Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFL: Egalitarian Fairness in Federated Learning for Mitigating Matthew Effect

Recent advances in federated learning (FL) enable collaborative training of machine learning (ML) models from large-scale and widely dispersed clients while protecting their privacy. However, when different clients' datasets are heterogeneous, traditional FL mechanisms produce a global model that does not adequately represent the poorer clients with limited data resources, resulting in lower accuracy and higher bias on their local data. According to the Matthew effect, which describes how the advantaged gain more advantage and the disadvantaged lose more over time, deploying such a global model in client applications may worsen the resource disparity among the clients and harm the principles of social welfare and fairness. To mitigate the Matthew effect, we propose Egalitarian Fairness Federated Learning (EFFL), where egalitarian fairness refers to the global model learned from FL has: (1) equal accuracy among clients; (2) equal decision bias among clients. Besides achieving egalitarian fairness among the clients, EFFL also aims for performance optimality, minimizing the empirical risk loss and the bias for each client; both are essential for any ML model training, whether centralized or decentralized. We formulate EFFL as a constrained multi-constrained multi-objectives optimization (MCMOO) problem, with the decision bias and egalitarian fairness as constraints and the minimization of the empirical risk losses on all clients as multiple objectives to be optimized. We propose a gradient-based three-stage algorithm to obtain the Pareto optimal solutions within the constraint space. Extensive experiments demonstrate that EFFL outperforms other state-of-the-art FL algorithms in achieving a high-performance global model with enhanced egalitarian fairness among all clients.
Recovering complete positivity of non-Markovian quantum dynamics with Choi-proximity regularization

A relevant problem in the theory of open quantum systems is the lack of complete positivity of dynamical maps obtained after weak-coupling approximations, a famous example being the Redfield master equation. A number of approaches exist to recover well-defined evolutions under additional Markovian assumptions, but much less is known beyond this regime. Here we propose a numerical method to cure the complete-positivity violation issue while preserving the non-Markovian features of an arbitrary original dynamical map. The idea is to replace its unphysical Choi operator with its closest physical one, mimicking recent work on quantum process tomography. We also show that the regularized dynamics is more accurate in terms of reproducing the exact dynamics: this allows to heuristically push the utilization of these master equations in moderate coupling regimes, where the loss of positivity can have relevant impact.
High-soft to Low-hard State Transition in Black Hole X-Ray Binaries with GRMHD Simulations

To understand the decaying phase of outbursts in the black hole (BH) X-ray binaries (BH-XRBs), we performed very long general relativistic magnetohydrodynamic simulations of a geometrically thin accretion disk around a Kerr BH with slowly rotating matter injected from outside. We thoroughly studied the flow properties, dynamical behavior of the accretion rate, magnetic flux rate, and jet properties during the temporal evolution. Due to the interaction between the thin disk and injected matter, the accretion flow near the BH goes through different phases. The sequence of phases is: soft state → soft-intermediate state → hard-intermediate state → hard state → quiescent state. For the accretion rate (and hence the luminosity) to decrease (as observed) in our model, the mass injection should not decay slower than the angular momentum injection. We also observed quasiperiodic oscillations (QPOs) in the accretion flow. Throughout the evolution, we observed low-frequency QPOs (∼10 Hz) and high-frequency QPOs (∼200 Hz). Our simple unified accretion flow model for state transitions is able to describe outbursts in BH-XRBs.
Optimization of Magnetized Electron Cooling with JSPEC

The Electron-Ion-Collider (EIC) will be a next-generation facility located at Brookhaven National Laboratory (BNL), built with the goal of accelerating heavy ions up to 275 GeV. To prevent ion beam size growth during the acceleration phase, cooling techniques will be required to keep the beam size from growing due to intra-beam scattering. The JSPEC (JLab Simulation Package for Electron Cooling) $\texttt{C++}$ package is a tool designed to numerically model magnetized and unmagnetized cooling through friction forces between co-propagating electron and ion bunches. Here we describe a feature that has been added to the JSPEC package, which implements a Nelder-Mead Simplex optimization algorithm to allow a user to optimize certain beam parameters in order to achieve a target cooling time.
A Study of Underwater Terrain Navigation based on the Robust Matching Method

Outliers in terrain data are an obstacle to achieving accurate and robust solutions of Underwater Terrain Relative Navigation (UTRN). If not handled properly, navigation may be degraded or even divergent. To address the problem, this paper proposes a terrain-matching algorithm based on the robust estimation theory. In contrast to the conventional approach, the proposed algorithm can significantly reduce the interference of the outliers. Experimental results confirm the good performance of the proposed method.
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures

