Miracle-dz/UniFAR-FTUs huggingface dataset:
Limitations on silicon in the outer core: Ultrasonic measurements at high temperatures and high dK/dP values of Fe‐Ni‐Si liquids at high pressures

The sound velocities of four iron‐nickel‐silicon liquids (Fe‐5 wt %Ni‐6 wt %Si, Fe‐5 wt %Ni‐10 wt %Si, Fe‐5 wt %Ni‐14 wt %Si, and Fe‐5 wt %Ni‐20 wt %Si) are measured between 1460 and 1925 K at ambient pressures using ultrasonic interferometry. The results constrain both the dependence on Si content of the bulk modulus of these liquids and the temperature dependence of their elasticity. These elastic data are utilized to assess both relatively low pressure (to 12 GPa) compressional data on Fe‐Si liquids and to extrapolate to higher‐pressure and higher‐temperature conditions. If a single equation of state for Fe‐Ni‐Si liquids of a given composition applies from low pressure to near core conditions, then our results imply that the isothermal pressure derivative of the bulk modulus of these liquids is high: likely 8 and above at high temperatures. This high value of the pressure derivative of the bulk modulus at low pressures in Fe‐Si liquids causes marked stiffening at higher pressures, leading to notable incompressibility and apparent low values of the pressure derivative of the bulk modulus at core conditions. These results reinforce the conclusion that silicon is not a major alloying component of Earth's core.
Compositionality and Bounds for Optimal Value Functions in Reinforcement Learning

An agent’s ability to reuse solutions to previously solved problems is critical for learning new tasks efﬁciently. Recent research using composition of value functions in reinforcement learning has shown that agents can utilize solutions of primitive tasks to obtain solutions for exponentially many new tasks. However, previous work has relied on restrictive assumptions on the dynamics, the method of composition, and the structure of reward functions. Here we consider the case of general composition functions without any restrictions on the structure of reward functions, applicable to both deterministic and stochastic dynamics. For this general setup, we provide bounds on the corresponding optimal value functions and characterize the value of corresponding policies. The theoretical results derived lead to improvements in training for both entropy-regularized and standard reinforcement learning, which we validate with numerical simulations.
A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration

Despite the impressive generalization capabilities of deep neural networks, they have been repeatedly shown to poorly estimate their predictive uncertainty - in other words, they are frequently overconfident when they are wrong. Fixing this issue is known as model calibration, and has consequently received much attention in the form of modified training schemes and post-training calibration procedures. In this work, we present a significant hurdle to the calibration of modern models: deep neural networks have large neighborhoods of almost certain confidence around their training points. We demonstrate in our experiments that this phenomenon consistently arises (in the context of image classification) across many model and dataset pairs. Furthermore, we prove that when this phenomenon holds, for a large class of data distributions with overlaps between classes, it is not possible to obtain a model that is asymptotically better than random (with respect to calibration) even after applying the standard post-training calibration technique of temperature scaling. On the other hand, we also prove that it is possible to circumvent this defect by changing the training process to use a modified loss based on the Mixup data augmentation technique.
Bayesian estimation of uncertainty in land surface‐atmosphere flux predictions

This study addresses the assessment of uncertainty associated with predictions of land surface-atmosphere fluxes using Bayesian Monte Carlo simulation within the generalized likelihood uncertainty estimation (GLUE) methodology. Even a simple soil vegetation-atmosphere transfer (SVAT) scheme is shown to lead to multiple acceptable parameterizations when calibration data are limited to timescales of typical intensive field campaigns. The GLUE methodology assigns a likelihood weight to each acceptable simulation. As more data become available, these likelihood weights may be updated by using Bayes equation. Application of the GLUE methodology can be shown to reveal deficiencies in model structure and the benefit of additional calibration data. The method is demonstrated with data sets taken from FIFE sites in Kansas, and ABRACOS data from the Amazon. Estimates of uncertainty are propagated for each data set revealing significant predictive uncertainty. The value of additional periods of data is then evaluated through comparing updated uncertainty estimates with previous estimates using the Shannon entropy measure.
When an Active Learner Meets a Black-box Teacher

Active learning maximizes the hypothesis updates to ﬁnd those desired unlabeled data. An inherent assumption is that this learning manner can derive those updates into the optimal hypothesis. However, its convergence may not be guaranteed well if those incremental updates are negative and disordered. In this paper, we introduce a machine teacher who provides a black-box teaching hypothesis for an active learner, where the teaching hypothesis is an eﬀective approximation for the optimal hypothesis. Theoretically, we prove that, under the guidance of this teaching hypothesis, the learner can converge into a tighter ∗ . Preliminary work was done when Xiaofeng Cao was a Research Assistant at AAII, UTS. † . The ﬁrst two authors made equal contributions.
Latitude‐dependent long‐term variations in polar mesospheric clouds from SBUV version 3 PMC data

