Miracle-dz/UniFAR-FTUs huggingface dataset:
Gravity-Informed Deep Learning Framework for Predicting Ship Traffic Flow and Invasion Risk of Non-Indigenous Species via Ballast Water Discharge

Invasive species in water bodies pose a major threat to the environment and biodiversity globally. Due to increased transportation and trade, non-native species have been introduced to new environments, causing damage to ecosystems and leading to economic losses in agriculture, forestry, and fisheries. Therefore, there is a pressing need for risk assessment and management techniques to mitigate the impact of these invasions. This study aims to develop a new physics-inspired model to forecast maritime shipping traffic and thus inform risk assessment of invasive species spread through global transportation networks. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Additionally, by analyzing the risk network of invasive species, we provide a comprehensive framework for assessing the invasion threat level given a pair of origin and destination. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% segmentation accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of invasive species risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing species invasion risks in a changing global landscape.
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems

Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-coveted property, especially when it comes to the multiple sources involved in the dialogue system. To better plan and incorporate the use of multiple sources in generating personalized response, we firstly decompose it into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation. We then propose a novel Unified Multi-Source Retrieval-Augmented Generation system (UniMS-RAG) Specifically, we unify these three sub-tasks with different formulations into the same sequence-to-sequence paradigm during the training, to adaptively retrieve evidences and evaluate the relevance on-demand using special tokens, called acting tokens and evaluation tokens. Enabling language models to generate acting tokens facilitates interaction with various knowledge sources, allowing them to adapt their behavior to diverse task requirements. Meanwhile, evaluation tokens gauge the relevance score between the dialogue context and the retrieved evidence. In addition, we carefully design a self-refinement mechanism to iteratively refine the generated response considering 1) the consistency scores between the generated response and retrieved evidence; and 2) the relevance scores. Experiments on two personalized datasets (DuLeMon and KBP) show that UniMS-RAG achieves state-of-the-art performance on the knowledge source selection and response generation task with itself as a retriever in a unified manner. Extensive analyses and discussions are provided for shedding some new perspectives for personalized dialogue systems.
Model Predictive Wave Disturbance Rejection for Underwater Soft Robotic Manipulators

Inspired by the octopus and other animals living in water, soft robots should naturally lend themselves to underwater operations, as supported by encouraging validations in deep water scenarios. This work deals with equipping soft arms with the intelligence necessary to move precisely in wave-dominated environments, such as shallow waters where marine renewable devices are located. This scenario is substantially more challenging than calm deep water since, at low operational depths, hydrodynamic wave disturbances can represent a significant impediment. We propose a control strategy based on Nonlinear Model Predictive Control that can account for wave disturbances explicitly, optimising control actions by considering an estimate of oncoming hydrodynamic loads. The proposed strategy is validated through a set of tasks covering set-point regulation, trajectory tracking and mechanical failure compensation, all under a broad range of varying significant wave heights and peak spectral periods. The proposed control methodology displays positional error reductions as large as 84% with respect to a baseline controller, proving the effectiveness of the method. These initial findings present a first step in the development and deployment of soft manipulators for performing tasks in hazardous water environments.
On the Statistical Mechanics of Mass Accommodation at Liquid-Vapor Interfaces

We propose a framework for describing the dynamics associated with the adsorption of small molecules to liquid-vapor interfaces, using an intermediate resolution between traditional continuum theories that are bereft of molecular detail and molecular dynamics simulations that are replete with them. In particular, we develop an effective single particle equation of motion capable of describing the physical processes that determine thermal and mass accommodation probabilities. The effective equation is parameterized with quantities that vary through space away from the liquid-vapor interface. Of particular importance in describing the early time dynamics is the spatially dependent friction, for which we propose a numerical scheme to evaluate from molecular simulation. Taken together with potentials of mean force computable with importance sampling methods, we illustrate how to compute the mass accommodation coefficient and residence time distribution. Throughout, we highlight the case of ozone adsorption in aqueous solutions and its dependence on electrolyte composition.
Continuous-time Riemannian SGD and SVRG Flows on Wasserstein Probabilistic Space

