Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.
* 2023 IEEE International Conference on Data Mining (ICDM)
This paper addresses the challenge of achieving information-theoretic security in semantic communication (SeCom) over a wiretap channel, where a legitimate receiver coexists with an eavesdropper experiencing a poorer channel condition. Despite previous efforts to secure SeCom against eavesdroppers, achieving information-theoretic security in such schemes remains an open issue. In this work, we propose a secure digital SeCom approach based on superposition codes, aiming to attain nearly information-theoretic security. Our proposed method involves associating semantic information with satellite constellation points within a double-layered constellation map, where cloud center constellation points are randomly selected. By carefully allocating power between these two layers of constellation, we ensure that the symbol error probability (SEP) of the eavesdropper decoding satellite constellation points is nearly equivalent to random guessing, while maintaining a low SEP for the legitimate receiver to successfully decode the semantic information. Simulation results showcase that the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) for the eavesdropper's reconstructed data, using our proposed method, can range from decoding Gaussian-distributed random noise to approaching the variance of the data. This validates the ability of our method to achieve nearly information-theoretic security, demonstrating superior data security compared to benchmark methods.
Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others. It inherently results in a trade-off between perception ability and communication costs. Previous works transmit complete full-frame high-dimensional feature maps among agents, resulting in substantial communication costs. To promote communication efficiency, we propose only transmitting the information needed for the collaborator's downstream task. This pragmatic communication strategy focuses on three key aspects: i) pragmatic message selection, which selects task-critical parts from the complete data, resulting in spatially and temporally sparse feature vectors; ii) pragmatic message representation, which achieves pragmatic approximation of high-dimensional feature vectors with a task-adaptive dictionary, enabling communicating with integer indices; iii) pragmatic collaborator selection, which identifies beneficial collaborators, pruning unnecessary communication links. Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration. The proposed PragComm promotes pragmatic communication and adapts to a wide range of communication conditions. We evaluate PragComm for both collaborative 3D object detection and tracking tasks in both real-world, V2V4Real, and simulation datasets, OPV2V and V2X-SIM2.0. PragComm consistently outperforms previous methods with more than 32.7K times lower communication volume on OPV2V. Code is available at github.com/PhyllisH/PragComm.
Precise estimation of treatment effects is crucial for evaluating intervention effectiveness. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they treat the entire population as a homogeneous group, overlooking the diversity of treatment effects across potential subgroups that have varying treatment effects. This limitation restricts the ability to precisely estimate treatment effects and provide subgroup-specific treatment recommendations. In this paper, we propose a novel treatment effect estimation model, named SubgroupTE, which incorporates subgroup identification in TEE. SubgroupTE identifies heterogeneous subgroups with different treatment responses and more precisely estimates treatment effects by considering subgroup-specific causal effects. In addition, SubgroupTE iteratively optimizes subgrouping and treatment effect estimation networks to enhance both estimation and subgroup identification. Comprehensive experiments on the synthetic and semi-synthetic datasets exhibit the outstanding performance of SubgroupTE compared with the state-of-the-art models on treatment effect estimation. Additionally, a real-world study demonstrates the capabilities of SubgroupTE in enhancing personalized treatment recommendations for patients with opioid use disorder (OUD) by advancing treatment effect estimation with subgroup identification.
Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face a challenge with conflicting requirements between codebook design and digital constellation modulation. Traditional codebooks need a wide index range, while modulation favors few discrete states. To address this, we propose a multilevel generative semantic communication system with a two-stage training framework. In the first stage, we train a high-quality codebook, using a multi-head octonary codebook (MOC) to compress the index range. We also integrate a residual vector quantization (RVQ) mechanism for effective multilevel communication. In the second stage, a noise reduction block (NRB) based on Swin Transformer is introduced, coupled with the multilevel codebook from the first stage, serving as a high-quality semantic knowledge base (SKB) for generative feature restoration. Experimental results highlight MOC-RVQ's superior performance over methods like BPG or JPEG, even without channel error correction coding.
