A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning predicts plant growth and yield in greenhouses.
Framework generates realistic crop images for growth modeling.
Interpretable neural network for plant traits and species identification.
We address the question of the growth of firm size. To this end, we analyze the Compustat data base comprising all publicly-traded United States manufacturing firms within the years 1974-1993. We find that the distribution of firm sizes remains stable for the 20 years we study, i.e., the mean value and standard deviati…
In this paper we look at ergodic BSDEs in the case where the forward dynamics are given by the solution to a non-autonomous (time-periodic coefficients) Ornstein-Uhlenbeck SDE with Lévy noise, taking values in a separable Hilbert space. We establish the existence of a unique bounded solution to an infinite horizon disc…
Iran's FiT policy boosts REs but risks financial crisis and plant failure.
The paper uses VGG-19 for plant species classification from leaf images.
Spectral algorithms solve optimal community detection and related problems.
Exact partitioning of high-order planted models achieved through convex optimization.
New insights link diverse statistical problems via secret leakage planted clique.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing tec…
Gradient descent trains both layers of a ReLU network to fit a linear model.
Paper proposes a new KPI for early fault detection in hydropower plants.
The main result of this article is that if a -manifold supports an Anosov flow, then the number of conjugacy classes in the fundamental group of grows exponentially fast with the length of the shortest orbit representative, hereby answering a question raised by Plante and Thurston in 1972. In fact we show th…
Study connects database alignment and planted matching using Gaussian features.
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures. For the purpose of supporting human operators of chemical plants, we are developing an AI system that can s…
We consider two closely related problems: planted clustering and submatrix localization. The planted clustering problem assumes that a random graph is generated based on some underlying clusters of the nodes; the task is to recover these clusters given the graph. The submatrix localization problem concerns locating hid…
Random Planted Forest interprets tree-based models by keeping some splits, leading to more interpretable predictions.
Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since…
Study information limits for community detection in sub-hypergraphs.
We consider the problem of online learning of optimal control for repeatedly operated systems in the presence of parametric uncertainty. During each round of operation, environment selects system parameters according to a fixed but unknown probability distribution. These parameters govern the dynamics of a plant. An ag…
Paper shows statistical-computational gaps in learning sparse mixtures and robust estimation.
Graph clustering involves the task of dividing nodes into clusters, so that the edge density is higher within clusters as opposed to across clusters. A natural, classic and popular statistical setting for evaluating solutions to this problem is the stochastic block model, also referred to as the planted partition model…
Power plant is a complex and nonstationary system for which the traditional machine learning modeling approaches fall short of expectations. The ensemble-based online learning methods provide an effective way to continuously learn from the dynamic environment and autonomously update models to respond to environmental c…
Paper explores limits of high-order clustering with planted structures.
Develops a new Gaussian process method for efficient Bayesian inference of plant root parameters in the Richards equation.
A neural collaborative filtering method predicts corn hybrid yield performance.
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process. Automatic data-driven approaches are widely needed and seen as a promising area of investment. This paper proposes an effective machine learning algorithm to predict industrial…
The recent liberalization of the electricity and gas markets has resulted in the growth of energy exchanges and modelling problems. In this paper, we modelize jointly gas and electricity spot prices using a mean-reverting model which fits the correlations structures for the two commodities. The dynamics are based on Or…
The paper uses a graph autoencoder to learn unbiased plant-pollinator interaction embeddings.
TMLE improves IPM estimation for ecological population dynamics.
Study shows it's impossible to count communities without finding them.
We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and cau…
Study on detecting and recovering hidden dense cycles in random graphs.
A controller learns to control a nonlinear plant with unknown model and partial observation using continuous deep Q-learning.
Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electric…
Graph neural networks help assess how global changes affect plant-pollinator networks.
Fourier-transform infra-red (FTIR) spectra of samples from 7 plant species were used to explore the influence of preprocessing and feature extraction on efficiency of machine learning algorithms. Wavelet Tensor Train (WTT) and Discrete Wavelet Transforms (DWT) were compared as feature extraction techniques for FTIR dat…
AI monitors social distancing and masks at manufacturing plants.
Study on Langevin dynamics for recovering planted signals in spiked matrix models.
In this paper, we present a method to determine the global horizontal irradiance (GHI) from the power measurements of one or more PV systems, located in the same neighborhood. The method is completely unsupervised and is based on a physical model of a PV plant. The precise assessment of solar irradiance is pivotal for …
Detection of dense cycles in graphs reveals a gap between easy detection and hard recovery.
We consider two problems that arise in machine learning applications: the problem of recovering a planted sparse vector in a random linear subspace and the problem of decomposing a random low-rank overcomplete 3-tensor. For both problems, the best known guarantees are based on the sum-of-squares method. We develop new …
We describe a natural way to plant cherry- and plumtrees at prescribed generic locations in an orchard.
Deep semi-supervised learning identifies tree species from natural images.
Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their char…
Machine learning predicts plant phenotypes from soil microbiome data.