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Human emotions can be conveyed through nuanced touch gestures. However, there is a lack of understanding of how consistently emotions can be conveyed to robots through touch. This study explores the consistency of touch-based emotional expression toward a robot by integrating tactile and auditory sensory reading of affective haptic expressions. We developed a piezoresistive pressure sensor and used a microphone to mimic touch and sound channels, respectively. In a study with 28 participants, each conveyed 10 emotions to a robot using spontaneous touch gestures. Our findings reveal a statistically significant consistency in emotion expression among participants. However, some emotions obtained low intraclass correlation values. Additionally, certain emotions with similar levels of arousal or valence did not exhibit significant differences in the way they were conveyed. We subsequently constructed a multi-modal integrating touch and audio features to decode the 10 emotions. A support vector machine (SVM) model demonstrated the highest accuracy, achieving 40% for 10 classes, with "Attention" being the most accurately conveyed emotion at a balanced accuracy of 87.65%.

A century or less separates the thermonuclear-powered eruptions of recurrent novae in the hydrogen-rich envelopes of massive white dwarfs. The colliding ejecta of successive recurrent nova events are predicted to always generate very large (tens of parsecs) super-remnants; only two examples are currently known. T CrB offers an excellent opportunity to test this prediction. As it will almost certainly undergo its next, once-in ~80-year recurrent nova event between 2024 and 2026, we carried out very deep narrowband and continuum imaging to search for the predicted, piled-up ejecta of the past millenia. While nothing is detected in continuum or narrowband [OIII] images, a ~30-parsec-diameter, faint nebulosity surrounding T CrB is clearly present in deep Halpha, [NII] and [SII] narrowband Condor Array Telescope imagery. We predict that these newly detected nebulosities, as well as the recent ejecta that have not yet reached the super-remnant, are far too optically-thin to capture all but a tiny fraction of the photons emitted by RN flashes. We thus predict that fluorescent light echoes will NOT be detectable following the imminent nova flash of T CrB. Dust may be released by the T CrB red giant wind in pre-eruption outbursts, but we have no reliable estimates of its quantity or geometrical distribution. While we cannot predict the morphology or intensity of dust-induced continuum light echoes following the coming flash, we encourage multi-epoch Hubble Space Telescope optical imaging as well as James Webb Space Telescope infrared imaging of T CrB during the year after it erupts., Comment: 14 pages, 6 Figures, in press in The Astrophysical Journal Letters

The Lyman alpha (Lya) forest in the spectra of z>5 quasars provides a powerful probe of the late stages of the Epoch of Reionization (EoR). With the recent advent of exquisite datasets such as XQR-30, many models have struggled to reproduce the observed large-scale fluctuations in the Lya opacity. Here we introduce a Bayesian analysis framework that forward-models large-scale lightcones of IGM properties, and accounts for unresolved sub-structure in the Lya opacity by calibrating to higher-resolution hydrodynamic simulations. Our models directly connect physically-intuitive galaxy properties with the corresponding IGM evolution, without having to tune "effective" parameters or calibrate out the mean transmission. The forest data, in combination with UV luminosity functions and the CMB optical depth, are able to constrain global IGM properties at percent level precision in our fiducial model. Unlike many other works, we recover the forest observations without evoking a rapid drop in the ionizing emissivity from z~7 to 5.5, which we attribute to our sub-grid model for recombinations. In this fiducial model, reionization ends at $z=5.44\pm0.02$ and the EoR mid-point is at $z=7.7\pm0.1$. The ionizing escape fraction increases towards faint galaxies, showing a mild redshift evolution at fixed UV magnitude, Muv. Half of the ionizing photons are provided by galaxies fainter than Muv~-12, well below direct detection limits of optical/NIR instruments including JWST. We also show results from an alternative galaxy model that does not allow for a redshift evolution in the ionizing escape fraction. Despite being decisively disfavored by the Bayesian evidence, the posterior of this model is in qualitative agreement with that from our fiducial model. We caution however that our conclusions regarding the early stages of the EoR and which sources reionized the Universe are more model-dependent., Comment: 21 pages, 12 figures, 3 tables (submitted to PASA)

KT Eri is a classical nova which went into outburst in 2009. Recent photometric analysis in quiescence indicates a relatively longer orbital period of 2.6 days, so that KT Eri could host a very bright accretion disk during the outburst like in the recurrent nova U Sco, the orbital period of which is 1.23 days. We reproduced the optical $V$ light curve as well as the supersoft X-ray light curve of KT Eri in outburst, assuming a large irradiated disk during a nova wind phase of the outburst while a normal size disk after the nova winds stop. This result is consistent with the temporal variation of wide-band $V$ brightness that varies almost with the intermediate-band Str\"omgren $y$ brightness, because the $V$ flux is dominated by continuum radiation, the origin of which is a photospheric emission from the very bright disk. We obtained the white dwarf mass to be $M_{\rm WD}= 1.3\pm0.02 ~M_\odot$, the hydrogen-burning turnoff epoch to be $\sim 240$ days after the outburst, the distance modulus in the $V$ band to be $(m-M)_V=13.4\pm 0.2$, and the distance to KT Eri to be $d=4.2\pm0.4$ kpc for the reddening of $E(B-V)= 0.08$. The peak absolute $V$ brightness is about $M_{V, \rm max}= -8.0$ and the corresponding recurrence time is $\sim 3,000$ yr from its ignition mass together with the mean mass-accretion rate of $\dot{M}_{\rm acc}\sim 1\times 10^{-9} ~M_\odot$ yr$^{-1}$ in quiescence. Thus, we suggest that KT Eri is not a recurrent nova., Comment: 33 pages, 12 figures, accepted for publication in ApJ

