Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation

Continual learning refers to the capability of a machine learning model to learn and adapt to new information, without compromising its performance on previously learned tasks. Although several studies have investigated continual learning methods for information retrieval tasks, a well-defined task formulation is still lacking, and it is unclear how typical learning strategies perform in this context. To address this challenge, a systematic task formulation of continual neural information retrieval is presented, along with a multiple-topic dataset that simulates continuous information retrieval. A comprehensive continual neural information retrieval framework consisting of typical retrieval models and continual learning strategies is then proposed. Empirical evaluations illustrate that the proposed framework can successfully prevent catastrophic forgetting in neural information retrieval and enhance performance on previously learned tasks. The results indicate that embedding-based retrieval models experience a decline in their continual learning performance as the topic shift distance and dataset volume of new tasks increase. In contrast, pretraining-based models do not show any such correlation. Adopting suitable learning strategies can mitigate the effects of topic shift and data augmentation.Comment: Submitted to Information Science

On extended state-space constructions for monte carlo methods

This thesis develops computationally efficient methodology in two areas. Firstly, we consider a particularly challenging class of discretely observed continuous-time point-process models. For these, we analyse and improve an existing filtering algorithm based on sequential Monte Carlo (smc) methods. To estimate the static parameters in such models, we devise novel particle Gibbs samplers. One of these exploits a sophisticated non-entred parametrisation whose benefits in a Markov chain Monte Carlo (mcmc) context have previously been limited by the lack of blockwise updates for the latent point process. We apply this algorithm to a Lévy-driven stochastic volatility model. Secondly, we devise novel Monte Carlo methods – based around pseudo-marginal and conditional smc approaches – for performing optimisation in latent-variable models and more generally. To ease the explanation of the wide range of techniques employed in this work, we describe a generic importance-sampling framework which admits virtually all Monte Carlo methods, including smc and mcmc methods, as special cases. Indeed, hierarchical combinations of different Monte Carlo schemes such as smc within mcmc or smc within smc can be justified as repeated applications of this framework

Efficient sequential Monte Carlo algorithms for integrated population models

In statistical ecology, state-space models are commonly used to represent the biological mechanisms by which population counts—often subdivided according to characteristics such as age group, gender or breeding status—evolve over time. As the counts are only noisily or partially observed, they are typically not sufficiently informative about demographic parameters of interest and must be combined with additional ecological observations within an integrated data analysis. Fitting integrated models can be challenging, especially if the constituent state-space model is nonlinear/non-Gaussian. We first propose an efficient particle Markov chain Monte Carlo algorithm to estimate demographic parameters without a need for linear or Gaussian approximations. We then incorporate this algorithm into a sequential Monte Carlo sampler to perform model comparison. We also exploit the integrated model structure to enhance the efficiency of both algorithms. The methods are demonstrated on two real data sets: little owls and grey herons. For the owls, we find that the data do not support an ecological hypothesis found in the literature. For the herons, our methodology highlights the limitations of existing models which we address through a novel regime-switching model. Supplementary materials accompanying this paper appear online

Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models

As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions. This paper proposes the AI-Reasoner, which extracts the morphological characteristics of defects (DefChars) from images and utilises decision trees to reason with the DefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e. charts) and textual explanations to provide insights into outputs made by masked-based defect detection and classification models. It also provides effective mitigation strategies to enhance data pre-processing and overall model performance. The AI-Reasoner was tested on explaining the outputs of an IE Mask R-CNN model using a set of 366 images containing defects. The results demonstrated its effectiveness in explaining the IE Mask R-CNN model's predictions. Overall, the proposed AI-Reasoner provides a solution for improving the performance of AI models in industrial applications that require defect analysis.Comment: 8 pages, 3 figures, 5 tables; submitted to 2023 IEEE symposium series on computational intelligence (SSCI

Limit theorems for sequential MCMC methods

Both sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Monte Carlo (sequential MCMC) methods constitute classes of algorithms which can be used to approximate expectations with respect to (a sequence of) probability distributions and their normalising constants. While SMC methods sample particles conditionally independently at each time step, sequential MCMC methods sample particles according to a Markov chain Monte Carlo (MCMC) kernel. Introduced over twenty years ago in [6], sequential MCMC methods have attracted renewed interest recently as they empirically outperform SMC methods in some applications. We establish an -inequality (which implies a strong law of large numbers) and a central limit theorem for sequential MCMC methods and provide conditions under which errors can be controlled uniformly in time. In the context of state-space models, we also provide conditions under which sequential MCMC methods can indeed outperform standard SMC methods in terms of asymptotic variance of the corresponding Monte Carlo estimators

Prognostic Factors Affecting Outcome after Allogeneic Transplantation for Hematological Malignancies from Unrelated Donors: Results from a Randomized Trial

