Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulation at the entrance of the Gulf of California from satellite altimeter and hydrographic observations

[1] The surface circulation around the entrance to the Gulf of California is described from satellite altimetry supported by 10 conductivity-temperature-depth (CTD) surveys. The sea surface height calculated from the 1/4° World Ocean Database 2001 climatology plus the Archiving, Validation, and Interpretation of Satellite Oceanographic altimeter data (October 1992 to January 2008) were RMS adjusted to the dynamic height calculated from CTD data; the 27.0 kg m−3 isopycnal provided the optimum reference. In the mean, the surface circulation shows a branch of the California Current heading landward toward the Gulf of California entrance, where it splits into two subbranches; these are separated by a cyclonic circulation attached to the coast south of Cabo Corrientes. This feature is produced by Sverdrup dynamics and is the first observational indication that the Mexican Coastal Current is generated locally by the wind stress curl, as previously suggested by numerical models. The global variance of the surface circulation can be separated into seasonal (explained variance 35%), interannual (explained variance 35%), and mesoscale (explained variance 30%) components. The seasonal signal, which shows the interplay of the poleward Mexican Coastal Current and the equatorward branch of the California Current, can be explained by a long Rossby wave model forced by the annual wind and by radiation from the coast. The interannual component is dominated by the El Nino-Southern Oscillation, which induces in the gulf entrance an anticyclonic (cyclonic) circulation during El Nino (La Nina); this circulation includes a poleward-flowing branch (during El Nino) parallel to the Pacific coast of the Baja California peninsula. The mesoscale variability is caused by intense eddy activity.
Rippled Scour Depressions on the Inner Continental Shelf Off Central California

ABSTRACT Side-scan sonar records taken during the recent Coastal Ocean Dynamics Experiment (CODE) show elongate, shore-normal rippled depressions of low relief on the inner continental shelf off central California between Bodega Bay and Point Arena. These features extend up to 2 km from the coast into water depths of up to 65 m. The proposed mechanism for their generation is storm-generated bottom currents associated with coastal downwelling during the late fall and winter which scour the surficial fine-sand sediment and expose the coarser-sand substrate in the depressions. The zones of most intense erosion and the irregular spacing of the features may be controlled by submerged rock ledges and other prominent coastal features. The large straight-crested ripples within the depressions (heights to 40 cm; wavelengths to 1.7 m) are probably formed by large-amplitude, long-period surface waves generated by winter storms.
Mean curvature flows of graphs sliding off to infinity in warped product manifolds

We study mean curvature flows in a warped product manifold defined by a closed Riemannian manifold and $\mathbb{R}$. In such a warped product manifold, we can define the notion of a graph, called a geodesic graph. We prove that the curve shortening flow preserves a geodesic graph for any warping function, and the mean curvature flow of hypersurfaces preserves a geodesic graph for some monotone convex warping functions. In particular, we consider some warping functions that go to zero at infinity, which means that the curves or hypersurfaces go to a point at infinity along the flow. In such a case, we prove the long-time existence of the flow and that the curvature and its higher-order derivatives go to zero along the flow.
Convective–Stratiform Precipitation Variability at Seasonal Scale from 8 Yr of TRMM Observations: Implications for Multiple Modes of Diurnal Variability

Abstract This study investigates the variability of convective and stratiform rainfall from 8 yr (1998–2005) of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI) measurements, focusing on seasonal diurnal variability. The main scientific goals are 1) to understand the climatological variability of these two dominant forms of precipitation across the four cardinal seasons and over continents and oceans separately and 2) to understand how differences in convective and stratiform rainfall variations ultimately determine how the diurnal variability of the total rainfall is modulated into multiple modes. There are distinct day–night differences for both convective and stratiform rainfall. Oceanic (continental) convective rainfall is up to 25% (50%) greater during nighttime (daytime) than daytime (nighttime). The seasonal variability of convective rainfall’s day–night difference is relatively small, while stratiform rainfall exhibits very apparent day–night vari.
Kootenai River Fisheries Investigations; Stock Status of Burbot and Rainbow Trout and Fisheries Inventory, 1995 Annual Report.

