Recovering core nodes from graph data with missing fringe interactions.
problem Recovering the core set from graph data with missing fringe interactions.
method Developed a theoretical framework and algorithms based on fixed-parameter tractability.
result Our algorithms outperform existing methods on various real-world datasets.
A variation of the preferential attachment random graph model of Barabási and Albert is defined that incorporates planted communities. The graph is built progressively, with new vertices attaching to the existing ones one-by-one. At every step, the incoming vertex is randomly assigned a label, which represents a commun…
This paper shows semi-equivelar toroidal maps are vertex-transitive covers.
problem Understanding the relationship between semi-equivelar and vertex-transitive toroidal maps.
method Proving semi-equivelar toroidal maps are quotients of vertex-transitive toroidal maps.
result Each semi-equivelar toroidal map has a finite vertex-transitive cover.
Small covers were introduced by Davis and Januszkiewicz in 1991. We introduce the notion of equilibrium triangulations for small covers. We study equilibrium and vertex minimal Z22-equivariant triangulations of 2-dimensional small covers. We discuss vertex minimal equilibrium triangulations of $\mathbb{R…
Study connects database alignment and planted matching using Gaussian features.
problem Identify matching between correlated user features in anonymized databases.
method Derived results for database alignment and planted matching, showing connections and thresholds.
result Performance thresholds for database alignment converge to planted matching when feature dimensionality is sufficiently high.
The study finds the bounds of vertex orbits in maps derived from specific lattices.
problem Determining the bounds of vertex orbits in maps derived from k-vertex-homogeneous lattices. method Analyzing maps as quotients of k-vertex-homogeneous lattices. result Sharp bounds of the number of vertex orbits are identified.
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…
We study Hurwitz spaces with regard to homological stabilization. By a Hurwitz space, we mean a moduli space of branched, not necessarily connected coverings of a disk with fixed structure group and number of branch points. We choose a sequence of subspaces of Hurwitz spaces which is suitable for our investigations. In…
A Seifert surgery is an integral surgery on a knot in S^3 producing a Seifert fiber space which may contain an exceptional fiber of index 0. The Seifert Surgery Network is a 1-dimensional complex whose vertices correspond to Seifert surgeries; its edges correspond to single twistings along "seiferters" or "annular pair…
Polynomial-time test for detecting dense subgraphs in heterogeneous networks.
problem Detecting a planted community in heterogeneous networks.
method Proposes a polynomial-time test with a standard normal distribution null limiting distribution.
result The test is efficient and performs well in both simulations and real data.
We give three constructions of a vertex-minimal triangulation of 4-dimensional real projective space RP4. The first construction describes a 4-dimensional sphere on 32 vertices, which is a double cover of a triangulated RP4 and has a large amount of symmetry. The second and third construct…
Tollefson described a variant of normal surface theory for 3-manifolds, called Q-theory, where only the quadrilateral coordinates are used. Suppose M is a triangulated, compact, irreducible, boundary-irreducible 3-manifold. In Q-theory, if M contains an essential surface, then the projective solution space has an e…
Given a flag in each of the vertex-transitive tessellations of the Euclidean plane by regular polygons, we determine the flag stabilizer under the action of the automorphism group of a regular cover. In so doing we give a presentation of these tilings as quotients of regular (infinite) polyhedra.
The article studies crystallizations of small covers over simple polytopes and finds unique crystallizations for the n-simplex.
problem Understanding crystallizations of small covers over simple polytopes.
method Examining crystallizations of small covers over the n-simplex and prism, proving uniqueness and counting equivalence classes. result Proves uniqueness of crystallization for RPn over n-simplex and counts equivalence classes for prism. The paper uses VGG-19 for plant species classification from leaf images.
problem Manual inspection of plant species by botanists is time-consuming.
method Transfer learning with VGG-19 for feature extraction and classification.
result The model achieves 99.70% accuracy in predicting plant species.
Spectral algorithms solve optimal community detection and related problems.
problem Optimal detection of community structures and related substructures.
method Spectral algorithms applied to various planted substructures.
result Spectral algorithms achieve optimal performance for a wide range of planted substructures.
Exact partitioning of high-order planted models achieved through convex optimization.
problem Efficiently partitioning hypergraphs generated by high-order planted models.
method Solving a computationally efficient convex optimization problem with a tensor nuclear norm constraint.
result Exact recovery of true underlying cluster structures with high probability.
AI system synthesizes chemical plant operation procedures for efficiency and stability.
problem Developing efficient and stable operation procedures for complex chemical plants.
method Integrates automated reasoning, deep reinforcement learning, and dynamic simulation with external knowledge.
result Synthesized procedure achieves faster recovery from malfunctions compared to standard PID control.
Every open Riemann surface can be triangulated with equilateral triangles.
problem The structure and triangulation of Riemann surfaces.
method Constructing a holomorphic branched covering to the Riemann sphere and glueing together equilateral triangles.
result Every open Riemann surface can be equilaterally triangulated.
New insights link diverse statistical problems via secret leakage planted clique.
problem Statistical-computational gaps in inference problems.
method Secret leakage planted clique as a new hardness assumption for reductions.
result Establishes tight statistical-computational tradeoffs for various problems.
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
problem Computational hardness of hypergraphic planted clique detection.
method No specific method mentioned; focuses on conjecturing equivalence.
result Equivalence of computational hardness between HPC and 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…
Semi-Equivelar maps are generalizations of Archimedean Solids (as are equivelar maps of the Platonic solids) to the surfaces other than 2−Sphere. We classify some semi equivelar maps on surface of Euler characteristic -1 and show that none of these are vertex transitive. We establish existence of 12-covered triangula…
The study shows how nonnegative Ricci curvature and metric cones imply the existence of abelian subgroups in the fundamental group of open manifolds.
problem Understanding the structure of fundamental groups of open manifolds with specific curvature properties.
method Analyzing the properties of the Riemannian universal cover and its asymptotic cones.
result The fundamental group of an open manifold with nonnegative Ricci curvature and certain geometric properties contains an abelian subgroup of finite index.
