Fracture and Friction: Stick-Slip Motion

We discuss the stick-slip motion of an elastic block sliding along a rigid substrate. We argue that for a given external shear stress this system shows a discontinuous nonequilibrium transition from a uniform stick state to uniform sliding at some critical stress which is nothing but the Griffith threshold for crack propagation. An inhomogeneous mode of sliding occurs, when the driving velocity is prescribed instead of the external stress. A transition to homogeneous sliding occurs at a critical velocity, which is related to the critical stress. We solve the elastic problem for a steady-state motion of a periodic stick-slip pattern and derive equations of motion for the tip and resticking end of the slip pulses. In the slip regions we use the linear viscous friction law and do not assume any intrinsic instabilities even at small sliding velocities. We find that, as in many other pattern forming system, the steady-state analysis itself does not select uniquely all the internal parameters of the pattern, especially the primary wavelength. Using some plausible analogy to first order phase transitions we discuss a ``soft'' selection mechanism. This allows to estimate internal parameters such as crack velocities, primary wavelength and relative fraction of the slip phase as function of the driving velocity. The relevance of our results to recent experiments is discussed.Comment: 12 pages, 7 figure

Predictive Uncertainty Estimation via Prior Networks

Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional mismatch between the test and training data distributions. Different actions might be taken depending on the source of the uncertainty so it is important to be able to distinguish between them. Recently, baseline tasks and metrics have been defined and several practical methods to estimate uncertainty developed. These methods, however, attempt to model uncertainty due to distributional mismatch either implicitly through model uncertainty or as data uncertainty. This work proposes a new framework for modeling predictive uncertainty called Prior Networks (PNs) which explicitly models distributional uncertainty. PNs do this by parameterizing a prior distribution over predictive distributions. This work focuses on uncertainty for classification and evaluates PNs on the tasks of identifying out-of-distribution (OOD) samples and detecting misclassification on the MNIST dataset, where they are found to outperform previous methods. Experiments on synthetic and MNIST data show that unlike previous non-Bayesian methods PNs are able to distinguish between data and distributional uncertainty

A hierarchical attention based model for off-topic spontaneous spoken response detection

Automatic spoken language assessment and training systems are becoming increasingly popular to handle the growing demand to learn languages. However, current systems often assess only fluency and pronunciation, with limited content-based features being used. This paper examines one particular aspect of content-assessment, off-topic response detection. This is important for deployed systems as it ensures that candidates understood the prompt, and are able to generate an appropriate answer. Previously proposed approaches typically require a set of prompt-response training pairs, which lim- its flexibility as example responses are required whenever a new test prompt is introduced. Recently, the attention based neural topic model (ATM) was presented, which can assess the relevance of prompt-response pairs regardless of whether the prompt was seen in training. This model uses a bidirectional Recurrent Neural Network (BiRNN) embedding of the prompt combined with an attention mechanism to attend over the hidden states of a BiRNN embedding of the response to compute a fixed-length embedding used to predict relevance. Unfortunately, performance on prompts not seen in the training data is lower than on seen prompts. Thus, this paper adds the following contributions: several im- provements to the ATM are examined; a hierarchical variant of the ATM (HATM) is proposed, which explicitly uses prompt similarity to further improve performance on unseen prompts by interpolating over prompts seen in training data given a prompt of interest via a second attention mechanism; an in-depth analysis of both models is conducted and main failure mode identified. On spontaneous spo- ken data, taken from BULATS tests, these systems are able to assess relevance to both seen and unseen prompt

Incorporating uncertainty into deep learning for spoken language assessment

There is a growing demand for automatic assessment of spoken English proficiency. These systems need to handle large vari- ations in input data owing to the wide range of candidate skill levels and L1s, and errors from ASR. Some candidates will be a poor match to the training data set, undermining the validity of the predicted grade. For high stakes tests it is essen- tial for such systems not only to grade well, but also to provide a measure of their uncertainty in their predictions, en- abling rejection to human graders. Pre- vious work examined Gaussian Process (GP) graders which, though successful, do not scale well with large data sets. Deep Neural Networks (DNN) may also be used to provide uncertainty using Monte-Carlo Dropout (MCD). This paper proposes a novel method to yield uncertainty and compares it to GPs and DNNs with MCD. The proposed approach explicitly teaches a DNN to have low uncertainty on train- ing data and high uncertainty on generated artificial data. On experiments conducted on data from the Business Language Test- ing Service (BULATS), the proposed ap- proach is found to outperform GPs and DNNs with MCD in uncertainty-based re- jection whilst achieving comparable grad- ing performance

Classical nonlinear response of a chaotic system: Langevin dynamics and spectral decomposition

We consider the classical response of a strongly chaotic Hamiltonian system. The spectrum of such a system consists of discrete complex Ruelle-Pollicott (RP) resonances which manifest themselves in the behavior of the correlation and response functions. We interpret the RP resonances as the eigenstates and eigenvalues of the Fokker-Planck operator obtained by adding an infinitesimal noise term to the first-order Liouville operator. We demonstrate how the deterministic expression for the linear response is reproduced in the limit of vanishing noise. For the second-order response we establish an equivalence of the spectral decomposition with infinitesimal noise and the long-time asymptotic expansion for the deterministic case.Comment: 16 pages, 1 figur

