Miracle-dz/UniFAR-FTUs huggingface dataset:
Wildfire and Aspect Effects on Hydrologic States after the 2010 Fourmile Canyon Fire

Wildfire is one of the most significant disturbances in mountainous landscapes, affecting water supply and ecologic function and setting the stage for natural hazards such as flash floods. The impacts of wildfire can affect the entire hydrologic cycle. Measurements of soil‐water content and matric potential in the near surface (top 30 cm) captured the hydrologic state in both burned and unburned hillslopes during the first spring through fall period (1 June–1 Oct. 2011) after the 2010 Fourmile Canyon Fire near Boulder, CO. This time span included different hydrologic periods characterized by cyclonic frontal storms (low‐intensity, long duration), convective storms (high‐intensity, short duration), and dry periods. In mountainous environments, aspect can also control hydrologic states, so north‐ vs. south‐facing slopes were compared. Wildfire tended to homogenize soil‐water contents across aspects and with depth in the soil, yet it also may have introduced an aspect control on matric potential that was not observed in unburned soils. Post‐wildfire changes in hydrologic state were observed in south‐facing soils, probably reflecting decreased soil‐water retention after wildfire. North‐facing soils were impacted the most, in terms of hydrologic state, by the loss of water storage in the combusted litter–duff layer and forest canopy, which had provided a large “hydrologic buffering” capacity when unburned. Unsaturated zone measurements showed increased variability in hydrologic states and more rapid state transitions in wildfire‐impacted soils. A simple, qualitative analysis suggested that the range of unsaturated‐zone processes along the gravity–capillarity–adsorption continuum was expanded by wildfire for a given soil. The small number of experimental plots in this study suggests that further work is needed before these conclusions can be generalized to other geographic areas.
Training a Large Video Model on a Single Machine in a Day

Videos are big, complex to pre-process, and slow to train on. State-of-the-art large-scale video models are trained on clusters of 32 or more GPUs for several days. As a consequence, academia largely ceded the training of large video models to industry. In this paper, we show how to still train a state-of-the-art video model on a single machine with eight consumer-grade GPUs in a day. We identify three bottlenecks, IO, CPU, and GPU computation, and optimize each. The result is a highly efficient video training pipeline. For comparable architectures, our pipeline achieves higher accuracies with $\frac{1}{8}$ of the computation compared to prior work. Code is available at https://github.com/zhaoyue-zephyrus/AVION.
The ups and downs of the TVZ: Geodetic observations of deformation around the Taupo Volcanic Zone, New Zealand

The 300 km long Taupo Volcanic Zone (TVZ) formed as a result of back‐arc rifting associated with subduction of the Pacific Plate beneath the Australian Plate, with current extension rates of 8–15 mm yr−1. Using GPS and interferometric synthetic aperture radar (InSAR) data, collected by the European (ESA) and Japanese (JAXA) space agencies, we present ground deformation observations from 2003 to 2011. Both InSAR and vertical GPS data show widespread subsidence across the central TVZ at rates of up to 20 mm yr−1. Using simple elastic dislocation models to represent the contraction of a sill like body at 6 km depth, we predict an annual volume change of 0.011–0.016 km3 beneath the central TVZ. We suggest that the majority of the observed subsidence is a result of the cooling and subsequent contraction of magma within the shallow crust.
RESEARCH RE: SEARCH & RE-SEARCH

Search algorithms are often categorized by their node expansion strategy. One option is the depth-first strategy, a simple backtracking strategy that traverses the search space in the order in which successor nodes are generated. An alternative is the best-first strategy, which was designed to make it possible to use domain-specific heuristic information. By exploring promising parts of the search space first, best-first algorithms are usually more efficient than depth-first algorithms. In programs that play minimax games such as chess and checkers, the efficiency of the search is of crucial importance. Given the success of best-first algorithms in other domains, one would expect them to be used for minimax games too. However, all high-performance game-playing programs are based on a depth-first algorithm. This study takes a closer look at a depth-first algorithm, AB, and a best-first algorithm, SSS. The prevailing opinion on these algorithms is that SSS offers the potential for a more efficient search, but that its complicated formulation and exponential memory requirements render it impractical. The theoretical part of this work shows that there is a surprisingly straightforward link between the two algorithms -- for all practical purposes, SSS is a special case of AB. Subsequent empirical evidence proves the prevailing opinion on SSS to be wrong: it is not a complicated algorithm, it does not need too much memory, and it is also not more efficient than depth-first search.
Food, fairness & ecology: an organic research agenda for a sustainable future.

