Miracle-dz/UniFAR-FTUs huggingface dataset:
Tectonostratigraphic subdivision and petrological characterisation of the deepest complexes of the Betic zone: a review

Abstract The Veleta and Mulhacén Complexes, previously considered together as the Nevado-Filábride Complex, constitute the deepest units of the Betic Cordilleras. The Veleta Complex (VC) is mainly formed of several thousand metres of graphite-bearing micaschists and crops out as a series of tectonic windows below the Mulhacén Complex (MC). This latter complex comprises a pile of thrust nappes of crustal origin, composed of a Palaeozoic basement and Mesozoic cover series, between which a Jurassic-Cretaceous ophiolitic nappe is tectonically intercalated. The Alpine metamorphism developed in eclogite facies followed by Ab-Ep amphibolite facies in the MC, and in Ab-Ep amphibolite facies followed by greenschist facies in the VC. Pre-Alpine metamorphism in high gradient amphibolite facies is locally well preserved in some basement levels of the MC. In contrast, only scarce relics of pre-Alpine metamorphism, in greenchists facies, have been preserved in the VC. Within the MC nappes of crustal origin there appear metagranitic rocks, which were generated during two stages of late Hercynian magmatic activity: one of syncollisional acidic magmatism and the other of postcollisional intraplate acidic-to-intermediate volcanism. The ophiolitic nappe is composed of basic, ultramafic and sedimentary rocks affected by ocean-floor and orogenic metamorphism. The basic rocks partly retain their original E-MORB geochemical character. The ultramafic rocks are serpentinites and secondary harzburgites containing partly rodingitized basaltic dykes. The Soportújar Formation of the MC is made up of continental and evaporitic metasediments and meta-andesitic rocks that originated during an intraorogenic relaxation period between the eo-Alpine and meso-Alpine metamorphic events. © 2002 Éditions scientifiques et médicales Elsevier SAS. All rights reserved.
Middle ro early late Viséan onset of late orogenic sedimentation in the Intra-Sudetic Basin, West Sudetes: miospore evidence and tectonic

The Early Carboniferous fluvial and deltaic sequence of the Intra-Sudetic Basin remained undated until recently, except for a Late Visean ammonoid fauna in its upper part. Current miospore data indicate that the oldest part of the sequence is not older than the mid Visean Knoxisporites triradiatus-Knoxisporites stephanephorus biozone of the west European miospore division. This palynological age determination is consistent with the recently obtained Ar-Ar cooling ages of white micas from sheared metamorphic rocks at the NW margin of the basin. This suggests that the rapid late orogenic denudation of the northern and western flanks of the Intra-Sudetic Basin must have started at or shortly after c. 335 Ma.
Spacing distribution for quantum Rabi models

The asymmetric quantum Rabi model (AQRM) is a fundamental model in quantum optics describing the interaction of light and matter. Besides its immediate physical interest, the AQRM possesses an intriguing mathematical structure which is far from being completely understood. In this paper, we focus on the distribution of the level spacing, the difference between consecutive eigenvalues of the AQRM in the limit of high energies, i.e. large quantum numbers. In the symmetric case, that is the quantum Rabi model (QRM), the spacing distribution for each parity (given by the $\mathbb{Z}_2$-symmetry) is fully clarified by an asymptotic expression derived by de Monvel and Zielinski, though some questions remain for the full spectrum spacing. However, in the general AQRM case, there is no parity decomposition for the eigenvalues. In connection with numerically exact studies for the first 40,000 eigenstates we describe the spacing distribution for the AQRM which is characterized by a new type of periodicity and symmetric behavior of the distribution with respect to the bias parameter. The results reflects the hidden symmetry of the AQRM known to appear for half-integer bias. In addition, we observe in the AQRM the excited state quantum phase transition for large values of the bias parameter, analogous to the QRM with large qubit energy, and an internal symmetry of the level spacing distribution for fixed bias. This novel symmetry is independent from the symmetry for half-integer bias and not explained by current theoretical knowledge.
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models

Large language models (LLMs) demonstrate remarkable performance on knowledge-intensive tasks, suggesting that real-world knowledge is encoded in their model parameters. However, besides explorations on a few probing tasks in limited knowledge domains, it is not well understood how to evaluate LLMs' knowledge systematically and how well their knowledge abilities generalize, across a spectrum of knowledge domains and progressively complex task formats. To this end, we propose KGQuiz, a knowledge-intensive benchmark to comprehensively investigate the knowledge generalization abilities of LLMs. KGQuiz is a scalable framework constructed from triplet-based knowledge, which covers three knowledge domains and consists of five tasks with increasing complexity: true-or-false, multiple-choice QA, blank filling, factual editing, and open-ended knowledge generation. To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains. Extensive experiments demonstrate that LLMs achieve impressive performance in straightforward knowledge QA tasks, while settings and contexts requiring more complex reasoning or employing domain-specific facts still present significant challenges. We envision KGQuiz as a testbed to analyze such nuanced variations in performance across domains and task formats, and ultimately to understand, evaluate, and improve LLMs' knowledge abilities across a wide spectrum of knowledge domains and tasks.
Real-Fake: Effective Training Data Synthesis Through Distribution Matching

