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In this work, we study a new image annotation task named Extractive Tags Summarization (ETS). The goal is to extract important tags from the context lying in an image and its corresponding tags. We adjust some state-of-the-art deep learning…

Computer Vision and Pattern Recognition · Computer Science 2020-11-10 Hieu Trong Phung , Anh Tuan Vu , Tung Dinh Nguyen , Lam Thanh Do , Giang Nam Ngo , Trung Thanh Tran , Ngoc C. Lê

This paper considers density evolution for lowdensity parity-check (LDPC) and multi-edge type low-density parity-check (MET-LDPC) codes over the binary input additive white Gaussian noise channel. We first analyze three singleparameter…

Information Theory · Computer Science 2016-05-17 Sachini Jayasooriya , Mahyar Shirvanimoghaddam , Lawrence Ong , Gottfried Lechner , Sarah J. Johnson

One of the main reasons to employ a description logic such as EL or EL++ is the fact that it has efficient, polynomial-time algorithmic properties such as deciding consistency and inferring subsumption. However, simply by adding negation of…

Logic in Computer Science · Computer Science 2019-08-29 Marcelo Finger

Steiner Tree Packing (STP) is a notoriously hard problem in classical complexity theory, which is of practical relevance to VLSI circuit design. Previous research has approached this problem by providing heuristic or approximate algorithms.…

Data Structures and Algorithms · Computer Science 2025-05-15 Niko Hastrich , Kirill Simonov

It is well known that LLM-based systems are data-hungry. Recent LLM-based TTS works typically employ complex data processing pipelines to obtain high-quality training data. These sophisticated pipelines require excellent models at each…

This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate…

Systems and Control · Electrical Eng. & Systems 2021-11-10 Mingzhou Yin , Andrea Iannelli , Mohammad Khosravi , Anilkumar Parsi , Roy S. Smith

Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth limits. Existing approaches transmit overfitted super-resolution (SR) model streams for…

Computer Vision and Pattern Recognition · Computer Science 2026-04-22 Yiying Wei , Hadi Amirpour , Jong Hwan Ko , Christian Timmerer

In this work, we investigate whether machine learning can be leveraged to identify promising states in dynamic programming algorithms, focusing on Elementary Resource Constrained Shortest Path Problems (ERCSPP). More in detail, we solved 41…

Optimization and Control · Mathematics 2026-05-06 Saverio Basso , Matteo Salani

This paper develops a general method for constructing entanglement-assisted quantum low-density parity-check (LDPC) codes, which is based on combinatorial design theory. Explicit constructions are given for entanglement-assisted quantum…

Information Theory · Computer Science 2012-08-28 Yuichiro Fujiwara , David Clark , Peter Vandendriessche , Maarten De Boeck , Vladimir D. Tonchev

Growing number of network devices and services have led to increasing demand for protective measures as hackers launch attacks to paralyze or steal information from victim systems. Intrusion Detection System (IDS) is one of the essential…

Machine Learning · Computer Science 2019-11-27 Hyeokmin Gwon , Chungjun Lee , Rakun Keum , Heeyoul Choi

In this research, we investigate the high-dimensional linear contextual bandit problem where the number of features $p$ is greater than the budget $T$, or it may even be infinite. Differing from the majority of previous works in this field,…

Machine Learning · Statistics 2025-06-27 Junpei Komiyama , Masaaki Imaizumi

The end-to-end TTS, which can predict speech directly from a given sequence of graphemes or phonemes, has shown improved performance over the conventional TTS. However, its predicting capability is still limited by the acoustic/phonetic…

Computation and Language · Computer Science 2019-04-10 Haohan Guo , Frank K. Soong , Lei He , Lei Xie

Echo-State Networks (ESNs) distil a key neurobiological insight: richly recurrent but fixed circuitry combined with adaptive linear read-outs can transform temporal streams with remarkable efficiency. Yet fundamental questions about…

Neural and Evolutionary Computing · Computer Science 2025-07-25 Pradeep Singh , Lavanya Sankaranarayanan , Balasubramanian Raman

Optimization problems such as the NP-complete 3-SAT provide an important benchmark for the difficult task of finding ground-states in strongly correlated many-body systems with rugged energy landscapes. The study of random 3-SAT problems as…

Statistical Mechanics · Physics 2026-05-21 J. Schwardt , J. C. Budich

The rapid adoption of deep learning has increasingly led to data-driven models replacing classical model-based algorithms, even in domains governed by well-understood physical laws. While data-driven models, such as long short-term memory…

Machine Learning · Computer Science 2026-05-20 Sooraj Sunil , Balakumar Balasingam

Generating high-performance code for diverse hardware and application domains is challenging. Functional array programming languages with patterns like map and reduce have been successfully combined with term rewriting to define and explore…

Programming Languages · Computer Science 2022-06-06 Thomas Koehler , Phil Trinder , Michel Steuwer

We present novel randomized approximation schemes for the Edit Distance (ED) problem and the Longest Common Subsequence (LCS) problem that, for any constant $\epsilon>0$, compute a $(1+\epsilon)$-approximation for ED and a…

Data Structures and Algorithms · Computer Science 2026-04-01 Xiao Mao , Aviad Rubinstein

This paper is concerned with construction and structural analysis of both cyclic and quasi-cyclic codes, particularly LDPC codes. It consists of three parts. The first part shows that a cyclic code given by a parity-check matrix in…

Information Theory · Computer Science 2010-12-16 Qin Huang , Qiuju Diao , Shu Lin , Khaled Abdel-Ghaffar

Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e.,\ compute clients) are idle much of the time, waiting for the next generation…

Neural and Evolutionary Computing · Computer Science 2024-01-02 Jason Liang , Hormoz Shahrzad , Risto Miikkulainen

Feature selection is a crucial step in machine learning, especially for high-dimensional datasets, where irrelevant and redundant features can degrade model performance and increase computational costs. This paper proposes a novel…

Neural and Evolutionary Computing · Computer Science 2024-10-30 Azam Asilian Bidgoli , Shahryar Rahnamayan
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