Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Model-Checkable Needles in Large Source Code Haystacks: Modular Bug-Finding via Static Analysis and Dynamic Invariant Discovery

In this paper, we present a novel marriage of static and dynamic analysis. Given a large code base with many functions and a mature test suite, we propose using static analysis to find functions 1) with assertions or other evident correctness properties (e.g., array bounds requirements or pointer access) and 2) with simple enough control flow and data use to be amenable to predicate-abstraction based or bounded model checking without human intervention. Because most such functions in realistic software systems in fact rely on many input preconditions not specified by the language's type system (or annotated in any way), we propose using dynamically discovered invariants based on a program's test suite to characterize likely preconditions, in order to reduce the problem of false positives. While providing little in the way of verification, this approach may provide an additional quick and highly scalable bug-finding method for programs that are usually considered "too large to model check. " We present a simple example showing that the technique can be useful for a more typically "model-checkable" code base, even in the presence of a poorly designed test suite and bad invariants.
Index interpolation: an approach to subsequence matching supporting normalization transform in time-series databases

In this paper, w epropose a subsequence matching algorithm that supports normalization transform in timeseries databases. Normalization transform enables nding sequences with similar uctuation patterns although they are not close to each other before the normalization transform. Application of the existing whole matching algorithm supporting normalization transform to the subsequence matching is feasible, but requires an index for ev ery possible length of the query sequence causing serious overhead on both storage space and update time. The proposed algorithm generates indexes only for a small number of di erent lengths of query sequences. F or subsequence matching it selects the most appropriate index among them. We can obtain better searc h performance by using more indexes. We call our approach index interp olation. We formally pro ve that the proposed algorithm does not cause false dismissal. F or performance evaluation, we have conducted experiments using the indexes for only ve di erent lengths out of the lengths 256 512 of the query sequence. The results show that the proposed algorithm outperforms the sequential scan by up to 14.6 times on the average when the selectivity of the query is 10 .
A Cucumber DELLA Homolog CsGAIP May Inhibit Staminate Development through Transcriptional Repression of B Class Floral Homeotic Genes

In hermaphroditic Arabidopsis, the phytohormone gibberellin (GA) stimulates stamen development by opposing the DELLA repression of B and C classes of floral homeotic genes. GA can promote male flower formation in cucumber (Cucumis sativus L.), a typical monoecious vegetable with unisexual flowers, and the molecular mechanism remains unknown. Here we characterized a DELLA homolog CsGAIP in cucumber, and we found that CsGAIP is highly expressed in stem and male flower buds. In situ hybridization showed that CsGAIP is greatly enriched in the stamen primordia, especially during the hermaphrodite stage of flower development. Further, CsGAIP protein is located in nucleus. CsGAIP can partially rescue the plant height, stamen development and fertility phenotypes of Arabidopsis rga-24/gai-t6 mutant, and ectopic expression of CsGAIP in wide-type Arabidopsis results in reduced number of stamens and decreased transcription of B class floral homeotic genes APETALA3 (AP3) and PISTILLATA (PI). Our data suggest that monoecious CsGAIP may inhibit staminate development through transcriptional repression of B class floral homeotic genes in Arabidopsis.
Model Checking Software Programs with First Order Logic Specifications using AIG Solvers

Static verification techniques leverage Boolean formula satisfiability solvers such as SAT and SMT solvers that operate on conjunctive normal form and first order logic formulae, respectively, to validate programs. They force bounds on variable ranges and execution time and translate the program and its specifications into a Boolean formula. They are limited to programs of relatively low complexity for the following reasons. (1) A small increase in the bounds can cause a large increase in the size of the translated formula. (2) Boolean satisfiability solvers are restricted to using optimizations that apply at the level of the formula. Finally, (3) the Boolean formulae often need to be regenerated with higher bounds to ensure the correctness of the translation. We present a method that uses sequential circuits, Boolean formulae with memory elements and hierarchical structure, and sequential circuit synthesis and verification frameworks to validate programs. (1) Sequential circuits are much more succinct than Boolean formulae with no memory elements and preserve the high-level structure of the program. (2) Encoding the problem as a sequential circuit enables the use of a number of powerful automated analysis techniques that have no counterparts for other Boolean formulae. Our method takes an imperative program with a first order logic specification consisting of a precondition and a postcondition pair, and a bound on the program variable ranges, and produces a sequential circuit with a designated output that is true when the program violates the specification.
Longitudinal identification of clinically distinct neurophenotypes in young children with fragile X syndrome

