High-level Ideas Research Articles

Conformal link prediction for false discovery rate control

AbstractMost link prediction methods return estimates of the connection probability of missing edges in a graph. Such output can be used to rank the missing edges from most to least likely to be a true edge, but does not directly provide a classification into true and nonexistent. In this work, we consider the problem of identifying a set of true edges with a control of the false discovery rate (FDR). We propose a novel method based on high-level ideas from the literature on conformal inference. The graph structure induces intricate dependence in the data, which we carefully take into account, as this makes the setup different from the usual setup in conformal inference, where data exchangeability is assumed. The FDR control is empirically demonstrated for both simulated and real data.

Co-creation in healthcare and research to improve service delivery for young people with chronic pain.

The process of co-creation can enable more effective, agile and integrated healthcare solutions achieving outcomes that effectively translate to healthcare delivery. Collaborative knowledge generation is particularly important in fields such as pediatric chronic pain where there is a complex interplay between biological, social, environmental, emotional, familial and school factors. The co-creation initiative described here was designed to amplify the voices of youth with chronic pain and their families and a variety of key stakeholders and generate novel approaches to the management of chronic pediatric pain in the setting of the South Australian Pediatric Chronic Pain Service. Stakeholders who were identified as influential in this ecosystem were allocated to 6 groups. A skilled facilitator co-prepared and delivered the workshop, engaging participants in three structured activities. Firstly, the challenges to service delivery were outlined, followed by the groups discussing what is currently working. The second activity involved lateral thinking without restrictions on time, resources or system to generate solutions to the key challenges presented. Finally, stakeholders were asked to agree on a generated solution from Activity 2 and build a case for actionable implementation of this solution. Data were summarised by the workshop facilitator and reflexive thematic analysis was used for coding and generating themes. From Activity 1, six themes collectively demonstrated that stakeholders valued many of the existing strengths of the service delivery, but some areas such as pain education was undervalued. Activity 2 generated solutions from high-level ideas to more day-to- day management strategies. Each of six groups generated unique solutions to an identified challenge for Activity 3. Engaging a wide variety of stakeholders in collaborative knowledge generation successfully provided the South Australian Pediatric Chronic Pain Service with a variety of novel, scalable solution across the healthcare continuum. Equally important is that this initiative helped to raise awareness about the complex issues faced in pediatric chronic pain care and helped to establish new partnerships that have led to enhanced service delivery.

Decentralized graph-based multi-agent reinforcement learning using reward machines

In multi-agent reinforcement learning (MARL), it is challenging for a collection of agents to learn complex temporally extended tasks. The difficulties lie in computational complexity and how to learn the high-level ideas behind reward functions. We study the graph-based Markov Decision Process (MDP), where the dynamics of neighboring agents are coupled. To learn complex temporally extended tasks, we use a reward machine (RM) to encode each agent’s task and expose reward function internal structures. RM has the capacity to describe high-level knowledge and encode non-Markovian reward functions. We propose a decentralized learning algorithm to tackle computational complexity, called decentralized graph-based reinforcement learning using reward machines (DGRM), that equips each agent with a localized policy, allowing agents to make decisions independently based on the information available to the agents. DGRM uses the actor-critic structure, and we introduce the tabular Q-function for discrete state problems. We show that the dependency of the Q-function on other agents decreases exponentially as the distance between them increases. To further improve efficiency, we also propose the deep DGRM algorithm, using deep neural networks to approximate the Q-function and policy function to solve large-scale or continuous state problems. The effectiveness of the proposed DGRM algorithm is evaluated by three case studies, two wireless communication case studies with independent and dependent reward functions, respectively, and COVID-19 pandemic mitigation. Experimental results show that local information is sufficient for DGRM and agents can accomplish complex tasks with the help of RM. DGRM improves the global accumulated reward by 119% compared to the baseline in the case of COVID-19 pandemic mitigation.

Separating weak integrated information theory into inspired and aspirational approaches.

