Miracle-dz/UniFAR-FTUs huggingface dataset:
K-Bearing stratospheric aerosols as indicators of low energy electromagnetic pollution

During the past five years, a team of Agrogeological Division of the Hungarian Geological and Geophysical Institute has studied the nutrient elements potential of the East-Hungarian sandy soils. Among these elements, the natural potassium sources are insufficient, i.e. neither the soil forming sediments, nor the groundwater cannot satisfy the consumption of the plants. Therefore, the unique K-source seems to be the meteoric water. The authors consulting more than three hundred geochemical, ecological, meteorological, astrophysical and radiological articles, identified the origin of atmospheric K-bearing aerosolsThus, in this paper, the K-salts, as the main aerosol particles in ice and rain nucleation, with stratospheric origin were discussed, mainly based on references. In the stratosphere, the solid particles show a well expressed stratification: aerosols from desertic, volcanic and anthropogenic origin are concentrated mainly at 20 km altitude, the cosmogenic particles are observable at 50–60 km, while the measurements prove a relatively uniform distribution of K bearing particles, decreasing abruptly in the troposphere, under cloud formation level. K-bearing aerosols form micron sized, soft flakes built up by very thin acicular crystals. They represent ideal nuclei for ice split and rain drop formation. Therefore, we suppose, that the stratospheric potassium is formed by cosmic radiation induced Ar/K reaction, the reverse of K/Ar decay. The increased K+ content in rainwater (or snow) samples above of highly populated centers is explained by the same Ar/K reaction, which is induced by low energy electromagnetic waves (UHF smog) due to (experimented) their Tunnel effect.
UniViLM: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation

With the recent success of the pre-training technique for NLP and image-linguistic tasks, some video-linguistic pre-training works are gradually developed to improve video-text related downstream tasks. However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks. This paper proposes UniVL: a Unified Video and Language pre-training model for both multimodal understanding and generation. It comprises four components, including two single-modal encoders, a cross encoder, and a decoder with the Transformer backbone. Five objectives, including video-text joint, conditioned masked language model (CMLM), conditioned masked frame model (CMFM), video-text alignment, and language reconstruction, are designed to train each of the components. We further develop two pre-training strategies, stage by stage pre-training (StagedP) and enhanced video representation (EnhancedV), to make the training process of the UniVL more effective. The pre-train is carried out on a sizeable instructional video dataset HowTo100M. Experimental results demonstrate that the UniVL can learn strong video-text representation and achieves state-of-the-art results on five downstream tasks.
The Synthesizability of Molecules Proposed by Generative Models

The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early-stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. These techniques can suggest novel molecular structures intended to maximize a multi-objective function, e.g., suitability as a therapeutic against a particular target, without relying on brute-force exploration of a chemical space. However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically-tractable chemical space, although doing so necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted.
Effective Capacity of NOMA with Finite Blocklength for Low-Latency Communications

In this paper, we investigate the link-layer rate of a two-users non-orthogonal multiple access (NOMA) network in finite blocklength (short packet communications) regime, where the two users are paired from a set of V users. The overall reliability consists of the transmission error probability and the queuing delay violation probability. The performance of the two-users NOMA network in finite blocklength is verified for achieving latency and reliability, using the effective capacity (EC) framework. Specifically, we derive closed-form expressions for the EC of the two-users NOMA network in finite blocklength regime, considering transmissions over Rayleigh fading channels. We also study a multiuser NOMA network and derive the total EC of two-users NOMA subsets, and show that the NOMA set with users having distinct channel conditions achieve maximum total EC. Focusing on a two-users NOMA network, we study the impact of the transmit signal-to-noise ratio, delay exponent, and transmission error probability, on the achievable EC of each user. The analysis shows that when the delay exponent is high, the delay violation probability does not improve below a certain value due to the dominant factor of the transmission error probability. The accuracy of the proposed closed-form expressions for the individual EC of a two-users NOMA is verified using the Monte-Carlo simulations.
The Big Three: A Methodology to Increase Data Science ROI by Answering the Questions Companies Care About

