Langevin Dynamics (LD) and its discrete proposal have been widely applied in the field of Combinatorial Optimization (CO). Both sampling-based and data-driven approaches have benefited significantly from these methods. However, LD's reliance on Gaussian noise limits its ability to escape narrow local optima, requires costly parallel chains, and performs poorly in rugged landscapes or with non-strict constraints. These challenges have impeded the development of more advanced approaches. To address these issues, we introduce Fractional Langevin Dynamics (FLD) for CO, replacing Gaussian noise with $\alpha$-stable L\'evy noise. FLD can escape from local optima more readily via L\'evy flights, and in multiple-peak CO problems with high potential barriers it exhibits a polynomial escape time that outperforms the exponential escape time of LD. Moreover, FLD coincides with LD when $\alpha = 2$, and by tuning $\alpha$ it can be adapted to a wider range of complex scenarios in the CO fields. We provide theoretical proof that our method offers enhanced exploration capabilities and improved convergence. Experimental results on the Maximum Independent Set, Maximum Clique, and Maximum Cut problems demonstrate that incorporating FLD advances both sampling-based and data-driven approaches, achieving state-of-the-art (SOTA) performance in most of the experiments.

Scalability is a major concern in implementing deep learning (DL) based methods in wireless communication systems. Given various channel reconstruction tasks, applying one DL model for one specific task is costly in both model training and model storage. In this paper, we propose a novel unsupervised deep plug-and-play prior method for three channel reconstruction tasks in the downlink of massive multiple-input multiple-output (MIMO) systems, including channel estimation, antenna extrapolation and channel state information (CSI) feedback. The proposed method corresponding to these three channel reconstruction tasks employs a common DL model, which greatly reduces the overhead of model training and storage. Unlike general multi-task learning, the DL model of the proposed method does not require further fine-tuning for specific channel reconstruction tasks. Extensive experiments are conducted on the DeepMIMO dataset to demonstrate the convergence, performance, and storage overhead of the proposed method for the three channel reconstruction tasks.

To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided one-for-all deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a DL based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. In this way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary compression ratio. The one-sided one-for-all framework reduces model storage space, relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness and advantages of the proposed method.

Good faith disagreements and healthy conflict management are essential to deliberative democracy and building strong relationships. People increasingly use computer-mediated communication during disagreements, which raises the question of how technology and design impact users' disagreements and relationships. We conducted a mixed-methods study with 257 participants to understand how design impacts disagreements across both existing social media platforms and novel, user-generated designs. Through interviews, a survey, and storyboard evaluations, we found that users often want to discuss challenging topics online but avoid them due to fear of hurting their relationships. Further, we found that users are most excited about design interventions that empower collective group action, humanize others online, or support channel switching to more private or socially rich contexts. Our results suggest that although technology has the potential to support users during conflict, it is also rife with possibilities to do more harm than good by diluting users' intentions, intruding, or backfiring. We introduce "interpersonal design,'' which centers relationships in the design process, an essential step in supporting users in the challenging task of arguing well.