Miracle-dz/UniFAR-FTUs huggingface dataset:
Food for Thought: Applying Behavioural Insights to Household Food Waste Recycling Behaviour.

Municipal waste production is one of the most widely recognised environmental issues in society today. In the UK, households are responsible for generating millions of tonnes of waste materials each year, with food waste proving to be a particularly problematic waste stream. Local authorities, who are responsible for waste management, have historically relied on changes to physical infrastructure or informational interventions to drive performance improvements. However, in times of increasing financial pressures, there has been a growing recognition that the transition to a sustainable, resilient and resourceful society will require fundamental changes to the way people think and behave. Indeed, what connects many modern-day sustainability challenges are their roots in human behaviour. While various ‘tools of government’ can be employed to realise strategic public policy objectives, emergent localism and the apparent ineffectiveness of this traditional approach catalysed a shift towards ensuring that statutory requirements were delivered more efficiently than ever before. This led to a widespread application of ‘insights’, synthesised from behavioural sciences, to inform the design, implementation and evaluation of new policy interventions. Enthusiasm to the so-called ‘nudge’ approach, which recognises that behaviour can be strongly and automatically influenced by the context in which it is situated, soon trickled down to local government, creating a growing appetite for the approach. These collective ‘behavioural insights’ provided local authorities with a powerful new set of policy tools that, if used correctly, could be used to influence waste behaviours. This research explored their application by evaluating the efficacy and affordability of those nudges that could feasibly be introduced at scale by local authority practitioners to produce a positive and sustained influence on household food waste recycling behaviour. By adopting a mixed-methods approach it was shown that, by making simple changes to the existing ‘choice environment’ in Surrey, it was possible to ‘nudge’ households towards engaging (more) in food waste recycling behaviour. Further, it was found that prompt-based nudges, using stickers as the medium of delivery, were particularly effective, with effects persisting for far longer than has typically been achieved using more ‘traditional’ informational policy interventions. While popular, the practice of ‘nudging’ has a range of issues, both conceptual and controversial, so it is important for policymakers to be aware of the differing philosophies, efficacy, methodologies and ethics associated with these types of intervention. While nudges may not be the ‘silver bullet’, it is argued that they are, at least for now, useful devices for policymakers to have in their ‘toolkit’.
One-shot distributed ridge regression in high dimensions

In many areas, practitioners need to analyze large datasets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Datasets are spread out over several computing units, which do most of the analysis locally, and communicate short messages. Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment? Ridge regression is an extremely popular method for supervised learning, and has several optimality properties, thus it is important to study. We study one-shot methods that construct weighted combinations of ridge regression estimators computed on each machine. By analyzing the mean squared error in a high dimensional random-effects model where each predictor has a small effect, we discover several new phenomena. 1. Infinite-worker limit: The distributed estimator works well for very large numbers of machines, a phenomenon we call "infinite-worker limit". 2. Optimal weights: The optimal weights for combining local estimators sum to more than unity, due to the downward bias of ridge. Thus, all averaging methods are suboptimal. We also propose a new optimally weighted one-shot ridge regression algorithm. We confirm our results in simulation studies and using the Million Song Dataset as an example. There we can save at least 100x in computation time, while nearly preserving test accuracy.
Modular graph functions and asymptotic expansions of Poincaré series

In this note we study $SL(2,\mathbb{Z})$-invariant functions such as modular graph functions or coefficient functions of higher derivative corrections in type IIB string theory. The functions solve inhomogeneous Laplace equations and we choose to represent them as Poincare series. In this way we can combine different methods for asymptotic expansions and obtain the perturbative and non-perturbative contributions to their zero Fourier modes. In the case of the higher derivative corrections, these terms have an interpretation in terms of perturbative string loop effects and pairs of instantons/anti-instantons.
Limits on Electromagnetic Counterparts of Gravitational-wave-detected Binary Black Hole Mergers

Unlike mergers of two compact objects containing a neutron star (NS), binary black hole (BBH) mergers are not accompanied by the production of tidally disrupted material and hence lack the most direct source of accretion to power a jet and generate electromagnetic (EM) radiation. However, following a tentative detection by the Fermi GBM of a γ-ray counterpart to GW150914, several ideas were proposed for driving a jet and producing EM radiation. If such jets were in fact produced, they would, however, lack the cocoon emission that makes jets from binary NSs also bright at large viewing angles. Here, via Monte Carlo simulations of a population of BBH mergers with properties consistent with those inferred from the existing LIGO/Virgo observations and the angular emission characteristic of jets propagating into the interstellar medium, we derive limits on the allowed energetics and Lorentz factors of such jets from EM follow-ups to GW-detected BBH merger events to date, and we make predictions that will help tighten these limits with broadband EM follow-ups to events in future LIGO/Virgo runs. The condition that ≲1 event out of 10 GW-detected BBH mergers be above the Fermi/GBM threshold imposes that any currently allowed emission model has to satisfy the condition (Eiso/1049 erg)(θjet/20°) ≲ 1.
Bright Mini-outburst Ends the 12 yr Long Activity of the Black Hole Candidate Swift J1753.5–0127