Image classification tasks are among the most prominent examples that can be reliably solved by classical machine learning models. In this study, we compare the performance of randomized classical and quantum neural networks as well as classical and quantum-classical hybrid convolutional neural networks for the task of binary image classification. To this end, we employ various data sets of increasing complexity - (i) an artificial hypercube dataset, (ii) MNIST handwritten digits, and (iii) real-world industrial images from laser cutting machines. We analyze the performance of the employed quantum models with respect to correlations between classification accuracy and various hyperparameters. For the random quantum neural networks, we additionally compare their performance with some known literature models and how top-performing models from one data set perform on the others. In general, we observe fairly similar performances of classical and quantum or hybrid models. Our study provides an industry perspective on the prospects of quantum machine learning for practical image classification tasks., Comment: 23 pages, 12 figures

We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) models using the Lyman-alpha forest. With this regression machinery, we constrain the mass of a potential WDM particle from observed Lyman-alpha sightlines directly from the density field. We design and train a Bayesian neural network on the supervised regression task of recovering the optical depth-weighted density field $\Delta_\tau$ as well as its reconstruction uncertainty from the Lyman-alpha forest flux field. We utilise the Sherwood-Relics simulation suite at $4.1\leq z \leq 5.0$ as the main training and validation dataset. Leveraging the density field recovered by our neural network, we construct an inference pipeline to constrain the WDM particle masses based on the probability distribution function of the density fields. We find that our trained Bayesian neural network can accurately recover within a $1\sigma$ error $\geq 85\%$ of the density field pixels from a validation simulated dataset that encompasses multiple WDM and thermal models of the IGM. When predicting on Lyman-alpha skewers generated using the alternative hydrodynamical code Nyx not included in the training data, we find a $1\sigma$ accuracy rate $\geq 75\%$. We consider 2 samples of observed Lyman-alpha spectra from the UVES and GHOST instruments, at $z=4.4$ and $z=4.9$ respectively and fit the density fields recovered by our Bayesian neural network to constrain WDM masses. We find lower bounds on the WDM particle mass of $m_{\mathrm{WDM}} \geq 3.8$ KeV and $m_{\mathrm{WDM}} \geq 2.2$ KeV at $2\sigma$ confidence, respectively. We are able to match current state-of-the-art WDM particle mass constraints using up to 40 times less observational data than Markov Chain Monte Carlo techniques based on the Lyman-alpha forest power spectrum., Comment: 12 pages, Submitted to A&A

The James Webb Space Telescope has uncovered a puzzling population of UV-faint broad-line active galactic nuclei (AGN), nicknamed ``Little Red Dots'' (LRD) owing to their compact morphology and red rest-frame optical colours. Interpreted as dust attenuated AGN, their inferred intrinsic luminosities and supermassive black hole (SMBH) masses rival those of UV-luminous quasars, although they are $>100$ times more abundant. If LRDs and quasars are members of the same underlying population, they should inhabit comparable mass dark matter halos, traced by similar overdensities of galaxies. Otherwise, they represent distinct populations with different physical properties and formation histories. Characterizing LRD environments thus provides a critical test of their nature. Here, we report the discovery of a LRD at $z=7.3$, attenuated by moderate amounts of dust, $A_V = {3.26}\,\rm{mag}$, with an intrinsic bolometric luminosity of $10^{46.7}\,\rm{erg}\,\rm{s}^{-1}$ and a SMBH mass of $7\times10^8\,\rm{M}_\odot$. Most notably, this object is embedded in an overdensity of eight nearby galaxies, allowing us to calculate the first spectroscopic estimate of the clustering of galaxies around LRDs. We find a LRD-galaxy cross-correlation length of $r_0\!=\!9\pm2\,\rm{h}^{-1}\,\rm{cMpc}$, comparable to that of $z\!\sim\!6$ UV-luminous quasars. The resulting estimate of their minimum dark matter halo mass of $\log_{10}(M_{\rm{halo, min}}/\rm{M}_{\odot})= 12.3_{-0.8}^{+0.7}$ indicates that nearly all halos above this mass must host actively accreting SMBHs at $z\approx7$, in strong contrast with the far smaller duty cycle of luminous quasars ($<1\%$). Our results, taken at face value, motivate a picture in which LRDs are the obscured counterparts of UV-luminous quasars, which provides a natural explanation for the short UV-luminous lifetimes inferred from both quasar clustering and quasar proximity zones., Comment: Submitted. Comments welcome!

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quickly overwhelmed by the large number of probable trees in the posterior distribution. In this paper we develop methods to sidestep this problem using a recently introduced structure called the subsplit directed acyclic graph (sDAG). This structure can represent many trees at once, and local rearrangements of trees translate to methods of enlarging the sDAG. Here we propose two methods of introducing, ranking, and selecting local rearrangements on sDAGs to produce a collection of trees with high posterior density. One of these methods successfully recovers the set of high posterior density trees across a range of data sets. However, we find that a simpler strategy of aggregating trees into an sDAG in fact is computationally faster and returns a higher fraction of probable trees.

Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent advances in the spatial modeling of the tumor microenvironment using graph neural networks, we present an explainable cell graph (xCG) approach for survival prediction. We validate our model on a public cohort of imaging mass cytometry (IMC) data for 416 cases of lung adenocarcinoma. We explain survival predictions in terms of known phenotypes on the cell level by computing risk attributions over cell graphs, for which we propose an efficient grid-based layer-wise relevance propagation (LRP) method. Our ablation studies highlight the importance of incorporating the cancer stage and model ensembling to improve the quality of risk estimates. Our xCG method, together with the IMC data, is made publicly available to support further research., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 11 pages

In this article, we establish some uniqueness and symmetry results of self-similar solutions to curvature flows by some homogeneous speed functions of principal curvatures in some warped product spaces. In particular, we proved that any compact star-shaped self-similar solution to any parabolic flow with homogeneous degree $-1$ (including the inverse mean curvature flow) in warped product spaces $I \times_{\phi} M^n$, where $M^n$ is a compact homogeneous manifold and $\phi'' \geq 0$, must be a slice. The same result holds for compact self-expanders when the degree of the speed function is greater than $-1$ and with an extra assumption $\phi' \geq 0$. Furthermore, we also show that any complete non-compact star-shaped, asymptotically concial expanding self-similar solutions to the flow by positive power of mean curvature in hyperbolic and anti-deSitter-Schwarzschild spaces are rotationally symmetric., Comment: 20 pages

In this work we study inside-out dissections of polygons and polyhedra. We first show that an arbitrary polygon can be inside-out dissected with $2n+1$ pieces, thereby improving the best previous upper bound of $4(n-2)$ pieces. Additionally, we establish that a regular polygon can be inside-out dissected with at most $6$ pieces. Lastly, we prove that any polyhedron that can be decomposed into finitely many regular tetrahedra and octahedra can be inside-out dissected., Comment: Keywords: Polygons, Polyhdra, Inside-out Dissections

We used the Condor Array Telescope to obtain deep imaging observations through luminance broad-band and He II, [O III], He I, H$\alpha$, [N II], and [S II] narrow-band filters of an extended region of the M81 Group spanning $\approx 8 \times 8$ deg$^2$ on the sky centered near M81 and M82. Here we report aspects of these observations that are specifically related to (1) a remarkable filament known as the "Ursa Major Arc" that stretches $\approx 30$ deg on the sky roughly in the direction of Ursa Major, (2) a "Giant Shell of Ionized Gas" that stretches $\approx 0.8$ deg on the sky located $\approx 0.6$ deg NW of M82, and (3) a remarkable network of ionized gaseous filaments revealed by the new Condor observations that appear to connect the arc, the shell, and various of the galaxies of the M81 Group and, by extension, the group itself. We measure flux ratios between the various ions to help to distinguish photoionized from shock-ionized gas, and we find that the flux ratios of the arc and shell are not indicative of shock ionization. This provides strong evidence against a previous interpretation of the arc as an interstellar shock produced by an unrecognized supernova. We suggest that all of these objects, including the arc, are associated with the M81 Group and are located at roughly the distance $\approx 3.6$ Mpc of M81, that the arc is an intergalactic filament, and that the objects are associated with the low-redshift cosmic web., Comment: 8 pages, 4 figures

We used the Condor Array Telescope to obtain deep imaging observations through the luminance broad-band and He II 468.6 nm, [O III] 500.7 nm, He I 587.5 nm, H$\alpha$, [N II] 658.4 nm, and [S II] 671.6 nm narrow-band filters of an extended region comprising 13 "Condor fields" spanning $\approx 8 \times 8$ deg$^2$ on the sky centered near M81 and M82. Here we describe the acquisition and processing of these observations, which together constitute unique very deep imaging observations of a large portion of the M81 Group through a complement of broad- and narrow-band filters. The images are characterized by an intricate web of faint, diffuse, continuum produced by starlight scattered from Galactic cirrus, and all prominent cirrus features identified in the broad-band image can also be identified in the narrow-band images. We subtracted the luminance image from the narrow-band images to leave more or less only line emission in the difference images, and we masked regions of the resulting images around stars at an isophotal limit. The difference images exhibit extensive extended structures of ionized gas in the direction of the M81 Group, from known galaxies of the M81 Group, clouds of gas, filamentary structures, and apparent or possible bubbles or shells. Specifically, the difference images show a remarkable filament known as the "Ursa Major Arc;" a remarkable network of criss-crossed filaments between M81 and NGC 2976, some of which intersect and overlap the Ursa Major Arc; and details of a "giant shell of ionized gas.", Comment: 15 pages, 8 figures

The goal of this study is to develop a computational model of the progression of changes in mitochondrial phenotype resulting from infection with pathogenic mycobacteria. This ultimately will enable a large-scale virulence screen of mutant bacterial libraries. Mycobacterium tuberculosis (Mtb) is an intracellular pathogen, but only a small number of its genes have been studied for roles in intracellular host cell survival and replication. Mitochondria are the powerhouse of the host cell and play critical roles in cell survival when attacked by certain pathogens. When Mtb bacteria invade host cells, they induce changes in mitochondrial morphology, making mitochondria a novel target for image processing and machine learning to determine virulence associations of genes in Mtb and potentially other related intracellular pathogens. By hypothesizing mitochondria as an instance of a dynamic and interconnected graph, we demonstrate a statistical approach for quantitatively recognizing novel mitochondrial phenotypes induced by invading pathogens.

