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.
New adaptive SGD algorithms for federated learning over physical channels.
Generative model learns wireless channel distributions efficiently.
Paper proposes online learning for MIMO channel estimation using neural networks.
Theory proposes neural networks can be initialized for optimal information transmission.
A new method detects hidden driving forces in systems with multiple observables.
The paper improves alignment methods for deep neural networks using geometric and spectral analysis.
Paper develops ML-based PLA verifiers that operate like the likelihood test.
The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. T…
ReQuestNet simplifies 5G channel estimation with a unified model.
The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver. Here we review a data-driven framework to symbol detection design which combines machine learning (ML…
DyFEn simulates blockchain for fee setting in payment channels.
From the viewpoint of physical-layer authentication, spoofing attacks can be foiled by checking channel state information (CSI). Existing CSI-based authentication algorithms mostly require a deep knowledge of the channel to deliver decent performance. In this paper, we investigate CSI-based authenticators that can spar…
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
Study shows physical drift affects put-call parity enforcement, not just option payoffs.
A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We com…
Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynam…
Proposes a secure communication method independent of eavesdropper's decoder.
Study on statistical estimation over Gaussian MAC, comparing analog and digital schemes.
Deep learning (DL) based autoencoder has shown great potential to significantly enhance the physical layer performance. In this paper, we present a DL based autoencoder for interference channel. Based on a characterization of a k-user Gaussian interference channel, where the interferences are classified as different le…
This paper considers probabilistic estimation of a low-rank matrix from non-linear element-wise measurements of its elements. We derive the corresponding approximate message passing (AMP) algorithm and its state evolution. Relying on non-rigorous but standard assumptions motivated by statistical physics, we characteriz…
Bayesian model improves BCI performance for ALS users.
Modeling supply chain disruptions from climate hazards with adaptive firms.
TIMeSynC combines financial service interactions for intent prediction.
Paper introduces REED for noncoherent OTA-FL, reducing latency without phase alignment.
We reformulate wealth taxation using Fokker-Planck equations to ensure tax neutrality.
In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying t…
In many real-world systems, information can be transmitted in two qualitatively different ways: by copying or by transformation. Copying occurs when messages are transmitted without modification, e.g., when an offspring receives an unaltered copy of a gene from its parent. Transformation occurs when messages are modifi…
This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node. We train DNNs such that the cross entropy loss is directl…
Multilayer (or deep) networks are powerful probabilistic models based on multiple stages of a linear transform followed by a non-linear (possibly random) function. In general, the linear transforms are defined by matrices and the non-linear functions are defined by information channels. These models have gained great p…
The liquid shape between two vertical parallel plates in a gravity field due to capillary forces is studied. When the physical system achieves its mechanical equilibrium, the capillary surface has mean curvature proportional to its height above a horizontal reference plane and it meets the vertical walls in a prescribe…
Proposes qIS for quantum generative models, extending classical inception score.
For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenge…
Estimates support in distributions with sampling artifacts and errors.
Attention mechanism is a hot spot in deep learning field. Using channel attention model is an effective method for improving the performance of the convolutional neural network. Squeeze-and-Excitation block takes advantage of the channel dependence, selectively emphasizing the important channels and compressing the rel…
CN normalizes channels for better time series model performance.
Proposes dynamic channel pruning during neural network training.
A new DVAE architecture improves channel estimation by incorporating temporal correlations.
Generative diffusion models improve channel sampling from limited data.
BestChanID identifies the channel with maximal capacity using training sequences.
A privacy-preserving method for transmitting data over a wiretap channel using generative networks.
We present a definition of discrete channel surfaces in Lie sphere geometry, which reflects several properties for smooth channel surfaces. Various sets of data, defined at vertices, on edges or on faces, are associated with a discrete channel surface that may be used to reconstruct the underlying particular discrete L…
VAE leverages MMSE channel estimation with data-driven modeling.
Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG signals. Spatial information contained in the placement of the electrodes can be exploited to accurately detect artifacts. However, when few…
New DL algorithm estimates OFDM channels without pilots.
MGLM models all possible language channel factorizations for improved multilingual generation.
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. Label learning is to let semantic channels match Shannon's channels and label selection is to let Shannon's channels match semantic channels. The Channel Matching (CM) algorithm is provided f…
Proposes GPCA module for channel attention in CNNs using Gaussian processes.
SOR-Mamba improves Mamba for robust time series forecasting by minimizing channel order bias.