Miracle-dz/UniFAR-FTUs huggingface dataset:
Cold electrons at comet 67P/Churyumov-Gerasimenko

Context. The electron temperature of the plasma is one important aspect of the environment. Electrons created by photoionization or impact ionization of atmospheric gas have energies ~10 eV. In an active comet coma, the gas density is high enough for rapid cooling of the electron gas to the neutral gas temperature (a few hundred kelvin). How cooling evolves in less active comets has not been studied before. Aims. We aim to investigate how electron cooling varied as comet 67P/Churyumov-Gerasimenko changed its activity by three orders of magnitude during the Rosetta mission. Methods. We used in situ data from the Rosetta plasma and neutral gas sensors. By combining Langmuir probe bias voltage sweeps and mutual impedance probe measurements, we determined at which time cold electrons formed at least 25% of the total electron density. We compared the results to what is expected from simple models of electron cooling, using the observed neutral gas density as input. Results. We demonstrate that the slope of the Langmuir probe sweep can be used as a proxy for the presence of cold electrons. We show statistics of cold electron observations over the two-year mission period. We find cold electrons at lower activity than expected by a simple model based on free radial expansion and continuous loss of electron energy. Cold electrons are seen mainly when the gas density indicates that an exobase may have formed. Conclusions. Collisional cooling of electrons following a radial outward path is not sufficient to explain the observations. We suggest that the ambipolar electric field keeps electrons in the inner coma for a much longer time, giving them time to dissipate energy by collisions with the neutrals. We conclude that better models are required to describe the plasma environment of comets. They need to include at least two populations of electrons and the ambipolar field.
Narrowband quantum emitters over large spectral range with Fourier-limited linewidth in hexagonal boron nitride

A. Dietrich, M. Bürk, E. S. Steiger, L. Antoniuk, T. T. Tran, M. Nguyen, I. Aharonovich, F. Jelezko, A. Kubanek 1 Institute for Quantum Optics, Ulm University,D-89081 Ulm, Germany School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, Australia 3 Center for Integrated Quantum Science and Technology (IQst), Ulm University,D-89081 Ulm, Germany (Dated: July 6, 2018)
VON NEUMANN RHO INVARIANTS AS OBSTRUCTIONS TO TORSION IN THE TOPOLOGICAL KNOT CONCORDANCE GROUP

We discuss an innite class of metabelian Von Neumann -invariants. Each one is a homomorphism from the monoid of knots to R. In general they are not well dened on the concordance group. Nonetheless, we show that they pass to well dened homomorphisms from the subgroup of the concordance group generated by anisotropic knots. Thus, the computation of even one of these invariants can be used to conclude that a knot is of innite order. We introduce a method to give a computable bound on these -invariants. Finally we compute this bound to get a new and explicit innite set of twist knots which is linearly independent in the concordance group and whose every member is of algebraic order 2 .
Cancer Treatment Patterns and Factors Affecting Receipt of Treatment in Older Adults: Results from the ASPREE Cancer Treatment Substudy (ACTS)

Cancer treatment planning in older adults is complex and requires careful balancing of survival, quality of life benefits, and risk of treatment-related morbidity and toxicity. As a result, treatment selection in this cohort tend to differ from younger patients. However, there are very few studies describing cancer treatment patterns in older cohorts. We used data from the ASPirin in Reducing Events in the Elderly (ASPREE) trial and the ASPREE Cancer Treatment Substudy (ACTS) to describe cancer treatment patterns in the elderly. We used a multivariate logistic regression model to identify factors affecting receipt of treatment. Of 1,893 eligible Australian and United States (US) participants with incident cancer, 1,569 (81%) received some form of cancer treatment. Non-metastatic breast cancers most frequently received treatment (98%), while haematological malignancy received the lowest rates of treatment (60%). Factors associated with not receiving treatment were older age (OR 0.94, 95% CI 0.91-0.96), residence in the US (OR 0.35, 95% CI 0.22-0.56), smoking (OR 0.60, 95% CI 0.37-0.98), and diabetes (OR 0.58, 95% CI 0.41-0.82). After adjustment for treatment patterns in sex-specific cancers, sex did not impact receipt of treatment. This study is one of the first describing cancer treatment patterns and factors affecting receipt of treatment across common cancer types in older adults. We found that most older adults with cancer received some form of cancer treatment, typically surgery or systemic therapy, although this varied with factors including cancer type, age, sex, and country of residence.
COVID-19 primary series and booster vaccination and immune imprinting

