Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network

Recently a line of researches has delved the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on the states of immediate neighbors is insufficient to imitate a centralized control policy. To address this limitation, prior studies proposed incorporating $L$-hop delayed states into the computation. While this approach shows promise, it can lead to a lack of consensus among distant flock members and the formation of small clusters, consequently resulting in the failure of cohesive flocking behaviors. Instead, our approach leverages spatiotemporal GNN, named STGNN that encompasses both spatial and temporal expansions. The spatial expansion collects delayed states from distant neighbors, while the temporal expansion incorporates previous states from immediate neighbors. The broader and more comprehensive information gathered from both expansions results in more effective and accurate predictions. We develop an expert algorithm for controlling a swarm of robots and employ imitation learning to train our decentralized STGNN model based on the expert algorithm. We simulate the proposed STGNN approach in various settings, demonstrating its decentralized capacity to emulate the global expert algorithm. Further, we implemented our approach to achieve cohesive flocking, leader following and obstacle avoidance by a group of Crazyflie drones. The performance of STGNN underscores its potential as an effective and reliable approach for achieving cohesive flocking, leader following and obstacle avoidance tasks.
CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving

Even though the collaboration between traditional and neuromorphic event cameras brings prosperity to frame-event based vision applications, the performance is still confined by the resolution gap crossing two modalities in both spatial and temporal domains. This paper is devoted to bridging the gap by increasing the temporal resolution for images, i.e., motion deblurring, and the spatial resolution for events, i.e., event super-resolving, respectively. To this end, we introduce CrossZoom, a novel unified neural Network (CZ-Net) to jointly recover sharp latent sequences within the exposure period of a blurry input and the corresponding High-Resolution (HR) events. Specifically, we present a multi-scale blur-event fusion architecture that leverages the scale-variant properties and effectively fuses cross-modality information to achieve cross-enhancement. Attention-based adaptive enhancement and cross-interaction prediction modules are devised to alleviate the distortions inherent in Low-Resolution (LR) events and enhance the final results through the prior blur-event complementary information. Furthermore, we propose a new dataset containing HR sharp-blurry images and the corresponding HR-LR event streams to facilitate future research. Extensive qualitative and quantitative experiments on synthetic and real-world datasets demonstrate the effectiveness and robustness of the proposed method. Codes and datasets are released at https://bestrivenzc.github. io/CZ-Net/.
Leveraging Optimization for Adaptive Attacks on Image Watermarks

Untrustworthy users can misuse image generators to synthesize high-quality deepfakes and engage in unethical activities. Watermarking deters misuse by marking generated content with a hidden message, enabling its detection using a secret watermarking key. A core security property of watermarking is robustness, which states that an attacker can only evade detection by substantially degrading image quality. Assessing robustness requires designing an adaptive attack for the specific watermarking algorithm. When evaluating watermarking algorithms and their (adaptive) attacks, it is challenging to determine whether an adaptive attack is optimal, i.e., the best possible attack. We solve this problem by defining an objective function and then approach adaptive attacks as an optimization problem. The core idea of our adaptive attacks is to replicate secret watermarking keys locally by creating surrogate keys that are differentiable and can be used to optimize the attack's parameters. We demonstrate for Stable Diffusion models that such an attacker can break all five surveyed watermarking methods at no visible degradation in image quality. Optimizing our attacks is efficient and requires less than 1 GPU hour to reduce the detection accuracy to 6.3% or less. Our findings emphasize the need for more rigorous robustness testing against adaptive, learnable attackers.
Structural and volcanic expression of a fast slipping Ridge-Transform-Ridge-Plate Boundary: Sea MARC I and photographic surveys at the Clipperton Transform Fault

A high-resolution near-bottom survey has been conducted of the Clipperton transform fault and adjoining segments of the East Pacific Rise (EPR), using the Sea MARC I side-looking sonar system and the Lamont-Doherty Geological Observatory Olympus-based camera system. The transform fault zone (TFZ) is a narrow, well-defined belt of transform-parallel lineaments, which varies along strike from a single, sharp-edged notch to a complex band of subparallel lineaments up to 1 km wide. The TFZ is set within a 5-km-wide band of unusually fine-grained side scan texture, which could indicate nonbasaltic seafloor and/or pervasively sheared and mass-wasted basaltic crust The fine-grained swath is surrounded by constructional volcanic terrain with no hint of strike-slip motion; this observation puts an upper limit of 5 km on the extent of lateral migration of the TFZ in the last 1.5 m.y. Both ridge transform intersections (RTIs) are dominated by bathymetric highs located on the old plate opposite the spreading center. A mantling of fresh-looking constructional volcanic terrain on side scan images suggests that the highs are built in part by recent extrusive and intrusive volcanism; thermal expansion may also play a part. The EPR south of Clipperton has recently experienced extrusion of high effusion rate basalts, burial of faults and fissures by lava flows, and development of vigorous hydrothermal circulation. On the EPR north of Clipperton, the axial zone of faults and fissures tapers toward the transform fault; this may reflect a change in the shape or size of the underlying shallow level magma feeders as a function of distance from the site of magma upwelling or distance toward the transform fault.
Soil Quality Mapping Studies Using Nematodes as Bioindicators

