Miracle-dz/UniFAR-FTUs huggingface dataset:
An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

In many parts of the world, electric power systems have seen a significant shift toward generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty between power grids and gas pipelines and brings coordination and uncertainty management challenges. To address these issues, we propose an uncertainty management framework for uncertain, but bounded gas consumption by gas-fired power plants. The admissible ranges are computed based on a joint optimization problem for the combined gas and electricity networks, which involves chance-constrained scheduling for the electric grid and a novel robust optimization formulation for the natural-gas network. This formulation ensures feasibility of the integrated system with a high probability, while providing a tractable numerical formulation. A key advance with respect to existing methods is that our method is based on a physically accurate, validated model for transient gas pipeline flows. Our case study benchmarks our proposed formulation against methods that ignore how reserve activation impacts the fuel use of gas power plants and only consider predetermined gas consumption. The results demonstrate the importance of considering uncertainty to avoid operating constraint violations and curtailment of gas to the generators.
The Quenching-Activation Behavior of the Gradient Descent Dynamics for Two-layer Neural Network Models

A numerical and phenomenological study of the gradient descent (GD) algorithm for training two-layer neural network models is carried out for different parameter regimes when the target function can be accurately approximated by a relatively small number of neurons. It is found that for Xavier-like initialization, there are two distinctive phases in the dynamic behavior of GD in the under-parametrized regime: An early phase in which the GD dynamics follows closely that of the corresponding random feature model and the neurons are effectively quenched, followed by a late phase in which the neurons are divided into two groups: a group of a few "activated" neurons that dominate the dynamics and a group of background (or "quenched") neurons that support the continued activation and deactivation process. This neural network-like behavior is continued into the mildly over-parametrized regime, where it undergoes a transition to a random feature-like behavior. The quenching-activation process seems to provide a clear mechanism for "implicit regularization". This is qualitatively different from the dynamics associated with the "mean-field" scaling where all neurons participate equally and there does not appear to be qualitative changes when the network parameters are changed.
Non-destructive estimation of sunflower leaf area and leaf area index under different water regime managements

Leaf area (LA) is a valuable parameter in many agronomic and plant physiological studies. Its measurement is time consuming and involves leaf destruction. Therefore, there is a tendency in using simple, fast, non-destructive, and electronic devices methods to estimate LA. The aim of this study was to estimate LA across different water regime treatments using a combination of leaf mass and leaf dimensions of sunflower (Helianthus annuus L.). For this purpose, different leaf sizes were collected from plants during the growing season on different time intervals. Experiment was conducted during 2012 summer time in Sari Agriculture Sciences and Natural Resources University, Iran. On field leaf dimension measurements were carried out, and leaves sketches were put on paper, scanned and then areas were measured using AutoCAD software. Multivariate linear and non-linear regression models were constructed between LA and other leaf components measured. All constructed models provided highly significant correlations (r = 0.90–0.99) between LA and different leaf components. The exponential model [LA = 0.619 [(L × W)0.5]2.019] provided the best estimation of sunflower LA (R2 = 0.993). In conclusion, the simple and quick models developed in this study could predict the sunflower LA and leaf area index (LAI) with high precision.
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals

Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on one-to-many sequence transduction problems, such as extracting multiple sequential sources from a mixture sequence. We extend the standard sequence-to-sequence model to a conditional multi-sequence model, which explicitly models the relevance between multiple output sequences with the probabilistic chain rule. Based on this extension, our model can conditionally infer output sequences one-by-one by making use of both input and previously-estimated contextual output sequences. This model additionally has a simple and efficient stop criterion for the end of the transduction, making it able to infer the variable number of output sequences. We take speech data as a primary test field to evaluate our methods since the observed speech data is often composed of multiple sources due to the nature of the superposition principle of sound waves. Experiments on several different tasks including speech separation and multi-speaker speech recognition show that our conditional multi-sequence models lead to consistent improvements over the conventional non-conditional models.
Statistical inference of assortative community structures

We develop a principled methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model. We show that this approach succeeds in finding statistically significant assortative modules in networks, unlike alternatives such as modularity maximization, which systematically overfits both in artificial as well as in empirical examples. In addition, we show that our method is not subject to a resolution limit, and can uncover an arbitrarily large number of communities, as long as there is statistical evidence for them. Our formulation is amenable to model selection procedures, which allow us to compare it to more general approaches based on the stochastic block model, and in this way reveal whether assortativity is in fact the dominating large-scale mixing pattern. We perform this comparison with several empirical networks, and identify numerous cases where the network's assortativity is exaggerated by traditional community detection methods, and we show how a more faithful degree of assortativity can be identified.
A solution formula and the R-boundedness for the generalized Stokes resolvent problem in an infinite layer with Neumann boundary condition