Several prognostic factors for the outcome after allogeneic hematopoietic stem-cell transplant (HSCT) from matched unrelated donors have been postulated from registry data; however, data from randomized trials are lacking. We present analyses on the effects of patient-related, donor-related, and treatment-related prognostic factors on acute GVHD (aGVHD), chronic GVHD (cGVHD), relapse, nonrelapse mortality (NRM), disease-free survival (DFS), and overall survival (OS) in a randomized, multicenter, open-label, phase III trial comparing standard graft-versus-host-disease (GVHD) prophylaxis with and without pretransplantation ATG-Fresenius (ATG-F) in 201 adult patients receiving myeloablative conditioning before HSCT from HLA-A, HLA-B antigen, HLA-DRB1, HLA-DQB1 allele matched unrelated donors. High-resolution testing (allele) of HLA-A, HLA-B, and HLA-C were obtained after study closure, and the impact of an HLA 10/10 4-digit mismatch on outcome and on the treatment effect of ATG-F versus control investigated. Advanced disease was a negative factor for relapse, DFS, and OS. Donor age ≥40 adversely affected the risk of aGVHD III-IV, extensive cGVHD, and OS. Younger donors are to be preferred in unrelated donor transplantation. Advanced disease patients need special precautions to improve outcome. The degree of mismatch had no major influence on the positive effect of ATG-F on the reduction of aGVHD and cGVHD

Harmonization guidelines for HLA-peptide multimer assays derived from results of a large scale international proficiency panel of the Cancer Vaccine Consortium

PURPOSE: The Cancer Vaccine Consortium of the Cancer Research Institute (CVC-CRI) conducted a multicenter HLA-peptide multimer proficiency panel (MPP) with a group of 27 laboratories to assess the performance of the assay. EXPERIMENTAL DESIGN: Participants used commercially available HLA-peptide multimers and a well characterized common source of peripheral blood mononuclear cells (PBMC). The frequency of CD8+ T cells specific for two HLA-A2-restricted model antigens was measured by flow cytometry. The panel design allowed for participants to use their preferred staining reagents and locally established protocols for both cell labeling, data acquisition and analysis. RESULTS: We observed significant differences in both the performance characteristics of the assay and the reported frequencies of specific T cells across laboratories. These results emphasize the need to identify the critical variables important for the observed variability to allow for harmonization of the technique across institutions. CONCLUSIONS: Three key recommendations emerged that would likely reduce assay variability and thus move toward harmonizing of this assay. (1) Use of more than two colors for the staining (2) collect at least 100,000 CD8 T cells, and (3) use of a background control sample to appropriately set the analytical gates. We also provide more insight into the limitations of the assay and identified additional protocol steps that potentially impact the quality of data generated and therefore should serve as primary targets for systematic analysis in future panels. Finally, we propose initial guidelines for harmonizing assay performance which include the introduction of standard operating protocols to allow for adequate training of technical staff and auditing of test analysis procedures

Comparison of matched sibling donors versus unrelated donors in allogeneic stem cell transplantation for primary refractory acute myeloid leukemia: a study on behalf of the Acute Leukemia Working Party of the EBMT

Background: Primary refractory acute myeloid leukemia (PRF-AML) is associated with a dismal prognosis. Allogeneic stem cell transplantation (HSCT) in active disease is an alternative therapeutic strategy. The increased availability of unrelated donors together with the significant reduction in transplant-related mortality in recent years have opened the possibility for transplantation to a larger number of patients with PRF-AML. Moreover, transplant from unrelated donors may be associated with stronger graft-mediated anti-leukemic effect in comparison to transplantations from HLA-matched sibling donor, which may be of importance in the setting of PRF-AML. Methods: The current study aimed to address the issue of HSCT for PRF-AML and to compare the outcomes of HSCT from matched sibling donors (n = 660) versus unrelated donors (n = 381), for patients with PRF-AML between 2000 and 2013. The Kaplan-Meier estimator, the cumulative incidence function, and Cox proportional hazards regression models were used where appropriate. Results: HSCT provide patients with PRF-AML a 2-year leukemia-free survival and overall survival of about 25 and 30%, respectively. In multivariate analysis, two predictive factors, cytogenetics and time from diagnosis to transplant, were associated with lower leukemia-free survival, whereas Karnofsky performance status at transplant >= 90% was associated with better leukemia-free survival (LFS). Concerning relapse incidence, cytogenetics and time from diagnosis to transplant were associated with increased relapse. Reduced intensity conditioning regimen was the only factor associated with lower non-relapse mortality. Conclusions: HSCT was able to rescue about one quarter of the patients with PRF-AML. The donor type did not have any impact on PRF patients' outcomes. In contrast, time to transplant was a major prognostic factor for LFS. For patients with PRF-AML who do not have a matched sibling donor, HSCT from an unrelated donor is a suitable option, and therefore, initiation of an early search for allocating a suitable donor is indicated