Ensemble approaches for uncertainty in spoken language assessment

Deep learning has dramatically improved the performance of automated systems on a range of tasks including spoken language assessment. One of the issues with these deep learning approaches is that they tend to be overconfident in the decisions that they make, with potentially serious implications for deployment of systems for high-stakes examinations. This paper examines the use of ensemble approaches to improve both the reliability of the scores that are generated, and the ability to detect where the system has made predictions beyond acceptable errors. In this work assessment is treated as a regression problem. Deep density networks, and ensembles of these models, are used as the predictive models. Given an ensemble of models measures of uncertainty, for example the variance of the predicted distributions, can be obtained and used for detecting outlier predictions. However, these ensemble approaches increase the computational and memory requirements of the system. To address this problem the ensemble is distilled into a single mixture density network. The performance of the systems is evaluated on a free speaking prompt-response style spoken language assessment test. Experiments show that the ensembles and the distilled model yield performance gains over a single model, and have the ability to detect outliers

On the diel rhythm of motor activity in perch [Translation from: Informatsionnyi Byulleten Biologiya Vnutrennikh Vod No.30, 12-14, 1976]

The river perch (Perca fluviatilis L.) is most active in the daytime hours, and displays seasonal changes of diel rhythm with a break of the rhythm in spring and autumn. In the present work data were obtained on the motor activity of 3 perch measuring l8-20 cm, caught by net in the littoral of a reservoir and spawned under laboratory conditions. The degree of intensity of movement of perch was judged by special experiments. The results are summarised in this short paper

Off-topic response detection for spontaneous spoken English assessment

Automatic spoken language assessment systems are becoming increasingly important to meet the demand for English second language learning. This is a challenging task due to the high error rates of, even state-of-the-art, non-native speech recognition. Consequently current systems primarily assess fluency and pronunciation. However, content assessment is essential for full automation. As a first stage it is important to judge whether the speaker responds on topic to test questions designed to elicit spontaneous speech. Standard approaches to off-topic response detection assess similarity between the response and question based on bag-of-words representations. An alternative framework based on Recurrent Neural Network Language Models (RNNLM) is proposed in this paper. The RNNLM is adapted to the topic of each test question. It learns to associate example responses to questions with points in a topic space constructed using these example responses. Classification is done by ranking the topic-conditional posterior probabilities of a response. The RNNLMs associate a broad range of responses with each topic, incorporate sequence information and scale better with additional training data, unlike standard methods. On experiments conducted on data from the Business Language Testing Service (BULATS) this approach outperforms standard approaches

Impact of ASR performance on free speaking language assessment

In free speaking tests candidates respond in spontaneous speech to prompts. This form of test allows the spoken language proficiency of a non-native speaker of English to be assessed more fully than read aloud tests. As the candidate's responses are unscripted, transcription by automatic speech recognition (ASR) is essential for automated assessment. ASR will never be 100% accurate so any assessment system must seek to minimise and mitigate ASR errors. This paper considers the impact of ASR errors on the performance of free speaking test auto-marking systems. Firstly rich linguistically related features, based on part-of-speech tags from statistical parse trees, are investigated for assessment. Then, the impact of ASR errors on how well the system can detect whether a learner's answer is relevant to the question asked is evaluated. Finally, the impact that these errors may have on the ability of the system to provide detailed feedback to the learner is analysed. In particular, pronunciation and grammatical errors are considered as these are important in helping a learner to make progress. As feedback resulting from an ASR error would be highly confusing, an approach to mitigate this problem using confidence scores is also analysed

N-terminus of pro-EMAP II regulates its binding with C-terminus, Arginyl-tRNA Synthetase, and Neurofilament light protein

Pro-EMAP II, one component of the Multi-Aminoacyl tRNA Synthetase (MSC) Complex, plays multiple roles in physiological and pathological processes of protein translation, signal transduction, immunity, lung development and tumor growth. Recent studies determined that pro-EMAP II has an essential role in maintaining axon integrity in central and peripheral neural systems where deletion of pro-EMAP IIs C-terminus was reported in a consanguineous Israeli Bedouin kindred suffering from Pelizaeus-Merzbacher-like disease. We hypothesized that pro-EMAP IIs N-terminus had an important role in the regulation of protein-protein interactions. Using a GFP reporter system, we defined a putative leucine-zipper in the N-terminus of human pro-EMAP II protein (amino acid residues 1-70), which can form specific strip-like punctate structures. Through GFP punctate analysis, we uncovered that pro-EMAP IIs C-terminus (147-312 amino acid residues) can repress the GFP punctate formation. Pull-down assays confirmed the binding between pro-EMAP II N-terminus and its C-terminus is mediated by a putative leucine-zipper. Furthermore, the pro-EMAP II 1-70 aa region was identified as the binding partner of the arginyl-tRNA synthetase (RARS), a polypeptide of MSC complex. We also determined that the punctate GFP pro-EMAP II 1-70aa aggregate co-localizes and binds to the neurofilament light (NFL) subunit protein that is associated with pathologic neurofilament network disorganization and degeneration of motor neurons. These findings indicate the structure and binding interaction of Pro-EMAP II protein and suggest a role of this protein in the pathological neurodegenerative diseases