The study of interactive proofs in the quantum setting has yielded profound insights in complexity theory and quantum information. A curious feature of these results is that the advantage, in terms of computational power, of quantum models over their classical counterparts is usually due to entanglement phenomena rather than quantum communication with the verifier. For example, it is known that QIP = IP = PSPACE, and QMIP with unentangled provers is equal to NEXP = MIP; on the other hand, MIP* = RE. In this work we initiate the general study of (quantum) positional multi-prover interactive proofs ((Q)PMIP), in which provers and verifiers positioned in space communicate freely save for the constraints imposed by the speed of light. We investigate how the class of languages decidable by (Q)PMIPs depends on the arrangement of the verifiers and (honest) provers. In the case of classical PMIPs, we show a dichotomy: if the arrangement satisfies what we call the "min-ball" condition, then the class is NEXP, otherwise it is PSPACE. We then exhibit an arrangement that does not satisfy the min-ball condition for which there is a quantum PMIP for EXP in the no pre-shared entanglement model. Our construction is based on positional cryptography and MIPs with no-signaling soundness. We introduce a new positional primitive, the positional hardcore bit, which allows a pair of spatially separated players to transmit a random bit to a particular location while guaranteeing that it remains strongly unguessable elsewhere.

Encoder-Decoder architectures are widely used in deep learning-based Deformable Image Registration (DIR), where the encoder extracts multi-scale features and the decoder predicts deformation fields by recovering spatial locations. However, current methods lack specialized extraction of features (that are useful for registration) and predict deformation jointly and homogeneously in all three directions. In this paper, we propose a novel expert-guided DIR network with Mixture of Experts (MoE) mechanism applied in both encoder and decoder, named SHMoAReg. Specifically, we incorporate Mixture of Attention heads (MoA) into encoder layers, while Spatial Heterogeneous Mixture of Experts (SHMoE) into the decoder layers. The MoA enhances the specialization of feature extraction by dynamically selecting the optimal combination of attention heads for each image token. Meanwhile, the SHMoE predicts deformation fields heterogeneously in three directions for each voxel using experts with varying kernel sizes. Extensive experiments conducted on two publicly available datasets show consistent improvements over various methods, with a notable increase from 60.58% to 65.58% in Dice score for the abdominal CT dataset. To the best of our knowledge, we are the first to introduce MoE mechanism into DIR tasks.

Active Visual Tracking (AVT) is a significant research area with extensive applications in fields such as drones and autonomous driving. AVT involves controlling camera motion based on visual observations to track target object(s). In dynamic environments, especially with the presence of distractors, AVT faces the challenge of scale variation. Existing methods struggle to effectively handle these scale changes. To address this problem, this paper proposes a novel Scale Variation Robust Active Visual Tracking method (SVR-AVT). We first introduce a multi-scale multi-stage curriculum learning approach. By progressively increasing the complexity of tracking tasks, the tracker adapts to target of various scales. Secondly, we design a scale attention network, which adaptively extracts important scale features through multiple convolutional branches with different receptive fields and a scale attention mechanism. Moreover, we employ maximum position entropy learning to encourage the target to explore the environment more extensively. Experimental results in 3D environments demonstrate that SVR-AVT significantly outperforms existing methods in handling distraction and scale variation, and exhibits strong generalization capability in unseen environments.