BACKGROUND: MicroRNAs (miRNAs) function in post-transcriptional regulation of gene expression by binding to target messenger RNAs (mRNAs). Because of the key part that miRNAs play, understanding the correct regulatory role of miRNAs in diverse patho-physiological conditions is of great interest. Although it is known that miRNAs act combinatorially to regulate genes, precise identification of miRNA-gene interactions and their specific functional roles in regulatory comodules remains a challenge. We developed THEIA, an effective method for simultaneously predicting miRNA-gene interactions and regulatory comodules, which group functionally related miRNAs and genes via non-negative matrix factorization (NMF). RESULTS: We apply THEIA to RNA sequencing data from breast invasive carcinoma samples and demonstrate its effectiveness in discovering biologically significant regulatory comodules that are significantly enriched in spatial miRNA clusters, biological pathways, and various cancers. CONCLUSIONS: THEIA is a theoretically rigorous optimization algorithm that simultaneously predicts the strength and direction (i.e., up-regulation or down-regulation) of the effect of modules of miRNAs on a gene. We posit that if THEIA is capable of recovering known clusters of genes and miRNA, then the clusters found by our method not previously identified by literature are also likely to have biological significance. We believe that these novel regulatory comodules found by our method will be a springboard for further research into the specific functional roles of these new functional ensembles of miRNAs and genes,especially those related to diseases like breast cancer.

Stress testing is an important task in software testing, which examines the behavior of a program under a heavy load. Symbolic execution is a useful tool to find out the worst-case input values for the stress testing. However, symbolic execution does not scale to a large program, since the number of paths to search grows exponentially with an input size. So far, such a scalability issue has been mostly managed by pruning out unpromising paths in the middle of searching based on heuristics, but this kind of work easily eliminates the true worst case as well, providing sub-optimal one only. Another way to achieve scalability is to learn a branching policy of worst-case complexity from small scale tests and apply it to a large scale. However, use cases of such a method are restricted to programs whose worst-case branching policy has a simple pattern. To address such limitations, we propose PySE that uses symbolic execution to collect the behaviors of a given branching policy, and updates the policy using a reinforcement learning approach through multiple executions. PySE's branching policy keeps evolving in a way that the length of an execution path increases in the long term, and ultimately reaches the worst-case complexity. PySE can also learn the worst-case branching policy of a complex or irregular pattern, using an artificial neural network in a fully automatic way. Experiment results demonstrate that PySE can effectively find a path of worst-case complexity for various Python benchmark programs and scales.

An important part of software testing is generation of worst-case test inputs, which exercise a program under extreme loads. For such a task, symbolic execution is a useful tool with its capability to reason about all possible execution paths of a program, including the one with the worst case behavior. However, symbolic execution suffers from the path explosion problem and frequent calls to a constraint solver, which make it impractical to be used at a large scale. To address the issue, this paper presents XSTRESSOR that is able to generate test inputs that can run specific loops in a program with the worst-case complexity in a large scale. XSTRESSOR synthetically generates the path condition for the large-scale, worst-case execution from a predictive model that is built from a set of small scale tests. XSTRESSOR avoids the scaling problem of prior techniques by limiting full-blown symbolic execution and run-time calls to constraint solver to small scale tests only. We evaluate XSTRESSOR against WISE and SPF-WCA, the most closely related tools to generate worst-case test inputs. Results show that XSTRESSOR can generate the test inputs faster than WISE and SPF-WCA, and also scale to much larger input sizes.

Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.

An emerging solution to privacy issues in smart grids is battery-based load hiding (BLH) that uses a rechargeable battery to decouple the meter readings from user activities. However, existing BLH algorithms have two significant limitations: (1) Most of them focus on flattening high-frequency variation of usage profile only, thereby still revealing a low-frequency shape, (2) Otherwise, they assume to know a statistical model of usage pattern. To overcome these limitations, we propose a new BLH algorithm, named RL-BLH. The RL-BLH hides both low-frequency and high-frequency usage patterns by shaping the meter readings to rectangular pulses. The RL-BLH learns a decision policy for choosing pulse magnitudes on the fly without prior knowledge of usage pattern. The decision policy is designed to charge and discharge the battery in the optimal way to maximize cost savings. We also provide heuristics to shorten learning time and improve cost savings.

