Miracle-dz/UniFAR-FTUs huggingface dataset:
Landslide monitoring using airphotos time series and GIS

Western Greece is suffering by landslides. The term landslide includes a wide range of ground movement, such as slides, falls, flows etc. mainly based on gravity with the aid of many conditioning and triggering factors. Landslides provoke enormous changes to the natural and artificial relief. The annual cost of repairing the damage amounts to millions of euros. In this paper a combined use of airphotos time series, high resolution remote sensing data and GIS for the landslide monitoring is presented. Analog and digital air-photos used covered a period of almost 70 years from 1945 until 2012. Classical analog airphotos covered the period from 1945 to 2000, while digital airphotos and satellite images covered the 2008-2012 period. The air photos have been orthorectified using the Leica Photogrammetry Suite. Ground control points and a high accuracy DSM were used for the orthorectification of the air photos. The 2008 digital air photo mosaic from the Greek Cadastral with a spatial resolution of 25 cm and the respective DSM was used as the base map for all the others data sets. The RMS error was less than 0.5 pixel. Changes to the artificial constructions provoked by the landslideswere digitized and then implemented in an ARCGIS database. The results are presented in this paper.
The Intracluster Light and Its Link with the Dynamical State of the Host Group/Cluster: The Role of the Halo Concentration

We investigate the role of halo concentration in the formation of intracluster light (ICL) in galaxy groups and clusters, as predicted by a state-of-the-art semianalytic model of galaxy formation, coupled with a set of high-resolution dark-matter-only simulations. The analysis focuses on how the fraction of ICL correlates with halo mass, concentration, and fraction of early-type galaxies (ETGs) in a large sample of groups and clusters with 13.0≤logMhalo≤15.0 . The fraction of ICL follows a normal distribution, a consequence of the stochastic nature of the physical processes responsible for the formation of the diffuse light. The fractional budget of ICL depends on both halo mass (very weakly) until group scales, and concentration (remarkably). More interestingly, the ICL fraction is higher in more concentrated objects, a result of the stronger tidal forces acting in the innermost regions of the halos where the concentration is the quantity playing the most relevant role. Our model predictions do not show any dependence between the ICL and ETGs fractions, and so we instead suggest the concentration rather than the mass, as recently claimed, to be the main driver of the ICL formation. The diffuse light starts to form in groups via stellar stripping and mergers and later assembled in more-massive objects. However, the formation and assembly keep going on group/cluster scales at lower redshift through the same processes, mainly via stellar stripping in the vicinity of the central regions where tidal forces are stronger.
Visual inspection for illicit items in X-ray images using Deep Learning

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage object detectors. However, no comparative experimental assessment of the various relevant DNN components/methods has been performed under a common evaluation protocol, which means that reliable cross-method comparisons are missing. This paper presents exactly such a comparative assessment, utilizing a public relevant dataset and a well-defined methodology for selecting the specific DNN components/modules that are being evaluated. The results indicate the superiority of Transformer detectors, the obsolete nature of auxiliary neural modules that have been developed in the past few years for security applications and the efficiency of the CSP-DarkNet backbone CNN.
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly

Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster. However, training or fine-tuning LLMs requires a vast amount of data, which can be challenging to access due to legal or technical restrictions and may require private computing resources. Federated Learning (FL) is a solution designed to overcome these challenges and expand data access for deep learning applications. This paper takes a hardware-centric approach to explore how LLMs can be brought to modern edge computing systems. Our study fine-tunes the FLAN-T5 model family, ranging from 80M to 3B parameters, using FL for a text summarization task. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions. Our contribution is twofold: First, we evaluate the current capabilities of edge computing systems and their potential for LLM FL workloads. Second, by comparing these systems with a data-center GPU, we demonstrate the potential for improvement and the next steps toward achieving greater computational efficiency at the edge.
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images