Weakly collisional plasmas contain a wealth of information about the dynamics of the plasma in the particle velocity distribution functions, yet our ability to exploit fully that information remains relatively primitive. Here we aim to present the fundamentals of a new technique denoted Plasma Seismology that aims to invert the information from measurements of the particle velocity distribution functions at a single point in space over time to enable the determination of the electric field variation over an extended spatial region. The fundamental mathematical tool at the heart of this technique is the Morrison $G$ Transform. Using kinetic numerical simulations of Langmuir waves in a Vlasov-Poisson plasma, we demonstrate the application of the standard Morrison $G$ Transform, which uses measurements of the particle velocity distribution function over all space at one time to predict the evolution of the electric field in time. Next, we introduce a modified Morrison $G$ Transform which uses measurements of the particle velocity distribution function at one point in space over time to determine the spatial variation of the electric field over an extended spatial region. We discuss the limitations of this approach, particularly for the numerically challenging case of Langmuir waves. The application of this technique to Alfven waves in a magnetized plasma holds the promise to apply the technique to existing spacecraft particle measurement instrumentation to determine the electric fields over an extended spatial region away from the spacecraft., Comment: 9 pages, 2 figures, 34 references

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

With the rapid expansion of the Internet of Things (IoT), efficient and durable energy harvesters for powering IoT devices operating indoors and outdoors are imperative. Promising materials for indoor photovoltaic (PV) technologies include transition metal dichalcogenides (TMDs) such as MoS2, MoSe2, WS2, and WSe2, mainly due to their high absorption coefficients and self-passivated surfaces. Here, we assess the performance of single-junction TMD solar cells under various indoor lighting conditions with a realistic detailed balance model including material-specific optical absorption, as well as radiative, Auger, and defect-assisted Shockley-Read-Hall recombination. We find TMD solar cells could achieve up to 36.5%, 35.6%, 11.2%, and 27.6% power conversion efficiency under fluorescent, LED, halogen, and low-light AM 1.5 G lighting, respectively, at 500 lux. Based on this, TMD solar cells could outperform commercial PV technologies in indoor scenarios, suggesting their viability for future IoT energy solutions.

Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it.

Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.

We present a data reduction pipeline (DRP) for Keck/NIRSPEC built as an addition to the PypeIt Python package. The DRP is capable of reducing multi-order echelle data taken both before and after the detector upgrade in 2018. As part of developing the pipeline, we implemented major improvements to the capabilities of the PypeIt package, including manual wavelength calibration for multi-order data and new output product that returns a coadded spectrum order-by-order. We also provide a procedure for correcting telluric absorption in NIRSPEC data by using the spectra of telluric standard stars taken near the time of the science spectra. At high resolutions, this is often more accurate than modeling-based approaches., Comment: 2 pages, 1 figure, accepted to RNAAS

In virtual assistant (VA) systems it is important to reject or redirect user queries that fall outside the scope of the system. One of the most accurate approaches for out-of-scope (OOS) rejection is to combine it with the task of intent classification on in-scope queries, and to use methods based on the similarity of embeddings produced by transformer-based sentence encoders. Typically, such encoders are fine-tuned for the intent-classification task, using cross-entropy loss. Recent work has shown that while this produces suitable embeddings for the intent-classification task, it also tends to disperse in-scope embeddings over the full sentence embedding space. This causes the in-scope embeddings to potentially overlap with OOS embeddings, thereby making OOS rejection difficult. This is compounded when OOS data is unknown. To mitigate this issue our work proposes to regularize the cross-entropy loss with an in-scope embedding reconstruction loss learned using an auto-encoder. Our method achieves a 1-4% improvement in the area under the precision-recall curve for rejecting out-of-sample (OOS) instances, without compromising intent classification performance., Comment: Appearing at Empirical Methods in Natural Language Processing 2024 - Industry Track

We examine the language capabilities of language models (LMs) from the critical perspective of human language acquisition. Building on classical language development theories, we propose a three-stage framework to assess the abilities of LMs, ranging from preliminary word understanding to complex grammar and complex logical reasoning. Using this framework, we evaluate the generative capacities of LMs using methods from linguistic research. Results indicate that although recent LMs outperform earlier models in overall performance, their developmental trajectory does not strictly follow the path of human language acquisition. Notably, in generation tasks, LMs are more similar to human performance in areas where information is easier to extract from the corpus, such as average word length, clauses, and auxiliary verbs. Newer LMs did not exhibit significant progress in terms of specific dimensions, such as clauses and auxiliary verbs, where the variation across corpora is relatively limited. Register theory offers a plausible explanation for these observations, suggesting that the linguistic features of the training data have a substantial impact on the models' abilities.

