Accurate 3D reconstruction of the small bowel skeleton is vital for understanding intestinal morphology, de-tecting structural abnormalities, and supporting diagnosis, yet limited resolution, organ adhesion, complex anatomy, and scarce annotations make continuous skeleton extraction from masks challenging. Voxel-based methods often struggle with the sparse topology and geometric directionality inherent in the small bowel skeleton, leading to inefficiency and high memory cost. To address these limitations, we propose a novel octree-based conditional diffusion model (i.e., Tree-Diffusion) that generates anatomically consistent small bowel skeletons guided by 3D segmentation masks. Specifically, we introduce two modules that captures structural priors from masks and topology characteristics from skeletons, ensuring cross-domain alignment and high-quality skeleton generation. Besides, we design a synthesis strategy to generate anatomically plausible skeleton-mask pairs, serving as topological priors to guide the diffusion model toward realis-tic structure predictions. To efficiently represent the elongated skeleton, we adopt an octree- based spatial encoding of hierarchical geometric features. Compared with baselines, our model achieves superior performance in anatomical fidelity, directional consistency, and inference efficiency. The code is available at: https://github.com/Small-Bowel-Skeleton-GenerationlCode

Coded caching is an effective technique to reduce the downlink traffic on the network. While coded caching has been extended to many scenarios, coded caching in satellite networks has not been well investigated in the literature. In this paper, we first introduce a novel model of coded caching in satellite networks, which consists of P satellites periodically moving in a given orbit and K users on Earth. In this model, at each timeslot, every satellite (regarded as a server) serves Q consecutive users in a regime, while each user could access one or more satellites at the same time. Due to the cyclic mobility of satellites, the connections between satellites and users could be predictable but also dynamically change in a cyclic wrap-around fashion. Thus, the connections between different satellites and different users at different timeslots could be highly coupled. Taking advantage of the predictable connections given the satellite constellation, we propose a centralized achievable scheme such that different satellites can serve the users jointly. For the converse bound, we introduce a novel method to select user groups and construct request patterns, such that the connections between users and satellites involved could be decoupled. Moreover, the gap between the achievable rate and the converse bound is shown to be at most a constant. Numerical results show the superior performance of the proposed scheme and converse bound.

Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there are limitations for Transformers in image fusion, such as lacks of local feature extraction and cross-modal feature interaction, resulting in insufficient multimodal feature extraction and integration. In addition, the computational cost of Transformers is higher. To address these challenges, in this work, we develop an adaptive cross-modal fusion strategy for unsupervised multimodal medical image fusion. Specifically, we propose a novel lightweight cross Transformer based on cross multi-axis attention mechanism. It includes cross-window attention and cross-grid attention to mine and integrate both local and global interactions of multimodal features. The cross Transformer is further guided by a spatial adaptation fusion module, which allows the model to focus on the most relevant information. Moreover, we design a special feature extraction module that combines multiple gradient residual dense convolutional and Transformer layers to obtain local features from coarse to fine and capture global features. The proposed strategy significantly boosts the fusion performance while minimizing computational costs. Extensive experiments, including clinical brain tumor image fusion, have shown that our model can achieve clearer texture details and better visual quality than other state-of-the-art fusion methods.

This paper researches on the power consumption of AIGC applications based on LLM with different parameter scales across different hardware platforms. Artificial Intelligence Generated Content (AIGC) represents a leading-edge application of AI technology, primarily driven by large language models (LLMs) and their associated technologies. The deployment of LLM typically relies on critical facilities with three layers, i.e., the hardware, model, and application layers. This empirical study aims to identify key factors in power consumption when a large model is serving in the inference stage, which will hint the insights for improving the energy efficiency of computational infrastructures. In the context of the "dual carbon" goals, i.e., carbon peaking and carbon neutrality, this study aims to find an effective way to reduce the energy cost of AIGC applications, thereby supporting sustainable AI development in industry.

Coded caching was introduced in partially cooperative D2D networks where some selfish users keep silent during delivery process. In existing works, unselfish users were randomly selected as delivery proxies for selfish users, resulting in asymmetric utilization of unselfish users. We observe that averaging the transmission load uniformly over unselfish users may achieve better performance. With this motivation, we propose scheme A, which symmetrically employs all unselfish users in delivery, and its transmission rate outperforms the schemes in existing works. Moreover, existing schemes applied the same symmetric cache placement and file splitting strategy as in the fully cooperative D2D networks, which ignored the asymmetry brought by the silent selfish users and thus incurred extra transmission. Consequently, we propose scheme B with an asymmetric file division strategy where the subfiles are exclusively designated to be sent by unselfish users, thus eliminating the proxy transmissions. To evaluate the performance of proposed schemes, a new converse bound is derived by the index coding approach. The joint performance of schemes A and B in certain regime is shown to be exact-optimal under uncoded placement when$S=1$, where$S$represents the number of selfish users. Similar to existing works, schemes A and B require an assumption that$S\leq t-1$, where$t$represents the caching redundancy. When$S\geq t$, we further propose scheme$C$employing uncoded placement, which outperforms the existing MDS-code based scheme in certain regime due to reduction on coding overhead. Numerical simulations are conducted to verify the superior performance of the proposed schemes.

