Miracle-dz/UniFAR-FTUs huggingface dataset:
The missing conformer: A comprehensive rotational spectroscopy study and astronomical search of two conformers of methyl cyanoacetate

We performed a comprehensive investigation of methyl cyanoacetate (MCA) using high-resolution Fourier transform rotational spectroscopy. Two low energy conformers of MCA were observed in the vibrational ground state, in selected frequency regions from 2 to 110 GHz. We report accurately determined line lists, rotational constants, centrifugal distortion constants, and nuclear quadrupole coupling constants for both conformers, as well as for several singly substituted heavy-atom isotopologues. One of the conformers was previously reported; however, the rotational transitions of the second conformer, which is newly described here, are observed to be generally more intense than the latter. The accurate predictions of rotational transitions into the millimeter-wave region can facilitate the detection of these conformers of MCA in the interstellar medium. Using the rotational spectroscopy data provided here, we searched for the two conformers of MCA in a deep and unbiased spectral survey of the molecular cloud G+0.693-0.027 located in the Galactic Center. None of the MCA conformers were detected. The upper limits for their abundances were derived with respect to the abundance of molecular hydrogen.
Determinants of default from full completion of vaccination among children between 12 and 23 months old in Yilmana Densa district, west Gojam zone, Ethiopia, 2019

Background Vaccination is one of the best cost-effective public health interventions to safeguard children from vaccine-preventable diseases. In Ethiopia, the prevalence of default to the full completion of child immunization is high. However, the determinants of default to full completion have not been thoroughly investigated in this study area. Therefore, this study assessed the determinants of default to the full compilation of vaccination among children between 12 and 23 months old in Yilmana Densa District, west Gojam, northwest Ethiopia. Methods A community-based unmatched case-control study design was employed in the Yilmana Densa district among 343 (111 cases and 232 controls) randomly selected 12–23 months old children. Face-to-face interviews were used to collect data using a multistage sampling method. For analysis, data were entered into epidata version 3.1 and exported to SPSS 23 software. Descriptive analysis followed by binary and multivariable logistic regression analysis was conducted. The statistical significance was declared at a p-value of 0.05. Result This study identified that mothers who had not attended ANC follow-up [AOR = 5.55, 95% CI: (1.789–17.217)], mothers who had not gotten information about vaccinations [AOR = 8.589, 95% CI: (4.414–16.714)], and mothers whose time taken to reach vaccination site is more than 39 min were at higher risk to default from completion of vaccination [AOR = 3.252, 95% CI: (1.952–5.417)]. Furthermore, maternal waiting time (>45 min) for child vaccination [AOR = 2.674, 95% CI: (1.517–4.714)] and home delivery [AOR 3.19, 95% CI: (1.751–5.814)] were risk factors to default child from full completion of vaccination. Conclusion Mothers delivered at home, mothers not attending ANC follow-up, mothers who did not get health information about the vaccine, mothers taking longer time to reach the vaccination site, and staying longer time for child vaccination are causes of default. Motivated institutional delivery services utilization is recommended. The district office should consider the distribution of vaccination sites by the opening of new outreach site to reduce the waiting time of mothers.
THE KCAL VERA 22 GHz CALIBRATOR SURVEY