Recently, optimization on the Riemannian manifold has provided new insights to the optimization community. In this regard, the manifold taken as the probability measure metric space equipped with the second-order Wasserstein distance is of particular interest, since optimization on it can be linked to practical sampling processes. In general, the oracle (continuous) optimization method on Wasserstein space is Riemannian gradient flow (i.e., Langevin dynamics when minimizing KL divergence). In this paper, we aim to enrich the continuous optimization methods in the Wasserstein space by extending the gradient flow into the stochastic gradient descent (SGD) flow and stochastic variance reduction gradient (SVRG) flow. The two flows on Euclidean space are standard stochastic optimization methods, while their Riemannian counterparts are not explored yet. By leveraging the structures in Wasserstein space, we construct a stochastic differential equation (SDE) to approximate the discrete dynamics of desired stochastic methods in the corresponded random vector space. Then, the flows of probability measures are naturally obtained by applying Fokker-Planck equation to such SDE. Furthermore, the convergence rates of the proposed Riemannian stochastic flows are proven, and they match the results in Euclidean space.
Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond

Thin-plate spline (TPS) is a principal warp that allows for representing elastic, nonlinear transformation with control point motions. With the increase of control points, the warp becomes increasingly flexible but usually encounters a bottleneck caused by undesired issues, e.g., content distortion. In this paper, we explore generic applications of TPS in single-image-based warping tasks, such as rotation correction, rectangling, and portrait correction. To break this bottleneck, we propose the coupled thin-plate spline model (CoupledTPS), which iteratively couples multiple TPS with limited control points into a more flexible and powerful transformation. Concretely, we first design an iterative search to predict new control points according to the current latent condition. Then, we present the warping flow as a bridge for the coupling of different TPS transformations, effectively eliminating interpolation errors caused by multiple warps. Besides, in light of the laborious annotation cost, we develop a semi-supervised learning scheme to improve warping quality by exploiting unlabeled data. It is formulated through dual transformation between the searched control points of unlabeled data and its graphic augmentation, yielding an implicit correction consistency constraint. Finally, we collect massive unlabeled data to exhibit the benefit of our semi-supervised scheme in rotation correction. Extensive experiments demonstrate the superiority and universality of CoupledTPS over the existing state-of-the-art (SoTA) solutions for rotation correction and beyond. The code and data will be available at https://github.com/nie-lang/CoupledTPS.
Quantum Oscillations in kagome metals CsTi3Bi5 and RbTi3Bi5

We report quantum oscillation measurements on the kagome compounds ATi$_3$Bi$_5$ (A=Rb, Cs) in magnetic fields up to 41.5 T and temperatures down to 350 mK. In addition to the frequencies observed in previous studies, we have observed multiple unreported frequencies above 2000 T in CsTi$_3$Bi$_5$ using a tunnel diode oscillator technique. We compare these results against density functional theory calculations and find good agreement with the calculations in the number of peaks observed, frequency, and the dimensionality of the Fermi surface. For RbTi$_3$Bi$_5$ we have obtained a different quantum oscillation spectrum, although calculated quantum oscillation frequencies for the Rb compound are remarkably similar to the Cs compound, calling for further studies.
Cosmology before noon with multiple galaxy populations

Near-future facilities observing the high-redshift universe ($2<z<5$) will have an opportunity to take advantage of"multi-tracer"cosmology by observing multiple tracers of the matter density field: Lyman alpha emitters (LAE), Lyman break galaxies (LBG), and CMB lensing $\kappa$. In this work we use Fisher forecasts to investigate the effect of multi-tracers on next-generation facilities. In agreement with previous work, we show that multiple tracers improve constraints primarily from degeneracy breaking, instead of the traditional intuition of sample variance cancellation. Then, we forecast that for both BBN and CMB primary priors, the addition of lensing and LAEs onto a LBG-only sample will gain 25\% or more in many parameters, with the largest gains being factor of $\sim10$ improvement for $f_{\rm EDE}$. We include a preliminary approach towards modelling the impact of radiative transfer (RT) on forecasts involving LAEs by introducing a simplified model at linear theory level. Our results, albeit preliminary, show that the while RT influences LAE-only forecasts strongly, its effect on composite multi-tracer forecasts are limited.
Enhanced quantum control of individual ultracold molecules using optical tweezer arrays

