Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness and Challenges in Generating Concurrent Tests for Thread-Safe Classes

Developing correct and efficient concurrent programs is difficult and error-prone, due to the complexity of thread synchronization. Often, developers alleviate such problem by relying on thread-safe classes, which encapsulate most synchronization-related challenges. Thus, testing such classes is crucial to ensure the reliability of the concurrency aspects of programs. Some recent techniques and corresponding tools tackle the problem of testing thread-safe classes by automatically generating concurrent tests. In this paper, we present a comprehensive study of the state-of-the-art techniques and an independent empirical evaluation of the publicly available tools. We conducted the study by executing all tools on the JaConTeBe benchmark that contains 47 well-documented concurrency faults. Our results show that 8 out of 47 faults (17%) were detected by at least one tool. By studying the issues of the tools and the generated tests, we derive insights to guide future research on improving the effectiveness of automated concurrent test generation.
Genetic Variant rs755622 Regulates Expression of the Multiple Sclerosis Severity Modifier D-Dopachrome Tautomerase in a Sex-Specific Way

Multiple sclerosis (MS) is a sex-specific autoimmune disease involving central nervous system. Previous studies determined that macrophage migration inhibitory factor (MIF) and its homologue D-dopachrome tautomerase (DDT) sex-specifically affect MS progression. Moreover, other studies reported that rs755622 polymorphism in promoter region of MIF gene is associated with risk of MS and affects the promoter activity to regulate MIF expression in a sex-specific way. Given that MIF and DDT share a part of promoter sequence, we surmise that rs755622 can also regulate DDT expression in a sex-specific way. However, this has not yet been studied. Here, we used five large-scale expression quantitative trait loci (eQTLs) and two RNA-seq datasets from brain and blood to assess the potential influence of rs755622 variant on expression of DDT in different genders by the linear regression and differential expression analysis. The results show that the minor allele frequency of rs755622 and expression of DDT are significantly increased in males for MS subjects and this minor allele variant can significantly upregulate DDT expression for males but not females, which suggests that the regulation of DDT expression level by rs755622 can affect MS progression in males. These findings further support and expand conclusions of previous studies and may help to better understand the mechanisms of MS.
Directed evolution of Escherichia coli with lower-than-natural plasmid mutation rates

Unwanted evolution of designed DNA sequences limits metabolic and genome engineering efforts. Engineered functions that are burdensome to host cells and slow their replication are rapidly inactivated by mutations, and unplanned mutations with unpredictable effects often accumulate alongside designed changes in large-scale genome editing projects. We developed a directed evolution strategy, Periodic Reselection for Evolutionarily Reliable Variants (PResERV), to discover mutations that prolong the function of a burdensome DNA sequence in an engineered organism. Here, we used PResERV to isolate E. coli cells that replicate ColE1 plasmids with higher fidelity. We found mutations in DNA polymerases I and IV and in RNase E that reduce plasmid mutation rates by 6-to 30-fold. The PResERV method implicitly selects to maintain the growth rate of host cells, and high plasmid copy numbers and gene expression levels are maintained in some of the evolved E. coli strains, indicating that it is possible to improve the genetic stability of cellular chassis without encountering trade-offs in other desirable performance characteristics. Utilizing these new antimutator E. coli and applying PResERV to other organisms in the future promises to prevent evolutionary failures and unpredictability to provide a more stable genetic foundation for synthetic biology.
Test Case Prioritization Based on Method Call Sequences

Test case prioritization is widely used in testing with the purpose of detecting faults as early as possible. Most existing techniques exploit coverage to prioritize test cases based on the hypothesis that a test case with higher coverage is more likely to catch bugs. Statement coverage and function coverage are the two widely used coverage granularity. The former typically achieves better test case prioritization in terms of fault detection capability, while the latter is more efficient because it incurs less overhead. In this paper we argue that static information such as statement and function coverage may not be the best criteria for guiding dynamic executions. Executions that cover the same set of statements /functions can may exhibit very different behavior. Therefore, the abstraction that reduces program behavior to statement/function coverage can be too simplistic to predicate fault detection capability. We propose a new approach that exploits function call sequences to prioritize test cases. This is based on the observation that the function call sequences rather than the set of executed functions is a better indicator of program behavior. Test cases that reveal unique function call sequences may have better chance to encounter faults. We choose function instead of statement sequences due to the consideration of efficiency. We have developed and implemented a new prioritization strategy AGC (Additional Greedy method Call sequence), that exploit function call sequences. We compare AGC against existing test case prioritization techniques on eight real-world open source Java projects. Our experiments show that our approach outperforms existing techniques on large programs (but not on small programs) in terms of bug detection capability. The performance shows a growth trend when the size of program increases.
An extracellular matrix fragment drives epithelial remodeling and airway hyperresponsiveness

