Improved track reconstruction using recurrent neural networks.
problem Reconstructing tracks from hits in detectors with many fake hits.
method Combining hit preprocessing and deep neural network in one stage.
result Proposed method is more accurate, faster, and does not require preprocessing.
New deep learning methods improve track reconstruction in particle physics.
problem Reconstructing particle tracks from detector hits in GEM detectors.
method Two-stage approach combining hits preprocessing and deep neural networks.
result Deep neural networks can accurately reconstruct tracks without preprocessing.
Gem theory helps estimate trisection genus of 4-manifolds.
problem Estimating the trisection genus of 4-manifolds.
method Using gem theory, a type of edge-colored graphs dual to colored triangulations.
result Regular genus is an upper bound for trisection genus of closed 4-manifolds.
A-GEM improves lifelong learning efficiency with minimal computational and memory cost.
problem Efficiency in lifelong learning with incremental task exposure.
method Introducing A-GEM, a new version of GEM, with a novel evaluation protocol and metric.
result A-GEM achieves the best trade-off between accuracy and efficiency in lifelong learning benchmarks.
Classifies semi-equivelar gems on surfaces with Euler characteristic -1.
problem Classifying semi-equivelar gems on surfaces with negative Euler characteristic.
method Regular colored graphs representing PL d-manifolds, cyclic sequence of face degrees around vertices. result Identifies 12 types of semi-equivelar gems for surfaces with Euler characteristic -1.
Review of gem theory's interactions with Kirby diagrams and trisections.
problem Representing and understanding PL 4-manifolds.
method Combining gem theory with Kirby diagrams and trisections.
result New gems representing 4-manifolds requiring 3-handles.
This paper classifies semi-equivelar gems on a double torus.
problem Classifying semi-equivelar gems on surfaces with negative Euler characteristic.
method Regular colored graphs representing the double torus, with identical cyclic face degree sequences around each vertex.
result 31 types of semi-equivelar gems on the double torus.
GEM-T generates synthetic tabular data by fitting moments, outperforming neural networks.
problem Generating synthetic tabular data from limited or sensitive real-world data.
method Generative Entropy Maximization (MaxEnt) for tables, capturing nth-order interactions.
result GEM-T matches or exceeds deep neural network approaches in 23 out of 34 datasets.
GEM improves recommendation by capturing complex feature interactions.
problem Capturing complex high-order interaction signals in feature-based recommendation models.
method Integrates graph convolution networks to generate high-order embeddings and combines with FM-based models.
result Significant improvement in recommendation performance over baselines.
Within crystallization theory, two interesting PL invariants for d-manifolds have been introduced and studied, namely {\it gem-complexity} and {\it regular genus}. In the present paper we prove that, for any closed connected PL 4-manifold M, its gem-complexity k(M) and its regular genus $ \mathcal G(M)…
The paper extends trisection theory to non-orientable 4-manifolds using colored triangulations.
problem Extending trisection theory to non-orientable 4-manifolds.
method Using colored triangulations and gems to induce trisections.
result Gem-induced trisections naturally give rise to trisections of closed 4-manifolds.
New schemes improve lifelong learning by balancing old and new tasks.
problem Catastrophic forgetting in deep neural networks when learning multiple tasks.
method Unified optimization perspective of episodic memory based approaches, introducing MEGA-I and MEGA-II schemes.
result Significant improvement in lifelong learning benchmarks, reducing error by up to 18%.
Given an special type of triangulation T for an oriented closed 3-manifold M3 we produce a framed link in S3 which induces the same M3 by an algorithm of complexity O(n2) where n is the number of tetrahedra in T . The special class is formed by the duals of the {\em solvable gems}. These are in practi…
Lower bounds for PL 4-manifolds with boundary are improved.
problem Estimating PL 4-manifolds with boundary.
method Proved inequalities for regular genus and gem-complexity.
result Improved lower bounds for PL 4-manifolds with boundary.
The paper generalizes trisections to compact PL 4-manifolds with boundary and introduces gem-induced trisections.
problem Generalizing trisections to compact PL 4-manifolds with boundary and any 5-colored graph encoding simply-connected 4-manifolds.
method Introducing gem-induced trisections and analyzing their properties for compact PL 4-manifolds with and without boundary.
result Conditions for gem-induced trisections to realize the G-trisection genus and direct determination of the genus from the graph.
