Miracle-dz/UniFAR-FTUs huggingface dataset:
Implicit cross-situational word learning in children with and without developmental language disorder and its relation to lexical-semantic knowledge

Introduction Research indicates that statistical learning plays a role in word learning by enabling the learner to track the co-occurrences between words and their visual referents, a process that is named cross-situational word learning. Word learning is problematic for children with developmental language disorder (DLD), and a deficit in statistical learning has been suggested to contribute to the language difficulties in these children. Therefore, we investigate whether children with DLD have more difficulty than TD children with learning novel word–referent pairs based on cross-situational statistics in an implicit task, and whether this ability is related to their lexical-semantic skills. Moreover, we look at the role of variability of the learning environment. Methods In our implicit cross-situational word learning task, each trial in the exposure phase was in itself ambiguous: two pictures of unknown objects were shown at the same time and two novel words were played consecutively, without indicating which word referred to which object. However, as every word occurred with its correct referent consistently, the children could learn the word–referent pairs across trials. The children were not explicitly instructed to learn the names of new objects. As an on-line measure of learning, eye-movements were recorded during the exposure phase. After exposure, word–referent knowledge was also tested using multiple choice questions. Several measures of lexical-semantic knowledge were administered to the children with DLD, as well as tasks measuring non-verbal intelligence and phonological processing. Contextual variability (the number of different distractors with which a particular word–referent pair occurs across trials) was manipulated between subjects by constructing two types of exposure conditions: low contextual diversity vs. high contextual diversity. Results Both groups of children performed significantly above chance level on the test phase, but the TD children significantly outperformed the children with DLD. We found no significant effect of contextual diversity. The eye-tracking data revealed some evidence of on-line learning, but no differences between groups. Finally, the regression analyses did not reveal any significant predictors of off-line or on-line cross-situational word learning ability. Discussion Our results indicate that although children with DLD are able to learn word-referent pairs in an implicit task, they have more difficulty than TD children. Possibly they need more input to achieve the same level.
Delta hedging bitcoin options with a smile

We analyse robust dynamic delta hedging of bitcoin options using a set of smile-implied and other smile-adjusted deltas that are either model-free, in the sense that they are the same for every scale-invariant stochastic and/or local volatility model, or they are based on simple regime-dependent parameterisations of local volatility. These deltas are popular with option market makers in traditional assets because they are very easy to implement. Previous empirical research on dynamic delta hedging is based solely on equity index options, but analysis of our unique data on hourly historical bitcoin option prices reveals that bitcoin implied volatility curves behave very differently from those of equity index options. For call and put options with a wide range of moneyness and with synthetic constant maturities of 10, 20 and 30 days, we compare the dynamic hedging performance of different smile-adjusted deltas over two one-year periods. We also examine the use of the perpetual contract rather than the standard futures as hedging instrument because the basis risk for the perpetual is very much smaller than it is for calendar futures. Results are presented as testable statistics of hedging error variance ratios. In certain periods the use of smile-implied hedge ratios can significantly out-perform the simple Black–Scholes delta hedge, especially when using the perpetual swap as hedging instrument, where efficiency gains can exceed 30% for out-of-the-money puts, and reach an average of 15% when hedging short-term out-of-the money calls during periods when the implied volatility curve slopes upwards. The advantage of using the perpetual contract is especially evident during 2021, for the longer-term contracts for which the basis is still rather large.
X-ray and ultraviolet observations of Markarian 421

La variabilite du spectre de l'objet de type BL Lac, Mkn 421 dans le domaine s'etendant de l'UV a RX est etudiee grâce aux donnees des satellites IUE et EXOSAT. Un modele de jet relativiste permet de decrire correctement ce spectre si l'on suppose que la coupure haute du rayonnement synchrotron varie radialement
Deep Reinforcement Learning-Aided Optimization of Multi-Interface Allocation for Short-Packet Communications

The severe spectrum scarcity and the stringent requirements of Beyond 5G applications call for an integrated use of low frequency Sub-6 GHz and high frequency millimeter Wave bands. Focusing on future Internet of Things (IoT) Short-Packet Communications (SPC), this paper investigates the optimized usage of such diverse wireless interfaces. We propose an unifying framework devoted to SPC that jointly optimizes the user partitioning over each band, and the radio resource scheduling within each band. Leveraging Deep Reinforcement Learning (DRL) tools, the proposed method enables to better tackle the challenges imposed by dynamically varying mobile environments such as the Line-of-Sight situations of each link, and the heterogeneity of individual Quality of Service (QoS) requirements, such as rate, delay and reliability. Regarding the DRL-based user partitioning to each band, we have investigated three different types of partitioning actions to obtain a high network performance as well as a rapid convergence. Regarding the proposed sub-schedulers within each band, we designed two optimization methods, i.e., one that leverages Difference of Convex Programming (DCP) technique, and the second that accelerates convergence to a local optimum. Numerical evaluations show that the proposed methods outperform conventional approaches in terms of sum-rate and QoS outage probabilities.
Diversity, Distribution, Systematics and Conservation Status of Podocarpaceae

