significant interannual variability in tracer ages and longer-term temporal trends in the tracer age biases complicate the interpretation of CFC data collected from repeat occupations on 5 to 10 year intervals as currently envisioned by CLIVAR. In particular, the tracer age differences from the instantaneous samples are likely not representative, at least directly, Tsumune et al., Interannual variability of the ages 2 of interdecadal variability in ocean ventilation.