Significance We present a research study that uses early structural brain growth as a metric for defining subgroups within individuals with fragile X syndrome (FXS). Topological data analysis, a type of multivariate pattern classification, identified two large subgroups based solely on the longitudinal structural brain images. Post hoc analysis indicated significant, unidirectional differences in cognition, adaptive functioning, and autism severity scores. Our results support the use of longitudinal topological data analysis (TDA) as a putative tool for differentiating individuals with FXS based on neuroanatomical data. This information may be used to predict outcomes and guide design of targeted therapies for individuals with FXS. Longitudinal TDA may be a useful analysis tool to explore variation in other neuropsychiatric disorders. Fragile X syndrome (FXS), due to mutations of the FMR1 gene, is the most common known inherited cause of developmental disability. The cognitive, behavioral, and neurological phenotypes observed in affected individuals can vary considerably, making it difficult to predict outcomes and determine the need for interventions. We sought to examine early structural brain growth as a potential marker for identification of clinically meaningful subgroups. Participants included 42 very young boys with FXS who completed a T1-weighted anatomical MRI and cognitive/behavioral assessment at two longitudinal time points, with mean ages of 2.89 y and 4.91 y. Topological data analysis (TDA), an unsupervised approach to multivariate pattern analysis, was applied to the longitudinal anatomical data to identify coherent but heretofore unknown subgroups. TDA revealed two large subgroups within the study population based solely on longitudinal MRI data. Post hoc comparisons of cognition, adaptive functioning, and autism severity scores between these groups demonstrated that one group was consistently higher functioning on all measures at both time points, with pronounced and significant unidirectional differences (P < 0.05 for time point 1 and/or time point 2 for each measure). These results support the existence of two longitudinally defined, neuroanatomically distinct, and clinically relevant phenotypes among boys with FXS. If confirmed by additional analyses, such information may be used to predict outcomes and guide design of targeted therapies. Furthermore, TDA of longitudinal anatomical MRI data may represent a useful method for reliably and objectively defining subtypes within other neuropsychiatric disorders.
Study on current status and climatic characteristics of wine regions in China

The aim of this paper is to improve the knowledge of the current status and climatic characteristics of Chinese wine regions. An investigation of Chinese winegrowing regions, which concerned the distribution, area and cultivars, was conducted using "questionnaires + expert consultation + available literature". On the basis of the results of the investigation, a map was drawn to depict the distribution of Chinese wine regions. Furthermore, observation records of weather stations located within winegrowing zones during 1982–2011 were employed to analyze the climatic characteristics of each region by using the climatic indices of frost-free season (FFS), dryness index (DI), and extremely low temperature (ELT). According to the findings of the investigation, wine grapes have been widely cultivated in 179 counties of China, with a total cultivation area of 163,200 ha. 'Cabernet Sauvignon' was the most widely cultivated variety. The analysis of the climatic characteristics revealed a regional difference within and between wine regions. Moreover, most wine regions were suitable for wine production in terms of the climatic indices, which in turn verified the applicability of the climate indices system.
Shrub patch configuration in relation to precipitation and soil properties in Northwest China

Shrub patch configuration is a long‐term evolutionary response to several environmental drivers and an equilibrium state in species competition. However, the variance in configuration along the precipitation gradient and its association with climate, soil, and adjacent vegetation remains unclear. Our study aimed to assess the variation of shrub patch configuration along a climatic gradient and the factors that affect shrub patch configuration, on the Inner Mongolia Plateau of Northwest China. Results showed that climate, soil, and vegetation contributed to 72.9% of the variance in shrub patch configuration. Interestingly, interspace/shrub radius ratio showed a decreasing trend with increasing precipitation transect. Patch size was positively correlated with aboveground biomass of interspace grass. Shrubs generally aggregated in high patches with low density on coarse soil texture, and vice versa. Moreover, shrub patches tended to occupy a larger percentage of landscape with increasing rainfall. To our knowledge, our study provides the first quantification of the effects of climate, soil, and vegetation on shrub patch configuration along a precipitation gradient in Northwest China and may contribute to a better understanding of the structure and functions of shrub ecosystems with a broader ecological application in arid and semiarid regions.
Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance.
Changes in titin and collagen underlie diastolic stiffness diversity of cardiac muscle.

