15,637 research outputs found
ADS_UNet: A Nested UNet for Histopathology Image Segmentation
The UNet model consists of fully convolutional network (FCN) layers arranged
as contracting encoder and upsampling decoder maps. Nested arrangements of
these encoder and decoder maps give rise to extensions of the UNet model, such
as UNete and UNet++. Other refinements include constraining the outputs of the
convolutional layers to discriminate between segment labels when trained end to
end, a property called deep supervision. This reduces feature diversity in
these nested UNet models despite their large parameter space. Furthermore, for
texture segmentation, pixel correlations at multiple scales contribute to the
classification task; hence, explicit deep supervision of shallower layers is
likely to enhance performance. In this paper, we propose ADS UNet, a stage-wise
additive training algorithm that incorporates resource-efficient deep
supervision in shallower layers and takes performance-weighted combinations of
the sub-UNets to create the segmentation model. We provide empirical evidence
on three histopathology datasets to support the claim that the proposed ADS
UNet reduces correlations between constituent features and improves performance
while being more resource efficient. We demonstrate that ADS_UNet outperforms
state-of-the-art Transformer-based models by 1.08 and 0.6 points on CRAG and
BCSS datasets, and yet requires only 37% of GPU consumption and 34% of training
time as that required by Transformers.Comment: To be published in Expert Systems With Application
A hybrid quantum algorithm to detect conical intersections
Conical intersections are topologically protected crossings between the
potential energy surfaces of a molecular Hamiltonian, known to play an
important role in chemical processes such as photoisomerization and
non-radiative relaxation. They are characterized by a non-zero Berry phase,
which is a topological invariant defined on a closed path in atomic coordinate
space, taking the value when the path encircles the intersection
manifold. In this work, we show that for real molecular Hamiltonians, the Berry
phase can be obtained by tracing a local optimum of a variational ansatz along
the chosen path and estimating the overlap between the initial and final state
with a control-free Hadamard test. Moreover, by discretizing the path into
points, we can use single Newton-Raphson steps to update our state
non-variationally. Finally, since the Berry phase can only take two discrete
values (0 or ), our procedure succeeds even for a cumulative error bounded
by a constant; this allows us to bound the total sampling cost and to readily
verify the success of the procedure. We demonstrate numerically the application
of our algorithm on small toy models of the formaldimine molecule
(\ce{H2C=NH}).Comment: 15 + 10 pages, 4 figure
BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Twitter bot detection has become a crucial task in efforts to combat online
misinformation, mitigate election interference, and curb malicious propaganda.
However, advanced Twitter bots often attempt to mimic the characteristics of
genuine users through feature manipulation and disguise themselves to fit in
diverse user communities, posing challenges for existing Twitter bot detection
models. To this end, we propose BotMoE, a Twitter bot detection framework that
jointly utilizes multiple user information modalities (metadata, textual
content, network structure) to improve the detection of deceptive bots.
Furthermore, BotMoE incorporates a community-aware Mixture-of-Experts (MoE)
layer to improve domain generalization and adapt to different Twitter
communities. Specifically, BotMoE constructs modal-specific encoders for
metadata features, textual content, and graphical structure, which jointly
model Twitter users from three modal-specific perspectives. We then employ a
community-aware MoE layer to automatically assign users to different
communities and leverage the corresponding expert networks. Finally, user
representations from metadata, text, and graph perspectives are fused with an
expert fusion layer, combining all three modalities while measuring the
consistency of user information. Extensive experiments demonstrate that BotMoE
significantly advances the state-of-the-art on three Twitter bot detection
benchmarks. Studies also confirm that BotMoE captures advanced and evasive
bots, alleviates the reliance on training data, and better generalizes to new
and previously unseen user communities.Comment: Accepted at SIGIR 202
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC
The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this
final state has yielded some of the most precise measurements of the particle. As
measurements of the Higgs boson become increasingly precise, greater import is
placed on the factors that constitute the uncertainty. Reducing the effects of these
uncertainties requires an understanding of their causes. The research presented
in this thesis aims to illuminate how uncertainties on simulation modelling are
determined and proffers novel techniques in deriving them.
The upgrade of the FastCaloSim tool is described, used for simulating events in
the ATLAS calorimeter at a rate far exceeding the nominal detector simulation,
Geant4. The integration of a method that allows the toolbox to emulate the
accordion geometry of the liquid argon calorimeters is detailed. This tool allows
for the production of larger samples while using significantly fewer computing
resources.
A measurement of the total Higgs boson production cross-section multiplied
by the diphoton branching ratio (σ × Bγγ) is presented, where this value was
determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement
with the Standard Model prediction. The signal and background shape modelling
is described, and the contribution of the background modelling uncertainty to the
total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production
mechanism.