The matrikine PGP is a mediator of pathological epithelial remodeling in asthma. Extracellular matrix extends its influence in asthma The interplay between immune cells and smooth muscle cells in asthma pathogenesis and response to treatment is under increasing scrutiny. Patel et al. focused on how leukotrienes can modulate immune inflammation and airway hypersensitivity. Mice lacking leukotriene A4 hydrolase had exaggerated epithelial remodeling but dampened immune responses in a house dust mite model of asthma. This was due to release of Pro-Gly-Pro (PGP), a component of the extracellular matrix, which was also elevated in the sputum from severe asthmatics in two clinical cohorts. Their results showcase how the extracellular matrix is a dynamic player in disease and reveal a mediator to be targeted in asthma therapy. It is anticipated that bioactive fragments of the extracellular matrix (matrikines) can influence the development and progression of chronic diseases. The enzyme leukotriene A4 hydrolase (LTA4H) mediates opposing proinflammatory and anti-inflammatory activities, through the generation of leukotriene B4 (LTB4) and degradation of proneutrophilic matrikine Pro-Gly-Pro (PGP), respectively. We show that abrogation of LTB4 signaling ameliorated inflammation and airway hyperresponsiveness (AHR) in a murine asthma model, yet global loss of LTA4H exacerbated AHR, despite the absence of LTB4. This exacerbated AHR was attributable to a neutrophil-independent capacity of PGP to promote pathological airway epithelial remodeling. Thus, we demonstrate a disconnect between airway inflammation and AHR and the ability of a matrikine to promote an epithelial remodeling phenotype that negatively affects lung function. Subsequently, we show that substantial quantities of PGP are detectable in the sputum of moderate-severe asthmatics in two distinct cohorts of patients. These studies have implications for our understanding of remodeling phenotypes in asthma and may rationalize the failure of LTA4H inhibitors in the clinic.
Comparative oncology: The paradigmatic example of canine and human mast cell neoplasms

In humans, advanced mast cell (MC) neoplasms are rare malignancies with a poor prognosis. Only a few preclinical models are available, and current treatment options are limited. In dogs, MC neoplasms are the most frequent malignant skin tumours. Unlike low‐grade MC neoplasms, high‐grade MC disorders usually have a poor prognosis with short survival. In both species, neoplastic MCs display activating KIT mutations, which are considered to contribute to disease evolution. Therefore, tyrosine kinase inhibitors against KIT have been developed. Unfortunately, clinical responses are unpredictable and often transient, which remains a clinical challenge in both species. Therefore, current efforts focus on the development of new improved treatment strategies. The field of comparative oncology may assist in these efforts and accelerate human and canine research regarding diagnosis, prognostication, and novel therapies. In this article, we review the current status of comparative oncology approaches and perspectives in the field of MC neoplasms.
Adjunctive Treatments for the Prevention of Chemotherapy- and Radiotherapy-Induced Mucositis

Background: Chemoradiotherapy-associated mucositis can manifest as pain, inflammation, dysphagia, diarrhea, weight loss, rectal bleeding, and infection. Mucositis is a major dose-limiting side effect of chemotherapy, affecting nutritional intake and oral and intestinal function. Despite several interventions being available, there is a need for safe and effective preventative and treatment options for treatment-induced mucositis. The goals of this review are to discuss interventions based on foods and natural products and present the research to date. Methods: A narrative literature review identified 60 clinical studies examining various nutritional compounds and 20 examining probiotics. 9 studies on probiotics for the prevention of diarrhea were also assessed on methodological quality and limitations identified. Results: Several compounds have been posited as useful adjuvants for cancer treatment–related mucositis. Probiotics demonstrate efficacy for the prevention and treatment of chemoradiotherapy-induced gastrointestinal toxicity without significant side effects. Glutamine and activated charcoal were reported to reduce chemotherapy-induced diarrhea but not radiation-induced intestinal mucositis. Honey has been reported to decrease treatment interruptions, weight loss, and delays the onset of oral mucositis. Zinc, glutamine, and topical vitamin E were demonstrated efficacy for oral mucositis. Conclusion: There is plausible clinical evidence for the administration of several adjunctive treatments for the prevention and treatment of mucositis. Probiotics were reported to reduce the burden of intestinal mucositis and treatment-induced diarrhea. Activated charcoal and glutamine are beneficial for chemotherapy-induced diarrhea, whereas the administration of honey, zinc, and glutamine reduce the risk of developing oral mucositis during chemotherapy or radiotherapy.
High‐Salt Exposure During Perinatal Development Enhances Stress Sensitivity