The potential of deep neural networks in skin lesion classification has already been demonstrated to be on-par if not superior to the dermatologists diagnosis. However, the performance of these models usually deteriorates when the test data differs significantly from the training data (i.e. domain shift). This concerning limitation for models intended to be used in real-world skin lesion classification tasks poses a risk to patients. For example, different image acquisition systems or previously unseen anatomical sites on the patient can suffice to cause such domain shifts. Mitigating the negative effect of such shifts is therefore crucial, but developing effective methods to address domain shift has proven to be challenging. In this study, we carry out an in-depth analysis of eight different unsupervised domain adaptation methods to analyze their effectiveness in improving generalization for dermoscopic datasets. To ensure robustness of our findings, we test each method on a total of ten distinct datasets, thereby covering a variety of possible domain shifts. In addition, we investigated which factors in the domain shifted datasets have an impact on the effectiveness of domain adaptation methods. Our findings show that all of the eight domain adaptation methods result in improved AUPRC for the majority of analyzed datasets. Altogether, these results indicate that unsupervised domain adaptations generally lead to performance improvements for the binary melanoma-nevus classification task regardless of the nature of the domain shift. However, small or heavily imbalanced datasets lead to a reduced conformity of the results due to the influence of these factors on the methods performance.
Boosting the Edelstein effect of two-dimensional electron gases by ferromagnetic exchange

Strontium titanate (SrTiO$_3$) two-dimensional electron gases (2DEGs) have broken spatial inversion symmetry and possess a finite Rashba spin-orbit coupling. This enables the interconversion of charge and spin currents through the direct and inverse Edelstein effects, with record efficiencies at low temperature, but more modest effects at room temperature. Here, we show that making these 2DEGs ferromagnetic enhances the conversion efficiency by nearly one order of magnitude. Starting from the experimental band structure of non-magnetic SrTiO$_3$ 2DEGs, we mimic magnetic exchange coupling by introducing an out-of-plane Zeeman term in a tight-binding model. We then calculate the band structure and spin textures for increasing internal magnetic fields and compute the Edelstein effect using a semiclassical Boltzmann approach. We find that the conversion efficiency first increases strongly with increasing magnetic field, then shows a maximum and finally decreases. This field dependence is caused by the competition of the exchange coupling with the effective Rashba interaction. While enhancing the splitting of band pairs amplifies the Edelstein effect, weakening the in-plane Rashba-type spin texture reduces it.
Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Text-to-image diffusion models have recently emerged at the forefront of image generation, powered by very large-scale unsupervised or weakly supervised text-to-image training datasets. Due to their unsupervised training, controlling their behavior in downstream tasks, such as maximizing human-perceived image quality, image-text alignment, or ethical image generation, is difficult. Recent works finetune diffusion models to downstream reward functions using vanilla reinforcement learning, notorious for the high variance of the gradient estimators. In this paper, we propose AlignProp, a method that aligns diffusion models to downstream reward functions using end-to-end backpropagation of the reward gradient through the denoising process. While naive implementation of such backpropagation would require prohibitive memory resources for storing the partial derivatives of modern text-to-image models, AlignProp finetunes low-rank adapter weight modules and uses gradient checkpointing, to render its memory usage viable. We test AlignProp in finetuning diffusion models to various objectives, such as image-text semantic alignment, aesthetics, compressibility and controllability of the number of objects present, as well as their combinations. We show AlignProp achieves higher rewards in fewer training steps than alternatives, while being conceptually simpler, making it a straightforward choice for optimizing diffusion models for differentiable reward functions of interest. Code and Visualization results are available at https://align-prop.github. io/.
Infrared spectroscopy of sulfuric acid/water aerosols: Freezing characteristics

A low-temperature flow cell has been used in conjunction with a Fourier transform infrared (FT-IR) spectrometer to study sulfuric acid/water aerosols. The aerosols were generated with a wide range of composition (28 to 85 wt%), including those characteristic of stratospheric sulfate aerosols, and studied over the temperature range from 240 K to 160 K. The particles exhibited deep supercooling, by as much as 100 K below the freezing point in some cases. Freezing of water ice was observed in the more dilute (<40 wt% sulfuric acid) particles, in agreement with the predictions of Jensen et al. and recent observations by Bertram et al. In contrast with theoretical predictions, however, the entire particle often does not immediately freeze, at least on the timescale of the present experiments (seconds to minutes). Freezing of the entire particle is observed at lower temperatures, well below that characteristic of the polar stratosphere.
Influence of Food Web Structure on Carbon Exchange Between Lakes and the Atmosphere