Classical nova eruptions result from thermonuclear-powered runaways in, and ejection of, the hydrogen-rich envelopes of white dwarf stars accreted from their close binary companions. Novae brighten to up to 1,000,000 solar luminosities, and recur thousands of times over their lifetimes spanning several billion years. Between eruptions, mass transfer from the donor star to the white dwarf proceeds via an accretion disk unless the white dwarf possesses a strong magnetic field which can partially or totally disrupt the disk. In that case, accretion is focussed by the white dwarf's magnetic field towards its magnetic poles. Optical spectroscopy and interferometric radio maps demonstrate the presence of bipolar jets, typically arcsec in angular size, and orders of magnitude smaller than one parsec in linear size, during the days to months after nova eruptions. These jets expel collimated matter from the white dwarfs in nova binary stars, but well-resolved images of them are lacking. Here we report the Condor telescope's detection of a hitherto unknown, highly resolved and braided jet, three degrees (at least 25 parsecs) in length. The jet originates at the white dwarf of the old nova GK Persei (nova Per 1901 CE). It precesses on a ~ 3600 yr timescale, and must be at least 7200 years old. Detected across four decades of wavelength, the jet's ultimate energy source is likely the strong accretion shocks near the white dwarf's magnetic poles., Comment: 18 pages, 4 figures

As premodern texts are passed down over centuries, errors inevitably accrue. These errors can be challenging to identify, as some have survived undetected for so long precisely because they are so elusive. While prior work has evaluated error detection methods on artificially-generated errors, we introduce the first dataset of real errors in premodern Greek, enabling the evaluation of error detection methods on errors that genuinely accumulated at some stage in the centuries-long copying process. To create this dataset, we use metrics derived from BERT conditionals to sample 1,000 words more likely to contain errors, which are then annotated and labeled by a domain expert as errors or not. We then propose and evaluate new error detection methods and find that our discriminator-based detector outperforms all other methods, improving the true positive rate for classifying real errors by 5%. We additionally observe that scribal errors are more difficult to detect than print or digitization errors. Our dataset enables the evaluation of error detection methods on real errors in premodern texts for the first time, providing a benchmark for developing more effective error detection algorithms to assist scholars in restoring premodern works.

This paper investigates the inverse problems of determining a space-dependent source for thermoelastic systems of type III under adequate time-averaged or final-in-time measurements and conditions on the time-dependent part of the sought source. Several numerical methods are proposed and examined, including a Landweber scheme and minimisation methods for the corresponding cost functionals, which are based on the gradient and conjugate gradient method. A shortcoming of these methods is that the values of the sought source are fixed ab initio and remain fixed during the iterations. The Sobolev gradient method is applied to overcome the possible inaccessibility of the source values at the boundary. Numerical examples are presented to discuss the different approaches and support our findings based on the implementation on the FEniCSx platform.

Reasoning about time and temporal relations is an integral aspect of human cognition, essential for perceiving the world and navigating our experiences. Though large language models (LLMs) have demonstrated impressive performance in many reasoning tasks, temporal reasoning remains challenging due to its intrinsic complexity. In this work, we first study an essential task of temporal reasoning -- temporal graph generation, to unveil LLMs' inherent, global reasoning capabilities. We show that this task presents great challenges even for the most powerful LLMs, such as GPT-3.5/4. We also notice a significant performance gap by small models (<10B) that lag behind LLMs by 50%. Next, we study how to close this gap with a budget constraint, e.g., not using model finetuning. We propose a new prompting technique tailored for temporal reasoning, Narrative-of-Thought (NoT), that first converts the events set to a Python class, then prompts a small model to generate a temporally grounded narrative, guiding the final generation of a temporal graph. Extensive experiments showcase the efficacy of NoT in improving various metrics. Notably, NoT attains the highest F1 on the Schema-11 evaluation set, while securing an overall F1 on par with GPT-3.5. NoT also achieves the best structural similarity across the board, even compared with GPT-3.5/4. Our code is available at https://github.com/launchnlp/NoT., Comment: EMNLP'24 Findings

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field., Comment: 16 pages, 2 figures, 1 table, 1 supplemental table

The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers have since achieved widespread success across various domains. However, the scalability limitations of Transformers - particularly with respect to sequence length - have sparked renewed interest in novel recurrent models that are parallelizable during training, offer comparable performance, and scale more effectively. In this work, we revisit sequence modelling from a historical perspective, focusing on Recurrent Neural Networks (RNNs), which dominated the field for two decades before the rise of Transformers. Specifically, we examine LSTMs (1997) and GRUs (2014). We demonstrate that by simplifying these models, we can derive minimal versions (minLSTMs and minGRUs) that (1) use fewer parameters than their traditional counterparts, (2) are fully parallelizable during training, and (3) achieve surprisingly competitive performance on a range of tasks, rivalling recent models including Transformers.

We present FastLexRank\footnote{https://github.com/LiMaoUM/FastLexRank}, an efficient and scalable implementation of the LexRank algorithm for text ranking. Designed to address the computational and memory complexities of the original LexRank method, FastLexRank significantly reduces time and memory requirements from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$ without compromising the quality or accuracy of the results. By employing an optimized approach to calculating the stationary distribution of sentence graphs, FastLexRank maintains an identical results with the original LexRank scores while enhancing computational efficiency. This paper details the algorithmic improvements that enable the processing of large datasets, such as social media corpora, in real-time. Empirical results demonstrate its effectiveness, and we propose its use in identifying central tweets, which can be further analyzed using advanced NLP techniques. FastLexRank offers a scalable solution for text centrality calculation, addressing the growing need for efficient processing of digital content.