Small (N2B) and large (N2BA) cardiac titin isoforms are differentially expressed in a species-specific and heart location-specific manner. To understand how differential expression of titin isoforms may influence passive stiffness of cardiac muscle we investigated the mechanical properties of mouse left ventricular (MLV) wall muscle (expressing predominantly the small titin isoform), bovine left atrial (BLA) wall muscle (predominantly the large isoform), and bovine left ventricular (BLV) wall muscle (expressing small and large isoforms at similar levels). Results indicate that the overall passive muscle stiffness of the muscle types varies nearly ten-fold, with stiffness increasing in the following order: BLA, BLV and MLV. To investigate the basis of the variation in the overall muscle stiffness, the contributions of titin and collagen to muscle stiffness were determined. Results showed that increased muscle stiffness results from increases in both titin- and collagen-based passive stiffness, indicating that titin and collagen change in a co-ordinated fashion. The expression level of the small titin isoform correlates with titin's contribution to overall muscle stiffness, suggesting that differential expression of titin isoforms is an effective means to modulate the filling behavior of the heart.
On the singularity probability of random Bernoulli matrices

Let $n$ be a large integer and $M_n$ be a random $n$ by $n$ matrix whose entries are i.i. d. Bernoulli random variables (each entry is $\pm 1$ with probability 1/2). We show that the probability that $M_n$ is singular is at most $(3/4 +o(1))^n$, improving an earlier estimate of Kahn, Koml\'os and Szemer\'edi, as well as earlier work by the authors. The key new ingredient is the applications of Freiman type inverse theorems and other tools from additive combinatorics.
The Optimization in News Search Engine Using Formal Concept Analysis

Being faced with the huge amount and rapid renewal of the news data, how to help users to obtain the news content they need rapidly, and how to use the resource of news information effectively, both come to the present problems. formal concept analysis(FCA) arose as a mathematical theory for the formalization of the concept of 'concept' and has more advantages in concept description and relation measurement than binary-relationship such as database. The technique of using FCA to optimize the personal news search engine is proposed in this paper, including users' query background construction using FCA , query keywords optimization based on users' background and the new layout strategy of search result based on "Concept Tree".
Changes in knowledge, perceptions, preventive behaviours and psychological responses in the pre-community outbreak phase of the H1N1 epidemic

SUMMARY To investigate the changes in community responsiveness during the pre-community-outbreak phase of the H1N1 epidemic in Hong Kong, a pooled sample of 999 adults was interviewed in three surveys (S1, S2, S3) from 7 May to 6 June 2009. Over time, fewer people felt confident in staying free from H1N1 infection in the following year (S1, 63·3%; S3, 46%; P<0·001). The level of distress due to H1N1 remained modest throughout the study period. People's confidence in the government's ability to control a large-scale H1N1 outbreak declined slightly at the third survey (S1, 80·5%; S3, 73·8%; P=0·025). Across the three surveys, respondents remained vigilant with frequent adoption of preventive measures (e.g. wearing face masks in public areas when suffering from influenza-like symptoms and frequent hand-washing). The public was generally supportive of the Hong Kong government although misconceptions regarding the disease were common. Provision of evidence-based public-health education is still warranted as the disease outbreak unfolds.
Scheduling disciplines for multimedia WLANs: embedded round robin and wireless dual queue

Wireless local area networks have developed into a promising solution to support advanced data services in untethered environments. Selection of an efficient packet-scheduling scheme is important for managing the bandwidth while satisfying QoS requirements of active sessions having diverse traffic characteristics. The key difficulty is the distributed nature of the queues in the uplink, resulting in the scheduler having to trade off polling greedy stations against wasting resources by polling potentially idle stations. In order to address this, we propose a novel scheduling scheme, "embedded round robin", which dynamically classifies stations as "busy" and "clear". We then extend the previously proposed dual queue scheduling discipline to the case of wireless networks.
An Economics-Driven Approach for Automated SLA Negotiation for Cloud Services Adoption: Aspoc2

With the growing number of cloud providers offering a variety of on-demand services, the process of adopting cloud and selecting a cloud provider is becoming laborious and time consuming involving complex and lengthy negotiations to mediate user requirements with that of cloud Service Level Agreement (SLA) provision. To make the method more efficient, there is a need for an effective, dynamic, and flexible automated approach on negotiation for resolving conflicts and mediating user requirements in SLAs. We contribute to a novel method for automated negotiation, which draws inspiration from two economic concepts - Pareto optimality and Bayesian updating. We describe a model which implements bilateral and multiple attribute negotiations. We exemplify the effectiveness of the approach using scenarios.
A bitplane based algorithm for lossless compression of DNA microarray images