Top predators and nutrient loading in lakes were manipulated to assess the influence of food web structure on carbon flux between lakes and the atmosphere. Nutrient enrichment increased primary production, causing lakes to become net sinks for atmospheric carbon (C atm ). Changes in top predators caused shifts in grazers. At identical nutrient loading, C atm invasion was greater to a lake with low grazing than to one with high grazing. Carbon stable-isotope distributions corroborated the drawdown of lake carbon dioxide and traced C atm transfer from algae to top predators. Thus, top predators altered ecosystem carbon fixation and linkages to the atmosphere.
Residual Multi-Fidelity Neural Network Computing

In this work, we consider the general problem of constructing a neural network surrogate model using multi-fidelity information. Motivated by rigorous error and complexity estimates for ReLU neural networks, given an inexpensive low-fidelity and an expensive high-fidelity computational model, we present a residual multi-fidelity computational framework that formulates the correlation between models as a residual function, a possibly non-linear mapping between 1) the shared input space of the models together with the low-fidelity model output and 2) the discrepancy between the two model outputs. To accomplish this, we train two neural networks to work in concert. The first network learns the residual function on a small set of high-fidelity and low-fidelity data. Once trained, this network is used to generate additional synthetic high-fidelity data, which is used in the training of a second network. This second network, once trained, acts as our surrogate for the high-fidelity quantity of interest. We present three numerical examples to demonstrate the power of the proposed framework. In particular, we show that dramatic savings in computational cost may be achieved when the output predictions are desired to be accurate within small tolerances.
Stability of homogeneous equilibria of the Hartree-Fock equation, for its equivalent formulation for random fields

The Hartree-Fock equation admits homogeneous states that model infinitely many particles at equilibrium. We prove their asymptotic stability in large dimensions, under assumptions on the linearised operator. Perturbations are moreover showed to scatter to linear waves. We obtain this result for the equivalent formulation of the Hartree-Fock equation in the framework of random fields. The main novelty is to study the full Hartree-Fock equation, including for the first time the exchange term in the study of these stationary solutions.
Analysis on Changes of Basic Climatic Elements and Extreme Events in Xinjiang, China during 1961–2010

Abstract By using the observation data from 89 weather stations in Xinjiang during 1961–2010, this paper analyzed the basic climatic elements including temperature, precipitation, wind speed, sunshine duration, water vapor pressure, and dust storm in the entire Xinjiang and the subareas: North Xinjiang, Tianshan Mountains, and South Xinjiang. The results indicate that from 1961 to 2010 the annual and seasonal mean temperatures in the entire Xinjiang show an increasing trend with the increasing rate rising from south to north. The increasing rate of annual mean minimum temperature is over twice more than that of the annual mean maximum temperature, contributing much to the increase in the annual averages. The magnitude of the decrease rate of low-temperature days is larger than the increase rate of high-temperature days. The increase of warm days and warm nights and the decrease of cold days and cold nights further reveal that the temperature increasing in Xinjiang is higher. In addition, annual and seasonal rainfalls have been increasing. South Xinjiang experiences higher increase in rainfall amounts than North Xinjiang and Tianshan Mountains. Annual rainy days, longest consecutive rainy days, the daily maximum precipitation and extreme precipitation events, annual torrential rain days and amount, annual blizzard days and amount, all show an increasing trend, corresponding to the increasing in annual mean water vapor pressure. This result shows that the humidity has increased with temperature increasing in the past 50 years. The decrease in annual mean wind speed and gale days lessen the impact of dust storm, sandstorm, and floating dust events. The increase in annual rainy days is the cause of the decrease in annual sunshine duration, while the increase in spring sunshine duration corresponds with the decrease in dust weather. Therefore, the increase in precipitation indicators, the decrease in gales and dust weather, and the increasing in sunshine duration in spring will be beneficial to crops growth. Citation Jiang, Y. -A. , Y. Chen, Y. -Z. Zhao, et al., 2013: Analysis on changes of basic climatic elements and extreme events in Xinjiang, China during 1961–2010. Adv. Clim. Change Res., 4(1), doi: 10.3724/SP.J.1248.2013. 020.
Concise and Organized Perception Facilitates Large Language Models for Deductive Reasoning