Among conifer families, Podocarpaceae is the second largest, with amazing diversity and functional traits, and it is the dominant Southern Hemisphere conifer family. However, comprehensive studies on diversity, distribution, systematic and ecophysiological aspects of the Podocarpaceae are sparse. We aim to outline and evaluate the current and past diversity, distribution, systematics, ecophysiological adaptations, endemism, and conservation status of podocarps. We analyzed data on the diversity and distribution of living and extinct macrofossil taxa and combined it with genetic data to reconstruct an updated phylogeny and understand historical biogeography. Podocarpaceae today contains 20 genera and approximately 219 taxa (201 species, 2 subspecies, 14 varieties and 2 hybrids) placed in three clades, plus a paraphyletic group/grade of four distinct genera. Macrofossil records show the presence of more than 100 podocarp taxa globally, dominantly from the Eocene–Miocene. Australasia (New Caledonia, Tasmania, New Zealand, and Malesia) is the hotspot of living podocarps diversity. Podocarps also show remarkable adaptations from broad to scale leaves, fleshy seed cones, animal dispersal, shrubs to large trees, from lowland to alpine regions and rheophyte to a parasite (including the only parasitic gymnosperm—Parasitaxus) and a complex pattern of seed and leaf functional trait evolution.
Electrical Switching of Infrared Light Using Graphene Integration with Plasmonic Fano Resonant Metasurfaces

Graphene has emerged as a promising optoelectronic material because its optical properties can be rapidly and dramatically changed using electric gating. Graphene’s weak optical response, especially in the infrared part of the spectrum, remains the key challenge to developing practical graphene-based optical devices such as modulators, infrared detectors, and tunable reflect-arrays. Here it is experimentally and theoretically demonstrated that a plasmonic metasurface with two Fano resonances can dramatically enhance the interaction of infrared light with single layer graphene. Graphene’s plasmonic response in the Pauli blocking regime is shown to cause strong spectral shifts of the Fano resonances without inducing additional nonradiative losses. It is shown that such electrically controllable spectral shift, combined with the narrow spectral width of the metasurface’s Fano resonances, enables reflectivity modulation by nearly an order of magnitude. We also demonstrate that metasurface-based enhancement of.
Coulomb potential inside a large finite crystal

We review long-range Coulomb interactions in systems consisting of periodically repeated replicas of identical unit cells containing a net dipole moment. Cases of both infinite and large finite samples of arbitrary shape are considered. The general result due to Smith (1981 Proc. Roy. Soc. Lond. A 375 475) for a large finite sample is considerably simplified. This allowed us to reveal further its physical meaning. We show that extra shape-dependent terms both in the electrostatic potential and energy have a pure macroscopic origin. We also suggest an alternative derivation for the Coulomb potential of a large finite sample of arbitrary shape which is believed to be simpler than the original one due to Smith.
Immune response and drug therapy based on ac4C-modified gene in pancreatic cancer typing

N-4 cytidine acetylation (ac4C) is an epitranscriptome modification catalyzed by N-acetyltransferase 10 (NAT10) and is essential for cellular mRNA stability, rRNA biosynthesis, cell proliferation, and epithelial-mesenchymal transition (EMT). Numerous studies have confirmed the inextricable link between NAT10 and the clinical characteristics of malignancies. It is unclear, however, how NAT10 might affect pancreatic ductal adenocarcinoma. We downloaded pancreatic ductal adenocarcinoma patients from the TCGA database. We obtained the corresponding clinical data for data analysis, model construction, differential gene expression analysis, and the GEO database for external validation. We screened the published papers for NAT10-mediated ac4C modifications in 2156 genes. We confirmed that the expression levels and genomic mutation rates of NAT10 differed significantly between cancer and normal tissues. Additionally, we constructed a NAT10 prognostic model and examined immune infiltration and altered biological pathways across the models. The NAT10 isoforms identified in this study can effectively predict clinical outcomes in pancreatic ductal adenocarcinoma. Furthermore, our study showed that elevated levels of NAT10 expression correlated with gemcitabine resistance, that aberrant NAT10 expression may promote the angiogenic capacity of pancreatic ductal adenocarcinoma through activation of the TGF-β pathway, which in turn promotes distal metastasis of pancreatic ductal adenocarcinoma, and that NAT10 knockdown significantly inhibited the migration and clonogenic capacity of pancreatic ductal adenocarcinoma cells. In conclusion, we proposed a predictive model based on NAT10 expression levels, a non-invasive predictive approach for genomic profiling, which showed satisfactory and effective performance in predicting patients’ survival outcomes and treatment response. Medicine and electronics will be combined in more interdisciplinary areas in the future.
Solvent-Free Aldol Condensation of Cyclopentanone with Natural Clay-Based Catalysts: Origin of Activity & Selectivity