GEM learns a manifold for cross-modal data, capturing structure without modality dependence.
problem Modality-specific neural models limit flexibility and custom architecture.
method Casts learning as manifold inference, enforcing coverage, linearity, and isometry.
result GEM learns latent structure across image, shape, audio, and cross-modal domains.
We solve the isomorphism problem for the whole class of Lins-Mandel gems (graphs encoded manifolds). We also present certain homeomorphisms of branched cyclic coverings of two-bridge hyperbolic links. As a consequence, we prove that, in in a wide subset of interesting cases, the isomorphism conditions for Lins-Mandel g…
We describe an algorithm to subdivide automatically a given set of PL n-manifolds (via coloured triangulations or, equivalently, via crystallizations) into classes whose elements are PL-homeomorphic. The algorithm, implemented in the case n=4, succeeds to solve completely the PL-homeomorphism problem among the catalogu…
The paper studies special crystallizations of 4-manifolds to minimize certain PL-invariants.
problem Minimizing combinatorially defined PL-invariants in crystallizations of compact 4-manifolds.
method Analysis of semi-simple and weak semi-simple crystallizations to minimize regular genus, Gurau degree, gem-complexity, and trisection genus.
result An original theorem on the minimization of PL-invariants for compact 4-manifolds with weak semi-simple crystallizations.
New model solves complex SDEs with high-dimensional spatial and stochastic spaces.
problem Solving SDEs with high-dimensional spatial and stochastic spaces.
method Physics-informed deep generative model (sPI-GeM) combining PI-BasisNet and PI-GeM.
result Scalable solution for high-dimensional SDE problems.
Paper analyzes a generalized EM algorithm for Gaussian mixtures in control systems.
problem Parametric distribution-based clustering in unsupervised learning.
method Proposes a generalized EM (GEM) algorithm for Gaussian mixture models, analyzing its convergence properties using control theory.
result GEM algorithm can be understood as a linear time-invariant system with feedback nonlinearity.
PHom-GeM uses topological features to assess generative models.
problem Generative models produce chaotic distributions during training.
method Persistent Homology for Generative Models (PHom-GeM) minimizes an objective function between true and reconstructed distributions.
result PHom-GeM is a topological distance measure for generative models.
Minimal crystallizations bound for 3-manifolds with boundary.
problem Bounding the gem-complexity of 3-manifolds with boundary.
method Proving bounds on gem-complexity and using crystallization properties.
result Sharp bounds for gem-complexity of 3-manifolds with boundary.
Graph Energy Matching improves generation quality for molecular graphs.
problem Discrete energy-based models struggle with efficient and high-quality sampling for graph generation.
method Inspired by transport-map optimization, Graph Energy Matching learns a permutation-invariant potential energy to guide sampling.
result GEM matches or surpasses discrete diffusion baselines on molecular graph benchmarks.
This is part 2 of a 3-part article where we provide an O(n2)-algorithm to produce a surgery presentation of a 3-manifold induced by a gem with a resolution. In this part we produce a sequence of colored simplicial 2-complexes which are inverses and dual to the sequence of gems produced in the first part. The refinem…
We study model evaluation and model selection from the perspective of generalization ability (GA): the ability of a model to predict outcomes in new samples from the same population. We believe that GA is one way formally to address concerns about the external validity of a model. The GA of a model estimated on a sampl…
The idea of computing Matveev complexity by using Heegaard decompositions has been recently developed by two different approaches: the first one for closed 3-manifolds via crystallization theory, yielding the notion of Gem-Matveev complexity; the other one for compact orientable 3-manifolds via generalized Heegaard dia…
COF-PAC converges with novel critic and learning method.
problem Convergent off-policy actor-critic with function approximation.
method Two-timescale approach with Gradient Emphasis Learning (GEM).
result First provably convergent COF-PAC with linear critics and nonlinear actor.
The notion of Gem-Matveev complexity has been introduced within crystallization theory, as a combinatorial method to estimate Matveev's complexity of closed 3-manifolds; it yielded upper bounds for interesting classes of such manifolds. In this paper we extend the definition to the case of non-empty boundary and prove …
We extend to dimension n≥3 the concept of ρ-pair in a coloured graph and we prove the existence theorem for minimal rigid crystallizations of handle-free, closed n-manifolds.
