Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Estuaries in South African Fisheries: Economic Importance and Management Implications

Existing catch data for 129 of 255 functional estuaries on the South African coastline were reviewed and the relationships between fish catch and estuary size, type and biogeographical region analysed using simple and multivariate models. The best predictive models were obtained by analysing data separately for each biogeographical region. Estuary size alone explained >80% of the variation in catch in the warm temperate region and >90% of the variation in catch in the cool temperate and subtropical region. Further analysis of the two main estuary types, i.e.permanently open and temporarily open/closed estuaries revealed a steeper regression slope and therefore greater productivity for the permanently open systems. Estuary size (ha) and type (five) were used to explain catches within the warm temperate and subtropical regions using general linear models. The models were able to explain 82 and 98% of the variance in catches for the two regions respectively, and both were significant (p < 0.001). These models were applied to estuary type and size data for all 246 estuaries in the two regions and a total catch of 1 840 tons per year was estimated. Including the cool temperate region the total estuarine catch in South Africa was estimated at 2 480 tons per year. In all, 50% of the estuarine catch was attributed to commercial seine and gillnet fisheries, 46% to recreational angling and 4% to traditional trap and spear fisheries. Total catch value was R433 million per year, of which 99% could be attributed to recreational angling. Estuarine contribution to the inshore marine fisheries was estimated at approximately R490 million per year, estuarydependent species constituting 83% of the catch of the recreational shore and commercial seine and gillnet fisheries and only 7% of the catch of the recreational spearfishery and commercial and recreational boat fisheries. In 2002, the total value of estuarine and estuary-dependent fisheries was estimated to be R1.251 billion.
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation

Thompson sampling has become a ubiquitous approach to online decision problems with bandit feedback. The key algorithmic task for Thompson sampling is drawing a sample from the posterior of the optimal action. We propose an alternative arm selection rule we dub TS-UCB, that requires negligible additional computational effort but provides significant performance improvements relative to Thompson sampling. At each step, TS-UCB computes a score for each arm using two ingredients: posterior sample(s) and upper confidence bounds. TS-UCB can be used in any setting where these two quantities are available, and it is flexible in the number of posterior samples it takes as input. This proves particularly valuable in heuristics for deep contextual bandits: we show that TS-UCB achieves materially lower regret on all problem instances in a deep bandit suite proposed in Riquelme et al. (2018). Finally, from a theoretical perspective, we establish optimal regret guarantees for TS-UCB for both the K-armed and linear bandit models.
Learning Halfspaces with Tsybakov Noise

We study the efficient PAC learnability of halfspaces in the presence of Tsybakov noise. In the Tsybakov noise model, each label is independently flipped with some probability which is controlled by an adversary. This noise model significantly generalizes the Massart noise model, by allowing the flipping probabilities to be arbitrarily close to $1/2$ for a fraction of the samples. Our main result is the first non-trivial PAC learning algorithm for this problem under a broad family of structured distributions -- satisfying certain concentration and (anti-)anti-concentration properties -- including log-concave distributions. Specifically, we given an algorithm that achieves misclassification error $\epsilon$ with respect to the true halfspace, with quasi-polynomial runtime dependence in $1/\epsilin$. The only previous upper bound for this problem -- even for the special case of log-concave distributions -- was doubly exponential in $1/\epsilon$ (and follows via the naive reduction to agnostic learning). Our approach relies on a novel computationally efficient procedure to certify whether a candidate solution is near-optimal, based on semi-definite programming. We use this certificate procedure as a black-box and turn it into an efficient learning algorithm by searching over the space of halfspaces via online convex optimization.
Characterization of a 27 kDa fibrinolytic enzyme from Bacillus amyloliquefaciens CH86-1 isolated from cheonggukjang

