Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple scatter correction for single plane Compton camera imaging in nuclear decommissioning

This paper focuses on the application of Compton cameras in nuclear decommissioning, aiming to develop efficient and affordable radiological characterization methods. Compton cameras offer advantages over traditional Anger cameras, including increased sensitivity and a larger field of view. We showcase the use of monolithic scintillation detectors in a Compton camera setup to compute two‐dimensional reconstructions of the source location of incident gamma rays. The presence of multiple scattering effects in the large, spatially non‐resolved detectors can lead to spatial artifacts in the reconstructed source activity. To address this, the paper proposes an extended forward model that accounts for double scattering interactions within a single detector element. Experimental and simulation results demonstrate the effectiveness of the proposed approach.
Believable Minecraft Settlements by Means of Decentralised Iterative Planning

Procedural city generation that focuses on believability and adaptability to random terrain is a difficult challenge in the field of Procedural Content Generation (PCG). Dozens of researchers compete for a realistic approach in challenges such as the Generative Settlement Design in Minecraft (GDMC), in which our method has won the 2022 competition. This was achieved through a decentralised, iterative planning process that is transferable to similar generation processes that aims to produce "organic" content procedurally.
How Do Analysts Understand and Verify AI-Assisted Data Analyses?

Data analysis is challenging as it requires synthesizing domain knowledge, statistical expertise, and programming skills. Assistants powered by large language models (LLMs), such as ChatGPT, can assist analysts by translating natural language instructions into code. However, AI-assistant responses and analysis code can be misaligned with the analyst's intent or be seemingly correct but lead to incorrect conclusions. Therefore, validating AI assistance is crucial and challenging. Here, we explore how analysts understand and verify the correctness of AI-generated analyses. To observe analysts in diverse verification approaches, we develop a design probe equipped with natural language explanations, code, visualizations, and interactive data tables with common data operations. Through a qualitative user study (n=22) using this probe, we uncover common behaviors within verification workflows and how analysts' programming, analysis, and tool backgrounds reflect these behaviors. Additionally, we provide recommendations for analysts and highlight opportunities for designers to improve future AI-assistant experiences.
The renal angina index accurately predicts low risk of developing severe acute kidney injury among children admitted to a low-resource pediatric intensive care unit

Abstract Background Acute kidney injury (AKI) increases the risk of adverse outcomes. The renal angina index (RAI) has previously been used to predict patients at risk of developing severe AKI (sAKI). Method This single-centre prospective observational study aimed to assess the prevalence of sAKI in PICU as the primary outcome and the duration of mechanical ventilation and PICU stay, RRT need, and mortality as secondary outcomes. The utility of the RAI in predicting day 3 sAKI was also assessed. We enrolled 122 patients aged 1 month to 16 years whose baseline characteristics were collected via questionnaire. RAI was calculated on day 0 with a score of ≥8 being considered positive. sAKI was defined as KDIGO stages 2 and 3. Results sAKI prevalence was 14.8% and its development was associated with longer duration of mechanical ventilation (p = 0.001) and higher mortality (p = 0.011). A positive Day 0 RAI predicted day 3 sAKI with sensitivity 55.6%, specificity 85.6%, PPV 40.0%, NPV 91.8%, and AUC of 0.77. Exclusion of children older than 5 years improved RAI performance (sensitivity 72.7%, specificity 88.0%, PPV 57.1%, NPV 93.6%, AUC 0.80). A modified RAI based on local AKI risk factors had equivalent performance to RAI (Z – score 0.78 (CI −0.077–0.033), p = 0.435) with sensitivity 72.2%, specificity 80.8%, PPV 39.4%, NPV 94.4% and AUC 0.80. Conclusion The RAI can be an effective tool in ruling out sAKI in patients and a modification of RAI based on population-based risk factors improves the test’s sensitivity and NPV.
2D-3D Pose Tracking with Multi-View Constraints

Camera localization in 3D LiDAR maps has gained increasing attention due to its promising ability to handle complex scenarios, surpassing the limitations of visual-only localization methods. However, existing methods mostly focus on addressing the cross-modal gaps, estimating camera poses frame by frame without considering the relationship between adjacent frames, which makes the pose tracking unstable. To alleviate this, we propose to couple the 2D-3D correspondences between adjacent frames using the 2D-2D feature matching, establishing the multi-view geometrical constraints for simultaneously estimating multiple camera poses. Specifically, we propose a new 2D-3D pose tracking framework, which consists: a front-end hybrid flow estimation network for consecutive frames and a back-end pose optimization module. We further design a cross-modal consistency-based loss to incorporate the multi-view constraints during the training and inference process. We evaluate our proposed framework on the KITTI and Argoverse datasets. Experimental results demonstrate its superior performance compared to existing frame-by-frame 2D-3D pose tracking methods and state-of-the-art vision-only pose tracking algorithms. More online pose tracking videos are available at \url{https://youtu.be/yfBRdg7gw5M}
Quasi-stationary behavior of the stochastic FKPP equation on the circle

