Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical methodology in the search for metallic ores

Atomic absorption and emission spectrography are the methods of analysis most widely used in geochemical exploration. Development of nonflame atomizers, particularly electrothermal devices and reduction cells for atom and metal-hydride generation, has expanded the application of atomic absorption spectrometry by pushing detection limits of many elements well into the parts per billion range and by reducing detection limits for others, such as As, Se, Te, and Sn, to levels useful in lithogeochemical surveys. The recent promotion of inductively-coupled plasma sources for excitation, as well as other variations, such as use of echelle gratings, has increased the number of available spectrographic methods for multielement surveys and has simplified the application of partial extraction techniques in emission spectrography. Other methods that require mass spectrometers and gas chromatographs are being used to measure volatile indicator elements and compounds such as helium and sulphur gases. Analytical techniques, including those based on voltammetry, ion-selective electrodes, and the use of partial or selective extractions, are finding increased application as analytical tools and as aids in determining metal speciation better to understand geochemical processes of dispersion and concentration. Current interest in uranium exploration has sparked a major effort to develop new analytical methods or improve existing ones for the determination of uranium and related radionuclides. Exploration geologists may now choose conventional fluorimetry, delayed neutron counting, X-ray fluorescence, laser-induced fluorescence, and nuclear-fission track techniques for the determination of uranium. The choice will depend on sensitivity required, sample media being analyzed, chemical species of the uranium to be determined, turnaround time required, and cost considerations. Two of the methods described, conventional fluorimetry and laser-induced fluorescence, can be adapted for use in the field. While recent developments of new techniques and apparatus have greatly expanded the number of usefUl analytical techniques in exploration geochemistry, each has its own problems and limitations as well as its applications. A panacea for analytical problems does not yet exist, except perhaps in the person of the skilled analyst, whose ingenuity in developing and applying new methods augments diligent application of tried and true procedures.
Spectral Lower Bounds for Local Search

Local search is a powerful heuristic in optimization and computer science, the complexity of which has been studied in the white box and black box models. In the black box model, we are given a graph $G = (V,E)$ and oracle access to a function $f : V \to \mathbb{R}$. The local search problem is to find a vertex $v$ that is a local minimum, i.e. with $f(v) \leq f(u)$ for all $(u,v) \in E$, using as few queries to the oracle as possible. We show that if a graph $G$ admits a lazy, irreducible, and reversible Markov chain with stationary distribution $\pi$, then the randomized query complexity of local search on $G$ is $\Omega\left( \frac{\sqrt{n}}{t_{mix} \cdot \exp(3\sigma)}\right)$, where $t_{mix}$ is the mixing time of the chain and $\sigma = \max_{u,v \in V(G)} \frac{\pi(v)}{\pi(u)}. $ This theorem formally establishes a connection between the query complexity of local search and the mixing time of the fastest mixing Markov chain for the given graph. We also get several corollaries that lower bound the complexity as a function of the spectral gap, one of which slightly improves a result from prior work.
Limit theorems for SDEs with irregular drifts

In this paper, concerning SDEs with H\"older continuous drifts, which are merely dissipative at infinity, and SDEs with piecewise continuous drifts, we investigate the strong law of large numbers and the central limit theorem for underlying additive functionals and reveal the corresponding rates of convergence. To establish the limit theorems under consideration, the exponentially contractive property of solution processes under the (quasi-)Wasserstein distance plays an indispensable role. In order to achieve such contractive property, which is new and interesting in its own right for SDEs with H\"older continuous drifts or piecewise continuous drifts, the reflection coupling method is employed and meanwhile a sophisticated test function is built.
Quantifying the cooling demand of urban areas using aerial images

Modelling cooling demands still imposes a challenge in urban energy modelling. This study focusses on estimating cooling capacities of air-cooled and hybrid condensers, rooftop units and cooling towers and cooling demands of respective buildings. The units are detected in aerial images using specifically trained object detection models. Nominal capacities are estimated using regression analysis using the number of condenser fans and unit footprint as proxy. By estimating operating times of the units based on building type, the annual cooling demand is estimated. The approach is applied to the Manhattan Financial District in New York City, where a capacity of 1.46 GW and a cooling demand of 1.71 TWh are estimated.
High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models

Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past thirty years, progress toward making high throughput phenotyping feasible. In this study, we demonstrate that a large language model and a hybrid NLP model (combining word vectors with a machine learning classifier) can perform high throughput phenotyping on physician notes with high accuracy. Large language models will likely emerge as the preferred method for high throughput deep phenotyping of physician notes.
Is ChatGPT More Empathetic than Humans?

