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Abstract We study spin dynamics and quantum magnetism with ultracold highly-magnetic atoms. In particular, we focus on the interactions among rare-earth atoms localized in a site of an optical-lattice potential, modeled as a cylindrically symmetric harmonic oscillator in the presence of a weak external magnetic field. The interactions between the atoms are modeled using a multi-channel Hamiltonian containing multiple spin–spin and anisotropic spin–orbit interactions with strengths that depend on the separation between the atoms. We studied the eigenenergies of the atom pair in a site for different lattice geometries and magnetic field strengths. In parallel, we compared these energies to those found from a simplified approach, where the complex-collisional physics is replaced by a two-length-scale pseudopotential containing the contact and magnetic dipole–dipole interactions. The eigenenergies of this model can be computed analytically within the Born approximation as well as non-perturbatively for strong contact interactions. We have shown that the pseudopotential model can accurately represent the multi-channel Hamiltonian in certain parameter regimes of the shape of the site of an optical lattice. The pseudopotential forms the starting point for many-body, condensed matter simulations involving many atom pairs in different sites of an optical lattice. 

Built infrastructure, such as seawalls and levees, has long been used to reduce shoreline erosion and protect coastal properties from flood impacts. In contrast, natural and nature-based features (NNBF), including marshes, mangroves, oyster reefs, coral reefs, and seagrasses, offer not only coastal protection but also a range of valuable ecosystem services. There is no clear understanding of the capacity of either natural habitats or NNBF integrated with traditional engineered infrastructure to withstand extreme events, nor are there well-defined breakpoints at which these habitats fail to provide coastal protection. Evaluating existing NNBF strategies using a standardized set of metrics can help to assess their effectiveness to better inform design criteria. This review identifies a selection of NNBF projects with long-term monitoring programs and synthesizes the monitoring data to provide a literature-based performance assessment. It also explores the integration of NNBF with existing gray infrastructure to enhance overall effectiveness. 

This paper presents a novel parametric scattering model (PSM) for sensing extended targets in integrated sensing and communication (ISAC) systems. The PSM addresses the limitations of traditional models by efficiently capturing the target’s angular characteristics through a compact set of key parameters, including the central angle and angular spread, enabling efficient optimization. Based on the PSM, we first derive the Cram´er-Rao bound (CRB) for parameter estimation and then propose a beamforming design algorithm to minimize the CRB while meeting both communication signal-to-interference-plusnoise ratio (SINR) and power constraints. By integrating the PSM into the beamforming optimization process, the proposed framework achieves superior CRB performance while balancing the tradeoff between sensing accuracy and communication quality. Simulation results demonstrate that the PSM-based approach consistently outperforms traditional unstructured and discrete scattering models, particularly in resource-limited scenarios, highlighting its practical applicability and scalability. 

Abstract—Multi-Object Tracking (MOT) is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms have yet been widely studied. In particular, incorrect association between the tracked objects and their assigned IDs can lead to severe consequences, such as wrong trajectory predictions. Previous attacks against MOT either focused on hijacking the trackers of individual objects, or manipulating the tracker IDs in MOT by attacking the integrated object detection (OD) module in the digital domain, which are model-specific, non-robust, and only able to affect specific samples in offline datasets. In this paper, we present ADVTRAJ, the first online and physical ID-manipulation attack against tracking-by-detection MOT, in which an attacker uses adversarial trajectories to transfer its ID to a targeted object to confuse the tracking system, without attacking OD. Our simulation results in CARLA show that ADVTRAJ can fool ID assignments with 100% success rate in various scenarios for white-box attacks against SORT, which also have high attack transferability (up to 93% attack success rate) against state-of-the-art (SOTA) MOT algorithms due to their common design principles. We characterize the patterns of trajectories generated by ADVTRAJ and propose two universal adversarial maneuvers that can be performed by a human walker/driver in daily scenarios. Our work reveals under-explored weaknesses in the object association phase of SOTA MOT systems, and provides insights into enhancing the robustness of such systems