Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Model for Joint Chinese Word Segmentation and Dependency Parsing

Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined on word-level, therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffers from error propagation. In this paper, we propose a unified model to integrate Chinese word segmentation and dependency parsing. Different from previous joint models, our proposed model is a graph-based model and more concise, which results in fewer efforts of feature engineering. Our joint model achieves better performance than previous joint models. Our joint model achieves the state-of-the-art results in both Chinese word segmentation and dependency parsing.
Modelling H3+ in planetary atmospheres: effects of vertical gradients on observed quantities

Since its detection in the aurorae of Jupiter approximately 30 years ago, the H3+ ion has served as an invaluable probe of giant planet upper atmospheres. However, the vast majority of monitoring of planetary H3+ radiation has followed from observations that rely on deriving parameters from column-integrated paths through the emitting layer. Here, we investigate the effects of density and temperature gradients along such paths on the measured H3+ spectrum and its resulting interpretation. In a non-isothermal atmosphere, H3+ column densities retrieved from such observations are found to represent a lower limit, reduced by 20% or more from the true atmospheric value. Global simulations of Uranus' ionosphere reveal that measured H3+ temperature variations are often attributable to well-understood solar zenith angle effects rather than indications of real atmospheric variability. Finally, based on these insights, a preliminary method of deriving vertical temperature structure is demonstrated at Jupiter using model reproductions of electron density and H3+ measurements. The sheer diversity and uncertainty of conditions in planetary atmospheres prohibits this work from providing blanket quantitative correction factors; nonetheless, we illustrate a few simple ways in which the already formidable utility of H3+ observations in understanding planetary atmospheres can be enhanced. This article is part of a discussion meeting issue ‘Advances in hydrogen molecular ions: H3+, H5+ and beyond’.
Text Repair Model for Neural Machine Translation

In this work, we train a text repair model as a post-processor for Neural Machine Translation (NMT). The goal of the repair model is to correct typical errors introduced by the translation process, and convert the "translationese" output into natural text. The repair model is trained on monolingual data that has been round-trip translated through English, to mimic errors that are similar to the ones introduced by NMT. Having a trained repair model, we apply it to the output of existing NMT systems. We run experiments for both the WMT18 English to German and the WMT16 English to Romanian task. Furthermore, we apply the repair model on the output of the top submissions of the most recent WMT evaluation campaigns. We see quality improvements on all tasks of up to 2.5 BLEU points.
Prime Sample Attention in Object Detection

It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP. Our study suggests that the samples in each mini-batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily result in higher mAP. Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance. We further develop a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) that directs the focus of the training process towards such samples. Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector. Particularly, on the MSCOCO dataset, PISA outperforms the random sampling baseline and hard mining schemes, \eg~OHEM and Focal Loss, consistently by around 2\% on both single-stage and two-stage detectors, even with a strong backbone ResNeXt-101. Code is available at: \url{https://github.com/open-mmlab/mmdetection}.
Bogomolov multiplier and the Lazard correspondence

Abstract In this paper, we extend the notion of CP covers for groups to the class of Lie algebras, and show that despite the case of groups, all CP covers of a Lie algebra are isomorphic. Moreover we show that CP covers of groups and Lie rings which are in Lazard correspondence, are in Lazard correspondence too, and the Bogomolov multipliers of the group and the Lie ring are isomorphic.
Creatine Supplementation Improves Phosphagen Energy Pathway During Supramaximal Effort, but Does Not Improve Anaerobic Capacity or Performance

This study aimed to investigate the effects of short-duration creatine monohydrate supplementation on anaerobic capacity (AC), anaerobic energy pathways, and time-to-exhaustion during high-intensity running. Fourteen healthy men underwent a graded exercise test (GXT) followed by a O2max confirmation test, 5 submaximal efforts, and 4 supramaximal running bouts at 115% of V˙O2max intensity (the first two supramaximal sessions were applied as familiarization trials) to measure the AC using two procedures; the maximum accumulated oxygen deficit (MAOD) and non-oxidative pathways energetics sum (AC[La-]+EPOCfast). The investigation was conducted in a single-blind and placebo-controlled manner, with participants performing the efforts first after being supplemented with a placebo (dextrose 20 g⋅day-1 for 5 days), and then, after a 7 day “placebo” washout period, they started the same procedure under creatine supplementation (20 g⋅day-1 for 5 days. This order was chosen due to the prolonged washout of creatine. MAOD was not different between placebo (3.35 ± 0.65 L) and creatine conditions (3.39 ± 0.79 L; P = 0.58) and presented a negligible effect [effect size (ES) = 0.08], similar to, AC[La-]+EPOCfast (placebo condition (3.66 ± 0.79 Land under creatine ingestion 3.82 ± 0.85 L; P = 0.07) presenting a small effect (ES = 0.20). The energetics from the phosphagen pathway increased significantly after creatine supplementation (1.66 ± 0.40 L) compared to the placebo condition (1.55 ± 0.42 L; P = 0.03). However, the glycolytic and oxidative pathways were not different between conditions. Furthermore, time to exhaustion did not differ between placebo (160.79 ± 37.76 s) and creatine conditions (163.64 ± 38.72; P = 0.49). Therefore, we can conclude that creatine supplementation improves the phosphagen energy contribution, but with no statistical effect on AC or time to exhaustion in supramaximal running.
No evidence of an association between parental mind-mindedness at 9 months and language development at either 9 or 25 months in Swedish infants