We observed a sample of 1536 sources with correlated flux densities brighter than 200 mJy at 8 GHz with the very long baseline interferometry (VLBI) array VLBI Exploration of Radio Astrometry at 22 GHz. One half of the target sources has been detected. The detection limit was around 200 mJy. We derived the correlated flux densities of 877 detected sources in three ranges of projected baseline lengths. The objective of these observations was to determine the suitability of given sources as phase calibrators for dual-beam and phase-referencing observations at high frequencies. Preliminary results indicate that the number of compact extragalactic sources at 22 GHz brighter than a given correlated flux density level is two times less than that at 8 GHz.
Cycle class consistency with distributional optimal transport and knowledge distillation for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a model trained on a labeled source domain to an unlabeled target domain. To this end, we propose in this paper a novel cycle class-consistent model based on optimal transport (OT) and knowledge distillation. The model consists of two agents, a teacher and a student cooperatively working in a cycle process under the guidance of the distributional optimal transport and distillation manner. The OT distance is designed to bridge the gap between the distribution of the target data and a distribution over the source class-conditional distributions. The optimal probability matrix then provides pseudo labels to learn a teacher that achieves a good classiﬁcation performance on the target domain. Knowledge distillation is performed in the next step in which the teacher distills and transfers its knowledge to the student. And ﬁnally, the student produces its prediction for the optimal transport step. This process forms a closed cycle in which the teacher and student networks are simultaneously trained to conduct transfer learning from the source to the target domain. Extensive experiments show that our proposed method outperforms existing meth-ods, especially the class-aware and OT-based ones on benchmark datasets including Ofﬁce-31, Ofﬁce-Home, and ImageCLEF-DA.
On the inductive bias of neural networks for learning read-once DNFs

Learning functions over Boolean variables is a fundamental problem in machine learning. But not much is known about learning such functions us-ing neural networks. Here we focus on learning read-once disjunctive normal forms (DNFs) under the uniform distribution with a convex neural network and gradient methods. We ﬁrst observe empirically that gradient methods converge to compact solutions with neurons that are aligned with the terms of the DNF. This is despite the fact that there are many zero training error networks that do not have this property. Thus, the learning process has a clear inductive bias towards such logical formulas. Following recent results which connect the inductive bias of gradient ﬂow (GF) to Karush-Kuhn-Tucker (KKT) points of minimum norm problems, we study these KKT points in our setting. We prove that zero training error solutions that memorize training points are not KKT points and therefore GF cannot converge to them. On the other hand, we prove that globally optimal KKT points correspond exactly to networks that are aligned with the DNF terms. These results suggest a strong connection between the inductive bias of GF and solutions that align with the DNF. We conclude with extensive experiments which verify our ﬁndings.
Convergence Analysis of Linear Coupling with Inexact Proximal Operator

Linear coupling is recently proposed to acceler-ate ﬁrst-order algorithms by linking gradient descent and mirror descent together, which is able to achieve an accelerated convergence rate. This work focuses on the convergence analysis of linear coupling for convex composite minimization when a proximal operator cannot be exactly computed. It is of particular interest to study the convergence of linear coupling because it not only achieves the accelerated convergence rate for ﬁrst-order algorithms but also works for generic norms. We present convergence analysis of linear coupling by allowing the proximal operator to be computed up to a certain precision. Our analysis illustrates that the accelerated convergence rate of linear coupling with an inexact proximal operator can be preserved if the error sequence of the inexact proximal operator decreases in a sufﬁciently fast rate. More im-portantly, our analysis leads to better bounds than existing works with inexact proximal operators. Experiment results on several real-world datasets verify our theoretical results.
Feature learning and random features in standard finite-width convolutional neural networks: An empirical study

The Neural Tangent Kernel is an important mile-stone in the ongoing eﬀort to build a theory for deep learning. Its prediction that suﬃciently wide neural networks behave as kernel methods, or equivalently as random feature models arising from linearized networks, has been conﬁrmed empirically for cer-tain wide architectures. In this paper, we compare the performance of two common ﬁnite-width convolutional neural networks, LeNet and AlexNet, to their linearizations on common benchmark datasets like MNIST and modiﬁed versions of it, CIFAR-10 and an ImageNet subset. We demonstrate empirically that ﬁnite-width neural networks, generally, greatly outperform the ﬁnite-width linearization of these architectures. When increasing the problem diﬃculty of the classiﬁcation task, we observe a larger gap which is in line with common intuition that ﬁnite-width neural networks perform feature learning which ﬁnite-width linearizations cannot. At the same time, ﬁnite-width linearizations im-prove dramatically with width, approaching the behavior of the wider standard networks which in turn perform slightly better than their standard width counterparts. Therefore, it appears that feature learning for non-wide standard networks is important but becomes less signiﬁcant with increasing width. We furthermore identify cases where both standard and linearized networks match in performance, in agreement with NTK theory, and a case where a wide linearization outperforms its standard width counterpart.
Stackmix: a complementary mix algorithm