The European Union Group of the International Federation of Organic Agriculture Movements (IFOAM EU Group) and the International Society of Organic Agriculture Research (ISOFAR) are developing a strategic research agenda focussing on ecological intensification, on sustainable rural regions, on high quality food for healthy nutrition and on ethical values of people vis-a-vis technology development in food production. The strategic research agenda (currently in its second draft, Niggli et al., 2008a) invites farmers, processors, traders, NGOs and scientists to debate on how practice and science should co-operate on future innovation. The final goal of the debate is a widely supported technology platform for organic agriculture and beyond.
DRAFT A First Look at 1 Gbps Fiber-ToThe-Home Traffic

Fiber-To-The-Home (FTTH) networks are on the cusp of bringing significantly higher capacities to residential users compared to today’s commercial broadband options. While fiber is available to some consumers now, the capacities offered commercially represent an incremental bump over more traditional DSL and cable options. However, a number of high capacity experimental FTTH networks have recently become operational. In this paper we aim to explore how users employ such high capacity in a residential setting by monitoring a neighborhood within an operational FTTH network that provides bi-directional 1 Gbps links to each house for nearly two years.
A quality index for spanish housing

The purpose of this article is to offer an empirical analysis of the geographical differences in Spanish housing quality. We derive an index of housing quality using the Multiple Correspondence Analysis (MCA) in the line with Arevalo (1999). The author obtains a housing quality index by using some structural characteristics of houses and those collected in the Encuesta Basica de Presupuestos Familiares (EBPF) in Spain for the periods 1980-81 and 1990-91. In addition, this paper includes location-specific characteristics of the house using in the analysis two new variables: city size and accessibility. Results confirm that location characteristics influence house quality. It is confirmed that the inclusion of these location-specific variables shows a more realistic view of geographical differences in the Spanish house quality. Firstly, urban characteristics reveal that houses located in densely populated cities do not significantly improve their quality whereas there is significant quality deterioration within sparsely populated cities. Secondly, as was to be expected, consideration of accessibility attributes reveals quality improvements of housing situated within those sparsely populated areas that have good road communication with densely populated cities.
Global Hotspot Reference Frames and Plate Motion

Most of the earth's new magmatic material is produced at volcanic centers associated with plate boundary processes. These are either arc volcanoes above subduction zones or linear spreading centers along divergent plate boundaries. Scattered about Earth's surface, however, are many volcanic centers that are not associated with plate boundary processes. These are usually found far from plate boundaries, and they continue to produce magma from a well-defined central location for many tens of millions of years. Geochemical studies of the products of these volcanic centers shows that their magma comes from deep within the mantle, below the tectonic plates that form the top 100 km of the earth. The term 'hotspot' has been coined to describe these volcanic features. If the tectonic plate on which the volcanic center is located moves relative to the underlying magma source, a trail of volcanic features can be produced on the plate, a 'hotspot track'. Probably the best-known example is associated with the island of Hawaii, with the Hawaii-Emperor seamount chain in the northern Pacific Ocean forming the 'track'. An extension of the hotspot concept is the proposal that all the global hotspots stay fixed relative to one another and thus provide a complete reference frame in which to describe plate motion. This would be incredibly useful, as no other reference frame exists in which to describe absolute plate motion through time. The hotspot reference frame was thought to provide this, and many published studies of plate motion make use of this reference frame. As studies of plate motions have advanced, however, it has become clear that the global hotspots do not stay fixed relative to each other. In this paper it is proposed that there are three global families of hotspots, a Pacific family, an Indo-Atlantic family and also Iceland, a major hotspot in terms of volcanic material but not fixed to the other two families. Hotspots within each of the first two families have remained relatively fixed for the past 80 million years.
Predicting performance difficulty from piano sheet music images

Estimating the performance difficulty of a musical score is crucial in music education for adequately designing the learning curriculum of the students. Although the Music Information Retrieval community has recently shown interest in this task, existing approaches mainly use machine-readable scores, leaving the broader case of sheet music images unaddressed. Based on previous works involving sheet music images, we use a mid-level representation, bootleg score, describing notehead positions relative to staff lines coupled with a transformer model. This architecture is adapted to our task by introducing an encoding scheme that reduces the encoded sequence length to one-eighth of the original size. In terms of evaluation, we consider five datasets -- more than 7500 scores with up to 9 difficulty levels -- , two of them particularly compiled for this work. The results obtained when pretraining the scheme on the IMSLP corpus and fine-tuning it on the considered datasets prove the proposal's validity, achieving the best-performing model with a balanced accuracy of 40.34\% and a mean square error of 1.33. Finally, we provide access to our code, data, and models for transparency and reproducibility.
Audio-Visual Speaker Verification via Joint Cross-Attention

