397 research outputs found
Machine learning applications in search algorithms for gravitational waves from compact binary mergers
Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe.
However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing.
In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software.
Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals
Development and implementation of in silico molecule fragmentation algorithms for the cheminformatics analysis of natural product spaces
Computational methodologies extracting specific substructures like functional groups or molecular scaffolds from input molecules can be grouped under the term “in silico molecule fragmentation”. They can be used to investigate what specifically characterises a heterogeneous compound class, like pharmaceuticals or Natural Products (NP) and in which aspects they are similar or dissimilar. The aim is to determine what specifically characterises NP structures to transfer patterns favourable for bioactivity to drug development. As part of this thesis, the first algorithmic approach to in silico deglycosylation, the removal of glycosidic moieties for the study of aglycones, was developed with the Sugar Removal Utility (SRU) (Publication A). The SRU has also proven useful for investigating NP glycoside space. It was applied to one of the largest open NP databases, COCONUT (COlleCtion of Open Natural prodUcTs), for this purpose (Publication B). A contribution was made to the Chemistry Development Kit (CDK) by developing the open Scaffold Generator Java library (Publication C). Scaffold Generator can extract different scaffold types and dissect them into smaller parent scaffolds following the scaffold tree or scaffold network approach. Publication D describes the OngLai algorithm, the first automated method to identify homologous series in input datasets, group the member structures of each group, and extract their common core. To support the development of new fragmentation algorithms, the open Java rich client graphical user interface application MORTAR (MOlecule fRagmenTAtion fRamework) was developed as part of this thesis (Publication E). MORTAR allows users to quickly execute the steps of importing a structural dataset, applying a fragmentation algorithm, and visually inspecting the results in different ways. All software developed as part of this thesis is freely and openly available (see https://github.com/JonasSchaub)
Metacomprehension Accuracy of Health-Related Information
As part of the production of written information, patient reader panels provide judgments of their understanding to evaluate the comprehensibility of draft documents. Previous research has suggested i) that there is a limited association, on average, between judgments of understanding and the comprehension demonstrated in tests of understanding and ii) that there is considerable variability between individuals in the direction and magnitude of this association. Unfortunately, while previous research implies, critically, that reader judgments of comprehensibility have limited utility, this research itself is characterized by important limitations that prevent firm conclusions. This thesis comprises three experimental studies. The study design, method of measurement, and the approach to analysis were motivated by a critical review of previous research. The specification of participant, text and question sample sizes was determined by a novel method of prospective study design analysis, evaluating the accuracy and precision in effect estimation. The robustness of effect estimates are established through the series of empirical replications and in analytical sensitivity checks. Across the studies, a weakly positive association between perceived and assessed comprehension was found across individuals, on average. Differences in reading ability and background knowledge did not reliably influence metacomprehension accuracy. Further, metacomprehension judgements were similarly predictive of performance on comprehension questions that targeted more versus less semantically central information. In contrast, metacomprehension judgements targeting specific ideas within texts were more predictive of understanding. The findings of this thesis indicate that metacomprehension judgements are not a gold-standard method of evaluation: judgements show some predictive validity of comprehension outcomes, yet provide little insight into whether critical elements of the documents are sufficiently understood. Overall, whilst situated within an applied context, the present research contributes more widely to the metacomprehension literature, making clear the need for a shift from traditional analytical approaches, in addition to greater theoretical precision
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Waiting for the Revolution to End: Syrian displacement, time and subjectivity
Waiting for the Revolution to End explores the Syrian revolution through the experiences of citizens in exile. Based on more than three years of embedded fieldwork with Syrians displaced in the border city of Gaziantep (southern Turkey), the book places the Syrian revolution and its tragic aftermath under ethnographic scrutiny. It charts the evolution from peaceful uprising (2011) to armed confrontation (2012), descent into fully fledged conflict (2013) and finally to proxy war (2015), to propose an understanding of revolution beyond success and failure.