Techniques combining multiple images as input/output have proven to be effective data aug-mentations for training convolutional neural networks. In this paper, we present StackMix: each input is presented as a concatenation of two images, and the label is the mean of the two one-hot labels. On its own, StackMix rivals other widely used methods in the “Mix” line of work. More importantly, unlike previous work, significant gains across a variety of benchmarks are achieved by combining StackMix with existing Mix augmentation, effectively mixing more than two images. E.g., by combining StackMix with CutMix, test error in the supervised setting is improved across a variety of settings over CutMix, including 0.8% on ImageNet, 3% on Tiny ImageNet, 2% on CIFAR-100, 0.5% on CIFAR-10, and 1.5% on STL-10. Similar results are achieved with Mixup. We further show that gains hold for robustness to common input corruptions and perturbations at varying severities with a 0.7% improvement on CIFAR-100-C, by combining StackMix with AugMix over AugMix. On its own, improvements with Stack-Mix hold across different number of labeled samples on CIFAR-100, maintaining approximately a 2% gap in test accuracy –down to using only 5% of the whole dataset– and is effective in the semi-supervised setting with a 2% improvement with the standard benchmark Π -model. Finally, we perform an extensive ablation study to better understand the proposed methodology.
Efficient inference for dynamic topic modeling with large vocabularies

Dynamic topic modeling is a well established tool for capturing the temporal dynamics of the topics of a corpus. In this work, we develop a scalable dynamic topic model by utilizing the correlation among the words in the vocabulary. By correlating previously independent temporal processes for words, our new model allows us to reliably estimate the topic representations containing less frequent words. We develop an amortised variational inference method with self-normalised importance sampling approximation to the word distribution that dramatically reduces the computational complexity and the number of variational parameters in order to handle large vocabularies. With extensive experiments on text datasets, we show that our method significantly outperforms the previous works by modeling word correlations, and it is able to handle real world data with a large vocabulary which could not be processed by previous continuous dynamic topic models.
Variational- and metric-based deep latent space for out-of-distribution detection

One popular deep-learning approach for the task of Out-Of-Distribution (OOD) detection is based on thresholding the values of per-class Gaussian likelihood of deep features. However, two issues arise with that approach: first, the distributions are often far from being Gaussian; second, many OOD data points fall within the effective support of the known classes’ Gaussians. Thus, either way it is hard to find a good threshold. In contrast, our proposed solution for OOD detection is based on a new latent space where: 1) each known class is well captured by a nearly-isotropic Gaussian; 2) those Gaussians are far from each other and from the origin of the space (together, these properties effectively leave the area around the origin free for OOD data). Concretely, given a (possibly-trained) backbone deep net of choice, we use it to train a conditional variational model via a Kullback Leibler loss, a triplet loss, and a new distancing loss that pushes classes away from each other. During inference, the class-dependent log-likelihood values of a deep feature ensemble of the test point are also weighted based on reconstruction errors, improving further the decision rule. Experiments on popular benchmarks show that our method yields state-of-the-art results, a feat achieved de-spite the fact that, unlike some competitors, we make no use of OOD data for training or hyperparameter tuning. Our code is available at https: //github.com/BGU-CS-VIL/vmdls .
Nonlinear Compton scattering in ultrashort laser pulses

