Paper develops efficient algorithms to estimate channel subspace from low-dimensional projections.
problem Estimating channel subspace information from limited projections in JSDM.
method Develops novel algorithms requiring sampling only O(2√M) elements for p-dim beamformer.
result Estimators return a p-dim beamformer with performance comparable to full knowledge.
Enhanced microfluidic sensing platform for particle detection.
problem Electronically acquiring spatially separated particle information in lab-on-a-chip devices.
method Microfluidic CODES combining resistive pulse sensing and code division multiple access.
result Non-orthogonal code waveforms and machine learning for improved multiplexing.
We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to station…
Machine learning improves PMD compensation in multiplexed systems.
problem Improving performance in multiplexed systems with PMD.
method Model-based machine learning parameterizing the Manakov-PMD equation.
result Performance close to PMD-free case achieved with hardware-friendly DBP and PMD compensation.
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than the…
Proposes a new model for clustering multiplex networks with compositional data.
problem Clustering multiplex networks with multiple types of relations and compositional data.
method Multiplex Dirichlet stochastic block model for compositional networks.
result Validated through simulation and applied to international export data.
Paper proposes neural network for efficient MIMO channel estimation and pilot reduction.
problem High overhead from pilot transmission in wideband MIMO systems.
method Neural network architecture for frequency-aware pilot design and channel estimation, with pruning technique.
result Neural network outperforms linear minimum mean square error (LMMSE) estimation.
Proposes CNN-based analog CSI feedback for FDD MIMO-OFDM systems.
problem High CSI feedback overhead in FDD MIMO systems.
method AnalogDeepCMC: maps downlink CSI to uplink channel input, reconstructs channel estimate.
result Significantly improves downlink spectral efficiency and simplifies operation.
New method predicts dynamic relationships in terrorist networks.
problem Dynamic co-evolution of multiplex graphs and nodal attributes in terrorism networks.
method Time-varying stochastic latent factor models with neural network Gaussian processes.
result Superior performance in predicting unobserved dynamic relationships.
New DL algorithm estimates OFDM channels without pilots.
problem Estimating OFDM channels in deep fading conditions.
method Deep learning (DL) for blind channel estimation.
result First theory on MSE performance of DL-based estimator.
Study multiplex networks of financial dependencies to detect financial stress.
problem Detect financial stress by analyzing complex financial data.
method Construct multiplex networks from four layers of financial correlations, analyze structural properties.
result Multiplex structure reveals unique features not seen in single-layer networks.
Model shows cascading failures are more severe in multiplex networks than single-layer networks.
problem Underestimation of risks in single-layer network analyses due to overlooked impact of weak layers.
method Simple model of cascading failure on multiplex networks of weight-heterogeneous layers.
result Multiplex model produces more catastrophic cascading failures than single-layer model.
Paper infers multiplex network structure from social interactions.
problem Challenges in understanding diffusion in social networks due to hidden structure and multiple interaction patterns.
method Proposes Multiplex Diffusion Model (MDM) using multivariate marked Hawkes process and topic model.
result MDM more effectively uncovers multiplex network structure compared to previous methods.
Understanding how spatial configurations of economic activity emerge is important when formulating spatial planning and economic policy. A simple model was proposed by Simon, who assumed that firms grow at a rate proportional to their size, and that new divisions of firms with certain probabilities relocate to other fi…
Unified framework for hyperbolic network embedding considering multiplex interactions.
problem Misleading results from ignoring multiplex interactions in real-world networks.
method Combines multiplex network hyperbolic embedding and community detection using random walk.
result Effective and efficient node embedding across multiplex channels compared to state-of-the-art.
LART detects communities across layers in multiplex networks.
problem Detecting shared communities across layers in multiplex networks.
method Locally Adaptive Random Transitions (LART) using random walks with layer similarity-dependent transition probabilities.
result LART outperforms existing algorithms in detecting communities across layers.
New algorithms detect clusters in multiplex networks.
problem Detecting clusters in networks with multiple layers.
method Network Fusion for Composite Community Extraction (NF-CCE) using non-negative matrix factorization.
result NF-CCE outperforms state-of-the-art methods on various multiplex networks.