[1] Previous studies have suggested that there should be secular trends in polar mesospheric cloud (PMC) occurrence frequency and brightness on decadal timescales and that those trends would be strongest at the lowest latitudes of the PMC existence region. We have analyzed the 27-year PMC data set created from Solar Backscatter Ultraviolet (SBUV, SBUV/2) satellite instruments for long-term variations in albedo using three latitude bands (50°–64°, 64°–74°, 74°–82°). The improved version 3 data set includes revisions to the PMC detection algorithm to produce more consistent results in all measurement conditions. A detailed error analysis yields an approximate uncertainty of 1–2% for seasonally averaged 252-nm albedo values. Adjustments for local time variations in PMC brightness between different satellite data sets were derived to ensure accurate trend calculations. Multiple linear regression fits show that albedo variations are anticorrelated with solar activity in all latitude bands, with a stronger response at high latitudes. The albedo increase from solar maximum to solar minimum ranges from +2% at 50°–64°S to +17% at 74°–82°N. Secular trends in albedo are positive, with long-term changes over 27 years ranging from +12% to +20% depending on hemisphere and latitude. The derived long-term trend in PMC albedo at 50°–64° is smaller than that of higher latitudes. This result contradicts previous suggestions that PMC brightness changes might be most rapid at low latitudes. The albedo response to solar variations is larger in the Northern Hemisphere, while long-term trends are approximately the same in both hemispheres.
Cognitive Artifacts in Support of Medical Shift Handover: An In Use, In Situ Evaluation

Abstract Technologies introduced to support complex and critical work practices merit rigorous and effective evaluation. However, evaluation strategies often fall short of evaluating real use by practitioners in the workplace and thereby miss an opportunity to gauge the true impact of the technology on the work. This article reports an in use, in situ evaluation of 2 cognitive artifacts that support the everyday work of handover in a healthcare setting. The evaluation drew inspiration from the theoretical viewpoint offered by distributed cognition, focusing on the information content, representational media, and context of use of the artifacts. The article discusses how this approach led to insights about the artifacts and their support of the work that could not have been obtained with more traditional evaluation techniques. Specifically, the argument is made that the ubiquitous approach of user testing with its reliance on think-alouds and observations of interaction is inadequate in this context and set an initial agenda for issues that should be addressed by in use, in situ evaluations.
Intensity Distribution from the 2004 M 9.0 Sumatra-Andaman Earthquake

The epicenter of the Mw 9.0 Sumatra-Andaman earthquake on 26 December 2004 at 00:58 UTC occurred near the northwestern end of Sumatra and ruptured northward along the Andaman and Nicobar Archipelago (Figure 1). The principal rupture had a duration of more than 10 minutes (Park et al., 2005) and was perceptible to a distance of 3,300 km westward and 2,600 km northward. The largest accelerations were perceived near the epicenter, but high intensities were also perceived along the archipelago with diminishing intensity northward. Presented here are 206 intensity reports interpreted in terms of EMS-98 (Grunthal, 1998), a scale with similarities to the MSK and Modified Mercalli Intensity scales but designed to take into account modern building practices. The data are culled from primary accounts, newspaper reports, and responses to an online intensity questionnaire (http://asc-india.org/menu/felt.htm). Insofar as additional epicentral data will be forthcoming in the next several months, the data are presented with a minimum of interpretation. Figure 1. Map showing the region of the rupture and its aftershocks (black dots). The location of the December mainshock is shown by a star. It is believed that the main rupture propagated to close to Port Blair. This study focuses on evidence for shaking intensity resulting from the earthquake, ignoring damage due to the tsunami, landslides, or liquefaction. Preliminary ground-based tsunami surveys are available (Borrero, 2005; NICEE, 2005); these provide some information about shaking intensities as well but are spatially limited. First-hand accounts of the disaster and damage reports that appeared in Web logs, conventional newspapers, and Web sites are evaluated along with 48 felt reports sent via the Internet by eyewitnesses in India. Multiple felt reports were received from places such as Bhubaneswar, Chennai, and Port Blair. The highest assigned intensity of 9 EMS was reported from Lameureum, on the northwest coast …
The Early Toarcian Anoxic Event in the Westernmost Tethys (Subbetic): Paleogeographic and Paleobiogeographic Significance