We consider the stochastic Fisher-Kolmogorov-Petrovsky-Piscunov (FKPP) equation on the circle $\mathbb{S}$, \begin{equation*} \partial_t u(t,x) \,= \frac{\alpha}{2}\Delta u +\beta\,u(1-u) + \sqrt{\gamma\,u(1-u)}\,\dot{W}, \qquad (t,x)\in(0,\infty)\times \mathbb{S}, \end{equation*} where $\dot{W}$ is space-time white noise. While any solution will eventually be absorbed at one of two states, the constant 1 and the constant 0 on the circle, essentially nothing had been established about the absorption time (also called the fixation time in population genetics), or about the long-time behavior prior to absorption. We establish the existence and uniqueness of the quasi-stationary distribution (QSD) for the solution of the stochastic FKPP. Moreover, we show that the solution conditioned on not being absorbed at time $t$ converges to this unique QSD as $t\to\infty$, for any initial distribution, and characterize the leading-order asymptotics for the tail distribution of the fixation time. We obtain explicit calculations in the neutral case ($\beta=0$), quantifying the effect of spatial diffusion on fixation time. We explicitly express the fixation rate in terms of the migration rate $\alpha$ for all $\alpha\in (0,\infty)$, finding in particular that the fixation rate is given by $\gamma[1-\frac{\gamma}{12\alpha}+\mathcal{O}(\frac{\gamma^2}{\alpha^2})]$ for fast migration and $\pi^2\alpha[1-\frac{8\alpha}{\gamma}+\mathcal{O}(\frac{\alpha^2}{\gamma^2})]$ for slow migration. Our proof relies on the observation that the absorbed (or killed) stochastic FKPP is dual to a system of $2$-type branching-coalescing Brownian motions killed when one type dies off, and on leveraging the relationship between these two killed processes.
Empowering Storage Systems Research with NVMeVirt: A Comprehensive NVMe Device Emulator

There have been drastic changes in the storage device landscape recently. At the center of the diverse storage landscape lies the NVMe interface, which allows high-performance and flexible communication models required by these next-generation device types. However, its hardware-oriented definition and specification are bottlenecking the development and evaluation cycle for new revolutionary storage devices. Furthermore, existing emulators lack the capability to support the advanced storage configurations that are currently in the spotlight. In this article, we present NVMeVirt, a novel approach to facilitate software-defined NVMe devices. A user can define any NVMe device type with custom features, and NVMeVirt allows it to bridge the gap between the host I/O stack and the virtual NVMe device in software. We demonstrate the advantages and features of NVMeVirt by realizing various storage types and configurations, such as conventional SSDs, low-latency high-bandwidth NVM SSDs, zoned namespace SSDs, and key-value SSDs with the support of PCI peer-to-peer DMA and NVMe-oF target offloading. We also make cases for storage research with NVMeVirt, such as studying the performance characteristics of database engines and extending the NVMe specification for the improved key-value SSD performance.
Stokes flows in a 2D bifurcation

The flow network model is an established approach to approximate pressure-flow relationships in a network, which has been widely used in many contexts. However, little is known about the impact of bifurcation geometry on such approximations, so the existing models mostly rely on unidirectional flow assumption and Poiseuille's law, and thus neglect the flow details at each bifurcation. In this work, we address these limitations by computing Stokes flows in a 2D bifurcation using LARS (Lightning-AAA Rational Stokes), a novel mesh-free algorithm for solving 2D Stokes flow problems utilising an applied complex analysis approach based on rational approximation of the Goursat functions. Using our 2D bifurcation model, we show that the fluxes in two child branches depend on not only pressures and widths of inlet and outlet branches, as most previous studies have assumed, but also detailed bifurcation geometries (e.g. bifurcation angle), which were not considered in previous studies. The 2D Stokes flow simulations allow us to represent the relationship between pressures and fluxes of a bifurcation using an updated flow network, which considers the bifurcation geometry and can be easily incorporated into previous flow network approaches. The errors in the flow conductance of a channel in a bifurcation approximated using Poiseuille's law can be greater than 16%, when the centreline length is twice the inlet channel width and the bifurcation geometry is highly asymmetric. In addition, we present details of 2D Stokes flow features, such as flow separation in a bifurcation and flows around fixed objects at different locations, which previous flow network models cannot capture. These findings suggest the importance of incorporating detailed flow modelling techniques alongside existing flow network approaches when solving complex flow problems.
Distributed multi‐vehicle task assignment in a time‐invariant drift field with obstacles

