12,718 research outputs found
Wallace_etal_2019_habenula_scseq
No full textsee read_ME.docx. Raw and processed data also available at NIH GEO database (GSE146983) here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146983.
Also see associated manuscript:
Wallace ML, Huang KW, Hochbaum D, Hyun M et al. Anatomical and single-cell transcriptional profiling of the murine habenular complex. Elife 2020 Feb 11;9. PMID: 3204396
Wallace_etal_2019_habenula_scseq
No full textsee read_ME.docx. Raw and processed data also available at NIH GEO database (GSE146983) here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146983.
Also see associated manuscript:
Wallace ML, Huang KW, Hochbaum D, Hyun M et al. Anatomical and single-cell transcriptional profiling of the murine habenular complex. Elife 2020 Feb 11;9. PMID: 3204396
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Novel asymmetric naphthalenediimide-polyamines conjugates as anticancer agents.
No full textAs the number of cases of cancer increases worldwide there is a need to develop better and more selective anticancer agents. Naphtalenediimide (NDI) scaffold is present in a wide number of anticancer agents which interact with DNA preferentially as intercalators (Brana and Ramos, 2001). Additionally, some derivatives characterized by this core have been optimized to exhibit bis-threading intercalating ability (Nojima et al., 2003), and to stabilize G-quadruplex DNA structure (Balasubramanian and Neidle, 2009). Recently, we reported a new series of NDI derivatives as antiproliferative agents (Tumiatti et al., 2009). These last compounds were characterized by NDI scaffold properly functionalized with two basic side chains. The most interesting compound was 1, characterized by one methoxy group on the two aromatic rings. Polyamine (PA) analogues have been extensively investigated for their potential pharmacological applications in different diseases, especially as anticancer agents (Casero and Woster, 2009; Wallace, 2009).
Furthermore, a polyamine transport system (PTS) was identified and several anthracene-polyamines conjugates demonstrated the ability to hit cancer cells selectively characterized by PTS (Palmer and Wallace, 2009). Very recently, symmetric NDI-PA conjugates were published showing an interesting biological profile as anticancer agents (Wang et al., 2012). On this basis new asymmetric NDI-polyamines conjugates (NDI-PA) were designed (see general structure in Figure 1) with the aim to improve the anticancer activity and/or selectivity of 1 through PTS interaction and transport inside the cells to reach several key targets including DNA. PAs architecture was altered and ranged from diamine to triamine and tetraamine systems. The novelty of such derivatives is represented by their asymmetric structure, which can offer the possibility to design future new conjugates characterized by NDI scaffold and PA chain, coupled with different anticancer pharmacophores.
All data related to the antiproliferative activity of these derivatives along with new insights on their molecular mechanisms, PTS interaction, and G-quadruplex stabilization will be shown and discussed
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
Using rationales and influential training examples to (attempt to) explain neural predictions in NLP
No full textPresented via Bluejeans Events on September 9, 2020 at 12:15 p.m.Byron Wallace is an assistant professor in the Khoury College of Computer Sciences at Northeastern University. Wallace’s research areas include artificial intelligence, data science, machine learning, natural language processing, and information retrieval, with emphasis on applications in health informatics.Runtime: 60:14 minutesModern deep learning models for natural language processing (NLP) achieve state-of-the-art predictive performance but are notoriously opaque. I will discuss recent work looking to address this limitation. I will focus specifically on approaches to: (i) Providing snippets of text (sometimes called "rationales") that support predictions, and; (ii) Identifying examples from the training data that influenced a given model output
Building a generalisable ML pipeline at ING
No full textAdvances in data science have caused an increase in the use of Artificial Intelligence (AI), specifically Machine Learning (ML), throughout various fields. Not only in research but in the industry as well, has ML been receiving increasing amounts of interest. Many companies rely on ML models to increase the efficiency of existing processes or offer new services and products. The industry, however, is facing several additional challenges compared to the academic context. One of those challenges is applying the Development Operations (DevOps) model to an ML application, also referred to as MLOps. This thesis sets out to find the specific challenges that practitioners encounter while operationalising ML models. To do so, we perform a single-case case study on an ML pipeline built by the Trade & Communication Surveillance team at the ING bank. This case study consists of conducting a set of interviews and performing a manual code inspection of the pipeline. The team faces challenges ranging from having insufficient time for operationalising each ML project individually to operating in the highlyregulated fintech context. Their pipeline is able to deploy a single ML model but it does not generalise well to other projects. We present the first version of an application that mitigates these challenges. The application is able to deploy ML models to the development environment at ING and can be operated by data scientists to reduce the effort of operationalising an ML model. Computer Science | Software Technolog
'Project smells' - Experiences in Analysing the Software Quality of ML Projects with mllint
No full textMachine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software EngineeringSoftware Technolog
Audiomobiles, Sculptures and Conundrums
No full textRoberto Gerhard was a pioneer of electronic music in England creating a number of substantial concert, theatre and radio works from as early as 1954. Gerhard’s electronic music is one of the richest repositories for understanding the development of the composer’s late compositional technique. Apart from the Symphony no.3, ‘Collages’, none of Gerhard’s electronic music is published. This paper will discuss aspects of Gerhard’s electronic music, focusing on Audiomobiles (1958-59) and Sculptures (1963)
- …