Companies may be achieving only a third of the value they could be getting from data science in industry applications. In this paper, we propose a methodology for categorizing and answering 'The Big Three' questions (what is going on, what is causing it, and what actions can I take that will optimize what I care about) using data science. The applications of data science seem to be nearly endless in today's modern landscape, with each company jockeying for position in the new data and insights economy. Yet, data scientists seem to be solely focused on using classification, regression, and clustering methods to answer the question 'what is going on'. Answering questions about why things are happening or how to take optimal actions to improve metrics are relegated to niche fields of research and generally neglected in industry data science analysis. We survey technical methods to answer these other important questions, describe areas in which some of these methods are being applied, and provide a practical example of how to apply our methodology and selected methods to a real business use case.
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction

Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the training data is fixed and applied directly in the test data. Although such assumption has made traditional machine learning techniques succeed in many real-world tasks, the highly dynamic nature of the stock market invalidates the strict assumption in stock prediction. To address this challenge, we propose the second-order identical distribution assumption, where the data distribution is assumed to be fluctuating over time with certain patterns. Based on such assumption, we develop a second-order learning paradigm with multi-scale patterns. Extensive experiments on real-world Chinese stock data demonstrate the effectiveness of our second-order learning paradigm in stock prediction.
Differentiable Bandit Exploration

Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution $\mathcal{P}$. In this work, we learn such policies for an unknown distribution $\mathcal{P}$ using samples from $\mathcal{P}$. Our approach is a form of meta-learning and exploits properties of $\mathcal{P}$ without making strong assumptions about its form. To do this, we parameterize our policies in a differentiable way and optimize them by policy gradients, an approach that is general and easy to implement. We derive effective gradient estimators and introduce novel variance reduction techniques. We also analyze and experiment with various bandit policy classes, including neural networks and a novel softmax policy. The latter has regret guarantees and is a natural starting point for our optimization. Our experiments show the versatility of our approach. We also observe that neural network policies can learn implicit biases expressed only through the sampled instances.
Seeded Binary Segmentation: A general methodology for fast and optimal changepoint detection

We propose seeded binary segmentation for large scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of changepoints based on these candidates can be done in various ways, adapted to the problem at hand. The method is thus easy to adapt to many changepoint problems, ranging from univariate to high-dimensional. Compared to recently popular random background intervals, seeded intervals lead to reproducibility and much faster computations. For the univariate Gaussian change in mean set-up, the methodology is shown to be asymptotically minimax optimal when paired with appropriate selection criteria. We demonstrate near-linear runtimes and competitive finite sample estimation performance. Furthermore, we illustrate the versatility of our method in high-dimensional settings.
A divide-and-conquer algorithm for computing gröbner bases of syzygies in finite dimension

Let f1, . , fm be elements in a quotient Rn/N which has finite dimension as a K-vector space, where R = K[X1, . , Xr] and N is an R-submodule of Rn. We address the problem of computing a Gröbner basis of the module of syzygies of (f1, ., fm), that is, of vectors (p1, ., pm) ∈ Rm such that p1f1 + . + pm fm = 0. An iterative algorithm for this problem was given by Marinari, Möller, and Mora (1993) using a dual representation of Rn/N as the kernel of a collection of linear functionals. Following this viewpoint, we design a divide-and-conquer algorithm, which can be interpreted as a generalization to several variables of Beckermann and Labahn's recursive approach for matrix Padé and rational interpolation problems. To highlight the interest of this method, we focus on the specific case of bivariate Padé approximation and show that it improves upon the best known complexity bounds.
Fully Distributed Resilient State Estimation Based on Distributed Median Solver

In this article, we present a scheme of fully distributed resilient state estimation for linear dynamical systems under sensor attacks. The proposed state observer consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. As a fully distributed scheme, it does not necessarily collect a majority of sensing data for the sake of attack identification, whereas the compromised sensors are eventually identified by the distributed network and excluded from the observers. For this, the overall network (not the individual local observer) is assumed to have redundant sensors and assumed to be connected. The proposed scheme is based on a novel design of a distributed median solver, which approximately recovers the median value of local estimates.
Coupled Thermal and Compositional Evolution of Photoevaporating Planet Envelopes

