The interaction of tumor cells with their environment and particularly the immune system is increasingly recognized as a key factor of tumorigenesis and tumor progression. However, the expression levels of genes alone cannot capture directly the complexity of these interactions, and a more elaborated framework is necessary. We propose a new cell-cell interactions (CCIs) feature space based on scRNA-Seq data of multiple patient samples to classify cancers into subtypes at the patient level. Specifically, we transform gene expression profiles into a redefined feature space that takes into account interactions between ligands and receptors across all possible pairs of cell types for each patient. To handle the ultrahigh dimensionality arising from the hundreds of thousands of CCIs, we propose an ensemble feature selection algorithm to prioritize the most discriminatory CCIs. Our machine learning pipeline identifies a set of CCIs that accurately classifies cancer samples into subtypes. We apply our computational framework, named pCCI, to the Pancreatic Ductal Adenocarcinoma and Glioblastoma Multiforme subtypes classification, achieving high classification performance. Our work provides compelling evidence that the CCI space can successfully reveal differences in the microenvironment of different cancer subtypes and identify novel pharmacological targets.

We propose an analytical framework for probabilistic timing analysis of the event-triggered Dynamic segment of the FlexRay communication protocol. Specifically, our framework computes the Deadline Miss Ratio of each message. The core problem is formulated as a Mixed Integer Linear Program (MILP). Given the intractability of the problem, we also propose several techniques that help to mitigate the running times of our tool. This includes the re-engineering of the problem to run it on GPUs as well as reformulating the MILP itself. Most importantly, we also show how our framework can handle correlations between the queuing events of messages. This is challenging because one cannot apply the convolution operator in the same way as in the case of independent queuing events.

In this paper we address the problem of computing the probability response time distribution of periodic tasks scheduled on a uniprocessor systems. Our framework assumes an arbitrary non-idling preemptive scheduling policy that may be either a fixed-priority scheduler (such as Rate Monotonic - RM) or a dynamic-priority scheduler (such as Earliest Deadline First - EDF). At the same time, our framework can handle arbitrary execution time distributions arbitrary deadlines providing numerically accurate results. We also show how the framework can be extended to compute the correlation coefficients between the response times of different jobs by performing the joint analysis.

We consider a real-time system of multiple tasks, each task having a plant to control. The overall quadratic control cost is to be optimized. We exploit the periodicity of the task response time, which corresponds to a periodic delay pattern in the feedback control loop. Perturbed periods are used as a tool to find a finite hyper period. We present an analytical procedure to design a periodic linear-quadratic-Gaussian (LQG) controller for tasks with fixed execution times as well as a numerical solution to the periodic -- stochastic LQG problem for tasks with variable execution times. The controllers are evaluated using simulations in real-time scheduling and control co-design examples.

We propose an analytical framework for probabilistic timing analysis of the event-triggered Dynamic segment of the Flex Ray communication protocol. Specifically, our framework computes the Deadline Miss Ratios of each message. The core problem is formulated as a Mixed Integer Linear Program (MILP). Given the intractability of the problem, we also propose several techniques that help to mitigate the running times of our tool. This includes the re-engineering of the problem to run it on GPUs as well as re-formulating the MILP itself.

FlexRay, developed by a consortium of over hundred automotive companies, is a real-time communication protocol for automotive networks. In this paper, we propose a new approach for timing analysis of the event-triggered component of FlexRay, known as the dynamic segment. Our technique accounts for the fact that the FlexRay standard allows slot multiplexing, i.e., the same priority can be assigned to more than one message. Existing techniques have either ignored slot multiplexing in their analysis or made simplifying assumptions that severely limit achieving high bandwidth utilization. Moreover, we show that our technique returns less pessimistic results compared to previously known techniques even in the case where slot multiplexing is ignored.

Application-specific instruction-set processors (ASIPs) allow the designer to extend the instruction set of the base processor with selected custom instructions to tailor-fit the application. In this paper, with the help of a motivational example, we first demonstrate that different custom instructions are vulnerable to faults with varying probabilities. This shows that by ignoring the vulnerability to faults, traditional methods of instruction set customization can provide no guarantees on the reliability of the system. Apart from such inherent disparity in error vulnerability across custom instructions, each custom instruction can have multiple implementation choices corresponding to varying hardened levels. Hardening reduces the vulnerability to errors but this comes at the overhead of area costs and reduced performance gain. In this paper, we propose a framework to select custom instructions and their respective hardening levels such that reliability is optimized while the performance gain is satisfied and area costs are met as well. Our framework is based on a novel analytical method to compute the overall system reliability based on the probability of failure of individual instructions. Wide range of experiments that were conducted illustrate how our tool navigates the design space to reveal interesting tradeoffs.

FlexRay is gaining wide acceptance as the next generation bus protocol for automotive networks. This has led to tremendous research interest in techniques for scheduling signals, which are generated by real-time applications, on the FlexRay bus. Signals are first packed together into frames at the application-level and the frames are then transmitted over the bus. To ensure reliability of frames in the presence of faults, frames must be retransmitted over the bus but this comes at the cost of higher bandwidth utilization. To address this issue, in this paper, we propose a novel frame packing method for FlexRay bus. Our method computes the required number of retransmissions of frames that ensures the specified reliability goal. The proposed frame packing method also ensures that none of the signals violates its deadline and that the desired reliability goal for guaranteeing fault-tolerance is met at the minimum bandwidth cost. Extensive experiments on synthetic as well as a industrial case study demonstrate the benefits of our method.

FlexRay has been widely accepted as the next generation bus protocol for automotive networks. This has led to tremendous research interest in techniques for scheduling messages on the FlexRay bus, in order to meet the hard real-time deadlines of the automotive applications. However, these techniques do not generate reliable schedules in the sense that they do not provide any performance guarantees in the presence of faults. In this work, we will present a framework for generating fault-tolerant message schedules on the time-triggered (static) segment of the FlexRay bus. We provide formal guarantees that the generated fault-tolerant schedules achieve the reliability goal even in the presence of transient and intermittent faults. Moreover, our technique minimizes the required number of re-transmissions of the messages in order to achieve such fault tolerant schedules, thereby, optimizing the bandwidth utilization. Towards this, we formulate the optimization problem in Constraint Logic Programming (CLP), which returns optimal results. However, this procedure is computationally intensive and hence, we also propose an efficient heuristic. The heuristic guarantees the reliability of the constructed schedules but might be sub-optimal with respect to bandwidth utilization. Extensive experiments run on synthetic test cases and real-life case studies illustrate that the heuristic performs extremely well. The experiments also establish that our heuristic scales significantly better than the CLP formulation.