AdaSearch improves adaptive sensing in noisy environments.
problem Adaptive source seeking in environments with variable background signals.
method Combines global trajectory planning with principled confidence intervals.
result AdaSearch outperforms uniform sampling and other methods in simulations and hardware tests.
BIND removes background noise from binary matrices, improving detection accuracy and fairness.
problem Real data often violates the i.i.d assumption for binary matrix entries, leading to inaccurate detection.
method BIND optimizes detection by estimating row- and column-wise mixture distributions and eliminating background noise.
result BIND effectively removes background noise and increases detection accuracy and fairness.
Proposes FSM-IRL to learn invariant network representations considering feature and structural shifts.
problem Spatial heterogeneity and temporal dynamics lead to OOD generalization issues in geographic networks.
method Introduces FSM-IRL model that accounts for feature and structural distribution shifts using causal attention and reweighting.
result Demonstrates strong learning capabilities on geographic and social network datasets in OOD scenarios.
Study identifies sepsis subpopulations for better ICU prediction.
problem Lack of stratified sepsis subpopulations in ICU EHR predictions.
method Stratified sepsis subpopulations identified using machine learning.
result Machine learning models perform better with stratified subpopulations.
The paper develops personalized DAG models for web user behavior.
problem Understanding user behavior transitions between websites with user heterogeneity and network dependency.
method Personalized Binomial DAG models with network-structured covariates, embedding network structure into a dimension-reduced covariate, learning node neighborhoods, and exploring variance-mean relation.
result The proposed algorithm outperforms state-of-the-art competitors in heterogeneous data.
Communication networks have evolved from specialized, research and tactical transmission systems to large-scale and highly complex interconnections of intelligent devices, increasingly becoming more commercial, consumer-oriented, and heterogeneous. Propelled by emergent social networking services and high-definition st…
ICAM creates interpretable feature attribution maps for brain images.
problem Challenges in predicting class relevance from brain images due to heterogeneity and background variation.
method A VAE-GAN framework for disentangling class relevance from background features.
result FA maps generated by ICAM outperform baseline methods and support phenotype variation exploration.
We consider bosonic supersymmetric backgrounds of ten-dimensional conformal supergravity. Up to local conformal isometry, we classify the maximally supersymmetric backgrounds, determine their conformal symmetry superalgebras and show how they arise as near-horizon geometries of certain half-BPS backgrounds or as a plan…
Derives path-integrals for superstrings on curved backgrounds using string geometry theory.
problem Calculating path-integrals for superstrings on curved backgrounds.
method Derives path-integrals from string geometry theory by considering fluctuations around string backgrounds.
result Derives path-integrals for perturbative superstrings on all string backgrounds.
Derives path integrals for perturbative strings on various backgrounds.
problem Calculating path integrals for strings on curved backgrounds.
method Derives path integrals from string geometry theory by considering fluctuations around string backgrounds.
result Derives path integrals of all order perturbative strings on various backgrounds.
The study explores highly supersymmetric backgrounds in 11D supergravity.
problem Understanding and constructing highly supersymmetric backgrounds in 11D supergravity.
method Definition of abstract symbols and a strong version of the Reconstruction Theorem, proposing a strategy to construct backgrounds, and providing an example with detailed computation.
result Bijective correspondence between highly supersymmetric backgrounds and abstract symbols, and a classical supersymmetry gap result.
Model predicts drug overdose hotspots using EMS and toxicology data.
problem Predicting drug overdose hotspots to focus limited services.
method Spatial-temporal point process model integrating EMS and toxicology data.
result Model improves prediction accuracy by integrating heterogeneous data.
We describe the construction of a Lie superalgebra associated to an arbitrary supersymmetric M-theory background, and discuss some examples. We prove that for backgrounds with more than 24 supercharges, the bosonic subalgebra acts locally transitively. In particular, we prove that backgrounds with more than 24 supersym…
New framework uses background knowledge to speed up causal discovery.
problem Scalable causal discovery for large datasets.
method Utilizes background knowledge during causal discovery process.
result Background knowledge reduces computational requirements and improves structure quality.
SRTC model for background/foreground separation with missing pixels.
problem Background/foreground separation with missing pixels in videos.
method Smooth robust tensor completion (SRTC) model with tensor proximal alternating minimization (tenPAM).
result Global convergence guarantee for the proposed algorithm.