Mediano etal. (The strength of weak integrated information theory. Trends Cogn Sci 2022;26: 646-55.) separate out strong and weak flavours of the integrated information theory (IIT) of consciousness. They describe 'strong IIT' as attempting to derive a universal formula for consciousness and 'weak IIT' as searching for empirically measurable correlates of aspects of consciousness. We put forward that their overall notion of 'weak IIT' may be too weak. Rather, it should be separated out to distinguish 'aspirational-IIT', which aims to empirically test IIT by making trade-offs to its proposed measures, and 'IIT-inspired' approaches, which adopt high-level ideas of IIT while dropping the mathematical framework it reaches through its introspective, first-principles approach to consciousness.

Convoluted Neighborhood-Based Ordered-Dither Block Truncation Coding for Ear Image Retrieval

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709.

Crash Consistency

Keeping data safe in the presence of crashes is a fundamental problem in storage systems. Although the high-level ideas for crash consistency are relatively well understood, realizing them in practice is surprisingly complex and full of challenges. The systems research community is actively working on solving this challenge, and the papers examined here offer three solutions.

Lifelong reinforcement learning with temporal logic formulas and reward machines

Continuously learning new tasks using high-level ideas or knowledge is a key capability of humans. In this paper, we propose lifelong reinforcement learning with sequential linear temporal logic formulas and reward machines (LSRM), which enables an agent to leverage previously learned knowledge to accelerate the learning of logically specified tasks. For a more flexible specification of tasks, we first introduce sequential linear temporal logic (SLTL), which is a supplement to the existing linear temporal logic (LTL) formal language. We then utilize reward machines (RMs) to exploit structural reward functions for tasks encoded with high-level events, and propose an automatic extension of RMs and efficient knowledge transfer over tasks for continuous lifelong learning. Experimental results show that LSRM outperforms methods that learn the target tasks from scratch by taking advantage of the task decomposition using SLTL and the knowledge transfer over RMs during the lifelong learning process.

Using codesign focus groups to develop an online COmmunity suPporting familiEs after Sudden Cardiac Death (COPE-SCD) in the young

ObjectiveTo codesign an online support intervention for families after sudden cardiac death (SCD) in the young (<35 years).DesignCodesign of an SCD family intervention by stakeholder focus groups.SettingFamilies and healthcare professionals...

Medical College Education Data Analysis Method Based on Improved Deep Learning Algorithm

Deep learning (DL) has become a popular study topic in the field of artificial intelligence (AI) in recent years, due to its significant role in various application areas. It leverages supercomputing capacity in the era of big data to uncover the high-level abstract ideas in the original dataset and serves as decision support in the application sector by increasing the number of channels and the scale of parameters. This study designs and implements a heterogeneous medical education data analysis system based on DL technology. The proposed system adopts DL technology to model, analyzes the heterogeneous medical education data, uses the decision-level fusion strategy for the data model, and designs and implements the voting method and the weighting method. The decision value is statistically calculated to realize the improved DL algorithm for the medical college education data analysis method. In addition, this study also uses the Alzheimer’s disease public dataset with various structures and modalities of medical education data to compare and evaluate the systematic data preprocessing model performance and the effect of fusion methods. The experimental result validates the proposed model’s performance, demonstrating that the way of evaluating complete heterogeneous multimodal data is not only closer to the genuine diagnostic process but also aids clinicians in grasping the patient’s entire state and obtaining outcomes. Further, the essential ideas and implementation techniques of convolutional neural network (CNN) and stacked autoencoder as well as its application cases in medical college education data analysis are thoroughly explained.

Statistical Methods for Microbiome Compositional Data Network Inference: A Survey

Microbes can be found almost everywhere in the world. They are not isolated, but rather interact with each other and establish connections with their living environments. Studying these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A widely used approach toward this objective involves the inference of microbiome interaction networks. However, owing to the compositional, high-dimensional, sparse, and heterogeneous nature of observed microbial data, applying network inference methods to estimate their associations is challenging. In addition, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this article, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks, and differential networks. Their assumptions, high-level ideas, advantages, as well as limitations, are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has, to date, captured all the aspects of interest. In addition, we discuss the challenges now confronting current microbial interaction study and future prospects. Finally, we point out several feasible directions of microbial network inference analysis and highlight that future research requires the joint promotion of statistical computation methods and experimental techniques.

