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A standard method to reduce the linewidth of semiconductor lasers involves the use of external optical feedback (EOF). However, feedback powers less than 1 % usually trigger coherence collapse (CC), leading to chaotic laser dynamics and linewidth broadening. This paper explores a method to mitigate CC through precise tuning of the feedback polarization depending on the feedback power. We report a semiconductor laser with a sub-kHz linewidth, achieved via EOF. The laser features a U-shaped cavity with two sampled grating distributed Bragg reflectors (SG-DBRs), enabling broad tunability across a 42 nm wavelength range (1513-1555 nm). By injecting optical feedback into both sides of the laser cavity via an external fiber-based cavity, we reduce the linewidth by more than three orders of magnitude, from MHz to sub-kHz across the laser's tuning range. Our approach achieves significant linewidth reduction while maintaining coherence at high feedback levels, marking an improvement over prior studies where CC limited performance. These results pave the way for ultra-narrow linewidth diode lasers with wide tunability, which would benefit fields like coherent optical communications and spectroscopy.

There is increasing interest in the adoption of LLMs in HCI research. However, LLMs may often be regarded as a panacea because of their powerful capabilities with an accompanying oversight on whether they are suitable for their intended tasks. We contend that LLMs should be adopted in a critical manner following rigorous evaluation. Accordingly, we present the evaluation of an LLM in identifying logical fallacies that will form part of a digital misinformation intervention. By comparing to a labeled dataset, we found that GPT-4 achieves an accuracy of 0.79, and for our intended use case that excludes invalid or unidentified instances, an accuracy of 0.90. This gives us the confidence to proceed with the application of the LLM while keeping in mind the areas where it still falls short. The paper describes our evaluation approach, results and reflections on the use of the LLM for our intended task.

Generative artificial intelligence (GenAI) can rapidly produce large and diverse volumes of content. This lends to it a quality of creativity which can be empowering in the early stages of design. In seeking to understand how creative ways to address practical issues can be conceived between humans and GenAI, we conducted a rapid ideation workshop with 21 participants where they used a large language model (LLM) to brainstorm potential solutions and evaluate them. We found that the LLM produced a greater variety of ideas that were of high quality, though not necessarily of higher quality than human-generated ideas. Participants typically prompted in a straightforward manner with concise instructions. We also observed two collaborative dynamics with the LLM fulfilling a consulting role or an assisting role depending on the goals of the users. Notably, we observed an atypical anti-collaboration dynamic where participants used an antagonistic approach to prompt the LLM.

With fact-checking by professionals being difficult to scale on social media, algorithmic techniques have been considered. However, it is uncertain how the public may react to labels by automated fact-checkers. In this study, we investigate the use of automated warning labels derived from misinformation detection literature and investigate their effects on three forms of post engagement. Focusing on political posts, we also consider how partisanship affects engagement. In a two-phases within-subjects experiment with 200 participants, we found that the generic warnings suppressed intents to comment on and share posts, but not on the intent to like them. Furthermore, when different reasons for the labels were provided, their effects on post engagement were inconsistent, suggesting that the reasons could have undesirably motivated engagement instead. Partisanship effects were observed across the labels with higher engagement for politically congruent posts. We discuss the implications on the design and use of automated warning labels.

In Singapore, there has been a rise in misinformation on mobile instant messaging services (MIMS). MIMS support both small peer-to-peer networks and large groups. Misinformation in the former may spread due to recipients' trust in the sender while in the latter, misinformation can directly reach a wide audience. The encryption of MIMS makes it difficult to address misinformation directly. As such, chatbots have become an alternative solution where users can disclose their chat content directly to fact checking services. To understand how effective fact checking chatbots are as an intervention and how trust in three different fact checkers (i.e., Government, News Outlets, and Artificial Intelligence) may affect this trust, we conducted a within-subjects experiment with 527 Singapore residents. We found mixed results for the fact checkers but support for the chatbot intervention overall. We also found a striking contradiction between participants' trust in the fact checkers and their behaviour towards them. Specifically, those who reported a high level of trust in the government performed worse and tended to follow the fact checking tool less when it was endorsed by the government.