Soil quality is one of the most important factors in sustaining the global biosphere and developing sustainable agricultural practices. Land use and management practices greatly impact the direction and degree of soil quality changes in time and space. Understanding the effects of land use and management practices on soil quality and its indicators has been identified as one of the most important goals for modern soil science. Soil quality mapping study represents a method for assessing and mapping soil quality changes in time and space in small units. For the present study, changes in the physical, chemical parameters and nematode density of the soils in the rural and urban areas of Thiruvananthapuram district, Kerala, were determined. The soil samples were collected from seven different categories of contaminated soils namely coastal area, sewage disposal area, industrial area, road-side area, agricultural area, market area and gasoline station area, and also from two control stations in rural and urban areas. The soil physico-chemical parameters and nematode density were determined. Geostatistics combined with GIS was applied to analyze the spatial variability of soil physico-chemical characteristics and nematode density. This soil quality mapping study provides a basis for identifying tension zones and serves as a triggering mechanism for implementation of soil contamination mitigating strategies.
Doubly heavy tetraquark states in the constituent quark model using diffusion Monte Carlo method

We use the diffusion Monte Carlo method to calculate the doubly heavy tetraquark $T_{cc}$ system in two kinds of constituent quark models, the pure constituent quark model AL1/AP1 and the chiral constituent quark model. When the discrete configurations are complete and no spatial clustering is preseted, the AL1/AP1 model gives an energy of $T_{cc}$ close to the $DD^*$ threshold, and the chiral constituent quark model yields a deeply bound state. We further calculate all doubly heavy tetraquark systems with $J^P=0^+,1^+,2^+$, and provide the binding energies of systems with bound states. The $I(J^P)=0(0^+)$ $bc\bar{n}\bar{n}$, $0(1^+)$ $bb\bar{n}\bar{n}$, $0(1^+)$ $bc\bar{n}\bar{n}$, $\frac{1}{2}(1^+)$ $bb\bar{s}\bar{n}$ systems have bound states in all three models. Since the DMC method has almost no restriction on the spatial part, the resulting bound states have greater binding energies than those obtained in previous works.
A Foundation Model for General Moving Object Segmentation in Medical Images

Medical image segmentation aims to delineate the anatomical or pathological structures of interest, playing a crucial role in clinical diagnosis. A substantial amount of high-quality annotated data is crucial for constructing high-precision deep segmentation models. However, medical annotation is highly cumbersome and time-consuming, especially for medical videos or 3D volumes, due to the huge labeling space and poor inter-frame consistency. Recently, a fundamental task named Moving Object Segmentation (MOS) has made significant advancements in natural images. Its objective is to delineate moving objects from the background within image sequences, requiring only minimal annotations. In this paper, we propose the first foundation model, named iMOS, for MOS in medical images. Extensive experiments on a large multi-modal medical dataset validate the effectiveness of the proposed iMOS. Specifically, with the annotation of only a small number of images in the sequence, iMOS can achieve satisfactory tracking and segmentation performance of moving objects throughout the entire sequence in bi-directions. We hope that the proposed iMOS can help accelerate the annotation speed of experts, and boost the development of medical foundation models.
An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC

Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability to prepare for the High Luminosity LHC experiment. Most approaches are based on the edge classification (EC) paradigm, wherein tracker hits are connected by edges, and a GNN is trained to prune edges, resulting in a collection of connected components representing tracks. These connected components are usually collected by a clustering algorithm and the resulting hit clusters are passed to downstream modules that may assess track quality or fit track parameters. In this work, we consider an alternative approach based on object condensation (OC), a multi-objective learning framework designed to cluster points belonging to an arbitrary number of objects, in this context tracks, and regress the properties of each object. We demonstrate that OC shows very promising results when applied to the pixel detector of the trackML dataset and can, in some cases, recover tracks that are not reconstructable when relying on the output of an EC alone. The results have been obtained with a modular and extensible open-source implementation that allows us to efficiently train and evaluate the performance of various OC architectures and related approaches.
Disturbance Observer-based Robust Integral Control Barrier Functions for Nonlinear Systems with High Relative Degree

