19 papers · ranked by Valyu relevance
Deval Shah, Tor M. Aamodt
Deep regression networks are widely used to tackle the problem of predicting a continuous value for a given input. Task-specialized approaches for training regression networks have shown significant improvement over generic approaches, such as direct regression. More recently, a generic approach based on regression by…
Carlos Gómez‐Rodríguez, Diego Roca, David Vilares
We introduce an encoding for syntactic parsing as sequence labeling that can represent any projective dependency tree as a sequence of 4-bit labels, one per word. The bits in each word's label represent (1) whether it is a right or left dependent, (2) whether it is the outermost (left/right) dependent of its parent…
Yuqing Gu, Chang He, Yuqing Zhang, Li Lin + 2 more
Anticounterfeiting labels based on physical unclonable functions (PUFs), as one of the powerful tools against counterfeiting, are easy to generate but difficult to duplicate due to inherent randomness. Gap-enhanced Raman tags (GERTs) with embedded Raman reporters show strong intensity enhancement and ultra-high…
Chanwut Kittivorawong
into Vega-Lite Authors: ['Chanwut Kittivorawong'] Legible labels should not overlap with other labels and other marks in a chart. When a chart contains a large number of data points, manually positioning these labels for each data point in the chart is a tedious task. A labeling algorithm is necessary to automatically…
Hua Ren, Guang-rong Bai, Tong-tong Chen, Zhen Yue + 1 more
With the rapid development of multimedia technology and the massive accumulation of user data, a huge amount of data is rapidly generated and shared over the network, while the problems of inappropriate data access and abuse persist. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that…
Yao-Yuan Yang, Chih‐Wei Chang, Hsuan-Tien Lin
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing. The methodology has been demonstrated to improve the performance of MLC algorithms…
Jia Lu, Ryan Tsoi, Nan Luo, Yuanchi Ha + 8 more
'Minjun Kwak' 'Yasa Baig' 'Nicole Moiseyev' 'Shari Tian' 'Alison Zhang' 'Neil Zhenqiang Gong' 'Lingchong You'] Title: Summary Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the…
Ann-Kathrin Zaiser, Patric Meyer, Regine Bader
There is evidence that rapid integration of novel associations into cortical networks is possible if associations are acquired through a learning procedure called fast mapping (FM). FM requires precise visual discrimination of sometimes highly similar pictures of a previously unknown and a known item, and linking an…
Qi Zhang, Shan Li, Bin Yu, Qingmei Zhang + 2 more
Multi-label proteins occur in two or more subcellular locations, which play a vital part in cell development and metabolism. Prediction and analysis of multi-label subcellular localization (SCL) can present new angle with drug target identification and new drug design. However, the prediction of multi-label protein SCL…
Jia Lu, Ryan Tsoi, Nan Luo, Yuanchi Ha + 8 more
Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns, combined with machine…
Scott Purdy
Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. In this white paper we describe how to encode data as Sparse Distributed Representations (SDRs) for use in HTM systems. We explain several existing encoders, which…
Le Zhang, Todd B. Krause, Harnimarta Deol, Bipin Pandey + 5 more
Sequence-defined polymers (SDPs) are currently being investigated for use as information storage media. As the number of monomers in the SDPs increases, with a corresponding increase in mathematical base, the use of tandem-MS for de novo sequencing becomes more challenging. In contrast, chain-end degradation routines…
Lorenzo Rosset, Martin Weigt, Francesco Zamponi
Accurately annotating and controlling protein function from sequence data remains a major challenge in protein engineering, especially when functional labels are scarce within large homologous families. Here, we study a two-stage light-supervision strategy for fine-grained functional annotation and label-aware sequence…
Tomasz Krokosz, Jarogniew Rykowski, Małgorzata Zajęcka, Robert Brzoza-Woch + 2 more
'Robert Brzoza-Woch' 'Leszek Rutkowski' 'Amitabh Mishra'] Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages…
Rıza Özçelik, Laura van Weesep, Sarah de Ruiter, Francesca Grisoni
In this work, we introduce peptidy -- a lightweight Python library that facilitates converting peptides (expressed as aminoacid sequences) to numerical representations suited to machine learning. peptidy is free from external dependencies, integrates seamlessly into modern Python environments, and supports a range of…
Jiayi Li, Hong-Sheng Lai, Litian Liang, Shiyi Du + 2 more
Codon sequence design is crucial for generating mRNA sequences with desired functional properties for tasks such as developing mRNA vaccines or gene editing therapies. Yet existing methods lack flexibility and controllability to adapt to various design objectives. We propose a novel machine learning-based framework…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
Authors not listed
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with…
José C. Rodríguez-Rodríguez, Gabriele S. de Blasio, Carmelo R. García, Alexis Quesada-Arencibia
'Alexis Quesada-Arencibia'] This paper expands upon a previous publication and is the natural continuation of an earlier study which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD. MONICOD is distinguished by its high operating speed, running at 200 frames per…