New link invariants derived from crossing multiplexing.
problem Creating new link invariants from classical to virtual links.
method Multiplexing crossings to create new virtual link diagrams.
result New link invariants derived from known welded link invariants.
Unified approach for clustering financial multiplex networks.
problem Lack of methods to capture interconnections between assets over time.
method Tensor-based unified local and global clustering coefficients for multiplex networks.
result Unified clustering coefficients effectively describe dependencies between assets over time.
MIM-Reasoner learns seed users for maximizing influence in multiplex networks.
problem Maximizing influence in multiplex networks with multiple layers of social interactions.
method Combining reinforcement learning with probabilistic graphical models.
result Established a theoretical guarantee for MIM-Reasoner's performance.
New model for multiplex networks learns shared structure.
problem Complex network structures not well modeled by existing methods.
method Latent space model for multiplex networks with shared structure.
result Model recovers latent positions with sufficient separation.
Credit risk analysis improved with a joint model for spatial and temporal effects.
problem Predicting borrower's time-to-event with spatial and temporal covariates.
method Spatio-Temporal Joint Model (STJM) using Bayesian hierarchical approach and INLA.
result Spatial effects improve joint model performance, but spatio-temporal interactions have less impact.
DMGI embeds multiplex networks with node attributes without supervision.
problem Existing methods fail to handle node attributes and multiple relation types in multiplex networks.
method Inspired by DGI, DMGI maximizes mutual information between local and global graph representations, integrating node embeddings from multiple graphs.
result DMGI outperforms state-of-the-art methods on various downstream tasks.
This paper optimizes caching and model multiplexing for large model inference.
problem Resource consumption and latency challenges in large model deployment.
method Jointly optimizing a caching algorithm (GDSF or LEC) and a model multiplexer for large model inference.
result Achieves optimal rates in offline and online settings with up to 50x improvement over baseline.
New heuristics for predicting links in multiplex networks.
problem Link prediction in networks with multiple types of connections.
method Proposed a general framework and three families of heuristics.
result Significantly outperformed baseline heuristics for ordinary networks.
Optimizes power allocation for WDM in RoFSO systems.
problem Maximizing total capacity with power and eye safety constraints.
method Model-based Stochastic Dual Gradient algorithm and model-free Primal-Dual Deep Learning algorithm.
result Deep Learning algorithm outperforms average equal power allocation.
New model detects communities in multiplex networks, accounting for layer dependencies.
problem Detecting communities in multiplex networks with layer-specific dependencies.
method Hierarchical Bayesian model with a hierarchical Dirichlet prior and slice sampler.
result Model automatically picks the number of communities at each layer, outperforming single-layer alternatives.
DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.
problem High CSI overhead in massive MIMO systems limits spectral efficiency.
method Deep learning-based fully convolutional neural network with residual layers and entropy coding.
result DeepCMC outperforms state-of-the-art schemes in CSI reconstruction quality for the same compression rate.
Multiplex Network Hawkes model for systemic risk measurement
problem Investigate how contagion in financial networks is affected by different transmission channels
method Multiplex Network Hawkes model
result Sparse contagion pathways, with systemic-risk transmission concentrated in outward flows from a small number of influential institutions
A method to create virtual links from virtual knots and calculate their invariants.
problem Calculating invariants of virtual links constructed from virtual knots.
method Introducing r-multiplexing of virtual knots and using invariants of the constructed r-component virtual links. result Every invariant of the r-component virtual link is an invariant of the original virtual knot. In economic and financial networks, the strength of each node has always an important economic meaning, such as the size of supply and demand, import and export, or financial exposure. Constructing null models of networks matching the observed strengths of all nodes is crucial in order to either detect interesting devi…
MXGNet tackles visual reasoning tasks using graph neural networks.
problem Abstract reasoning, especially in the visual domain, is challenging for AI.
method Combines object-level representations, graph neural networks, and multiplex graphs.
result Achieves state-of-the-art accuracy on Euler Diagram Syllogisms and outperforms state-of-the-art models on RPM datasets.
Coherent Multiplex analyzes real-time wavelet coherence among multiple signals.
problem Identifying and visualizing coherence among multiple time series.
method Fast spectral similarity based on cosine similarity metrics of Fourier-transformed signals and sparse time-frequency wavelet coherence.
result Scalable real-time system for low-latency inference and monitoring of inter-signal relationships.
