Miracle-dz/UniFAR-FTUs huggingface dataset:
Revealing the chemical structure of the Class I disc Oph-IRS 67

Context. Recent results suggest that the first steps towards planet formation may be already taking place in protoplanetary discs during the first 100 000 yr after stars form. It is therefore crucial to unravel the physical and chemical structures of such discs in their earliest stages while they are still embedded in their natal envelopes and compare them with more evolved systems. Aims. The purpose of this paper is to explore the structure of a line-rich Class I protobinary source, Oph-IRS 67, and analyse the differences and similarities with Class 0 and Class II sources. Methods. We present a systematic molecular line study of IRS 67 with the Submillimeter Array (SMA) on 1–2′′ (150–300 AU) scales. The wide instantaneous band-width of the SMA observations (~30 GHz) provide detections of a range of molecular transitions that trace different physics, such as CO isotopologues, sulphur-bearing species, deuterated species, and carbon-chain molecules. Results. We see significant differences between different groups of species. For example, the CO isotopologues and sulphur-bearing species show a rotational profile and are tracing the larger-scale circumbinary disc structure, while CN, DCN, and carbon-chain molecules peak at the southern edge of the disc at blue-shifted velocities. In addition, the cold gas tracer DCO+ is seen beyond the extent of the circumbinary disc. Conclusions. The detected molecular transitions can be grouped into three main components: cold regions far from the system, the circumbinary disc, and a UV-irradiated region likely associated with the surface layers of the disc that are reached by the UV radiation from the sources. The different components are consistent with the temperature structure derived from the ratio of two H2CO transitions, that is, warm temperatures are seen towards the outflow direction, lukewarm temperatures are associated with the UV-radiated region, and cold temperatures are related with the circumbinary disc structure. The chemistry towards IRS 67 shares similarities with both Class 0 and Class II sources, possibly due to the high gas column density and the strong UV radiation arising from the binary system. IRS 67 is, therefore, highlighting the intermediate chemistry between deeply embedded sources and T-Tauri discs.
Separable Layers Enable Structured Efficient Linear Substitutions

In response to the development of recent efficient dense layers, this paper shows that something as simple as replacing linear components in pointwise convolutions with structured linear decompositions also produces substantial gains in the efficiency/accuracy tradeoff. Pointwise convolutions are fully connected layers and are thus prepared for replacement by structured transforms. Networks using such layers are able to learn the same tasks as those using standard convolutions, and provide Pareto-optimal benefits in efficiency/accuracy, both in terms of computation (mult-adds) and parameter count (and hence memory). Code is available at this https URL.
Long-term soft and hard X-ray investigation of the colliding wind WN+O binary WR 25

We investigated the long-term behaviour in X-rays of the colliding wind binary WR 25, using archival data obtained with Suzaku, Swift, XMM–Newton, and NuSTAR spanning over ∼16 yr. Our analysis reveals phase-locked variations repeating consistently over many consecutive orbits, in agreement with an X-ray emission fully explained by thermal emission from the colliding winds in the 208-d orbit. We report on a significant deviation of the X-ray flux with respect to the 1/D trend (expected for adiabatic shocked winds) close to periastron passage. The absence of a drop in post-shock plasma temperature close to periastron suggests this break in trend cannot be explained in terms of reduced pre-shock velocities in this part of the orbit. Finally, NuSTAR data reveal a lack of hard X-ray emission (above 10.0 keV) above the background level. Upper limits on a putative non-thermal emission strongly suggest that the sensitivity of present hard X-ray observatories is not sufficient to detect non-thermal emission from massive binaries above 10 keV, unless the wind kinetic power is large enough to significantly feed particle acceleration in the wind–wind interaction.
Towards Unified Acceleration of High-Order Algorithms under Hölder Continuity and Uniform Convexity