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal.
RMDA: A Regressive Multiple-Source Domain Adaption Approach for Early Prediction of Lithium-ion Battery Lifetime

Early prediction of battery lifetime is crucial for manufacturers to sort and group lithium-ion batteries (LIBs). This article introduces a regressive multiple-source domain adaption (RMDA) method for accurate early lifetime prediction with multiple LIB datasets. First, a common feature extractor is used to extract common features from different domains. Then, the domain-specific extractors followed by a maximum mean discrepancy loss are utilized to reduce the shift of source domains. Next, the domain-specific regressors are adopted to predict the lifetime of the source domains and the target domain. The effectiveness of the proposed RMDA method is validated on three LIB datasets with different chemistries and operating conditions.
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models' ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available at https://github.com/FudanDISC/DISC-LawLLM.
Improving Opioid Use Disorder Risk Modelling through Behavioral and Genetic Feature Integration

Opioids are an effective analgesic for acute and chronic pain, but also carry a considerable risk of addiction leading to millions of opioid use disorder (OUD) cases and tens of thousands of premature deaths in the United States yearly. Estimating OUD risk prior to prescription could improve the efficacy of treatment regimens, monitoring programs, and intervention strategies, but risk estimation is typically based on self-reported data or questionnaires. We develop an experimental design and computational methods that combine genetic variants associated with OUD with behavioral features extracted from GPS and Wi-Fi spatiotemporal coordinates to assess OUD risk. Since both OUD mobility and genetic data do not exist for the same cohort, we develop algorithms to (1) generate mobility features from empirical distributions and (2) synthesize mobility and genetic samples assuming an expected level of disease co-occurrence. We show that integrating genetic and mobility modalities improves risk modelling using classification accuracy, area under the precision-recall and receiver operator characteristic curves, and $F_1$ score. Interpreting the fitted models suggests that mobility features have more influence on OUD risk, although the genetic contribution was significant, particularly in linear models. While there exist concerns with respect to privacy, security, bias, and generalizability that must be evaluated in clinical trials before being implemented in practice, our framework provides preliminary evidence that behavioral and genetic features may improve OUD risk estimation to assist with personalized clinical decision-making.
Inverse problem of analog gravity systems

Analog gravity models of black holes and exotic compact objects provide a unique opportunity to study key properties of such systems in controlled laboratory environments. In contrast to astrophysical systems, analog gravity systems can be prepared carefully and their dynamical aspects thus investigated in unprecedented ways. While gravitational wave scattering properties of astrophysical compact objects are more connected to quasinormal modes, laboratory experiments can also access the transmission and reflection coefficients, which are otherwise mostly relevant for Hawking radiation related phenomena. In this work, we report two distinct results. First, we outline a semiclassical, nonparametric method that allows for the reconstruction of the effective perturbation potential from the knowledge of transmission and reflection coefficients for certain types of potentials in the Schr\"odinger wave equation admitting resonant tunneling. Second, we show how to use our method by applying it to an imperfect draining vortex, which has been suggested as an analog of extreme compact objects. Although the inverse problem is, in general, not unique, choosing physically motivated assumptions and requiring the validity of semiclassical theory, we demonstrate that the method provides efficient and accurate results.
Integral Sliding Mode Trajectory Tracking Control for UG Based on RBF Neural Network Observer

In order to study the longitudinal trajectory tracking problem of underwater glider (UG), an integral sliding mode control based on radial basis function (RBF) neural network observer is proposed in this paper. Firstly, a hydrodynamic model of longitudinal motion of UG is constructed. Then, an RBF neural network (NN) is used to approximate the nonlinear function of UG hydrodynamic model, and an adaptive observer is designed to observe the state of the system (i.e., pitch angle and pitch velocity). In the process of parameter configuration of the neural network, the adaptive learning rate is introduced to improve the accuracy of the hydrodynamic model of trajectory tracking. Furthermore, in the design of integral sliding mode controller, improved hyperbolic tangent function is used to eliminate chattering phenomenon. The simulation results show that the control scheme designed can effectively track the control instructions of the glider with global robustness.
Regime shifts in lake ecosystems : pattern and variation

