Dynamics of cell-type transition mediated by epigenetic modifications

Maintaining tissue homeostasis requires appropriate regulation of stem cell differentiation. The Waddington landscape posits that gene circuits in a cell form a potential landscape of different cell types, wherein cells follow attractors of the probability landscape to develop into distinct cell types. However, how adult stem cells achieve a delicate balance between self-renewal and differentiation remains unclear. We propose that random inheritance of epigenetic states plays a pivotal role in stem cell differentiation and present a hybrid model of stem cell differentiation induced by epigenetic modifications. Our comprehensive model integrates gene regulation networks, epigenetic state inheritance, and cell regeneration, encompassing multi-scale dynamics ranging from transcription regulation to cell population. Through model simulations, we demonstrate that random inheritance of epigenetic states during cell divisions can spontaneously induce cell differentiation, dedifferentiation, and transdifferentiation. Furthermore, we investigate the influences of interfering with epigenetic modifications and introducing additional transcription factors on the probabilities of dedifferentiation and transdifferentiation, revealing the underlying mechanism of cell reprogramming. This \textit{in silico} model provides valuable insights into the intricate mechanism governing stem cell differentiation and cell reprogramming and offers a promising path to enhance the field of regenerative medicine.Comment: 34 pages, 12 figure

A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.Comment: Long paper accepted at AAAI 202

Cost design for opportunistic multi-hop routing in Cognitive Radio networks

Abstract-Cognitive Radio (CR) is a revolutionary tech-nology with promising applications in military areas since it enables CR users in the field to dynamically access to the vacant licensed frequency bands if no primary users are present. In practice, multi-hop routing in CR networks presents a great challenge due to unreliable traditional links and time varying unlicensed CR links. To improve the performance of multi-hop routing, opportunistic routing (OR) has been proposed and investigated extensively. Instead of using a single next hop, OR forwards a packet to an ordered set of candidate nodes and one node is chosen to relay the packet towards the destination. Most OR protocols prioritize the candidates and make the selection based on the cost defined as expected transmission times (ETX). Actually, ETX, as well as other existing criteria, does not always lead to the best forwarder choice for OR in CR networks since it ignores numerous potential CR links. In this paper, we propose a novel cost criterion for oppor-tunistic multi-hop routing in CR networks, which leverages the unlicensed CR links to prioritize the candidate nodes and optimally selecting the forwarder. Simulation results show that our design efficiently decreases the number of transmissions, and etTectively increases the throughput for most node pairs when compared with OR and traditional single-path routing. I

Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting

Pre-trained language models (PLMs) have played an increasing role in multimedia research. In terms of vision-language (VL) tasks, they often serve as a language encoder and still require an additional fusion network for VL reasoning, resulting in excessive memory overhead. In this paper, we focus on exploring PLMs as a stand-alone model for VL reasoning tasks. Inspired by the recently popular prompt tuning, we first prove that the processed visual features can be also projected onto the semantic space of PLMs and act as prompt tokens to bridge the gap between single- and multi-modal learning. However, this solution exhibits obvious redundancy in visual information and model inference, and the placement of prompt tokens also greatly affects the final performance. Based on these observations, we further propose a novel transfer learning approach for PLMs, termed Dynamic Visual Prompting (DVP). Concretely, DVP first deploys a cross-attention module to obtain text-related and compact visual prompt tokens, thereby greatly reducing the input length of PLMs. To obtain the optimal placement, we also equip DVP with a reinforcement-learning based search algorithm, which can automatically merge DVP with PLMs for different VL tasks via a very short search process. In addition, we also experiment DVP with the recently popular adapter approach to keep the most parameters of PLMs intact when adapting to VL tasks, helping PLMs achieve a quick shift between single- and multi-modal tasks. We apply DVP to two representative PLMs, namely BERT and T5, and conduct extensive experiments on a set of VL reasoning benchmarks including VQA2.0, GQA and SNLIVE. The experimental results not only show the advantage of DVP on efficiency and performance, but also confirm its superiority in adapting pre-trained language models to VL tasks

Global hybrid simulations of soft X-ray emissions in the Earth’s magnetosheath

Earth’s magnetopause is a thin boundary separating the shocked solar wind plasma from the magnetospheric plasmas, and it is also the boundary of the solar wind energy transport to the magnetosphere. Soft X-ray imaging allows investigation of the large-scale magnetopause by providing a two-dimensional (2-D) global view from a satellite. By performing 3-D global hybrid-particle-in-cell (hybrid-PIC) simulations, we obtain soft X-ray images of Earth’s magnetopause under different solar wind conditions, such as different plasma densities and directions of the southward interplanetary magnetic field. In all cases, magnetic reconnection occurs at low latitude magnetopause. The soft X-ray images observed by a hypothetical satellite are shown, with all of the following identified: the boundary of the magnetopause, the cusps, and the magnetosheath. Local X-ray emissivity in the magnetosheath is characterized by large amplitude fluctuations (up to 160%); however, the maximum line-of-sight-integrated X-ray intensity matches the tangent directions of the magnetopause well, indicating that these fluctuations have limited impact on identifying the magnetopause boundary in the X-ray images. Moreover, the magnetopause boundary can be identified using multiple viewing geometries. We also find that solar wind conditions have little effect on the magnetopause identification. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission will provide X-ray images of the magnetopause for the first time, and our global hybrid-PIC simulation results can help better understand the 2-D X-ray images of the magnetopause from a 3-D perspective, with particle kinetic effects considered