Embedded systems are ubiquitous in every aspect of modern life. As the Internet of Thing expands, our dependence on these systems increases. Many of these interconnected systems are and will be low cost bare-metal systems, executing without an operating system. Bare-metal systems rarely employ any security protection mechanisms and their development assumptions (unrestricted access to all memory and instructions), and constraints(runtime, energy, and memory) makes applying protections challenging. To address these challenges we present EPOXY, an LLVM-based embedded compiler. We apply a novel technique, called privilege overlaying, wherein operations requiring privileged execution are identified and only these operations execute in privileged mode. This provides the foundation on which code-integrity, adapted control-flow hijacking defenses, and protections for sensitive IO are applied. We also design fine-grained randomization schemes, that work within the constraints of bare-metal systems to provide further protection against control-flow and data corruption attacks. These defenses prevent code injection attacks and ROP attacks from scaling across large sets of devices. We evaluate the performance of our combined defense mechanisms for a suite of 75 benchmarks and 3 real-world IoT applications. Our results for the application case studies show that EPOXY has, on average, a 1.8% increase in execution time and a 0.5% increase in energy usage.

Fast allocation of large blocks of physically contiguous memory plays a crucial role to boost the performance of multimedia applications in modern memory-constrained portable devices, such as smartphones, tablets, etc. Existing systems have addressed this issue by provisioning a large statically reserved memory area (SRA) in which only dedicated applications can allocate pages. However, this in turn degrades the performance of applications that are prohibited to utilize the SRA due to the reduced available memory pool. To overcome this drawback while maintaining the benefits of the SRA, we propose a new memory management scheme that uses a special memory region, called page-cache-preferred area (PCPA), in concert with a quick memory reclaiming algorithm. The key of the proposed scheme is to enhance the memory utilization efficiency by enabling to allocate page-cached pages of all applications in the PCPA until predetermined applications require to allocate big chunks of contiguous memory. At this point, clean page-cached pages in the PCPA are rapidly evicted without write-back to a secondary storage. Compared to the SRA scheme, experimental results show that the average launch time of real-world applications and the execution time of I/O-intensive benchmarks are reduced by 9.2% and 24.7%, respectively.

A specific application for wastewater monitoring and actuation, called CSOnet, deployed city-wide in a mid-sized US city, South Bend, Indiana, posed some challenges to a time synchronization protocol. The nodes in CSOnet have a low duty cycle (2% in current deployment) and use an external clock, called the Real Time Clock (RTC), for triggering the sleep and the wake-up. The RTC has a very low drift (2 ppm) over the wide range of temperature fluctuations that the CSOnet nodes have, while having a low power consumption (0.66 mW). However, these clocks will still have to be synchronized occasionally during the long lifetime of the CSOnet nodes and this was the problem we confronted with our time synchronization protocol. The RTC to fit within the power and the cost constraints makes the tradeoff of having a coarse time granularity of only 1 second. Therefore, it is not sufficient to synchronize the RTC itself---that would mean a synchronization error of up to 1 second would be possible even with a perfect synchronization protocol. This would be unacceptable for the low duty cycle operation---each node stays awake for only 6 seconds in a 5 minute time window. This was the first of three challenges for time synchronization. The second challenge is that the synchronization has to be extremely fast since ideally the entire network should be synchronized during the 6 second wake-up period. Third, the long range radio used for the metropolitan-scale CSOnet does not make its radio stack software available, as is seen with several other radios for long-range ISM band RF communication. Therefore, a common technique for time synchronization---MAC layer time-stamping---cannot be used. Additionally, MAC layer time-stamping is known to be problematic with high speed radios (even at 250 kbps).We solve these challenges and design a synchronization protocol called Harmonia. It has three design innovations. First, it uses the finely granular microcontroller clock to achieve synchronization of the RTC, such that the synchronization error, despite the coarse granularity of the RTC, is in the microsecond range. Second, Harmonia pipelines the synchronization messages through the network resulting in fast synchronization of the entire network. Third, Harmonia provides failure handling for transient node and link failures such that the network is not overburdened with synchronization messages and the recovery is done locally. We evaluate Harmonia on CSOnet nodes and compare the two metrics of synchronization error and synchronization speed with FTSP. It performs slightly worse in the former and significantly better in the latter.