Background: Epidemiological evidence for immune imprinting was investigated in immune histories related to vaccination in Qatar from onset of the omicron wave, on December 19, 2021, through September 15, 2022. Methods: Matched, retrospective, cohort studies were conducted to investigate differences in incidence of SARS-CoV-2 reinfection in the national cohort of persons who had a primary omicron infection, but different vaccination histories. History of primary-series (two-dose) vaccination was compared to that of no vaccination, history of booster (three-dose) vaccination was compared to that of two-dose vaccination, and history of booster vaccination was compared to that of no vaccination. Associations were estimated using Cox proportional-hazards regression models. Results: The adjusted hazard ratio comparing incidence of reinfection in the two-dose cohort to that in the unvaccinated cohort was 0.43 (95% CI: 0.38-0.48). The adjusted hazard ratio comparing incidence of reinfection in the three-dose cohort to that in the two-dose cohort was 1.38 (95% CI: 1.16-1.65). The adjusted hazard ratio comparing incidence of reinfection in the three-dose cohort to that in the unvaccinated cohort was 0.53 (95% CI: 0.44-0.63). All adjusted hazard ratios appeared stable over 6 months of follow-up. Divergence in cumulative incidence curves in all comparisons increased markedly when incidence was dominated by BA.4/BA.5 and BA.2. 75*. No reinfection in any cohort progressed to severe, critical, or fatal COVID-19. Conclusions: History of primary-series vaccination enhanced immune protection against omicron reinfection, but history of booster vaccination compromised protection against omicron reinfection. These findings do not undermine the short-term public health utility of booster vaccination.
Childhood deprivation and Health related quality of life and associated factors among Pediatric cancer patients at National Hospital, Tanzania

Introduction Pediatrics cancer is one of the most important life-threatening, non-communicable diseases worldwide. However, pediatric cancer patients suffer from physical disabilities associated with cancer treatment. However, there is unclear information about level of health-related quality of life and associated factors. Aimed of this study was to assess level of Health-Related Quality of Life among pediatric cancer patients. Methods A hospital-based Analytical cross-sectional study design with a quantitative approach was employed among 91 conveniently sampled pediatric cancer patients with their parents/caregivers. Self-administered questionnaires were used to collect data. Data were analyzed using SPSS computer software program version 25. One-way ANOVA and linear regression were used to quantify and establish an association between Childhood Deprivation, Health-Related Quality of Life, and associated factors among Pediatric Cancer Patients at 95% CI, and a 5% level of significance. Results Results, the response rate was 92.3% (n = 84). The most prominent kind of pediatric cancer was Acute Lymphoblastic Leukemia 30 (35.7%) and Hodgkin lymphoma 11(13%). The mean age of pediatric cancer patients was 10.26{+/-}3.90 years while 59 (70.2%) were male. In linear regression, Childhood Deprivation ({beta}=-1.640; P-value< 0.05) on PedsQLTM 4.0 and ({beta}= -2.175; P-value< 0.01) on PedsQLTM 3.0. Findings imply that decreases the level of Childhood Deprivation, the chance of improving Quality of life increases. Conclusion The magnitude of acute lymphoblastic leukemia is higher among pediatric cancer patients. There was a significant link between pediatric cancers and childhood deprivation and thus, compromised quality of life. Innovative pediatric cancer care policies, guidelines, and or strategies may need to be advocated to address the problem accordingly.
Stark effect upon the effective mass and radius in a tight-binding exciton model

With a Green's function formalism we obtain the eigenvalue spectrum of a tight-binding one-dimensional exciton model characterized by a contact interaction, a Coulombic electron and hole attraction, the Heller-Marcus exciton-hopping energy and an external constant and homogeneous electric field. The resulting eigenvalue spectrum, in the form of an unevenly spaced Wannier-Stark ladder with envelope profiles, is used to obtain the effective mass of the exciton by the application of the Mattis-Gallinar effective mass formula [D. C. Mattis and J.-P. Gallinar, {\it Phys. Rev. Lett. } {\bf{53}}, 1391 (1984)]. We obtain positive and negative effective masses for the exciton. The inverse effective mass may oscillate periodically as a function of the inverse of the electric field, with the frequency of oscillation linearly dependent upon the tight-binding hopping matrix element. The exciton radius is also obtained with the Green's function formalism, and it too exhibits Keldysh-like field dependent oscillations, as well as abrupt variations associated to strongly avoided crossings in the eigenvalue spectrum. Finally, some comments are made about the experimental relevance of our results.
On detecting oscillations of gamma rays into axion-like particles in turbulent and coherent magnetic fields