In this paper, through a very intuitive vanilla proximal method perspective, we derive accelerated high-order optimization algorithms for minimizing a convex function that has Holder continuous derivatives. In this general convex setting, we propose a concise unified acceleration framework (UAF), which reconciles the two different high-order acceleration approaches, one by Nesterov and Baes [29, 3, 33] and one by Monteiro and Svaiter [25]. As result, the UAF unifies the high-order acceleration instances [29, 3, 33, 15, 16, 25, 19, 6, 14] of the two approaches by only two problem-related parameters and two additional parameters for framework design. Furthermore, the UAF (and its analysis) is the first approach to make high-order methods applicable for high-order smoothness conditions with respect to non-Euclidean norms. If the function is further uniformly convex, we propose a general restart scheme for the UAF. The iteration complexities of instances of both the UAF and the restarted UAF match existing lower bounds in most important cases [2, 16]. For practical implementation, we introduce a new and effective heuristic that significantly simplifies the binary search procedure required by the framework. We use experiments to verify the effectiveness of the heuristic and demonstrate clear and consistent advantages of high-order acceleration methods over first-order ones, in terms of run-time complexity. Finally, the UAF is proposed directly in the general composite convex setting, thus show that the existing high-order algorithms [29, 3, 33, 16, 6, 14] can be naturally extended to the general composite convex setting.
CO observations of major merger pairs at z =  0: molecular gas mass and star formation

We present CO observations of 78 spiral galaxies in local merger pairs. These galaxies represent a subsample of a Ks-band-selected sample consisting of 88 close major-merger pairs (HKPAIRs), 44 spiral–spiral (S+S) pairs, and 44 spiral–elliptical (S+E) pairs, with separation <20 h−1 kpc and mass ratio <2.5. For all objects, the star formation rate (SFR) and dust mass were derived from Herschel PACS and SPIRE data, and the atomic gas mass, MHI, from the Green Bank Telescope HI observations. The complete data set allows us to study the relation between gas (atomic and molecular) mass, dust mass, and SFR in merger galaxies. We derive the molecular gas fraction (MH2/M*), molecular-to-atomic gas mass ratio (MH2/MHI), gas-to-dust mass ratio and SFE (= SFR/MH2) and study their dependences on pair type (S+S compared to S+E), stellar mass, and the presence of morphological interaction signs. We find an overall moderate enhancement (∼2×) in both molecular gas fraction (MH2/M*) and molecular-to-atomic gas ratio (MH2/MHI) for star-forming galaxies in major-merger pairs compared to non-interacting comparison samples, whereas no enhancement was found for the SFE nor for the total gas mass fraction ((MHI + MH2)/M*). When divided into S+S and S+E, low mass and high mass, and with and without interaction signs, there is a small difference in SFE, a moderate difference in MH2/M*, and a strong difference in MH2/MHI between subsamples. For the molecular-to-atomic gas ratio MH2/MHI, the difference between S+S and S+E subsamples is 0.55 ± 0.18 dex and between pairs with and without interaction sign 0.65 ± 0.16 dex. Together, our results suggest that (1) star formation enhancement in close major-merger pairs occurs mainly in S+S pairs after the first close encounter (indicated by interaction signs) because the HI gas is compressed into star-forming molecular gas by the tidal torque; and (2) this effect is much weakened in the S+E pairs.
Learning to solve the credit assignment problem

Backpropagation is driving today's artificial neural networks (ANNs). However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative: neurons can randomly introduce change, and use unspecific feedback signals to observe their effect on the cost and thus approximate their gradient. However, the convergence rate of such learning scales poorly with the number of involved neurons. Here we propose a hybrid learning approach. Each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide. We provide proof that our approach converges to the true gradient for certain classes of networks. In both feedforward and convolutional networks, we empirically show that our approach learns to approximate the gradient, and can match or the performance of exact gradient-based learning. Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules.
Gender-preserving Debiasing for Pre-trained Word Embeddings

Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the down-stream NLP applications that use those word embeddings. Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings. Specifically, we consider four types of information: feminine, masculine, gender-neutral and stereotypical, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the gender-related information in feminine and masculine words, (b) preserves the neutrality in gender-neutral words, and (c) removes the biases from stereotypical words. Experimental results on several previously proposed benchmark datasets show that our proposed method can debias pre-trained word embeddings better than existing SoTA methods proposed for debiasing word embeddings while preserving gender-related but non-discriminative information.
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression

Neural networks have been shown to be vulnerable against minor adversarial perturbations of their inputs, especially for high dimensional data under $\ell_\infty$ attacks. To combat this problem, techniques like adversarial training have been employed to obtain models which are robust on the training set. However, the robustness of such models against adversarial perturbations may not generalize to unseen data. To study how robustness generalizes, recent works assume that the inputs have bounded $\ell_2$-norm in order to bound the adversarial risk for $\ell_\infty$ attacks with no explicit dimension dependence. In this work we focus on $\ell_\infty$ attacks on $\ell_\infty$ bounded inputs and prove margin-based bounds. Specifically, we use a compression based approach that relies on efficiently compressing the set of tunable parameters without distorting the adversarial risk. To achieve this, we apply the concept of effective sparsity and effective joint sparsity on the weight matrices of neural networks. This leads to bounds with no explicit dependence on the input dimension, neither on the number of classes. Our results show that neural networks with approximately sparse weight matrices not only enjoy enhanced robustness, but also better generalization.
Enhancing Stock Movement Prediction with Adversarial Training

This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution [Xu and Cohen, 2018] with 3.11% relative improvements on average w.r. t. accuracy, validating the usefulness of adversarial training for stock prediction task.
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary. The proposed family includes both untargeted and targeted attacks optimized for ℓ and ℓ∞ similarity metrics respectively. Theoretical analysis is provided for the proposed algorithms and the gradient direction estimate. Experiments show HopSkipJumpAttack requires significantly fewer model queries than several state-of-the-art decision-based adversarial attacks. It also achieves competitive performance in attacking several widely-used defense mechanisms.
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning. As most of these applications involve continuous nonconvex-nonconcave formulations, a very basic question arises---"what is a proper definition of local optima? " Most previous work answers this question using classical notions of equilibria from simultaneous games, where the min-player and the max-player act simultaneously. In contrast, most applications in machine learning, including GANs and adversarial training, correspond to sequential games, where the order of which player acts first is crucial (since minimax is in general not equal to maximin due to the nonconvex-nonconcave nature of the problems). The main contribution of this paper is to propose a proper mathematical definition of local optimality for this sequential setting---local minimax, as well as to present its properties and existence results. Finally, we establish a strong connection to a basic local search algorithm---gradient descent ascent (GDA): under mild conditions, all stable limit points of GDA are exactly local minimax points up to some degenerate points.
Ultranarrow Linewidth Photonic‐Atomic Laser

Lasers with high spectral purity can enable a diverse application space, including precision spectroscopy, coherent high‐speed communications, physical sensing, and manipulation of quantum systems. Already, meticulous design and construction of bench Fabry–Perot cavities has made possible dramatic achievements in active laser‐linewidth reduction, predominantly for optical‐atomic clocks. Yet, there is increasing demand for miniaturized laser systems operating with high performance in ambient environments. Here, a compact and robust photonic‐atomic laser comprising a 2.5 centimeter long, 20 000 finesse, monolithic Fabry–Perot cavity integrated with a micromachined rubidium vapor cell is presented. By leveraging the short‐time frequency stability of the cavity and the long‐time frequency stability of atoms, an ultranarrow‐linewidth laser that enables integration for extended measurements is realized. Specifically, the laser supports a fractional‐frequency stability of 1×10−13 at an averaging time of 20 millisecond, 7×10−13 at 300 second, an integrated linewidth of 25 Hz that results from thermal noise, frequency noise floor as low as 0.06 Hz2 Hz−1, and a passive vibration immunity as low as 10−10 g−1. The present work explores hybrid laser systems with monolithic photonic and atomic packages based on physical design.
Pairwise hydrodynamic interactions of synchronized spermatozoa

The journey of mammalian spermatozoa in nature is well-known to be reliant on their individual motility. Often swimming in crowded microenvironments, the progress of any single swimmer is likely dependent on their interactions with other nearby swimmers. Whilst the complex dynamics of lone spermatozoa have been well-studied, the detailed effects of hydrodynamic interactions between neighbors remain unclear, with inherent nonlinearity in the governing hydrodynamics and potential dependence on the details of swimmer morphology. In this study we will attempt to elucidate the pairwise swimming behaviors of virtual spermatozoa, forming a computational representation of an unbound swimming pair and evaluating the details of their interactions via a high-accuracy boundary element method. We have explored extensive regions of parameter space to determine the pairwise interactions of synchronized spermatozoa, with synchronized swimmers often being noted in experimental observations, and have found that two-dimensional reduced autonomous dynamical systems capture the anisotropic nature of the swimming speed and stability arising from near-field hydrodynamic interactions. Focusing on two configurations of spermatozoa, namely those with swimmers located side-by-side or above and below one another, we have found that side-by-side cells attract each other, and the trajectories in the phase plane are well captured by a recently-proposed coarse-graining method of microswimmer dynamics via superposed regularised Stokeslets. In contrast, the above-below pair exhibit a remarkable stable pairwise swimming behavior, corresponding to a stable configuration of the plane autonomous system with swimmers lying approximately parallel to one another.
Paradox of Integration — Cellular Automata Approach