Photoevaporative mass loss sculpts the atmospheric evolution of tightly orbiting sub-Neptune-mass exoplanets. To date, models of the mass loss from warm Neptunes have assumed that the atmospheric abundances remain constant throughout the planet’s evolution. However, the cumulative effects of billions of years of escape modulated by diffusive separation and preferential loss of hydrogen can lead to planetary envelopes that are enhanced in helium and metals relative to hydrogen. We have performed the first self-consistent calculations of the coupled thermal, mass-loss, and compositional evolution of hydrogen–helium envelopes surrounding sub-Neptune-mass planets. We extended the Modules for Experiments in Stellar Astrophysics stellar evolution code to model the evolving envelope abundances of photoevaporating planets. We demonstrate that H–He fractionation can lead to planetary envelopes that are significantly enriched in helium and metals compared to their initial primordial compositions. A subset of our model planets—having Rp ≲ 3.00 R⊕, initial fenv < 0.5%, and irradiation flux ∼101–103 times that of Earth—obtain final helium mass fractions in excess of Y = 0.40 after several billion years of mass loss. GJ 436b, the planet that originally inspired Hu et al. to propose the formation of helium-enhanced planetary atmospheres, requires a primordial envelope that is too massive to become helium enhanced. Planets with envelope helium fractions of Y = 0.40 have radii that are between 0.5% and 10% smaller (depending on their mass, irradiation flux, and envelope mass fraction) than similar planets with solar composition (Y = 0.24) envelopes. The results of preferential loss of hydrogen may have observable consequences for the Mp − Rp relations and atmospheric spectra of sub-Neptune populations.
Directed Graph Hashing

This paper presents an algorithm for structurally hashing directed graphs. The algorithm seeks to fulfill the recursive principle that a hash of a node should depend only on the hash of its neighbors. The algorithm works even in the presence of cycles, which prevents a naive recursive algorithm from functioning. We also discuss the implications of the recursive principle, limitations of the algorithm, and potential use cases.
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories

We address the problem of learning to discover 3D parts for objects in unseen categories. Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches. Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion. At the core of our approach is to restrict the local context for extracting part-level features, which guarantees the generalizability to novel categories. On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples. Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.
Scalable Dyadic Independence Models with Local and Global Constraints

An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large networks. By utilizing matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models, while being able to meaningfully model local information (degrees) as well as global information (clustering coefficient, assortativity, etc.) if desired. This allows one to efficiently generate random networks with similar properties as an observed network, scalable up to sparse graphs consisting of millions of nodes. Empirical evaluation demonstrates its competitiveness in terms of accuracy with state-of-the-art methods for link prediction and network reconstruction.
New and improved rotational periods of magnetic CP stars from ASAS-3, KELT, and MASCARA data

Magnetic chemically peculiar (mCP) stars allow the investigation of such diverse phenomena as atomic diffusion, magnetic fields, and stellar rotation. The aim of the present investigation is to enhance our knowledge of the rotational properties ofmCP stars by increasing the sample of objects with accurately determined rotational periods. To this end, archival photometric time-series data from the ASAS-3, KELT, and MASCARA surveys were employed to improve existing rotational period information and derive rotational periods for mCP stars hitherto not known to be photometric variables. Our final sample consists of 294 mCP stars, a considerable amount of which (more than 40 per cent) are presented here as photometric variables for the first time. In addition, we identified 24 mCP star candidates that show light variability in agreement with rotational modulation but lack spectroscopic confirmation. The rotational period distribution of our sample agrees well with the literature. Most stars are between 100 Myr and 1Gyr old, with an apparent lack of very young stars. No objects were found on the zero-age main sequence; several stars seem to have evolved to the subgiant stage, albeit well before the first dredge-up. We identified four eclipsing binaries (HD244391, HD247441, HD248784, and HD252519), which potentially host an mCP star. This is of great interest because mCP stars are very rarely found in close binary systems, particularly eclipsing ones. Using archival spectra, we find strong evidence that the HD252519 system indeed harbours an mCP star component.
Sparse Coresets for SVD on Infinite Streams

