Miracle-dz/UniFAR-FTUs huggingface dataset:
A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV

Modern surveys of gravitational microlensing events have progressed to detecting thousands per year, and surveys are capable of probing Galactic structure, stellar evolution, lens populations, black hole physics, and the nature of dark matter. One of the key avenues for doing this is the microlensing Einstein radius crossing time (t E) distribution. However, systematics in individual light curves as well as oversimplistic modeling can lead to biased results. To address this, we developed a model to simultaneously handle the microlensing parallax due to Earth's motion, systematic instrumental effects, and unlensed stellar variability with a Gaussian process model. We used light curves for nearly 10,000 OGLE-III and -IV Milky Way bulge microlensing events and fit each with our model. We also developed a forward model approach to infer the t E distribution by forward modeling from the data rather than using point estimates from individual events. We find that modeling the variability in the baseline removes a source of significant bias in individual events, and the previous analyses overestimated the number of t E > 100 day events due to their oversimplistic model ignoring parallax effects. We use our fits to identify the hundreds filling a regime in the microlensing parameter space that are 50% pure of black holes. Finally, we have released the largest-ever catalog of Markov Chain Monte Carlo parameter estimates for microlensing events.
General cut method for computing Szeged‐like topological indices with applications to molecular graphs

Szeged, PI and Mostar indices are some of the most investigated distance-based molecular descriptors. Recently, many different variations of these topological indices appeared in the literature and sometimes they are all together called Szeged-like topological indices. In this paper, we formally introduce the concept of a general Szeged-like topological index, which includes all mentioned indices and also infinitely many other topological indices that can be defined in a similar way. As the main result of the paper, we provide a cut method for computing a general Szeged-like topological index for any strength-weighted graph. This greatly generalizes various methods known for some of the mentioned indices and therefore rounds off such investigations. Moreover, we provide applications of our main result to benzenoid systems, phenylenes, and coronoid systems, which are well-known families of molecular graphs. In particular, closed-form formulas for some subfamilies of these graphs are deduced.
Fears for COVID-19: The crash risk of stock market

This paper investigates the impact of COVID-19 epidemic on the Chinese stock market crash risk. We first estimate conditional skewness of the return distribution from the GARCH-S model as the proxy of the equity market crash risk for the Shanghai Exchange Stock Market. Then, we construct a fear index for COVID-19 using the data from Baidu Index. Our findings show that the conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the epidemic increases the crash risk of stock market. Furthermore, we find that the fear sentiment also exacerbates the crash risk. In particular, the fear sentiment plays a significant role in the impact of COVID-19 on the crash risk. When the fear sentiment among people is high, the stock market crash risk is affected by the epidemic more seriously. Evidence from the daily deaths and global cases shows the robustness.
Distributional Generalization: A New Kind of Generalization

We introduce a new notion of generalization -- Distributional Generalization -- which roughly states that outputs of a classifier at train and test time are close *as distributions*, as opposed to close in just their average error. For example, if we mislabel 30% of dogs as cats in the train set of CIFAR-10, then a ResNet trained to interpolation will in fact mislabel roughly 30% of dogs as cats on the *test set* as well, while leaving other classes unaffected. This behavior is not captured by classical generalization, which would only consider the average error and not the distribution of errors over the input domain. Our formal conjectures, which are much more general than this example, characterize the form of distributional generalization that can be expected in terms of problem parameters: model architecture, training procedure, number of samples, and data distribution. We give empirical evidence for these conjectures across a variety of domains in machine learning, including neural networks, kernel machines, and decision trees. Our results thus advance our empirical understanding of interpolating classifiers.
Decoupling Representation Learning from Reinforcement Learning

In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy learning. To this end, we introduce a new unsupervised learning (UL) task, called Augmented Temporal Contrast (ATC), which trains a convolutional encoder to associate pairs of observations separated by a short time difference, under image augmentations and using a contrastive loss. In online RL experiments, we show that training the encoder exclusively using ATC matches or outperforms end-to-end RL in most environments. Additionally, we benchmark several leading UL algorithms by pre-training encoders on expert demonstrations and using them, with weights frozen, in RL agents; we find that agents using ATC-trained encoders outperform all others. We also train multi-task encoders on data from multiple environments and show generalization to different downstream RL tasks. Finally, we ablate components of ATC, and introduce a new data augmentation to enable replay of (compressed) latent images from pre-trained encoders when RL requires augmentation. Our experiments span visually diverse RL benchmarks in DeepMind Control, DeepMind Lab, and Atari, and our complete code is available at this https URL.
Caching in Networks without Regret