Background radiation fields pervade the Universe, and above a certain energy any γ-ray flux emitted by an extragalactic source should be attenuated due to e+e- pair production. The opacity could be alleviated if photons oscillated into hypothetical axion-like particles (ALPs) in ambient magnetic fields, leading to a γ-ray excess especially at high optical depths that could be detected with imaging air Cherenkov telescopes (IACTs). Here, we introduce a method to search for such a signal in γ-ray data and to estimate sensitivities for future observations. Different magnetic fields close to the γ-ray source are taken into account in which photons can convert into ALPs that then propagate unimpeded over cosmological distances until they re-convert in the magnetic field of the Milky Way. Specifically, we consider the coherent field at parsec scales in a blazar jet as well as the turbulent field inside a galaxy cluster. For the latter, we explicitly derive the transversal components of a magnetic field with gaussian turbulence which are responsible for the photon-ALP mixing. To illustrate the method, we apply it to a mock IACT array with characteristics similar to the Cherekov Telescope Array and investigate the dependence of the sensitivity to detect a γ-ray excess on the magnetic-field parameters.
Pixel-Wise Contrastive Distillation

We present a simple but effective pixel-level self-supervised distillation framework friendly to dense prediction tasks. Our method, called Pixel-Wise Contrastive Distillation (PCD), distills knowledge by attracting the corresponding pixels from student’s and teacher’s output feature maps. PCD includes a novel design called SpatialAdaptor which "reshapes" a part of the teacher network while preserving the distribution of its output features. Our ablation experiments suggest that this reshaping behavior enables more informative pixel-to-pixel distillation. Moreover, we utilize a plug-in multi-head self-attention module that explicitly relates the pixels of student’s feature maps to enhance the effective receptive field, leading to a more competitive student. PCD outperforms previous self-supervised distillation methods on various dense prediction tasks. A backbone of ResNet-18-FPN distilled by PCD achieves 37.4 APbbox and 34.0 APmask on COCO dataset using the detector of Mask R-CNN. We hope our study will inspire future research on how to pre-train a small model friendly to dense prediction tasks in a self-supervised fashion.
ON MEASURING THE METALLICITY OF A TYPE IA SUPERNOVA’S PROGENITOR

In Type Ia Supernovae (SNe Ia) the relative abundances of chemical elements are affected by the neutron excess in the composition of the progenitor white dwarf. Since these products leave signatures in the spectra near maximum light, spectral features may be used to constrain the composition of the progenitor. We calculate the nucleosynthetic yields for three SN Ia simulations, assuming single degenerate, Chandrasekhar-mass progenitors, for a wide range of progenitor metallicities, and calculate synthetic light curves and spectra to explore correlations between progenitor metallicity and the strength of spectral features. We use two two-dimensional simulations of the deflagration–detonation–transition scenario with different 56Ni yields and the W7 simulation to control for differences between explosion models and total yields. While the overall yields of intermediate-mass elements (16 < A 40) differ between the three cases, trends in the yields are similar. With increasing metallicity, 28Si yields remain nearly constant, 40Ca yields decline, and Ti and 54Fe yields increase. In the synthetic spectra, we identify two features at 30 days post-explosion that appear to deepen with progenitor metallicity: a Ti feature around 4200 Å and an Fe feature around 5200 Å. In all three simulations, their pseudo equivalent widths show a systematic trend with progenitor metallicity. This suggests that these two features may allow for differentiation among progenitor metallicities of observed SNe Ia and potentially help to reduce the intrinsic Hubble scatter.
Learning to Navigate Wikipedia by Taking Random Walks

A fundamental ability of an intelligent web-based agent is seeking out and acquiring new information. Internet search engines reliably find the correct vicinity but the top results may be a few links away from the desired target. A complementary approach is navigation via hyperlinks, employing a policy that comprehends local content and selects a link that moves it closer to the target. In this paper, we show that behavioral cloning of randomly sampled trajectories is sufficient to learn an effective link selection policy. We demonstrate the approach on a graph version of Wikipedia with 38M nodes and 387M edges. The model is able to efficiently navigate between nodes 5 and 20 steps apart 96% and 92% of the time, respectively. We then use the resulting embeddings and policy in downstream fact verification and question answering tasks where, in combination with basic TF-IDF search and ranking methods, they are competitive results to the state-of-the-art methods.
Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important problem has many practical applications. Here we propose a novel method and neural network model that enables better grasp success rates relative to what is available in the literature. The method takes standard point cloud data as input and works well with single-view point clouds observed from arbitrary viewing directions. Videos and code are available at https://haojhuang.github. io/edge_grasp_page/.
When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain

Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training scheme to train standard model architectures, without completely leveraging the richness of the financial data. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks. Our models, code and benchmark data will be made publicly available on Github and Huggingface.
The Geometry of the G29-38 White Dwarf Dust Disk from Radiative Transfer Modeling

