7,006 research outputs found
Modular lifelong machine learning
Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge.
Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand.
This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems.
First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures.
Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations.
Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods.
Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future
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
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Quantitative synthesis of the role of birds in carrying ticks and tick-borne pathogens in North America
Birds play a central role in the ecology of tick-borne pathogens. They expand tick populations and pathogens across vast distances and serve as reservoirs that maintain and amplify transmission locally. Research into the role of birds for supporting ticks and tick-borne pathogens has largely been descriptive and focused in small areas. To expand inference beyond these studies, we conducted a quantitative review at the scale of North America to identify avian life history correlates of tick infestation and pathogen prevalence, calculate species-level indices of importance for carrying ticks, and identify research gaps limiting understanding of tick-borne pathogen transmission. Across studies, 78 of 162 bird species harbored ticks, yielding an infestation prevalence of 1981 of 38,929 birds (5.1�%). Avian foraging and migratory strategies interacted to influence infestation. Ground-foraging species, especially non-migratory ground foragers, were disproportionately likely to have high prevalence and intensity of tick infestation. Studies largely focused on Borrelia burgdorferi, the agent of Lyme disease, and non-migratory ground foragers were especially likely to carry B. burgdorferi-infected ticks, a finding that highlights the potential importance of resident birds in local pathogen transmission. Based on infestation indices, all 'super-carrier' bird species were passerines. Vast interior areas of North America, many bird and tick species, and most tick-borne pathogens, remain understudied, and research is needed to address these gaps. More studies are needed that quantify tick host preferences, host competence, and spatiotemporal variation in pathogen prevalence and vector and host abundance. This information is crucial for predicting pathogen transmission dynamics under future global change.Peer reviewedNatural Resource Ecology and ManagementEntomology and Plant Patholog
Estudo da remodelagem reversa miocárdica através da análise proteómica do miocárdio e do líquido pericárdico
Valve replacement remains as the standard therapeutic option for aortic
stenosis patients, aiming at abolishing pressure overload and triggering
myocardial reverse remodeling. However, despite the instant hemodynamic
benefit, not all patients show complete regression of myocardial hypertrophy,
being at higher risk for adverse outcomes, such as heart failure. The current
comprehension of the biological mechanisms underlying an incomplete reverse
remodeling is far from complete. Furthermore, definitive prognostic tools and
ancillary therapies to improve the outcome of the patients undergoing valve
replacement are missing. To help abridge these gaps, a combined myocardial
(phospho)proteomics and pericardial fluid proteomics approach was followed,
taking advantage of human biopsies and pericardial fluid collected during
surgery and whose origin anticipated a wealth of molecular information
contained therein.
From over 1800 and 750 proteins identified, respectively, in the myocardium
and in the pericardial fluid of aortic stenosis patients, a total of 90 dysregulated
proteins were detected. Gene annotation and pathway enrichment analyses,
together with discriminant analysis, are compatible with a scenario of increased
pro-hypertrophic gene expression and protein synthesis, defective ubiquitinproteasome system activity, proclivity to cell death (potentially fed by
complement activity and other extrinsic factors, such as death receptor
activators), acute-phase response, immune system activation and fibrosis.
Specific validation of some targets through immunoblot techniques and
correlation with clinical data pointed to complement C3 β chain, Muscle Ring
Finger protein 1 (MuRF1) and the dual-specificity Tyr-phosphorylation
regulated kinase 1A (DYRK1A) as potential markers of an incomplete
response. In addition, kinase prediction from phosphoproteome data suggests
that the modulation of casein kinase 2, the family of IκB kinases, glycogen
synthase kinase 3 and DYRK1A may help improve the outcome of patients
undergoing valve replacement. Particularly, functional studies with DYRK1A+/-
cardiomyocytes show that this kinase may be an important target to treat
cardiac dysfunction, provided that mutant cells presented a different response
to stretch and reduced ability to develop force (active tension).