A detailed analysis of the photon emission spectra of an electron scattered by a laser pulse containing only very few cycles of the carrying electromagnetic field is presented. The analysis is performed in the framework of strong-field quantum electrodynamics, with the laser field taken into account exactly in the calculations. We consider different emission regimes depending on the laser intensity, placing special emphasis on the regime of one-cycle beams and of high laser intensities, where the emission spectra depend nonperturbatively on the laser intensity. In this regime, we, in particular, present an accurate stationary phase analysis of the integrals that are shown to determine the computed emission spectra. The emission spectra show significant differences with respect to those in a long pulsed or monochromatic laser field: The emission lines obtained here are much broader, and more important, no dressing of the electron mass is observed.
Implicit kernel meta-learning using kernel integral forms

Meta-learning algorithms have made signiﬁcant progress in the context of meta-learning for image classiﬁcation but less attention has been given to the regression setting. In this paper we propose to learn the probability distribution representing a random feature kernel that we wish to use within kernel ridge regression (KRR). We introduce two instances of this meta-learning framework, learning a neural network pushforward for a translation-invariant kernel and an afﬁne pushforward for a neural network random feature kernel, both mapping from a Gaussian latent distribution. We learn the parameters of the pushforward by minimizing a meta-loss associated to the KRR objective. Since the resulting kernel does not admit an ana-lytical form, we adopt a random feature sampling approach to approximate it. We call the resulting method Implicit Kernel Meta-Learning (IKML). We derive a meta-learning bound for IKML, which shows the role played by the number of tasks T , the task sample size n , and the number of random features M . In particular the bound implies that M can be the chosen independently of T and only mildly dependent on n . We introduce one synthetic and two real-world meta-learning regression benchmark datasets. Experiments on these datasets show that IKML performs best or close to best when compared against competitive meta-learning methods.
Asymptotic optimality for active learning processes

Active Learning (AL) aims to optimize basic learned model(s) iteratively by selecting and anno-tating unlabeled data samples that are deemed to best maximise the model performance with minimal required data. However, the learned model is easy to overﬁt due to the biased distribution ( sampling bias and dataset shift ) formed by non-uniform sampling used in AL. Considering AL as an iterative sequential optimization process, we ﬁrst provide a perspective on AL in terms of statistical properties, i.e., asymptotic unbiasedness, consistency and asymptotic efﬁciency, with respect to basic estimators when the sample size (size of labeled set) becomes large, and in the limit as sample size tends to inﬁnity. We then discuss how bi-ases affect AL. Finally, we proposed a ﬂexible AL framework that aims to mitigate the impact of bias in AL by minimizing generalization error and importance-weighted training loss simultaneously.
Meta-learning without data via Wasserstein distributionally-robust model fusion

Existing meta-learning works assume that each task has available training and testing data. However, we can only use many available pre-trained models without accessing their training data in practice. We often need a single model to solve different tasks simultaneously as this is much more convenient to deploy the models. Our work aims to meta-learn a model initialization from these pre-trained models without using corresponding training data. We name this challenging problem setting Data-Free Learning To Learn (DFL2L). We pro-pose a distributionally robust optimization (DRO) framework to learn a black-box model to fuse and compress all the pre-trained models into a single network to address this problem. The proposed DRO framework diversiﬁes the learned task embedding associated with each pre-trained model to cover the diversity in the underlying training task distributions, encouraging good generalization to unseen new tasks. We sample a meta-initialization from the black-box network during meta-testing for fast adaptation to unseen new tasks. Extensive experiments on ofﬂine and online DFL2L settings and several real image datasets demonstrate the effectiveness of the proposed methods.
Lepton flavor universality violation in semileptonic tree level weak transitions

The recent deviations of the experimental data on some parameters of the tree-level semileptonic B and Bc mesons decays from the standard model (SM) predictions indicate considerable violations of the lepton flavor universality, and as a result possible new physics (NP) effects. To better understand the possible NP effects it is necessary to study deeply the physical quantities defining these decays from many aspects. The calculations of the physical quantities require the determinations of the hadronic form factors entering the matrix elements of the considered transitions as the main inputs. We calculate the form factors governing the tree-level Bc→J/ψlν and Bc→ηclν transitions within the QCD sum rules method. The obtained form factors are used in the calculations of the branching ratios (BRs) of the Bc→J/ψlν and Bc→ηclν transitions as well as R(J/ψ) and R(ηc). Our result on R(J/ψ) supports the present tension between the SM theory prediction and the experimental data. Our result on R(ηc) can be checked in future experiments.
Learning a neural Pareto manifold extractor with constraints