The author sampled 33 burbot Lota lota in the Kootenay River in British Columbia, Canada. Burbot catch from November 1994 to February 1995 averaged 0.047 fish/net-day. Total length ranged from 3854 mm to 958 mm and weighed from 272 g to 4,086 g (mean = 982 g). Twelve burbot were implanted with sonic transmitters and released at capture sites. Two additional burbot had active transmitters from the previous season. Telemetry of burbot during the pre-spawn, spawning, and post-spawning periods was conducted. Burbot were located a total of 203 times from November 1994 through August 8, 1995. Ripe burbot were captured and they appeared to have an affinity to water <2C. The author believes burbot spawned in the Goat River, British Columbia. Burbot with sonic transmitters did not reach Idaho until after the spawning period. Statistical analysis of burbot movement and discharge from Libby Dam indicated there was a significant relation between winter power production and spawning migration of burbot. A controlled test is needed to verify this relation. Zooplankton samples from the Kootenai River were substantially lower than the delta of Kootenay Lake, British Columbia, Canada.
AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs’ capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs’ potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.
Distilling ODE Solvers of Diffusion Models into Smaller Steps

Abstract Diffusion models have recently gained prominence as a novel category of generative models. Despite their success, these models face a notable drawback in terms of slow sampling speeds, requiring a high number of function evaluations (NFE) in the order of hundreds or thousands. In response, both learning-free and learning-based sampling strategies have been explored to expedite the sampling process. Learning-free sampling employs various ordinary differential equation (ODE) solvers based on the formulation of diffusion ODEs. However, it encounters challenges in faithfully tracking the true sampling trajectory, particularly for small NFE. Conversely, learning-based sampling methods, such as knowledge distillation, demand extensive additional training, limiting their practical applicability. To overcome these limitations, we introduce Distilled-ODE solvers (D-ODE solvers), a straightforward distillation approach grounded in ODE solver formulations. Our method seamlessly integrates the strengths of both learning-free and learning-based sampling. D-ODE solvers are constructed by introducing a single parameter adjustment to existing ODE solvers. Furthermore, we optimize D-ODE solvers with smaller steps using knowledge distillation from ODE solvers with larger steps across a batch of samples. Comprehensive experiments demonstrate the superior performance of D-ODE solvers compared to existing ODE solvers, including DDIM, PNDM, DPM-Solver, DEIS, and EDM, particularly in scenarios with fewer NFE. Notably, our method incurs negligible computational overhead compared to previous distillation techniques, facilitating straightforward and rapid integration with existing samplers. Qualitative analysis reveals that D-ODE solvers not only enhance image quality but also faithfully follow the target ODE trajectory.
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective

Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The former negatively impacts model accuracy, while the latter usually relies on globally clear boundaries between benign and infected model updates. However, in reality, model updates can easily become mixed and scattered throughout due to the diverse distributions of local data. This work focuses on excluding infected models in FL. Unlike previous perspectives from a global view, we propose Snowball, a novel anti-backdoor FL framework through bidirectional elections from an individual perspective inspired by one principle deduced by us and two principles in FL and deep learning. It is characterized by a) bottom-up election, where each candidate model update votes to several peer ones such that a few model updates are elected as selectees for aggregation; and b) top-down election, where selectees progressively enlarge themselves through picking up from the candidates. We compare Snowball with state-of-the-art defenses to backdoor attacks in FL on five real-world datasets, demonstrating its superior resistance to backdoor attacks and slight impact on the accuracy of the global model.
A Simulation Model for Meandering Rivers

A computer model for the dynamics of meandering rivers has been used to study the interplay between the migrating river and the changing sedimentary environment created by the meandering river itself. The model is based on the theory of Ikeda et al. [1981] and is closely related to that proposed by Howard [1983]. Coarser sands, which are often associated with high credibility, are deposited in the point bars formed when the river migrates away from its former bank. Fine-grained material eventually fills the oxbow lakes created by cutoff processes and forms erosion-resistant plugs. In the simulations, geometric forms of individual meanders observed in different natural sedimentary environments have been reproduced by changing the credibility of the corresponding sedimentary materials, such as point bar deposits, flood plain deposits, and oxbow lake deposits. The simulations indicate that the typical meander wavelength is determined mainly by hydraulic factors such as the flow in the channel and the inclination of the underlying flood plain and is independent of the difference in the credibilities of sedimentary deposits. The computational approach permits exploration of long-term changes in the floodplain geology, mediated by the meandering river. As an initial demonstration, the formation of meander belts is investigated using the model. The results suggest that a meander belt will be formed by a river's own cutoff loops only if the characteristic time of deposition and solidification of an oxbow lake is longer than the typical time that it takes the river to migrate downstream over the distance of a meander-loop wavelength.
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification

The correlation between the sharpness of loss minima and generalisation in the context of deep neural networks has been subject to discussion for a long time. Whilst mostly investigated in the context of selected benchmark data sets in the area of computer vision, we explore this aspect for the acoustic scene classification task of the DCASE2020 challenge data. Our analysis is based on two-dimensional filter-normalised visualisations and a derived sharpness measure. Our exploratory analysis shows that sharper minima tend to show better generalisation than flat minima -even more so for out-of-domain data, recorded from previously unseen devices-, thus adding to the dispute about better generalisation capabilities of flat minima. We further find that, in particular, the choice of optimisers is a main driver of the sharpness of minima and we discuss resulting limitations with respect to comparability. Our code, trained model states and loss landscape visualisations are publicly available.
Lunar degree 2 potential Love number determination from satellite tracking data

The lunar potential Love number k2 has been determined using available satellite tracking data coming from the Lunar Orbiters I‐V, the Apollo 15 and 16 subsatellites, Clementine and Lunar Prospector. Some SMART‐1 spacecraft tracking data are also included. A previous determination using spacecraft data reported k2 = 0.026 ± 0.003 (ten times the formal error), whereas lunar laser ranging results report lower values, around k2 = 0.0210 ± 0.0025. The best estimate obtained here results in k2 = 0.0213 ± 0.0075 (ten times the formal error as well), closer in agreement with the lunar laser ranging values.
Seawater calcium isotope ratios across the Eocene-Oligocene transition

During the Eocene-Oligocene transition (EOT, ca. 34 Ma), Earth's climate cooled significantly from a greenhouse to an icehouse climate, while the calcite (CaCO3) compensation depth (CCD) in the Pacific Ocean increased rapidly. Fluctuations in the CCD could result from various processes that create an imbalance between calcium (Ca) sources to, and sinks from, the ocean (e.g., weathering and CaCO3 deposition), with different effects on the isotopic composition of dissolved Ca in the oceans due to differences in the Ca isotopic composition of various inputs and outputs. We used Ca isotope ratios (δ44/40Ca) of coeval pelagic marine barite and bulk carbonate to evaluate changes in the marine Ca cycle across the EOT. We show that the permanent deepening of the CCD was not accompanied by a pronounced change in seawater δ44/40Ca, whereas time intervals in the Neogene with smaller carbonate depositional changes are characterized by seawater δ44/40Ca shifts. This suggests that the response of seawater δ44/40Ca to changes in weathering fluxes and to imbalances in the oceanic alkalinity budget depends on the chemical composition of seawater. A minor and transient fluctuation in the Ca isotope ratio of bulk carbonate may reflect a change in isotopic fractionation associated with CaCO3 precipitation from seawater due to a combination of factors, including changes in temperature and/or in the assemblages of calcifying organisms.
Ethnobotany of Berta and Gumuz people in western Ethiopia

Abstract This paper studies the use and management of local botanical resources by the Berta and Gumuz people of western Ethiopia. The study focused on plants that are important in their lives, including those that grow in natural habitats and those maintained in fields and cultivated home gardens. Interviews were conducted along with informal discussions with the purpose of collecting ethnobotanical information on useful plants. The interviews included 125 informants; most of whom were from farming families. Information was provided on 185 plant species that serve as sources of food, medicine and other products essential for local livelihoods. The study showed that the Berta and Gumuz people rely on an assortment of plants, 30% through cultivation and 70% through direct collection from wild stands. The ethnobotanical knowledge of the people was equated with the age and educational level of the informants. Areas of conservation concern are over-harvesting of wild plants that give immediate economic returns, change in plant use due to increased dependence on fabricated materials, cultivation of crops in direct response to market needs and domestication of new plants. Recommendations are made for intervention through in-situ and ex-situ conservation. The traditional conservation options that have ensured the maintenance of the plant diversity and the indigenous knowledge base to date must be encouraged and enhanced through application of modern approaches to biodiversity conservation. This study attempts to provide baseline information that can be used in conservation planning and sustainable resource development programs in addition to documenting the cultural heritage of the people.
MWA rapid follow-up of gravitational wave transients: Prospects for detecting prompt radio counterparts