During the past years, the development of microarray technology has been remarkable, and it is becoming a daily tool in many genomic research laboratories. The widespread adoption of this technology, coupled with the significant volume of data generated per experiment, in the form of images, have led to significant challenges in storage and query-retrieval. In this paper, we present a lossless bitplane based method for efficient compression of microarray images. This method is based on arithmetic coding driven by image-dependent multi-bitplane finite-context models. It produces an embedded bitstream that allows progressive, lossy-to-lossless decoding. We compare the compression efficiency of the proposed method with three image compression standards (JPEG2000, JPEG-LS and JBIG) and also with the two most recent specialized methods for microarray image coding. The proposed method gives better results for all images of the test sets and confirms the effectiveness of bitplane based methods and finite-context modeling for the lossless compression of microarray images.
WikiWalk: Random walks on Wikipedia for Semantic Relatedness

Computing semantic relatedness of natural language texts is a key component of tasks such as information retrieval and summarization, and often depends on knowledge of a broad range of real-world concepts and relationships. We address this knowledge integration issue by computing semantic relatedness using personalized PageRank (random walks) on a graph derived from Wikipedia. This paper evaluates methods for building the graph, including link selection strategies, and two methods for representing input texts as distributions over the graph nodes: one based on a dictionary lookup, the other based on Explicit Semantic Analysis. We evaluate our techniques on standard word relatedness and text similarity datasets, finding that they capture similarity information complementary to existing Wikipedia-based relatedness measures, resulting in small improvements on a state-of-the-art measure.
Cross-Lingual Speaker Adaptation for Statistical Speech Synthesis Using Limited Data

Cross-lingual speaker adaptation with limited adaptation data has many applications such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis (SSS) systems using limited adaptation data. To that end, we propose two techniques exploiting a bilingual Turkish-English speech database that we collected. In one approach, speaker-speciﬁc state-mapping is proposed for cross-lingual adaptation which performed signiﬁcantly better than the baseline state-mapping algorithm in adapting the excitation parameter both in objective and subjective tests. In the second approach, eigenvoice adaptation is done in the input language which is then used to estimate the eigenvoice weights in the output language using weighted linear regression. The second approach performed signiﬁcantly better than the baseline system in adapting the spectral envelope parameters both in ob-jective and subjective tests.
Spectro-temporal analysis of speech using 2-d Gabor filters

We present a 2-D spectro-temporal Gabor filterbank based on the 2-D Fast Fourier Transform, and show how it may be used to analyze localized patches of a spectrogram. We argue that the 2-D Gabor filterbank has the capacity to decompose a patch into its underlying dominant spectro-temporal components, and we illustrate the response of our filterbank to different speech phenomena such as harmonicity, formants, vertical onsets/offsets, noise, and overlapping simultaneous speakers.
Decreased Level of Nurr1 in Heterozygous Young Adult Mice Leads to Exacerbated Acute and Long-Term Toxicity after Repeated Methamphetamine Exposure

The abuse of psychostimulants, such as methamphetamine (METH), is prevalent in young adults and could lead to long-term adaptations in the midbrain dopamine system in abstinent human METH abusers. Nurr1 is a gene that is critical for the survival and maintenance of dopaminergic neurons and has been implicated in dopaminergic neuron related disorders. In this study, we examined the synergistic effects of repeated early exposure to methamphetamine in adolescence and reduction in Nurr1 gene levels. METH binge exposure in adolescence led to greater damage in the nigrostrial dopaminergic system when mice were exposed to METH binge later in life, suggesting a long-term adverse effect on the dopaminergic system. Compared to naïve mice that received METH binge treatment for the first time, mice pretreated with METH in adolescence showed a greater loss of tyrosine hydroxylase (TH) immunoreactivity in striatum, loss of THir fibers in the substantia nigra reticulata (SNr) as well as decreased dopamine transporter (DAT) level and compromised DA clearance in striatum. These effects were further exacerbated in Nurr1 heterozygous mice. Our data suggest that a prolonged adverse effect exists following adolescent METH binge exposure which may lead to greater damage to the dopaminergic system when exposed to repeated METH later in life. Furthermore, our data support that Nurr1 mutations or deficiency could be a potential genetic predisposition which may lead to higher vulnerability in some individuals.