This paper investigates the empathetic responding capabilities of ChatGPT, particularly its latest iteration, GPT-4, in comparison to human-generated responses to a wide range of emotional scenarios, both positive and negative. We employ a rigorous evaluation methodology, involving a between-groups study with 600 participants, to evaluate the level of empathy in responses generated by humans and ChatGPT. ChatGPT is prompted in two distinct ways: a standard approach and one explicitly detailing empathy's cognitive, affective, and compassionate counterparts. Our findings indicate that the average empathy rating of responses generated by ChatGPT exceeds those crafted by humans by approximately 10%. Additionally, instructing ChatGPT to incorporate a clear understanding of empathy in its responses makes the responses align approximately 5 times more closely with the expectations of individuals possessing a high degree of empathy, compared to human responses. The proposed evaluation framework serves as a scalable and adaptable framework to assess the empathetic capabilities of newer and updated versions of large language models, eliminating the need to replicate the current study's results in future research.
Efficient Estimation of the Convective Cooling Rate of Photovoltaic Arrays with Various Geometric Configurations: a Physics-Informed Machine Learning Approach

Convective heat transfer is crucial for photovoltaic (PV) systems, as the power generation of PV is sensitive to temperature. The configuration of PV arrays have a significant impact on convective heat transfer by influencing turbulent characteristics. Conventional methods of quantifying the configuration effects are either through Computational Fluid Dynamics (CFD) simulations or empirical methods, which face the challenge of either high computational demand or low accuracy, especially when complex array configurations are considered. This work introduces a novel methodology to quantify the impact of geometric configurations of PV arrays on their convective heat transfer rate in wind field. The methodology combines Physics Informed Machine Learning (PIML) and Deep Convolution Neural Network (DCNN) to construct a robust PIML-DCNN model to predict convective heat transfer rates. In addition, an innovative loss function, termed Pocket Loss is proposed to enhance the interpretability of the PIML-DCNN model. The model exhibits promising performance, with a relative error of 1.9\% and overall $R^2$ of 0.99 over all CFD cases in estimating the coefficient of convective heat transfer, when compared with full CFD simulations. Therefore, the proposed model has the potential to efficiently guide the configuration design of PV arrays for power generation enhancement in real-world operations.
The Geometry of Cyclical Social Trends

We investigate the emergence of periodic behavior in opinion dynamics and its underlying geometry. For this, we use a bounded-confidence model with contrarian agents in a convolution social network. This means that agents adapt their opinions by interacting with their neighbors in a time-varying social network. Being contrarian, the agents are kept from reaching consensus. This is the key feature that allows the emergence of cyclical trends. We show that the systems either converge to nonconsensual equilibrium or are attracted to periodic or quasi-periodic orbits. We bound the dimension of the attractors and the period of cyclical trends. We exhibit instances where each orbit is dense and uniformly distributed within its attractor. We also investigate the case of randomly changing social networks.
Urban-Rural Classification using Census data, 1851-1911

© Harry Smith and Robert J. Bennett, University of Cambridge, members of the Cambridge Group for the History of Population and Social Structure asserts their moral right to be identified as the authors of this paper; it may be referenced provided full acknowledgement is made: Cite (Harvard format): Smith, Harry and Bennett Robert, J. (2017), Urban-Rural Classification using Census data, 1851-1911, Working Paper 6: ESRC project ES/M0010953: ‘Drivers of Entrepreneurship and Small Businesses’, University of Cambridge, Department of Geography and Cambridge Group for the History of Population and Social Structure.
Speeding up 6-DoF Grasp Sampling with Quality-Diversity

Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generative models. However, the interaction data remains the bottleneck for generalization. Getting data for grasping is a critical challenge, as this skill is required to complete many manipulation tasks. Quality-Diversity (QD) algorithms optimize a set of solutions to get diverse, high-performing solutions to a given problem. This paper investigates how QD can be combined with priors to speed up the generation of diverse grasps poses in simulation compared to standard 6-DoF grasp sampling schemes. Experiments conducted on 4 grippers with 2-to-5 fingers on standard objects show that QD outperforms commonly used methods by a large margin. Further experiments show that QD optimization automatically finds some efficient priors that are usually hard coded. The deployment of generated grasps on a 2-finger gripper and an Allegro hand shows that the diversity produced maintains sim-to-real transferability. We believe these results to be a significant step toward the generation of large datasets that can lead to robust and generalizing robotic grasping policies.
Vosh: Voxel-Mesh Hybrid Representation for Real-Time View Synthesis