In streaming Singular Value Decomposition (SVD), $d$-dimensional rows of a possibly infinite matrix arrive sequentially as points in $\mathbb{R}^d$. An $\epsilon$-coreset is a (much smaller) matrix whose sum of square distances of the rows to any hyperplane approximates that of the original matrix to a $1 \pm \epsilon$ factor. Our main result is that we can maintain a $\epsilon$-coreset while storing only $O(d \log^2 d / \epsilon^2)$ rows. Known lower bounds of $\Omega(d / \epsilon^2)$ rows show that this is nearly optimal. Moreover, each row of our coreset is a weighted subset of the input rows. This is highly desirable since it: (1) preserves sparsity; (2) is easily interpretable; (3) avoids precision errors; (4) applies to problems with constraints on the input. Previous streaming results for SVD that return a subset of the input required storing $\Omega(d \log^3 n / \epsilon^2)$ rows where $n$ is the number of rows seen so far. Our algorithm, with storage independent of $n$, is the first result that uses finite memory on infinite streams. We support our findings with experiments on the Wikipedia dataset benchmarked against state-of-the-art algorithms.
A closer look at the approximation capabilities of neural networks

The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions $\sigma$, then a standard feedforward neural network with one hidden layer is able to approximate any continuous multivariate function $f$ to any given approximation threshold $\varepsilon$, if and only if $\sigma$ is non-polynomial. In this paper, we give a direct algebraic proof of the theorem. Furthermore we shall explicitly quantify the number of hidden units required for approximation. Specifically, if $X\subseteq \mathbb{R}^n$ is compact, then a neural network with $n$ input units, $m$ output units, and a single hidden layer with $\binom{n+d}{d}$ hidden units (independent of $m$ and $\varepsilon$), can uniformly approximate any polynomial function $f:X \to \mathbb{R}^m$ whose total degree is at most $d$ for each of its $m$ coordinate functions. In the general case that $f$ is any continuous function, we show there exists some $N\in \mathcal{O}(\varepsilon^{-n})$ (independent of $m$), such that $N$ hidden units would suffice to approximate $f$. We also show that this uniform approximation property (UAP) still holds even under seemingly strong conditions imposed on the weights. We highlight several consequences: (i) For any $\delta > 0$, the UAP still holds if we restrict all non-bias weights $w$ in the last layer to satisfy $|w| 0$ (depending only on $f$ and $\sigma$), such that the UAP still holds if we restrict all non-bias weights $w$ in the first layer to satisfy $|w|>\lambda$. (iii) If the non-bias weights in the first layer are \emph{fixed} and randomly chosen from a suitable range, then the UAP holds with probability $1$.
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition

While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely varies based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameters for each task as a sum of task-shared and sparse task-adaptive parameters. With our Additive Parameter Decomposition (APD), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, we can achieve even better scalability with APD using hierarchical knowledge consolidation, which clusters the task-adaptive parameters to obtain hierarchically shared parameters. We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness.
First Order Optimization in Policy Space for Constrained Deep Reinforcement Learning

In reinforcement learning, an agent attempts to learn high-performing behaviors through interacting with the environment, such behaviors are often quantified in the form of a reward function. However some aspects of behavior, such as ones which are deemed unsafe and are to be avoided, are best captured through constraints. We propose a novel approach called First Order Constrained Optimization in Policy Space (FOCOPS) which maximizes an agent's overall reward while ensuring the agent satisfies a set of cost constraints. Using data generated from the current policy, FOCOPS first finds the optimal update policy by solving a constrained optimization problem in the nonparameterized policy space. FOCOPS then projects the update policy back into the parametric policy space. Our approach provides a guarantee for constraint satisfaction throughout training and is first-order in nature therefore extremely simple to implement. We provide empirical evidence that our algorithm achieves better performance on a set of constrained robotics locomotive tasks compared to current state of the art approaches.