This paper studies the fundamental limit of semantic communications over the discrete memoryless channel. We consider the scenario to send a semantic source consisting of an observation state and its corresponding semantic state, both of which are recovered at the receiver. To derive the performance limitation, we adopt the semantic rate-distortion function (SRDF) to study the relationship among the minimum compression rate, observation distortion, semantic distortion, and channel capacity. For the case with unknown semantic source distribution, while only a set of the source samples is available, we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution. Furthermore, for a special case where the semantic state is a deterministic function of the observation, we design a cascade neural network to estimate the SRDF. For the case with perfectly known semantic source distribution, we propose a general Blahut-Arimoto algorithm to effectively compute the SRDF. Finally, experimental results validate our proposed algorithms for the scenarios with ideal Gaussian semantic source and some practical datasets.
DTC is a technical system that reflects the raw channel fading states and variations in a digital form at the virtual space, to actively adapt to novel communication techniques of the wireless communication system (WCS) at the physical or link level. To realize DTC, in this article, the concept and construction method of the radio environment knowledge pool (REKP) is proposed, which possesses the advantages of being controllable, interpretable, renewable, and generalized. Concretely, it is a collection that represents the regular pattern representations and interconnections between propagation environment information (PEI) and channel data. It also has the ability to update knowledge based on environment changes, human cognition, and technological developments. Firstly, the current state of knowledge-based research in the communication field and that for acquiring channel knowledge and achieving DTC are summarized. Secondly, how to construct and update REKP to conduct key communication tasks is given. Then, the typical cases with extensive numerical results are presented to demonstrate the great potential of REKP in enabling DTC. Finally, how to utilize REKP to address key challenges in implementing DTC and 6G WCS are discussed, including interpretability and generalization of DTC, and enhancing performance and reducing costs in the 6G WCS.
In-band full-duplex (IBFD) is a theoretically effective solution to increase the overall throughput for the future wireless communications system by enabling transmission and reception over the same time-frequency resources. However, reliable source reconstruction remains a great challenge in the practical IBFD systems due to the non-ideal elimination of the self-interference and the inherent limitations of the separate source and channel coding methods. On the other hand, artificial intelligence-enabled semantic communication can provide a viable direction for the optimization of the IBFD system. This article introduces a novel IBFD paradigm with the guidance of semantic communication called semantics-division duplexing (SDD). It utilizes semantic domain processing to further suppress self-interference, distinguish the expected semantic information, and recover the desired sources. Further integration of the digital and semantic domain processing can be implemented so as to achieve intelligent and concise communications. We present the advantages of the SDD paradigm with theoretical explanations and provide some visualized results to verify its effectiveness.
Sleep staging has become a critical task in diagnosing and treating sleep disorders to prevent sleep related diseases. With growing large scale sleep databases, significant progress has been made toward automatic sleep staging. However, previous studies face critical problems in sleep studies; the heterogeneity of subjects' physiological signals, the inability to extract meaningful information from unlabeled data to improve predictive performances, the difficulty in modeling correlations between sleep stages, and the lack of an effective mechanism to quantify predictive uncertainty. In this study, we propose a neural network based sleep staging model, DREAM, to learn domain generalized representations from physiological signals and models sleep dynamics. DREAM learns sleep related and subject invariant representations from diverse subjects' sleep signals and models sleep dynamics by capturing interactions between sequential signal segments and between sleep stages. We conducted a comprehensive empirical study to demonstrate the superiority of DREAM, including sleep stage prediction experiments, a case study, the usage of unlabeled data, and uncertainty. Notably, the case study validates DREAM's ability to learn generalized decision function for new subjects, especially in case there are differences between testing and training subjects. Uncertainty quantification shows that DREAM provides prediction uncertainty, making the model reliable and helping sleep experts in real world applications.