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on non-corrupted measurements while controlling th…
In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on non-corrupted measurements while controlling th…
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a …
GEM detects malicious accounts using adaptive embeddings from heterogeneous graphs.
problem Detecting malicious accounts on a leading mobile payment platform.
method Adaptive learning of discriminative embeddings from heterogeneous account-device graphs with attention mechanism for node importance.
result GEM consistently outperforms competitive methods in detecting malicious accounts.
HeMPPCAT improves PCA for data with varying noise.
problem PCA's suboptimal performance on data with heterogeneous noise.
method HeMPPCAT uses a GEM algorithm to estimate factors, means, and noise variances.
result Improved factor estimates and clustering accuracy compared to MPPCA.
Adversarial perturbations fool deepfake detectors with high accuracy.
problem Improving deepfake detection accuracy against adversarial attacks.
method Used adversarial perturbations and two defenses: Lipschitz regularization and Deep Image Prior (DIP).
result Deepfake detectors achieved 27% accuracy on perturbed images, compared to 95% on unperturbed.
Distributed asynchronous SGD has become widely used for deep learning in large-scale systems, but remains notorious for its instability when increasing the number of workers. In this work, we study the dynamics of distributed asynchronous SGD under the lens of Lagrangian mechanics. Using this description, we introduce …
This paper studies concept drift detectors for financial time series.
problem Improving accuracy on financial time series with concept drifts.
method Three simple concept drift detectors tailored to financial time series.
result Two of the detectors are as effective as state-of-the-art detectors.
From a pseudo-triangulation with n tetrahedra T of an arbitrary closed orientable connected 3-manifold (for short, {\em a 3D-space}) M3, we present a gem J′, inducing $\IS^3$, with the following characteristics: (a) its number of vertices is O(n); (b) it has a set of p pairwise disjoint couples of vertices …
Graph networks improve particle reconstruction in irregular detectors.
problem Handling irregular particle-detector geometries in particle reconstruction.
method Introduce distance-weighted graph network architectures (GarNet, GravNet layers) for irregular geometry detectors.
result The proposed graph networks provide equally performing or less resource-demanding solutions compared to existing methods.
In this paper, deep neural network (DNN) is utilized to improve the belief propagation (BP) detection for massive multiple-input multiple-output (MIMO) systems. A neural network architecture suitable for detection task is firstly introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed based on two m…
ATSDLN adapts to time series data for anomaly detection.
problem Challenges in selecting and optimizing anomaly detectors for time series data.
method Adaptive Time Series Detector Learning Network (ATSDLN) that selects and optimizes detectors and parameters.
result ATSDLN outperforms other methods in anomaly detection across various datasets.
New method uses eigenspectrum to optimize object detector architectures.
problem Understanding the effects of ImageNet pre-training on object detectors.
method Analysis of eigenspectrum dynamics of feature maps in object detectors.
result Object detectors trained from scratch and ImageNet pre-trained models behave differently.
New method uses neural networks to estimate parameters without needing detector simulations.
problem Estimating parameters in high-energy physics with detector effects.
method Two-level fitting approach: SRGN (Simulation-level fit based on Reweighting Generator-level events with Neural networks).
result Demonstrated using simulated datasets, SRGN can estimate parameters without detector effects.
We introduce a Bayesian defect detector to facilitate the defect detection on the motion blurred images on rough texture surfaces. To enhance the accuracy of Bayesian detection on removing non-defect pixels, we develop a class of reflected non-local prior distributions, which is constructed by using the mode of a distr…
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper p…
TomOpt optimizes muon detector designs using differentiable programming.
problem Designing efficient particle detectors for muon tomography.
method Differentiable programming for muon interaction modeling, inference, and optimisation.
result Demonstrated end-to-end differentiable and inference-aware optimisation of particle physics instruments.
DCSO dynamically selects top-performing base detectors for outlier ensembles.
problem Challenges in selecting and combining outlier scores from different detectors.
method DCSO dynamically selects top-performing base detectors based on local k-nearest neighbors.
result DCSO provides consistent performance improvement over static combination approaches.