Excess consumption of dietary sodium during pregnancy has been shown to impair offspring cardiovascular function and enhance salt preference in adulthood, but little is known regarding the long‐term impact of this nutritional surplus on offspring brain morphology and behavior. Using a combination of cellular and behavioral approaches, we examined the impact of maternal salt intake during the perinatal period on structural plasticity in the prefrontal cortex (PFC) and nucleus accumbens (NAc) in weanling and adult offspring as well as reward‐ and stress‐driven behaviors in adult offspring. We found that weanling rats born to 4% NaCl‐fed dams exhibited an increase and decrease in thin spine density in the infralimbic PFC (IL‐PFC) and prelimbic PFC (PL‐PFC), respectively, as well as an increase in mushroom spine density in the NAc shell, compared to 1% NaCl‐fed controls. Structural changes in the IL‐PFC and NAc shell persisted into adulthood, the latter of which is a phenotype that has been observed in rats exposed to early life stress. There was no effect of maternal salt intake on reward‐driven behaviors, including sucrose preference, conditioned place preference (CPP) for cocaine, and forced swim stress (FSS)‐induced reinstatement of cocaine‐induced CPP. However, rats born to high‐salt fed dams spent less time swimming in the FSS and displayed heightened plasma CORT levels in response to the FSS compared to controls, suggesting that early salt exposure increases stress sensitivity. Overall, our results suggest that perinatal salt exposure evokes lasting impacts on offspring physiology and behavior.
Mode selection schemes for unicasting device‐to‐device communications supported by network coding

Device‐to‐device (D2D) communication in a cellular spectrum increases the spectral and energy efficiency of local communication sessions, while also taking advantage of accessing licensed spectrum and higher transmit power levels than when using unlicensed bands. To realize the potential benefits of D2D communications, appropriate mode selection algorithms that select between the cellular and D2D communication modes must be designed. On the other hand, physical‐layer network coding (NWC) at a cellular base station—which can be used without D2D capability—can also improve the spectral efficiency of a cellular network that carries local traffic. In this paper, we ask whether cellular networks should support D2D communications, physical‐layer NWC, or both. To this end, we study the performance of mode selection algorithms that can be used in cellular networks that use physical‐layer NWC and support D2D communications. We find that the joint application of D2D communication and NWC scheme yields additional gains compared with a network that implements only one of these schemes, provided that the network implements proper mode selection and resource allocation algorithms. We propose 2 mode selection schemes that aim to achieve high signal‐to‐interference‐plus‐noise ratio and spectral efficiency, respectively, and take into account the NWC and D2D capabilities of the network.
VolCE: An Efficient Tool for Solving #SMT(LA) Problems

We present VolCE, a tool for computing and estimating the size of the solution space of SMT(LA) constraints. VolCE supports the SMT-LIB format. It integrates SMT solving with volume computation/estimation and integer solution counting for convex polytopes. Several effective techniques are adopted, which enable the tool to deal with high-dimensional problem instances efficiently.
A Characterwise Windowed Approach to Hebrew Morphological Segmentation

This paper presents a novel approach to the segmentation of orthographic word forms in contemporary Hebrew, focusing purely on splitting without carrying out morphological analysis or disambiguation. Casting the analysis task as character-wise binary classification and using adjacent character and word-based lexicon-lookup features, this approach achieves over 98% accuracy on the benchmark SPMRL shared task data for Hebrew, and 97% accuracy on a new out of domain Wikipedia dataset, an improvement of ≈4% and 5% over previous state of the art performance.
ScalaExtrap: Trace-based communication extrapolation for SPMD programs

Performance modeling for scientific applications is important for assessing potential application performance and systems procurement in high-performance computing (HPC). Recent progress on communication tracing opens up novel opportunities for communication modeling due to its lossless yet scalable trace collection. Estimating the impact of scaling on communication efficiency still remains nontrivial due to execution-time variations and exposure to hardware and software artifacts. This work contributes a fundamentally novel modeling scheme. We synthetically generate the application trace for large numbers of nodes via extrapolation from a set of smaller traces. We devise an innovative approach for topology extrapolation of single program, multiple data (SPMD) codes with stencil or mesh communication. Experimental results show that the extrapolated traces precisely reflect the communication behavior and the performance characteristics at the target scale for both strong and weak scaling applications. The extrapolated trace can subsequently be (a) replayed to assess communication requirements before porting an application, (b) transformed to autogenerate communication benchmarks for various target platforms, and (c) analyzed to detect communication inefficiencies and scalability limitations. To the best of our knowledge, rapidly obtaining the communication behavior of parallel applications at arbitrary scale with the availability of timed replay, yet without actual execution of the application, at this scale, is without precedence and has the potential to enable otherwise infeasible system simulation at the exascale level.
Don't Use Large Mini-Batches, Use Local SGD

Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks. Drastic increases in the mini-batch sizes have lead to key efficiency and scalability gains in recent years. However, progress faces a major roadblock, as models trained with large batches often do not generalize well, i.e. they do not show good accuracy on new data. As a remedy, we propose a \emph{post-local} SGD and show that it significantly improves the generalization performance compared to large-batch training on standard benchmarks while enjoying the same efficiency (time-to-accuracy) and scalability. We further provide an extensive study of the communication efficiency vs. performance trade-offs associated with a host of \emph{local SGD} variants.
Evidence for Reduced Autobiographical Memory Episodic Specificity in Cognitively Normal Middle-Aged and Older Individuals at Increased Risk for Alzheimer’s Disease Dementia

Abstract Objectives: Alzheimer’s disease (AD) typically eludes clinical detection for years, if not decades. The identification of subtle cognitive decline associated with preclinical AD would not only advance understanding of the disease, but also provide clinical targets to assess preventative and early intervention treatments. Disrupted retrieval of detailed episodic autobiographical memories may be a sensitive indicator of subtle cognitive decline, because this type of memory taxes a core neural network affected by preclinical AD neuropathology. Methods: To begin to address this idea, we assessed the episodic specificity of autobiographical memories retrieved by cognitively normal middle-aged and older individuals who are carriers of the apolipoprotein E ε4 allele – a population at increased risk for subtle cognitive decline related to neuropathological risk factors for AD. We compared the ε4 carriers to non-carriers of ε4 similar in age, education, and gender. Results: The ε4 carriers did not perform worse than the non-carriers on a comprehensive battery of neuropsychological tests. In contrast, as a group, the ε4 carriers generated autobiographical memories that were reduced in “internal” or episodic details relative to non-carriers. Conclusions: These findings support the notion that reduced autobiographical episodic detail generation may be a marker of subtle cognitive decline associated with AD. (JINS, 2018, 24, 1073–1183)
The Research of Chinese Word Segmentation Disambiguation Dased on Context Information

Chinese segmentation technology is a fundamental issue in the field of Natural Language Processing, and ambiguity segmentation is one of its difficulties. Therefore, this paper integrates contextual information such as word length, part of speech and semantic similarity to simulate the process of human thinking and disambiguate the context. By improving the TextRank graph model algorithm for keyword extraction, and then using a disambiguation algorithm based on semantic relevance, a Chinese word segmentation disambiguation model based on context information was proposed to obtain the correct segmentation results of ambiguity. The experimental results show that the precision of eliminating ambiguity by disambiguation model is better than that of the disambiguation field, which proves the effectiveness of the method.
A Note on Inexact Condition for Cubic Regularized Newton's Method

This note considers the inexact cubic-regularized Newton's method (CR), which has been shown in \cite{Cartis2011a} to achieve the same order-level convergence rate to a secondary stationary point as the exact CR \citep{Nesterov2006}. However, the inexactness condition in \cite{Cartis2011a} is not implementable due to its dependence on future iterates variable. This note fixes such an issue by proving the same convergence rate for nonconvex optimization under an inexact adaptive condition that depends on only the current iterate. Our proof controls the sufficient decrease of the function value over the total iterations rather than each iteration as used in the previous studies, which can be of independent interest in other contexts.
DeepRoad: GAN-Based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems

While Deep Neural Networks (DNNs) have established the fundamentals of image-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To address the safety issues in autonomous driving systems, a recent set of testing techniques have been designed to automatically generate artificial driving scenes to enrich test suite, e.g., generating new input images transformed from the original ones. However, these techniques are insufficient due to two limitations: first, many such synthetic images often lack diversity of driving scenes, and hence compromise the resulting efficacy and reliability. Second, for machine-learning-based systems, a mismatch between training and application domain can dramatically degrade system accuracy, such that it is necessary to validate inputs for improving system robustness. In this paper, we propose DeepRoad, an unsupervised DNN-based framework for automatically testing the consistency of DNN-based autonomous driving systems and online validation. First, DeepRoad automatically synthesizes large amounts of diverse driving scenes without using image transformation rules (e.g. scale, shear and rotation). In particular, DeepRoad is able to produce driving scenes with various weather conditions (including those with rather extreme conditions) by applying Generative Adversarial Networks (GANs) along with the corresponding real-world weather scenes. Second, DeepRoad utilizes metamorphic testing techniques to check the consistency of such systems using synthetic images. Third, DeepRoad validates input images for DNN-based systems by measuring the distance of the input and training images using their VGGNet features. We implement DeepRoad to test three well-recognized DNN-based autonomous driving systems in Udacity self-driving car challenge. The experimental results demonstrate that DeepRoad can detect thousands of inconsistent behaviors for these systems, and effectively validate input images to potentially enhance the system robustness as well.