The conversion of biomass resources into high-value fuels and chemicals using thermochemical methods has become an attractive method of energy utilization. In this study, natural minerals were used as raw materials; the acidic sites were introduced by ball-milling modification, and the aldol condensation reaction of the biomass-based cyclopentanone molecule was carried out under solvent-free conditions. It was found that the SO3H-APG catalyst—with strong medium-based sites when the -SO3H loading was 4 mmol/g—exhibited excellent acid–base co-activation effects and a significant catalytic effect in the cyclopentanone condensation reaction. The optimization of the reaction conditions showed that the conversion of cyclopentanone reached 85.53% at the reaction temperature of 150 °C and reaction time of 4 h. The selectivity of the dimer and trimer was 69.04% and 28.41%, respectively. The investigation of the cyclopentanone condensation mechanism and kinetic analysis showed that the acid–base presence of an acid–base bifunctional catalyst was important to facilitate the condensation reaction. This research route is in line with the concept of sustainable green production and also provides a promising pathway for catalyst design and the synthesis of long-chain hydrocarbons.
Genetic Localization and Homologous Genes Mining for Barley Grain Size

Grain size is an important agronomic trait determining barley yield and quality. An increasing number of QTLs (quantitative trait loci) for grain size have been reported due to the improvement in genome sequencing and mapping. Elucidating the molecular mechanisms underpinning barley grain size is vital for producing elite cultivars and accelerating breeding processes. In this review, we summarize the achievements in the molecular mapping of barley grain size over the past two decades, highlighting the results of QTL linkage analysis and genome-wide association studies. We discuss the QTL hotspots and predict candidate genes in detail. Moreover, reported homologs that determine the seed size clustered into several signaling pathways in model plants are also listed, providing the theoretical basis for mining genetic resources and regulatory networks of barley grain size.
Radiomic Features from Post-Operative 18F-FDG PET/CT and CT Imaging Associated with Locally Recurrent Rectal Cancer: Preliminary Findings

Locally Recurrent Rectal Cancer (LRRC) remains a major clinical concern; it rapidly invades pelvic organs and nerve roots, causing severe symptoms. Curative-intent salvage therapy offers the only potential for cure but it has a higher chance of success when LRRC is diagnosed at an early stage. Imaging diagnosis of LRRC is very challenging due to fibrosis and inflammatory pelvic tissue, which can mislead even the most expert reader. This study exploited a radiomic analysis to enrich, through quantitative features, the characterization of tissue properties, thus favoring an accurate detection of LRRC by Computed Tomography (CT) and 18F-FDG-Positron Emission Tomography/CT (PET/CT). Of 563 eligible patients undergoing radical resection (R0) of primary RC, 57 patients with suspected LRRC were included, 33 of which were histologically confirmed. After manually segmenting suspected LRRC in CT and PET/CT, 144 Radiomic Features (RFs) were generated, and RFs were investigated for univariate significant discriminations (Wilcoxon rank-sum test, p < 0.050) of LRRC from NO LRRC. Five RFs in PET/CT (p < 0.017) and two in CT (p < 0.022) enabled, individually, a clear distinction of the groups, and one RF was shared by PET/CT and CT. As well as confirming the potential role of radiomics to advance LRRC diagnosis, the aforementioned shared RF describes LRRC as tissues having high local inhomogeneity due to the evolving tissue’s properties.
Family reunion policy for resettled refugees: Governance, challenges and impacts

The past decade has seen renewed efforts to establish resettlement as a durable solution for refugees, both as a protection tool and a mechanism to equitably distribute them among countries. Although the right to a family life is widely recognised as a fundamental human right, whether refugees can arrive with their family or be reunited with family once resettled varies across receiving countries. Little is known about family reunion policies in countries leading the resettlement efforts, and about the impact of these policies on the lives and experiences of resettled refugees. This paper addresses this gap though a systematic scoping review of academic and policy literature on family reunion policies for resettlement refugees, and on the impact of such policy on their lives. Based on a review of 42 papers published between 2010 and 2021, we outline the policies implemented in different receiving countries to enable resettled refugees to reunite with family, documenting at the same time the challenges refugees face in the process, as well as the impact of policy on their experiences. The findings evidence a tension between the refugees' own understanding of family and definitions of family in policy in receiving countries, which often results in family separation or reconfiguration. Additionally, high costs and other administrative barriers, as well as long waiting times associated with family reunification, lead to delayed or denied reunion, having detrimental effects on resettled refugees' well-being in the present and on their future prospects.
The IITM earth system model