The neural radiance field (NeRF) has emerged as a prominent methodology for synthesizing realistic images of novel views. While neural radiance representations based on voxels or mesh individually offer distinct advantages, excelling in either rendering quality or speed, each has limitations in the other aspect. In response, we propose a pioneering hybrid representation named Vosh, seamlessly combining both voxel and mesh components in hybrid rendering for view synthesis. Vosh is meticulously crafted by optimizing the voxel grid of NeRF, strategically with selected voxels replaced by mesh. Therefore, it excels in fast rendering scenes with simple geometry and textures through its mesh component, while simultaneously enabling high-quality rendering in intricate regions by leveraging voxel component. The flexibility of Vosh is showcased through the ability to adjust hybrid ratios, providing users the ability to control the balance between rendering quality and speed based on flexible usage. Experimental results demonstrates that our method achieves commendable trade-off between rendering quality and speed, and notably has real-time performance on mobile devices.
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation

Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical translation. We propose to use marginal L1 average calibration error (mL1-ACE) as a novel auxiliary loss function to improve pixel-wise calibration without compromising segmentation quality. We show that this loss, despite using hard binning, is directly differentiable, bypassing the need for approximate but differentiable surrogate or soft binning approaches. Our work also introduces the concept of dataset reliability histograms which generalises standard reliability diagrams for refined visual assessment of calibration in semantic segmentation aggregated at the dataset level. Using mL1-ACE, we reduce average and maximum calibration error by 45% and 55% respectively, maintaining a Dice score of 87% on the BraTS 2021 dataset. We share our code here: https://github.com/cai4cai/ACE-DLIRIS
Spatial features of CO2 for occupancy detection in a naturally ventilated school building

Accurate occupancy information helps to improve building energy efficiency and occupant comfort. Occupancy detection methods based on CO2 sensors have received attention due to their low cost and low intrusiveness. In naturally ventilated buildings, the accuracy of CO2-based occupancy detection is generally low in related studies due to the complex ventilation behavior and the difficulty in measuring the actual air exchange through windows. In this study, we present two novel features for occupancy detection based on the spatial distribution of the CO2 concentration. After a quantitative analysis with Support Vector Machine (SVM) as classifier, it was found that the accuracy of occupancy state detection in naturally ventilated rooms could be improved by up to 14.8 percentage points compared to the baseline, reaching 83.2 % (F1 score 0.84) without any ventilation information. With ventilation information, the accuracy reached 87.6 % (F1 score 0.89). The performance of occupancy quantity detection was significantly improved by up to 25.3 percentage points versus baseline, reaching 56 %, with root mean square error (RMSE) of 11.44 occupants, using only CO2-related features. Additional ventilation information further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By incorporating spatial features, the model using only CO2-related features revealed similar performance as the model containing additional ventilation information, resulting in a better low-cost occupancy detection method for naturally ventilated buildings.
Variance, norms and cooperative behavior in public goods games

This study examines the relationship between the variance of others’ contributions, social norms (empirical and normative expectations), and cooperative behavior using a classic linear public goods game. The following results are observed. First, the variance of a participant’s group members’ contributions had a negative impact on their contributions, empirical expectations, and normative expectations. Second, deviations from the mean, whether negative or positive, were deemed less socially appropriate. Third, while there was a strong relationship between variance, social norms, and cooperative behavior, the mediating effect of social norms was found to be insignificant. Finally, there were some notable findings regarding behavior type. Although free riders and cooperators exhibited distinct behavioral patterns, their normative expectations were similar. Free riders expected others to cooperate, but their empirical expectations were significantly lower than cooperators’ expectations, which were aligned with their actual contributions. These findings contribute to research on the relationship between distribution heterogeneity, social norms and cooperative behavior. Furthermore, these findings provide valuable insights into management practices.
Galaxy Morphologies Revealed with Subaru HSC and Super-Resolution Techniques II: Environmental Dependence of Galaxy Mergers at z~2-5

We super-resolve the seeing-limited Subaru Hyper Suprime-Cam (HSC) images for 32,187 galaxies at z~2-5 in three techniques, namely, the classical Richardson-Lucy (RL) point spread function (PSF) deconvolution, sparse modeling, and generative adversarial networks to investigate the environmental dependence of galaxy mergers. These three techniques generate overall similar high spatial resolution images but with some slight differences in galaxy structures, for example, more residual noises are seen in the classical RL PSF deconvolution. To alleviate disadvantages of each technique, we create combined images by averaging over the three types of super-resolution images, which result in galaxy sub-structures resembling those seen in the Hubble Space Telescope images. Using the combined super-resolution images, we measure the relative galaxy major merger fraction corrected for the chance projection effect, f_merg, for galaxies in the ~300 deg^2-area data of the HSC Strategic Survey Program and the CFHT Large Area U-band Survey. Our f_merg measurements at z~3 validate previous findings showing that f_merg is higher in regions with a higher galaxy overdensity delta at z~2-3. Thanks to the large galaxy sample, we identify a nearly linear increase in f_merg with increasing delta at z~4-5, providing the highest-z observational evidence that galaxy mergers are related to delta. In addition to our f_merg measurements, we find that the galaxy merger fractions in the literature also broadly align with the linear f_merg-delta relation across a wide redshift range of z~2-5. This alignment suggests that the linear f_merg-delta relation can serve as a valuable tool for quantitatively estimating the contributions of galaxy mergers to various environmental dependences. This super-resolution analysis can be readily applied to datasets from wide field-of-view space telescopes such as Euclid and Roman.
Designing Digital Educational Games by Integrating Teaching Process into the Technology Platform of Entertainment Games