We consider the online $\textsf{Bipartite Caching}$ problem where $n$ users are connected to $m$ caches in the form of a bipartite network. Each of the $m$ caches has a file storage capacity of $C$. There is a library consisting of $N >C$ distinct files. Each user can request any one of the files from the library at each time slot. We allow the file request sequences to be chosen in an adversarial fashion. A user's request at a time slot is satisfied if the requested file is already hosted on at least one of the caches connected to the user at that time slot. Our objective is to design an efficient online caching policy with minimal regret. In this paper, we propose $\textsf{LeadCache,}$ an online caching policy based on the $\textsf{Follow the Perturbed Leader}$ (FTPL) paradigm. We show that $\textsf{LeadCache}$ is regret optimal up to a multiplicative factor of $\tilde{O}(n^{0.375}). $ As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area. Our new rounding algorithm substantially improves upon the currently best-known complexity for this problem. Moreover, we show the surprising result that under mild Strong-Law-type assumptions on the file request sequence, the rate of file fetches to the caches approaches to zero under the $\textsf{LeadCache}$ policy. Finally, we derive a tight universal regret lower bound for the $\textsf{Bipartite Caching}$ problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio.
Shaping the structure of a GMC with radiation and winds

We study the effect of stellar feedback (photodissociation/ionization, radiation pressure, and winds) on the evolution of a Giant Molecular Cloud (GMC), by means of a 3D radiative transfer, hydrosimulation implementing a complex chemical network featuring H2 formation and destruction. We track the formation of individual stars with mass $M\gt 1\, {\rm M}_{\odot }$ with a stochastic recipe. Each star emits radiation according to its spectrum, sampled with 10 photon bins from near-infrared to extreme ultraviolet bands; winds are implemented by energy injection in the neighbouring cells. We run a simulation of a GMC with mass $M=10^5\, {\rm M}_{\odot }$, following the evolution of different gas phases. Thanks to the simultaneous inclusion of different stellar feedback mechanisms, we identify two stages in the cloud evolution: (1) radiation and winds carve ionized, low-density bubbles around massive stars, while FUV radiation dissociates most H2 in the cloud, apart from dense, self-shielded clumps; (2) rapid star formation (SFR$\simeq 0.1\, {\rm M}_{\odot }\, {\rm yr}^{-1}$) consumes molecular gas in the dense clumps, so that UV radiation escapes and ionizes the remaining $\mathrm{H\,{\small I}}$ gas in the GMC. H2 is exhausted in 1.6 Myr, yielding a final star formation efficiency of 36 per cent. The average intensity of FUV and ionizing fields increases almost steadily with time; by the end of the simulation (t = 2.5 Myr) we find 〈G0〉 ≃ 103 (in Habing units), and a ionization parameter 〈Uion〉 ≃ 102, respectively. The ionization field has also a more patchy distribution than the FUV one within the GMC. Throughout the evolution, the escape fraction of ionizing photons from the cloud is fion, esc ≲ 0.03.
The role of collision speed, cloud density, and turbulence in the formation of young massive clusters via cloud–cloud collisions

Young massive clusters (YMCs) are recently formed astronomical objects with unusually high star formation rates. We propose the collision of giant molecular clouds (GMCs) as a likely formation mechanism of YMCs, consistent with the YMC conveyor-belt formation mode concluded by other authors. We conducted smoothed particle hydrodynamical simulations of cloud-cloud collisions and explored the effect of the clouds' collision speed, initial cloud density, and the level of cloud turbulence on the global star formation rate and the properties of the clusters formed from the collision. We show that greater collision speed, greater initial cloud density and lower turbulence increase the overall star formation rate and produce clusters with greater cluster mass. In general, collisions with relative velocity $\gtrsim 25$ km/s, initial cloud density $\gtrsim 250$ cm$^{-3}$, and turbulence of $\approx 2.5$ km/s can produce massive clusters with properties resembling the observed Milky Way YMCs.
VALES: VII. Molecular and ionized gas properties in the pressure balanced interstellar medium of starburst galaxies at z ∼ 0.15

Context. Spatially resolved observations of the ionized and molecular gas are critical for understanding the physical processes that govern the interstellar medium (ISM) in galaxies. Aims. To study the morpho-kinematic properties of the ionized and molecular gas in three dusty starburst galaxies at $z = 0.12-0.17$ to explore the relation between molecular ISM gas phase dynamics and the star-formation activity. Methods. We analyse $\sim$kpc-scale ALMA CO(1--0) and seeing limited SINFONI Paschen-$\alpha$ observations. We use a dynamical mass model, which accounts for beam-smearing effects, to constrain the CO-to-H$_2$ conversion factor. Results. One starburst galaxy shows irregular morphology which may indicate a major merger, while the other two systems show disc-like morpho-kinematics. The two disc-like starbursts show molecular gas velocity dispersion values comparable with that seen in local LIRG/ULIRGs, but in an ISM with molecular gas fraction and surface density values consistent to that reported for local star-forming galaxies. These molecular gas velocity dispersion values can be explained by assuming vertical pressure equilibrium. The star-formation activity is correlated with the molecular gas content suggesting depletion times of the order of $\sim 0.1-1$ Gyr. The star formation rate surface density ($\Sigma_{\rm SFR}$) correlates with the ISM pressure set by self-gravity ($P_{\rm grav}$) following a power law with an exponent close to 0.8. Conclusions. In dusty disc-like starburst galaxies, our data support the scenario in which the molecular gas velocity dispersion values are driven by the ISM pressure set by self-gravity, responsible to maintain the vertical pressure balance. The correlation between $\Sigma_{\rm SFR}$ and $P_{\rm grav}$ suggests that, in these dusty starbursts galaxies, the star formation activity arises as a consequence of the ISM pressure balance.
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA

Many NLP tasks have benefited from transferring knowledge from contextualized word embeddings, however the picture of what type of knowledge is transferred is incomplete. This paper studies the types of linguistic phenomena accounted for by language models in the context of a Conversational Question Answering (CoQA) task. We identify the problematic areas for the finetuned RoBERTa, BERT and DistilBERT models through systematic error analysis - basic arithmetic (counting phrases), compositional semantics (negation and Semantic Role Labeling), and lexical semantics (surprisal and antonymy). When enhanced with the relevant linguistic knowledge through multitask learning, the models improve in performance. Ensembles of the enhanced models yield a boost between 2.2 and 2.7 points in F1 score overall, and up to 42.1 points in F1 on the hardest question classes. The results show differences in ability to represent compositional and lexical information between RoBERTa, BERT and DistilBERT.
Quantile-based Iterative Methods for Corrupted Systems of Linear Equations

Often in applications ranging from medical imaging and sensor networks to error correction and data science (and beyond), one needs to solve large-scale linear systems in which a fraction of the measurements have been corrupted. We consider solving such large-scale systems of linear equations $\mathbf{A}\mathbf{x}=\mathbf{b}$ that are inconsistent due to corruptions in the measurement vector $\mathbf{b}$. We develop several variants of iterative methods that converge to the solution of the uncorrupted system of equations, even in the presence of large corruptions. These methods make use of a quantile of the absolute values of the residual vector in determining the iterate update. We present both theoretical and empirical results that demonstrate the promise of these iterative approaches.
Few-Shot Unsupervised Continual Learning through Meta-Examples

In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unbalanced, unlabeled and can be available as a stream. Hence many existing deep learning solutions suffer from a limited range of applications, in particular in the case of online streaming data that evolve over time. To narrow this gap, in this work we introduce a novel and complex setting involving unsupervised meta-continual learning with unbalanced tasks. These tasks are built through a clustering procedure applied to a fitted embedding space. We exploit a meta-learning scheme that simultaneously alleviates catastrophic forgetting and favors the generalization to new tasks, even Out-of-Distribution ones. Moreover, to encourage feature reuse during the meta-optimization, we exploit a single inner loop taking advantage of an aggregated representation achieved through the use of a self-attention mechanism. Experimental results on few-shot learning benchmarks show competitive performance even compared to the supervised case. Additionally, we empirically observe that in an unsupervised scenario, the small tasks and the variability in the clusters pooling play a crucial role in the generalization capability of the network. Further, on complex datasets, the exploitation of more clusters than the true number of classes leads to higher results, even compared to the ones obtained with full supervision, suggesting that a predefined partitioning into classes can miss relevant structural information.
SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms Under Uncertainty

We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and performs reciprocal collision avoidance. We account for the uncertainty in position and velocity by formulating the collision constraints as chance constraints, which describe a set of velocities that avoid collisions with a specified confidence level. We present two different methods for formulating and solving the chance constraints: our first method assumes a Gaussian noise distribution. Our second method is its extension to the non-Gaussian case by using a Gaussian Mixture Model (GMM). We reformulate the linear chance constraints into equivalent deterministic constraints, which are used with an MPC framework to compute a local collision-free trajectory for each quadrotor. We evaluate the proposed algorithm in simulations on benchmark scenarios and highlight its benefits over prior methods. We observe that both the Gaussian and non-Gaussian methods provide improved collision avoidance performance over the deterministic method. On average, the Gaussian method requires $\sim \! 5$ ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires $\sim \! 9$ ms on average in scenarios with 4 agents.
Improved Quantum Boosting

Boosting is a general method to convert a weak learner (which generates hypotheses that are just slightly better than random) into a strong learner (which generates hypotheses that are much better than random). Recently, Arunachalam and Maity gave the first quantum improvement for boosting, by combining Freund and Schapire's AdaBoost algorithm with a quantum algorithm for approximate counting. Their booster is faster than classical boosting as a function of the VC-dimension of the weak learner's hypothesis class, but worse as a function of the quality of the weak learner. In this paper we give a substantially faster and simpler quantum boosting algorithm, based on Servedio's SmoothBoost algorithm.
EFEITO DA PROFUNDIDADE DE TRABALHO NA QUALIDADE DA OPERAÇÃO DE SUBSOLAGEM PARA IMPLANTAÇÃO FLORESTAL