A method for estimating the number of events in a Monte Carlo background
sample required to model the shape is detailed. It was found that the size of
the nominal γγ background events sample required a multiplicative increase by
a factor of 3.60 to adequately model the background with a confidence level of
68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate,
0.5 billion additional simulated events were produced, substantially reducing the
background modelling uncertainty.
A technique is detailed for emulating the effects of Monte Carlo event generator
differences using multivariate reweighting. The technique is used to estimate the
event generator uncertainty on the signal modelling of tHqb events, improving the
reliability of estimating the tHqb production cross-section. Then this multivariate
reweighting technique is used to estimate the generator modelling uncertainties
on background V γγ samples for the first time. The estimated uncertainties were
found to be covered by the currently assumed background modelling uncertainty
Visualisation of Fundamental Movement Skills (FMS): An Iterative Process Using an Overarm Throw
Fundamental Movement Skills (FMS) are precursor gross motor skills to more complex or specialised skills and are recognised as important indicators of physical competence, a key component of physical literacy. FMS are predominantly assessed using pre-defined manual methodologies, most commonly the various iterations of the Test of Gross Motor Development. However, such assessments are time-consuming and often require a minimum basic level of training to conduct. Therefore, the overall aim of this thesis was to utilise accelerometry to develop a visualisation concept as part of a feasibility study to support the learning and assessment of FMS, by reducing subjectivity and the overall time taken to conduct a gross motor skill assessment. The overarm throw, an important fundamental movement skill, was specifically selected for the visualisation development as it is an acyclic movement with a distinct initiation and conclusion. Thirteen children (14.8 ± 0.3 years; 9 boys) wore an ActiGraph GT9X Link Inertial Measurement Unit device on the dominant wrist whilst performing a series of overarm throws. This thesis illustrates how the visualisation concept was developed using raw accelerometer data, which was processed and manipulated using MATLAB 2019b software to obtain and depict key throw performance data, including the trajectory and velocity of the wrist during the throw. Overall, this thesis found that the developed visualisation concept can provide strong indicators of throw competency based on the shape of the throw trajectory. Future research should seek to utilise a larger, more diverse, population, and incorporate machine learning. Finally, further work is required to translate this concept to other gross motor skills
Breast mass segmentation from mammograms with deep transfer learning
Abstract. Mammography is an x-ray imaging method used in breast cancer screening, which is a time consuming process. Many different computer assisted diagnosis have been created to hasten the image analysis. Deep learning is the use of multilayered neural networks for solving different tasks. Deep learning methods are becoming more advanced and popular for segmenting images. One deep transfer learning method is to use these neural networks with pretrained weights, which typically improves the neural networks performance.
In this thesis deep transfer learning was used to segment cancerous masses from mammography images. The convolutional neural networks used were pretrained and fine-tuned, and they had an an encoder-decoder architecture. The ResNet22 encoder was pretrained with mammography images, while the ResNet34 encoder was pretrained with various color images. These encoders were paired with either a U-Net or a Feature Pyramid Network decoder. Additionally, U-Net model with random initialization was also tested. The five different models were trained and tested on the Oulu Dataset of Screening Mammography (9204 images) and on the Portuguese INbreast dataset (410 images) with two different loss functions, binary cross-entropy loss with soft Jaccard loss and a loss function based on focal Tversky index.
The best models were trained on the Oulu Dataset of Screening Mammography with the focal Tversky loss. The best segmentation result achieved was a Dice similarity coefficient of 0.816 on correctly segmented masses and a classification accuracy of 88.7% on the INbreast dataset. On the Oulu Dataset of Screening Mammography, the best results were a Dice score of 0.763 and a classification accuracy of 83.3%. The results between the pretrained models were similar, and the pretrained models had better results than the non-pretrained models. In conclusion, deep transfer learning is very suitable for mammography mass segmentation and the choice of loss function had a large impact on the results.Rinnan massojen segmentointi mammografiakuvista syvä- ja siirto-oppimista hyödyntäen. Tiivistelmä. Mammografia on röntgenkuvantamismenetelmä, jota käytetään rintäsyövän seulontaan. Mammografiakuvien seulonta on aikaa vievää ja niiden analysoimisen avuksi on kehitelty useita tietokoneavusteisia ratkaisuja. Syväoppimisella tarkoitetaan monikerroksisten neuroverkkojen käyttöä eri tehtävien ratkaisemiseen. Syväoppimismenetelmät ovat ajan myötä kehittyneet ja tulleet suosituiksi kuvien segmentoimiseen. Yksi tapa yhdistää syvä- ja siirtooppimista on hyödyntää neuroverkkoja esiopetettujen painojen kanssa, mikä auttaa parantamaan neuroverkkojen suorituskykyä.