Concretely efficient secure multi-party computation protocols: survey and more

Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on their private inputs, and reveals nothing but the output of the function. In the last decade, MPC has rapidly moved from a purely theoretical study to an object of practical interest, with a growing interest in practical applications such as privacy-preserving machine learning (PPML). In this paper, we comprehensively survey existing work on concretely efficient MPC protocols with both semi-honest and malicious security, in both dishonest-majority and honest-majority settings. We focus on considering the notion of security with abort, meaning that corrupted parties could prevent honest parties from receiving output after they receive output. We present high-level ideas of the basic and key approaches for designing different styles of MPC protocols and the crucial building blocks of MPC. For MPC applications, we compare the known PPML protocols built on MPC, and describe the efficiency of private inference and training for the state-of-the-art PPML protocols. Furthermore, we summarize several challenges and open problems to break though the efficiency of MPC protocols as well as some interesting future work that is worth being addressed. This survey aims to provide the recent development and key approaches of MPC to researchers, who are interested in knowing, improving, and applying concretely efficient MPC protocols.

Using co-design to develop an online intervention for families after a sudden cardiac death in the young

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): National Heart Foundation of Australia / NHMRC Co-funded Post Graduate Scholarship Introduction Sudden cardiac death (SCD) is a devastating complication of many genetic heart diseases. The psychological impact on surviving family members is significant and lifelong. Previous needs analysis found medical and psychological needs are of high importance but remain unmet in some cases. Co-design is an increasingly popular research method for the development of interventions and services that include users as contributing members of the research team. Previous research has shown co-design leads to innovative ideas that better address user needs. Purpose To use co-design to develop an online support intervention for families after SCD. Methods Semi-structured moderated online focus groups were held with key stakeholders, including family members who had experienced young SCD, healthcare professionals and researchers. Guided discussions were used to co-design an online support intervention. Thematic analysis of discussions and iterative feedback on draft materials guided content development. Results Four focus groups were held (average 11 participants per group). Stakeholder involvement facilitated development of high-level ideas and priority issues. Key benefits included creative content and materials developed based on user preference for stories, narratives and information reflecting everyday experience of families navigating the legal and medical processes surrounding SCD, normalising and supporting grief responses in the context of family relationships, and fostering hope. Conclusion Co-design supported creative presentation, tailoring of information and development of psychosocial interventions to meet the needs of SCD families. Further work is needed to pilot and trial the intervention, prior to it becoming a key resource in the care of these families.

Machine Learning Modeling: A New Way to do Quantitative Research in Social Sciences in the Era of AI

Improvements in big data and machine learning algorithms have helped AI technologies reach a new breakthrough and have provided a new opportunity for quantitative research in the social sciences. Traditional quantitative models rely heavily on theoretical hypotheses and statistics but fail to acknowledge the problem of overfitting, causing the research results to be less generalizable, and further leading to societal predictions in the social sciences being ignored when they should have been meaningful. Machine learning models that use cross validation and regularization can effectively solve the problem of overfitting, providing support for the societal predictions based on these models. This paper first discusses the sources and internal mechanisms of overfitting, and then introduces machine learning modeling by discussing its high-level ideas, goals, and concrete methods. Finally, we discuss the shortcomings and limiting factors of machine learning models. We believe that using machine learning in social sciences research is an opportunity and not a threat. Researchers should adopt an objective attitude and make sure that they know how to combine traditional methods with new methods in their research based on their needs.

Computational Psycholinguistics

Computational Psycholinguistics

Deep learning acceleration based on in-memory computing

Performing computations on conventional von Neumann computing systems results in a significant amount of data being moved back and forth between the physically separated memory and processing units. This costs time and energy, and constitutes an inherent performance bottleneck. In-memory computing is a novel non-von Neumann approach, where certain computational tasks are performed in the memory itself. This is enabled by the physical attributes and state dynamics of memory devices, in particular, resistance-based nonvolatile memory technology. Several computational tasks such as logical operations, arithmetic operations, and even certain machine learning tasks can be implemented in such a computational memory unit. In this article, we first introduce the general notion of in-memory computing and then focus on mixed-precision deep learning training with in-memory computing. The efficacy of this new approach will be demonstrated by training the MNIST multilayer perceptron network achieving high accuracy. Moreover, we show how the precision of in-memory computing can be further improved through architectural and device-level innovations. Finally, we present system aspects, such as high-level system architecture, including core-to-core interconnect technologies, and high-level ideas and concepts of the software stack .