In this paper, we consider the problem of safe control synthesis of general controlled nonlinear systems in the presence of bounded additive disturbances. Towards this aim, we first construct a governing augmented state space model consisting of the equations of motion of the original system, the integral control law and the nonlinear disturbance observer. Next, we propose the concept of Disturbance Observer based Integral Control Barrier Functions (DO-ICBFs) which we utilize to synthesize safe control inputs. The characterization of the safe controller is obtained after modifying the governing integral control law with an additive auxiliary control input which is computed via the solution of a quadratic problem. In contrast to prior methods in the relevant literature which can be unnecessarily cautious due to their reliance on the worst case disturbance estimates, our DO-ICBF based controller uses the available control effort frugally by leveraging the disturbance estimates computed by the disturbance observer. By construction, the proposed DO-ICBF based controller can ensure state and input constraint satisfaction at all times. Further, we propose Higher Order DO-ICBFs that extend our proposed method to nonlinear systems with higher relative degree with respect to the auxiliary control input. Finally, numerical simulations are provided to validate our proposed approach.
Assessment of soil quality in conservation reserve program and wheat-fallow soils

Chemical and microbial aspects of soil quality are an important consideration when evaluating the benefits of soil conservation efforts such as the Conservation Reserve Program (CRP). The objective of this study was to evaluate the quality of CRP and wheat-fallow (W-F) soils using soil biological and chemical parameters and C and N mineralization processes. The study was conducted on 20 CRP/W-F paired sites in eastern Washington, on Ritzville silt loam (coarse-silty, mixed, mesic Calciorthidic Haploxerolls). Soils collected from the paired fields were analyzed for chemical and biological parameters that have been suggested as indicators of soil quality. Potential enzyme activities and soil N were higher in the CRP soil than the W-F soil. Although there were no significant differences in total organic carbon (TOC) or microbial biomass carbon (MBC) the C mineralization potentials and C pools were significantly different between the CRP and W-F soils. Soil biota measurements showed there was greater active bacterial biomass in the CRP soil but greater fungal-feeding nematodes, flagellates, and amoebae in the W-F soil. The C mineralization study suggests that there is a significant increase in the secondary C pool of the CRP soil, which may indicate a buildup of higher quality soil organic matter and the potential for higher enzyme levels. When grass or straw was added to each soil type, the W-F soil produced more CO 2 with either substrate than the CRP soil, indicating C limiting conditions in the W-F soil. Since it is unknown what constitutes good soil quality, these shifts in chemical and biological parameters may seem subtle. However, in general, trends in the data indicated that soil quality in the CRP was improved after 4 to 7 yr, compared with its previous management in W-F cropland.
Feature Interaction Aware Automated Data Representation Transformation

Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively. Recent advancements in automated feature engineering (AutoFE) have made significant progress in addressing various challenges associated with representation learning, issues such as heavy reliance on intensive labor and empirical experiences, lack of explainable explicitness, and inflexible feature space reconstruction embedded into downstream tasks. However, these approaches are constrained by: 1) generation of potentially unintelligible and illogical reconstructed feature spaces, stemming from the neglect of expert-level cognitive processes; 2) lack of systematic exploration, which subsequently results in slower model convergence for identification of optimal feature space. To address these, we introduce an interaction-aware reinforced generation perspective. We redefine feature space reconstruction as a nested process of creating meaningful features and controlling feature set size through selection. We develop a hierarchical reinforcement learning structure with cascading Markov Decision Processes to automate feature and operation selection, as well as feature crossing. By incorporating statistical measures, we reward agents based on the interaction strength between selected features, resulting in intelligent and efficient exploration of the feature space that emulates human decision-making. Extensive experiments are conducted to validate our proposed approach.
Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination

This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated with classification problems. The objective is to enhance domain alignment and reduce domain shifts between these domains by leveraging their unique characteristics. Our approach proposes a novel loss function along with carefully selected existing loss functions tailored to address the challenges specific to histology images. This loss combination not only makes the model accurate and robust but also faster in terms of training convergence. We specifically focus on leveraging histology-specific features, such as tissue structure and cell morphology, to enhance adaptation performance in the histology domain. The proposed method is extensively evaluated in accuracy, robustness, and generalization, surpassing state-of-the-art techniques for histology images. We conducted extensive experiments on the FHIST dataset and the results show that our proposed method - Domain Adaptive Learning (DAL) significantly surpasses the ViT-based and CNN-based SoTA methods by 1.41% and 6.56% respectively.
Subset-Reach Estimation in Cross-Media Measurement