Researchers found no AdS4 backgrounds with >16 supersymmetries in IIB supergravity, and unique ones in IIA and 11D.
problem Exploring AdS4 backgrounds with >16 supersymmetries in 10 and 11 dimensions.
method Examined all possible fluxes and isometry constraints in IIB, IIA, and 11D supergravities.
result No AdS4 backgrounds with >16 supersymmetries in IIB supergravity, unique ones in IIA and 11D.
Study optimal reinsurance for insurers with a reinsurer's default risk.
problem Optimal reinsurance for insurers with a reinsurer's default risk.
method Analytical solution for two types of reinsurance contracts.
result Joint effect of reinsurer's default and background risk on reinsurance demand.
Motivated by the search for new gravity duals to M2 branes with N>4 supersymmetry --- equivalently, M-theory backgrounds with Killing superalgebra osp(N∣4) for N>4 --- we classify homogeneous M-theory backgrounds with symmetry Lie algebra so(n)⊕so(3,2) for n=5,6,7. We f…
We extend the Falceto-Zambon version of Marsden-Ratiu Poisson reduction to Poisson quasi-Nijenhuis structures with background on manifolds. We define gauge transformations of Poisson quasi-Nijenhuis structures with background, study some of their properties and show that they are compatible with reduction procedure. We…
Generative Adversarial Networks generate PXD background noise efficiently.
problem Efficiently generate statistically independent PXD background noise samples.
method Conditional Generative Adversarial Networks (GANs) with contrastive learning.
result On-demand PXD background generator reduces storage requirements.
Heterotic backgrounds described using generalised geometry, preserving minimal supersymmetry.
problem Characterizing heterotic backgrounds preserving minimal supersymmetry in four dimensions.
method Using generalised geometry, characterizing backgrounds by an SU(3)imesSpin(6+n) structure and an involutive subbundle of the generalised tangent bundle. result The analysis of infinitesimal deformations reproduces known cohomologies of massless moduli.
Background doesn't affect personality predictions in deep networks.
problem Understanding how background images influence personality attribution in deep learning models.
method Explicitly studied the effect of background images on personality prediction in deep residual networks, controlling for confounds.
result Adding background information to input decreases model performance for personality trait prediction.
Paper proposes a method to train ML on sPlot background data without negative weights.
problem Training machine learning on data with sPlot background subtraction leads to negative weights and algorithm divergence.
method Proposes a rigorous mathematical approach to handle negative weights in sPlot background data.
result Allows the use of any machine learning method on sPlot background data samples without encountering negative weights.
The paper examines how background risk affects portfolio selection and optimal reinsurance design.
problem Maximizing the probability of reaching a financial goal in the presence of background risk.
method Quantile formulation method to derive optimal solutions explicitly.
result The presence of background risk does not change the solution shape but alters the parameter values.
Study of AdS backgrounds preserving supersymmetry using generalised geometry.
problem Characterize AdS backgrounds preserving supersymmetry.
method Use generalised geometry to study supersymmetric AdS backgrounds of eleven-dimensional or type II supergravity.
result Show that a class of AdS backgrounds correspond to spaces with weak generalised holonomy.
We prove that supersymmetry backgrounds of (1,0) and (2,0) six-dimensional supergravity theories preserving more than one half of the supersymmetry are locally homogeneous. As a byproduct we also establish that the Killing spinors of such a background generate a Lie superalgebra.
Investment models account for both probabilistic and possibilistic risks.
problem Investment and background risks are modeled probabilistically and possibilistically.
method Developed three investment models combining probabilistic and possibilistic risks.
result Approximate calculation formula for optimal investment solutions proved.
Defines Killing spinors and bosonic backgrounds in 5D supergravity.
problem Characterizing backgrounds in 5D supergravity.
method Calculates Spencer cohomology, defines Killing spinors, and imposes constraints on spinor connection curvature.
result Recover field equations of 5D supergravity and find new field equations for sp(1)-valued one-form. In this paper we study homogeneous backgrounds of type IIB supergravity where the underlying geometry is that of a symmetric space. We determine which ten-dimensional lorentzian symmetric spaces (up to local isometry) admit such backgrounds and in about two thirds of the cases we determine fully their moduli space.
PCA++ improves robustness to background noise in contrastive learning.
problem Recovering shared signal subspaces from positive pairs in high-dimensional data with structured background noise.
method PCA++ uses hard uniformity-constrained contrastive learning to enforce identity covariance on projected features.
result PCA++ outperforms standard PCA and alignment-only PCA+ in simulations and real-world datasets.
Method identifies causal drivers from background features.
problem Distinguishing causal influence from hidden confounding.
method Stability of regression coefficients measured by statistic V.
result V converges to zero if and only if no causal drivers exist.
