A bibliometric study of the research field of experimental philosophy of language

The past eighteen years witnessed the rapid development of experimental philosophy of language. Adopting a bibliometric approach, this study examines the research trends and status quo of this burgeoning field based on a corpus of 237 publications retrieved from PhilPapers. It is observed that experimental philosophy of language has undergone three stages, the initiation stage, the development stage, and the extension stage, across which there is a clear upward trend in the annual number of publications. Michael Devitt, Edouard Machery, John Turri, Nat Hansen, et al., are found to be the most productive philosophers, testifying their leading positions in this field. Journals, instead of books, are the major homes of works in this area. The analysis also yields a list of influential works, including the seminal work “Semantics, Cross-cultural Style” and other significant publications on the semantics of various types of expressions. Relatedly, the major research themes are found to include not only intuitions about the reference of proper names, but also a wide array of philosophically and linguistically interesting issues like the meaning of color adjectives, epistemic modals, and predicates of personal taste, the norms of assertions and the essence of lies, etc. These findings showcase that experimental philosophy of language has broadened the research territory and offered deep insights into central issues of philosophy of language that are beyond the reach of the conventional armchair methodology

Multilingual Text Detection with Nonlinear Neural Network

Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work

Molecular Dynamics Simulation of Macromolecules Using Graphics Processing Unit

Molecular dynamics (MD) simulation is a powerful computational tool to study the behavior of macromolecular systems. But many simulations of this field are limited in spatial or temporal scale by the available computational resource. In recent years, graphics processing unit (GPU) provides unprecedented computational power for scientific applications. Many MD algorithms suit with the multithread nature of GPU. In this paper, MD algorithms for macromolecular systems that run entirely on GPU are presented. Compared to the MD simulation with free software GROMACS on a single CPU core, our codes achieve about 10 times speed-up on a single GPU. For validation, we have performed MD simulations of polymer crystallization on GPU, and the results observed perfectly agree with computations on CPU. Therefore, our single GPU codes have already provided an inexpensive alternative for macromolecular simulations on traditional CPU clusters and they can also be used as a basis to develop parallel GPU programs to further speedup the computations.Comment: 21 pages, 16 figure

Study of neurodegenerative diseases with novel MRI techniques

Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive degeneration of structure and function of the nervous system. They include diseases such as Alzheimer’s disease (AD), multiple sclerosis (MS) and others. The main aims of this thesis were to study functional and/or structural brain changes in AD and MS using novel magnetic resonance imaging (MRI) techniques. The concentration of β-amyloid1-42 (Aβ42), total tau (T-tau) and tau phosphorylated at position threonine 181 (P-tau181p) in cerebrospinal fluid (CSF) may reflect brain pathophysiological processes in AD. We found a positive correlation between functional connectivity within the default mode network (DMN) and the ratio of Aβ42/P-tau181p in sporadic AD (Paper I). Furthermore, there were correlations between AD CSF biomarkers and changes of gray matter volume, fractional anisotropy (FA) and mean diffusivity (MD). The majority of brain regions with statistically significant correlation with biomarkers of AD overlapped with the DMN (Paper II). These findings implicate that the brain functional connectivity and structure are affected by pathological changes at an early stage in AD. We also found a significantly increased MD in pre-symptomatic mutation carriers (pre-MCs) of AD compared with non-carriers (NCs), and increased MD associated with AD CSF biomarkers (Paper III). Similar results were observed both in sporadic and familial AD, which suggests that MD may reflect pathology of early stage AD. Although the exact causes of these changes are difficult to identify, the increased MD may be explained by myelin loss. In MS, myelin loss is one of the characteristic events of the pathological process. By combining susceptibility-weighted MRI with analysis of the ! ∗ decay curves, we were able to characterize and quantify myelin loss (Paper IV). In conclusion, pathological changes in AD and MS could be detected by novel MRI techniques. This suggests that these techniques may also be helpful in further understanding pathology in other neurodegenerative diseases. As non-invasive tools, these novel MRI techniques are possible to screen individuals susceptible to and/or manifesting early neurodegeneration

Investigation of nonlinear flame response to dual-frequency disturbances

The two-way interaction between the unsteady flame heat release rate and acoustic waves can lead to combustion instability within combustors. To understand and quantify the flame response to oncoming acoustic waves, previous studies have typically considered the flame dynamic response to pure tone forcing and assumed a dynamically linear or weakly nonlinear response. In this study, the introduction of excitation with two distinct frequencies denoted $St_1$ and $St_2$ is considered, including the effect of excitation amplitude in order to gain more insight into the nature of flame nonlinearities and these associated with combustion instabilities. Corresponding results are obtained by combining a low-order asymptotic analysis (up to third order in normalised excitation amplitude) with numerical methods based on the model framework of the $G$-equation. The influence paths of the disturbance at $St_2$ on the flame dynamic response at $St_1$ are studied in detail. Due to the flame propagating forward normally to itself (named flame kinematic restoration), the perturbation at $St_2$ acts together with that at $St_1$ to induce a third-order nonlinear interaction in the flame kinematics, impressively suppressing the spatial wrinkling of the flame at $St_1$. Additionally, introducing the perturbation at $St_2$ alters the effective flame displacement speed, which is responsible for the calculation of the flame heat release rate and further affects the global response at $St_1$. Taking into account the above two factors, the nonlinear response of the flame at $St_1$ is completely quantified and the corresponding characteristics are clearly interpreted

jaw-1D: a gain-of-function mutation responsive to paramutation-like induction of epigenetic silencing

The Arabidopsis thaliana gain-of-function T-DNA insertion mutant jaw-1D produces miR319A, a microRNA that represses genes encoding CIN-like TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTORs (TCPs), a family of transcription factors that play key roles in leaf morphogenesis. In this study, we show that jaw-1D is responsive to paramutation-like epigenetic silencing. A genetic cross of jaw-1D with the polycomb gene mutant curly leaf-29 (clf-29) leads to attenuation of the jaw-1D mutant plant phenotype. This induced mutation, jaw-1D*, was associated with down-regulation of miR319A, was heritable independently from clf-29, and displayed paramutation-like non-Mendelian inheritance. Down-regulation of miR319A in jaw-1D* was linked to elevated levels of histone H3 lysine 9 dimethylation and DNA methylation at the CaMV35S enhancer located within the activation-tagging T-DNA of the jaw-1D locus. Examination of 21 independent T-DNA insertion mutant lines revealed that 11 could attenuate the jaw-1D mutant phenotype in a similar way to the paramutation induced by clf-29. These paramutagenic mutant lines shared the common feature that their T-DNA insertion was present as multi-copy tandem repeats and contained high levels of CG and CHG methylation. Our results provide important insights into paramutation-like epigenetic silencing, and caution against the use of jaw-1D in genetic interaction studies

A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification

Computer systems are becoming extremely complex, while system anomalies dramatically influence the availability and usability of systems. Online anomaly prediction is an important approach to manage imminent anomalies, and the high accuracy relies on precise system monitoring data. However, precise monitoring data is not easily achievable because of widespread noise. In this paper, we present a method which integrates an improved Evidential Markov model and ensemble classification to predict anomaly for systems with noise. Traditional Markov models use explicit state boundaries to build the Markov chain and then make prediction of different measurement metrics. A Problem arises when data comes with noise because even slight oscillation around the true value will lead to very different predictions. Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario. The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream. This study further applies ensemble classification to identify system anomaly based on the predicted metrics. Extensive experiments on anomaly data collected from 66 metrics in PlanetLab have confirmed that our approach can achieve high prediction accuracy and time efficiency