Miracle-dz/UniFAR-FTUs huggingface dataset:
CHIPS: Complete History of Interaction-powered Supernovae

We present the public release of the Complete History of Interaction-Powered Supernovae (CHIPS) code, which is suited to model a variety of transients that arise from interaction with a dense circumstellar medium (CSM). Contrary to existing modelings, which mostly attach the CSM by hand, CHIPS self-consistently simulates both the creation of the CSM from mass eruption of massive stars prior to core collapse, and the subsequent supernova light curve. We demonstrate the performance of CHIPS by presenting examples of the density profiles of the CSM and the light curves. We show that the gross light-curve properties of putative interaction-powered transients (e.g., Type IIn supernovae, rapidly evolving transients and recently discovered fast blue optical transients) can be comprehensively explained with the output of CHIPS.
Ain’t No Mountain High Enough: Semiparametric Modeling of LIGO–Virgo’s Binary Black Hole Mass Distribution

We introduce a semiparametric model for the primary mass distribution of binary black holes (BBHs) observed with gravitational waves (GWs) that applies a cubic-spline perturbation to a power law. We apply this model to the 46 BBHs included in the second gravitational-wave transient catalog (GWTC-2). The spline perturbation model recovers a consistent primary mass distribution with previous results, corroborating the existence of a peak at 35 M ⊙ (>97% credibility) found with the Powerlaw+Peak model. The peak could be the result of pulsational pair-instability supernovae. The spline perturbation model finds potential signs of additional features in the primary mass distribution at lower masses similar to those previously reported by Tiwari and Fairhurst. However, with fluctuations due to small-number statistics, the simpler Powerlaw+Peak and BrokenPowerlaw models are both still perfectly consistent with observations. Our semiparametric approach serves as a way to bridge the gap between parametric and nonparametric models to more accurately measure the BBH mass distribution. With larger catalogs we will be able to use this model to resolve possible additional features that could be used to perform cosmological measurements and will build on our understanding of BBH formation, stellar evolution, and nuclear astrophysics.
r-GAT: Relational Graph Attention Network for Multi-Relational Graphs

Graph Attention Network (GAT) focuses on modelling simple undirected and single relational graph data only. This limits its ability to deal with more general and complex multi-relational graphs that contain entities with directed links of different labels (e.g., knowledge graphs). Therefore, directly applying GAT on multi-relational graphs leads to sub-optimal solutions. To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations. Specifically, each channel corresponds to a latent semantic aspect of an entity. This enables us to aggregate neighborhood information for the current aspect using relation features. We further propose a query-aware attention mechanism for subsequent tasks to select useful aspects. Extensive experiments on link prediction and entity classification tasks show that our r-GAT can model multi-relational graphs effectively. Also, we show the interpretability of our approach by case study.
Modeling age-specific incidence of colon cancer via niche competition

Cancer development is a multistep process often starting with a single cell in which a number of epigenetic and genetic alterations have accumulated thus transforming it into a tumor cell. The progeny of such a single benign tumor cell expands in the tissue and can at some point progress to malignant tumor cells until a detectable tumor is formed. The dynamics from the early phase of a single cell to a detectable tumor with billions of tumor cells are complex and still not fully resolved, not even for the well-known prototype of multistage carcinogenesis, the adenoma-adenocarcinoma sequence of colorectal cancer. Mathematical models of such carcinogenesis are frequently tested and calibrated based on reported age-specific incidence rates of cancer, but they usually require calibration of four or more parameters due to the wide range of processes these models aim to reflect. We present a cell-based model, which focuses on the competition between wild-type and tumor cells in colonic crypts, with which we are able reproduce epidemiological incidence rates of colon cancer. Additionally, the fraction of cancerous tumors with precancerous lesions predicted by the model agree with clinical estimates. The correspondence between model and reported data suggests that the fate of tumor development is majorly determined by the early phase of tumor growth and progression long before a tumor becomes detectable. Due to the focus on the early phase of tumor development, the model has only a single fit parameter, the time scale set by an effective replacement rate of stem cells in the crypt. We find this effective rate to be considerable smaller than the actual replacement rate, which implies that the time scale is limited by the processes succeeding clonal conversion of crypts.
FBXO2/SCF ubiquitin ligase complex directs xenophagy through recognizing bacterial surface glycan