Bacillus amyloliquefaciens CH86-1 isolated from cheonggukjang was found to have strong fibrinolytic activity when grown on Luria-Bertani medium, and this activity increased sharply when the cells entered the stationary phase. The major fibrinolytic enzyme, AprE86-1, was purified from culture supernatant and identified by tandem mass spectrometry. The molecular weight of the mature enzyme was determined to be 27 kDa by sodium dodecyl sulfatepolyacrylamide gel electrophoresis. The optimum pH of partially purified AprE86-1 was 6.0-7.0 and it was stable at up to 45°C.
Probabilities of first order sentences on sparse random relational structures: An application to definability on random CNF formulas

We extend the convergence law for sparse random graphs proven by Lynch to arbitrary relational languages. We consider a finite relational vocabulary $\sigma $ and a first-order theory $T$ for $\sigma $ composed of symmetry and anti-reflexivity axioms. We define a binomial random model of finite $\sigma $-structures that satisfy $T$ and show that first-order properties have well defined asymptotic probabilities when the expected number of tuples satisfying each relation in $\sigma $ is linear. It is also shown that these limit probabilities are well behaved with respect to several parameters that represent the density of tuples in each relation $R$ in the vocabulary $\sigma $. An application of these results to the problem of random Boolean satisfiability is presented. We show that in a random $k$-CNF formula on $n$ variables, where each possible clause occurs with probability $\sim c/n^{k-1}$, independently any first-order property of $k$-CNF formulas that implies unsatisfiability does almost surely not hold as $n$ tends to infinity.
A model for the spread of an epidemic from local to global: A case study of COVID-19 in India

In this paper we propose an epidemiological model for the spread of COVID-19. The dynamics of the spread is based on four fundamental categories of people in a population: Tested and infected, Non-Tested but infected, Tested but not infected, and non-Tested and not infected. The model is based on two levels of dynamics of spread in the population: at local level and at the global level. The local level growth is described with data and parameters which include testing statistics for COVID-19, preventive measures such as nationwide lockdown, and the migration of people across neighboring locations. In the context of India, the local locations are considered as districts and migration or traffic flow across districts are defined by normalized edge weight of the metapopulation network of districts which are infected with COVID-19. Based on this local growth, state level predictions for number of people tested with COVID-19 positive are made. Further, considering the local locations as states, prediction is made for the country level. The values of the model parameters are determined using grid search and minimizing an error function while training the model with real data. The predictions are made based on the present statistics of testing, and certain linear and log-linear growth of testing at state and country level. Finally, it is shown that the spread can be contained if number of testing can be increased linearly or log-linearly by certain factors along with the preventive measures in near future. This is also necessary to prevent the sharp growth in the count of infected and to get rid of the second wave of pandemic.
FRagmentation and Evolution of Dense Cores Judged by ALMA (FREJA). I. Overview: Inner ∼1000 au Structures of Prestellar/Protostellar Cores in Taurus

We have performed survey-type observations in 1 mm continuum and molecular lines toward dense cores (32 prestellar + 7 protostellar) with an average density of ≳105 cm−3 in the Taurus molecular clouds using the Atacama Large Millimeter/submillimeter Array–Atacama Compact Array (ALMA-ACA) stand-alone mode with an angular resolution of 6. ″5 (∼900 au). The primary purpose of this study is to investigate the innermost part of dense cores with view to understanding the initial condition of star formation. In the protostellar cores, contributions from protostellar disks dominate the observed continuum flux with a range of 35%–90%, except for the very low-luminosity object. For the prestellar cores, we have successfully confirmed continuum emission from dense gas with a density of ≳3 × 105 cm−3 toward approximately one-third of the targets. Thanks to the lower spatial frequency coverage with the ACA 7 m array, the detection rate is significantly higher than that of the previous surveys, which have zero or one continuum-detected sources among a large number of starless samples using the ALMA Main Array. The statistical counting method tells us that the lifetime of prestellar cores until protostar formation therein approaches the freefall time as the density increases. Among the prestellar cores, at least two targets have possible internal substructures, which are detected in continuum emission with the size scale of ∼1000 au if we consider the molecular line (C18O and N2D+) distributions. These results suggest that small-scale fragmentation/coalescence processes occur in a region smaller than 0.1 pc, which may determine the final core mass associated with individual protostar formation before starting the dynamical collapse of the core with a central density of ∼(0.3–1) × 106 cm−3.
Higher-order interactions in statistical physics and machine learning: A non-parametric solution to the inverse problem