This study opens many avenues in post-aortic valve replacement reverse
remodeling research. In the future, gain-of-function and/or loss-of-function
studies with isolated cardiomyocytes or with animal models of aortic bandingdebanding will help disclose the efficacy of targeting the surrogate therapeutic
targets. Besides, clinical studies in larger cohorts will bring definitive proof of
complement C3, MuRF1 and DYRK1A prognostic value.A substituição da válvula aórtica continua a ser a opção terapêutica de
referência para doentes com estenose aórtica e visa a eliminação da
sobrecarga de pressão, desencadeando a remodelagem reversa miocárdica.
Contudo, apesar do benefício hemodinâmico imediato, nem todos os pacientes
apresentam regressão completa da hipertrofia do miocárdio, ficando com maior
risco de eventos adversos, como a insuficiência cardíaca. Atualmente, os
mecanismos biológicos subjacentes a uma remodelagem reversa incompleta
ainda não são claros. Além disso, não dispomos de ferramentas de
prognóstico definitivos nem de terapias auxiliares para melhorar a condição
dos pacientes indicados para substituição da válvula. Para ajudar a resolver
estas lacunas, uma abordagem combinada de (fosfo)proteómica e proteómica
para a caracterização, respetivamente, do miocárdio e do líquido pericárdico
foi seguida, tomando partido de biópsias e líquidos pericárdicos recolhidos em
ambiente cirúrgico.
Das mais de 1800 e 750 proteínas identificadas, respetivamente, no miocárdio
e no líquido pericárdico dos pacientes com estenose aórtica, um total de 90
proteínas desreguladas foram detetadas. As análises de anotação de genes,
de enriquecimento de vias celulares e discriminativa corroboram um cenário de
aumento da expressão de genes pro-hipertróficos e de síntese proteica, um
sistema ubiquitina-proteassoma ineficiente, uma tendência para morte celular
(potencialmente acelerada pela atividade do complemento e por outros fatores
extrínsecos que ativam death receptors), com ativação da resposta de fase
aguda e do sistema imune, assim como da fibrose.
A validação de alguns alvos específicos através de immunoblot e correlação
com dados clínicos apontou para a cadeia β do complemento C3, a Muscle
Ring Finger protein 1 (MuRF1) e a dual-specificity Tyr-phosphoylation
regulated kinase 1A (DYRK1A) como potenciais marcadores de uma resposta
incompleta. Por outro lado, a predição de cinases a partir do fosfoproteoma,
sugere que a modulação da caseína cinase 2, a família de cinases do IκB, a
glicogénio sintase cinase 3 e da DYRK1A pode ajudar a melhorar a condição
dos pacientes indicados para intervenção. Em particular, a avaliação funcional
de cardiomiócitos DYRK1A+/- mostraram que esta cinase pode ser um alvo
importante para tratar a disfunção cardíaca, uma vez que os miócitos mutantes
responderam de forma diferente ao estiramento e mostraram uma menor
capacidade para desenvolver força (tensão ativa).
Este estudo levanta várias hipóteses na investigação da remodelagem reversa.
No futuro, estudos de ganho e/ou perda de função realizados em
cardiomiócitos isolados ou em modelos animais de banding-debanding da
aorta ajudarão a testar a eficácia de modular os potenciais alvos terapêuticos
encontrados. Além disso, estudos clínicos em coortes de maior dimensão
trarão conclusões definitivas quanto ao valor de prognóstico do complemento
C3, MuRF1 e DYRK1A.Programa Doutoral em Biomedicin
On the Mechanism of Building Core Competencies: a Study of Chinese Multinational Port Enterprises
This study aims to explore how Chinese multinational port enterprises (MNPEs) build
their core competencies. Core competencies are firms’special capabilities and sources
to gain sustainable competitive advantage (SCA) in marketplace, and the concept led
to extensive research and debates. However, few studies include inquiries about the
mechanisms of building core competencies in the context of Chinese MNPEs.
Accordingly, answers were sought to three research questions:
1. What are the core competencies of the Chinese MNPEs?
2. What are the mechanisms that the Chinese MNPEs use to build their core
competencies?
3. What are the paths that the Chinese MNPEs pursue to build their resources bases?
The study adopted a multiple-case study design, focusing on building mechanism of
core competencies with RBV. It selected purposively five Chinese leading MNPEs
and three industry associations as Case Companies.
