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The first JWST data revealed an unexpected population of red galaxies that appear to have redshifts of $z\sim 7-9$ and high masses of $M_*$ $\sim$ 10$^{10}$ M$_{\odot}$ (Labbé et al. 2023). Here we fit Sérsic profiles to the F200W NIRCam images of the 13 massive galaxy candidates of Labbé et al., to determine their structural parameters. Satisfactory fits were obtained for nine galaxies. We find that their effective radii are extremely small, ranging from $r_{\rm e}\sim 80$ pc to $r_{\rm e} \sim 300$ pc, with a mean of $\langle r_{\rm e}\rangle \approx 150$ pc. For their apparent stellar masses, the galaxies are smaller than any other galaxy population that has been observed at any other redshift. We use the fits to derive circularized three-dimensional stellar mass profiles of the galaxies, and compare these to the mass profiles of massive quiescent galaxies at $z\sim$2.3 and nearby elliptical galaxies. We find that, despite the high redshift galaxies having $10-20$ times smaller half-light radii, the central stellar densities are comparable to those of their putative descendants at later times. The most straightforward interpretation is that the dense compact inner regions of the most massive ellipticals today were already in place $\sim 600$ Myr after the Big Bang. We caution that the redshifts and masses of the galaxies remain to be confirmed, and that the complex NIRCam point spread function is not yet fully characterized., Submitted to ApJ Letters

We use CEERS JWST/NIRCam imaging to measure rest-frame near-IR light profiles of $>$500 $M_\star>10^{10}~M_\odot$ galaxies in the redshift range $0.510^{11}~M_\odot$) quiescent galaxies between $z=0.5$ and $z=2.3$, again comparing $R_{\rm{opt}}$, \rmass~and \rmassd. We conclude that the main tenets of the size evolution narrative established over the past 20 years, based on rest-frame optical light profile analysis, still hold in the era of JWST/NIRCam observations in the rest-frame near-IR., Submitted to ApJ. Comments welcome

This article summarizes the literature on trust of digital technologies from a human-centric perspective. We summarize literature on trust in face-to-face interactions from other fields, followed by a discussion of organizational trust, technology-mediated trust, trust of software products, trust of AI, and blockchain. This report was created for the Science for Technological Innovation Veracity Spearhead supported by New Zealand's National Science Challenges.

This paper investigates deceptive reinforcement learning for privacy preservation in model-free and continuous action space domains. In reinforcement learning, the reward function defines the agent's objective. In adversarial scenarios, an agent may need to both maximise rewards and keep its reward function private from observers. Recent research presented the ambiguity model (AM), which selects actions that are ambiguous over a set of possible reward functions, via pre-trained $Q$-functions. Despite promising results in model-based domains, our investigation shows that AM is ineffective in model-free domains due to misdirected state space exploration. It is also inefficient to train and inapplicable in continuous action space domains. We propose the deceptive exploration ambiguity model (DEAM), which learns using the deceptive policy during training, leading to targeted exploration of the state space. DEAM is also applicable in continuous action spaces. We evaluate DEAM in discrete and continuous action space path planning environments. DEAM achieves similar performance to an optimal model-based version of AM and outperforms a model-free version of AM in terms of path cost, deceptiveness and training efficiency. These results extend to the continuous domain., Comment: 8 pages, 1 reference page, 4 appendix pages, Accepted into International Conference on Automated Planning and Scheduling (ICAPS) 2023

A standard practice in extragalactic population studies is the fitting of parametric models to galaxy images. From such fits, key structural parameters of galaxies such as total flux and effective radius (size) can be extracted. One of the most popular parametric forms is that of the S\'ersic profile, which is flexible enough to reasonably fit the light distribution of nearly all galaxies. Here we present pysersic, a Bayesian framework created to facilitate the inference of structural parameters from galaxy images. Pysersic is written in pure Python, and is built using the jax framework, allowing for just-in-time compilation, auto-differentiation and seamless execution on CPUs, GPUs or TPUs. Inference is performed with the numpyro package using gradient based methods, e.g., No U-Turn Sampling, for efficient and robust posterior estimation in only a few minutes on a modern laptop. Pysersic is designed to have a user-friendly interface, allowing users to fit single or multiple sources in a few lines of code, while also being flexible enough for integration into current and future analysis pipelines. In addition to sampling, pysersic can produce point estimates of the best model via optimization in several seconds, and approximate the posterior via stochastic variational inference. The use of the numpyro probabilistic language provides future extensibility to arbitrary models beyond the S\'ersic., Comment: Submitted to the Journal of Open Source Software (JOSS)