This paper shows GNNs can learn good approximations for graph problems.
problem Learning good approximations for combinatorial graph problems.
method Developed new GNNs and bridged GNN theory with distributed local algorithms.
result Most powerful GNNs can learn approximations for minimum dominating set and vertex cover problems with specific ratios.
Paper proposes a new KPI for early fault detection in hydropower plants.
problem Early detection and maintenance of faults in hydropower plants.
method Developed and tested a novel Key Performance Indicator (KPI).
result The KPI outperforms conventional multivariable process control charts.
The study explores maps of 2- and 3-uniform tilings on the torus.
problem Understanding the number of vertex orbits in quotient maps of 2- and 3-uniform tilings.
method Analyzing the quotient maps of 2- and 3-uniform tilings on the torus.
result Bounds on the number of vertex orbits in quotient maps of 2- and 3-uniform tilings.
With the [0,1,2]-family of cyclic triangulations we introduce a rich class of vertex-transitive triangulations of surfaces. In particular, there are infinite series of cyclic q-equivelar triangulations of orientable and non-orientable surfaces for every q=3k, k≥2, and every q=3k+1, k≥3. Series of cy…
Study compares WTT and DWT for FTIR data feature extraction of medicinal plants.
problem Improving machine learning efficiency with FTIR spectra of medicinal plants.
method Comparison of WTT and DWT for feature extraction, varying preprocessing steps.
result WTT and DWT yield similar results, improving clustering and classification accuracy.
Study examines how disturbances affect financial returns in Austrian forests.
problem Financial impact of disturbances on timberland returns in Austria.
method Applied probability theory to analyze two management regimes: even-aged and semi-stationary.
result Severe disturbances can lead to a shift from continuous-cover to even-aged forestry, affecting financial sensitivity.
There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs. Montanari (2015) showed t…
Random Planted Forest interprets tree-based models by keeping some splits, leading to more interpretable predictions.
problem Estimating the unknown regression function from lower-order interaction terms.
method Modifying the random forest algorithm by keeping certain leaves instead of deleting them, resulting in non-binary trees called planted trees.
result The random planted forest achieves asymptotically optimal convergence rates up to a logarithmic factor when the interaction bound is low.
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.
problem Identify limits for exact community detection in sub-hypergraphs.
method Use Fano's inequality to define model parameters and identify success and failure regions.
result Identify regions where algorithms succeed or fail in exact recovery.
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.
problem Statistical-computational gaps in learning sparse mixtures and robust estimation.
method Average-case reduction techniques, Imbalanced Sparse Gaussian Mixtures, and algorithmic change of measure.
result New hardness results for robust sparse mean estimation, semirandom planted dense subgraph, and universality principle for sparse mixture problems.
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…
The study connects triangulated surfaces to complex projective structures and circle patterns.
problem Understanding circle patterns on complex projective tori.
method Using discrete holomorphic quadratic differentials, the approach involves cross ratio systems and Delaunay angles.
result For any triangulated torus, the projection map is a covering map with at most one branch point.
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 proposes a predictive maintenance system for solar plants using big data.
problem Fault prediction in photovoltaic plants to reduce downtime and maintenance costs.
method Data-driven approach with unsupervised clustering and Pattern Recognition Neural Network.
result Effective prediction of both generic and specific faults, up to 7 days in advance.
Paper explores limits of high-order clustering with planted structures.
problem Statistical and computational limits of high-order clustering with planted structures.
method Developed methods for detection and recovery of clusters, identified signal-to-noise ratio boundaries.
result Sharp boundaries of signal-to-noise ratio for statistical and computational feasibility.
PLIT identifies plant lncRNAs from RNA-seq data with high accuracy.
problem Inaccurate identification of lncRNAs in plant transcriptomic datasets.
method PLIT uses L1 regularization and iRF classification to select optimal features from sequence and codon-bias data.
result PLIT outperforms existing CPC tools in identifying lncRNAs in plant RNA-seq datasets.
Interpretable neural network for plant traits and species identification.
problem Plant phenotyping and identification.
method Neural network trained on UPWINS spectral library, with visualization of weights for trait-based spectral features.
result 90% accuracy in species identification with interpretable neural network.
Develops a new Gaussian process method for efficient Bayesian inference of plant root parameters in the Richards equation.
problem Estimating unknown parameters in nonlinear PDEs for agricultural studies.
method Gaussian process collocation with importance sampling and Bayesian optimization.
result Our method yields robust estimates with uncertainty quantification for plant root parameters.
A neural collaborative filtering method predicts corn hybrid yield performance.
problem Predicting yield performance of untested hybrid combinations in plant breeding.
method Ensemble of matrix factorization and neural networks.
result The model significantly outperformed other models in the Syngenta Crop Challenge.
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…
Vertex distortion detects if a knot is unknot.
problem Determining if a knot is the unknot.
method Using Denne-Sullivan's bound on Gromov distortion, the vertex distortion of nontrivial lattice knots is bounded. Then, it is shown that trivial vertex distortion implies the unknot.
result The conjecture that trivial vertex distortion implies the unknot is proven.
Deep learning predicts plant growth and yield in greenhouses.
problem Predicting plant growth and yield for better greenhouse management.
method Utilized a new deep recurrent neural network (RNN) with LSTM neurons to model growth parameters.
result Deep learning models outperformed traditional ML methods in predicting plant growth and yield.