Mind-mindedness (MM), the parent’s propensity to treat their young child as an individual with a mind of their own, has repeatedly been found to be positively associated with subsequent child development outcomes. In the current Swedish study, the first aim was to investigate the main features of MM in this cultural context and the second aim was to investigate its association with early child language development. Sixty-three parent-child dyads participated. MM was assessed by videotaped laboratory-based parent-child dyad free-play sessions. Language development was assessed using the parent questionnaire Swedish Early Communicative Development Inventory (SECDI), a Swedish adaptation of the internationally used MacArthur-Bates Communicative Development Inventories (MB-CDI). The ratio between Appropriate MM and Non-attuned MM was 4:1 and there was no statistically significant correlation between these two variables. There were no statistically significant correlations between Total MM or Appropriate MM and language ability ratings at either 9 or 25 months. This may be due to methodological issues concerning elicitation of MM in a Swedish context. We emphasize the importance of further theoretical and empirical studies of cross-cultural validation of MM.
Band structure of Ge1−xSnx alloy: a full-zone 30-band k · p model

A full-zone 30-band k · p model is developed as an efficient and reliable tool to compute electronic band structure in Ge1−xSnx alloy. The model was first used to reproduce the electronic band structures in Ge and α-Sn obtained with empirical tight binding and ab initio methods. Input parameters for the 30-band k · p model are carefully calibrated against prior empirical predications and experimental data. Important material properties such as effective mass for electrons and holes, Luttinger parameters, and density of states are obtained for Ge1−xSnx alloy with the composition range 0 < x < 0.3. The 30-band k · p model that requires far less computing resources is a necessary capability for optimization of sophisticated devices made from Ge1−xSnx alloy with a large parameter space to explore.
Discrimination between prompt and long-lived particles using convolutional neural network

Sophisticated machine learning techniques, like computer vision, are state of the art in modern day research. These technologically advanced algorithms have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Most of the computer vision tasks are surrounded around convolutional neural networks (CNN), which can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard Model. We demonstrate the usefulness of CNN by using a couple of physics examples from well motivated BSM scenarios predicting long-lived particles giving rise to displaced jets. Our work suggests that modern machine-learning techniques have potential to discriminate between energy deposition patterns of prompt and long-lived particles, and thus, they can be useful tools in such searches.
Convergence of Message-Passing for Distributed Convex Optimisation with Scaled Diagonal Dominance

This paper studies the convergence properties the well-known message-passing algorithm for convex optimisation. Under the assumption of pairwise separability and scaled diagonal dominance, asymptotic convergence is established and a simple bound for the convergence rate is provided for message-passing. In comparison with previous results, our results do not require the given convex program to have known convex pairwise components and that our bound for the convergence rate is tighter and simpler. When specialised to quadratic optimisation, we generalise known results by providing a very simple bound for the convergence rate.
Nonlinear chiral refrigerators

We investigate a mesoscopic refrigerator based on chiral quantum Hall edge channels. We discuss a three-terminal cooling device in which charge transport occurs between a pair of voltage-biased terminals only. The third terminal, which is to be cooled, is set as a voltage probe with vanishing particle flux. This largely prevents the generation of direct Joule heating which ensures a high coefficient of performance. Cooling operation is based on energy-dependent quantum transmissions. The latter are implemented with the aid of two tunable scattering resonances (quantum dots). To find the optimal performance point and the largest temperature difference created with our refrigerator, it is crucial to address the nonlinear regime of transport, accounting for electron-electron interaction effects. Our numerical simulations show that the maximal cooling power can be tuned with the quantum dot couplings and energy levels. Further, we provide analytical expressions within a weakly nonlinear scattering-matrix formalism which allow us to discuss the conditions for optimal cooling in terms of generalized thermopowers. Our results are important for the assessment of chiral conductors as promising candidates for efficient quantum refrigerators with low dissipation.
A statistical study of fast magnetic reconnection in turbulent accretion disks and jets