This study presents a method for designing Digital Educational Games (DEGs) that integrates educational content with entertainment game technology. The purpose is to create engaging learning experiences by leveraging theories of learning, emotion, and motivation. Methodology includes selecting popular entertainment games and incorporating educational objectives to enhance both learning and gameplay. This design has been applied into teaching Soft Skills according to the game based learning approach. This application was evaluated to find the effectiveness of this proposal and the way to improve the scenario of the “The Energy Bus Journey”. Results indicate this approach increases student engagement and learning efficacy. The conclusion suggests that while promising, further optimization is needed to perfect the balance between educational content and gaming enjoyment.
Composition of rough singular integral operators on rearrangement invariant Banach type spaces

Let $\Omega$ be a homogeneous function of degree zero and enjoy the vanishing condition on the unit sphere $\mathbb{S}^{n-1}(n\geq 2)$. Let $T_{\Omega}$ be the convolution singular integral operator with kernel ${\Omega(x)}{|x|^{-n}}$. In this paper, when $\Omega \in L^{\infty}(\mathbb {S}^{n-1})$, we consider the quantitative weighted bounds of the composite operators of $T_{\Omega}$ on rearrangement invariant Banach function spaces. These spaces contain the classical Lorentz spaces and Orlicz spaces as special examples. Weighted boundedness of the composite operators on rearrangement invariant quasi-Banach spaces were also given.
Nitrate Isotope Distribution in the Subarctic and Subtropical North Pacific

Nitrogen and oxygen isotopic compositions of nitrate (δ15NNitrate and δ18ONitrate) were determined along 47°N in the subarctic North Pacific and 149°E in the western North Pacific. In the western subarctic gyre, known as a high‐nutrient, low‐chlorophyll region, the δ15NNitrate and the differences between δ15NNitrate and δ18ONitrate (herein Δ(15–18)) in the intermediate water were significantly lower than in the surrounding area. The 15N‐depleted nitrate was generated by nitrified nitrate from the remineralization of organic matter produced by the partial consumption of the surface nitrate pool. Between the western subarctic gyre and the Alaskan gyre, there was a 0.2‰ increase in δ15NNitrate and a 0.3‰ increase in Δ(15–18) in the intermediate water associated with a 7.2‐μM decrease in the nitrate concentration at the surface. Assuming Rayleigh distillation kinetics, the increase in utilization of the surface nitrate pool between the western subarctic gyre and the Alaskan gyre (from 29% to 85%) corresponds to a change in δ15N of organic matter from 2.6‰ to 4.9‰. The 15N enrichment of intermediate nitrate toward the east resulted from the increase in utilization. Moreover, the 15N‐depleted nitrate in the mixed‐layer water of the western subtropical gyre was affected by N2 fixation in the Kuroshio water, whereas the 15N‐enriched nitrate in the intermediate water at the western margin of North America was affected by water column denitrification. The δ15N sediment record in the western subarctic North Pacific likely reflects past changes in the high‐nutrient, low‐chlorophyll region but may also be controlled by water column denitrification and N2 fixation.
Positivity and asymptotic behaviour of solutions to a generalized nonlocal fast diffusion equation

We study the positivity and asymptotic behaviour of nonnegative solutions of a general nonlocal fast diffusion equation, \[\partial_t u + \mathcal{L}\varphi(u) = 0,\] and the interplay between these two properties. Here $\mathcal{L}$ is a stable-like operator and $\varphi$ is a singular nonlinearity. We start by analysing positivity by means of a weak Harnack inequality satisfied by a related elliptic (nonlocal) equation. Then we use this positivity to establish the asymptotic behaviour: under certain hypotheses on the nonlocal operator and nonlinearity, our solutions behave asymptotically as the Barenblatt solution of the standard fractional fast diffusion equation. The main difficulty stems from the generality of the operator, which does not allow the use of the methods that were available for the fractional Laplacian. Our results are new even in the case where $\varphi$ is a power.