We propose two novel approaches to address a critical problem of reach measurement across multiple media -- how to estimate the reach of an unobserved subset of buying groups (BGs) based on the observed reach of other subsets of BGs. Specifically, we propose a model-free approach and a model-based approach. The former provides a coarse estimate for the reach of any subset by leveraging the consistency among the reach of different subsets. Linear programming is used to capture the constraints of the reach consistency. This produces an upper and a lower bound for the reach of any subset. The latter provides a point estimate for the reach of any subset. The key idea behind the latter is to exploit the conditional independence model. In particular, the groups of the model are created by assuming each BG has either high or low reach probability in a group, and the weights of each group are determined through solving a non-negative least squares (NNLS) problem. In addition, we also provide a framework to give both confidence interval and point estimates by integrating these two approaches with training points selection and parameter fine-tuning through cross-validation. Finally, we evaluate the two approaches through experiments on synthetic data.
Agricultural soil erosion and global carbon cycle: controversy over?

Recent research on the contribution of soil erosion on agricultural land to atmospheric carbon dioxide (CO2) emphasizes either the contribution of soil organic matter (SOM) mineralization during transport as source for atmospheric CO2, or the deep burial of SOM‐rich sediment in agricultural landscapes as a sink. The contribution of either process is subject to a controversial debate. In this letter, we present preliminary results on our research on interrill carbon (C) erosion, SOM transport by rill erosion and the stationarity of C erosion during the Holocene. None of those issues has been incorporated comprehensively and with global coverage in the debate on the role of C erosion in the global C cycle. Therefore, we argue that only an eco‐geomorphologic perspective on organic C movement through landscapes can reconcile the two positions. Copyright © 2009 John Wiley & Sons, Ltd.
Interpreting shear and strain fine structure from a neutrally buoyant float

The 1–5 m shear and strain spectra from a neutrally buoyant float reveal more strain in the near-inertial band (f < ω < 2f ∼ M2) and more shear in the continuum internal wave band (2f < ω < N) than can be accounted for by linear internal gravity waves. The shape of the 1-m shear spectrum can be reproduced by vertically advecting low-frequency ∼2-m wavelength fine structure past the float sensors with the observed displacement time series. Advection smears the encounter frequency across the internal wave band, aliasing and Doppler shifting some low intrinsic frequency variance into the continuum band and some high intrinsic frequency wave variance into the near-inertial band. Past observations of the excess shear variance at high frequencies led to speculation that it may be stratified two-dimensional turbulence having the same spatial scales as internal waves but carrying fine-scale potential vorticity anomalies (Holloway, 1983). The high shear relative to strain (Vz/( N¯ζz) = 2.2) and high Froude number (Vz/ N¯ = 1.23) would restrict stratified two-dimensional turbulence to having average relative vorticity more negative than −f. This limitation makes a stratified turbulence explanation for the fine structure impossible. On the other hand, the shear and strain characteristics are consistent with the Garrett and Munk (1979) internal wave model and could easily be explained by near-inertial internal waves. There may be no efficient means of generating potential vorticity anomalies away from boundaries.
White Paper on Radio Channel Modeling and Prediction to Support Future Environment-aware Wireless Communication Systems

COST INTERACT working group (WG)1 aims at increasing the theoretical and experimental understanding of radio propagation and channels in environments of interest and at deriving models for design, simulation, planning and operation of future wireless systems. Wide frequency ranges from sub-GHz to terahertz (THz), potentially high mobility, diverse and highly cluttered environments, dense networks, massive antenna systems, and the use of intelligent surfaces, are some of the challenges for radio channel measurements and modeling for next generation systems. As indicated in [1], with increased number of use cases (e.g., those identified by one6G [2] and shown in Fig. 1) to be supported and a larger number of frequency bands, a paradigm shift in channel measurements and modeling will be required. To address the particular challenges that come with such a paradigm shift, WG1 started the work on relevant topics, ranging from channel sounder design, metrology and measurement methodologies, measurements, modeling, and systematic dataset collection and analysis. In addition to the core activities of WG1, based on the strong interest of the participants, two sub-working groups (subWGs) have been initiated as part of WG1: i) subWG1.1 on millimeter-wave (mmWave) and THz sounding (subWG THz) and ii) subWG1.2 on propagation aspects related to reconfigurable intelligent surfaces (RIS) (subWG RIS). This white paper has two main goals: i) it summarizes the state-of-theart in radio channel measurement and modeling and the key challenges that the scientific community will have to face over the next years to support the development of 6G networks, as identified by WG1 and its subWGs; and ii) it charts the main directions for the work of WG1 and subWGs for the remainder of COST INTERACT duration (i.e., until October 2025).