We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses

Many practical applications of dialogue technology require the generation of responses according to a particular developer-specified persona. While a variety of personas can be elicited from recent large language models, the opaqueness and unpredictability of these models make it desirable to be able to specify personas in an explicit form. In previous work, personas have typically been represented as sets of one-off pieces of self-knowledge that are retrieved by the dialogue system for use in generation. However, in realistic human conversations, personas are often revealed through story-like narratives that involve rich habitual knowledge -- knowledge about kinds of events that an agent often participates in (e.g., work activities, hobbies, sporting activities, favorite entertainments, etc.), including typical goals, sub-events, preconditions, and postconditions of those events. We capture such habitual knowledge using an explicit schema representation, and propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses. Furthermore, we demonstrate a method for bootstrapping the creation of such schemas by first generating generic passages from a set of simple facts, and then inducing schemas from the generated passages

Get the gist? Using large language models for few-shot decontextualization

In many NLP applications that involve interpreting sentences within a rich context -- for instance, information retrieval systems or dialogue systems -- it is desirable to be able to preserve the sentence in a form that can be readily understood without context, for later reuse -- a process known as ``decontextualization''. While previous work demonstrated that generative Seq2Seq models could effectively perform decontextualization after being fine-tuned on a specific dataset, this approach requires expensive human annotations and may not transfer to other domains. We propose a few-shot method of decontextualization using a large language model, and present preliminary results showing that this method achieves viable performance on multiple domains using only a small set of examples

Cross-Cultural Variations in Identifying Embedded Figures : Comparisons from the United States, Germany, Russia, and Malaysia

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Previous studies have found cross-cultural variation in field dependence. In this study, cross-cultural differences were expected depending on the degree of individualism or collectivism of the respective cultures. Samples were taken from two prototypically individualist cultures, the United States and Germany, and two collectivist cultures, Russia and Malaysia. As predicted, field dependence did not differ between those samples that represented the same type of culture, either collectivist or individualist. However, a clear difference could be found between the two types of cultures; that is, U.S. and German participants were more field independent than were Russian and Malaysian participants

Improved proteasomal cleavage prediction with positive-unlabeled learning

Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer. An important step of such pathway is the degradation of the vaccine into smaller peptides by the proteasome, some of which are going to be presented to T cells by the MHC complex. While predicting MHC-peptide presentation has received a lot of attention recently, proteasomal cleavage prediction remains a relatively unexplored area in light of recent advancesin high-throughput mass spectrometry-based MHC ligandomics. Moreover, as such experimental techniques do not allow to identify regions that cannot be cleaved, the latest predictors generate decoy negative samples and treat them as true negatives when training, even though some of them could actually be positives. In this work, we thus present a new predictor trained with an expanded dataset and the solid theoretical underpinning of positive-unlabeled learning, achieving a new state-of-the-art in proteasomal cleavage prediction. The improved predictive capabilities will in turn enable more precise vaccine development improving the efficacy of epitope-based vaccines. Pretrained models are available on GitHubComment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 8 page

Advanced Immunoinformatics Approaches for Precision Medicine

Genomic sequencing and other ’-omic’ technologies are slowly changing biomedical practice. As a result, patients now can be treated based on their molecular profile. Especially the immune system’s variability, in particular that of the human leukocyte antigen (HLA) gene cluster, makes such a paradigm indispensable when treating illnesses such as cancer, autoimmune diseases, or infectious diseases. It can be, however, costly and time-consuming to determine the HLA genotype with traditional means, as these methods do not utilize often pre-existing sequencing data. We therefore proposed an algorithmic approach that can use these data sources to infer the HLA genotype. HLA genotyping inference can be cast into a set covering problem under special biological constraints and can be solved efficiently via integer linear programming. Our proposed approach outperformed previously published methods and remains one of the most accurate methods to date. We then introduced two applications in which a HLA-based stratification is vital for the efficacy of the treatment and the reduction of its adverse effects. In the first example, we dealt with the optimal design of string-of-beads vaccines (SOB). We developed a mathematical model that maximizes the efficacy of such vaccines while minimizing their side effects based on a given HLA distribution. Comparisons of our optimally designed SOB with experimentally tested designs yielded promising results. In the second example, we considered the problem of anti-drug antibody (ADA) formation of biotherapeutics caused by HLA presented peptides. We combined a new statistical model for mutation effect prediction together with a quantitative measure of immunogenicity to formulate an optimization problem that finds alterations to reduce the risk of ADA formation. To efficiently solve this bi-objective problem, we developed a distributed solver that is up to 25-times faster than state-of-the art solvers. We used our approach to design the C2 domain of factor VIII, which is linked to ADA formation in hemophilia A. Our experimental evaluations of the proposed designs are encouraging and demonstrate the prospects of our approach. Bioinformatics is an integral part of modern biomedical research. The translation of advanced methods into clinical use is often complicated. To ease the translation, we developed a programming library for computational immunology and used it to implement a Galaxy-based web server for vaccine design and a KNIME extension for desktop PCs. These platforms allow researchers to develop their own immunoinformatics workflows utilizing the platform’s graphical programming capabilities