We present optical, UV, and X-ray monitoring of the short orbital period black hole X-ray binary candidate Swift J1753.5–0127, focusing on the final stages of its 12 yr long outburst that started in 2005. From 2016 September onward, the source started to fade, and within 3 months, the optical flux almost reached the quiescent level. Soon after that, using a new proposed rebrightening classification method, we recorded a mini-outburst and a reflare in the optical light curves, peaking in 2017 February (V ∼ 17.0) and May (V ∼ 17.9), respectively. Remarkably, the mini-outburst has a peak flux consistent with the extrapolation of the slow decay before the fading phase preceding it. The following reflare was fainter and shorter. We found from optical colors that the temperature of the outer disk was ∼11,000 K when the source started to fade rapidly. According to the disk instability model, this is close to the critical temperature when a cooling wave is expected to form in the disk, shutting down the outburst. The optical color could be a useful tool to predict decay rates in some X-ray transients. We notice that all X-ray binaries that show mini-outbursts following a main outburst are short orbital period systems (<7 hr). In analogy with another class of short-period binaries showing similar mini-outbursts, the cataclysmic variables of the RZ LMi type, we suggest that mini-outbursts could occur if there is a hot inner disk at the end of the outburst decay.
Principal components in linear mixed models with general bulk

We study the principal components of covariance estimators in multivariate mixed-effects linear models. We show that, in high dimensions, the principal eigenvalues and eigenvectors may exhibit bias and aliasing effects that are not present in low-dimensional settings. We derive the first-order limits of the principal eigenvalue locations and eigenvector projections in a high-dimensional asymptotic framework, allowing for general population spectral distributions for the random effects and extending previous results from a more restrictive spiked model. Our analysis uses free probability techniques, and we develop two general tools of independent interest-- strong asymptotic freeness of GOE and deterministic matrices and a free deterministic equivalent approximation for bilinear forms of resolvents.
A Novel Independent RNN Approach to Classification of Seizures against Non-seizures

In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions. Manual reviews can be laborious and error prone. Automatic and accurate seizure/non-seizure classification methods are desirable. A critical challenge is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure features, this paper leverages an emerging deep learning model, the independently recurrent neural network (IndRNN), to construct a new approach for the seizure/non-seizure classification. This new approach gradually expands the time scales, thereby extracting temporal and spatial features from the local time duration to the entire record. Evaluations are conducted with cross-validation experiments across subjects over the noisy data of CHB-MIT. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art methods. In addition, we explore how the segment length affects the classification performance. Thirteen different segment lengths are assessed, showing that the classification performance varies over the segment lengths, and the maximal fluctuating margin is more than 4%. Thus, the segment length is an important factor influencing the classification performance.
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

Structured pruning of filters or neurons has received increased focus for compressing convolutional neural networks. Most existing methods rely on multi-stage optimizations in a layer-wise manner for iteratively pruning and retraining which may not be optimal and may be computation intensive. Besides, these methods are designed for pruning a specific structure, such as filter or block structures without jointly pruning heterogeneous structures. In this paper, we propose an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner. To accomplish this, we first introduce a soft mask to scale the output of these structures by defining a new objective function with sparsity regularization to align the output of baseline and network with this mask. We then effectively solve the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end-to-end manner. By forcing more scale factors in the soft mask to zero, the fast iterative shrinkage-thresholding algorithm (FISTA) can be leveraged to fast and reliably remove the corresponding structures. Extensive experiments demonstrate the effectiveness of GAL on different datasets, including MNIST, CIFAR-10 and ImageNet ILSVRC 2012. For example, on ImageNet ILSVRC 2012, the pruned ResNet-50 achieves 10.88% Top-5 error and results in a factor of 3.7x speedup. This significantly outperforms state-of-the-art methods.
Strengthening the Healthy Adult Self in Art Therapy: Using Schema Therapy as a Positive Psychological Intervention for People Diagnosed With Personality Disorders