Study systemic risk in multiplex networks with asymmetric coupling and feedback.
problem Understanding systemic risk in interconnected networks with asymmetric feedback.
method Analytical branching process approximation to calculate systemic risk.
result Systemic risk in multiplex networks is lower than in aggregated networks below a critical coupling strength, but increases above it.
New method preserves GCM spatial dependencies for better climate projections.
problem Systemic biases in GCM output and loss of spatial/temporal dependencies.
method SPECD approach using Vecchia approximation and semi-parametric quantile regression.
result SPECD preserves key marginal and joint distribution properties of precipitation and temperature.
AI improves OFDM receivers for robust real-world communication.
problem Performance gap between simulation and real-world OFDM systems.
method Comparison of AI-aided OFDM receivers and development of SwitchNet receiver.
result SwitchNet receiver adapts to real channels online, improving robustness.
The interbank market is considered one of the most important channels of contagion. Its network representation, where banks and claims/obligations are represented by nodes and links (respectively), has received a lot of attention in the recent theoretical and empirical literature, for assessing systemic risk and identi…
ASTPN improves video-based person re-identification by jointly attending to spatial and temporal features.
problem Video-based person re-identification in surveillance and HCI.
method Joint Spatial and Temporal Attention Pooling Network (ASTPN).
result ASTPN outperforms state-of-the-art methods on multiple datasets.
Proposes KStar Diffuser for kinematics-aware bimanual robotic manipulation.
problem Challenges in applying imitation learning to bimanual robotic tasks.
method Integrates physical robot structure into action prediction using a dynamic spatial-temporal graph and differentiable kinematics.
result Effective generation of kinematics-aware actions in both simulation and real-world environments.
A new proof of an extension theorem with bounded generators.
problem Extension theorems in complex analysis.
method Skoda-type L2 division theorem with bounded generators. result The new division theorem allows α to be 1 in the norm of the datum. A-MMSE uses attention to learn efficient OFDM channel estimation.
problem Accurate OFDM channel estimation requires second-order statistics, which are hard to obtain in practice.
method A-MMSE is a model-based DNN framework that learns linear MMSE filters via Attention Transformer, reducing inference complexity.
result A-MMSE outperforms other methods in normalized MSE across various SNR conditions.
MxPool learns graph features from diverse graphs using a hierarchical structure.
problem Learning graph features from diverse graphs with varying properties and sizes.
method MxPool uses a multiplex structure with multiple graph convolution/pooling networks in a hierarchical learning structure.
result MxPool outperforms state-of-the-art methods on graph classification benchmarks.
Unique convex divisible domain found in Grassmannian.
problem Finding convex divisible domains in flag manifolds.
method Analyzing projective isomorphism and Lie groups.
result Only one convex divisible domain in Grassmannian.
Deep learning improves one-bit OFDM receiver performance.
problem One-bit quantization complicates accurate channel estimation and data detection in OFDM receivers.
method Developed deep neural networks for channel estimation and data detection, using a two-step training policy.
result Deep learning-based designs achieve lower BER than unquantized OFDM at moderate SNRs.
Solves division problem for L. Hörmander's systems.
problem Division problem for L. Hörmander's overdetermined systems.
method Formulates and proves divisibility criterion, coherence theorem.
result Establishes effective divisibility criterion and extends coherence theorem.
EFDM models spatial point processes with variable cardinality using existence variables.
problem Challenges in extending diffusion models to variable-cardinality spatial point processes.
method Existence-field diffusion model (EFDM) that jointly models spatial locations and cardinality without discrete transitions.
result EFDM achieves improved modeling capability on datasets with varying cardinality.
This paper models and evaluates contagion and stabilisation in interconnected financial markets.
problem Understanding and managing contagion and resilience in multilayer financial networks.
method Formulates an interconnected multiplex structure, models contagion mechanism, and designs minimum-cost stabilisation strategies.
result Empirically validated minimum-cost stabilisation strategies for multichannel contagion containment.
Proves divisibility relations for symplectic curve polynomials.
problem Divisibility relations for symplectic curve polynomials.
method New proofs of divisibility relations for Oka and Alexander polynomials of symplectic curves.
result Proves Libgober's divisibility relations for symplectic curves.