Synthetic training data has gained prominence in numerous learning tasks and scenarios, offering advantages such as dataset augmentation, generalization evaluation, and privacy preservation. Despite these benefits, the efficiency of synthetic data generated by current methodologies remains inferior when training advanced deep models exclusively, limiting its practical utility. To address this challenge, we analyze the principles underlying training data synthesis for supervised learning and elucidate a principled theoretical framework from the distribution-matching perspective that explicates the mechanisms governing synthesis efficacy. Through extensive experiments, we demonstrate the effectiveness of our synthetic data across diverse image classification tasks, both as a replacement for and augmentation to real datasets, while also benefits such as out-of-distribution generalization, privacy preservation, and scalability. Specifically, we achieve 70.9% top1 classification accuracy on ImageNet1K when training solely with synthetic data equivalent to 1 X the original real data size, which increases to 76.0% when scaling up to 10 X synthetic data.
Controls on suspended-sediment transfer at a High-Arctic glacier, determined from statistical modelling

Simple linear regression models have been widely employed in the analysis of suspended-sediment concentration (SSC) time series from glacierized catchments, although they have many limitations. This paper builds regression models which address these shortcomings and permit inferences concerning the controls on suspended-sediment transfer from a glacier at 78°N in the Svalvard archipelago. A bivariate regression model, deterministically predicting SSC from discharge alone, explained less than 15 per cent of the variance in SSC. A multivariate model, incorporating additional potentially explanatory variables, offered little improvement. Diurnal hysteresis in the data gives rise to quasi-autocorrelation in the residual series from regression models. This was effectively removed by incorporating dummy diurnal variables into the multivariate model. The presence of a first-order autoregressive, stochastic process gives rise to true autocorrelation in the residual series from regression models. This was accommodated by incorporating an ARIMA (1,0,0) term into a multivariate autoregression model. The model-building process yielded a systematic progression in the explanation of variance in SSC, stripping away pattern in the autocorrelation function of the residual series; mean model error was reduced from 54 per cent to 6 per cent. The dependence of SSC on the magnitude of discharge is weak and highly variable, whereas the dependence of current SSC on recent values of SSC, revealed through the stochastic term, is an order of magnitude greater and relatively constant during the melt season. The dominant control on SSC throughout the melt season is therefore short-term sediment availability. The simple and largely unchanging stochastic process generally responsible for generating the observed SSC series implies a simple and unchanging glacier drainage system. Copyright © 1999 John Wiley & Sons, Ltd.
Aspects of higher-curvature gravities with covariant derivatives

We study various aspects of higher-curvature theories of gravity built from contractions of the metric, the Riemann tensor and the covariant derivative, $\mathcal{L}(g^{ab},R_{abcd},\nabla_a)$. We characterise the linearized spectrum of these theories and compute the modified Newton potential in the general case. Then, we present the first examples of Generalized Quasi-topological (GQT) gravities involving covariant derivatives of the Riemann tensor. We argue that they always have second-order equations on maximally symmetric backgrounds. Focusing on four spacetime dimensions, we find new densities of that type involving eight and ten derivatives of the metric. In the latter case, we find new modifications of the Schwarzschild black hole. These display thermodynamic properties which depart from the ones of polynomial GQT black holes. In particular, the relation between the temperature and the mass of small black holes, $T\sim M^{1/3}$, which universally holds for general polynomial GQT modifications of Einstein gravity, gets modified in the presence of the new density with covariant derivatives to $T\sim M^{3}$. Finally, we consider brane-world gravities induced by Einstein gravity in the AdS bulk. We show that the effective quadratic action for the brane-world theory involving arbitrary high-order terms in the action can be written explicitly in a closed form in terms of Bessel functions. We use this result to compute the propagator of metric perturbations on the brane and its pole structure in various dimensions, always finding infinite towers of ghost modes, as well as tachyons and more exotic modes in some cases.
University of Birmingham The puzzling difficulty of tool innovation : why can’t children piece their knowledge together?