We propose a model-independent definition of $n$-point interaction within a system of binary and categorical random variables from first principles, via the non-parametric framework of Targeted Learning, a subfield of mathematical statistics. This definition provides an interpretation for both magnitude and sign of $2$-point, $3$-point, and general $n$-point interactions. We show that the sign of an $n$-point interaction is interpretable relative to an $(n-1)$-point interaction obtained by fixing any one of the $n$ variables. The non-parametric definition of interaction is fundamentally unbiased and reduces to familiar notions of interaction in parametric statistical physics models. Moreover, by taking into account information on conditional independence and without any further assumptions, the accuracy of interactions estimated directly from data is substantially increased whilst the number of samples required and the computational run time are both reduced. We illustrate these concepts both analytically and numerically on (i) the $2$-dimensional Ising model, (ii) an Ising-like model with non-zero $2$-point, $3$-point, and $4$-point interactions, (iii) the Restricted Boltzmann Machine (RBM), and argue that the formulation applies to energy-based models more generally. The non-parametric formulation allows for the direct reconstruction of the Hamiltonian from the data it generated. Finally, we discuss novel applications of this work, namely estimating causal molecular interactions leading to physiological outcomes, in population biomedicine.
Antioxidative and Bactericidal Properties of Phenolic Compounds in Rapeseeds

Antioxidative and bactericidal properties were investigated in isolated and fractionated phenolic compounds of rapeseeds. Among the investigated groups of compounds (phenolic acids, sinapine and the products of its hydrolysis) phenolic acids, and especially sinapic acid, were found to be the most active. Due to valuable properties of these compounds they can be considered to be applied for food and fodder preserving. Antioxidative und antibakterielle Eigenschaften der Rapsphenolverbindungen Antioxidative und antibakterielle Eigenschaften wurden in isolierten und fraktionierten Rapsphenolverbindungen untersucht. Unter den untersuchten Phenolverbindungen (Phenolsauren, Sinapin und Produkte ihrer Hydrolyse) zeichneten sich Phenolsauren sowie substantielle Ableitungen der Sinapinsaure durch die hochste Aktivitat aus. Diese wertvollen Eigenschaften konnten bei der Haltbarmachung von Lebens- und Futtermitteln genutzt werden.
A Ray–Knight representation of up-down Chinese restaurants

We study composition-valued continuous-time Markov chains that appear naturally in the framework of Chinese Restaurant Processes (CRPs). As time evolves, new customers arrive (up-step) and existing customers leave (down-step) at suitable rates derived from the ordered CRP of Pitman and Winkel (2009). We relate such up-down CRPs to the splitting trees of Lambert (2010) inducing spectrally positive Levy processes. Conversely, we develop theorems of Ray-Knight type to recover more general up-down CRPs from the heights of Levy processes with jumps marked by integer-valued paths. We further establish limit theorems for the Levy process and the integer-valued paths to connect to work by Forman et al. (2018+) on interval partition diffusions and hence to some long-standing conjectures.
The SOFIA Massive (SOMA) Star Formation Survey. III. From Intermediate- to High-mass Protostars