In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making. In early decision support systems, we assumed that we could give people recommendations and that they would consider them, and then follow them when required. However, research found that people often ignore recommendations because they do not trust them; or perhaps even worse, people follow them blindly, even when the recommendations are wrong. Explainable artificial intelligence mitigates this by helping people to understand how and why models give certain recommendations. However, recent research shows that people do not always engage with explainability tools enough to help improve decision making. The assumption that people will engage with recommendations and explanations has proven to be unfounded. We argue this is because we have failed to account for two things. First, recommendations (and their explanations) take control from human decision makers, limiting their agency. Second, giving recommendations and explanations does not align with the cognitive processes employed by people making decisions. This position paper proposes a new conceptual framework called Evaluative AI for explainable decision support. This is a machine-in-the-loop paradigm in which decision support tools provide evidence for and against decisions made by people, rather than provide recommendations to accept or reject. We argue that this mitigates issues of over- and under-reliance on decision support tools, and better leverages human expertise in decision making.

With just a month of data, JWST is already transforming our view of the Universe, revealing and resolving starlight in unprecedented populations of galaxies. Although ``HST-dark" galaxies have previously been detected at long wavelengths, these observations generally suffer from a lack of spatial resolution which limits our ability to characterize their sizes and morphologies. Here we report on a first view of starlight from a subset of the HST-dark population that are bright with JWST/NIRCam (4.4$μ$m

In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showing confidence scores in human-agent interaction systems can help build trust between humans and AI systems. However, most existing research only used the confidence score as a form of communication, and we still lack ways to explain why the algorithm is confident. This paper also presents two methods for understanding model confidence using counterfactual explanation: (1) based on counterfactual examples; and (2) based on visualisation of the counterfactual space., Comment: 8 pages, Accepted to IJCAI Workshop on Explainable Artificial Intelligence 2022

This paper evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neural Networks (CNNs) models and boost the fidelity and performance of the used explainer. CNNs for computer vision have shown exceptional performance in critical industries. However, it is a significant barrier when deploying CNNs due to their complexity and lack of interpretability. Recent studies to explain computer vision models have shifted from extracting low-level features (pixel-based explanations) to mid-or high-level features (concept-based explanations). The current research direction tends to use extracted features in developing approximation algorithms such as linear or decision tree models to interpret an original model. In this work, we modify one of the state-of-the-art concept-based explanations and propose an alternative framework named TreeICE. We design a systematic evaluation based on the requirements of fidelity (approximate models to original model's labels), performance (approximate models to ground-truth labels), and interpretability (meaningful of approximate models to humans). We conduct computational evaluation (for fidelity and performance) and human subject experiments (for interpretability) We find that Tree-ICE outperforms the baseline in interpretability and generates more human readable explanations in the form of a semantic tree structure. This work features how important to have more understandable explanations when interpretability is crucial.

This paper presents an argument for why we are not measuring trust sufficiently in explainability, interpretability, and transparency research. Most studies ask participants to complete a trust scale to rate their trust of a model that has been explained/interpreted. If the trust is increased, we consider this a positive. However, there are two issues with this. First, we usually have no way of knowing whether participants should trust the model. Trust should surely decrease if a model is of poor quality. Second, these scales measure perceived trust rather than demonstrated trust. This paper showcases three methods that do a good job at measuring perceived and demonstrated trust. It is intended to be starting point for discussion on this topic, rather than to be the final say. The author invites critique and discussion.

A paper presented at the Workshop on Contestability in Algorithmic Systems at CSCW 2019. Challenging algorithmic decisions is important to decision subjects, yet numerous factors can limit a person's ability to contest such decisions. We propose that administrative law systems, which were created to ensure that governments are kept accountable for their actions and decision making in relation to individuals, can provide guidance on how to design contestation systems for algorithmic decision-making.

Deception is virtually ubiquitous in warfare, and should be a central consideration for military operations research. However, studies of agent behaviour in simulated operations have typically neglected to include explicit models of deception. This paper proposes that a computational model that approximates the human deceptive planning process would enable the authentic representation of strategic deception in multi-agent systems. The proposed deceptive planning model provides a framework for studying, explaining, and discovering deceptive behaviours, enabling the generation of novel solutions to adversarial planning problems., Comment: 8 pages, 2 figures, Presented at the 2nd International Workshop on Deceptive AI @IJCAI2021. See https://sites.google.com/view/deceptai2021/program

Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the \textit{layers of explanation} (LEx), a lens through which we can assess the appropriateness of different types of explanations. The framework uses the notions of \textit{sensitivity} (emotional responsiveness) of features and the level of \textit{stakes} (decision's consequence) in a domain to determine whether different types of explanations are \textit{appropriate} in a given context. We demonstrate how to use the framework to assess the appropriateness of different types of explanations in different domains., Comment: 6 pages