Xenophagy, also known as antibacterial selective autophagy, degrades invading bacterial pathogens such as group A Streptococcus (GAS) to defend cells. Although invading bacteria are known to be marked with ubiquitin and selectively targeted by xenophagy, how ubiquitin ligases recognize invading bacteria is poorly understood. Here, we show that FBXO2, a glycoprotein‐specific receptor for substrate in the SKP1/CUL1/F‐box protein (SCF) ubiquitin ligase complex, mediates recognition of GlcNAc side chains of the GAS surface carbohydrate structure and promotes ubiquitin‐mediated xenophagy against GAS. FBXO2 targets cytosolic GAS through its sugar‐binding motif and GlcNAc expression on the GAS surface. FBXO2 knockout resulted in decreased ubiquitin accumulation on intracellular GAS and xenophagic degradation of bacteria. Furthermore, SCF components such as SKP1, CUL1, and ROC1 are required for ubiquitin‐mediated xenophagy against GAS. Thus, SCFFBXO2 recognizes GlcNAc residues of GAS surface carbohydrates and functions in ubiquitination during xenophagy.
Pediatric Primary Tympanoplasty Outcomes With Autologous and Non-autologous Grafts

Supplemental Digital Content is available in the text Objective: To compare rates of successful tympanic membrane (TM) closure in primary pediatric tympanoplasty between various autologous and non-autologous tissues. Methods: A retrospective chart review was performed examining all primary pediatric tympanoplasties over a 20-year period at a single institution. Results: In 564 pediatric tympanoplasties, no statistically significant difference existed between success rates of autologous and non-autologous grafts (p = 0.083). Compared with fascia, the hazard ratios (and 95% confidence intervals [CI]) for failure for each graft were as follows: human pericardial collagen (HR 0.90, CI 0.54–1.50, p = 0.680), porcine submucosal collagen (HR 1.07, CI 0.56–2.05, p = 0.830), human acellular dermal collagen (HR 1.66, CI 0.95–2.87, p = 0.073), and “multiple grafts” (HR 0.72, CI 0.26–1.98, p = 0.520). Survival curves demonstrated that 75% of graft failures occurred by 6 months after surgery, the rest occurring between 6 and 12 months postoperatively. Larger perforations encompassing more than or equal to 50% of the TM had lower success rates (HR 1.50, CI 1.02–2.21, p = 0.041) than smaller perforations encompassing less than 50% of the TM. Age was not correlated with success (HR 0.98, CI 0.93–1.03, p = 0.390). Conclusion: This study found that non-autologous collagen grafts provide equivalent rates of healing when compared with autologous tissue in primary pediatric tympanoplasty. In addition to the potential for reduced operative time and donor site morbidity, these materials provide a viable graft alternative in fascia-depleted ears. Level of Evidence: Level 4
Constraint-Driven Optimal Control of Multiagent Systems: A Highway Platooning Case Study

Platooning has been exploited as a method for vehicles to minimize energy consumption. In this letter, we present a constraint-driven optimal control framework that yields emergent platooning behavior for connected and automated vehicles operating in an open transportation system. Our approach combines recent insights in constraint-driven optimal control with the physical aerodynamic interactions between vehicles in a highway setting. The result is a set of equations that describes when platooning is an appropriate strategy, as well as a descriptive optimal control law that yields emergent platooning behavior. Finally, we demonstrate these properties in simulation.
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting

Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified by extracting the spatial and temporal patterns of traffic data separately. We argue that such correlations are universal and play a pivotal role in traffic flow. We put forward spacetime interval learning as a paradigm to explicitly capture these correlations through a unified analysis of both spatial and temporal features. Unlike the state-of-the-art methods, which are restricted to a particular road network, we model the universal spatio-temporal correlations that are transferable from cities to cities. To this end, we propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor comprising the data from its neighbors within close time points. Based on this idea, we introduce local spacetime neural network (STNN), which employs novel spacetime convolution and attention mechanism to learn the universal spatio-temporal correlations. The proposed STNN captures local traffic patterns, which does not depend on a specific network structure. As a result, a trained STNN model can be applied on any unseen traffic networks. We evaluate the proposed STNN on two public real-world traffic datasets and a simulated dataset on dynamic networks. The experiment results show that STNN not only improves prediction accuracy by 15% over state-of-the-art methods, but is also effective in handling the case when the traffic network undergoes dynamic changes as well as the superior generalization capability.
Fundamental limits of over-the-air optimization: Are analog schemes optimal?

We consider convex optimization on a $d$ dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance $\sigma^{2}$. The codewords satisfy an average power constraint $P$, resulting in the signal-to-noise ratio (SNR) of $P/\sigma^{2}$. Many schemes have been proposed for this problem, termed over-the-air optimization, in recent years. We present lower and upper bounds for the convergence rates for over-the-air optimization. Our first result is a lower bound for the convergence rate showing that any code must slowdown the convergence rate by a factor of roughly $\sqrt{d/\log(1+\text{SNR})}$. Next, we consider a popular class of schemes called analog coding, where a linear function of the gradient is sent. We show that a simple scaled transmission analog coding scheme results in a slowdown in convergence rate by a factor of $\sqrt{d(1+1/\text{SNR})}$. This matches the previous lower bound up to constant factors for low SNR, making the scaled transmission scheme optimal at low SNR. However, we show that this slowdown is necessary for any analog coding scheme. In particular, a slowdown in convergence by a factor of $\sqrt{d}$ remains even when SNR tends to infinity, a clear shortcoming of analog coding schemes at high SNR. Remarkably, we present a simple quantize-and-modulate scheme that uses Amplitude Shift Keying and almost attains the optimal convergence rate at all SNRs.
COSMic: A Coherence-Aware Generation Metric for Image Descriptions

Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by introducing the first discourse-aware learned generation metric for evaluating image descriptions. Our approach is inspired by computational theories of discourse for capturing information goals using coherence. We present a dataset of image$\unicode{x2013}$description pairs annotated with coherence relations. We then train a coherence-aware metric on a subset of the Conceptual Captions dataset and measure its effectiveness$\unicode{x2014}$its ability to predict human ratings of output captions$\unicode{x2014}$on a test set composed of out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient for our proposed metric with the human judgments for the results of a number of state-of-the-art coherence-aware caption generation models when compared to several other metrics including recently proposed learned metrics such as BLEURT and BERTScore.
FBERT: A Neural Transformer for Identifying Offensive Content

Transformer-based models such as BERT, XLNET, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and hate speech, an important problem in social media. In this paper, we present fBERT, a BERT model retrained on SOLID, the largest English offensive language identification corpus available with over $1.4$ million offensive instances. We evaluate fBERT's performance on identifying offensive content on multiple English datasets and we test several thresholds for selecting instances from SOLID. The fBERT model will be made freely available to the community.
Plasticization and thermal behavior of hydroxyl and nitrogen rich group-containing tung-oil-based ester plasticizers for PVC

Hydroxyl and nitrogen rich group-containing tung-oil-based ester (GEHTMA-1, GEHTMA-2, GEHTMA-3 and GEHTMA-4) plasticizers were successfully synthesized from tung-maleic anhydride and utilized to plasticize poly(vinyl chloride) (PVC). In comparison to petroleum-based plasticizers (e.g. DOTP), the mechanical test shows that bio-based GEHTMA-3 displays better mechanical properties, which means that GEHTMA-3 could endow PVC resins with well-balanced properties of flexibility and strength. With the addition of GEHTMA-3 into DOTP, PVC/DOTP/GEHTMA-3 exhibits better mechanical properties, plasticization performance, migration resistance and thermal stability compared with the PVC/DOTP system. Above all, PVC/28DOTP/12GEHTMA-3 shows the highest tensile strength and elongation of 32.19 MPa and 345.20%, respectively. PVC/32DOTP/8GEHTMA-3 displays the best thermal stability. The superior performance is attributed to the molecular interaction of GEHTMA-3 and DOTP with PVC. The interaction originates from the simultaneous introduction of hydroxyl, epoxy, benzene ring, ester and nitrogen rich groups into GEHTMA-3 structures. Besides, GEHTMA-3 presented good miscibility with PVC resins, particularly when a mixture of DOTP and GEHTMA-3 was used. In addition, possible plasticization of the PVC/DOTP/GEHTMA-3 system was presented.
Instance-Conditioned GAN

Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as ImageNet and COCO-Stuff remains challenging in unconditional settings. In this paper, we take inspiration from kernel density estimation techniques and introduce a non-parametric approach to modeling distributions of complex datasets. We partition the data manifold into a mixture of overlapping neighborhoods described by a datapoint and its nearest neighbors, and introduce a model, called instance-conditioned GAN (IC-GAN), which learns the distribution around each datapoint. Experimental results on ImageNet and COCO-Stuff show that IC-GAN significantly improves over unconditional models and unsupervised data partitioning baselines. Moreover, we show that IC-GAN can effortlessly transfer to datasets not seen during training by simply changing the conditioning instances, and still generate realistic images. Finally, we extend IC-GAN to the class-conditional case and show semantically controllable generation and competitive quantitative results on ImageNet; while improving over BigGAN on ImageNet-LT. Code and trained models to reproduce the reported results are available at https://github.com/facebookresearch/ic_gan.
The mathematics of adversarial attacks in AI - Why deep learning is unstable despite the existence of stable neural networks

The unprecedented success of deep learning (DL) makes it unchallenged when it comes to classification problems. However, it is well established that the current DL methodology produces universally unstable neural networks (NNs). The instability problem has caused an enormous research effort -- with a vast literature on so-called adversarial attacks -- yet there has been no solution to the problem. Our paper addresses why there has been no solution to the problem, as we prove the following mathematical paradox: any training procedure based on training neural networks for classification problems with a fixed architecture will yield neural networks that are either inaccurate or unstable (if accurate) -- despite the provable existence of both accurate and stable neural networks for the same classification problems. The key is that the stable and accurate neural networks must have variable dimensions depending on the input, in particular, variable dimensions is a necessary condition for stability. Our result points towards the paradox that accurate and stable neural networks exist, however, modern algorithms do not compute them. This yields the question: if the existence of neural networks with desirable properties can be proven, can one also find algorithms that compute them? There are cases in mathematics where provable existence implies computability, but will this be the case for neural networks? The contrary is true, as we demonstrate how neural networks can provably exist as approximate minimisers to standard optimisation problems with standard cost functions, however, no randomised algorithm can compute them with probability better than 1/2.
Correction of Ocular Aberrations with Prismatic Rigid Gas-permeable Contact Lenses in Keratoconic Eyes