Many white dwarfs host disks of dust produced by disintegrating planetesimals and revealed by infrared excesses. The disk around G29-38 was the first to be discovered and is now well-observed, yet we lack a cohesive picture of its geometry and dust properties. Here we model the G29-38 disk for the first time using radiative transfer calculations that account for radial and vertical temperature and optical depth gradients. We arrive at a set of models that can match the available infrared measurements well, although they overpredict the width of the 10 μm silicate feature. The resulting set of models has a disk inner edge located at 92–100 R WD (where R WD is the white dwarf radius). This is farther from the star than inferred by previous modeling efforts due to the presence of a directly illuminated front edge to the disk. The radial width of the disk is narrow (≤10 R WD); such a feature could be explained by inefficient spreading or the proximity of the tidal disruption radius to the sublimation radius. The models have a half-opening angle of ≥1. °4. Such structure would be in strong contradiction with the commonly employed flat-disk model analogous to the rings of Saturn, and in line with the vertical structure of main-sequence debris disks. Our results are consistent with the idea that disks are collisionally active and continuously fed with new material, rather than evolving passively after the disintegration of a single planetesimal.
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model

Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and deep equilibrium models (DEQ) are two DMBA frameworks that have received significant attention. The key difference between the two is that the image prior in DEQ is trained by using a specific measurement model, while that in PnP is trained as a general image denoiser. This difference is behind a common assumption that PnP is more robust to changes in the measurement models compared to DEQ. This paper investigates the robustness of DEQ priors to changes in the measurement models. Our results on two imaging inverse problems suggest that DEQ priors trained under mismatched measurement models outperform image denoisers.
Learning in colloids: Synapse-like ZnO + DMSO colloid

Colloids submitted to electrical stimuli exhibit a reconfiguration that could be used to store information and, potentially, compute. We investigated learnign, memorization, and time and stimulation's voltage dependence of conductive network formation in a colloidal suspension of ZnO nanoparticles in DMSO. Relations between critical resistance and stimulation time were reconstructed. The critical voltage, i.e. the stimulation voltage necessary for dropping the resistance, was shown to decrease in response to an increase in stimulation time.
CEERS Key Paper. IV. A Triality in the Nature of HST-dark Galaxies

The new capabilities that JWST offers in the near- and mid-infrared (IR) are used to investigate in unprecedented detail the nature of optical/near-IR-faint, mid-IR-bright sources, with HST-dark galaxies among them. We gather JWST data from the CEERS survey in the Extended Groth Strip, jointly with HST data, and analyze spatially resolved optical-to-mid-IR spectral energy distributions to estimate photometric redshifts in two dimensions and stellar population properties on a pixel-by-pixel basis for red galaxies detected by NIRCam. We select 138 galaxies with F150W − F356W > 1.5 mag and F356W < 27.5 mag. The nature of these sources is threefold: (1) 71% are dusty star-forming galaxies (SFGs) at 2 < z < 6 with 9100 Gyr−1); (2) 18% are quiescent/dormant (i.e., subject to reignition/rejuvenation) galaxies (QGs) at 3 < z < 5, with logM⋆/M⊙∼10 and poststarburst mass-weighted ages (0.5–1.0 Gyr); and (3) 11% are strong young starbursts with indications of high equivalent width emission lines (typically, [O iii]+Hβ) at 6 < z < 7 (XELG-z6) and logM⋆/M⊙∼9.5 . The sample is dominated by disk-like galaxies with remarkable compactness for XELG-z6 (effective radii smaller than 0.4 kpc). Large attenuations in SFGs, 2 < A(V) < 5 mag, are found within 1.5 times the effective radius, approximately 2 kpc, while QGs present A(V) ∼ 0.2 mag. Our SED-fitting technique reproduces the expected dust emission luminosities of IR-bright and submillimeter galaxies. This study implies high levels of star formation activity between z ∼ 20 and z ∼ 10, where virtually 100% of our galaxies had already formed 108 M ⊙, 60% had assembled 109 M ⊙, and 10% up to 1010 M ⊙ (in situ or ex situ).
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation

The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for current natural language understanding systems. To facilitate the future development of models that can process negation effectively, we present CONDAQA, the first English reading comprehension dataset which requires reasoning about the implications of negated statements in paragraphs. We collect paragraphs with diverse negation cues, then have crowdworkers ask questions about the implications of the negated statement in the passage. We also have workers make three kinds of edits to the passage—paraphrasing the negated statement, changing the scope of the negation, and reversing the negation—resulting in clusters of question-answer pairs that are difficult for models to answer with spurious shortcuts. CONDAQA features 14,182 question-answer pairs with over 200 unique negation cues and is challenging for current state-of-the-art models. The best performing model on CONDAQA (UnifiedQA-v2-3b) achieves only 42% on our consistency metric, well below human performance which is 81%. We release our dataset, along with fully-finetuned, few-shot, and zero-shot evaluations, to facilitate the development of future NLP methods that work on negated language.