Controllable Neural Story Plot Generation via Reinforcement Learning

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201

Event Representations for Automated Story Generation with Deep Neural Nets

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.Comment: Submitted to AAAI'1

Clinical Applicability of MRI Texture Analysis

Radiologisten kuvien tulkinta on perinteisesti perustunut asiantuntijan näköhavaintoihin. Tietokoneavusteisten menetelmien käyttö lisääntyy radiologisessa diagnostiikassa. Tekstuuria eli kuviorakennetta on käytetty erottelevana ominaisuutena kudoksia luokiteltaessa ja karakterisoitaessa. Kuvan kuviorakenteen ominaisuuksia kuvaavia tekstuuriparametreja voidaan laskea erilaisilla matemaattisilla ja signaalinkäsittelymenetelmillä. Tekstuurianalyysi on antanut lupaavia tuloksia magneettikuvien tarkastelussa. Sen avulla on voitu määrittää sekä pieniä hajanaisia että suurempia paikallisia muutoksia. Menetelmällä on mahdollista havaita ihmissilmälle näkymättömiä sekä näkyviä muutoksia. Menetelmää tulisi tutkia edelleen, koska kliinisen menetelmän kehittämistä varten tarvitaan lisätietoa sen soveltuvuudesta erilaisille aineistoille sekä analyysimenetelmän eri vaiheiden optimoimisesta. Tämän väitöstutkimuksen tavoite oli selvittää magneettikuvauksen tekstuurianalyysin kliinistä käytettävyyttä eri kannoilta. Tutkimusaineisto koostui kolmesta potilasmateriaalista ja yhdestä terveiden urheilijoiden joukosta sekä heidän verrokeistaan. Aineisto kerättiin osina Tampereen yliopistollisessa sairaalassa toteutettuja laajempia tutkimusprojekteja, ja mukaan otettiin yhteensä 220 osallistujaa. Ensimmäisessä osatyössä tarkasteltiin pehmytkudoskuvantamista, non-Hodgkin-lymfooman hoitovasteen arviointia tekstuurianalyysilla. Kaksi seuraavaa osatyötä käsitteli keskushermoston kuvantamista: lieviä aivovammoja sekä MS-tautia. Viimeisessä osatyössä arvioitiin liikunnan vaikutusta urheilijoiden ja verrokkien reisiluun kaulan luurakenteeseen. Kudosten ja muutosten vertailuissa oli edustettuna sekä ympäröivästä kudoksesta visuaalisella tarkastelulla erottumattomia että selkeästi erottuvia rakenteita. Lisäksi tutkimuksessa selvitettiin mielenkiintoalueen käsityönä tehtävän rajaamisen ja magneettikuvaussekvenssin valinnan vaikutusta analyysiin. Yhteenvetona todetaan, että tekstuurimenetelmällä on mahdollista havaita ja karakterisoida tutkimukseen valikoidun aineiston edustamia etiologialtaan erilaisia muutoksia kliinisistä 1.5 Teslan magneettikuvista. Tutkimuksessa käsitellyt yksityiskohdat MRI-kuvasarjojen valinnasta sekä mielenkiintoalueiden piirtämisestä antavat pohjaa kliinisen protokollan kehittämiseen. Osa tutkimusaineistoista oli kokeellisia, ja niiden tulokset tulisi vahvistaa laajemmilla kliinisillä tutkimuksilla.The usage of computerised methods in radiological image interpretation is becoming more common. Texture analysis has shown promising results as an image analysis method for detecting non-visible and visible lesions, with a number of applications in magnetic resonance imaging (MRI). Although several recent studies have investigated this topic, there remains a need for further analyses incorporating different clinical materials and taking protocol planning for clinical analyses into account. The purpose of this thesis was to determine the clinical applicability of MRI texture analysis from different viewpoints. This study is based on three patient materials and one collection of healthy athletes and their referents. A total of 220 participants in wider on-going study projects at Tampere University Hospital were included in this thesis. The materials include a study on non-Hodgkin lymphoma, representing soft tissue imaging with malignant disease treatment monitoring; and two studies on central nervous system diseases, mild traumatic brain injury and multiple sclerosis. A musculoskeletal imaging study investigated load-associated physiological changes in healthy participants? bones. Furthermore, manual Region of Interest (ROI) definition methods and the selection of MRI sequences for analyses of visible and non-visible lesions were evaluated. In summary, this study showed that non-visible lesions and physiological changes as well as visible focal lesions of different aetiologies could be detected and characterised by texture analysis of routine clinical 1.5 T scans. The details of MRI sequence selection and ROI definition in this study may serve as guidelines for the development of clinical protocols. However, these studies are partly experimental and need to be validated with larger sample sizes

“Model age-based” and “copy when uncertain” biases in children’s social learning of a novel task