AbstractWith the goal of building an Earth system model appropriate for detection, attribution, and projection of changes in the South Asian monsoon, a state-of-the-art seasonal prediction model, namely the Climate Forecast System version 2 (CFSv2) has been adapted to a climate model suitable for extended climate simulations at the Indian Institute of Tropical Meteorology (IITM), Pune, India. While the CFSv2 model has been skillful in predicting the Indian summer monsoon (ISM) on seasonal time scales, a century-long simulation with it shows biases in the ocean mixed layer, resulting in a 1.5°C cold bias in the global mean surface air temperature, a cold bias in the sea surface temperature (SST), and a cooler-than-observed troposphere. These biases limit the utility of CFSv2 to study climate change issues. To address biases, and to develop an Indian Earth System Model (IITM ESMv1), the ocean component in CFSv2 was replaced at IITM with an improved version, having better physics and interactive ocean biogeo.
Emerging artificial intelligence methodologies in uncertainty analysis and modeling

This paper presents an introductory overview of various methods used in representing and solving uncertainty problems. The emerging methods help us to capture both the subjective judgment and incomplete information and data in decision analysis under uncertainty. The type of method to be used for a problem depends on the situation. Although, the emerging methods appear to address uncertainty very well, problems are usually encountered. The emerging methods involve complicated mathematics, which are fairly difficult to understand and handle for the average transportation and pavement engineer. As the sample space grows, it becomes very difficult to handle and analyze uncertainties using the emerging methods. The solution becomes difficult and labor intensive. The availability of software will be a very promising step. Some of the emerging methods like influence diagrams and valuation-based systems handle asymmetric problems.
Numerical integration over the triangle

describes a method for the evaluation of integrals over a triangle, based on a formula developed in [1]. The mid-points of the sides of the triangle are used as the points of evaluation in this method. Other possibilities may be used; for example, the trisection points of the medians that are not the centroid could be used as the points of evaluation. These points are symmetrically spaced and the weights, wi, again are equal. The centroid could also be used as a single point of evaluation with wi equal to the area of the triangle. As another modification to this method, one could calculate the appropriate mid-points of the reference triangle for a reasonable number of subdivisions, then retain them as constants rather than calculate each point as it is used. Recently, (1958) Mr. Walter Leffin carried out computations by the use of these methods.
Theory of excitation transfer in collisions between alkali atoms. I. Identical partners

Possible mechanisms are considered for the transfer of electronic excitation energy in collisions between an excited alkali atom MA*(2Pj) and an unexcited atom MB(2S1/2). It is shown that a dipole–dipole interaction which is responsible for the transfer of electronic excitation energy in collisions between identical partners (MA = MB) is not sufficient to explain the observed magnitudes of the cross sections and that, therefore, the exchange interaction can no longer be neglected. If the exchange interaction is taken into account, it can be shown that there are regions of nonadiabaticity in the energy diagram, which are probably responsible for the change in the electronic energy states of the collision partners. The calculated cross sections are compared with experimental values.
A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model

—Despite the continuous development of online education models, the effectiveness of online distance education has never been able to meet people’s expectations due to individual difference of learners. How to personalize teaching in a targeted manner and stimulate learners’ independent learning ability has become a key issue. In this study, the multidimensional features of the learning process are mined with the help of the BORUTA feature selection model, and the DKVMN-BORUTA model incorporating multidimensional features is established. This optimized deep knowledge tracking method is combined with graph structure rules. Then, an intelligent knowledge recommendation algorithm based on reinforcement learning is used to construct a fusion approach-based model for distanced personalized teaching and learning of English. The results show that the research proposed fused deep-directed graph knowledge tracking with graph structure rules for remote personalized English teaching model has the lowest AUC value of 0.893 and the highest AUC value of 0.921 on each dataset. The prediction accuracy of the research model is 94.3% and the F1 score is 0.92, which is the highest among the studied models, indicating that the proposed model has a strong performance. The fusion model proposed in the study has a higher accuracy rate of knowledge personalization recommendation than the traditional deep knowledge tracking model, and it can help learners save revision time effectively and improve their overall English performance.