Traffic-Aware Transmission Mode Selection in D2D-enabled Cellular Networks with Token System

We consider a D2D-enabled cellular network where user equipments (UEs) owned by rational users are incentivized to form D2D pairs using tokens. They exchange tokens electronically to "buy" and "sell" D2D services. Meanwhile the devices have the ability to choose the transmission mode, i.e. receiving data via cellular links or D2D links. Thus taking the different benefits brought by diverse traffic types as a prior, the UEs can utilize their tokens more efficiently via transmission mode selection. In this paper, the optimal transmission mode selection strategy as well as token collection policy are investigated to maximize the long-term utility in the dynamic network environment. The optimal policy is proved to be a threshold strategy, and the thresholds have a monotonicity property. Numerical simulations verify our observations and the gain from transmission mode selection is observed.Comment: 7 pages, 6 figures. A shorter version is submitted to EUSIPC

Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension and generation tasks. We have the following main findings. First, for the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks and sometimes even better than small SOTA models specifically fine-tuned on each downstream task. We also find that larger instructed LLMs are not always better on code-related tasks. Second, for the few-shot setting, we find that adding demonstration examples substantially helps instructed LLMs perform better on most code comprehension and generation tasks; however, the examples would sometimes induce unstable or even worse performance. Furthermore, we find widely-used BM25-based shot selection strategy significantly outperforms the basic random selection or fixed selection only on generation problems. Third, for the fine-tuning setting, we find that fine-tuning could further improve the model performance on downstream code comprehension and generation tasks compared to the zero-shot/one-shot performance. In addition, after being fine-tuned on the same downstream task dataset, instructed LLMs outperform both the small SOTA models and similar-scaled LLMs without instruction tuning. Based on our findings, we further present practical implications on model and usage recommendation, performance and cost trade-offs, and future direction

USP21 deubiquitylates Nanog to regulate protein stability and stem cell pluripotency

The homeobox transcription factor Nanog has a vital role in maintaining pluripotency and self-renewal of embryonic stem cells (ESCs). Stabilization of Nanog proteins is essential for ESCs. The ubiquitin–proteasome pathway mediated by E3 ubiquitin ligases and deubiquitylases is one of the key ways to regulate protein levels and functions. Although ubiquitylation of Nanog catalyzed by the ligase FBXW8 has been demonstrated, the deubiquitylase that maintains the protein levels of Nanog in ESCs yet to be defined. In this study, we identify the ubiquitin-specific peptidase 21 (USP21) as a deubiquitylase for Nanog, but not for Oct4 or Sox2. USP21 interacts with Nanog protein in ESCs in vivo and in vitro. The C-terminal USP domain of USP21 and the C-domain of Nanog are responsible for this interaction. USP21 deubiquitylates the K48-type linkage of the ubiquitin chain of Nanog, stabilizing Nanog. USP21-mediated Nanog stabilization is enhanced in mouse ESCs and this stabilization is required to maintain the pluripotential state of the ESCs. Depletion of USP21 in mouse ESCs leads to Nanog degradation and ESC differentiation. Overall, our results demonstrate that USP21 maintains the stemness of mouse ESCs through deubiquitylating and stabilizing Nanog