In Elmarakeby et al.,"Biologically informed deep neural network for prostate cancer discovery", a feedforward neural network with biologically informed, sparse connections (P-NET) was presented to model the state of prostate cancer. We verified the reproducibility of the study conducted by Elmarakeby et al., using both their original codebase, and our own re-implementation using more up-to-date libraries. We quantified the contribution of network sparsification by Reactome biological pathways, and confirmed its importance to P-NET's superior performance. Furthermore, we explored alternative neural architectures and approaches to incorporating biological information into the networks. We experimented with three types of graph neural networks on the same training data, and investigated the clinical prediction agreement between different models. Our analyses demonstrated that deep neural networks with distinct architectures make incorrect predictions for individual patient that are persistent across different initializations of a specific neural architecture. This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.
Novel Discrete Composite Distributions with Applications to Infectious Disease Data (preprint)

It was observed that the number of cases and deaths for infectious diseases were associated with heavy-tailed power law distributions such as the Pareto distribution. While Pareto distribution was widely used to model the cases and deaths of infectious diseases, a major limitation of Pareto distribution is that it can only fit a given data set beyond a certain threshold. Thus, it can only model part of the data set. Thus, we proposed some novel discrete composite distributions with Pareto tails to fit the real infectious disease data. To provide necessary statistical inference for the tail behavior of the data, we developed a hypothesis testing procedure to test the tail index parameter. COVID-19 reported cases in Singapore and monkeypox reported cases in France were analyzed to evaluate the performance of the newly created distributions. The results from the analysis suggested that the discrete composite distributions could demonstrate competitive performance compared to the commonly used discrete distributions. Furthermore, the analysis of the tail index parameter can provide great insights into preventing and controlling infectious diseases.
OceanChat: Piloting Autonomous Underwater Vehicles in Natural Language

In the trending research of fusing Large Language Models (LLMs) and robotics, we aim to pave the way for innovative development of AI systems that can enable Autonomous Underwater Vehicles (AUVs) to seamlessly interact with humans in an intuitive manner. We propose OceanChat, a system that leverages a closed-loop LLM-guided task and motion planning framework to tackle AUV missions in the wild. LLMs translate an abstract human command into a high-level goal, while a task planner further grounds the goal into a task sequence with logical constraints. To assist the AUV with understanding the task sequence, we utilize a motion planner to incorporate real-time Lagrangian data streams received by the AUV, thus mapping the task sequence into an executable motion plan. Considering the highly dynamic and partially known nature of the underwater environment, an event-triggered replanning scheme is developed to enhance the system's robustness towards uncertainty. We also build a simulation platform HoloEco that generates photo-realistic simulation for a wide range of AUV applications. Experimental evaluation verifies that the proposed system can achieve improved performance in terms of both success rate and computation time. Project website: \url{https://sites.google. com/view/oceanchat}
Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores

Data-driven decision-making and AI applications present exciting new opportunities delivering widespread benefits. The rapid adoption of such applications triggers legitimate concerns about loss of privacy and misuse of personal data. This leads to a growing and pervasive tension between harvesting ubiquitous data on the Web and the need to protect individuals. Decentralised personal data stores (PDS) such as Solid are frameworks designed to give individuals ultimate control over their personal data. But current PDS approaches have limited support for ensuring privacy when computations combine data spread across users. Secure Multi-Party Computation (MPC) is a well-known subfield of cryptography, enabling multiple autonomous parties to collaboratively compute a function while ensuring the secrecy of inputs (input privacy). These two technologies complement each other, but existing practices fall short in addressing the requirements and challenges of introducing MPC in a PDS environment. For the first time, we propose a modular design for integrating MPC with Solid while respecting the requirements of decentralisation in this context. Our architecture, Libertas, requires no protocol level changes in the underlying design of Solid, and can be adapted to other PDS. We further show how this can be combined with existing differential privacy techniques to also ensure output privacy. We use empirical benchmarks to inform and evaluate our implementation and design choices. We show the technical feasibility and scalability pattern of the proposed system in two novel scenarios -- 1) empowering gig workers with aggregate computations on their earnings data; and 2) generating high-quality differentially-private synthetic data without requiring a trusted centre. With this, we demonstrate the linear scalability of Libertas, and gained insights about compute optimisations under such an architecture.
Variational Principles for Nonbarotropic Fluid Dynamics