Control over the quantum states of individual molecules is crucial in the quest to harness their rich internal structure and dipolar interactions for applications in quantum science. In this paper, we develop a toolbox of techniques for the control and readout of individually trapped polar molecules in an array of optical tweezers. Starting with arrays of up to eight Rb and eight Cs atoms, we assemble arrays of RbCs molecules in their rovibrational and hyperfine ground state with an overall efficiency of 48(2)%. We demonstrate global microwave control of multiple rotational states of the molecules and use an auxiliary tweezer array to implement site-resolved addressing and state control. We show how the rotational state of the molecule can be mapped onto the position of Rb atoms and use this capability to readout multiple rotational states in a single experimental run. Further, using a scheme for the mid-sequence detection of molecule formation errors, we perform rearrangement of assembled molecules to prepare small defect-free arrays. Finally, we discuss a feasible route to scaling to larger arrays of molecules.
Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter

Object recognition and object pose estimation in robotic grasping continue to be significant challenges, since building a labelled dataset can be time consuming and financially costly in terms of data collection and annotation. In this work, we propose a synthetic data generation method that minimizes human intervention and makes downstream image segmentation algorithms more robust by combining a generated synthetic dataset with a smaller real-world dataset (hybrid dataset). Annotation experiments show that the proposed synthetic scene generation can diminish labelling time dramatically. RGB image segmentation is trained with hybrid dataset and combined with depth information to produce pixel-to-point correspondence of individual segmented objects. The object to grasp is then determined by the confidence score of the segmentation algorithm. Pick-and-place experiments demonstrate that segmentation trained on our hybrid dataset (98.9%, 70%) outperforms the real dataset and a publicly available dataset by (6.7%, 18.8%) and (2.8%, 10%) in terms of labelling and grasping success rate, respectively. Supplementary material is available at https://sites.google. com/view/synthetic-dataset-generation.
Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images Using a 3D Convolutional Auto-Encoder

This study presents a deep convolutional auto-encoder network for filtering reverberation artifacts, from transthoracic echocardiographic (TTE) image sequences. Given the spatiotemporal nature of these artifacts, the filtering network was built using 3D convolutional layers to suppress the clutter patterns throughout the cardiac cycle. The network was designed by taking advantage of: i) an attention mechanism to focus primarily on cluttered regions and ii) residual learning to preserve fine structures of the image frames. To train the deep network, a diverse set of artifact patterns was simulated and the simulated patterns were superimposed onto artifact-free ultra-realistic synthetic TTE sequences of six ultrasound vendors to generate input of the filtering network. The artifact-free sequences served as ground-truth. Performance of the filtering network was evaluated using unseen synthetic as well as in-vivo artifactual sequences. Satisfactory results obtained using the latter dataset confirmed the good generalization performance of the proposed network which was trained using the synthetic sequences and simulated artifact patterns. Suitability of the clutter-filtered sequences for further processing was assessed by computing segmental strain curves from them. The results showed that the large discrepancy between the strain profiles computed from the cluttered segments and their corresponding segments in the clutter-free images was significantly reduced after filtering the sequences using the proposed network. The trained deep network could process an artifactual TTE sequence in a fraction of a second and can be used for real-time clutter filtering. Moreover, it can improve the precision of the clinical indexes that are computed from the TTE sequences. The source code of the proposed method is available at: https://github.com/MahdiTabassian/Deep-Clutter-Filtering/tree/main.
A Kriging Based Forecasting and Scheduling System for Scientific Computing Cloud Applications

Regarding to the theories and techniques of cloud computing having been developed and applied in scientific computing field, tasks can be conveniently managed by the cloud platform on the basis of standardized scheduling system with cost (resources consumed) recorded. However, there are two issues which drag the customers’ attention: 1) When will the tasks expect for termination (response time) under a specific resource scheduling; 2) What is the best scheduling solution by considering cost. In order to reply these two questions, a Kriging based forecasting and scheduling system has been proposed in this paper. With the cooperation between the scientific designer and the cloud designer, the design variables for evaluating the cloud applications can be achieved; Kriging surrogate model is then introduced to simulate the approximate functional relationship between the design variables and the response time of the tasks; Sequential quadratic programming optimization algorithm then provides the best scheduling solution for the tasks if cost constraints are to be met. Two real scientific computing cloud applications have been testified on an OpenStack cloud platform, with consequences described in details. The work in this paper has put forward a novel way for the designers and the customers on predictable and reasonable scheduling strategies for the various resource-intensive scientific computing cloud applications with surrogate models and optimization algorithms.
Domain-specific Cognitive Impairments, Mood and Quality of Life 6 Months After Stroke

