1,129 research outputs found
Safe passage for attachment systems:Can attachment security at international schools be measured, and is it at risk?
Relocations challenge attachment networks. Regardless of whether a person moves or is moved away from, relocation produces separation and loss. When such losses are repeatedly experienced without being adequately processed, a defensive shutting down of the attachment system could result, particularly when such experiences occur during or across the developmental years. At schools with substantial turnover, this possibility could be shaping youth in ways that compromise attachment security and young people’s willingness or ability to develop and maintain deep long-term relationships. Given the well-documented associations between attachment security, social support, and long-term physical and mental health, the hypothesis that mobility could erode attachment and relational health warrants exploration. International schools are logical settings to test such a hypothesis, given their frequently high turnover without confounding factors (e.g. war trauma or refugee experiences). In addition, repeated experiences of separation and loss in international school settings would seem likely to create mental associations for the young people involved regarding how they and others tend to respond to such situations in such settings, raising the possibility that people at such schools, or even the school itself, could collectively be represented as an attachment figure. Questions like these have received scant attention in the literature. They warrant consideration because of their potential to shape young people’s most general convictions regarding attachment, which could, in turn, have implications for young people’s ability to experience meaning in their lives
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
Less is More: Restricted Representations for Better Interpretability and Generalizability
Deep neural networks are prevalent in supervised learning for large amounts of tasks such as image classification, machine translation and even scientific discovery.
Their success is often at the sacrifice of interpretability and generalizability. The increasing complexity of models and involvement of the pre-training process make the inexplicability more imminent. The outstanding performance when labeled data are abundant while prone to overfit when labeled data are limited demonstrates the difficulty of deep neural networks' generalizability to different datasets.
This thesis aims to improve interpretability and generalizability by restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in a low-data regime.
We consider two strategies of restricting representations: (1) adding bottleneck, and (2) introducing compression. Given input x, suppose we want to learn y with the latent representation z (i.e. x→z→y), adding bottleneck means adding function R such that L(R(z)) < L(z) and introducing compression means adding function R so that L(R(y)) < L(y) where L refers to the number of bits. In other words, the restriction is added either in the middle of the pipeline or at the end of it.
We first introduce how adding information bottleneck can help attribution analysis and apply it to investigate BERT's behavior on text classification in Chapter 3.
We then extend this attribution method to analyze passage reranking in Chapter 4, where we conduct a detailed analysis to understand cross-layer and cross-passage behavior.
Adding bottleneck can not only provide insight to understand deep neural networks but can also be used to increase generalizability.
In Chapter 5, we demonstrate the equivalence between adding bottleneck and doing neural compression. We then leverage this finding with a framework called Non-Parametric learning by Compression with Latent Variables (NPC-LV), and show how optimizing neural compressors can be used in the non-parametric image classification with few labeled data.
To further investigate how compression alone helps non-parametric learning without latent variables (NPC), we carry out experiments with a universal compressor gzip on text classification in Chapter 6.
In Chapter 7, we elucidate methods of adopting the perspective of doing compression but without the actual process of compression using T5.
Using experimental results in passage reranking, we show that our method is highly effective in a low-data regime when only one thousand query-passage pairs are available.
In addition to the weakly supervised scenario, we also extend our method to large language models like GPT under almost no supervision --- in one-shot and zero-shot settings. The experiments show that without extra parameters or in-context learning, GPT can be used for semantic similarity, text classification, and text ranking and outperform strong baselines, which is presented in Chapter 8.
The thesis proposes to tackle two big challenges in machine learning --- "interpretability" and "generalizability" through restricting representation. We provide both theoretical derivation and empirical results to show the effectiveness of using information-theoretic approaches. We not only design new algorithms but also provide numerous insights on why and how "compression" is so important in understanding deep neural networks and improving generalizability
30th European Congress on Obesity (ECO 2023)
This is the abstract book of 30th European Congress on Obesity (ECO 2023
Time-Lock Puzzles from Randomized Encodings
Time-lock puzzles, introduced by May, Rivest, Shamir and Wagner, is a mechanism for sending messages ``to the future\u27\u27. A sender
can quickly generate a puzzle with a solution that remains hidden until a moderately large amount of time has elapsed. The solution should be hidden from any adversary that runs in time significantly less than , including resourceful parallel adversaries with polynomially many processors.
While the notion of time-lock puzzles has been around for 22 years, there has only been a *single* candidate proposed. Fifteen years ago, Rivest, Shamir and Wagner suggested a beautiful candidate time-lock puzzle based on the assumption that exponentiation modulo an RSA integer is an ``inherently sequential\u27\u27 computation.
We show that various flavors of {\em randomized encodings} give rise to time-lock puzzles of varying strengths, whose security can be shown assuming *the existence* of non-parallelizing languages, which are languages that require circuits of depth at least to decide, in the worst-case. The existence of such languages is necessary for the existence of time-lock puzzles.
We instantiate the construction with different randomized
encodings from the literature, where increasingly better efficiency is obtained based on increasingly stronger cryptographic assumptions, ranging from one-way functions to indistinguishability obfuscation. We also observe that time-lock puzzles imply one-way functions, and thus the reliance on some cryptographic assumption is necessary.
Finally, generalizing the above, we construct other types of puzzles such as *proofs of work* from randomized encodings and a
suitable worst-case hardness assumption (that is necessary for such puzzles to exist)
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea
Ministry of Science, ICT & Future Planning, Republic of Korea
Ministry of Science & ICT (MSIT), Republic of Korea
2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program
IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068
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)
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Designing a New Tactile Display Technology and its Disability Interactions
People with visual impairments have a strong desire for a refreshable tactile interface that can provide immediate access to full page of Braille and tactile graphics. Regrettably, existing devices come at a considerable expense and remain out of reach for many. The exorbitant costs associated with current tactile displays stem from their intricate design and the multitude of components needed for their construction. This underscores the pressing need for technological innovation that can enhance tactile displays, making them more accessible and available to individuals with visual impairments. This research thesis delves into the development of a novel tactile display technology known as Tacilia. This technology's necessity and prerequisites are informed by in-depth qualitative engagements with students who have visual impairments, alongside a systematic analysis of the prevailing architectures underpinning existing tactile display technologies. The evolution of Tacilia unfolds through iterative processes encompassing conceptualisation, prototyping, and evaluation. With Tacilia, three distinct products and interactive experiences are explored, empowering individuals to manually draw tactile graphics, generate digitally designed media through printing, and display these creations on a dynamic pin array display. This innovation underscores Tacilia's capability to streamline the creation of refreshable tactile displays, rendering them more fitting, usable, and economically viable for people with visual impairments
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