Objetivou-se, com este trabalho, realizar uma avaliacao da profundidade de trabalho na qualidade da operacao de subsolagem para implantacao de povoamentos florestais. O experimento foi realizado em uma empresa florestal localizada no Municipio de Telemaco Borba, Estado do Parana, Brasil, em um Latossolo de textura media. O equipamento utilizado foi um trator de esteiras com potencia de 170 HP e peso operacional de 19.000 kg, equipado com lâmina tipo V-shear, tracionando um subsolador de arrasto mono-haste com peso de 5.390 kg, com a operacao de subsolagem nas profundidades de 40 e 50 cm. A qualidade da subsolagem foi determinada com base nos aspectos de profundidade, formato e distância entre as linhas de sulcos, com uso de graficos de controle. Os resultados indicaram a falta de controle no processo relacionado com a profundidade e formato das linhas de preparo do solo na operacao de subsolagem em ambos os tratamentos. O preparo do solo realizado a 50 cm de profundidade apresentou os melhores padroes de qualidade nas condicoes estudadas.
Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization

This work addresses inverse linear optimization where the goal is to infer the unknown cost vector of a linear program. Specifically, we consider the data-driven setting in which the available data are noisy observations of optimal solutions that correspond to different instances of the linear program. We introduce a new formulation of the problem that, compared to other existing methods, allows the recovery of a less restrictive and generally more appropriate admissible set of cost estimates. It can be shown that this inverse optimization problem yields a finite number of solutions, and we develop an exact two-phase algorithm to determine all such solutions. Moreover, we propose an efficient decomposition algorithm to solve large instances of the problem. The algorithm extends naturally to an online learning environment where it can be used to provide quick updates of the cost estimate as new data becomes available over time. For the online setting, we further develop an effective adaptive sampling strategy that guides the selection of the next samples. The efficacy of the proposed methods is demonstrated in computational experiments involving two applications, customer preference learning and cost estimation for production planning. The results show significant reductions in computation and sampling efforts.
Cultivar Type and Nut-Sowing Orientation Influence Germination and Plant Vigor in Cashew (Anacardium occidentale L.)

The germination of cashew nuts and the quantitative plant vigor of the seedlings as influenced by small, medium, and large Indian and Brazilian cashew cultivars and five nut-sowing orientations (nut-side/side-positioning, stalk-end facing up, stalk-end facing down, scar-end facing up, and scar-end facing down) were investigated. The results of data collected on percentage germination, stem girth (cm), plant height (cm), number of leaves, leaf area (cm2), and dry matter yield showed that genotype and nut-sowing orientations significantly influence percentage germination (P < 0.05). Brazilian cultivars with medium nuts, irrespective of sowing orientations, had better germination than other genotypes. Brazilian cultivars with large nuts sown on nut-side and Indian cultivars with medium nuts sown on nut-side and with stalk-end up had germination percentage ranging between 86.7% and 100% at 4 weeks after sowing (WAS), while Brazilian cultivars with large nuts, Indian cultivars with medium and small nuts sown with scar-end down, and Indian small with stalk-end down had the lowest germination percentage of between 26.7% and 46.7%. The Brazilian cultivars with large and medium nuts both sown on the nut-side and stalk-end up produced higher dry matter yield of between 6.9 and 7.4 g/plant, while Indian cultivars with small nuts, in all sowing orientations, produced the least dry matter yield of between 0.73 and 2.5 g/plant. This empirical information will guide the farmers in the selection of planting materials and seed propagation for the successful establishment of cashew plantations with healthy and vigorous seedlings.
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning

Pretrained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and computational speed on hardware platforms have impeded the popularity of pretrained models, especially in the era of edge computing. In this work, we propose an efficient transformer-based large-scale language representation using hardware-friendly block structure pruning. We incorporate the reweighted group Lasso into block-structured pruning for optimization. Besides the significantly reduced weight storage and computation, the proposed approach achieves high compression rates. Experimental results on different models (BERT, RoBERTa, and DistilBERT) on the General Language Understanding Evaluation (GLUE) benchmark tasks show that we achieve up to 5.0x with zero or minor accuracy degradation on certain task(s). Our proposed method is also orthogonal to existing compact pretrained language models such as DistilBERT using knowledge distillation, since a further 1.79x average compression rate can be achieved on top of DistilBERT with zero or minor accuracy degradation. It is suitable to deploy the final compressed model on resource-constrained edge devices.