Tool innovation—designing and making novel tools to solve tasks— is extremely difﬁcult for young children. To discover why this might be, we highlighted different aspects of tool making to children aged 4 to 6 years ( N = 110). Older children successfully inno-vated the means to make a hook after seeing the pre-made target tool only if they had a chance to manipulate the materials during a warm-up. Older children who had not manipulated the materials and all younger children performed at ﬂoor. We conclude that children’s difﬁculty is likely to be due to the ill-structured nature of tool innovation problems, in which components of a solution must be retrieved and coordinated. Older children struggled to bring to mind components of the solution but could coordinate them, whereas younger children could not coordinate components even when explicitly provided.
Duration of Residence in the 1855 Census of New York State

Migration in nineteenth-century New York State is analyzed using data from the 1855 state census. This census included a question on consecutive years of residence in the town or city of enumeration. The author first examines the quality of the data and finds them accurate. The resulting estimates of migration are compared with estimates made using nominal record linkage. (ANNOTATION)
Dynamic Link Prediction for New Nodes in Temporal Graph Networks

Modelling temporal networks for dynamic link prediction of new nodes has many real-world applications, such as providing relevant item recommendations to new customers in recommender systems and suggesting appropriate posts to new users on social platforms. Unlike old nodes, new nodes have few historical links, which poses a challenge for the dynamic link prediction task. Most existing dynamic models treat all nodes equally and are not specialized for new nodes, resulting in suboptimal performances. In this paper, we consider dynamic link prediction of new nodes as a few-shot problem and propose a novel model based on the meta-learning principle to effectively mitigate this problem. Specifically, we develop a temporal encoder with a node-level span memory to obtain a new node embedding, and then we use a predictor to determine whether the new node generates a link. To overcome the few-shot challenge, we incorporate the encoder-predictor into the meta-learning paradigm, which can learn two types of implicit information during the formation of the temporal network through span adaptation and node adaptation. The acquired implicit information can serve as model initialisation and facilitate rapid adaptation to new nodes through a fine-tuning process on just a few links. Experiments on three publicly available datasets demonstrate the superior performance of our model compared to existing state-of-the-art methods.
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking

Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, but ignore unlabelled data in the target domain. We transform zero-shot DST into few-shot DST by utilising such unlabelled data via joint and self-training methods. Our method incorporates auxiliary tasks that generate slot types as inverse prompts for main tasks, creating slot values during joint training. Cycle consistency between these two tasks enables the generation and selection of quality samples in unknown target domains for subsequent fine-tuning. This approach also facilitates automatic label creation, thereby optimizing the training and fine-tuning of DST models. We demonstrate this method's effectiveness on large language models in zero-shot scenarios, improving average joint goal accuracy by $8\%$ across all domains in MultiWOZ.
Large Language Model-Aware In-Context Learning for Code Generation

Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. LLMs take a prompt consisting of requirement-code examples and a new requirement as input, and output new programs. Existing studies have found that ICL is highly dominated by the examples and thus arises research on example selection. However, existing approaches randomly select examples or only consider the textual similarity of requirements to retrieve, leading to sub-optimal performance. In this paper, we propose a novel learning-based selection approach named LAIL (LLM-Aware In-context Learning) for code generation. Given a candidate example, we exploit LLMs themselves to estimate it by considering the generation probabilities of ground-truth programs given a requirement and the example. We then label candidate examples as positive or negative through the probability feedback. Based on the labeled data, we import a contrastive learning objective to train an effective retriever that acquires the preference of LLMs in code generation. We apply LAIL to three LLMs and evaluate it on three representative datasets (e.g., MBJP, MBPP, and MBCPP). LATA outperforms the state-of-the-art baselines by 11.58%, 6.89%, and 5.07% on CodeGen, and 4.38%, 2.85%, and 2.74% on GPT-3.5 in terms of Pass@1, respectively.
Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World

We introduce Bongard-OpenWorld, a new benchmark for evaluating real-world few-shot reasoning for machine vision. It originates from the classical Bongard Problems (BPs): Given two sets of images (positive and negative), the model needs to identify the set that query images belong to by inducing the visual concepts, which is exclusively depicted by images from the positive set. Our benchmark inherits the few-shot concept induction of the original BPs while adding the two novel layers of challenge: 1) open-world free-form concepts, as the visual concepts in Bongard-OpenWorld are unique compositions of terms from an open vocabulary, ranging from object categories to abstract visual attributes and commonsense factual knowledge; 2) real-world images, as opposed to the synthetic diagrams used by many counterparts. In our exploration, Bongard-OpenWorld already imposes a significant challenge to current few-shot reasoning algorithms. We further investigate to which extent the recently introduced Large Language Models (LLMs) and Vision-Language Models (VLMs) can solve our task, by directly probing VLMs, and combining VLMs and LLMs in an interactive reasoning scheme. We even conceived a neuro-symbolic reasoning approach that reconciles LLMs&VLMs with logical reasoning to emulate the human problem-solving process for Bongard Problems. However, none of these approaches manage to close the human-machine gap, as the best learner achieves 64% accuracy while human participants easily reach 91%. We hope Bongard-OpenWorld can help us better understand the limitations of current visual intelligence and facilitate future research on visual agents with stronger few-shot visual reasoning capabilities.
Holocene Coral Reef Terraces and Coseismic Uplift of Huon Peninsula, Papua New Guinea

