Cell-Probe Bounds for Online Edit Distance and Other Pattern Matching Problems

We give cell-probe bounds for the computation of edit distance, Hamming distance, convolution and longest common subsequence in a stream. In this model, a fixed string of $n$ symbols is given and one $\delta$-bit symbol arrives at a time in a stream. After each symbol arrives, the distance between the fixed string and a suffix of most recent symbols of the stream is reported. The cell-probe model is perhaps the strongest model of computation for showing data structure lower bounds, subsuming in particular the popular word-RAM model. * We first give an $\Omega((\delta \log n)/(w+\log\log n))$ lower bound for the time to give each output for both online Hamming distance and convolution, where $w$ is the word size. This bound relies on a new encoding scheme and for the first time holds even when $w$ is as small as a single bit. * We then consider the online edit distance and longest common subsequence problems in the bit-probe model ($w=1$) with a constant sized input alphabet. We give a lower bound of $\Omega(\sqrt{\log n}/(\log\log n)^{3/2})$ which applies for both problems. This second set of results relies both on our new encoding scheme as well as a carefully constructed hard distribution. * Finally, for the online edit distance problem we show that there is an $O((\log n)^2/w)$ upper bound in the cell-probe model. This bound gives a contrast to our new lower bound and also establishes an exponential gap between the known cell-probe and RAM model complexities.Comment: 32 pages, 4 figure

Personality Traits, Perceived Stress, and Tinnitus-Related Distress in Patients With Chronic Tinnitus: Support for a Vulnerability-Stress Model

Background: Despite vulnerability-stress models underlying a variety of distress-related emotional syndromes, few studies have investigated interactions between personality factors and subjectively experienced stressors in accounting for tinnitus-related distress. Aim: The present study compared personality characteristics between patients with chronic tinnitus and the general population. Within the patient sample, it was further examined whether personality dimensions predicted tinnitus-related distress and, if so, whether differential aspects or levels of perceived stress mediated these effects. Method: Applying a cross-sectional design, 100 patients with chronic tinnitus completed the Freiburger Persönlichkeitsinventar (FPI-R) measuring personality, the Perceived Stress Questionnaire (PSQ-20) measuring perceived stress and the German version of the Tinnitus Questionnaire (TQ) measuring tinnitus-related distress. FPI-R scores were compared with normed values obtained from a representative German reference population. Mediation analyses were computed specifying FPI-R scores as independent, PSQ20 scores as mediating and the TQ-total score as dependent variables. Results: Patients with chronic tinnitus significantly differed from the general population across a variety of personality indices. Tinnitus-related distress was mediated by differential interactions between personality factors and perceived stress dimensions. Conclusion: In conceptualizing tinnitus-related distress, idiosyncratic assessments of vulnerability-stress interactions are crucial for devising effective psychological treatment strategies. Patients' somatic complaints and worries appear to be partly informed by opposing tendencies reflecting emotional excitability vs. aggressive inhibition - suggesting emotion-focused treatment strategies as a promising new direction for alleviating distress

A theoretical and methodological framework for machine learning in survival analysis: Enabling transparent and accessible predictive modelling on right-censored time-to-event data

Survival analysis is an important field of Statistics concerned with mak- ing time-to-event predictions with ‘censored’ data. Machine learning, specifically supervised learning, is the field of Statistics concerned with using state-of-the-art algorithms in order to make predictions on unseen data. This thesis looks at unifying these two fields as current research into the two is still disjoint, with ‘classical survival’ on one side and su- pervised learning (primarily classification and regression) on the other. This PhD aims to improve the quality of machine learning research in survival analysis by focusing on transparency, accessibility, and predic- tive performance in model building and evaluation. This is achieved by examining historic and current proposals and implementations for models and measures (both classical and machine learning) in survival analysis and making novel contributions. In particular this includes: i) a survey of survival models including a crit- ical and technical survey of almost all supervised learning model classes currently utilised in survival, as well as novel adaptations; ii) a survey of evaluation measures for survival models, including key definitions, proofs and theorems for survival scoring rules that had previously been missing from the literature; iii) introduction and formalisation of composition and reduction in survival analysis, with a view on increasing transparency of modelling strategies and improving predictive performance; iv) imple- mentation of several R software packages, in particular mlr3proba for machine learning in survival analysis; and v) the first large-scale bench- mark experiment on right-censored time-to-event data with 24 survival models and 66 datasets. Survival analysis has many important applications in medical statistics, engineering and finance, and as such requires the same level of rigour as other machine learning fields such as regression and classification; this thesis aims to make this clear by describing a framework from prediction and evaluation to implementation

Concert recording 2017-05-03a

[Track 1]. Concerto in D major. I. Allegro moderato [Track 2]. II. Andante [Track 3]. III. Allegro grazioso / Giuseppe Tartini -- [Track 4]. Elegy in memoriam Gustav Mahler / Miroslaw Gasieniec -- [Track 5]. Variations on a theme from Norma / Jean-Baptiste Arban -- [Track 6]. The adventures of / Kevin McKee -- [Track 7]. Cantus / Eric Nathan -- [Track 8]. Talking -- [Track 9]. The lightning fields. III. Marfa lights / Michael Daugherty -- [Track 10]. Farewell to Stromness / Peter Maxwell Davies