Fast magnetic reconnection events can play an important role in accretion disk systems. A potential model to explain the non-thermal very-high-energy (VHE) emission (from GeV to TeV) observed in black-hole binaries (BHBs) and Active Galatic Nuclei (AGNs) can be attributed to fast magnetic reconnection induced in the turbulent corona of accretion disks and/or jets. In this work, we will discuss the results of global general relativistic MHD (GRMHD) simulations of accretion disks around black holes, whose turbulence is naturally driven by MHD instabilities, such as the magnetorotational instability (MRI). We will also present studies of magnetic reconnection driven by kink instabilities inside jets employing special relativistic MHD (SRMHD) simulations. As we expect, our simulations reveal the development of a nearly steady-state turbulence driven by these instabilities. We have performed a detailed statistical analysis to identify the presence of current sheets in the turbulent regions of both the accretion flow and jet. We then determined the magnetic reconnection rates in these locations obtaining average reconnection velocities in Alfven speed units of the order of $0.01-0.7$, which are consistent with the predictions of the theory of turbulence-induced fast reconnection.
Extragalactic neutrinos as tracers of dark matter?

Neutrinos produced in extragalactic sources may experience flavor-oscillations and decoherence on their way to Earth due to their interaction with dark matter (DM). As a result, they may be detected in pointer-states other than the flavor states produced at the source. The oscillation pattern and the structure of the pointer-states can give us information on the characteristics of the DM and the kind of interaction that has taken place. From this perspective, neutrinos can be viewed as DM-tracers. We study the local evolution of neutrino flavor-eigenstates due to local effects produced by the presence of DM. To explore the sensitivity of the model, we consider different DM density profiles, masses and interactions. Starting from the eigenstates of the neutrino mass Hamiltonian, we construct flavor-states with the neutrino mixing-matrix in vacuum. We then include local interactions with DM, acting along the neutrino path towards the Earth, and analyze the resulting probabilities. In doing so, we adopt different DM density profiles, e.g. a constant, a local isotropic, and a Navarro–Frenk–White density distribution. Finally, by following the time-evolution of the flavor-states, we identify pointer-states and interpret the results in terms of the adopted DM model. In addition, neutrinos may experience changes in the oscillation parameters and resonances (MSW effect), the extent of which depends on the DM density profile. Thus, the presence of DM may enhance or suppress the oscillation pattern. The suppression of components of the neutrino wave-packet (decoherence effects) may also take place. The features of the calculated response seem to support the notion that these neutrinos can be taken as DM tracers. From a theoretical point of view, the coexistence and/or competition of decoherence and matter effects is sustained by the results.
Inula Viscosa Extract Inhibits Growth of Colorectal Cancer Cells in vitro and in vivo Through Induction of Apoptosis

Colorectal cancer (CRC) is the second most common cancer in females and the third in males worldwide. Conventional therapy of CRC is limited by severe side effects and by the development of resistance. Therefore, additional therapies are needed in order to combat the problem of selectivity and drug resistance in CRC patients. Inula viscosa (IV) is a well-known medicinal perennial herb in traditional medicine. It is used for different therapeutic purposes, such as; topical anti-inflammatic, diuretic, hemostatic, antiseptic, antiphlogistic, and in the treatment of diabetes. Several studies attempted to reveal the anti-cancer activity of different extracts prepared by different organic solvents from different parts of the IV plant. The aim of the present study is to examine the potential beneficial effects of IV leaf aqueous extract on the growth of colon cancer cells in vitro and in vivo. The results indicated that exposure of colorectal cancer cells to IV extract, significantly reduced cell viability in a dose and time dependent manner. Moreover, treatment of cells with 300 μg/ml of IV extract induced apoptosis, as it was detected by Annexin V/FITC/PI, TUNEL assay, and the activation of caspases. In vivo studies revealed that treatment with 150 or 300 mg/kg IV extract inhibited tumor growth in mice transplanted with MC38 cells. Tumors' weight and volume were significantly (P < 0.001) reduced when compared to untreated-control group. Staining of the paraffin section of tumors revealed that IV treatment inhibited cell proliferation and induced apoptosis. Additionally, no side effects such as; weight loss, behavior changes, ruffled fur or changes in kidney, and liver functions were observed. These results may indicate that active doses of IV extract are not toxic. Further studies are needed in order to identify the structure of the active compounds. Results from this study may contribute to the development of new and efficient strategies for treatment of human colon cancer.
Characterizing Livestock Markets, Primary Diseases, and Key Management Practices Along the Livestock Supply Chain in Cameroon