We present SOFIA–FORCAST images of 14 intermediate-mass protostar candidates as part of the SOFIA Massive (SOMA) Star Formation Survey. We build spectral energy distributions, also using archival Spitzer, Herschel, and IRAS data. We then fit the spectral energy distributions with radiative transfer models of Zhang & Tan, based on turbulent core accretion theory, to estimate key protostellar properties. With the addition of these intermediate-mass sources, based on average properties derived from SED fitting, SOMA protostars span luminosities from , current protostellar masses from , and ambient clump mass surface densities, , from . A wide range of evolutionary states of the individual protostars and of the protocluster environments is also probed. We have also considered about 50 protostars identified in infrared dark clouds that are expected to be at the earliest stages of their evolution. With this global sample, most of the evolutionary stages of high- and intermediate-mass protostars are probed. The best-fitting models show no evidence that a threshold value of the protocluster clump mass surface density is required to form protostars up to . However, to form more massive protostars, there is tentative evidence that needs to be . We discuss how this is consistent with expectations from core accretion models that include internal feedback from the forming massive star.
Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context

We present a novel data augmentation technique, CRA (Contextual Response Augmentation), which utilizes conversational context to generate meaningful samples for training. We also mitigate the issues regarding unbalanced context lengths by changing the input output format of the model such that it can deal with varying context lengths effectively. Specifically, our proposed model, trained with the proposed data augmentation technique, participated in the sarcasm detection task of FigLang2020, have won and achieves the best performance in both Reddit and Twitter datasets.
Major difference in the expression of δ‐ and μ‐opioid receptors between turtle and rat brain

The reptilian turtle brain has a remarkably higher endurance for anoxia than mammalian brains. Since the response to O2 deprivation is dependent in a major way on the expression and regulation of membrane proteins, differences in such proteins may play a role in the species‐related differences in hypoxic responses. Because opioid system is involved in the regulation of hypoxic responses, we asked whether there are differences between rat and turtle brains in terms of opioid receptor expression. In this work, we compared the expression and distribution of δ‐and μ‐opioid receptors in the turtle and rat brains. Our results show that (1) the dissociation constant (Kd) for δ‐receptor binding was approximately four times lower and Bmax was more than double in the turtle brain homogenates than in rat ones; (2) the δ‐receptor binding density was heterogeneously distributed in the turtle brain, with a higher density in the rostral regions than in the brainstem and spinal cord, and was generally much higher than in rat brains from the cortex to spinal cord; (3) the δ‐opioid receptors in the rat brains were mostly located in the cortex, caudate putamen, and amygdala with an extremely low density in most subcortical (e.g., hippocampus and thalamus) and almost all brainstem regions; and (4) in sharp contrast to δ‐opioid receptors, μ‐opioid receptor density was much lower in all turtle brain regions compared with the rat ones. Our results demonstrate that the turtle brain is actually an organ of δ‐opioid receptors, whereas the rat brain has predominantly μ‐opioid receptors. Because we have recently found that δ‐opioid receptors protect neurons against glutamate and hypoxic stress, we speculate that the unique pattern of δ‐receptor receptor expression and distribution plays a critical role in the tolerance of turtle brain to stressful situations characterized by glutamate excitotoxicity. J. Comp. Neurol. 436:202–210, 2001. © 2001 Wiley‐Liss, Inc.
VINTERGATAN – II. The history of the Milky Way told by its mergers

Using the VINTERGATAN cosmological zoom simulation, we explore the contributions of the in situ and accreted material, and the effect of galaxy interactions and mergers in the assembly of a Milky Way-like galaxy. We find that the initial growth phase of galaxy evolution, dominated by repeated major mergers, provides the necessary physical conditions for the assembly of a thick, kinematically hot disk populated by high-[$\alpha$/Fe] stars, formed both in situ and in accreted satellite galaxies. We find that the diversity of evolutionary tracks followed by the simulated galaxy and its progenitors leads to very little overlap of the in situ and accreted populations for any given chemical composition. At a given age, the spread in [$\alpha$/Fe] abundance ratio results from the diversity of physical conditions in VINTERGATAN and its satellites, with an enhancement in [$\alpha$/Fe] found in stars formed during starburst episodes. Later, the cessation of the merger activity promotes the in situ formation of stars in the low-[$\alpha$/Fe] regime, in a radially extended, thin and overall kinematically colder disk, thus establishing chemically bimodal thin and thick disks, in line with observations. We draw links between notable features in the [Fe/H] - [$\alpha$/Fe] plane with their physical causes, and propose a comprehensive formation scenario explaining self-consistently, in the cosmological context, the main observed properties of the Milky Way.
Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics

Automatic metrics are fundamental for the development and evaluation of machine translation systems. Judging whether, and to what extent, automatic metrics concur with the gold standard of human evaluation is not a straightforward problem. We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, which often leads to falsely confident conclusions about a metric’s efficacy. Finally, we turn to pairwise system ranking, developing a method for thresholding performance improvement under an automatic metric against human judgements, which allows quantification of type I versus type II errors incurred, i.e., insignificant human differences in system quality that are accepted, and significant human differences that are rejected. Together, these findings suggest improvements to the protocols for metric evaluation and system performance evaluation in machine translation.
A Note on Multiple-Processor Multitask Scheduling

In this paper we study the multiple-processor multitask scheduling problem in the deterministic and stochastic models. We consider and analyze M-SRPT, a simple modification of the shortest remaining processing time algorithm, which always schedules jobs according to SRPT whenever possible, while processes tasks in an arbitrary order. The modified SRPT algorithm is shown to achieve an competitive ratio of $\Theta(\log \alpha +\beta)$ for minimizing flow time, where $\alpha$ denotes the ratio of maximum job workload and minimum job workload, $\beta$ represents the ratio between maximum non-preemptive task workload and minimum job workload. The algorithm is shown to be optimal (up to a constant factor) when there are constant number of machines. We further consider the problem under poisson arrival and general workload distribution, M-SRPT is proved to be asymptotic optimal when the traffic intensity $\rho$ approaches $1$, if the task size is upper bound by the derived upper bound $\eta$.
String Stability of a Vehicular Platoon With the Use of Macroscopic Information

The paper investigates the possibility of using macroscopic information to improve control performance of a vehicular platoon composed of autonomous vehicles. A mesoscopic traffic modeling framework is proposed, and closed loop String Stability is achieved by exploiting Input-to-State Stability (ISS) for the analysis of the platoon. Examples of mesoscopic control laws are illustrated and simulations show the effectiveness of our approach in ensuring String Stability and improving the platoon behavior.
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles

The strong correlation between neurons or filters can significantly weaken the generalization ability of neural networks. Inspired by the well-known Tammes problem, we propose a novel diversity regularization method to address this issue, which makes the normalized weight vectors of neurons or filters distributed on a hypersphere as uniformly as possible, through maximizing the minimal pairwise angles (MMA). This method can easily exert its effect by plugging the MMA regularization term into the loss function with negligible computational overhead. The MMA regularization is simple, efficient, and effective. Therefore, it can be used as a basic regularization method in neural network training. Extensive experiments demonstrate that MMA regularization is able to enhance the generalization ability of various modern models and achieves considerable performance improvements on CIFAR100 and TinyImageNet datasets. In addition, experiments on face verification show that MMA regularization is also effective for feature learning.
Exhaustive Goodness of Fit Via Smoothed Inference and Graphics

Abstract Classical tests of goodness of fit aim to validate the conformity of a postulated model to the data under study. Given their inferential nature, they can be considered a crucial step in confirmatory data analysis. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. The main goal of this work is to establish a comprehensive framework for goodness of fit which naturally integrates modeling, estimation, inference and graphics. Modeling and estimation focus on a novel formulation of smooth tests that easily extends to arbitrary distributions, either continuous or discrete. Inference and adequate post-selection adjustments are performed via a specially designed smoothed bootstrap and the results are summarized via an exhaustive graphical tool called CD-plot. Technical proofs, codes and data are provided in the supplementary material.