Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpolating sequences for weighted Bergman spaces on strongly pseudoconvex bounded domains

Let $0 -1$, and $\Omega$ be a strongly pseudoconvex bounded domain with a smooth boundary in $\mathbb{C}^n$. We will study the interpolation problem for weighted Bergman spaces $A^p_\beta(\Omega)$. In the case, $1\leq p \max \{n(2p-1)-1, n(2q-1)-1\}$, where $q$ is the conjugate exponent of $p$ (let $q=1$, for $p=1$), we show that a sequence in $\mathbb{B}_n$, the unit ball in $\mathbb{C}^n$, is interpolating for $A^p_\beta(\mathbb{B}_n)$ if and only if it is separated.
Broadband Selection, Spectroscopic Identification, and Physical Properties of a Population of Extreme Emission-line Galaxies at 3 < z < 3.7

We present the selection, spectroscopic identification, and physical properties of extreme emission-line galaxies (EELGs) at 3 < z < 3.7, aiming at studying physical properties of an analog population of star-forming galaxies (SFGs) at the epoch of reionization. The sample is selected based on the excess in the observed Ks broadband flux relative to the best-fit stellar continuum model flux. By applying a 0.3 mag excess as a primary criterion, we select 240 EELG candidates with intense emission lines and estimated an observed-frame equivalent width (EW) of ≳1000 Å over the UltraVISTA-DR2 ultra-deep stripe in the COSMOS field. We then carried out HK-band follow-up spectroscopy for 23 of the candidates with Subaru/MOIRCS, and we find that 19 and 2 of them are at z > 3 with intense [O iii] emission and Hα emitters at z ≃ 2, respectively. These spectroscopically identified EELGs at z ≃ 3.3 show, on average, higher specific star formation rates (sSFRs) than the star-forming main sequence, low dust attenuation of E(B − V) ≲ 0.1 mag, and high [O iii]/[O ii]ratios of ≳3. We also find that our EELGs at z ≃ 3.3 have higher hydrogen-ionizing photon production efficiencies (ξion) than the canonical value (≃1025.2 erg−1 Hz), indicating that they are efficient in ionizing their surrounding interstellar medium. These physical properties suggest that they are low-metallicity galaxies with higher ionizing parameters and harder UV spectra than normal SFGs, which is similar to galaxies with Lyman continuum (LyC) leakage. Among our EELGs, those with the largest [O iii]/[O ii] and EW([O iii]) values would be the most promising candidates to search for LyC leakage.
A Complex Luminosity Function for the Anomalous Globular Clusters in NGC 1052-DF2 and NGC 1052-DF4

NGC 1052-DF2 and NGC 1052-DF4 are ultradiffuse galaxies that have extremely low velocity dispersions, indicating that they have little or no dark matter. Both galaxies host anomalously luminous globular clusters (GCs), with a peak magnitude of their GC luminosity function (GCLF) that is ∼1.5 mag brighter than the near-universal value of M V ≈ −7.5. Here we present an analysis of the joint GCLF of the two galaxies, making use of new Hubble Space Telescope photometry and Keck spectroscopy and a recently improved distance measurement. We apply a homogeneous photometric selection method to the combined GC sample of DF2 and DF4. The new analysis shows that the peak of the combined GCLF remains at M V ≈ −9 mag. In addition, we find a subpopulation of less luminous GCs at M V ≈ −7.5 mag, where the near-universal GCLF peak is located. The number of GCs in the magnitude range of −5 to −8 is in DF2 and in DF4, similar to that expected from other galaxies of the same luminosity. The total GC number between M V of −5 and −11 is for DF2 and for DF4, calculated from the background-subtracted GCLF. The data are consistent with both galaxies having two GC populations: the one expected for their luminosity, and a very luminous population composing ∼90% of the total cluster mass. The number of GCs does not scale with the halo mass in either DF2 or DF4, suggesting that N GC is not directly determined by the merging of halos.
Karyology of the northern Baltic peracaridan Crustacea

Abstract To elucidate cytotaxonomic relationships of the species of Isopoda, Amphipoda, and Mysidacea inhabiting the northern Baltic Sea, their mitotic or meiotic chromosome configurations were investigated using orceine squash and Giemsa-stained air spread techniques. The twenty one examined species and their haploid chromosome numbers were Idotea granulosa (n = 28), I. baltica and I. chelipes (n = 29), Mesidotea entomon (n = 30), Jaera ischiosetosa (n = 15), Asellus aquaticus (n = 8), Gammarus oceanicus, G. locusta, G. salinus, G. zaddachi, and G. duebeni (n = 26–27), Calliopius laeviusculus (n = 9), Leptocheirus pilosus (n = 11), Corophium volutator (n = 14), Pontoporeia femorata (n = 14), P. affinis (n = 26), Neomysis integer (n = 34), Mysis mixta, M. relicta, and Praunus inermis (n = 52) and P. flexuosus (n = 49). The observed karyological features are in accordance with phylogenetic reconstructions based on morphology and zoogeographical histories of the species. Mysids are the connecting link with .
Compressive Summarization with Plausibility and Salience Modeling

Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be deleted, then learn a model of what to actually delete by optimizing for content selection (ROUGE). In this work, we propose to relax the rigid syntactic constraints on candidate spans and instead leave compression decisions to two data-driven criteria: plausibility and salience. Deleting a span is plausible if removing it maintains the grammaticality and factuality of a sentence, and spans are salient if they contain important information from the summary. Each of these is judged by a pre-trained Transformer model, and only deletions that are both plausible and not salient can be applied. When integrated into a simple extraction-compression pipeline, our method achieves strong in-domain results on benchmark summarization datasets, and human evaluation shows that the plausibility model generally selects for grammatical and factual deletions. Furthermore, the flexibility of our approach allows it to generalize cross-domain: our system fine-tuned on only 500 samples from a new domain can match or exceed an in-domain extractive model trained on much more data.
Tokenization Repair in the Presence of Spelling Errors

We consider the following tokenization repair problem: Given a natural language text with any combination of missing or spurious spaces, correct these. Spelling errors can be present, but it’s not part of the problem to correct them. For example, given: “Tispa per isabout token izaionrep air”, compute “Tis paper is about tokenizaion repair”. We identify three key ingredients of high-quality tokenization repair, all missing from previous work: deep language models with a bidirectional component, training the models on text with spelling errors, and making use of the space information already present. Our methods also improve existing spell checkers by fixing not only more tokenization errors but also more spelling errors: once it is clear which characters form a word, it is much easier for them to figure out the correct word. We provide six benchmarks that cover three use cases (OCR errors, text extraction from PDF, human errors) and the cases of partially correct space information and all spaces missing. We evaluate our methods against the best existing methods and a non-trivial baseline. We provide full reproducibility under https://ad.informatik. uni-freiburg.de/publications.
RNNs Can Generate Bounded Hierarchical Languages with Optimal Memory

Recurrent neural networks empirically generate natural language with high syntactic fidelity. However, their success is not well-understood theoretically. We provide theoretical insight into this success, proving in a finite-precision setting that RNNs can efficiently generate bounded hierarchical languages that reflect the scaffolding of natural language syntax. We introduce Dyck-($k$,$m$), the language of well-nested brackets (of $k$ types) and $m$-bounded nesting depth, reflecting the bounded memory needs and long-distance dependencies of natural language syntax. The best known results use $O(k^{\frac{m}{2}})$ memory (hidden units) to generate these languages. We prove that an RNN with $O(m \log k)$ hidden units suffices, an exponential reduction in memory, by an explicit construction. Finally, we show that no algorithm, even with unbounded computation, can suffice with $o(m \log k)$ hidden units.
On the role of interactions in trans-sonically flowing atomic condensates

We provide a joint numerical-analytical study of the physics of a flowing atomic Bose-Einstein condensate in the combined presence of an external trap and a step potential which accelerates the atoms out of the condensate creating a pair of neighbouring black- and white-hole horizons. In particular, we focus on the rapidly growing density modulation pattern that appears in the supersonic region, an experimentally observed feature that was related to black-hole lasing phenomena. A direct assessment of the role of interactions in this process suggests an interpretation of the experimental data in terms of linear interference of atomic waves rather than collective effects. Our conclusions are further supported by an analytical solution of the Schrödinger equation in terms of Airy wave functions.
Scheduling Opportunistic Links in Two-Tiered Reconfigurable Datacenters

Reconfigurable optical topologies are emerging as a promising technology to improve the efficiency of datacenter networks. This paper considers the problem of scheduling opportunistic links in reconfigurable datacenters such as ProjecToR. We study the online setting and aim to minimize flow completion times. The problem is a two-tier generalization of classic switch scheduling problems. We present a stable-matching algorithm which is O(ε^-2 )-competitive against an optimal offline algorithm, in a resource augmentation model: the online algorithm runs 2+ε times faster. Our algorithm and result are fairly general and allow for different link delays and also apply to hybrid topologies which combine fixed and reconfigurable links. Our analysis is based on LP relaxation and dual fitting.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data

Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.
On Cross-Dataset Generalization in Automatic Detection of Online Abuse

NLP research has attained high performances in abusive language detection as a supervised classification task. While in research settings, training and test datasets are usually obtained from similar data samples, in practice systems are often applied on data that are different from the training set in topic and class distributions. Also, the ambiguity in class definitions inherited in this task aggravates the discrepancies between source and target datasets. We explore the topic bias and the task formulation bias in cross-dataset generalization. We show that the benign examples in the Wikipedia Detox dataset are biased towards platform-specific topics. We identify these examples using unsupervised topic modeling and manual inspection of topics’ keywords. Removing these topics increases cross-dataset generalization, without reducing in-domain classification performance. For a robust dataset design, we suggest applying inexpensive unsupervised methods to inspect the collected data and downsize the non-generalizable content before manually annotating for class labels.
A Deeper Look at the Layerwise Sparsity of Magnitude-based Pruning

Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on "how to choose," the layerwise sparsities are mostly selected algorithm-by-algorithm, often resorting to handcrafted heuristics or an extensive hyperparameter search. To fill this gap, we propose a novel importance score for global pruning, coined layer-adaptive magnitude-based pruning (LAMP) score; the score is a rescaled version of weight magnitude that incorporates the model-level $\ell_2$ distortion incurred by pruning, and does not require any hyperparameter tuning or heavy computation. Under diverse datasets and models, LAMP consistently outperforms popular existing schemes for layerwise sparsity selection. Furthermore, we observe that LAMP continues to outperform baselines even in weight-rewinding setups, while the connectivity-oriented layerwise sparsity (the strongest baseline overall) performs worse than a simple global magnitude-based pruning in this case.
Multi-Agent Trust Region Policy Optimization

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement learning (MARL) for partially observable Markov games (POMGs). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents' observation is sufficient. By using a local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratios with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method.
Revisiting Projection-free Online Learning: the Strongly Convex Case

Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe method, have gained significant interest in the machine learning community in recent years due to their ability to handle convex constraints that are popular in many applications, but for which computing projections is often computationally impractical in high-dimensional settings, and hence prohibit the use of most standard projection-based methods. In particular, a significant research effort was put on projection-free methods for online learning. In this paper we revisit the Online Frank-Wolfe (OFW) method suggested by Hazan and Kale \cite{Hazan12} and fill a gap that has been left unnoticed for several years: OFW achieves a faster rate of $O(T^{2/3})$ on strongly convex functions (as opposed to the standard $O(T^{3/4})$ for convex but not strongly convex functions), where $T$ is the sequence length. This is somewhat surprising since it is known that for offline optimization, in general, strong convexity does not lead to faster rates for Frank-Wolfe. We also revisit the bandit setting under strong convexity and prove a similar bound of $\tilde O(T^{2/3})$ (instead of $O(T^{3/4})$ without strong convexity). Hence, in the current state-of-affairs, the best projection-free upper-bounds for the full-information and bandit settings with strongly convex and nonsmooth functions match, up to logarithmic factors, in $T$.
Consensus on simplicial complexes, or: The nonlinear simplicial Laplacian

We consider a nonlinear flow on simplicial complexes related to the simplicial Laplacian, and show that it is a generalization of various consensus and synchronization models commonly studied on networks. In particular, our model allows us to formulate flows on simplices of any dimension, so that it includes edge flows, triangle flows, etc. We show that the system can be represented as the gradient flow of an energy functional, and use this to deduce the stability of various steady states of the model. Finally, we demonstrate that our model contains higher-dimensional analogues of structures seen in related network models.
A simulation framework for particle magnetization dynamics of large ensembles of single domain particles: Numerical treatment of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging

Magnetic nanoparticles and their magnetization dynamics play an important role in many applications. We focus on magnetization dynamics in large ensembles of single domain nanoparticles being characterized by either Brownian or N\'{e}el rotation mechanisms. Simulations of the respective behavior are obtained by solving advection-diffusion equations on the sphere, for which a unified computational framework is developed and investigated. This builds the basis for solving two parameter identification problems, which are formulated in the context of the chosen application, magnetic particle imaging. The functionality of the computational framework is illustrated by numerical results in the parameter identification problems either compared quantitatively or qualitatively to measured data.
Stuides on the Chemical Constituents of Euphorbia ebracteolata

