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.
Hierarchical FL reduces latency in HCNs by sharing model updates.
A novel method for efficient CDRL over wireless networks.
ChemCPA predicts cellular responses to novel drugs using transfer learning.
We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The interactions between the cells, most notably due to their coupled interference render this optimization prohibitively complex. Utilizing a single agent rei…
Paper uses RL to optimize SFC deployment and VNF management in NFV networks.
Method learns cell interaction rules from individual trajectories.
Solves challenges of drone communication in cellular networks.
Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and model…
Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here we present OhmNet, a hierarchy-aware unsupervised node feature le…
This paper detects anomalies in cellular network traffic using hybrid methods.
We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through ex…
LOT framework embeds high-dimensional cell data into interpretable Euclidean space.
Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environme…
Optimizes antenna tilt for better QoS in cellular networks.
Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches hav…
In this paper we generalize cellular algebras by allowing different partial orderings relative to fixed idempotents. For these relative cellular algebras we classify and construct simple modules, and we obtain other characterizations in analogy to cellular algebras. We also give several examples of algebras that are re…
Until recently, transcriptomics was limited to bulk RNA sequencing, obscuring the underlying expression patterns of individual cells in favor of a global average. Thanks to technological advances, we can now profile gene expression across thousands or millions of individual cells in parallel. This new type of data has …
The paper optimizes UAV path and power for QoS in cellular networks.
TrajectoryNet models dynamic cellular trajectories using optimal transport.
CT improves neural network performance on cell complex data.
This article introduces descriptive cellular homology on cell complexes, which is an extension of J.H.C. Whitehead's CW topology. A main result is that a descriptive cellular complex is a topology on fibres in a fibre bundle. An application of two forms of cellular homology is given in terms of the persistence of shape…
The study classifies cellular pseudomanifolds and their properties.
The paper studies Morse theory on manifolds with boundaries, constructing cellular structures and estimating critical points.
Quantum cellular automata form a homology theory.
MoReL models multi-omics data to find hidden molecular interactions.
sgdGMF efficiently estimates generalized matrix factorization models for single-cell RNA sequencing data.
Novel framework predicts cell responses to perturbations using GRNs.
Proposes CCCVAE for better single-cell clustering with cell-cell communication.
We present a construction of cellular BF theory (in both abelian and non-abelian variants) on cobordisms equipped with cellular decompositions. Partition functions of this theory are invariant under subdivisions, satisfy a version of the quantum master equation, and satisfy Atiyah-Segal-type gluing formula with respect…
Optimizes natural frequencies of cellular composites with various microstructures.
LUNAR uses cellular automata for real-time data classification in fast streams.
New method learns cell trajectories and network interactions from single-cell data.
For leveled spatial graphs, we find a surface embedding that allows cellular embedding.
We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are to 1) use RL to solve performance tuning problems in an indoor cellular network for voic…
We give examples of harmonic cellular maps between negatively curved manifolds which are not diffeomorphisms but are homotopic to diffeomorphisms.
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell …
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an adaptation property relies heavily on cellular neuromodulation,…
The notion of cellular stratified spaces was introduced in a joint work of the author with Basabe, González, and Rudyak [1009.1851] with the aim of constructing a cellular model of the configuration space of a sphere. In particular, it was shown that the classifying space (order complex) of the face poset of a totally …
Study immersions of punctured 4-manifolds for quantum automata applications.
The paper extends Gaussian processes to model complex interactions in cellular complexes.
In this work we develop a cellular equivariant homology functor and apply it to prove an equivariant Euler-Poincare formula and an equivariant Lefschetz theorem.
For enabling automatic deployment and management of cellular networks, the concept of self-organizing network (SON) was introduced. SON capabilities can enhance network performance, improve service quality, and reduce operational and capital expenditure (OPEX/CAPEX). As an important component in SON, self-healing is de…
Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long…
IH-GAN models cellular structures accurately and improves structural performance.
New findings on hyperbolicity of augmented links in thickened surfaces.
scICML integrates multi-omics data from single cells using co-clustering.
DVNet efficiently segments large neurovascular datasets using skip connections.
CURIE uses cellular automata to detect concept drift in data streams.