The study revealed three main findings. First, it identified three generic core
competencies possessed by Case Companies, i.e., innovation in business models and
operations, utilisation of technologies, and acquisition of strategic resources. Second,
it developed the conceptual framework of the Mechanism of Building Core
Competencies (MBCC), which is a process of change of collective learning in
effective and efficient utilization of resources of a firm in response to critical events.
Third, it proposed three paths to build core competencies, i.e., enhancing collective
learning, selecting sustainable processes, and building resource base.
The study contributes to the knowledge of core competencies and RBV in three ways:
(1) presenting three generic core competencies of the Chinese MNPEs, (2) proposing
a new conceptual framework to explain how Chinese MNPEs build their core
competencies, (3) suggesting a solid anchor point (MBCC) to explain the links among
resources, core competencies, and SCA. The findings set benchmarks for Chinese
logistics industry and provide guidelines to build core competencies
Recommended from our members
Abstinence from compulsive sexual behavior and problematic pornography use: phenomenological experiences and short-term effects
The investigation of abstinence from compulsive sexual behavior (CSB) and problematic pornography use (PPU) is important for filling gaps in current knowledge about their assessment and intervention. These gaps include: (i) abstinence-based interventions for CSB and PPU being common yet little understood, and (ii) uncertainty about whether withdrawal-related symptomatology manifest when regular pornography users with varying levels of self-reported PPU attempt to abstain from pornography.
The first part of the present thesis comprised two systematic reviews that were conducted to review the state of the evidence in the following two areas: the theoretical conceptualization and psychometric assessment of PPU (Systematic Review 1) and short-term abstinence effects across multiple potential behavioral addictions (Systematic Review 2). Findings of Systematic Review 1 indicated a lack of consensus in the field about the theoretical conceptualization and operationalization of CSB and PPU and highlighted a need for further empirical research into less agreed-upon components of addiction (e.g., withdrawal). Findings of Systematic Review 2 showed that negative abstinence effects (potentially reflective of withdrawal-related symptoms) and/or positive abstinence effects (potentially reflective of benefits of abstinence as an intervention) manifested to varying extents across six potential behavioral addictions (exercise, gambling, gaming, mobile phone use, pornography use and social media use). Importantly, the review highlighted that no prospective study had examined the effects of short-term abstinence from pornography specifically on the manifestation of withdrawal-related symptoms.
The second part of the present thesis comprised three empirical studies that contributed to filling the aforementioned gaps in the literature by achieving two aims. First, qualitative analyses of phenomenological experiences of abstinence-based recovery were conducted on abstinence journals of members of a pornography 'rebooting' forum (Empirical Study 1; N = 104) and in-depth semi-structured interviews with members of a 12-step group for CSB (Empirical Study 2; N = 14). Descriptions and sense-making of abstinence-based recovery experiences across both communities revealed commonalities (e.g., the importance of lifestyle changes and social support from the group in achieving successful abstinence), but also notable dissimilarities (e.g., nuances in etiological conceptualization of the problematic behavior leading to differences in sense-making about the meaning of abstinence). Second, a randomized controlled design was used to examine whether there were any effects of a seven-day pornography abstinence period on withdrawal-related symptoms (and whether abstinence effects were moderated by self-reported PPU) in a sample of regular pornography users (Empirical Study 3; N = 176). Contrary to confirmatory hypotheses, no evidence of abstinence effects on withdrawal-related symptoms was found, and this was not dependent on level of PPU (but with the caveat that the sample was 64.2% female and had relatively low levels of PPU). However, because exploratory analyses found an abstinence effect on craving when PPU was high and past four-week frequency of pornography use (FPU) crossed the threshold of daily use, the possibility that withdrawal-related symptoms may manifest only at high levels of FPU and PPU needs to be investigated in future prospective studies. Overall, the present thesis provides novel insights into abstinence-based interventions for CSB and PPU and contributes to current theorizing about whether withdrawal-related symptomatology manifests in relation to PPU
- …