748 research outputs found
t-Deletion-s-Insertion-Burst Correcting Codes
Motivated by applications in DNA-based storage and communication systems, we
study deletion and insertion errors simultaneously in a burst. In particular,
we study a type of error named -deletion--insertion-burst (-burst
for short) which is a generalization of the -burst error proposed by
Schoeny {\it et. al}. Such an error deletes consecutive symbols and inserts
an arbitrary sequence of length at the same coordinate. We provide a
sphere-packing upper bound on the size of binary codes that can correct a
-burst error, showing that the redundancy of such codes is at least
. For , an explicit construction of binary -burst
correcting codes with redundancy is given. In
particular, we construct a binary -burst correcting code with redundancy
at most , which is optimal up to a constant.Comment: Part of this work (the (t,1)-burst model) was presented at ISIT2022.
This full version has been submitted to IEEE-IT in August 202
Facial Expression Recognition using Vanilla ViT backbones with MAE Pretraining
Humans usually convey emotions voluntarily or involuntarily by facial
expressions. Automatically recognizing the basic expression (such as happiness,
sadness, and neutral) from a facial image, i.e., facial expression recognition
(FER), is extremely challenging and attracts much research interests. Large
scale datasets and powerful inference models have been proposed to address the
problem. Though considerable progress has been made, most of the state of the
arts employing convolutional neural networks (CNNs) or elaborately modified
Vision Transformers (ViTs) depend heavily on upstream supervised pretraining.
Transformers are taking place the domination of CNNs in more and more computer
vision tasks. But they usually need much more data to train, since they use
less inductive biases compared with CNNs. To explore whether a vanilla ViT
without extra training samples from upstream tasks is able to achieve
competitive accuracy, we use a plain ViT with MAE pretraining to perform the
FER task. Specifically, we first pretrain the original ViT as a Masked
Autoencoder (MAE) on a large facial expression dataset without expression
labels. Then, we fine-tune the ViT on popular facial expression datasets with
expression labels. The presented method is quite competitive with 90.22\% on
RAF-DB, 61.73\% on AfectNet and can serve as a simple yet strong ViT-based
baseline for FER studies.Comment: 3 page
Continuous chromatographic processes with a small number of columns: Comparison of simulated moving bed with Varicol, PowerFeed, and ModiCon
The Simulated Moving Bed process and its recent extensions called Varicol, PowerFeed and ModiCon are studied, in the case where a small number of columns are used, i.e. from three to five. A multiobjective optimization approach, using genetic algorithms and a detailed model of the multicolumn chromatographic process, is applied to optimize each process separately, and allow for comparison of the different operating modes. The non-standard SMB processes achieve better performance than SMB, due to the availability of more degrees of freedom in the operating conditions of the process, namely the way to carry out asynchronous switches for Varicol, and the different flow rates and feed concentration during the switching interval for PowerFeed and for ModiCon, respectively. We also consider the possibility of combining two non-standard operating modes in a new hybrid process, and evaluate also in this case the possible performance. Finally, a critical assessment of the results obtained and of the potential for practical implementation of the different techniques is reporte
Towards interpretable-by-design deep learning algorithms
The proposed framework named IDEAL (Interpretable-by-design DEep learning
ALgorithms) recasts the standard supervised classification problem into a
function of similarity to a set of prototypes derived from the training data,
while taking advantage of existing latent spaces of large neural networks
forming so-called Foundation Models (FM). This addresses the issue of
explainability (stage B) while retaining the benefits from the tremendous
achievements offered by DL models (e.g., visual transformers, ViT) pre-trained
on huge data sets such as IG-3.6B + ImageNet-1K or LVD-142M (stage A). We show
that one can turn such DL models into conceptually simpler,
explainable-through-prototypes ones.
The key findings can be summarized as follows: (1) the proposed models are
interpretable through prototypes, mitigating the issue of confounded
interpretations, (2) the proposed IDEAL framework circumvents the issue of
catastrophic forgetting allowing efficient class-incremental learning, and (3)
the proposed IDEAL approach demonstrates that ViT architectures narrow the gap
between finetuned and non-finetuned models allowing for transfer learning in a
fraction of time \textbf{without} finetuning of the feature space on a target
dataset with iterative supervised methods
Principal Component Analysis of Galaxy Clustering in Hyperspace of Galaxy Properties
Ongoing and upcoming galaxy surveys are providing precision measurements of
galaxy clustering. However a major obstacle in its cosmological application is
the stochasticity in the galaxy bias. We explore whether the principal
component analysis (PCA) of galaxy correlation matrix in hyperspace of galaxy
properties (e.g. magnitude and color) can reveal further information on
mitigating this issue. Based on the hydrodynamic simulation TNG300-1, we
analyze the cross power spectrum matrix of galaxies in the magnitude and color
space of multiple photometric bands. (1) We find that the first principal
component is an excellent proxy of the galaxy deterministic bias
, in that . Here denotes
the -th galaxy sub-sample. is the largest eigenvalue and
is the matter power spectrum. We verify that this relation holds for
all the galaxy samples investigated, down to Mpc. Since
is a direct observable, we can utilize it to design a linear weighting scheme
to suppress the stochasticity in the galaxy-matter relation. For an LSST-like
magnitude limit galaxy sample, the stochasticity can
be suppressed by a factor of \ga 2 at Mpc. This reduces the
stochasticity-induced systematic error in the matter power spectrum
reconstruction combining galaxy clustering and galaxy-galaxy lensing from to at Mpc. (2) We also find that
increases monotonically with and .
quantify the fractional contribution of other
eigenmodes to the galaxy clustering and are direct observables. Therefore the
two provide extra information on mitigating galaxy stochasticity
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels
Segmentation algorithms of medical image volumes are widely studied for many
clinical and research purposes. We propose a novel and efficient framework for
medical image segmentation. The framework functions under a deep learning
paradigm, incorporating four novel contributions. Firstly, a residual
interconnection is explored in different scale encoders. Secondly, four copy
and crop connections are replaced to residual-block-based concatenation to
alleviate the disparity between encoders and decoders, respectively. Thirdly,
convolutional attention modules for feature refinement are studied on all scale
decoders. Finally, an adaptive denoising learning strategy(ADL) based on the
training process from underfitting to overfitting is studied. Experimental
results are illustrated on a publicly available benchmark database of spine
CTs. Our segmentation framework achieves competitive performance with other
state-of-the-art methods over a variety of different evaluation measures
- …