Exploiting large language models (LLMs) to tackle deductive reasoning has garnered growing attention. It still remains highly challenging to achieve satisfactory results in complex deductive problems, characterized by plenty of premises (i.e., facts or rules) entailing intricate relationships among entities and requiring multi-hop reasoning. One intuitive solution is to decompose the original task into smaller sub-tasks, and then chain the multiple casual reasoning steps together in a forward (e.g., Selection-Inference) or backward (e.g., LAMBADA) direction. However, these techniques inevitably necessitate a large number of overall stages, leading to computationally expensive operations and a higher possibility of making misleading steps. In addition to stage-by-stage decomposition, we draw inspiration from another aspect of human problem-solving. Humans tend to distill the most relevant information and organize their thoughts systematically (e.g., creating mind maps), which assists them in answering questions or drawing conclusions precisely and quickly. In light of this, we propose a novel reasoning approach named Concise and Organized Perception (COP). COP carefully analyzes the given statements to efficiently identify the most pertinent information while eliminating redundancy. It then prompts the LLMs in a more organized form that adapts to the model's inference process. By perceiving concise and organized proofs, the deductive reasoning abilities of LLMs can be better elicited, and the risk of acquiring errors caused by excessive reasoning stages is mitigated. Furthermore, our approach can be combined with the aforementioned ones to further boost their performance. Extensive experimental results on three popular deductive benchmarks (i.e., ProofWriter, PrOntoQA and PrOntoQA-OOD) show that COP significantly outperforms previous state-of-the-art methods.
An Extremely Massive White Dwarf Escaped from the Hyades Star Cluster

We searched the Gaia DR3 database for ultramassive white dwarfs with kinematics consistent with having escaped the nearby Hyades open cluster, identifying three such candidates. Two of these candidates have masses estimated from Gaia photometry of approximately 1.1 solar masses; their status as products of single-stellar evolution that have escaped the cluster was deemed too questionable for immediate follow-up analysis. The remaining candidate has an expected mass >1.3 solar masses, significantly reducing the probability of it being an interloper. Analysis of follow-up Gemini GMOS spectroscopy for this source reveals a nonmagnetized hydrogen atmosphere white dwarf with a mass and age consistent with having formed from a single star. Assuming a single-stellar-evolution formation channel, we estimate a 97.8% chance that the candidate is a true escapee from the Hyades. With a determined mass of 1.317 solar masses, this is potentially the most massive known single-evolution white dwarf and is by far the most massive with a strong association with an open cluster.
Variational Inference for GARCH-family Models

The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust approach for Bayesian inference in complex machine learning models; however, its adoption in econometrics and finance is limited. This paper discusses the extent to which Variational Inference constitutes a reliable and feasible alternative to Monte Carlo sampling for Bayesian inference in GARCH-like models. Through a large-scale experiment involving the constituents of the S& P 500 index, several Variational Inference optimizers, a variety of volatility models, and a case study, we show that Variational Inference is an attractive, remarkably well-calibrated, and competitive method for Bayesian learning.
Rethinking Fairness for Human-AI Collaboration

Existing approaches to algorithmic fairness aim to ensure equitable outcomes if human decision-makers comply perfectly with algorithmic decisions. However, perfect compliance with the algorithm is rarely a reality or even a desirable outcome in human-AI collaboration. Yet, recent studies have shown that selective compliance with fair algorithms can amplify discrimination relative to the prior human policy. As a consequence, ensuring equitable outcomes requires fundamentally different algorithmic design principles that ensure robustness to the decision-maker's (a priori unknown) compliance pattern. We define the notion of compliance-robustly fair algorithmic recommendations that are guaranteed to (weakly) improve fairness in decisions, regardless of the human's compliance pattern. We propose a simple optimization strategy to identify the best performance-improving compliance-robustly fair policy. However, we show that it may be infeasible to design algorithmic recommendations that are simultaneously fair in isolation, compliance-robustly fair, and more accurate than the human policy; thus, if our goal is to improve the equity and accuracy of human-AI collaboration, it may not be desirable to enforce traditional fairness constraints.
TimeGPT-1

In this paper, we introduce TimeGPT, the first foundation model for time series, capable of generating accurate predictions for diverse datasets not seen during training. We evaluate our pre-trained model against established statistical, machine learning, and deep learning methods, demonstrating that TimeGPT zero-shot inference excels in performance, efficiency, and simplicity. Our study provides compelling evidence that insights from other domains of artificial intelligence can be effectively applied to time series analysis. We conclude that large-scale time series models offer an exciting opportunity to democratize access to precise predictions and reduce uncertainty by leveraging the capabilities of contemporary advancements in deep learning.