There is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those systems. But there is much debate about what form these regulations should take and how they should be implemented. Most work in this area has been qualitative, and has not been able to make formal predictions. Here, we propose that evolutionary game theory can be used to quantitatively model the dilemmas faced by users, AI creators, and regulators, and provide insights into the possible effects of different regulatory regimes. We show that creating trustworthy AI and user trust requires regulators to be incentivised to regulate effectively. We demonstrate the effectiveness of two mechanisms that can achieve this. The first is where governments can recognise and reward regulators that do a good job. In that case, if the AI system is not too risky for users then some level of trustworthy development and user trust evolves. We then consider an alternative solution, where users can condition their trust decision on the effectiveness of the regulators. This leads to effective regulation, and consequently the development of trustworthy AI and user trust, provided that the cost of implementing regulations is not too high. Our findings highlight the importance of considering the effect of different regulatory regimes from an evolutionary game theoretic perspective.

Far-UVC light in the wavelength range of 200-230 nm has attracted renewed interest because of its safety for human exposure and effectiveness in inactivating pathogens. Here we present a compact solid-state far-UVC laser source based on second-harmonic generation (SHG) using a low-cost commercially-available blue laser diode pump. Leveraging the high intensity of light in a nanophotonic waveguide and heterogeneous integration, our approach achieves Cherenkov phase-matching across a bonded interface consisting of a silicon nitride (SiN) waveguide and a beta barium borate (BBO) nonlinear crystal. Through systematic investigations of waveguide dimensions and pump power, we analyze the dependencies of Cherenkov emission angle, conversion efficiency, and output power. Experimental results confirm the feasibility of generating far-UVC, paving the way for mass production in a compact form factor. This solid-state far-UVC laser source shows significant potential for applications in human-safe disinfection, non-line-of-sight free-space communication, and deep-UV Raman spectroscopy.

The massive volume of online information along with the issue of misinformation has spurred active research in the automation of fact-checking. Like fact-checking by human experts, it is not enough for an automated fact-checker to just be accurate, but also be able to inform and convince the user of the validity of its predictions. This becomes viable with explainable artificial intelligence (XAI). In this work, we conduct a study of XAI fact-checkers involving 180 participants to determine how users' actions towards news and their attitudes towards explanations are affected by the XAI. Our results suggest that XAI has limited effects on users' agreement with the veracity prediction of the automated fact-checker and on their intent to share news. However, XAI nudges users towards forming uniform judgments of news veracity, thereby signaling their reliance on the explanations. We also found polarizing preferences towards XAI and raise several design considerations on them.

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the 'quiet revolution' of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable skill for both global metrics and extreme events, when verified against both the operational analysis and synoptic observations. Increasing forecast smoothness and bias drift with forecast lead time are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

We present a study of rotation across 30 square degrees of the Orion Star-forming Complex, following a $\sim$200 d photometric monitoring campaign by the Next Generation Transit Survey (NGTS). From 5749 light curves of Orion members, we report periodic signatures for 2268 objects and analyse rotation period distributions as a function of colour for 1789 stars with spectral types F0$-$M5. We select candidate members of Orion using $\textit{Gaia}$ data and assign our targets to kinematic sub-groups. We correct for interstellar extinction on a star-by-star basis and determine stellar and cluster ages using magnetic and non-magnetic stellar evolutionary models. Rotation periods generally lie in the range 1$-$10 d, with only 1.5 per cent of classical T Tauri stars or Class I/II young stellar objects rotating with periods shorter than 1.8 d, compared with 14 per cent of weak-line T Tauri stars or Class III objects. In period$-$colour space, the rotation period distribution moves towards shorter periods among low-mass (>M2) stars of age 3$-$6 Myr, compared with those at 1$-$3 Myr, with no periods longer than 10 d for stars later than M3.5. This could reflect a mass-dependence for the dispersal of circumstellar discs. Finally, we suggest that the turnover (from increasing to decreasing periods) in the period$-$colour distributions may occur at lower mass for the older-aged population: $\sim$K5 spectral type at 1$-$3 Myr shifting to $\sim$M1 at 3$-$6 Myr., Comment: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society. Published by Oxford University Press on behalf of the Royal Astronomical Society. 20 pages. 21 figures