V745 Sco is a Galactic symbiotic recurrent nova with nova eruptions in 1937, 1989 and 2014. We study the behavior of V745 Sco at radio wavelengths (0.6-37,GHz), covering both its 1989 and 2014 eruptions and informed by optical, X-ray, and $\gamma$-ray data. The radio light curves are synchrotron-dominated. Surprisingly, compared to expectations for synchrotron emission from explosive transients such as radio supernovae, the light curves spanning 0.6-37 GHz all peak around the same time ($\sim$18-26 days after eruption) and with similar flux densities (5-9 mJy).We model the synchrotron light curves as interaction of the nova ejecta with the red giant wind, but find that simple spherically symmetric models with wind-like circumstellar material (CSM) cannot explain the radio light curve. Instead, we conclude that the shock suddenly breaks out of a dense CSM absorbing screen around 20 days after eruption, and then expands into a relatively low density wind ($\dot{M}_{out} \approx 10^{-9}-10^{-8}$ M$_{\odot}$ yr$^{-1}$ for $v_w = 10$ km s$^{-1}$) out to $\sim$1 year post-eruption. The dense, close-in CSM may be an equatorial density enhancement or a more spherical red giant wind with $\dot{M}_{in} \approx [5-10] \times 10^{-7}$ M$_{\odot}$ yr$^{-1}$, truncated beyond several $\times 10^{14}$ cm. The outer lower-density CSM would not be visible in typical radio observations of Type Ia supernovae: V745 Sco cannot be ruled out as a Type Ia progenitor based on CSM constraints alone.Complementary constraints from the free-free radio optical depth and the synchrotron luminosity imply the shock is efficient at accelerating relativistic electrons and amplifying magnetic fields, with $\epsilon_e$ and $\epsilon_B \approx 0.01-0.1$., Comment: 21 pages, 20 figures

Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly lensed SN, the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky") at z = 0.3544. We measure updated, template-subtracted photometry for SN Zwicky and derive improved time delays and magnifications. This is possible because SNe are transient, fading away after reaching their peak brightness. Specifically, we measure point spread function (PSF) photometry for all four images of SN Zwicky in three Hubble Space Telescope WFC3/UVIS passbands (F475W, F625W, F814W) and one WFC3/IR passband (F160W), with template images taken $\sim 11$ months after the epoch in which the SN images appear. We find consistency to within $2\sigma$ between lens model predicted time delays ($\lesssim1$ day), and measured time delays with HST colors ($\lesssim2$ days), including the uncertainty from chromatic microlensing that may arise from stars in the lensing galaxy. The standardizable nature of SNe Ia allows us to estimate absolute magnifications for the four images, with images A and C being elevated in magnification compared to lens model predictions by about $6\sigma$ and $3\sigma$ respectively, confirming previous work. We show that millilensing or differential dust extinction is unable to explain these discrepancies and find evidence for the existence of microlensing in images A, C, and potentially D, that may contribute to the anomalous magnification., Comment: Submitted to ApJ

For a set of red and blue points in the plane, a minimum bichromatic spanning tree (MinBST) is a shortest spanning tree of the points such that every edge has a red and a blue endpoint. A MinBST can be computed in $O(n\log n)$ time where $n$ is the number of points. In contrast to the standard Euclidean MST, which is always plane (noncrossing), a MinBST may have edges that cross each other. However, we prove that a MinBST is quasi-plane, that is, it does not contain three pairwise crossing edges, and we determine the maximum number of crossings. Moreover, we study the problem of finding a minimum plane bichromatic spanning tree (MinPBST) which is a shortest bichromatic spanning tree with pairwise noncrossing edges. This problem is known to be NP-hard. The previous best approximation algorithm, due to Borgelt et al. (2009), has a ratio of $O(\sqrt{n})$. It is also known that the optimum solution can be computed in polynomial time in some special cases, for instance, when the points are in convex position, collinear, semi-collinear, or when one color class has constant size. We present an $O(\log n)$-factor approximation algorithm for the general case., Comment: ISAAC 2024

The escaping ionising efficiency from galaxies, $f_{\rm esc}\xi_{\rm ion}$, is a crucial ingredient for understanding their contribution to hydrogen reionisation, but both of its components, $f_{\rm{esc}}$ and $\xi_{\rm{ion}}$, are extremely difficult to measure. We measure the average escaping ionising efficiency $\langle f_{\rm{esc}} \xi_{\rm{ion}}\rangle$ of galaxies at $z=5$ implied by the mean level of ionisation in the intergalactic medium via the Lyman-$\alpha$ forest. We use the fact that $\dot{N}_{\rm{ion}} = \rho_{\rm{UV}} f_{\rm{esc}} \xi_{\rm{ion}}$, the product of the ionising output and the UV density $\rho_{\rm{UV}}$, can be calculated from the known average strength of the UV background and the mean free path of ionising photons. These quantities, as well as $\rho_{\rm{UV}}$, are robustly measured at $z\leq6$. We calculate the missing factor of $\langle f_{\rm{esc}} \xi_{\rm{ion}}\rangle$ at $z=5$, during a convenient epoch after hydrogen reionisation has completed and the intergalactic medium has reached ionisation equilibrium, but before bright quasars begin to dominate the ionising photon production. Intuitively, our constraint corresponds to the required escaping ionising production from galaxies in order to avoid over- or under-ionising the Lyman-$\alpha$ forest. We obtain a measurement of $\log \langle f_{\rm{esc}} \xi_{\rm{ion}}\rangle /$erg Hz$^{-1}$ $ = 24.28_{-0.20}^{+0.21}$ at $z=5$ when integrating $\rho_\text{UV}$ down to a limiting magnitude $M_\text{lim}=-11$. Our measurement of the escaping ionising efficiency of galaxies is in rough agreement with both observations and most models., Comment: Accepted for publication in A&A; 4 pages, 1 figure