This article reviews recent theoretical contributions and studies about art therapy that attempt to capture aspects of healthy ego functioning and presents an inventory of art therapy interventions to strengthen the Healthy Adult. It discusses how art therapy can offer interventions that focus specifically and directly on the integration and growth of the whole person, and which interventions are suitable for this purpose in the treatment of people diagnosed with severe personality disorders. A link is made between the Healthy Adult as a familiar concept from Schema Therapy, and Positive Psychology, with its focus on well-being, strength and positive affect. On the basis of our present knowledge, available studies and experiences, art therapy seems promising in contributing to the development of Healthy Adult functioning. Art therapy addresses the different areas of healthy adult functioning. Specific art therapy interventions are discussed on a concrete level. Art therapy appears to offer ways to strengthen the Healthy Adult and helps people to free themselves from destructive patterns and to work on satisfying their basic needs as independent and responsibly functioning individuals in a positive connectedness with themselves and their surroundings. The strength of art therapy may be the experiential level and the appeal to the capacity to play, to flexibility, and to be creative. This makes experiences easier to reach, and developing from there into a Healthy Adult mode is possible in a manner that is more felt than thought. Creativity can be regarded as the ability of the Healthy Adult to be flexible and to find different solutions to a problem. This appeal of art therapy fits well the therapeutic goal of empowerment and well-being, which is at the heart of positive psychology. More research is needed to verify the effects and working mechanisms of art therapy interventions.
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder

One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis. However, previous studies relied on humans defining macro actions or assumed macro actions as repetitions of the same primitive actions. We present Factorized Macro Action Reinforcement Learning (FaMARL) which autonomously learns disentangled factor representation of a sequence of actions to generate macro actions that can be directly applied to general reinforcement learning algorithms. FaMARL exhibits higher scores than other reinforcement learning algorithms on environments that require an extensive amount of search.
νbhlight: Radiation GRMHD for Neutrino-driven Accretion Flows

The 2017 detection of the in-spiral and merger of two neutron stars was a landmark discovery in astrophysics. We now know that such mergers are central engines of short gamma-ray bursts and sites of r-process nucleosynthesis, where the heaviest elements in our universe are formed. In the coming years, we expect many more such mergers. Modeling such systems presents a significant computational challenge along with the observational one. To meet this challenge, we present , a scheme for solving general relativistic magnetohydrodynamics with energy-dependent neutrino transport in full (3 + 1) dimensions, facilitated by Monte Carlo methods. We present a suite of tests demonstrating the accuracy, efficacy, and necessity of our scheme. We demonstrate the potential of our scheme by running a sample calculation in a domain of interest—the dynamics and composition of the accretion disk formed by a binary neutron star merger.
Varied origins of up-bending breaks in galaxy disks

Aims. Using a sample of 175 low-inclination galaxies from the S4G, we investigate the origins of up-bending (Type III) breaks in the 3.6 μm surface brightness profiles of disk galaxies. Methods. We reanalyzed a sample of previously identified Type III disk break-hosting galaxies using a new, unbiased break-finding algorithm, which uncovered many new, sometimes subtle disk breaks across the whole sample. We classified each break by its likely origin through close examination of the galaxy images across wavelengths, and compare samples of galaxies separated by their outermost identified break types in terms of their stellar populations and local environments. Results. We find that more than half of the confirmed Type III breaks in our sample can be attributed to morphological asymmetry in the host galaxies. As these breaks are mostly an artifact of the azimuthal averaging process, their status as physical breaks is questionable. Such galaxies occupy some of the highest density environments in our sample, implying that much of this asymmetry is the result of tidal disturbance. We also find that Type III breaks related to extended spiral arms or star formation often host down-bending (Type II) breaks at larger radius which were previously unidentified. Such galaxies reside in the lowest density environments in our sample, in line with previous studies that found a lack of Type II breaks in clusters. Galaxies occupying the highest density environments most often show Type III breaks associated with outer spheroidal components. Conclusions. We find that Type III breaks in the outer disks of galaxies arise most often through environmental influence: either tidal disturbance (resulting in disk asymmetry) or heating through, for example, galaxy harrassment (leading to spheroidal components). Galaxies hosting the latter break types also show bimodal distributions in central g − r color and morphological type, with more than half of such galaxies classified as Sa or earlier; this suggests these galaxies may be evolving into early-type galaxies. By contrast, we find that Type III breaks related to apparently secular features (e.g., spiral arms) may not truly define their hosts’ outer disks, as often in such galaxies additional significant breaks can be found at larger radius. Given this variety in Type III break origins, we recommend in future break studies making a more detailed distinction between break subtypes when seeking out, for example, correlations between disk breaks and environment, to avoid mixing unlike physical phenomena.
Stellar content, planetary nebulae, and globular clusters of [KKS2000]04 (NGC 1052-DF2)

