Advances in All-Neural Speech Recognition

This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology

When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks

We introduce a framework for the modeling of sequential data capturing pathways of varying lengths observed in a network. Such data are important, e.g., when studying click streams in information networks, travel patterns in transportation systems, information cascades in social networks, biological pathways or time-stamped social interactions. While it is common to apply graph analytics and network analysis to such data, recent works have shown that temporal correlations can invalidate the results of such methods. This raises a fundamental question: when is a network abstraction of sequential data justified? Addressing this open question, we propose a framework which combines Markov chains of multiple, higher orders into a multi-layer graphical model that captures temporal correlations in pathways at multiple length scales simultaneously. We develop a model selection technique to infer the optimal number of layers of such a model and show that it outperforms previously used Markov order detection techniques. An application to eight real-world data sets on pathways and temporal networks shows that it allows to infer graphical models which capture both topological and temporal characteristics of such data. Our work highlights fallacies of network abstractions and provides a principled answer to the open question when they are justified. Generalizing network representations to multi-order graphical models, it opens perspectives for new data mining and knowledge discovery algorithms.Comment: 10 pages, 4 figures, 1 table, companion python package pathpy available on gitHu

The Happiness-Income Paradox Revisited

The striking thing about the happiness-income paradox is that over the long-term – usually a period of 10 y or more – happiness does not increase as a country's income rises. Heretofore the evidence for this was limited to developed countries. This article presents evidence that the long term nil relationship between happiness and income holds also for a number of developing countries, the eastern European countries transitioning from socialism to capitalism, and an even wider sample of developed countries than previously studied. It also finds that in the short-term in all three groups of countries, happiness and income go together, i.e., happiness tends to fall in economic contractions and rise in expansions. Recent critiques of the paradox, claiming the time series relationship between happiness and income is positive, are the result either of a statistical artifact or a confusion of the short-term relationship with the long-term one.Easterlin Paradox, life satisfaction, subjective well-being

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Simple Classification of Light Baryons

We introduce a classification number $n$ which describes the baryon mass information in a fuzzy manner. According to $n$ and $J^p$ of baryons, we put all known light baryons in a simple table in which some baryons with same ($n$, $J^p$) are classified as members of known octets or decuplets. Meanwhile, we predict two new possible octets.Comment: 5 latex pages, 5 tables, no figur

The power of Bayesian evidence in astronomy

We discuss the use of the Bayesian evidence ratio, or Bayes factor, for model selection in astronomy. We treat the evidence ratio as a statistic and investigate its distribution over an ensemble of experiments, considering both simple analytical examples and some more realistic cases, which require numerical simulation. We find that the evidence ratio is a noisy statistic, and thus it may not be sensible to decide to accept or reject a model based solely on whether the evidence ratio reaches some threshold value. The odds suggested by the evidence ratio bear no obvious relationship to the power or Type I error rate of a test based on the evidence ratio. The general performance of such tests is strongly affected by the signal to noise ratio in the data, the assumed priors, and the threshold in the evidence ratio that is taken as `decisive'. The comprehensiveness of the model suite under consideration is also very important. The usefulness of the evidence ratio approach in a given problem can be assessed in advance of the experiment, using simple models and numerical approximations. In many cases, this approach can be as informative as a much more costly full-scale Bayesian analysis of a complex problem.Comment: 11 pages; MNRAS in pres

What makes a phase transition? Analysis of the random satisfiability problem

In the last 30 years it was found that many combinatorial systems undergo phase transitions. One of the most important examples of these can be found among the random k-satisfiability problems (often referred to as k-SAT), asking whether there exists an assignment of Boolean values satisfying a Boolean formula composed of clauses with k random variables each. The random 3-SAT problem is reported to show various phase transitions at different critical values of the ratio of the number of clauses to the number of variables. The most famous of these occurs when the probability of finding a satisfiable instance suddenly drops from 1 to 0. This transition is associated with a rise in the hardness of the problem, but until now the correlation between any of the proposed phase transitions and the hardness is not totally clear. In this paper we will first show numerically that the number of solutions universally follows a lognormal distribution, thereby explaining the puzzling question of why the number of solutions is still exponential at the critical point. Moreover we provide evidence that the hardness of the closely related problem of counting the total number of solutions does not show any phase transition-like behavior. This raises the question of whether the probability of finding a satisfiable instance is really an order parameter of a phase transition or whether it is more likely to just show a simple sharp threshold phenomenon. More generally, this paper aims at starting a discussion where a simple sharp threshold phenomenon turns into a genuine phase transition

How to Tango: a manual for implementing Spine Tango

The generic approach of the Spine Tango documentation system, which uses web-based technologies, is a necessity for reaching a maximum number of participants. This, in turn, reduces the potential for customising the Tango according to the individual needs of each user. However, a number of possibilities still exist for tailoring the data collection processes to the user's own hospital workflow. One can choose between a purely paper-based set-up (with in-house scanning, data punching or mailing of forms to the data centre at the University of Bern) and completely paper-free online data entry. Many users work in a hybrid mode with online entry of surgical data and paper-based recording of the patients' perspectives using the Core Outcome Measures Index (COMI) questionnaires. Preoperatively, patients can complete their questionnaires in the outpatient clinic at the time of taking the decision about surgery or simply at the time of hospitalisation. Postoperative administration of patient data can involve questionnaire completion in the outpatient clinic, the handing over the forms at the time of discharge for their mailing back to the hospital later, sending out of questionnaires by post with a stamped addressed envelope for their return or, in exceptional circumstances, conducting telephone interviews. Eurospine encourages documentation of patient-based information before the hospitalisation period and surgeon-based information both before and during hospitalisation; both patient and surgeon data should be acquired for at least one follow-up, at a minimum of three to six months after surgery. In addition, all complications that occur after discharge, and their consequences should be recorde