This paper studies the pricing of contingent claims of American style, using indifference pricing by fully dynamic convex risk measures. We provide a general definition of risk-indifference prices for buyers and sellers in continuous time, in a setting where buyer and seller have potentially different information, and show that these definitions are consistent with no-arbitrage principles. Specifying to stochastic volatility models, we characterize indifference prices via solutions of Backward Stochastic Differential Equations reflected at Backward Stochastic Differential Equations and show that this characterization provides a basis for the implementation of numerical methods using deep learning., Comment: 18 pages, 3 figures

We have used the UKIRT Hemisphere Survey (UHS) combined with the UKIDSS Galactic Cluster Survey (GCS), the UKIDSS Galactic Plane Survey (GPS), and the CatWISE2020 catalog to search for new substellar members of the nearest open cluster to the Sun, the Hyades. Eight new substellar Hyades candidate members were identified and observed with the Gemini/GNIRS near-infrared spectrograph. All eight objects are confirmed as brown dwarfs with spectral types ranging from L6 to T5, with two objects showing signs of spectral binarity and/or variability. A kinematic analysis demonstrates that all eight new discoveries likely belong to the Hyades cluster, with future radial velocity and parallax measurements needed to confirm their membership. CWISE J042356.23$+$130414.3, with a spectral type of T5, would be the coldest ($T_{\rm eff}$$\approx$1100 K) and lowest-mass ($M$$\approx$30 $M_{\rm Jup}$) free-floating member of the Hyades yet discovered. We further find that high-probability substellar Hyades members from this work and previous studies have redder near-infrared colors than field-age brown dwarfs, potentially due to lower surface gravities and super-solar metallicities., Comment: Accepted to AJ

Unmeasured confounding is a major challenge for identifying causal relationships from non-experimental data. Here, we propose a method that can accommodate unmeasured discrete confounding. Extending recent identifiability results in deep latent variable models, we show theoretically that confounding can be detected and corrected under the assumption that the observed data is a piecewise affine transformation of a latent Gaussian mixture model and that the identity of the mixture components is confounded. We provide a flow-based algorithm to estimate this model and perform deconfounding. Experimental results on synthetic and real-world data provide support for the effectiveness of our approach.

Reconstructing the evolutionary history relating a collection of molecular sequences is the main subject of modern Bayesian phylogenetic inference. However, the commonly used Markov chain Monte Carlo methods can be inefficient due to the complicated space of phylogenetic trees, especially when the number of sequences is large. An alternative approach is variational Bayesian phylogenetic inference (VBPI) which transforms the inference problem into an optimization problem. While effective, the default diagonal lognormal approximation for the branch lengths of the tree used in VBPI is often insufficient to capture the complexity of the exact posterior. In this work, we propose a more flexible family of branch length variational posteriors based on semi-implicit hierarchical distributions using graph neural networks. We show that this semi-implicit construction emits straightforward permutation equivariant distributions, and therefore can handle the non-Euclidean branch length space across different tree topologies with ease. To deal with the intractable marginal probability of semi-implicit variational distributions, we develop several alternative lower bounds for stochastic optimization. We demonstrate the effectiveness of our proposed method over baseline methods on benchmark data examples, in terms of both marginal likelihood estimation and branch length posterior approximation., Comment: 26 pages, 7 figures

A polyiamond is a polygon composed of unit equilateral triangles, and a generalized deltahedron is a convex polyhedron whose every face is a convex polyiamond. We study a variant where one face may be an exception. For a convex polygon P, if there is a convex polyhedron that has P as one face and all the other faces are convex polyiamonds, then we say that P can be domed. Our main result is a complete characterization of which equiangular n-gons can be domed: only if n is in {3, 4, 5, 6, 8, 10, 12}, and only with some conditions on the integer edge lengths., Comment: 25 pages, 19 figures, 8 references

The amount of turbulent pressure in galaxy clusters is still debated, especially as for the impact of the dynamical state and the hydro-method used for simulations. We study the turbulent pressure fraction in the intra cluster medium of massive galaxy clusters. We aim to understand the impact of the hydrodynamical scheme, analysis method, and dynamical state on the final properties of galaxy clusters from cosmological simulations. We perform non-radiative simulations of a set of zoom-in regions of seven galaxy clusters with Meshless Finite Mass (MFM) and Smoothed Particle Hydrodynamics (SPH). We use three different analysis methods based on: $(i)$ the deviation from hydrostatic equilibrium, $(ii)$ the solenoidal velocity component obtained by a Helmholtz-Hodge decomposition, and $(iii)$ the small-scale velocity obtained through a multi-scale filtering approach. We split the sample of simulated clusters into active and relaxed clusters. Our simulations predict an increased turbulent pressure fraction for active compared to relaxed clusters. This is especially visible for the velocity-based methods. For these, we also find increased turbulence for the MFM simulations compared to SPH, consistent with findings from more idealized simulations. The predicted non-thermal pressure fraction varies between a few percent for relaxed clusters and $\approx13\%$ for active ones within the cluster center and increases towards the outskirts. No clear trend with redshift is visible. Our analysis quantitatively assesses the importance played by the hydrodynamical scheme and the analysis method to determine the non-thermal/turbulent pressure fraction. While our setup is relatively simple (non-radiative runs), our simulations show agreement with previous, more idealized simulations, and make a step further toward the understanding of turbulence., Comment: 15 pages, 10 figures, submitted to A&A

