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.
Paper proposes a new Markov model for efficient PLC system design.
Improved error correction using neural networks and belief propagation.
Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination. Our research shows that BGP announcements that are associated with disruptive updates tend to occur in groups of relatively high frequency, followed by periods …
We investigate large changes, bursts, of the continuous stochastic signals, when the exponent of multiplicativity is higher than one. Earlier we have proposed a general nonlinear stochastic model which can be transformed into Bessel process with known first hitting (first passage) time statistics. Using these results w…
PRGDS models count tensors with sparsity and burstiness.
Paper proposes a new method to handle missing data in medical records using sequential variational autoencoders.
In this paper, we present a deep learning (DL) algorithm for channel estimation in communication systems. We consider the time-frequency response of a fast fading communication channel as a two-dimensional image. The aim is to find the unknown values of the channel response using some known values at the pilot location…
Paper proposes using CNN for stock trading with data normalization.
Optimizes CNNs by directing gradients along output channels.
While the channel capacity reflects a theoretical upper bound on the achievable information transmission rate in the limit of infinitely many bits, it does not characterise the information transfer of a given encoding routine with finitely many bits. In this note, we characterise the quality of a code (i. e. a given en…
New DL algorithm estimates OFDM channels without pilots.
Adaptive neural network improves MIMO detection on real-world channels.
Paper proposes IABF to improve NECST robustness.
Study neural communication systems with bandwidth-limited channels.
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural network…
Proposes dynamic channel pruning during neural network training.
The problem to accurately and parsimoniously characterize random series of events (RSEs) present in the Web, such as e-mail conversations or Twitter hashtags, is not trivial. Reports found in the literature reveal two apparent conflicting visions of how RSEs should be modeled. From one side, the Poissonian processes, o…
Paper proposes online learning for MIMO channel estimation using neural networks.
Inspired by recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded b…
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions b…
New method uses VAEs for blind channel equalization and decoding.
BestChanID identifies the channel with maximal capacity using training sequences.
VAE leverages MMSE channel estimation with data-driven modeling.
In this paper, we consider signal detection algorithms in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel with one source, one relay, and one destination. The existing suboptimal near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector a…
Complex autonomous control systems are subjected to sensor failures, cyber-attacks, sensor noise, communication channel failures, etc. that introduce errors in the measurements. The corrupted information, if used for making decisions, can lead to degraded performance. We develop a framework for using adversarial deep r…
Novel beamforming method reduces errors in wireless networks.
This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption, but makes accurate channel estimation and…
ReQuestNet simplifies 5G channel estimation with a unified model.
Combustion reaction kinetics models are used for the description of a special class of bursty Financial Time Series. The small number of parameters they depend upon enable financial analysts to predict the time as well as the magnitude of the jump of the value of the portfolio. Several Financial Time Series are analyse…
New ML models improve VVLC channel characterization for vehicular OWC.
Improved Granger causality method for dynamic time series data.
Dual U-net models improve multi-channel MRI image reconstruction.
Paper defends deep learning classifiers against channel-aware adversarial attacks.
Paper proposes neural network for efficient MIMO channel estimation and pilot reduction.
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical representation of the Bessel K pro…
We present a new, efficient method for automatically detecting severe conflicts `edit wars' in Wikipedia and evaluate this method on six different language WPs. We discuss how the number of edits, reverts, the length of discussions, the burstiness of edits and reverts deviate in such pages from those following the gene…
Adversaries can fool deep learning modulator classifiers over wireless channels.
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model. To this goal, we develop a theoretical framework based on probabilistic finite-state automata to define novel encoding and decoding schemes that ensur…
A new distillation method transfers channel information from teacher to student.
Estimates support in distributions with sampling artifacts and errors.
This paper considers a transmission control problem in network-coded two-way relay channels (NC-TWRC), where the relay buffers random symbol arrivals from two users, and the channels are assumed to be fading. The problem is modeled by a discounted infinite horizon Markov decision process (MDP). The objective is to find…
Study the tradeoff between signal distortion and human perception over finite channels.
The paper applies communication theory to improve language model reranking.
Federated edge learning improves with CSIT-free model aggregation using RIS.
New method separates and deconvolves signals from single-channel mixtures.
A common approach to analyze a covariate-sample count matrix, an element of which represents how many times a covariate appears in a sample, is to factorize it under the Poisson likelihood. We show its limitation in capturing the tendency for a covariate present in a sample to both repeat itself and excite related ones…
A-MMSE uses attention to learn efficient OFDM channel estimation.
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes. However, this decomposition can fall short in the finite bit-length regime, as it requires non-trivial tuning of hand-…