Tässä diplomityössä tutkittiin syvä- ja siirto-oppimisen käyttöä syöpäisten massojen segmentoimiseen mammografiakuvista. Käytetyt konvoluutioneuroverkot olivat esikoulutettuja ja hienosäädettyjä. Lisäksi niillä oli enkooderi-dekooderi arkkitehtuuri. ResNet22 enkooderi oli esikoulutettu mammografia kuvilla, kun taas ResNet34 enkooderi oli esikoulutettu monenlaisilla värikuvilla. Näihin enkoodereihin yhdistettiin joko U-Net:n tai piirrepyramidiverkon dekooderi. Lisäksi käytettiin U-Net mallia ilman esikoulutusta. Nämä viisi erilaista mallia koulutettiin ja testattiin sekä Oulun Mammografiaseulonta Datasetillä (9204 kuvaa) että portugalilaisella INbreast datasetillä (410 kuvaa) käyttäen kahta eri tavoitefunktiota, jotka olivat binääriristientropia yhdistettynä pehmeällä Jaccard-indeksillä ja fokaaliin Tversky indeksiin perustuva tavoitefunktiolla.
Parhaat mallit olivat koulutettu Oulun datasetillä fokaalilla Tversky tavoitefunktiolla. Parhaat tulokset olivat 0,816 Dice kerroin oikeissa positiivisissa segmentaatioissa ja 88,7 % luokittelutarkkuus INbreast datasetissä. Esikoulutetut mallit antoivat parempia tuloksia kuin mallit joita ei esikoulutettu. Oulun datasetillä parhaat tulokset olivat 0,763:n Dice kerroin ja 83,3 % luokittelutarkkuus. Tuloksissa ei ollut suurta eroa esikoulutettujen mallien välillä. Tulosten perusteella syvä- ja siirto-oppiminen soveltuvat hyvin massojen segmentoimiseen mammografiakuvista. Lisäksi tavoitefunktiovalinnalla saatiin suuri vaikutus tuloksiin
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
Increased lifetime of Organic Photovoltaics (OPVs) and the impact of degradation, efficiency and costs in the LCOE of Emerging PVs
Emerging photovoltaic (PV) technologies such as organic photovoltaics (OPVs) and perovskites (PVKs) have the potential to disrupt the PV market due to their ease of fabrication (compatible with cheap roll-to-roll processing) and installation, as well as their significant efficiency improvements in recent years. However, rapid degradation is still an issue present in many emerging PVs, which must be addressed to enable their commercialisation. This thesis shows an OPV lifetime enhancing technique by adding the insulating polymer PMMA to the active layer, and a novel model for quantifying the impact of degradation (alongside efficiency and cost) upon levelized cost of energy (LCOE) in real world emerging PV installations.
The effect of PMMA morphology on the success of a ternary strategy was investigated, leading to device design guidelines. It was found that either increasing the weight percent (wt%) or molecular weight (MW) of PMMA resulted in an increase in the volume of PMMA-rich islands, which provided the OPV protection against water and oxygen ingress. It was also found that adding PMMA can be effective in enhancing the lifetime of different active material combinations, although not to the same extent, and that processing additives can have a negative impact in the devices lifetime.
A novel model was developed taking into account realistic degradation profile sourced from a literature review of state-of-the-art OPV and PVK devices. It was found that optimal strategies to improve LCOE depend on the present characteristics of a device, and that panels with a good balance of efficiency and degradation were better than panels with higher efficiency but higher degradation as well. Further, it was found that low-cost locations were more favoured from reductions in the degradation rate and module cost, whilst high-cost locations were more benefited from improvements in initial efficiency, lower discount rates and reductions in install costs
Exploring the Structure of Scattering Amplitudes in Quantum Field Theory: Scattering Equations, On-Shell Diagrams and Ambitwistor String Models in Gauge Theory and Gravity
In this thesis I analyse the structure of scattering amplitudes in super-symmetric gauge and gravitational theories in four dimensional spacetime, starting with a detailed review of background material accessible to a non-expert. I then analyse the 4D scattering equations, developing the theory of how they can be used to express scattering amplitudes at tree level. I go on to explain how the equations can be solved numerically using a Monte Carlo algorithm, and introduce my Mathematica package treeamps4dJAF which performs these calculations. Next I analyse the relation between the 4D scattering equations and on-shell diagrams in N = 4 super Yang-Mills, which provides a new perspective on the tree level amplitudes of the theory. I apply a similar analysis to N = 8 supergravity, developing the theory of on-shell diagrams to derive new Grassmannian integral formulae for the amplitudes of the theory. In both theories I derive a new worldsheet expression for the 4 point one loop amplitude supported on 4D scattering equations. Finally I use 4D ambitwistor string theory to analyse scattering amplitudes in N = 4 conformal supergravity, deriving new worldsheet formulae for both plane wave and non-plane wave amplitudes supported on 4D scattering equations. I introduce a new prescription to calculate the derivatives of on-shell variables with respect to momenta, and I use this to show that certain non-plane wave amplitudes can be calculated as momentum derivatives of amplitudes with plane wave states
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