In this thesis, I explore academics’ methods of presenting knowledge in academia. My central concern is academics’ expectations of the use of Microsoft PowerPoint and similar software in routine academic presentations. I argue that academics’ expectations of presentations are informed by design, the breaching of which reveals new knowledge of these expectations. In this research I draw on design, ethnomethodology’s breaching experiments and actor-network theory’s notion of script to develop the notion of quasi-design. This methodology is developed to be applicable in academia through the case studies of this research. In these case studies I undertook participant observation among academics who go about presenting knowledge in conference presentations, lectures and mock research interviews. I then breached these presentation scripts. Subsequently, I developed a method of design-led research that involves breaching not only scripts informing interaction between people and things but quasi-scripts containing atmospheres. These atmospheres are important as they affect my research participants who reveal their expectations of presentations by adopting, resisting or transforming disruptive breaches into quasi-breaches. Breaching presentation quasi-scripts therefore affects academics who reveal their expectations of presentations as informed by design. Through this, I inform our understanding of ethnomethodological breaching experiments, actor-network theory’s notion of script, interdisciplinary social research situated between design and sociology and presentations given in academic settings. To conclude, I outline quasi-design as involving the breaching of quasi-scripts to explore people’s expectations revealed inadopting, resisting or developing quasi-breaches in situations of presentation inacademia and perhaps beyond.

Intelligent agents powered by AI planning assist people in complex scenarios, such as managing teams of semi-autonomous vehicles. However, AI planning models may be incomplete, leading to plans that do not adequately meet the stated objectives, especially in unpredicted situations. Humans, who are apt at identifying and adapting to unusual situations, may be able to assist planning agents in these situations by encoding their knowledge into a planner at run-time. We investigate whether people can collaborate with agents by providing their knowledge to an agent using linear temporal logic (LTL) at run-time without changing the agent's domain model. We presented 24 participants with baseline plans for situations in which a planner had limitations, and asked the participants for workarounds for these limitations. We encoded these workarounds as LTL constraints. Results show that participants' constraints improved the expected return of the plans by 10% ($p < 0.05$) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience. However, participants used more declarative than control constraints over time, but declarative constraints produced plans less similar to the expectation of the participants, which could lead to potential trust issues., International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 2021

In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions. Our starting point is the observation that causal models can generate opportunity chains that take the form of `A enables B and B causes C'. Using insights from an analysis of 240 explanations generated in a human-agent experiment, we define a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. A recurrent neural network is employed to learn opportunity chains, and decision trees are used to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 reinforcement learning benchmarks using different reinforcement learning algorithms. From a study with 90 human participants, we show that our distal explanation model results in improved outcomes over three scenarios compared with two baseline explanation models.

Research has shown that personalization of health interventions can contribute to an improved effectiveness. Reinforcement learning algorithms can be used to perform such tailoring. In this paper, we present a cluster-based reinforcement learning approach which learns optimal policies for groups of users. Such an approach can speed up the learning process while still giving a level of personalization. We apply both online and batch learning to learn policies over the clusters and introduce a publicly available simulator which we have developed to evaluate the approach. The results show batch learning significantly outperforms online learning. Furthermore, near-optimal clustering is found which proves to be beneficial in learning significantly better policies compared to learning per user and learning across all users.

Health information is generally fragmented across silos. Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization. Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more degrees confederated machine learning. We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.

The deployment of Electronic Health Records (EHRs) across Australia has provided a platform of possibilities for new technology developers. However, the lack of enthusiasm by multiple health-care providers and patients reveals how many existing applications fail to address the needs of these stakeholders. We conducted interviews with GPs and software company representatives (being partners of our Living Lab) to understand their goals of engaging with EHR-enabled technology, with a focus on their emotional goals. We then constructed a motivational goal model to represent the technology goals that are relevant to our case study. This approach to gathering technology requirements enabled us to elicit, organise and communicate the functional, quality and emotional goals for EHR-enabled technology. Results illustrate our living lab process and reflect on a variety of goals that may be supported by EHR- enabled technology. The goal model will be evolved and utilised in the planning of future phases of this living lab project.