We discuss the self-deprecating strategy introduced by Peter Blau as one of stages of the process of social integration. Recently we have introduced a two-dimensional space of status, real and surface one ($A$ and $B$), and we have demonstrated that with this setup, the self-deprecating strategy efficiently prevents the rejection [K. Malarz and K. Kulakowski, International Journal of Modern Physics C 30, 1950040 (2019)]. There, the process of reducing the conflict was described by master equations, i.e. a set of differential equations describing evolution of density $v(A,B)$ of actors of status $(A,B)$. Here we reformulate the problem in terms of probabilistic cellular automata. The obtained results for number $n(A,B)$ of actors of status $(A,B)$ are qualitatively the same as in the previous approach, both for synchronous and asynchronous version of the automaton. Namely, an enhancement of the surface status compensates a deficiency of the real one.
Young age increases risk for lymph node positivity but decreases risk for non-small cell lung cancer death

Non-small cell lung cancer (NSCLC) prognosis and risk of lymph node positivity (LN+) are reference points for reasonable treatments. The aim of the current study was to investigate the effect of age on LN+ and NSCLC death. Data from the Surveillance, Epidemiology, and End Results (SEER) registry were used to identify 82,253 patients with NSCLC diagnosed between 1988 and 2008. All the patients underwent standard lung cancer surgery with lymph node examination. Demographic and clinicopathological parameters were extracted and compared among each age group. Impact of age on LN+ and NSCLC death was evaluated by the Cochran–Armitage trend test and logistic univariate and multivariate analyses for all T stages. Overall, 22,711 (27.60%) patients of the entirety had lymph node metastasis and 28,968 (35.22%) patients died of NSCLC within 5 years. With the increase in age, LN+ rates decreased regardless of T stages (P<0.001), whereas NSCLC-specific mortality increased in stages T1–T3 (P<0.001). Controlling other covariates in multivariable logistic regression, age remained an independent risk factor for LN+ in all T stages (P<0.05) and in stages T1–T3 (P<0.05). Our SEER analysis demonstrated a higher rate of LN+ and lower mortality in younger patients with NSCLC, after accounting for other covariates.
Endometritis in infertility

Chronic endometritis is an asymptomatic condition. It results from persistent inflammation of the endometrial lining caused by the presence of microorganisms in the uterine cavity. It is often associated with infertility and poor reproductive outcome. The pathogenesis of chronic endometritis and its association with infertility has been reviewed. Diagnostic modalities and treatment for chronic endometritis are also discussed.
Economic Inequality in Life Satisfaction and Self-perceived Health in Iranian Children and Adolescents: The CASPIAN IV Study

Background: The purpose of this study was to assess socioeconomic status (SES) inequality in life satisfaction (LS) and good self-perceived health (SPH) in Iranian children and adolescents. Methods: This nationwide study was conducted as part of a fourth national school-based surveillance program performed on 14880 students aged 6–18 years who were living in urban and rural areas of 30 provinces of Iran between 2011 and 2012. Using principle component analysis, the SES of participants was constructed as single variable. SES inequality in LS and good SPH across the SES quintiles was assessed using the concentration index (C) and slope index of inequality (SII). The determinants of this inequality are investigated by the Oaxaca Blinder decomposition method. Results: Frequency of LS along with the SES quintiles shifted significantly from 73.28% (95% CI: 71.49, 75.08) in the lowest quintile to 86.57% (95% CI:85.20, 87.93) in the highest SES quintile. Frequency of favorable SPH linearly increased from lowest SES quintile (76.18% (95% CI: 74.45, 77.92)) to highest SES quintile (83.39% (95% CI: 81.89, 84.89)). C index for LS and good SPH was negative, which suggests inequality was in favor of high SES group. SII for LS and SPH was 15.73 (95% CI: 12.10, 19.35) and 8.21 (95% CI: 5.46, 10.96)]. Living area and passive smoking were the most contributed factors in SES inequality of LS. Also passive smoking and physical activity were the most contributed factors in SES inequality of SPH. Conclusions: SES inequality in LS and good SPH was in favor of high SES group. These findings are useful for health policies, better programming and future complementary analyses.