A positive $$\delta^{13}C$$ excursion reveals an oceanic anoxic event in the Subbetic, corresponding to the westernmost end of the Tethys during the Early Jurassic. This positive $$\delta^{13}C$$ excursion exists in five stratigraphic sections consisting of Lower and Middle Toarcian pelagic sediments. The positive $$\delta^{13}C$$ excursion in the Subbetic is located in the upper part of the Early Toarcian, serpentinus ($$\approx$$ falciferum) biozone, while in other Tethyan regions (Alps, Apennines, and Dinarides) it has been found in the lower part of the same biozone. The most significant positive $$\delta^{13}C$$ excursion detected in the Subbetic is therefore about half a million years younger, although other relative peaks had been detected. This isotopic anomaly can be correlated with a maximum transgression that occurred earlier in the regions closer to the open sea of the Tethys ocean. Other features of the anoxic event (faunal crises and black-shale deposits), appear at different ages, related to the activity of the transgressive phenomenon that occurred through the Early Toarcian.
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis

In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and flow-based models for image generation, we propose Mol2Image: a flow-based generative model for molecule to cell image synthesis. To generate cell features at different resolutions and scale to high-resolution images, we develop a novel multi-scale flow architecture based on a Haar wavelet image pyramid. To maximize the mutual information between the generated images and the molecular interventions, we devise a training strategy based on contrastive learning. To evaluate our model, we propose a new set of metrics for biological image generation that are robust, interpretable, and relevant to practitioners. We show quantitatively that our method learns a meaningful embedding of the molecular intervention, which is translated into an image representation reflecting the biological effects of the intervention.
Leveraging Large Language Models for Mental Health Prediction via Online Text Data

The recent technology boost of large language models (LLMs) has empowered a variety of applications. However, there is very little research on understanding and improving LLMs’ capability for the mental health domain. In this work, we present the first comprehensive evaluation of multiple LLMs, including Alpaca, Alpaca-LoRA, and GPT-3.5, on various mental health prediction tasks via online text data. We conduct a wide range of experiments, covering zero-shot prompting, few-shot prompting, and instruction finetuning. The results indicate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for mental health tasks. More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously. Our best-finetuned model, Mental-Alpaca, outperforms GPT-3.5 (25 times bigger) by 16.7% on balanced accuracy and performs on par with the state-of-the-art task-specific model. We summarize our findings into a set of action guidelines for future researchers, engineers, and practitioners on how to empower LLMs with better mental health domain knowledge and become an expert in mental health prediction tasks.
ARCH-COMP18 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2018. In this year, six tools CORA, CORA/SX, C2E2, Flow*, Isabelle/HOL, and SymReach (in alphabetic order) participated. They are applied to solve reachability analysis problems on four benchmarks problems, one of them with hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools.
Concave-bank benches on the Squamish River, British Columbia, Canada

The form, sediments, and possible origin of two concave-bank benches on the Squamish River, British Columbia, are briefly described. They contain the only fine-grained within-channel deposits in this gravel-bed stream and are generally similar to features on the Barwon River in Australia.One concave-bank bench on the Squamish River is a mature feature and the other is currently being formed. Evidence from the latter example indicates that the features, like those on the Barwon River, are deposited in the separation zones developed at the concave bank of very sharp bends. Sediments appear to be supplied from suspensions in dissipating vortices advected into the separation zone. Two processes of vortex advection are briefly discussed.
Speaker-specific Thresholding for Robust Imposter Identification in Unseen Speaker Recognition

Speaker identification systems are deployed in diverse environments, often different from the lab conditions on which they are trained and tested. In this paper, first, we show the problem of generalization using fixed thresholds computed using the equal error rate metric. Secondly, we introduce a novel and generalizable speaker-specific thresholding technique for robust imposter identification in unseen speaker identification. We propose a speaker-specific adaptive threshold, which can be computed us-ing the enrollment audio samples, for identifying imposters in unseen speaker identification. Furthermore, we show the efficacy of the proposed technique on VoxCeleb1, VCTK and the FFSVC 2022 datasets, beating the baseline fixed thresholding by up to 25%. Finally, we exhibit that the proposed algorithm is also generalizable, demonstrating its performance on ResNet50, ECAPA-TDNN and RawNet3 speaker encoders.
BigTrans: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages

Large language models (LLMs) demonstrate promising translation performance among various natural languages. However, many LLMs especially the open-sourced ones, such as BLOOM (Scao et al., 2023) and LLaMA (Tou-vron et al., 2023), are English-dominant and support only dozens of natural languages, making the potential of LLMs on language translation less explored. In this work, we present Big-Trans which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languages. BigTrans is built upon LLaMA-13B and it is optimized in three steps. First, we continue training LLaMA with massive Chinese monolingual data. Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages. Third, we instruct-tune the foundation model with multilingual translation instructions, leading to our BigTrans model. The preliminary experiments on multilingual translation show that BigTrans performs comparably with Chat-GPT and Google Translate in many languages and even outperforms ChatGPT in 8 language pairs. We release the BigTrans model 1 and hope it can advance the research progress.
Microstructure of Omani expansive soils

The fundamental factors controlling the expansive nature of Omani soils are their characteristics with respect to geology, mineralogy, engineering, and microstructure. The first three factors have been extensively studied, however, the fourth has not yet been fully investigated and no work has been published on this aspect. Therefore, this paper deals with the microstructure characteristics of these soils. The scanning electron microscope was employed in this investigation. Three samples selected from different sites where expansive soils problems were known (Al-Khod, Al-Murtafa'a, and Duqm) were investigated. Two specimens from each sample, representing the vertical and horizontal planes, were tested. In addition, two samples that were subjected to swelling were tested to investigate the microfabric changes caused by swelling. The fabric of the Omani expansive soils studied generally consists of dense clay matrices, although clay-granular matrices are occasionally observed. In addition, no connectors hav.
Extracting Image Characteristics to Predict Crowdfunding Success

Despite an increase in the empirical study of crowdfunding platforms and the prevalence of visual information, operations management and marketing literature has yet to explore the role that image characteristics play in crowdfunding success. The authors of this manuscript begin by synthesizing literature on visual processing to identify several image characteristics that are likely to shape crowdfunding success. After detailing measures for each image characteristic, they use them as part of a machine-learning algorithm (Bayesian additive trees), along with project characteristics and textual information, to predict crowdfunding success. Results show that the inclusion of these image characteristics substantially improves prediction over baseline project variables, as well as textual features. Furthermore, image characteristic variables exhibit high importance, similar to variables linked to the number of pictures and number of videos. This research therefore offers valuable resources to researchers and managers who are interested in the role of visual information in ensuring new product success.
REVISED CARBONATE-WATER ISOTOPIC TEMPERATURE SCALE

The relationship between temperature and O^(18) content relative to that for a Cretaceous belemnite of the Pee Dee formation previously reported (Epstein, Buchsbaum, Lowenstam, and Urey, 1951) has been re-determined using modified procedures for removing organic matter from shells, and is found to be t(°C) = 16.5 - 4. 3δ + 0. 14δ^2 where δ is the difference in per mil of the O^(18) to O^(16) ratio between the sample and reference gas. The new relationship agrees with that determined by McCrea (1950) for inorganically precipitated calcium carbonate. Carbonate-carbon dioxide exchange experiments were done to determine the direct and indirect effects of organic matter in the shell on the mass spectrometer analyses.
Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

ABSTRACT This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real-world individual’s affective states. It is also important to ensure that the measurement can be applied regardless of the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes in duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.
Concurrent variation between the East Asian subtropical jet and polar front jet during persistent snowstorm period in 2008 winter over southern China

The concurrent variation features between the East Asian subtropical jet and polar front jet were investigated during persistent snowstorm period in 2007/2008 winter over southern China. The East Asian subtropical jet was divided into two parts: (1) the plateau jet, located along the southern side of the Tibetan Plateau, and (2) the ocean jet, situated at the southeastern Japan Island. The concurrent intensity variation among the polar front jet, plateau jet, and ocean jet and the associated atmospheric anomalous signals were examined. A possible mechanism for concurrent variation among the three jets was also investigated from a perspective of synoptic‐scale transient eddy activities (STEA). The enhanced plateau jet was simultaneously correlated with the weakened polar front jet, while the variation of the ocean jet lagged the variation of the plateau jet (polar front jet) about 5 days. The concurrent variation between the plateau jet and the polar front jet acted as an important bridge that linked the snowstorm to the atmospheric anomalous signals associated with the cold and warm air activities. Due to the opposite trends of STEA variation over the southern and northern sides of the Tibetan Plateau, the plateau jet and the polar front jet exhibited a significant concurrent variation feature. The STEA anomalies over the plateau jet and polar front jet regions propagated downstream to the East Asian coast as a wave train along the southern and northern sides of the Tibetan Plateau, respectively, resulting in a 5 day lag variation relationship between the ocean jet and the plateau jet (polar front jet).