Master of Science

thesisConcurrent programs are extremely important for efficiently programming future HPC systems. Large scientific programs may employ multiple processes or threads to run on HPC systems for days. Reliability is an essential requirement of existing concurrent programs. Therefore, verification of concurrent programs becomes increasingly important. Today we have two significant challenges in developing concurrent program verification tools: The first is scalability. Since new types of concurrent programs keep being created, verification tools need to scale to handle all these new types of programs. The second is providing formal coverage guarantee. Dynamic verification tools always face a huge schedule space. Both these capabilities must exist for testing programs that follow multiple concurrency models. Most current dynamic verification tools can only explore either thread level or process level schedules. Consequently, they fail to verify hybrid programs. Exploring mixed process and thread level schedules is not an ideal solution because the state space will grow exponentially in both levels. It is hard to systematically traverse these mixed schedules. Therefore, our approach is to determinize all concurrent APIs except one API whose schedules will then be explored. To improve search efficiency, we proposed a random-walk based heuristic algorithm. We observed many concurrent programs and concluded some common structures of them. Based on the existence of these structures, we can make dynamic verification tools focusing on specific regions and bypassing regions of less interest. We propose a random sampling of executions in the regions of less interest

Efficient search for inputs causing high floating-point errors

pre-printTools for floating-point error estimation are fundamental to program understanding and optimization. In this paper, we focus on tools for determining the input settings to a floating point routine that maximizes its result error. Such tools can help support activities such as precision allocation, performance optimization, and auto-tuning. We benchmark current abstraction-based precision analysis methods, and show that they often do not work at scale, or generate highly pessimistic error estimates, often caused by non-linear operators or complex input constraints that define the set of legal inputs. We show that while concrete-testing-based error estimation methods based on maintaining shadow values at higher precision can search out higher error-inducing inputs, suitable heuristic search guidance is key to finding higher errors. We develop a heuristic search algorithm called Binary Guided Random Testing (BGRT). In 45 of the 48 total benchmarks, including many real-world routines, BGRT returns higher guaranteed errors. We also evaluate BGRT against two other heuristic search methods called ILS and PSO, obtaining better results

Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1

Src-homology 2 domain-containing tyrosine phosphatase 2 promotes oral cancer invasion and metastasis

BACKGROUND: Tumor invasion and metastasis represent a major unsolved problem in cancer pathogenesis. Recent studies have indicated the involvement of Src-homology 2 domain-containing tyrosine phosphatase 2 (SHP2) in multiple malignancies; however, the role of SHP2 in oral cancer progression has yet to be elucidated. We propose that SHP2 is involved in the progression of oral cancer toward metastasis. METHODS: SHP2 expression was evaluated in paired oral cancer tissues by using immunohistochemical staining and real-time reverse transcription polymerase chain reaction. Isogenic highly invasive oral cancer cell lines from their respective low invasive parental lines were established using a Boyden chamber assay, and changes in the hallmarks of the epithelial-mesenchymal transition (EMT) were assessed to evaluate SHP2 function. SHP2 activity in oral cancer cells was reduced using si-RNA knockdown or enforced expression of a catalytically deficient mutant to analyze migratory and invasive ability in vitro and metastasis toward the lung in mice in vivo. RESULTS: We observed the significant upregulation of SHP2 in oral cancer tissues and cell lines. Following SHP2 knockdown, the oral cancer cells markedly attenuated migratory and invasion ability. We observed similar results in phosphatase-dead SHP2 C459S mutant expressing cells. Enhanced invasiveness was associated with significant upregulation of E-cadherin, vimentin, Snail/Twist1, and matrix metalloproteinase-2 in the highly invasive clones. In addition, we determined that SHP2 activity is required for the downregulation of phosphorylated ERK1/2, which modulates the downstream effectors, Snail and Twist1 at a transcript level. In lung tissue sections of mice, we observed that HSC3 tumors with SHP2 deletion exhibited significantly reduced metastatic capacity, compared with tumors administered control si-RNA. CONCLUSIONS: Our data suggest that SHP2 promotes the invasion and metastasis of oral cancer cells. These results provide a rationale for further investigating the effects of small-molecule SHP2 inhibitors on the progression of oral cancer, and indicate a previously unrecognized SHP2-ERK1/2-Snail/Twist1 pathway that is likely to play a crucial role in oral cancer invasion and metastasis

State diagram for packed granular particles under shear: two types of /quaking/ and "shear unjamming"

Understanding intermittency, an ubiquitous behavior in flows of packed grains, is pivotal for establishing the rheology of granular material. A straightforward explanation has been missing despite the long development of theories at different levels of abstraction. In this work, we propose the use of a Stribeck-Hertz model that starts with the classic Coulomb friction but also takes into account the tribology between particles, i.e. the reduction of friction coefficient with speed as is commonly observed. Our numerical studies reveal a state diagram covering a wide range of packing fractions, and produce the quaking intermittency in the mid-range of a dimensionless shear rate defined accordingly, in consistence with our recent experimental observation [Phys. Rev. Lett. 126.128001 (2021)]. Monitoring the change of mean contact number allows us to distinguish two types of quaking. Above the random-close-packing density, the quakes are exclusively of the first type, occurred with a sudden increase of the contact number. At lower packing fractions, the dominant quaking depends in part on the dimensionless shear rate. The second type of quaking is identified as the prelude for a granular packing to "unjam" upon increase of the dimensionless shear rate -- a phenomenon that occurs only when the essential tribology is taken into accoun