We present two different families of eleven-dimensional manifolds that admit non-restricted extensions of the isometry algebras to geometric superalgebras. Both families admit points for which the superalgebra extends to a super Lie algebra; on the one hand, a family of N=1, ν=3/4 supergravity backgrounds a…
New method simplifies causal inference with tiered background knowledge.
problem Large equivalence classes of DAGs limit causal information.
method Integrates tiered background knowledge to create 'tiered MPDAGs' with simplified structure.
result Tiered MPDAGs are chain graphs with chordal components, simplifying causal effect estimation.
Study evaluates saliency maps on artificial data with different backgrounds.
problem Objective evaluation of saliency methods on artificial data with varying backgrounds.
method Developed a framework to generate artificial data with synthetic lesions and a known ground truth map, evaluated two data sets with different backgrounds (Perlin noise and 2D brain MRI slices).
result Heatmaps vary strongly between saliency methods and backgrounds.
Researchers prove a nonlinear gluing theorem for gravitational fields near static backgrounds.
problem Proving a nonlinear gluing theorem for gravitational fields near static backgrounds.
method Proved a nonlinear characteristic Ck-gluing theorem for vacuum gravitational fields in Bondi gauge. result Generalized the C2-gluing theorem near light cones to a wider class of hypersurfaces. We construct rigid supersymmetric gauge theories on Riemannian five-manifolds. We follow a holographic approach, realizing the manifold as the conformal boundary of a six-dimensional bulk supergravity solution. This leads to a systematic classification of five-dimensional supersymmetric backgrounds with gravity duals. …
The paper improves itemset quality assessment by incorporating background knowledge.
problem Assessing the quality of discovered itemsets is challenging due to many patterns being explainable by background knowledge.
method The authors introduce a maximum entropy approach to efficiently infuse additional background knowledge such as row margins, lazarus counts, and bounds of ones.
result More sophisticated models that incorporate background knowledge fit the data better and improve frequency prediction of itemsets.
Paper explores using EEG for better speaker identification, even in noisy environments.
problem Speaker identification performance degrades in background noise.
method Uses EEG signals to enhance speaker identification systems, comparing with acoustic features.
result Speaker identification system using only EEG features outperforms one using only acoustic features in high background noise.
We define the Poisson quasi-Nijenhuis structures with background on Lie algebroids and we prove that to any generalized complex structure on a Courant algebroid which is the double of a Lie algebroid is associated such a structure. We prove that any Lie algebroid with a Poisson quasi-Nijenhuis structure with background…
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …
A geometric string solution has background fields in overlapping coordinate patches related by diffeomorphisms and gauge transformations, while for a non-geometric background this is generalised to allow transition functions involving duality transformations. Non-geometric string backgrounds arise from T-duals and mirr…
Paper characterizes and represents pairwise causal background knowledge for improved causal inference.
problem Improving causal inference by handling pairwise causal constraints.
method Graphical characterization, direct causal clause (DCC), unified representation, MPDAG, polynomial-time algorithms.
result Pairwise causal background knowledge uniquely decomposes into MPDAG and DCCs, improving causal effect identification.
A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with a relatively large number of variable-width basis functions, and the parameters are estimated by a Bayesian method…
A user-friendly interface constructs effective background knowledge from ER diagrams.
problem Inefficient construction of background knowledge by domain experts in ILP systems.
method Design of a graphical user interface to interact with Entity Relationship diagrams to construct modes for a probabilistic logic learning system.
result Domain experts can construct effective background knowledge on par with experts using the graphical interface.
CNNs improve signal-background classification in particle physics experiments.
problem Improving accuracy in classifying signal from background in particle physics experiments.
method Extensive convolutional neural architecture search for 2D and 3D image data.
result Achieved high accuracy for signal/background discrimination with CNNs, less parameters than ResNet.
Develops machine learning models for clinical predictions from complex health data.
problem Complexity and uncertainty in medical data.
method Statistical Relational Learning approaches.
result Outstanding performance in medical research and real-world applications.
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. Our algorithm, based on recent theoretical results for ℓ1-$…
Model identifies order splitting and liquidity replenishment as necessary for the square-root law of market impact.
problem Quantifying the square-root law of market impact and identifying its underlying mechanisms.
method Minimal limit-order-book model with heterogeneous interacting agents calibrated against real data. Counterfactual ablation to isolate mechanisms.
result Order splitting and liquidity replenishment are necessary for the square-root law of market impact.