This work was supported by a John Templeton Foundation grant (40128).Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N = 140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children’s continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of model.Publisher PDFPeer reviewe

Social motives vs social influence: an experiment on interdependent time preferences

We report experimental evidence on the effects of social preferences on intertemporal decisions. To this aim, we design an intertemporal Dictator Game to test whether Dictators modify their discounting behavior when their own decision is imposed on their matched Recipients. We run four different treatments to identify the effect of payoffs externalities from those related to information and beliefs. Our descriptive statistics show that heterogeneous social time preferences and information about others’ time preferences are significant determinants of choices: Dictators display a marked propensity to account for the intertemporal preferences of Recipients, both in the presence of externalities (social motives) and/or when they know about the decisions of their matched partners (social influence). We also perform a structural estimation exercise to control for heterogeneity in risk attitudes. As for individual behavior, our estimates confirm previous studies in that high risk aversion is associated with low discounting. As for social behavior, we find that social motives outweigh social influence, especially when we restrict our sample to pairs of Dictators and Recipients who satisfy minimal consistency conditions

The Impact of Austerity Measures on People with Intellectual Disabilities in England

Context. UK austerity measures following the 2008 financial crisis included budget reductions for health and social care. We aimed to investigate the extent to which austerity-measures had impacted the lives of people with intellectual disabilities in England, and whether their support costs were associated with their characteristics, needs and outcomes. Objectives. We report on what services people with intellectual disabilities were using, whether they had lost care, the costs of their support, and what impact any loss of benefits and services had on individuals’ lives. Methods. 150 participants with intellectual disabilities across England were interviewed about their services and their well-being. Service and individual support costs were calculated. Statistical and thematic analyses were employed. Results. The largest proportion (42%) of our sample had lost care. 14% had experienced changed care, and care had remained the same for 36%. Only 7% said their care had improved. No associations were found between costs and characteristics and needs except for whether the person had mild or severe intellectual disabilities. Those who had lost care engaged in fewer activities and had significantly lower self-esteem and quality-of-life scores compared with those who had not lost care. Loss of care impacted on individuals’ independence and future aspirations. Limitations. A comparative study of austerity impacts across the whole of England was not possible. Our costs data may be underestimated because full information on support from home, key, or support workers was unavailable. Implications. In attempting to mitigate against COVID-19 impacts on people with intellectual disabilities, policy-decisions will need to consider the backlog of a decade of cuts

Effect of slice thickness on brain magnetic resonance image texture analysis

Background The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients. Methods We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy. Results Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets. Conclusions Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue.BioMed Central Open acces

Long-range interactions between ultracold atoms and molecules including atomic spin-orbit

We investigate theoretically the long-range electrostatic interactions between a ground-state homonuclear alkali-metal dimer and an excited alkali-metal atom taking into account its fine-structure. The interaction involves the combination of first-order quadrupole-quadrupole and second-order dipole-dipole effects. Depending on the considered species, the atomic spin-orbit may be comparable to the atom-molecule electrostatic energy and to the dimer rotational structure. Here we extend our general description in the framework of the second-order degenerate perturbation theory [M. Lepers and O. Dulieu, Eur. Phys. J. D, 2011] to various regimes induced by the magnitude of the atomic spin-orbit. A complex dynamics of the atom-molecule may take place at large distances, which may have consequences for the search for an universal model of ultracold inelastic collisions as proposed for instance in [Z. Idziaszek and P. S. Julienne, Phys. Rev. Lett. \textbf{104}, 113202 (2010)].Comment: Submitted to Phys. Chem. Chem. Phys., special issue on cold molecule

Comprehensive identification of essential Staphylococcus aureus genes using Transposon-Mediated Differential Hybridisation (TMDH).

BACKGROUND: In recent years there has been an increasing problem with Staphylococcus aureus strains that are resistant to treatment with existing antibiotics. An important starting point for the development of new antimicrobial drugs is the identification of "essential" genes that are important for bacterial survival and growth. RESULTS: We have developed a robust microarray and PCR-based method, Transposon-Mediated Differential Hybridisation (TMDH), that uses novel bioinformatics to identify transposon inserts in genome-wide libraries. Following a microarray-based screen, genes lacking transposon inserts are re-tested using a PCR and sequencing-based approach. We carried out a TMDH analysis of the S. aureus genome using a large random mariner transposon library of around a million mutants, and identified a total of 351 S. aureus genes important for survival and growth in culture. A comparison with the essential gene list experimentally derived for Bacillus subtilis highlighted interesting differences in both pathways and individual genes. CONCLUSION: We have determined the first comprehensive list of S. aureus essential genes. This should act as a useful starting point for the identification of potential targets for novel antimicrobial compounds. The TMDH methodology we have developed is generic and could be applied to identify essential genes in other bacterial pathogens.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are