[KKS2000]04 (NGC1052-DF2) has become a controversial and well-studied galaxy after the claims suggesting a lack of dark matter and the presence of an anomalously bright globular cluster (GC) system around it. A precise determination of its overall star formation history (SFH) as well as a better characterisation of its GC or planetary nebulae (PN) systems are crucial aspects to: i) understand its real nature, in particular placing it within the family of ultra diffuse galaxies; ii) shed light on its possible formation, evolution, and survival in the absence of dark matter. With this purpose we expand on the knowledge of [KKS2000]04 from the analysis of OSIRIS@GTC spectroscopic data. On the one hand, we claim the possible detection of two new PNe and confirm membership of 5 GCs. On the other hand, we find that the stars shaping [KKS2000]04 are intermediate-age to old (90\% of its stellar mass older than 5 Gyr, average age of 8.7 $\pm$ 0.7 Gyr) and metal-poor ([M/H] $\sim$ -1.18 $\pm$ 0.05), in general agreement with previous results. We do not find any clear hints of significant changes in its stellar content with radius. In addition, the possibility of [KKS2000]04 being a tidal dwarf galaxy with no dark matter is highly disfavoured.
Vectorial and spinorial perturbations in Galileon black holes: Quasinormal modes, quasiresonant modes, and stability

In this work we have considered a model that includes the interaction of gravity and matter fields with Galilean invariance (the so-called derivative coupling) as well as some corresponding black hole type solutions. Quasinormal perturbations of two kinds of matter fields have been computed by different methods. The effect of the derivative coupling in the quasinormal spectrum has been analyzed and evaluated.
S-band Polarization All-Sky Survey (S-PASS): survey description and maps

We present the S-Band Polarization All Sky Survey (S-PASS), a survey of polarized radio emission over the southern sky at Dec. <−1° taken with the Parkes radio telescope at 2.3 GHz. The main aim was to observe at a frequency high enough to avoid strong depolarization at intermediate Galactic latitudes (still present at 1.4 GHz) to study Galactic magnetism, but low enough to retain ample signal-to-noise ratio (S/N) at high latitudes for extragalactic and cosmological science. We developed a new scanning strategy based on long azimuth scans and a corresponding map-making procedure to make recovery of the overall mean signal of Stokes Q and U possible, a long-standing problem with polarization observations. We describe the scanning strategy, map-making procedure and validation tests. The overall mean signal is recovered with a precision better than 0.5 per cent. The maps have a mean sensitivity of 0.81 mK on beam-size scales and show clear polarized signals, typically to within a few degrees of the Galactic plane, with ample S/N everywhere (the typical signal in low-emission regions is 13 mK and 98.6 per cent of pixels have S/N > 3). The largest depolarization areas are in the inner Galaxy, associated with the Sagittarius Arm. We have also computed a rotation measure map combining S-PASS with archival data from the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck experiments. A Stokes I map has been generated, with sensitivity limited to the confusion level of 9 mK.
Evolution of the galaxy stellar mass functions and UV luminosity functions at z = 6−9 in the Hubble Frontier Fields

We present new measurements of the evolution of the galaxy stellar mass functions (GSMF) and UV luminosity functions (UV LF) for galaxies from $z=6-9$ within the Frontier Field cluster MACSJ0416.1-2403 and its parallel field. To obtain these results, we derive the stellar masses of our sample by fitting synthetic stellar population models to their observed spectral energy distribution with the inclusion of nebular emission lines. This is the deepest and farthest in distance mass function measured to date and probes down to a level of M$_{*} = 10^{6.8}M_{\odot}$. The main result of this study is that the low-mass end of our GSMF to these limits and redshifts appears to become steeper from $-1. 98_{-0.07}^{+0.07}$ at $z=6$ to $-2. 38_{-0.88}^{+0.72}$ at $z=9$, steeper than previously observed mass functions at slightly lower redshifts, and we find no evidence of turnover in the mass range probed. We furthermore demonstrate that the UV LF for these system also appears to show a steepening at the highest redshifts, without any evidence of turnover in the luminosity range probed. Our $M_{\mathrm{UV}}-M_{*}$ relation exhibit shallower slopes than previously observed and are in accordance with a constant mass-to-light ratio. Integrating our GSMF, we find that the stellar mass density increases by a factor of $\sim15_{-6}^{+21}$ from $z=9$ to $z=6$. We estimate the dust-corrected star formation rates (SFRs) to calculate the specific star formation rates ($\mathrm{sSFR}=\mathrm{SFR/M_{*}}$) of our sample, and find that for a fixed stellar mass of $5\times10^{9}M_{\odot}$, sSFR $\propto(1+z)^{2.01\pm0.16}$. Finally, from our new measurements, we estimate the UV luminosity density ($\rho_{\textrm{UV}}$) and find that our results support a smooth decline of $\rho_{\textrm{UV}}$ towards high redshifts.