Abstract As many as six levels of emerged Holocene coral terraces occur along 40 km of coastline on the Huon Peninsula, Papua New Guinea, recording uplift history since culmination of the postglacial transgression. The Holocene reef crest, ca. 6000 yr B.P., is tilted down to the northwest, parallel to the coast and concordant with the deformation of the last interglacial coral reef terrace, and descends from 23 to 12 m in the study area. The pattern and rate of deformation have been uniform in the late Quaternary because average uplift rates have remained the same since the last interglaciation. The Holocene terraces described here are erosional features with regressive encrusting corals, developed upon the Holocene transgressive reef. The multiple levels represent episodic, probably coseismic uplift, which has occurred repeatedly in the last ca. 6000 yr. Significant longshore variation in the age of the lowest terrace, from 1700 to 2500 yr B.P., suggests independent coseismic uplift on different sectors of the coast. This is supported by age-height relationships of the higher Holocene terraces. Nonlinear uplift during the Holocene, with recurrence intervals increasing toward the present, is clearly recorded by the regressive terraces in each subregion. Some of the Holocene regressive terraces grade laterally into fluvial terraces capped with debris-flow deposits, probably reflecting seismically triggered mass movement.
Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition

Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effective, can be time-consuming and inconsistent. With the rise of advanced technologies like robotics and artificial intelligence, there is increasing interest in automating such teaching processes. In this study, we examine the potential of a virtual AI teacher in emulating the techniques of human educators for motor skill acquisition. We introduce an AI teacher model that captures the distinct characteristics of human instructors. Using a reinforcement learning environment tailored to mimic teacher-learner interactions, we tested our AI model against four guiding hypotheses, emphasizing improved learner performance, enhanced rate of skill acquisition, and reduced variability in learning outcomes. Our findings, validated on synthetic learners, revealed significant improvements across all tested hypotheses. Notably, our model showcased robustness across different learners and settings and demonstrated adaptability to handwriting. This research underscores the potential of integrating Imitation and Reinforcement Learning models with robotics in revolutionizing the teaching of critical motor skills.
XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility

In the realm of Mobility-on-Demand (MoD) systems, the forecasting of rider demand is a cornerstone for operational decision-making and system optimization. Traditional forecasting methodologies primarily yield point estimates, thereby neglecting the inherent uncertainty within demand projections. Moreover, MoD demand levels are profoundly influenced by both endogenous and exogenous factors, leading to high and dynamic volatility. This volatility significantly undermines the efficacy of conventional time series forecasting methods. In response, we propose an Extended Recurrent Mixture Density Network (XRMDN), a novel deep learning framework engineered to address these challenges. XRMDN leverages a sophisticated architecture to process demand residuals and variance through correlated modules, allowing for the flexible incorporation of endogenous and exogenous data. This architecture, featuring recurrent connections within the weight, mean, and variance neural networks, adeptly captures demand trends, thus significantly enhancing forecasting precision, particularly in high-volatility scenarios. Our comprehensive experimental analysis, utilizing real-world MoD datasets, demonstrates that XRMDN surpasses the existing benchmark models across various metrics, notably excelling in high-demand volatility contexts. This advancement in probabilistic demand forecasting marks a significant contribution to the field, offering a robust tool for enhancing operational efficiency and customer satisfaction in MoD systems.
UAVSAR Instrument: Current Operations and Planned Upgrades

The Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument is a pod-based Lband polarimetric synthetic aperture radar (SAR), specifically designed to acquire airborne repeat track SAR data for differential interferometric measurements. This instrument is currently installed on the NASA GulfstreamIII (G-III) aircraft with precision real-time Global Positioning System (GPS) and a sensor-controlled flight management system for precision repeat-pass data acquisitions. UAVSAR has conducted engineering and preliminary science data flights since October 2007 on the G-III. We are porting the radar to the Global Hawk Unmanned Airborne Vehicle (UAV) to enable long duration/long range data campaigns. We plan to install two radar pods (each with its own active array antenna) under the wings of the Global Hawk to enable the generation of precision topographic maps and single pass polarimetric-interferometry (SPI) providing vertical structure of ice and vegetation. Global Hawk’s range of ~ 8000 nm will enable regional surveys with far fewer sorties as well as measurements of remote locations without the need for long and complicated deployments. We are also developing P-band polarimetry and Ka-band single-pass interferometry capabilities on UAVSAR by replacing the radar antenna and front-end electronics to operate at these frequencies.