Performance Enhancement of Submerged Ocean Wave Energy Converter using Nonlinear Stiffness

Ocean waves are a source of renewable energy with an enormous potential to augment current renewable energy markets. Historically, the levelised cost of wave energy has been higher than conventional renewable energy sources such as wind or solar. While significant progress has been made in improving the economic viability of wave energy, a robust control system for wave energy converters is an important step to progress their technology readiness level. Utility scale wave energy systems typically require large capital investment. Therefore, tools are required to accurately and reliably model systems to predict the dynamic response and performance of potential control systems. This thesis presents a passive control system in the form of a nonlinear stiffness to improve the robustness of wave energy systems in situ as the ocean wave conditions change over time. In the preceding work in the literature, two common shortcomings, which may undermine the investigations, are: (i) the lack of comparisons against optimal conditions; and, (ii) the simplistic representation of hydrodynamic forces in fluid-structure interactions. These two gaps underpin the purpose of each chapter of this thesis and are systematically addressed in the context of a submerged point absorbing wave energy converter. Many differing designs of wave energy converters have been proposed in literature, with fundamentally different modes of operation. This thesis initially compares the application of a passive control system to point absorbing wave energy devices in both floating and submerged contexts. It was found that the application of nonlinear stiffness did not improve upon a system controlled by an optimised linear stiffness in both floating and submerged scenarios for regular wave excitation. Since many floating point absorbers experience a large hydrostatic stiffness, mechanisms to provide large negative stiffness are required for tuning purposes. The nonlinear stiffness — which can provide negative stiffness—offers a notable improvement in power production capacity compared to the scenario with no control stiffness in floating systems. For a submerged system, a position-dependent force is inherently required to counteract the constant buoyancy force, so the system may be optimally tuned by a linear stiffness. For irregular waves, which are more representative of ocean conditions, a floating system without an optimised linear stiffness experiences a significant benefit, while systems with optimal linear parameters do not benefit in terms of the power converted. However, as ocean conditions change in terms of significant wave height, energy period, and wave phase relationships, the addition of a nonlinear stiffness mechanism provides an improvement by enhancing the robustness to changing ocean conditions and by desensitising the system to wave phasing. The fidelity of simulations involving nonlinear stiffness may be improved by extending the model to three degrees of freedom to capture geometric nonlinearities and dynamic coupling between different degrees of freedom. In this work, the nonlinear stiffness was parametrised and varied to demonstrate how and why the system responds either positively or negatively depending on particular wave conditions. It was shown that when the system is optimally tuned for a regular wave, the nonlinear stiffness is not able to improve the amount of power generated. For irregular waves, the optimal performance is observed when the system is tuned with a linear stiffness to give a particular natural frequency—depending on wave condition. However, the same performance is also achieved with a nonlinear stiffness augmentation when the system is oscillating about any equilibrium point if the position dependent natural frequency is close to the optimal natural frequency. A consistent beneficial trend is seen under different irregular wave excitations. The nonlinear stiffness exposes the system to a changing effective resonance frequency varying with position. As a result, performance improvements over the linear system are observed when the system is tuned for one irregular wave and excited by a different irregular wave. Therefore, the primary benefit of a nonlinear augmentation is the improvement to robustness of such systems for varying sea conditions. The hydrodynamic modelling of the fluid-structure interaction of a submerged wave energy device is often achieved using linear potential flow theory. This limitation is explored by comparing both linear and nonlinear hydrodynamic models (using a validated computational fluid dynamics simulation) with a novel pseudo-nonlinear model, which extends the linear model to incorporate pose-dependent hydrodynamic parameters during simulation through pre-calculated values. The results showed that linear hydrodynamics do not adequately represent all the important nonlinear effects. The trends in motion also indicates the presence of frequency dependent fluid-structure interactions associated with the resonance of body of water above the buoy. It is not possible to represent such phenomena using standard linear potential flow methods. Therefore, higher fidelity models should be employed to obtain more reliable indications of performance. The three degrees of freedom model was further extended by including nonlinear stiffness into the validated computational fluid dynamics model. It was shown that inclusion of nonlinear hydrodynamics shifts the optimal natural frequency of the system. For regular waves, the nonlinear stiffness did not provide a consistent improvement. Under irregular conditions, a small amount of nonlinear stiffness was shown to provide a 5.5% improvement. The nonlinear stiffness was parametrised relative to the potential energy of the incident wave, leading to the observation that the peak in time-averaged power generation occurred when the nonlinear stiffness potential at the nominal equilibrium position was around 25% of the potential energy of the incident wave. While the trend in power results between the models using linear and nonlinear hydrodynamics with the nonlinear stiffness were reasonably similar, in the nonlinear hydrodynamics model, the nonlinear stiffness more rapidly detunes the system than in the linear model. This finding indicates that a nonlinear stiffness mechanism may be an effective method to detune the device to protect components from extreme operating conditions.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 202

Comparison of wave-body interaction modelling methods for the study of reactively controlled point absorber wave energy converter

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