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About 10,000 papers for “decoding complexity”

Decoding Complexity

Marko Grobelnik · 2025

AI comprises a suite of tools and techniques aimed at managing complexity in diverse ways. Its core advantage lies in identifying both evident and surprising patterns within data, utilizing reasoning techniques to often yield insights surpassing what humans can achieve. Typically, while individuals may excel at specific tasks, it's improbable for one person to match AI's performance across a wide array of activities. This paper aims to explore the multifaceted role of AI in public relations, highlighting known applications, emerging possibilities, and speculative future realities. AI is already facilitating numerous micro scenarios in PR, where specialists determine campaign strategies or responses to specific situations. However, AI's scope extends further. It is anticipated that AI technology will soon manage broad contexts with near real-time response capabilities, surpassing current human abilities. This includes a profound understanding of the world's causal structure—actions and reactions—potentially enhancing or diminishing the uncertainties in PR decision-making.

Cited by 0

Decoding complexity

Hannah Meijs · 2024

Chapter 1 General introduction Chapter 2 A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study Chapter 3 A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS Chapter 4 A deep learning derived transdiagnostic EEG signature indexing hypoarousal and impulse control: Implications for treatment prediction in ADHD and MDD Chapter 5 General discussion References English Summary Nederlandse samenvatting Impact Paragraph Curriculum Vitae List of Publications Conference Contribution and other Presentations Dankwoord(ADHD), with remission to methylphenidate in children with ADHD in a sex-specific manner, and with remission to antidepressant medication in adults with a major depressive disorder in a drug-specific manner.Our results demonstrate the value of the RDoC approach in psychiatry research for the discovery of biomarkers with diagnostic and treatment prediction capacities.

Cited by 0Open access

Complexity and Decoding

Stanislav Bulygin, Ruud Pellikaan, Xin-Wen Wu · Cambridge University Press eBooks · 2017

Cited by 0

Low Complexity Turbo Decoding

Peter Haase, Hermann Rohling · 2002

In this paper a truncated turbo decoding with reduced computational complexity is proposed. In the course of iterative decoding, bits are detected that will be changed very unlikely in the current iteration step. These bits are excluded from decoding (in the current iteration step) which yields a turbo decoding with much lower complexity while the loss of bit error rate (BER) is quiet small.

Cited by 0

Decoding Complexity of Aging

Kritika Raj · Cell & Developmental Biology · 2012

Aging, the process of growing old, is characterized by the gradual deterioration of normal cellular functions, leading to a steady decline in the biological, physical and psychological abilities. It is a phenomenon which is genetically determined and environmentally modulated [1]. It also increases the susceptibility for a number of complex diseases, regardless of whether or not they are ultimately responsible for the death of the individual developing them. Aging activates some irreversible series of biological changes that inevitably result in death of the organism. Although, the causes of these changes may be entirely different in different cases implying no common mechanism, yet they often imply a mutual element of descent. Therefore, aging is a most common yet mysterious aspect of biological studies, even after being a subject of interest to humans since the beginning of recorded history.

Cited by 9Open access

Decoding Complexity Study II.

L. Rudolph · 1977

Abstract : This report presents the most recent results of an investigation into the complexity of decoding error-correcting codes and the development of efficient and practical decoding techniques. The most important result has been the discovery of a new general mathematical framework in which digital decoding and analog demodulation become special cases of a more general class of decoding-demodulation functions. Although we have only just begun to explore the many possibilities opened up by this discovery, results of practical importance have already been produced, including an optimum soft-decision symbol-by-symbol decoding algorithm for linear codes whose complexity varies inversely with code rate. (Author)

Cited by 0

2017 Annual Report: Decoding Complexity

Issue Lab (Candid) · 2017

Consider one of the cell's most basic functions: replication and division. Without this fundamental process, none of us multicellular organisms would exist. But then consider the sheer number of parts involved. Three billion proteins, 30 billion lipids, 50 million RNAs, two meters of mission-critical DNA—all being duplicated in less than a day. Biological systems, even small-scale ones, are stunningly complex.The only way we will ever make sense of this complexity is to reveal its organizing principles. At the Allen Institute, we are creating models that can understand how brain circuits will behave, predict where parts of a cell will be during its various stages, and untangle how our cells communicate to build entire organisms. We are also setting foundational standards in the form of scienti?c tools and resources—including gene edited stem cell and mouse lines—and working toward a periodic table of cell types in the brain.Our big, team and open science approach has given us a unique ability to pursue these projects at a large and exciting scale. We hope that our efforts to decode biology's complexity, and to freely share what we discover and create, will drive scienti?c progress in labs and institutions around the globe.

Cited by 0

Decoding the complexity of metastasis

Yingcheng Wu, Tiancheng Zhang, Xiaoming Zhang et al. · Cancer Biology and Medicine · 2022

Cancer metastasis remains one of the most confounding questions in oncology[1][1],[2][2]. Although current cutting-edge techniques enable very early detection of tumors, profiling whether a tumor has already begun to spread and where it has attempted to colonize remains a major hurdle. Indeed,

Cited by 8Open access

Reduced-complexity lattice spherical decoding

Asma Mejri, Ghaya Rekaya-Ben Othman · 2015

Lattice sequential decoders based on a spherical search region, such as the Sphere Decoder and the SB-Stack decoder, implement a tree-search strategy to find the ML solution while visiting only the lattice points that belong to a sphere of a predefined radius. Their computational complexity depends then critically on the choice of the initial sphere radius. We propose in this work novel initial sphere radius selection methods for spherical-region based sequential lattice decoders and show through simulations the complexity reduction allowed by such methods when the Sphere Decoder is used while maintaining ML performance.

Cited by 1

Complexity-Constrained D-SCFlip Decoding

Anna Fominykh, Daria Ustinova, Alexey Frolov et al. · IEEE Open Journal of the Communications Society · 2026

Dynamic successive cancellation flip (D-SCFlip) decoding has emerged as a low-complexity alternative to successive cancellation list (SCL) decoding for polar codes, bridging the performance gap between conventional successive cancellation (SC) and SCL decoding. While D-SCFlip enhances SC decoding with adaptive bit-flip capabilities, its practical efficiency remains constrained by several factors, including the rapid growth of the search space with code dimension, lack of spatial error localization, and uniform resource allocation for correcting bit error decisions. This work presents a complexity-constrained D-SCFlip decoding algorithm that leverages segmented cyclic redundancy check (CRC) as a tool for fine-grained complexity budgeting, enabling dynamic allocation of fixed computational resources based on localized error information. First, we adapt the segmented CRC approach to the D-SCFlip context, replacing the global CRC with localized checks across optimally sized segments to constrain the flip search space to specific error regions. Second, we introduce a computational complexity budgeting mechanism that efficiently allocates decoding resources across segments based on their error susceptibility and correction potential. Our contribution also includes rigorous optimization of segment configuration specifically designed for complexity-constrained decoding. Simulation results demonstrate that our approach achieves better block error rate performance compared to conventional D-SCFlip decoding under equivalent worst-case complexity constraints, with gains of 0.05-0.14 dB across various code rates and lengths.

Cited by 0Open access