We present a comprehensive theoretical study of interacting and disordered topological phases of coupled Kitaev wires, which may support further realistic applications of Majorana fermions. We develop a variety of analytical, mathematical and numerical methods for one and two-coupled wires, associated with a topological marker accessible from real-space correlation functions on the wire(s). We verify the stability of the topological superconducting phase and quantify disorder effects close to the quantum phase transitions, e.g. through two-point correlation functions or using a renormalization group (RG) analysis of disorder. We show for the first time that the double critical Ising (DCI) phase -- a fractional Majorana liquid characterized by a pair of half central charges and topological numbers -- is stabilized by strong interactions against disorder which respects the inversion symmetry between the wires (ie. parity conservation on each wire). In the presence of an inter-wire hopping term, the DCI phase turns into a protected topological phase with a bulk gap. We study the localization physics developing along the critical line for weaker interactions., Comment: 13 pages, 11 figures (+ Appendices)

Painting classification plays a vital role in organizing, finding, and suggesting artwork for digital and classic art galleries. Existing methods struggle with adapting knowledge from the real world to artistic images during training, leading to poor performance when dealing with different datasets. Our innovation lies in addressing these challenges through a two-step process. First, we generate more data using Style Transfer with Adaptive Instance Normalization (AdaIN), bridging the gap between diverse styles. Then, our classifier gains a boost with feature-map adaptive spatial attention modules, improving its understanding of artistic details. Moreover, we tackle the problem of imbalanced class representation by dynamically adjusting augmented samples. Through a dual-stage process involving careful hyperparameter search and model fine-tuning, we achieve an impressive 87.24\% accuracy using the ResNet-50 backbone over 40 training epochs. Our study explores quantitative analyses that compare different pretrained backbones, investigates model optimization through ablation studies, and examines how varying augmentation levels affect model performance. Complementing this, our qualitative experiments offer valuable insights into the model's decision-making process using spatial attention and its ability to differentiate between easy and challenging samples based on confidence ranking.

The UKIRT Hemisphere Survey covers the northern sky in the infrared from 0-60 degrees declination. Current data releases include both J and K bands, with H-band data forthcoming. Here we present a novel pipeline to recover asteroids from this survey data. We recover 26,138 reliable observations, corresponding to 23,399 unique asteroids, from these public data. We measure J-K colors for 601 asteroids. Our survey extends about two magnitudes deeper than 2MASS. We find that our small inner main belt objects are less red than larger inner belt objects, perhaps because smaller asteroids are collisionally younger, with surfaces that have been less affected by space weathering. In the middle and outer main belt, we find our small asteroids to be redder than larger objects in their same orbits, possibly due to observational bias or a disproportionate population of very red objects among these smaller asteroids. Future work on this project includes extracting moving object measurements from H and Y band data when it becomes available., Comment: 15 pages, 10 figures

The current paper examines the possibility of replacing conventional synchronous single-attempt exam with more flexible and accessible multi-attempt asynchronous assessments in introductory-level physics by using large isomorphic problem banks. We compared student's performance on both numeric and conceptual problems administered on a multi-attempt, asynchronous quiz to their performance on isomorphic problems administered on a subsequent single-attempt, synchronous exam. We computed the phi coefficient and the McNemar's test statistic for the correlation matrix between paired problems on both assessments as a function of the number of attempts considered on the quiz. We found that for the conceptual problems, a multi-attempt quiz with five allowed attempts could potentially replace similar problems on a single-attempt exam, while there was a much weaker association for the numerical questions beyond two quiz attempts.

Language models (LMs) are often expected to generate strings in some formal language; for example, structured data, API calls, or code snippets. Although LMs can be tuned to improve their adherence to formal syntax, this does not guarantee conformance, especially with smaller LMs suitable for large-scale deployment. In addition, tuning requires significant resources, making it impractical for uncommon or task-specific formats. To prevent downstream parsing errors we would ideally constrain the LM to only produce valid output, but this is severely complicated by tokenization, which is typically both ambiguous and misaligned with the formal grammar. We solve these issues through the application of automata theory, deriving an efficient closed-form solution for the regular languages, a broad class of formal languages with many practical applications, including API calls or schema-guided JSON and YAML. We also discuss pragmatic extensions for coping with the issue of high branching factor, and extend our techniques to deterministic context-free languages, which similarly admit an efficient closed-form solution. Previous work on this topic (Willard and Louf, 2023) layers bespoke solutions onto automata, leading to problems with speed, correctness, and extensibility. Instead, we reformulate the entire task in terms of automata so we can leverage well-studied and well-optimized algorithms. Our system compiles constraints ~7,000x faster, is provably correct, and can be extended in a modular fashion., Comment: COLM 2024 Camera-ready version, responding to feedback from reviewers