AIM:To investigate the chemical constituents of the roots of Euphorbia ebracteolata Hayata. METHOD:Various chromatographic techniques were used for isolation and purification of the constituents. The structures were elucidated by chemical evidence and spectral methods. RESULT:Seven compounds were isolated. The structures were identified by spectroscopic methods and compared with authentic samples as octacosylferulate,24-methylenecycoartanol,β-sitosterol,β-amyrin acetate,β-amyrin,1-octacosanol and sucrose, respectively. CONCLUSION:Octacosyl ferulate,β-amyrin acetate, β-amyrin,1-octacosanol and sucrose were isolated from Euphorbia ebracteolata for the first time.
Assessment of heavy metal pollution and human health risk in urban soils of a coal mining city in East China

ABSTRACT The aims of this study were to determine the concentrations, distribution, potential ecological risk (PER), and human health risk (Risk) of heavy metals in urban soils from a coal mining city in China. A total of 36 topsoil samples from Huainan city, Anhui, East China, were collected and analyzed for As, Hg, Pb, Cd, Cr, and Cu. The PER was calculated to assess the pollution level. The hazard index (HI) and carcinogenic risk were used to assess the human health risk of heavy metals in the study area. The average concentration of As, Hg, Pb, Cd, Cr, and Cu were 12.54, 0.21, 24.21, 0.19, 49.39, and 21.74 mg kg−1, respectively. The correlations between heavy metals indicated that Cu, Cr, Cd, and Pb mainly originated from automobile exhaust emissions, coal gangue, fly ash, and industrial wastewater, and that As and Hg mainly came from coal combustion exhaust. The PER index values of heavy metals decreased in the following order: Hg > Cd > As> Cu > Pb > Cr. The HI and Risk values indicated that the noncarcinogenic and carcinogenic risks of selected metals in the urban soil were both below the threshold values.
Efeito da suplementação isocalórica de sacarina e sacarose no ganho de peso, ingestão calórica, tolerância à glicose e consumo basal de oxigênio em ratos Wistar

Introduction: The use of non-caloric sweeteners (ANC) can interfere with the regulation of appetite, promoting greater food intake, greater weight gain (WG) and increased adiposity. In previous data, the results showed that the animals that consumed yogurt saccharin and aspartame had a greater increase in weight compared to the group using sucrose. However, as the total calorie intake was similar between the groups, the weight increase could not be explained by the increase in caloric intake. We concluded that weight gain may be associated with decreased energy expenditure induced by artificial sweetener. Previous studies have suggested that saccharin could induce weight gain, but no study to date has evaluated the consumption of oxygen basal animals. In this sense, it is possible that saccharin may be determining reduction in energy expenditure and possibly contributing to an increase in blood glucose. Thus, this study include saccharin analyze the effect on basal oxygen consumption. Materials and Methods: We conducted a controlled experiment with 37 adult male Wistar rats weighing 180-220 g were randomly divided into three groups according to the type of exposure for both non-caloric sweetener (sugar-SAC), calorie sweetener (sucrose-SUC) or control (CONT). The supplements were given daily over a period of 12 weeks. Weight gain, food intake and water control were determined weekly, basal oxygen consumption determined at rest (VO2) and RER were measured at baseline, 5 and 12 weeks and tolerance test oral glucose was determined at week 6 and 12. Results: The use of saccharin promoted greater weight gain than sucrose (p =0.031). The total caloric intake (kcal/g) differ between the groups (p = 0.029), the animals that consumed saccharin ate more food. The groups differed in water intake, and the sugar group with the highest consumption (ml/g) (p = 0.018). However, the oxygen consumption and the respiratory exchange ratio were not significant. Conclusion: The cumulative weight gain in the animals fed saccharin can not be attributed to a reduction in energy expenditure, measured by oxygen consumption, but can be explained by increased food and water intake.
Coupled day–night models of exoplanetary atmospheres

We provide a new framework to model the day side and night side atmospheres of irradiated exoplanets using 1-D radiative transfer by incorporating a self-consistent heat flux carried by circulation currents (winds) between the two sides. The advantages of our model are its physical motivation and computational efficiency, which allows for an exploration of a wide range of atmospheric parameters. We use this forward model to explore the day and night side atmosphere of WASP-76~b, an ultra-hot Jupiter which shows evidence for a thermal inversion and Fe condensation, and WASP-43~b, comparing our model against high precision phase curves and general circulation models. We are able to closely match the observations as well as prior theoretical predictions for both of these planets with our model. We also model a range of hot Jupiters with equilibrium temperatures between 1000-3000~K and reproduce the observed trend that the day-night temperature contrast increases with equilibrium temperature up to $\sim$2500~K beyond which the dissociation of H$_2$ becomes significant and the relative temperature difference declines.