Barotropic fluid flows with the same circulation structure as steady flows generically have comoving physical surfaces on which the vortex lines lie. These become Bernoullian surfaces when the flow is steady. When these surfaces are nested (vortex line foliation) with the topology of cylinders, toroids or a combination of both, a Clebsch representation of the flow velocity can be introduced. This is then used to reduce the number of functions to be varied in the variational principles for such flows. Here we extend the work to non barotropic flows and study the implication for variational analysis and conserved quantities of topological significance such as circulation and helicity.
Soil Quality and Soil Health: A Review

Soil health and soil quality are defined as the capacity of soil to function as a vital living system within land use boundaries. This function which sustains biological productivity of soil also maintains the quality of surrounding environment and human health. Thus the two terms are used interchangeably although it is important to distinguish that, soil quality is related to soil function, whereas soil health presents the soil as a finite non-renewable and dynamic living resource. In this review, we deal with soil health concept which includes interactions between plant inputs and soil in creating a healthy environment. Adverse effects on soil health and soil quality arise from nutrient imbalance in soil, excessive fertilization, soil pollution and soil loss processes that are increasingly becoming common in developing countries. This review will examine the development of soil health approaches as well as the content of soil health and soil quality information and its application to reduce negative impacts on agricultural productivity and long term sustainability.
Atomic superheterodyne receiver Sensitivity estimation based on homodyne readout

The electric field measurement sensitivity based on the Rydberg atomic vapor cell has great theoretical advantages over traditional dipole antennas. We combine the Rydberg atomic heterodyne receiver and the Mach-Zehnder interferometer (MZI) with high phase detection sensitivity to evaluate the system reception sensitivity based on the transmitted laser phase shift. We conduct a theoretical investigation into the impacts of local microwave electric field frequency detuning, and laser frequency detuning on enhancing the sensitivity of heterodyne Rydberg atomic receiver based on MZI. To optimize the output signal amplitude given the input microwave signal, we derive the steady-state solutions of the atomic density matrix. Numerical results show that laser frequency detuning and local microwave electric field frequency detuning can improve the system detection sensitivity, which can help the system achieve extra sensitivity gain. It also shows that the phase-based readout scheme of heterodyne Rydberg atomic receiver based on MZI can achieve better sensitivity than the intensity-based readout scheme of heterodyne Rydberg atomic receiver.
Integrated Satellite Observations of the 2006 Eruption of Augustine Volcano

Satellite observations played an important role in monitoring the 2006 eruption of Augustine Volcano. It represented the first opportunity for observers to use, in an operational setting, new Web-based tools and techniques developed by the Alaska Volcano Observatory remote sensing group. The “Okmok Algorithm” was used to analyze thermal infrared satellite data and highlight changes in the style and phases of activity. Temperature measurements were used to estimate ash cloud heights, which compared favorably to radar and ground-based observations, although larger discrepancies were seen when compared to pilot reports. Brightness temperature difference techniques were used to locate and track the 14 ash clouds produced during the explosive phase of the eruption. Stacking of these analyses allowed the creation of composite maps showing the distribution of airborne ash. The data from these maps were further combined with information from local reports and samples of ashfall to create a prototype of a concentration map that could be used to assess the potential hazard an eruption represents to aircraft, infrastructure and human health.
Use of Unmanned Aerial Systems for multispectral survey and tree classification: a test in a park area of northern Italy

Abstract In the frame of project FoGLIE (Fruition of Goods and Landscape in Interactive Environment), UAS were used to survey a park area in its less accessible zones, for scenic and stereoscopic videos, 3D modeling and vegetation monitoring. For this last application, specifically, through the acquisition of very high resolution images taken with two UAS-borne compact cameras (RGB and NIR), a DSM of a small vegetated area and the corresponding orthoimages were produced and co-registered. Planimetric and height accuracies in block adjustments and orthophotos are in the range of 0.10 m horizontally and 0.15 m in height. Then, after the derivation of synthetic channels, both unsupervised classification and supervised one were performed in order to test the algorithms' ability to distinguish between different bushes and trees species: some of them were correctly classified by the latter method but misclassifications still remain. The overall accuracy for the unsupervised classification is about 50% while the supervised one yields an overall accuracy around 80%.
Label Augmentation Method for Medical Landmark Detection in Hip Radiograph Images

This work reports the empirical performance of an automated medical landmark detection method for predict clinical markers in hip radiograph images. Notably, the detection method was trained using a label-only augmentation scheme; our results indicate that this form of augmentation outperforms traditional data augmentation and produces highly sample efficient estimators. We train a generic U-Net-based architecture under a curriculum consisting of two phases: initially relaxing the landmarking task by enlarging the label points to regions, then gradually eroding these label regions back to the base task. We measure the benefits of this approach on six datasets of radiographs with gold-standard expert annotations.
Repeated short-term bioproductivity changes in a coastal lake on Store Koldewey, northeast Greenland: an indicator of varying sea-ice coverage?