We consider the generalized Stokes resolvent problem in an infinite layer with Neumann boundary conditions. This problem arises from a free boundary problem describing the motion of incompressible viscous one-phase fluid flow without surface tension in an infinite layer bounded both from above and from below by free surfaces. We derive a new exact solution formula to the generalized Stokes resolvent problem and prove the $\mathscr{R}$-boundedness of the solution operator families with resolvent parameter $\lambda$ varying in a sector $\Sigma_{\varepsilon,\gamma_0}$ for any $\gamma_0>0$ and $0 \gamma_0 \}$. As applications, we obtain the maximal $L_p$-$L_q$ regularity for the nonstationary Stokes problem and then establish the well-posedness locally in time of the nonlinear free boundary problem mentioned above in $L_p$-$L_q$ setting. We make full use of the solution formula to take $\gamma_0>0$ arbitrarily, while in general domains we only know the $\mathscr{R}$-boundedness for $\gamma_0\gg1$ from the result by Shibata. As compared with the case of Neumann-Dirichlet boundary condition studied by Saito, analysis is even harder on account of higher singularity of the symbols in the solution formula.
Newton-type Methods for Minimax Optimization

Differential games, in particular two-player sequential games (a.k.a. minimax optimization), have been an important modelling tool in applied science and received renewed interest in machine learning due to many recent applications. To account for the sequential and nonconvex nature, new solution concepts and algorithms have been developed. In this work, we provide a detailed analysis of existing algorithms and relate them to two novel Newton-type algorithms. We argue that our Newton-type algorithms nicely complement existing ones in that (a) they converge faster to (strict) local minimax points; (b) they are much more effective when the problem is ill-conditioned; (c) their computational complexity remains similar. We verify our theoretical results by conducting experiments on training GANs.
Percolation thresholds for robust network connectivity

Communication networks, power grids, and transportation networks are all examples of networks whose performance depends on reliable connectivity of their underlying network components even in the presence of usual network dynamics due to mobility, node or edge failures, and varying traffic loads. Percolation theory quantifies the threshold value of a local control parameter such as a node occupation (resp., deletion) probability or an edge activation (resp., removal) probability above (resp., below) which there exists a giant connected component (GCC), a connected component comprising of a number of occupied nodes and active edges whose size is proportional to the size of the network itself. Any pair of occupied nodes in the GCC is connected via at least one path comprised of active edges and occupied nodes. The mere existence of the GCC itself does not guarantee that the long-range connectivity would be robust, e.g. to random link or node failures due to network dynamics. In this paper, we explore new percolation thresholds that guarantee not only spanning network connectivity, but also robustness. We define and analyze four measures of robust network connectivity, explore their interrelationships, and numerically evaluate the respective robust percolation thresholds for the 2D square lattice.
Effects of seed origin, growing medium and mini-plug density on early growth and quality of black locust (Robinia pseudoacacia (L.)) seedlings

The objective of this study was to identify optimal growing conditions for the production of high-quality mini-plug seedlings of black locust (Robinia pseudoacacia (L.)). Two seed origins (Greek and Hungarian), two growing media (enriched peat and stabilized medium) and four mini-plug densities (975; 1,460; 1,820 and 3,500 mini-plugs·m -2 ) were tested in two phases, in growth chambers and in a nursery after transplanting the mini-plugs into larger contain - ers. Measurements included seedling survival, growth traits (root length, shoot height, leaf area, root dry mass, shoot dry mass, root/shoot ratio) and root growth potential (RGP). In addition, the ratio of variable to maximal fluorescence (F v /F m ) and the effective quantum yield of photosystem II (ΔF/F' m ) were measured. It was found that growing medium produced significant effects mainly in the first phase and seed origin in the second phase while mini-plug density was important in both phases. The F v /F m ratio was found to be significantly affected by growing medium while the effec - tive quantum yield was influenced by density. Early survival of seedlings was closely correlated with fluorescence and growth traits (particularly with plant height) suggesting that these parameters might be useful for the grading of black locust seedlings prior to transplanting.
Unsupervised Cross-lingual Representation Learning for Speech Recognition