Abstract We present and evaluate the prospects for detecting coherent radio counterparts to gravitational wave (GW) events using Murchison Widefield Array (MWA) triggered observations. The MWA rapid-response system, combined with its buffering mode ( $\sim$ 4 min negative latency), enables us to catch any radio signals produced from seconds prior to hours after a binary neutron star (BNS) merger. The large field of view of the MWA ( $\sim$ $1\,000\,\textrm{deg}^2$ at 120 MHz) and its location under the high sensitivity sky region of the LIGO-Virgo-KAGRA (LVK) detector network, forecast a high chance of being on-target for a GW event. We consider three observing configurations for the MWA to follow up GW BNS merger events, including a single dipole per tile, the full array, and four sub-arrays. We then perform a population synthesis of BNS systems to predict the radio detectable fraction of GW events using these configurations. We find that the configuration with four sub-arrays is the best compromise between sky coverage and sensitivity as it is capable of placing meaningful constraints on the radio emission from 12.6% of GW BNS detections. Based on the timescales of four BNS merger coherent radio emission models, we propose an observing strategy that involves triggering the buffering mode to target coherent signals emitted prior to, during or shortly following the merger, which is then followed by continued recording for up to three hours to target later time post-merger emission. We expect MWA to trigger on $\sim$ $5-22$ BNS merger events during the LVK O4 observing run, which could potentially result in two detections of predicted coherent emission.
SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation

Microscopic image segmentation is a challenging task, wherein the objective is to assign semantic labels to each pixel in a given microscopic image. While convolutional neural networks (CNNs) form the foundation of many existing frameworks, they often struggle to explicitly capture long-range dependencies. Although transformers were initially devised to address this issue using self-attention, it has been proven that both local and global features are crucial for addressing diverse challenges in microscopic images, including variations in shape, size, appearance, and target region density. In this paper, we introduce SA2-Net, an attention-guided method that leverages multi-scale feature learning to effectively handle diverse structures within microscopic images. Specifically, we propose scale-aware attention (SA2) module designed to capture inherent variations in scales and shapes of microscopic regions, such as cells, for accurate segmentation. This module incorporates local attention at each level of multi-stage features, as well as global attention across multiple resolutions. Furthermore, we address the issue of blurred region boundaries (e.g., cell boundaries) by introducing a novel upsampling strategy called the Adaptive Up-Attention (AuA) module. This module enhances the discriminative ability for improved localization of microscopic regions using an explicit attention mechanism. Extensive experiments on five challenging datasets demonstrate the benefits of our SA2-Net model. Our source code is publicly available at \url{https://github.com/mustansarfiaz/SA2-Net}.
A More General Theory of Diagnosis from First Principles

Model-based diagnosis has been an active research topic in different communities including artificial intelligence, formal methods, and control. This has led to a set of disparate approaches addressing different classes of systems and seeking different forms of diagnoses. In this paper, we resolve such disparities by generalising Reiter's theory to be agnostic to the types of systems and diagnoses considered. This more general theory of diagnosis from first principles defines the minimal diagnosis as the set of preferred diagnosis candidates in a search space of hypotheses. Computing the minimal diagnosis is achieved by exploring the space of diagnosis hypotheses, testing sets of hypotheses for consistency with the system's model and the observation, and generating conflicts that rule out successors and other portions of the search space. Under relatively mild assumptions, our algorithms correctly compute the set of preferred diagnosis candidates. The main difficulty here is that the search space is no longer a powerset as in Reiter's theory, and that, as consequence, many of the implicit properties (such as finiteness of the search space) no longer hold. The notion of conflict also needs to be generalised and we present such a more general notion. We present two implementations of these algorithms, using test solvers based on satisfiability and heuristic search, respectively, which we evaluate on instances from two real world discrete event problems. Despite the greater generality of our theory, these implementations surpass the special purpose algorithms designed for discrete event systems, and enable solving instances that were out of reach of existing diagnosis approaches.
Thin Current Sheets in the Magnetotail at Lunar Distances: Statistics of ARTEMIS Observations

The magnetotail current sheet's (CSs) spatial configuration and stability control the onset of magnetic reconnection ‐ the driving process for magnetospheric substorms. The near‐Earth CS has been thoroughly investigated by numerous missions, whereas the midtail CS has not been adequately explored. This is especially the case for the long‐term variation of its configuration in response to the solar wind. We present a statistical analysis of 1261 magnetotail CS crossings by the Acceleration, Reconnection, Turbulence and Electrodynamics of Moon's Interaction with the Sun (ARTEMIS) mission orbiting the moon (X ∼ −60 RE), collected during the entirety of Solar Cycle 24. We demonstrate that the magnetotail CS typically remains extremely thin, with a characteristic thickness comparable to the thermal ion gyroradius, even at large distances from Earth's dipole. We also find that a substantial fraction (∼one quarter) of the observed CSs have a partially force‐free magnetic field configuration, with a significant contribution of the magnetic field shear component to the pressure balance. Further, we quantify the impact of the changing solar wind driving conditions on the properties of the midtail around the lunar orbit. During active solar wind driving conditions, we observe an increase in the occurrence rate of thin CSs, whereas quiet solar wind driving conditions seem to favor the formation of partially force‐free CSs.