A lacustrine sediment core from Store Koldewey, northeast Greenland, was biogeochemically, biologically and sedimentologically investigated in order to reconstruct long- and short-term climatic and environmental variability. The chronology of the uppermost 189 cm of the record is based on ten 14C AMS age determinations of aquatic mosses. The record covers almost the entire Holocene and revealed changes on multidecadal to centennial scales. Dating of the oldest mosses shows that lacustrine biogenic productivity already began at around 11 cal. kyr BP. This age pre-dates the onset of biogenic productivity in other lakes on Store Koldewey by about 2 kyr. In spite of the early onset of biogenic production organic matter accumulation remained low and minerogenic sedimentation dominated. At about 9.5 cal. kyr BP moss, sulphur, organic carbon and biogenic silica content started to increase, indicating that the environment stabilized and the biogenic production in the lake adjusted to more preferable conditions. Subsequently, the biogenic productivity experienced repeated changes and varied both on long- and short-term scales. The long-term trend shows a maximum during the early Holocene thus responding to increased temperatures during the Holocene Thermal Maximum. Superimposed on the long-term trend, biogenic productivity also experienced repeated short-term fluctuations that match partly the NGRIP temperatures. The most pronounced decrease of biogenic productivity occurred at around 8.2 cal. kyr BP. Perennial lake ice coverage resulting from low temperatures is supposed to have caused decreased lacustrine biogenic productivity. From the middle Holocene to the present repeated decreases of productivity occurred that could be related to periods with severe sea-ice conditions of the East Greenland Current. Besides the dependence on air temperature it therefore demonstrates the sensitivity of lacustrine biogenic productivity in coastal high arctic areas to short-term cold spells that are mediated by the currents emanating from the Arctic Ocean. However, the data also emphasize the difficulties associated with the interpretation of lacustrine records.
N2O Emissions from a Nitrogen-Enriched River

Nitrous oxide (N2O) emissions from the South Platte River in Colorado were measured using closed chambers in the fall, winter, and summer of 1994−1995. The South Platte River was enriched in inorganic N (9−800 μM) derived from municipal wastewater effluent and groundwater return flows from irrigated agricultural fields. River water was as much as 2500% supersaturated with N2O, and median N2O emission rates from the river surface ranged from less than 90 to 32 600 μg-N m-2 d-1. Seventy-nine percent of the variance in N2O emission rates was explained by concentrations of total inorganic N in river water and by water temperature. The estimated total annual N2O emissions from the South Platte River were 2 × 1013−6 × 1013 μg-N yr-1. This amount of annual N2O emissions was similar to the estimated annual N2O emissions from all primary municipal wastewater treatment processes in the United States (1). Results from this study indicate that N-enriched rivers could be important anthropogenic sources of N2O to the a.
Radiation transport model for ablation hollows on snowfields

[1] The ablation hollows or “suncups” that form on the surface of snowfields in summer are a wonderful example of pattern formation in nature. Suncups reduce the albedo of the snow and set a characteristic length for interaction of wind with the snowpack. They also contain information about the properties of the snow and its ablation rate, which could be extracted if we had a more quantitative understanding of how suncups form. A mathematical model is proposed that explains the shape, size, and dynamical behavior of suncups in terms of the interaction of solar radiation with the snowpack. Using a perturbation method, we derive a nonlinear partial differential equation for the time-dependent shape of the snow surface from an approximate physical model for the interaction of solar radiation with snow. The resulting equation, which is similar to the Kuramoto-Sivashinsky equation in fluid mechanics, has solutions with characteristic length and amplitude. We find expressions for the characteristic size of suncups in terms of the spectrally averaged diffusion length of solar radiation in snow. The model correctly describes the shape of suncups, with their spatially ordered patterns of parabolic valleys and V-shaped ridges. It is also in remarkably good agreement with the observed length scales and growth rates. Depending on the relative values of the coefficients of the nonlinear terms in the differential equation, the suncup patterns can be either stationary in time or chaotic.