Multi-objective optimization (MOO) problems require balancing competing objectives, often under constraints. The Pareto optimal solution set deﬁnes all possible optimal trade-offs over such objectives. In this work, we present a novel method for Pareto-front learning : inducing the full Pareto manifold at train-time so users can pick any desired optimal trade-off point at run-time. Our key insight is to exploit Fritz-John Conditions for a novel guided double gradient descent strategy. Evaluation on synthetic benchmark problems allows us to vary MOO problem difﬁculty in controlled fashion and measure accuracy vs. known analytic solutions. We further test scalability and generalization in learning optimal neural model parameterizations for Multi-Task Learning (MTL) on image classiﬁcation. Results show consistent improvement in accuracy and efﬁciency over prior MTL methods as well as techniques from operations research.
Supernova shock breakout through a wind

The breakout of a supernova shock wave through the progenitor star's outer envelope is expected to appear as an X-ray flash. However, if the supernova explodes inside an optically thick wind, the breakout flash is delayed. We present a simple model for estimating the conditions at shock breakout in a wind based on the general observable quantities in the X-ray flash light curve; the total energy EX, and the diffusion time after the peak, tdiff. We base the derivation on the self-similar solution for the forward–reverse shock structure expected for an ejecta plowing through a pre-existing wind at large distances from the progenitor's surface. We find simple quantitative relations for the shock radius and velocity at breakout. By relating the ejecta density profile to the pre-explosion structure of the progenitor, the model can also be extended to constrain the combination of explosion energy and ejecta mass. For the observed case of XRO08109/SN2008D, our model provides reasonable constraints on the breakout radius, explosion energy and ejecta mass, and predicts a high shock velocity which naturally accounts for the observed non-thermal spectrum.
Indefinite Morse 2-functions; broken fibrations and generalizations

A Morse 2-function is a generic smooth map from a smooth manifold to a surface. In the absence of definite folds (in which case we say that the Morse 2-function is indefinite), these are natural generalizations of broken (Lefschetz) fibrations. We prove existence and uniqueness results for indefinite Morse 2-functions mapping to arbitrary compact, oriented surfaces. "Uniqueness" means there is a set of moves which are sufficient to go between two homotopic indefinite Morse 2-functions while remaining indefinite throughout. We extend the existence and uniqueness results to indefinite, Morse 2-functions with connected fibers.
Associations between attachment, therapeutic alliance, and engagement in black people with psychosis living in the UK

Abstract Background Compared to other ethnic groups in the UK, Black people have the highest rates of psychosis. This may partly be explained by both assessment bias and structural racism. Mental health services often find it difficult to develop therapeutic relationships with Black people with psychosis. Attachment theory posits that the quality of previous caregiving experiences influence current interpersonal functioning and emotional regulation. In this study, we applied the theory to improve the understanding of therapeutic relationships with people with psychosis. Aims This is the first study to examine associations between attachment difficulties, therapeutic alliance, and service engagement in a Black sample with psychosis. Method Fifty-one participants completed self-report measures of attachment and alliance. Staff completed measures of alliance and service engagement. Results Higher attachment avoidance was related to poorer alliance ratings. These significant findings were not upheld in a regression model controlling for total symptom scores and perceived ethnic/racial discrimination in services. Attachment anxiety was generally not associated with alliance ratings. Neither attachment anxiety nor attachment avoidance was significantly associated with service engagement. Conclusions Staff should be supported to better understand the needs of service users with avoidant attachment behaviours and to develop mutually-agreed treatment goals and therapeutic bonds.