This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on a concurrently introduced self-supervised model which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to the strongest comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages.
Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India

The ongoing novel coronavirus epidemic was announced a pandemic by the World Health Organization on March 11, 2020, and the Government of India declared a nationwide lockdown on March 25, 2020 to prevent community transmission of the coronavirus disease (COVID)-19. Due to the absence of specific antivirals or vaccine, mathematical modeling plays an important role in better understanding the disease dynamics and in designing strategies to control the rapidly spreading infectious disease. In our study, we developed a new compartmental model that explains the transmission dynamics of COVID-19. We calibrated our proposed model with daily COVID-19 data for four Indian states, namely, Jharkhand, Gujarat, Andhra Pradesh, and Chandigarh. We study the qualitative properties of the model, including feasible equilibria and their stability with respect to the basic reproduction number R0. The disease-free equilibrium becomes stable and the endemic equilibrium becomes unstable when the recovery rate of infected individuals increases, but if the disease transmission rate remains higher, then the endemic equilibrium always remains stable. For the estimated model parameters, R0>1 for all four states, which suggests the significant outbreak of COVID-19. Short-time prediction shows the increasing trend of daily and cumulative cases of COVID-19 for the four states of India.
Stability and instability in scalar balance laws: fronts and periodic waves

We complete a full classification of non-degenerate traveling waves of scalar balance laws from the point of view of spectral and nonlinear stability/instability under (piecewise) smooth perturbations. A striking feature of our analysis is the elucidation of the prominent role of characteristic points in the determination of both the spectra of the linearized operators and the phase dynamics involved in the nonlinear large-time evolution. For a generic class of equations an upshot of our analysis is a dramatic reduction from a tremendously wide variety of entropic traveling waves to a relatively small range of stable entropic traveling waves.
On the intermediate dimensions of Bedford-McMullen carpets

The intermediate dimensions of a set $\Lambda$, elsewhere denoted by $\dim_{\theta}\Lambda$, interpolates between its Hausdorff and box dimensions using the parameter $\theta\in[0,1]$. Determining a precise formula for $\dim_{\theta}\Lambda$ is particularly challenging when $\Lambda$ is a Bedford-McMullen carpet with distinct Hausdorff and box dimension. In this direction, answering a question of Fraser, we show that $\dim_{\theta}\Lambda$ is strictly less than the box dimension of $\Lambda$ for every $\theta<1$, moreover, the derivative of the upper bound is strictly positive at $\theta=1$. We also improve on the lower bound obtained by Falconer, Fraser and Kempton.
Pareto Active Learning with Gaussian Processes and Adaptive Discretization

We consider the problem of optimizing a vector-valued objective function $\boldsymbol{f}$ sampled from a Gaussian Process (GP) whose index set is a well-behaved, compact metric space $({\cal X},d)$ of designs. We assume that $\boldsymbol{f}$ is not known beforehand and that evaluating $\boldsymbol{f}$ at design $x$ results in a noisy observation of $\boldsymbol{f}(x)$. Since identifying the Pareto optimal designs via exhaustive search is infeasible when the cardinality of ${\cal X}$ is large, we propose an algorithm, called Adaptive $\boldsymbol{\epsilon}$-PAL, that exploits the smoothness of the GP-sampled function and the structure of $({\cal X},d)$ to learn fast. In essence, Adaptive $\boldsymbol{\epsilon}$-PAL employs a tree-based adaptive discretization technique to identify an $\boldsymbol{\epsilon}$-accurate Pareto set of designs in as few evaluations as possible. We provide both information-type and metric dimension-type bounds on the sample complexity of $\boldsymbol{\epsilon}$-accurate Pareto set identification. We also experimentally show that our algorithm outperforms other Pareto set identification methods on several benchmark datasets.
Experimental and theoretical investigations of four amine derivatives as effective corrosion inhibitors for mild steel in HCl medium

