Cooperative control of connected automated vehicles (CAVs) offers a promising solution for reducing traffic congestion and accidents. However, existing optimization-based and search-based methods for trajectory planning and vehicle scheduling struggle with real-time multi-vehicle control. This paper introduces a hybrid bi-level approach that nests optimization modelling within deep reinforcement learning (DRL) to jointly optimize vehicle sequences, lane selections, and trajectories, providing a rapid, safe, and high-quality solution to enhance traffic performance at multi-lane freeway merging sections. Specifically, we approach the problem of lane selection and vehicle sequencing for multiple vehicles as a multi-step decision-making process. At the upper level, we design a DRL agent with an attention-based encoder-decoder structure that auto-regressively constructs driving sequences and lane choices. It generates a probability matrix to select the next passing vehicle and target lane based on prior decisions at each step. The attention mechanism enables the centralized upper level to adapt to scenarios with varying vehicle counts without the need to retrain. At the lower level, we formulate a model predictive control (MPC) planner to generate safe trajectories. The resulting travel delay guides the upper-level DRL agent learning to maximize overall traffic efficiency. Moreover, we introduce a leader-and-lane-specific credit assignment mechanism that leverages domain knowledge to link each action with associated travel delays. This mechanism enables the agent to accurately recognize the impact of decisions on total delay, enhancing learning performance. Simulation results suggest that the proposed approach’s superior real-time performance and scalability from several to over a dozen vehicles, making it well-suited for practical automated merging tasks.

AutopilotingConnected and Autonomous Vehicles(CAVs) is an important application for mobile computing. A promising context to realize autopiloting CAVs is cooperative driving on dedicated highways. For such a context, an indispensable driving scenario isCooperative Lane Change(CLC). Due to the safety concerns of this driving scenario, a verifiably safe solution is needed (at least, the solution design should be formally provably safe). However, this demand is complicated by the inherently unreliable wireless communications between the CAVs. In this paper, we focus on the well-adoptedConstant Time Headway(CTH) safety rule. We propose a CLC protocol, and formally prove its guarantee of the CTH safety and liveness, even under arbitrary wireless packet losses. These theoretical claims are further confirmed by our simulations. The simulation results also show that our proposed protocol significantly improves lane change success rates (by$5.3\% \sim +\infty \%$) than other alternatives under adverse conditions. Furthermore, the sensitivity study results also show our protocol can tolerate reasonable disturbances.

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.

Intensive interactions among vehicles at freeway on-ramp merging areas lead to congestion and accidents. The emergence of connected automated vehicles (CAVs) has shown great potential to improve this issue. In this paper, a mixed integer nonlinear programming (MINLP) model is proposed and solved for the task of a cooperative merging of two traffic streams at a freeway on-ramp merging section. The proposed model simultaneously optimizes multiple vehicles’ trajectories and their merging sequence to improve traffic efficiency and ensure safety. Unlike conventional treatments, which match one mainline facilitating vehicle with one merging vehicle, the proposed model determines the optimal number of facilitating vehicles and which mainline vehicles should serve as the facilitating vehicles to cooperatively minimize disruption from ramps. The safety and feasibility of the planned vehicle trajectories are guaranteed at any time. We propose an integrated solution algorithm that incorporates an iterative linear programming method into a novel search process based on a necessary condition for optimality that we identify and prove. The algorithm is highly efficient because it enjoys a significantly reduced search space. The proposed approach, consisting of the MINLP model and the solution algorithm, is evaluated under different traffic demands and mainline-ramp demand ratios and real vehicle arrival patterns from the NGSIM dataset. The performance of the proposed method outperforms benchmark CAV control algorithms, and the computational efficiency is promising for real-time automated merging tasks.