Purpose To identify which acute and 6-month domain-specific cognitive impairments impact mood functioning, participation, and stroke-related quality of life 6 months after stroke. Materials and Methods A prospective cohort of 430 stroke survivors completed the Oxford Cognitive Screen (OCS) acutely and 6 months post-stroke. Participants completed the Stroke Impact Scale (SIS) and Hospital Depression and Anxiety Scale (HADS) at 6 months. Multivariable regression analyses assessed whether severity of, and domain-specific, cognitive impairment acutely and at 6 months was associated with composite 6-month SIS scores, each SIS subscale, and HADS scores. Results Increased severity of cognitive impairment acutely and at 6 months was associated with lower 6-month SIS composite scores independent of age, sex, education years, and stroke severity (both p<0.001). Domain-specific impairments in memory (p<0.001) and attention (p=0.002) acutely, and language (p<0.001), memory (p=0.001) and number processing (p=0.006) at 6 months showed the strongest associations with worse SIS composite scores. Severity of acute and 6-month cognitive impairment was associated with poorer functioning in each SIS subscale, as well as greater levels of depression (acute p=0.021, 6-months p<0.001), but not anxiety (p=0.174, p=0.129). Conclusions Both acute and 6-month domain-specific cognitive impairments, particularly in memory, were found to negatively impact overall functional and mood outcomes 6 months post-stroke.
Borrelia burgdorferi PlzA is a cyclic-di-GMP dependent DNA-and RNA-binding protein 1

17 The PilZ domain-containing protein, PlzA, is the only cyclic di-GMP binding protein encoded 18 by all Lyme disease spirochetes. PlzA has been implicated in the regulation of many borrelial 19 processes, but the effector mechanism of PlzA was not previously known. Here we report that 20 PlzA can bind DNA and RNA, and that nucleic acid binding requires c-di-GMP. In studies with 21 the promoter and 5’ UTR of the glycerol catabolism operon, glpFKD, the affinity of PlzA for 22 nucleic acids increased as concentrations of c-di-GMP were increased. A mutant PlzA that is 23 incapable of binding c-di-GMP did not bind to any tested nucleic acids. PlzA is a dual-domain 24 protein consisting of a PilZ-like N-terminal domain linked to a canonical C-terminal PilZ 25 domain. Dissection of the domains demonstrated that the separated N-terminal domain bound 26 nucleic acids independently of c-di-GMP. The C-terminal domain, which includes the c-di27 GMP binding motifs, did not bind nucleic acids under any tested conditions. Our data are 28 supported by computational docking, which predicts that c-di-GMP binding at the C-terminal 29 domain stabilizes the overall protein structure and facilitates PlzA-DNA interactions via 30 residues in the N-terminal domain. Based on our data, we propose that levels of c-di-GMP 31 during the various stages of the enzootic life cycle dictate PlzA regulatory targets and 32 functions. 33
Impact of maternal obesogenic diet on maternal and offspring microbiome development

Pregnancy can alter gut microbiota composition, but how an obesogenic diet impacts maternal gut microbiota, and the extent to which this influences offspring microbiome can be obscured by confounding factors. This study examined changes in gut microbiota composition across pre-pregnancy, gestation and lactation in rat dams fed either a high-fat, high-sugar Cafeteria (Caf) diet or Chow. Microbiome development was assessed in their offspring weaned onto chow. Caf diet consumption during pregnancy increased weight gain and adiposity, and compromised subsequent maternal nursing behaviour. α- and β diversity measures in Caf-fed dams showed a different trajectory across the progression of pregnancy, with no change in Bacteroidetes and Firmicutes abundance compared with Chow dams. Offspring born to Caf dams exhibited greater adiposity and plasma leptin at weaning and 14 weeks of age than those born to Chow dams. Maternal Caf diet induced clear differences in β diversity in weanlings but not α diversity. SourceTracker analysis revealed similarities in the gut microbiota of Chow weanlings and maternal gut microbiota in lactation, whereas the microbiota of Caf weanlings was similar to the maternal gut microbiota during gestation. Maternal Caf diet exerted only marginal effects on gut microbiota composition in 14-week-old offspring.