The application of Very Long Baseline Interferometry (VLBI) to the Search for Extraterrestrial Intelligence (SETI) has been limited to date, despite the technique offering many advantages over traditional single-dish SETI observations. In order to further develop interferometry for SETI, we used the European VLBI Network (EVN) at $21$~cm to observe potential secondary phase calibrators in the Kepler field. Unfortunately, no secondary calibrators were detected. However, a VLBA primary calibrator in the field, J1926+4441, offset only $\sim1.88'$ from a nearby exoplanet Kepler-111~b, was correlated with high temporal $\left(0.25 \ \rm{s}\right)$ and spectral $\left(16384 \times 488\ \rm{Hz \ channels}\right)$ resolution. During the analysis of the high-resolution data, we identified a spectral feature that was present in both the auto and cross-correlation data with a central frequency of $1420.424\pm0.0002$ MHz and a width of 0.25 MHz. We demonstrate that the feature in the cross-correlations is an artefact in the data, associated with a significant increase in each telescope's noise figure due to the presence of \ion{H}{i} in the beam. This would typically go unnoticed in data correlated with standard spectral resolution. We flag (excluded from the subsequent analysis) these channels and phase rotate the data to the location of Kepler-111~b aided by the GAIA catalogue and search for signals with $\rm{SNR}>7$. At the time of our observations, we detect no transmitters with an Equivalent Isotropically Radiated Power (EIRP) > $\sim4\times10^{15}$ W., Comment: 11 pages, 12 figures, accepted MNRAS

The dipper is a novel class of young stellar object associated with large drops in flux on the order of 10 to 50 per cent lasting for hours to days. Too significant to arise from intrinsic stellar variability, these flux drops are currently attributed to disk warps, accretion streams, and/or transiting circumstellar dust. Dippers have been previously studied in young star forming regions including the Orion Complex. Using Next Generation Transit Survey (NGTS) data, we identified variable stars from their lightcurves. We then applied a machine learning random forest classifier for the identification of new dipper stars in Orion using previous variable classifications as a training set. We discover 120 new dippers, of which 83 are known members of the Complex. We also investigated the occurrence rate of disks in our targets, again using a machine learning approach. We find that all dippers have disks, and most of these are full disks. We use dipper periodicity and model-derived stellar masses to identify the orbital distance to the inner disk edge for dipper objects, confirming that dipper stars exhibit strongly extended sublimation radii, adding weight to arguments that the inner disk edge is further out than predicted by simple models. Finally, we determine a dipper fraction (the fraction of stars with disks which are dippers) for known members of 27.8 plus minus 2.9 per cent. Our findings represent the largest population of dippers identified in a single cluster to date., Comment: 28 pages, 34 figures

As we transition to renewable energy sources, addressing their inflexibility during peak demand becomes crucial. It is therefore important to reduce the peak load placed on our energy system. For households, this entails spreading high-power appliance usage like dishwashers and washing machines throughout the day. Traditional approaches to spreading out usage have relied on differential pricing set by a centralised utility company, but this has been ineffective. Our previous research investigated a decentralised mechanism where agents receive an initial allocation of time-slots to use their appliances, which they can exchange with others. This was found to be an effective approach to reducing the peak load when we introduced social capital, the tracking of favours, to incentivise agents to accept exchanges that do not immediately benefit them. This system encouraged self-interested agents to learn socially beneficial behaviour to earn social capital that they could later use to improve their own performance. In this paper we expand this work by implementing real world household appliance usage data to ensure that our mechanism could adapt to the challenging demand needs of real households. We also demonstrate how smaller and more diverse populations can optimise more effectively than larger community energy systems., Comment: arXiv admin note: text overlap with arXiv:2006.14526