Live animal markets are common hotspots for the dispersal of multiple infectious diseases in various production systems globally. In Cameroon livestock trade occurs predominantly via a system of livestock markets. Improving the understanding of the risks associated with livestock trade systems and markets is, therefore, key to design targeted and evidence-based interventions. In the current study, official transaction records for a 12-month period were collected from 62 livestock markets across Central and Southern Cameroon, in combination with a questionnaire-based survey with the livestock markets stakeholders. The available information collected at these markets was used to characterize their structural and functional organization. Based on trade volume, cattle price and the intensity of stakeholder attendance, four main classes of livestock markets were identified. Despite an evident hierarchical structure of the system, a relatively limited pool of infectious diseases was consistently reported as predominant across market classes, highlighting homogeneous disease risks along the livestock supply chain. Conversely, the variable livestock management practices reported (e.g., traded species, husbandry practices, and transhumance habits) highlighted diverse potential risks for disease dissemination among market classes. Making use of readily available commercial information at livestock markets, this study describes a rapid approach for market characterization and classification. Simultaneously, this study identifies primary diseases and management practices at risk and provides the opportunity to inform evidence-based and strategic communication, surveillance and control approaches aiming at mitigating these risks for diseases dissemination through the livestock supply chain in Cameroon.
Influence of Groundcover Vegetation, Soil Physicochemical Properties, and Irrigation Practices on Soil Fungi in Semi-arid Vineyards

Although plants are known to have a strong influence on soil biota, the effect of groundcover vegetation in perennial cropping systems on soil fungi has been little explored. We surveyed extensively managed vineyards to determine how plant community functional characteristics, soil factors, and irrigation management related to the abundance of two guilds of soil fungi that may play a role in plant-soil feedback (entomopathogenic fungi represented by Beauveria bassiana, and the pathogenic species complex, Ilyonectria spp.). We found that plant community characteristics were related to fungal abundance for both fungi assayed. Beauveria bassiana increased with native species, annual plants, and legumes consistently across sampling periods. Ilyonectria spp. increased with the abundance of forbs and exotic species, though only the relationship with forbs was consistent across sampling periods. Both fungal guilds increased with increasing soil organic matter. The use of dual or sprinkler irrigation systems also increased Beauveria bassiana and Ilyonectria spp. in vineyard soils. Overall, groundcover vegetation played a significant role in driving abundance of these important groups of soil fungi. Groundcover management may therefore be a viable tool to manipulate soil fungi with the potential for improving ecosystem services such as conservation biological control of soil dwelling insect pests and deterring pathogens in perennial cropping systems.
Domain Reduction for Monotonicity Testing: A o(d) Tester for Boolean Functions in d-Dimensions

We describe a $\tilde{O}(d^{5/6})$-query monotonicity tester for Boolean functions $f:[n]^d \to \{0,1\}$ on the $n$-hypergrid. This is the first $o(d)$ monotonicity tester with query complexity independent of $n$. Motivated by this independence of $n$, we initiate the study of monotonicity testing of measurable Boolean functions $f:\mathbb{R}^d \to \{0,1\}$ over the continuous domain, where the distance is measured with respect to a product distribution over $\mathbb{R}^d$. We give a $\tilde{O}(d^{5/6})$-query monotonicity tester for such functions. Our main technical result is a domain reduction theorem for monotonicity. For any function $f:[n]^d \to \{0,1\}$, let $\epsilon_f$ be its distance to monotonicity. Consider the restriction $\hat{f}$ of the function on a random $[k]^d$ sub-hypergrid of the original domain. We show that for $k = \text{poly}(d/\epsilon)$, the expected distance of the restriction is $\mathbb{E}[\epsilon_{\hat{f}}] = \Omega(\epsilon_f)$. Previously, such a result was only known for $d=1$ (Berman-Raskhodnikova-Yaroslavtsev, STOC 2014). Our result for testing Boolean functions over $[n]^d$ then follows by applying the $d^{5/6}\cdot \text{poly}(1/\epsilon,\log n, \log d)$-query hypergrid tester of Black-Chakrabarty-Seshadhri (SODA 2018). To obtain the result for testing Boolean functions over $\mathbb{R}^d$, we use standard measure theoretic tools to reduce monotonicity testing of a measurable function $f$ to monotonicity testing of a discretized version of $f$ over a hypergrid domain $[N]^d$ for large, but finite, $N$ (that may depend on $f$). The independence of $N$ in the hypergrid tester is crucial to getting the final tester over $\mathbb{R}^d$.
Dense vs. Sparse Representations for News Stream Clustering

The abundance of news being generated on a daily basis has made it hard, if not impossible, to monitor all news developments. Thus, there is an increasing need for accurate tools that can organize the news for easier exploration. Typically, this means clustering the news stream, and then connecting the clusters into story lines. Here, we focus on the clustering step, using a local topic graph and a community detection algorithm. Traditionally, news clustering was done using sparse vector representations with TF–IDF weighting, but more recently dense representations have emerged as a popular alternative. Here, we compare these two representations, as well as combinations thereof. The evaluation results on a standard dataset show a sizeable improvement over the state of the art both for the standard F1 as well as for a BCubed version thereof, which we argue is more suitable for the task.