This paper investigates the unsourced random access (URA) problem with a massive multiple-input multiple-output receiver that serves wireless devices in the near-field of radiation. We employ an uncoupled transmission protocol without appending redundancies to the slot-wise encoded messages. To exploit the channel sparsity for block length reduction while facing the collapsed sparse structure in the angular domain of near-field channels, we propose a sparse channel sampling method that divides the angle-distance (polar) domain based on the maximum permissible coherence. Decoding starts with retrieving active codewords and channels from each slot. We address the issue by leveraging the structured channel sparsity in the spatial and polar domains and propose a novel turbo-based recovery algorithm. Furthermore, we investigate an off-grid compressed sensing method to refine discretely estimated channel parameters over the continuum that improves the detection performance. Afterward, without the assistance of redundancies, we recouple the separated messages according to the similarity of the users’ channel information and propose a modifiedK-medoids method to handle the constraints and collisions involved in channel clustering. Simulations reveal that via exploiting the channel sparsity, the proposed URA scheme achieves high spectral efficiency and surpasses existing multi-slot-based schemes. Moreover, with more measurements provided by the overcomplete channel sampling, the near-field-suited scheme outperforms its counterpart of the far-field.

Coded caching is an effective technique to reduce the downlink traffic on the network. While coded caching has been extended to many scenarios, coded caching in satellite networks has not been well investigated in the literature. In this paper, we introduce a novel model of coded caching in satellite networks, which consists of P satellites periodically moving in a given orbit and K users on the earth. In this model, at each timeslot, every satellite (regarded as a server) serves Q consecutive users in a regime, while each user can access one satellite. Due to the cyclic mobility of satellites, the connections between satellites and users could be predictable but also dynamically change in a cyclic shift pattern. Thus, the connections between different satellites and different users at different timeslots could be highly coupled. Taking advantage of the predictable connections, we propose a centralized achievable scheme such that different satellites can serve the users jointly. For the converse bound, we introduce a novel method to construct request patterns such that the connections between users and satellites involved could be decoupled. The gap between the achievable rate and the converse bound is shown to be at most a constant. Numerical results for the performance of our scheme are also demonstrated.

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced$ K $-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes.

Coded caching is an effective way to reduce the network load by exploiting multicast opportunities between distinct users. In [1], a hierarchical network with two layers of caches is investigated. It is shown that the achievable rate of each layer is bounded from the corresponding converse bound within a constant multiplicative and additive gap, and can be achieved simultaneously. In this regard (with the additive gap), there is no tension between the rates of the two layers [1]. This paper takes a further investigation on this topic and shows that there is tension using a toy model. With the toy model, we derive new lower bounds and propose novel achievable schemes, which are shown to be optimal in an average sense. The involved techniques in both the lower bounds and achievable schemes could be of interest for future studies on coded caching in hierarchical networks.

Coded caching has been studied extensively and extended to many scenarios because it can exploit multicast opportunities to reduce the downlink traffic on the network. The basic model of coded caching is that a server with$N$files of the same size, is connected to several users with cache size$M$through a shared link. Prior work [1] has derived the optimal rate-memory tradeoff of this system with the constraint of uncoded prefetching. However, when heterogeneity of file sizes is taken into account, there remains open questions. In this paper, we consider two users and$N$files with distinct file sizes. When the cache size is large or small compared to the overall file size, i.e., 0 ≤$M$≤ ½ N F1or$M$≥ H(W) - ½ N F1where$F$1denotes the minimum file size and H (W) represents the overall information entropy of all files in the database, we show that our proposed scheme is optimal under all possible schemes. When the cache size is mediate, i.e., ½ N F1$M$1we show that our proposed scheme is optimal under the constraint of uncoded prefetching. To obtain these results, we introduce new techniques to extend the novel cut-set bound in [2], and propose a new achievable scheme based on both files and caches sizes.

This paper investigates the massive random access for a huge amount of user devices served by a base station (BS) equipped with a massive number of antennas. We consider a grant-free unsourced random access (U-RA) scheme where all users possess the same codebook and the BS aims at declaring a list of transmitted codewords and recovering the messages sent by active users. Most of the existing works concentrate on applying U-RA in the oversimplified independent and identically distributed (i.i.d.) channels. In this paper, we consider a fairly general joint-correlated MIMO channel model with line-of-sight components for the realistic outdoor wireless propagation environments. We conduct the activity detection for the emitted codewords by performing an improved coordinate descent approach with Bayesian learning automaton to solve a covariance-based maximum likelihood estimation problem. The proposed algorithm exhibits a faster convergence rate than traditional descent approaches. We further employ a coupled coding scheme to resolve the issue that the dimensions of the common codebook expand exponentially with user payload size in the practical massive machine-type communications scenario. Our simulations reveal that to achieve an error probability of 0.05 for reliable communications in correlated channels, one must pay a 0.9 to 1.3 dB penalty comparing to the minimum signal to noise ratio needed in i.i.d. channels on condition that a sufficient number of receiving antennas is equipped at the BS.