Speaker verification has been widely explored using speech signals, which has shown significant improvement using deep models. Recently, there has been a surge in exploring faces and voices as they can offer more complementary and comprehensive information than relying only on a single modality of speech signals. Though current methods in the literature on the fusion of faces and voices have shown improvement over that of individual face or voice modalities, the potential of audio-visual fusion is not fully explored for speaker verification. Most of the existing methods based on audio-visual fusion either rely on score-level fusion or simple feature concatenation. In this work, we have explored cross-modal joint attention to fully leverage the inter-modal complementary information and the intra-modal information for speaker verification. Specifically, we estimate the cross-attention weights based on the correlation between the joint feature presentation and that of the individual feature representations in order to effectively capture both intra-modal as well inter-modal relationships among the faces and voices. We have shown that efficiently leveraging the intra- and inter-modal relationships significantly improves the performance of audio-visual fusion for speaker verification. The performance of the proposed approach has been evaluated on the Voxceleb1 dataset. Results show that the proposed approach can significantly outperform the state-of-the-art methods of audio-visual fusion for speaker verification.
The strain on scientific publishing

Scientists are increasingly overwhelmed by the volume of articles being published. Total articles indexed in Scopus and Web of Science have grown exponentially in recent years; in 2022 the article total was 47% higher than in 2016, which has outpaced the limited growth, if any, in the number of practising scientists. Thus, publication workload per scientist (writing, reviewing, editing) has increased dramatically. We define this problem as the strain on scientific publishing. To analyse this strain, we present five data-driven metrics showing publisher growth, processing times, and citation behaviours. We draw these data from web scrapes, requests for data from publishers, and material that is freely available through publisher websites. Our findings are based on millions of papers produced by leading academic publishers. We find specific groups have disproportionately grown in their articles published per year, contributing to this strain. Some publishers enabled this growth by adopting a strategy of hosting special issues, which publish articles with reduced turnaround times. Given pressures on researchers to publish or perish to be competitive for funding applications, this strain was likely amplified by these offers to publish more articles. We also observed widespread year-over-year inflation of journal impact factors coinciding with this strain, which risks confusing quality signals. Such exponential growth cannot be sustained. The metrics we define here should enable this evolving conversation to reach actionable solutions to address the strain on scientific publishing.
HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)

Machine learning (ML) is crucial in network anomaly detection for proactive threat hunting, reducing detection and response times significantly. However, challenges in model training, maintenance, and frequent false positives impact its acceptance and reliability. Explainable AI (XAI) attempts to mitigate these issues, allowing cybersecurity teams to assess AI-generated alerts with confidence, but has seen limited acceptance from incident responders. Large Language Models (LLMs) present a solution through discerning patterns in extensive information and adapting to different functional requirements. We present HuntGPT, a specialized intrusion detection dashboard applying a Random Forest classifier using the KDD99 dataset, integrating XAI frameworks like SHAP and Lime for user-friendly and intuitive model interaction, and combined with a GPT-3.5 Turbo, it delivers threats in an understandable format. The paper delves into the system's architecture, components, and technical accuracy, assessed through Certified Information Security Manager (CISM) Practice Exams, evaluating response quality across six metrics. The results demonstrate that conversational agents, supported by LLM and integrated with XAI, provide robust, explainable, and actionable AI solutions in intrusion detection, enhancing user understanding and interactive experience.
FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation

Neural radiance fields with stochasticity have garnered significant interest by enabling the sampling of plausible radiance fields and quantifying uncertainty for downstream tasks. Existing works rely on the independence assumption of points in the radiance field or the pixels in input views to obtain tractable forms of the probability density function. However, this assumption inadvertently impacts performance when dealing with intricate geometry and texture. In this work, we propose an independence-assumption-free probabilistic neural radiance field based on Flow-GAN. By combining the generative capability of adversarial learning and the powerful expressivity of normalizing flow, our method explicitly models the density-radiance distribution of the whole scene. We represent our probabilistic NeRF as a mean-shifted probabilistic residual neural model. Our model is trained without an explicit likelihood function, thereby avoiding the independence assumption. Specifically, We downsample the training images with different strides and centers to form fixed-size patches which are used to train the generator with patch-based adversarial learning. Through extensive experiments, our method demonstrates state-of-the-art performance by predicting lower rendering errors and more reliable uncertainty on both synthetic and real-world datasets.
Accuracy of a commercial multiplex PCR for the diagnosis of bacterial vaginosis