We propose a simple and novel method to actively resolve the temporary congestion caused by a freeway mainline vehicle’s facilitating maneuver of creating a gap for on-ramp merging vehicles, under under-critical mainline conditions. We first present an analytical finding derived from the kinematic wave model with a triangular fundamental diagram. That is, when the prevailing mainline traffic is under-critical, the total delay of the mainline vehicles affected by the gap creation does not depend on the choice of speed by which the gap is created, namely the facilitating speed. This is because the recovery wave speed is constantly equal to the characteristic wave speed of the congestion regime,$-w$. In light of this observation, to improve mainline traffic efficiency, we propose the strategy of active congestion resolving, which features a recovery wave of a speed higher than$-w$. Characterizing CAVs’ car-following behaviors by Newell’s simplified car-following model, we analytically show that such a recovery wave can be achieved by properly modifying the values of the affected CAVs’ car-following characteristic parameters and adopting the modified values in a proper way during congestion resolving. Then, in the presence of this active congestion resolving strategy, an optimization program is formulated to seek an optimal facilitating speed that can balance between traffic efficiency and speed variation. Simulation experiments are conducted to validate the effectiveness of the proposed method.

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of natural killer (NK) and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for four-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

Based on dynamic research perspectives of time slicing, this paper shows an endeavor on mining dynamic features of the scientific teams. Traditionally the static method of network structure analysis can successfully be used to analyze the distribution of network resource structure. But it cannot be used to explore the dynamic features of research groups, because of its lacks on the influence of some variables in research activities. These variables include researchers, research hotspots and research funds, etc. which are all in varying state due to the changing world. This paper proposes a new approach to analyze the dynamic characteristics of scientific teams, by using time slicing incorporated with traditional static method. KP(core members Keep-Rate) is used as an index of the dynamic transitions of scientific teams in two sequential time slices, and an algorithm is proposed to identify the successor team(s).

The problem of discovering covert nodes in social network has been widely studied because of its tremendous number of applications in determining critical points in social network, such as detecting terrorist, recommending item for possible customer, finding the source of spreading gossip, etc. In this paper, we utilize game theory to solve this problem. Firstly, we propose the model which analyzes game in the influence transmission. Then we obtain each nodes contribution by calculating the nodes earning in game. The feasibility and effectiveness of our method were verified on a simulation dataset and a real dataset.

The decreasing cost of sequencing is leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. Global system-wide effects of variants in coding genes are particularly poorly understood. It is known that while variants in some genes can lead to diseases, complete disruption of other genes, called 'loss-of-function tolerant', is possible with no obvious effect. Here, we build a systems-based classifier to quantitatively estimate the global perturbation caused by deleterious mutations in each gene. We first survey the degree to which gene centrality in various individual networks and a unified 'Multinet' correlates with the tolerance to loss-of-function mutations and evolutionary conservation. We find that functionally significant and highly conserved genes tend to be more central in physical protein-protein and regulatory networks. However, this is not the case for metabolic pathways, where the highly central genes have more duplicated copies and are more tolerant to loss-of-function mutations. Integration of three-dimensional protein structures reveals that the correlation with centrality in the protein-protein interaction network is also seen in terms of the number of interaction interfaces used. Finally, combining all the network and evolutionary properties allows us to build a classifier distinguishing functionally essential and loss-of-function tolerant genes with higher accuracy (AUC = 0.91) than any individual property. Application of the classifier to the whole genome shows its strong potential for interpretation of variants involved in mendelian diseases and in complex disorders probed by genome-wide association studies.

Collaboration networks are among some of the social networks and offer us the opportunity to study the structure underlying the networks. In this paper, we utilize the social network analysis (SNA) framework to understand what characterizes the social structure of the CSCWD conference's paper co-authorship network. It can potentially provide us with an understanding of the individuals and the network. We consider two scientists to be connected if they have authored a paper together, system like the co-authorship network of the conference is inherently dynamic, and so we represent it as a time-varying graph. Each graph is a static cumulative network. We then give results for the average distance and the diameter they show that this network forms a “small world”. We then also give the clustering coefficient demonstrating the presence of clustering of the collaboration between the scientists, as well as the increasing proportion of the giant component, it can serve as an additional support that the community would work well if it is densely connected. Through those figures, we also want to find some potential actions that can be taken to further develop the conference and other similar conferences.