Four amine derivative compounds were synthesized: 2-[(phenylamino)methyl]phenol, 2-{[(4-hydroxyphenyl)amino]methyl}phenol, 2-[(2-hydroxybenzyl)amino]benzonitrile and 2-{[(3-chlorophenyl)amino]methyl}phenol. The structure of the organic molecules was confirmed by FT-IR, 13C NMR and 1H NMR spectroscopy analyses. Their corrosion inhibition performances on mild steel in 1 M HCl were investigated using electrochemical measurements and surface analysis. Scanning electron microscopy analysis confirms the presence on the mild steel surface of a protective film of the as-prepared organic compounds, which depends on the substituent groups. Moreover, density functional theory and molecular dynamics simulation were employed in order to determine the adsorption mechanism and the position of amine derivative molecules towards the mild steel surface in an aggressive solution and to confirm the electrochemical results. The inhibition efficiency (IE) decreases with a decrease in concentration and the adsorption obeyed the Langmuir isotherm. The substitution of the OH group on the aromatic ring by Cl or CN increases IE to 90.23 and 92.56%, respectively. Molecular dynamics simulations attested that the four molecules were adsorbed on the Fe (110) surface in a flat position in the presence of water and HCl with high interaction between the different groups of the inhibitors and mild steel surface.
Virialization of the Inner CGM in the FIRE Simulations and Implications for Galaxy Disks, Star Formation, and Feedback

We use the FIRE-2 cosmological simulations to study the formation of a quasi-static, virial-temperature gas phase in the circumgalactic medium (CGM) at redshifts 0 < z < 5 and how the formation of this virialized phase affects the evolution of galactic disks. We demonstrate that when the halo mass crosses ∼1012 M ⊙, the cooling time of shocked gas in the inner CGM (∼0.1R vir, where R vir is the virial radius) exceeds the local free-fall time. The inner CGM then experiences a transition from on average subvirial temperatures (T ≪ T vir), large pressure fluctuations, and supersonic inflow/outflow velocities to virial temperatures (T ∼ T vir), uniform pressures, and subsonic velocities. This transition occurs when the outer CGM (∼0.5R vir) is already subsonic and has a temperature ∼T vir, indicating that the longer cooling times at large radii allow the outer CGM to virialize at lower halo masses than the inner CGM. This outside-in CGM virialization scenario is in contrast with inside-out scenarios commonly envisioned based on more idealized simulations. We demonstrate that inner CGM virialization coincides with abrupt changes in the central galaxy and its stellar feedback: the galaxy settles into a stable rotating disk, star formation transitions from “bursty” to “steady,” and stellar-driven galaxy-scale outflows are suppressed. Our results thus suggest that CGM virialization is initially associated with the formation of rotation-dominated thin galactic disks, rather than with the quenching of star formation as often assumed.
Searching Towards Class-Aware Generators for Conditional Generative Adversarial Networks

Conditional generative adversarial networks (cGANs) are designed to generate images based on the provided conditions, e.g., class-level distributions, semantic label maps, etc. Existing methods have used the same generator architecture for all classes. This paper presents an idea that adopts neural architecture search (NAS) to find a class-aware architecture for each class. The search space contains regular and class-modulated convolutions, where the latter is designed to introduce class-specific information while avoiding the reduction of training data for each class generator. The search algorithm follows a weight-sharing pipeline with mixed-architecture optimization so that the search cost does not grow with the number of classes. To learn the sampling policy, a Markov decision process is embedded into the search algorithm, and a moving average is applied for better stability. Class-aware generators show advantages over class-agnostic architectures experimentally. Moreover, we discover two intriguing phenomena that are inspirational to craft cGANs by hand.
Learning Task-General Representations with Generative Neuro-Symbolic Modeling

A hallmark of human intelligence is the ability to interact directly with raw data and acquire rich, general-purpose conceptual representations. In machine learning, symbolic models can capture the compositional and causal knowledge that enables flexible generalization, but they struggle to learn from raw inputs, relying on strong abstractions and simplifying assumptions. Neural network models can learn directly from raw data, but they struggle to capture compositional and causal structure and typically must retrain to tackle new tasks. To help bridge this gap, we propose Generative Neuro-Symbolic (GNS) Modeling, a framework for learning task-general representations by combining the structure of symbolic models with the expressivity of neural networks. Concepts and conceptual background knowledge are represented as probabilistic programs with neural network sub-routines, maintaining explicit causal and compositional structure while capturing nonparametric relationships and learning directly from raw data. We apply GNS to the Omniglot challenge of learning simple visual concepts at a human level. We report competitive results on 4 unique tasks including one-shot classification, parsing, generating new exemplars, and generating new concepts. To our knowledge, this is the strongest neurally-grounded model to complete a diverse set of Omniglot tasks.