5,902 research outputs found
MolFM: A Multimodal Molecular Foundation Model
Molecular knowledge resides within three different modalities of information
sources: molecular structures, biomedical documents, and knowledge bases.
Effective incorporation of molecular knowledge from these modalities holds
paramount significance in facilitating biomedical research. However, existing
multimodal molecular foundation models exhibit limitations in capturing
intricate connections between molecular structures and texts, and more
importantly, none of them attempt to leverage a wealth of molecular expertise
derived from knowledge graphs. In this study, we introduce MolFM, a multimodal
molecular foundation model designed to facilitate joint representation learning
from molecular structures, biomedical texts, and knowledge graphs. We propose
cross-modal attention between atoms of molecular structures, neighbors of
molecule entities and semantically related texts to facilitate cross-modal
comprehension. We provide theoretical analysis that our cross-modal
pre-training captures local and global molecular knowledge by minimizing the
distance in the feature space between different modalities of the same
molecule, as well as molecules sharing similar structures or functions. MolFM
achieves state-of-the-art performance on various downstream tasks. On
cross-modal retrieval, MolFM outperforms existing models with 12.13% and 5.04%
absolute gains under the zero-shot and fine-tuning settings, respectively.
Furthermore, qualitative analysis showcases MolFM's implicit ability to provide
grounding from molecular substructures and knowledge graphs. Code and models
are available on https://github.com/BioFM/OpenBioMed.Comment: 31 pages, 15 figures, and 15 table
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
The future of cosmology? A case for CMB spectral distortions
This thesis treats the topic of CMB Spectral Distortions (SDs), which
represent any deviation from a pure black body shape of the CMB energy
spectrum. As such, they can be used to probe the inflationary, expansion and
thermal evolution of the universe both within CDM and beyond it. The
currently missing observation of this rich probe of the universe makes of it an
ideal target for future observational campaigns. In fact, while the
CDM signal guarantees a discovery, the sensitivity to a wide variety
of new physics opens the door to an enormous uncharted territory. In light of
these considerations, the thesis opens by reviewing the topic of CMB SDs in a
pedagogical and illustrative fashion, aimed at waking the interest of the
broader community. This introductory premise sets the stage for the first main
contribution of the thesis to the field of SDs: their implementation in the
Boltzmann solver CLASS and the parameter inference code MontePython. The
CLASS+MontePython pipeline is publicly available, fast, it includes all sources
of SDs within CDM and many others beyond that, and allows to
consistently account for any observational setup. By means of these numerical
tools, the second main contribution of the thesis consists in showcasing the
versatility and competitiveness of SDs for several cosmological models as well
as for a number of different mission designs. Among others, the results cover
features in the primordial power spectrum, primordial gravitational waves,
non-standard dark matter properties, primordial black holes, primordial
magnetic fields and Hubble tension. Finally, the manuscript is disseminated
with (20) follow-up ideas that naturally extend the work carried out so far,
highlighting how rich of unexplored possibilities the field of CMB SDs still
is. The hope is that these suggestions will become a propeller for further
interesting developments.Comment: PhD thesis. Pedagogical review of theory, experimental status and
numerical tools (CLASS+MontePython) with broad overview of applications.
Includes 20 original follow-up idea
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Unfair behaviors of Machine Learning (ML) software have garnered increasing
attention and concern among software engineers. To tackle this issue, extensive
research has been dedicated to conducting fairness testing of ML software, and
this paper offers a comprehensive survey of existing studies in this field. We
collect 100 papers and organize them based on the testing workflow (i.e., how
to test) and testing components (i.e., what to test). Furthermore, we analyze
the research focus, trends, and promising directions in the realm of fairness
testing. We also identify widely-adopted datasets and open-source tools for
fairness testing
PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors
Graph neural networks (GNNs) have recently emerged as a promising learning
paradigm in learning graph-structured data and have demonstrated wide success
across various domains such as recommendation systems, social networks, and
electronic design automation (EDA). Like other deep learning (DL) methods, GNNs
are being deployed in sophisticated modern hardware systems, as well as
dedicated accelerators. However, despite the popularity of GNNs and the recent
efforts of bringing GNNs to hardware, the fault tolerance and resilience of
GNNs have generally been overlooked. Inspired by the inherent algorithmic
resilience of DL methods, this paper conducts, for the first time, a
large-scale and empirical study of GNN resilience, aiming to understand the
relationship between hardware faults and GNN accuracy. By developing a
customized fault injection tool on top of PyTorch, we perform extensive fault
injection experiments on various GNN models and application datasets. We
observe that the error resilience of GNN models varies by orders of magnitude
with respect to different models and application datasets. Further, we explore
a low-cost error mitigation mechanism for GNN to enhance its resilience. This
GNN resilience study aims to open up new directions and opportunities for
future GNN accelerator design and architectural optimization
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
People Talking and AI Listening: How Stigmatizing Language in EHR Notes Affect AI Performance
Electronic health records (EHRs) serve as an essential data source for the
envisioned artificial intelligence (AI)-driven transformation in healthcare.
However, clinician biases reflected in EHR notes can lead to AI models
inheriting and amplifying these biases, perpetuating health disparities. This
study investigates the impact of stigmatizing language (SL) in EHR notes on
mortality prediction using a Transformer-based deep learning model and
explainable AI (XAI) techniques. Our findings demonstrate that SL written by
clinicians adversely affects AI performance, particularly so for black
patients, highlighting SL as a source of racial disparity in AI model
development. To explore an operationally efficient way to mitigate SL's impact,
we investigate patterns in the generation of SL through a clinicians'
collaborative network, identifying central clinicians as having a stronger
impact on racial disparity in the AI model. We find that removing SL written by
central clinicians is a more efficient bias reduction strategy than eliminating
all SL in the entire corpus of data. This study provides actionable insights
for responsible AI development and contributes to understanding clinician
behavior and EHR note writing in healthcare.Comment: 54 pages, 9 figure
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
The Characterisation and Treatment of Resistant Hypertension
Hypertension is a highly prevalent condition and, as a risk factor for vascular disease in particular, a leading contributory cause of death worldwide. Recent consensus guidelines suggest that moderate and severe (grade II and III) hypertension should be treated rapidly to achieve targets though, prior to the inception of this thesis, the evidence for the safety and efficacy of this approach, together with the physiological consequences of rapid hypertension treatment in moderate and severe disease, was limited.
This thesis explores the clinical consequences of an 18-week treatment programme for individuals with grade II and III hypertension, using guideline- recommended pharmacological treatment, delivered over an accelerated timeframe. The blood pressure response to treatment is reported, together with the tolerance and safety of the protocol, as defined by the protocol completion rate, frequency of medication side effects and clinically significant adverse events. The programme also provided an opportunity to study health-rated quality of life in patients with moderate and severe hypertension and the effect of rapid treatment on health-related quality of life. This allowed for the first validation (according to modern standards) of an English language disease-specific instrument for measuring health-related quality of life in hypertension, following translation of the original MINICHAL disease-specific instrument from the original Spanish.
In addition, the clinical treatment programme provided an opportunity to study the microvascular response to rapid treatment of moderate and severe hypertension, particularly with relevance to the rarefaction of hypertension and its reversal with treatment. Moreover, the morphological and functional myocardial consequences of treatment were determined, using cardiac MR imaging.
Accordingly, this thesis presents evidence supporting the rapid treatment of moderate and severe hypertension, providing an opportunity for this to be studied in future investigations, with the aim of exploring whether this approach is prognostically advantageous for patients
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