To generate trust with their users, Explainable Artificial Intelligence (XAI) systems need to include an explanation model that can communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an explanation in order to propose a human explanation dialog model. We follow a bottom-up approach to derive the model by analysing transcripts of 398 different explanation dialog types. We use grounded theory to code and identify key components of which an explanation dialog consists. We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog. We present a generalized state model obtained by the analysis and compare it with an existing conceptual dialog model of explanation., Comment: 15 pages, First international workshop on socio-cognitive systems at Federated AI Meeting (FAIM) 2018

In organizational and commercial settings, people often have clear roles and workflows against which functional and non-functional requirements can be extracted. However, in more social settings, such as platforms for enhancing social interaction, successful applications are driven more by using emotional engagement than functionality, and the drivers of user engagement are difficult to identify. A key challenge is to understand people's emotional goals so that they can be incorporated into the design. This paper proposes a novel framework called the Emotional Attachment Framework, which is based on existing models and theories of emotional attachment. Its aim is to facilitate the process of getting a deeper insight into emotional goals in software engineering. To demonstrate the framework in use, emotional goals are elicited for a software application that aims to provide help for homeless people. To measure the effectiveness and efficiency of the proposed technique in this study, a series of evaluations are undertaken: a semi-controlled experiment, a comparison analysis, and domain expert and end-user evaluation. The results indicate that the Emotional Attachment Framework has the potential to give better insight during analysis of emotional goals., Comment: 44 pages

As part of the follow-up of bright 1.4mm sources from the South Pole Telescope 2500 square degree survey, we have identified a unique proto-cluster at z=4.3. Using ALMA CO(4-3) and [CII] spectroscopy, we have spectroscopically confirmed at least 12 members within a radius of 13 arcseconds. These 12 members have a combined star formation rate of 6800 M_sol/yr, which may increase up to 22000 M_sol/yr if the entire LABOCA 870µm flux of 100 mJy is accounted for. The 12 members have a velocity dispersion of 500 km/s, implying a virial mass of ~2x1013 M_sol. This makes SPT2349-56 a truly unique proto-cluster both in terms of mass and star-formation rate., Poster presentation at GCF2017.

Section 6 is the final section of I-69 from Evansville to Indianapolis and will link Martinsville to I-465 in Indianapolis. In this session, project officials will provide a summary of the activities associated with the project’s Tier 2 Environmental Impact Statement since the project resumed in October 2014, including defining the project scope, public involvement, environmental analysis, and preliminary design.

Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.

We present a search for companion [CII] emitters to known luminous sources at $6, Accepted for publication in ApJ

We describe the Scene-Graph-As-Bus technique (SGAB), the first step in a staircase of solutions for sharing software components for virtual environments. The goals of SGAB are to allow, with minimal effort, independently designed applications to share component functionality; and for multiple users to share applications designed for single users. This paper reports on the SGAB design for transparently conjoining different applications by unifying the state information contained in their scene graphs. SGAB monitors and maps changes in the local scene graph of one application to a neutral scene graph representation (NSG), distributes the NSG changes over the network to remote peer applications, and then maps the NSG changes to the local scene graph of the remote application. The fundamental contribution of SGAB is that both the local and remote applications can be completely unaware of each other; that is, both applications can interoperate without code or binary modification despite each having no knowledge of networking or interoperability.

In this paper we investigate the use of next generation remote collaboration tools for supporting design education. "Chinese Whispers" [ref] refers to the concept of mediating between remote studios with new forms of hybrid designing and real time online collaboration. We conducted a sequence of experiments which explore the concept of linkage and slippage, occurring at the boundaries of converging technologies as a means of generating innovative and unexpected design outcomes -- real and virtual, tangible and intangible. The project is framed in a contemporary context with background research into current concepts and theories centered on learning ecology, and user-generated design into future trends and state of the art technologies.Chinese Whisper involved linking hard- and soft-wares that are not immediately compatible in a remote networked environment, to facilitate an educational design process in both remote and real environments. Through this process students engaged in 3D scanning, downloading, visualizing, analyzing, remote simultaneous modeling in stereo and deciding when to hit '3D print' at any given stage to invent a new design methodology. Refereed/Peer-reviewed

An important step in Reinforcement Learning (RL) research is to create mechanisms that give higher level insights into the black-box policy models used nowadays and provide explanations for these learned behaviors or motivate the choices behind certain decision steps. In this paper, we illustrate how Soft Decision Tree (SDT) distillation can be used to make policies that are learned through RL more interpretable. Soft Decision Trees create binary trees of predetermined depth, where each branching node represents a hierarchical filter that influences the classification of input data. We distill SDTs from a deep neural network RL policy for the Mario AI benchmark and inspect the learned hierarchy of filters, showing which input features lead to specific action distributions in the episode. We realize preliminary steps towards interpreting the learned behavior of the policy and discuss future improvements., info:eu-repo/semantics/published