We have a long way to go if we want to realize the promise of the “Decade on Ecosystem Restoration”

We have a long way to go if we want to realize the promise of the “Decade on Ecosystem Restoration”

Characterizing Human Summarization Strategies for Text Reuse and Transformation in Literature Review Writing

Citations are useful signals of information salience, but little research has identified the patterns of information selection, transformation, and organization that they espouse. This paper investigated the summarization strategies followed in the writing of literature review sections of information science research papers. We found that the summarization strategies followed are different for the two major styles of literature review writing, descriptive versus integrative literature reviews. Descriptive literature reviews, which focus on individual descriptions of research papers, are more likely to reference the Method and the Result sections of the cited paper and copy-paste text the referenced text. In contrast, integrative literature reviews, which synthesize the main ideas for many papers together, have more critiques and focus mainly on the Conclusion sections. These findings, based on a hand-annotated dataset, have the potential to scale up into a transformation-invariant neural architecture for scientific summarization that can generate different summaries of the input text with integrative or descriptive characteristics. Article Highlights: • Citations encapsulate strategies of information selection, transformation, and organization. • Authors who cite more results in their literature review sections are less likely to provide an integrative summary of the main research ideas. • Authors who synthesize the high-level ideas focus more on the implications mentioned in the cited papers and are less likely to copy text from them. • The findings can inspire novel and creative approaches to scientific summarization in more data-intensive neural architectures.

Differentiation Analysis of Continuous Electroencephalographic Activity Triggered by Video Clip Contents.

While viewing a video clip, we experience a wide variety of contents, from low-level features of the images to high-level ideas such as the storyline. Each change in our experience must be supported by some corresponding change in neurophysiological activity. Differentiation analysis, which quantifies the differences in brain activity by measuring the distances between observed brain states, was applied here to continuous high-density electroencephalographic data recorded while participants watched short video clips. These clips were manipulated in various ways to change the degree of meaningfulness of their contents. We found that neurophysiological differentiation mirrored that of phenomenal differentiation, being higher for meaningful clips and lower for phase-scrambled versions or random noise. The distinction between meaningful and meaningless clips was present even at the individual level, and moreover, differentiation values correlated with individual subjective reports of meaningfulness. Spatial and spectral breakdowns of the overall effect showed frontal and posterior ROIs and highlighted specific roles for different spectral bands. Comparing the results with a multivariate decoding approach reveals that the two methods are capturing different aspects of brain activity and highlights a crucial theoretical distinction between the level and pattern of activity. In future applications, differentiation analysis may be used to evaluate the subjective meaningfulness of stimuli when behavioral responses may be inadequate, as with disorders of consciousness.

Understanding and maintaining tactics graphically OR how we are learning that a diagram can be worth more than 10K LoC

The use of a functional language to implement proof strategies as proof tactics in interactive theorem provers, often provides short, concise and elegant implementations. Whilst being elegant, the use of higher order features and combinator languages often results in a very procedural view of a strategy, which may deviate significantly from the high-level ideas behind it. This can make a tactic hard to understand and hence difficult to to debug and maintain for experts and non-experts alike: one often has to tear apart complex combinations of lower level tactics manually in order to analyse a failure in the overall strategy. In an industrial technology transfer project, we have been working on porting a very large and complex proof tactic into PSGraph, a graphical language for representing proof strategies. The goal of this work is to improve understandability and maintainability of tactics. Motivated by some initial successes with this, we here extend PSGraph with additional features for development and debugging. Through the re-implementation and refactoring of several existing tactics, we demonstrates the advantages of PSGraph compared with a typical sentential tactic language with respect to debugging, readability and maintenance. In order to act as guidance for others, we give a fairly detailed comparison of the user experience with the two approaches. The paper is supported by a web page providing further details about the implementation as well as interactive illustrations of the examples.

An invitation to game semantics

Game semantics is a flexible semantic theory that has led in recent years to an unprecedented number of full abstraction results for various programming paradigms. We present a gentle introduction to the subject, focussing on high-level ideas and examples with a view to providing a bridge to more technical literature.