SIGNIFICANCE A base-down prism was incorporated on the anterior surface of rigid gas-permeable (RGP) contact lenses to explore potential effects on the residual ocular aberrations after contact lens fitting in keratoconic eyes. PURPOSE This study aimed to evaluate the correction of ocular aberrations with corneal prismatic RGP contact lenses in keratoconic eyes and their impact on visual function. METHODS A cross-sectional and randomized study was performed. Seventeen eyes of 17 keratoconus patients (34.6 ± 11.1 years) were evaluated. Two designs (standard and prismatic) of a corneal RGP contact lens (KAKC; Conoptica, Barcelona, Spain) were fitted to the same eye of each patient in a random order: a standard RGP contact lens as control and a prismatic RGP contact lens with a base-down prism of 1.6 prism diopters. Ocular aberrations were measured for a pupil diameter of 3 mm with and without both contact lenses, whereas high-contrast distance visual acuity, low-contrast distance visual acuity, and contrast sensitivity were measured under photopic and mesopic conditions. RESULTS Both contact lenses improved oblique primary astigmatism, defocus, vertical coma, coma-like, and root-mean-square higher-order aberrations compared with the unaided eyes (P < .05). Furthermore, the prismatic RGP contact lenses offered lower values of vertical coma and root-mean-square higher-order aberrations than the standard RGP contact lenses (P < .05). Both designs (standard and prismatic) produced a positive vertical coma of lower magnitude than the negative vertical coma of the unaided eyes. On the other hand, the improvement achieved in all visual function variables was the same for both contact lens designs (P ≥ .05). CONCLUSIONS The prismatic RGP contact lenses corrected higher levels of higher-order aberrations compared with the standard RGP contact lenses. However, both contact lens designs with the same refractive power were equally efficient at improving visual function.
The In Situ Origins of Dwarf Stellar Outskirts in FIRE-2

Extended, old, and round stellar halos appear to be ubiquitous around high-mass dwarf galaxies (108.5 < M ⋆/M ⊙ < 109.6) in the observed universe. However, it is unlikely that these dwarfs have undergone a sufficient number of minor mergers to form stellar halos that are composed of predominantly accreted stars. Here, we demonstrate that FIRE-2 (Feedback in Realistic Environments) cosmological zoom-in simulations are capable of producing dwarf galaxies with realistic structures, including both a thick disk and round stellar halo. Crucially, these stellar halos are formed in situ, largely via the outward migration of disk stars. However, there also exists a large population of “nondisky” dwarfs in FIRE-2 that lack a well-defined disk/halo and do not resemble the observed dwarf population. These nondisky dwarfs tend to be either more gas-poor or to have burstier recent star formation histories than the disky dwarfs, suggesting that star formation feedback may be preventing disk formation. Both classes of dwarfs underscore the power of a galaxy’s intrinsic shape—which is a direct quantification of the distribution of the galaxy’s stellar content—to interrogate the feedback implementation in simulated galaxies.
Inhomogeneities in the 2-Flavor Chiral Gross-Neveu Model

We investigate the finite temperature and density chiral Gross-Neveu (cGN) model with an axial UA(1) symmetry in 1 + 1 dimensions on the lattice. In the limit where the number of flavors Nf tends to infinity the continuum model has been solved analytically and shows two phases: a symmetric high-temperature phase with a vanishing condensate and a low-temperature phase in which the complex condensate forms a chiral spiral which breaks translation invariance. In the lattice simulations we employ chiral SLAC fermions with exact axial symmetry. Similarly to Nf →∞, we find for 8 flavors, where quantum and thermal fluctuations are suppressed, two distinct regimes in the (T, μ) phase diagram, characterized by qualitatively different behavior of the two-point functions of the condensate fields. More surprisingly, at Nf = 2, where fluctuations are no longer suppressed, the model still behaves similarly to the Nf →∞ model and we conclude that the chiral spiral leaves its footprints even on systems with a small number of flavors. For example, at low temperature the two-point functions are still dominated by chiral spirals with pitches proportional to the inverse chemical potential, although in contrast to large-Nf their amplitudes decrease with distance. With Dyson-Schwinger equations we calculate the decay of the UA(1)-invariant fermion four-point function in search for a BKT phase at zero temperature.