Purpose. Bacterial vaginosis (BV) is a common clinical condition characterized by odorous vaginal discharge, vaginal itching and/or burning. BV can occur when vaginal lactobacilli are depleted and replaced by diverse anaerobic bacteria. We evaluated a commercial multiplex PCR (ATRiDA) for the diagnosis of BV. Methods. Cervicovaginal samples were included from women reporting urogenital symptoms and from women notified for sexually transmitted infections (STI) ‐ who were not (necessarily) symptomatic. Clinical BV diagnoses were obtained from electronic patient files. The ATRiDA test measures the loads of Gardnerella vaginalis, Atopobium vaginae and Lactobacillus species in relation to overall bacterial load. The ATRiDA test outcome was compared to the clinical BV diagnosis and to vaginal microbiota composition, determined by 16SrRNA gene sequencing. Results. We included samples from 185 women reporting urogenital symptoms, of whom 81 had BV and 93 women who were notified for an STI, of whom 16 had BV. Overall, compared to the clinical BV diagnosis, the ATRiDA test demonstrated high sensitivity (96.9 %) and moderate specificity (70.2 %). The negative predictive value was high (>97.3). The positive predictive value differed by study group and was highest in women reporting urogenital symptoms (78.2 %). Sequencing showed that 54 % of women who had an ATRiDA BV‐positive test outcome, but who were not clinically diagnosed with BV, had diverse anaerobic vaginal microbiota (asymptomatic vaginal dysbiosis). Conclusion. The ATRiDA test is a sensitive method for the detection of BV but, given the high occurrence of asymptomatic vaginal dysbiosis, a positive test outcome should be interpreted together with clinical symptoms.
DIGITAL SIMULATION IN HYDROLOGY' STANFORD WATERSHED MODEL 4

TECHNIQUES DEVELOPED FOR LARGE-SCALE DIGITAL COMPUTERS USING DATA ON RAINFALL, EVAPOTRANSPIRATION AND RUNOFF WERE DESCRIBED. A BRIEF INTRODUCTION INTO SIMULATION METHODS AND HYDROLOGIC MODELS IS FOLLOWED BY A DISCUSSION INTO THE HYDROLOGIC CYCLE OF INFILTRATION, OVERLAND FLOW AND EVAPOTRANSPIRATION. A GENERAL SIMULATION MODEL IS THEN DEVELOPED MATHEMATICALLY, THE OPERATION AND SIMULATION RESULTS BEING PRESENTED. THE APPLICATIONS OF SIMULATION TO WEATHER MODIFICATION, URBANIZATION AND HYDROMETEOROLOGICAL NETWORKS ARE ALSO MENTIONED. /RRL/
“ Health in Eastern Europe and Former Soviet Union ” Mortality Crisis in Russia : New Health Threats

In 1992 and 1998 Russia experienced two economic crises accompanied by subsequent mortality upsurge. Using exploratory factor analysis of age-adjusted mortality from major causes of death, this study shows that more than 95% of the total variation in mortality in Russia can be explained by 3 factors: (1) factor related to social stress, correlated with mortality from ischaemic heart disease and alcohol poisoning; (2) factor of asocial behavior, correlated to mortality from tuberculosis, drug dependence, AIDS, sexually transmitted diseases; and (3) factor related to selective mortality, correlated to mortality from some chronic diseases (diabetes, asthma). Our analysis demonstrates that diseases related to the second mortality factor will determine the future mortality changes in Russia. These diseases, more common among young adult age groups, are becoming a serious threat for population health in Russia and may affect other countries.
Using chemically reactive tracers to determine temperature characteristics of geothermal reservoirs

The rates of most chemical reactions are very sensitive to temperature, and this property can be used to measure temperature patterns in geothermal reservoirs. Two approaches are considered: reverse-flush and flow-through. Both of these can indicate thermal drawdown faster than the measurement of produced-fluid temperature. The reverse-flush approach is more difficult to carry out and interferes more with normal operation, but it gives the earlier indication of thermal drawdown and yields more information when using a single reaction. In both approaches, injecting a suite of reactants can yield bivariate time-temperature distributions which give temperature distributions as functions of fluid residence time. Applications to Hot Dry Rock systems are considered throughout the paper. Results of early kinetics studies of candidate tracer reactions also are reported.
TLS data denoising by range image processing

The work described in this paper deals with denoising of fine detail in terrestrial laser scanner (TLS) data for close‐range applications. In contrast to other denoising methods described in the literature, the method described here puts the problem back into two dimensions. In the proposed method, data from each instrument station is processed by denoising the range image as a 2D function. Then, after denoising, the registration process is applied to obtain the final 3D point cloud. Two image denoising methods are tested. Wavelet analysis and a “non‐local means” (NL‐means) algorithm are applied to archaeological objects of sizes described as medium (around one cubic metre) and small (around one cubic decimetre). The rich relief of these objects proves the efficiency of the method on all kinds of surface shape. The experience shows that the NL‐means method achieves good results: the standard deviation of 2 mm in the raw data is decreased to 1 mm. Besides, NL‐means denoising requires very few parameters to be set (generally two) whilst wavelet denoising requires many. Hence, the extreme simplicity of the NL‐means method makes it much more efficient and suitable for use by non‐specialists. The method, which slightly modifies the standard production process and inherits the algorithmic complexity of the classical 2D image processing schemes, allows the range of applications of TLS to be extended to small and finely detailed objects.