While the Assad regime remains in place, the Syrian revolution (al-thawra) still holds a transformational power that can be located on intimate and world-making scales. Charlotte Al-Khalili traces the unintended consequences of revolution and its unexpected consequences to reveal the reshaping of Syrian life-worlds and exiles’ evolving theorizations, experiences and imaginations of al-thawra. She describes the in-between spatio-temporal realm inhabited by Syrians displaced to Turkey as they await the revolution’s outcomes, and maps the revolution’s multidimensional and multi-scalar effects on their everyday life. By following the chronology of events inside Syria and Syrians’ geography of displacement, the book makes the relation between revolution and displacement its centerpiece, both as an ethnographic object and an analytical device
The Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity
Community detection is a classic problem in network science with extensive
applications in various fields. Among numerous approaches, the most common
method is modularity maximization. Despite their design philosophy and wide
adoption, heuristic modularity maximization algorithms rarely return an optimal
partition or anything similar. We propose a specialized algorithm, Bayan, which
returns partitions with a guarantee of either optimality or proximity to an
optimal partition. At the core of the Bayan algorithm is a branch-and-cut
scheme that solves an integer programming formulation of the problem to
optimality or approximate it within a factor. We demonstrate Bayan's
distinctive accuracy and stability over 21 other algorithms in retrieving
ground-truth communities in synthetic benchmarks and node labels in real
networks. Bayan is several times faster than open-source and commercial solvers
for modularity maximization making it capable of finding optimal partitions for
instances that cannot be optimized by any other existing method. Overall, our
assessments point to Bayan as a suitable choice for exact maximization of
modularity in networks with up to 3000 edges (in their largest connected
component) and approximating maximum modularity in larger networks on ordinary
computers.Comment: 6 pages, 2 figures, 1 tabl
Case and Gender Loss in Germanic, Romance, and Balkan Sprachbund Languages
My dissertation investigates the loss of morphological case and grammatical gender in the Germanic, Romance, and Balkan Sprachbund languages. Crucial language-internal and language-external motivations are considered. To illustrate the changes of morphological cases, the languages are divided into historical stages. Every change in nominal inflection between stages is attributed to either sound change or analogical change; these choices are justified through consideration of historical sound changes and the motivations behind analogical processes. The changes are also discussed in terms of their effects on number syncretism, case and gender mergers, order of case loss, and the relationship between gender and declension.These motivations can be classified as language-internal or language-external. Phonological, morphosyntactic, and semantic factors are among the former. Different types of sound change can neutralize inflection differences, but two closely related types, prosodic change, and vowel reduction have been suggested as key causes in case and gender loss in IE languages. A usual direction of change in morphological case loss includes variation between two or more cases in one or more functions, followed by functional narrowing and occasionally a complete functional merger of the case markings. Similarly, there can be differences between a case and an analytic construction, which can lead to the former being replaced by the latter in some or all functions. External motivations for case and gender loss include the kinds of contact conditions that cause or accelerate simplification in internal developments. Essential contact situation is the establishment of a sprachbund, or linguistic region, which usually entails structural convergence among surrounding languages during a long period of profound contact. Interactions among number, case, and gender are analyzed using original quantitative measures of number syncretism on nouns and gender syncretism on agreement targets. Overall, the results of my study support the general hypothesis that the loss of case and gender categories can be explained by the neutralization of distinctions in these categories as a direct result of sound change and by the profiling of a more relevant category through analogical processes
Changing Priorities. 3rd VIBRArch
In order to warrant a good present and future for people around the planet and to safe the care of the planet itself, research in architecture has to release all its potential. Therefore, the aims of the 3rd Valencia International Biennial of Research in Architecture are:
- To focus on the most relevant needs of humanity and the planet and what architectural research can do for solving them.
- To assess the evolution of architectural research in traditionally matters of interest and the current state of these popular and widespread topics.
- To deepen in the current state and findings of architectural research on subjects akin to post-capitalism and frequently related to equal opportunities and the universal right to personal development and happiness.
- To showcase all kinds of research related to the new and holistic concept of sustainability and to climate emergency.
- To place in the spotlight those ongoing works or available proposals developed by architectural researchers in order to combat the effects of the COVID-19 pandemic.
- To underline the capacity of architectural research to develop resiliency and abilities to adapt itself to changing priorities.
- To highlight architecture's multidisciplinarity as a melting pot of multiple approaches, points of view and expertise.
- To open new perspectives for architectural research by promoting the development of multidisciplinary and inter-university networks and research groups.
For all that, the 3rd Valencia International Biennial of Research in Architecture is open not only to architects, but also for any academic, practitioner, professional or student with a determination to develop research in architecture or neighboring fields.Cabrera Fausto, I. (2023). Changing Priorities. 3rd VIBRArch. Editorial Universitat Politècnica de València. https://doi.org/10.4995/VIBRArch2022.2022.1686
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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