Professor Stephen R. Carpenter has been elected by the ECI Jury chaired by Professor Wolfgang Wieser (University of Innsbruck, Austria), for his outstanding work on the food web structure of lake ecosystems, which has led to novel concepts on ecosystem functioning and to a unification of fisheries biological and limnological approaches. Born in Kansas City, Missouri, USA, in 1952, Stephen Carpenter is a Professor at the University of Wisconsin, Madison. He has received numerous international awards and he is a former President of the Ecological Society of America.
A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries

Image segmentation is a complex mathematical problem, especially for images that contain intensity inhomogeneity and tightly packed objects with missing boundaries in between. For instance, Magnetic Resonance (MR) muscle images often contain both issues, making muscle segmentation especially difficult. In this paper we propose a novel intensity correction and a semi-automatic active contour-based segmentation approach. The approach uses a geometric flow that incorporates a Reproducing Kernel Hilbert Space (RKHS) edge detector and a geodesic distance penalty term from a set of markers and anti-markers. We test the proposed scheme on MR muscle segmentation and compare with some state-of-the-art methods. To help deal with the intensity inhomogeneity in this kind of image, a new approach to estimate the bias field using a fat fraction image, called Prior Bias-Corrected Fuzzy C-means (PBCFCM), is introduced. Numerical experiments show that the proposed scheme leads to significantly better results than compared ones. The average dice values of the proposed method are 92.5%, 85.3%, 85.3% for quadriceps, hamstrings and other muscle groups while other approaches are at least 10% worse.
A Study on the Use of the Phyto-Courier Technology in Tobacco Leaves Infected by Agrobacterium tumefaciens

Climate change results in exceptional environmental conditions and drives the migration of pathogens to which local plants are not adapted. Biotic stress disrupts plants’ metabolism, fitness, and performance, ultimately impacting their productivity. It is therefore necessary to develop strategies for improving plant resistance by promoting stress responsiveness and resilience in an environmentally friendly and sustainable way. The aim of this study was to investigate whether priming tobacco plants with a formulation containing silicon-stabilised hybrid lipid nanoparticles functionalised with quercetin (referred to as GS3 phyto-courier) can protect against biotic stress triggered by Agrobacterium tumefaciens leaf infiltration. Tobacco leaves were primed via infiltration or spraying with the GS3 phyto-courier, as well as with a buffer (B) and free quercetin (Q) solution serving as controls prior to the biotic stress. Leaves were then sampled four days after bacterial infiltration for gene expression analysis and microscopy. The investigated genes increased in expression after stress, both in leaves treated with the phyto-courier and control solutions. A trend towards lower values was observed in the presence of the GS3 phyto-courier for genes encoding chitinases and pathogenesis-related proteins. Agroinfiltrated leaves sprayed with GS3 confirmed the significant lower expression of the pathogenesis-related gene PR-1a and showed higher expression of peroxidase and serine threonine kinase. Microscopy revealed swelling of the chloroplasts in the parenchyma of stressed leaves treated with B; however, GS3 preserved the chloroplasts’ mean area under stress. Furthermore, the UV spectrum of free Q solution and of quercetin freshly extracted from GS3 revealed a different spectral signature with higher values of maximum absorbance (Amax) of the flavonoid in the latter, suggesting that the silicon-stabilised hybrid lipid nanoparticles protect quercetin against oxidative degradation.
Dynamic Ex Vivo Porcine Eye Model to Measure Ophthalmic Drug Penetration under Simulated Lacrimal Flow

Animal models are still used in the research and development of ophthalmic drug products, mainly due to the difficulty in simulating natural physiological conditions with in vitro models, as there is a lack of dynamic protection mechanisms. Therefore, developing alternative ophthalmic models that evaluate drug penetration in the cornea while applying dynamic protection barriers is a contemporary challenge. This study aimed to develop a dynamic ex vivo model using porcine eyes with a simulated lacrimal flow to evaluate the performance of pharmaceutical drug products. A glass donor cell to support a simulated tear flow was designed, optimized, and custom-made. The system was challenged with different formulations (with fluconazole) including excipients with different viscosities (poloxamer 407) and mucoadhesive properties (chitosan). The results were compared to those obtained from a conventional excised cornea model mounted in Franz-type diffusion cells. The dynamic model could differentiate formulations, while the static model did not, overestimating ex vivo drug penetrated amounts. Hence, the dynamic model with simulated tear flow showed to be a simple and promising new alternative method for the drug penetration of ophthalmic formulations that ultimately can reduce the number of animals used in research.