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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
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
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods
Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection.
In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application.
The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings.
Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection
Trust and transparency in an age of surveillance
Investigating the theoretical and empirical relationships between transparency and trust in the context of surveillance, this volume argues that neither transparency nor trust provides a simple and self-evident path for mitigating the negative political and social consequences of state surveillance practices.
Dominant in both the scholarly literature and public debate is the conviction that transparency can promote better-informed decisions, provide greater oversight, and restore trust damaged by the secrecy of surveillance. The contributions to this volume challenge this conventional wisdom by considering how relations of trust and policies of transparency are modulated by underlying power asymmetries, sociohistorical legacies, economic structures, and institutional constraints. They study trust and transparency as embedded in specific sociopolitical contexts to show how, under certain conditions, transparency can become a tool of social control that erodes trust, while mistrust - rather than trust - can sometimes offer the most promising approach to safeguarding rights and freedom in an age of surveillance. The first book addressing the interrelationship of trust, transparency, and surveillance practices, this volume will be of interest to scholars and students of surveillance studies as well as appeal to an interdisciplinary audience given the contributions from political science, sociology, philosophy, law, and civil society
Principles of Massively Parallel Sequencing for Engineering and Characterizing Gene Delivery
The advent of massively parallel sequencing and synthesis technologies have ushered in a new paradigm of biology, where high throughput screening of billions of nucleid acid molecules and production of libraries of millions of genetic mutants are now routine in labs and clinics. During my Ph.D., I worked to develop data analysis and experimental methods that take advantage of the scale of this data, while making the minimal assumptions necessary for deriving value from their application. My Ph.D. work began with the development of software and principles for analyzing deep mutational scanning data of libraries of engineered AAV capsids. By looking at not only the top variant in a round of directed evolution, but instead a broad distribution of the variants and their phenotypes, we were able to identify AAV variants with enhanced ability to transduce specific cells in the brain after intravenous injection. I then shifted to better understand the phenotypic profile of these engineered variants. To that end, I turned to single-cell RNA sequencing to seek to identify, with high resolution, the delivery profile of these variants in all cell types present in the cortex of a mouse brain. I began by developing infrastructure and tools for dealing with the data analysis demands of these experiments. Then, by delivering an engineered variant to the animal, I was able to use the single-cell RNA sequencing profile, coupled with a sequencing readout of the delivered genetic cargo present in each cell type, to define the variant’s tropism across the full spectrum of cell types in a single step. To increase the throughput of this experimental paradigm, I then worked to develop a multiplexing strategy for delivering up to 7 engineered variants in a single animal, and obtain the same high resolution readout for each variant in a single experiment. Finally, to take a step towards translation to human diagnostics, I leveraged the tools I built for scaling single-cell RNA sequencing studies and worked to develop a protocol for obtaining single-cell immune profiles of low volumes of self-collected blood. This study enabled repeat sampling in a short period of time, and revealed an incredible richness in individual variability and time-of-day dependence of human immune gene expression. Together, my Ph.D. work provides strategies for employing massively parallel sequencing and synthesis for new biological applications, and builds towards a future paradigm where personalized, high-resolution sequencing might be coupled with modular, customized gene therapy delivery.</p
The new age of fear: an analysis of crisis framing by right-wing populist parties in Greece and France
From the 2009 Eurozone economic downturn, to the 2015 mass movement of forcibly displaced migrants and the current COVID-19 pandemic, crises have seemingly become a ‘new normal’ feature of European politics. During this decade, rolling crises generated a wave of public discontent that damaged the legitimacy of national governments and the European Union and heralded a renaissance of populism. The central message of populist parties, which helped them rise in popularity or enter parliament for the first time, is simple but very effective: democratic representation has been undermined by national and global elites. This has provoked a wealth of studies seeking to explain the rise or breakthrough of populist fringe parties, without adequate consideration of how crises transform, not only the demand side, but also the supply of populist arguments, which has received scarce attention.
This thesis seeks to address this imbalance by synthesising insights from the crisis framing literature, which facilitates an understanding and operationalisation of populism as a style of discourse. To assess how far-right parties employ this discourse, and the implications of this for their electoral prospects, a comparative case-study design is employed, exploring the discourse of parties, the National Rally (NR) in France and Golden Dawn (GD) in Greece. Their ideologically similar profile but differential electoral performance, allows for a more nuanced analysis of their respective framing strategies.
The thesis examines the discourse of the two parties MPs on month by month basis over a four year period, 2012-2015 for GD and 2012-2013 and 2016-2017 for NR, via the use of the NVivo software. Their respective discourses are quantified and broken down into four key areas associated with Foreign Policy, the Economy, the Political System and Society, analysing the content, frequency and salience of key crisis frames. Discourse analysis of excerpts adds a qualitative element to the analysis that showcases the substantial differences between the two case studies. The analysis demonstrates that references to ‘the people’ and anti-elitism were the centrepieces of each case study’s discourse with strong nativist and nationalist elements.
The two parties were extremely similar in the diagnostic stage of their framing and the way which they attribute blame for the crises. However, their discursive strategies diverge regarding their proposed solutions to the crises. Golden Dawn remained a single issue party in terms of discourse, since it never presented a comprehensive plan for ending the crises. As a result, Golden Dawn’s discourse remained one-dimensional throughout its brief period of success, being centred solely on attributing blame and attacking its political opponents and the European Union. On the other hand, National Rally’s framing was more elaborate and ambitious both in terms of the variety of issues raised and, especially, the proposed solutions if advocated. This, it is argued, contributed to the evolution of RN into a mainstream competitor that is no longer dependent on a niche part of the electoral market, while the inability of GD to develop equally successful crisis frames offers a unique understanding as to why the party failed electorally and was unable to enter Parliament in the 2019 elections. The overall analysis produces a rich framework that maps out the key elements of populist crisis discourse by far-right parties, which has implications for electoral politics and for our understanding of populism, more broadly
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