164 research outputs found
Deep Span Representations for Named Entity Recognition
Span-based models are one of the most straightforward methods for named
entity recognition (NER). Existing span-based NER systems shallowly aggregate
the token representations to span representations. However, this typically
results in significant ineffectiveness for long-span entities, a coupling
between the representations of overlapping spans, and ultimately a performance
degradation. In this study, we propose DSpERT (Deep Span Encoder
Representations from Transformers), which comprises a standard Transformer and
a span Transformer. The latter uses low-layered span representations as
queries, and aggregates the token representations as keys and values, layer by
layer from bottom to top. Thus, DSpERT produces span representations of deep
semantics.
With weight initialization from pretrained language models, DSpERT achieves
performance higher than or competitive with recent state-of-the-art systems on
eight NER benchmarks. Experimental results verify the importance of the depth
for span representations, and show that DSpERT performs particularly well on
long-span entities and nested structures. Further, the deep span
representations are well structured and easily separable in the feature space
Efficient Mixed Transformer for Single Image Super-Resolution
Recently, Transformer-based methods have achieved impressive results in
single image super-resolution (SISR). However, the lack of locality mechanism
and high complexity limit their application in the field of super-resolution
(SR). To solve these problems, we propose a new method, Efficient Mixed
Transformer (EMT) in this study. Specifically, we propose the Mixed Transformer
Block (MTB), consisting of multiple consecutive transformer layers, in some of
which the Pixel Mixer (PM) is used to replace the Self-Attention (SA). PM can
enhance the local knowledge aggregation with pixel shifting operations. At the
same time, no additional complexity is introduced as PM has no parameters and
floating-point operations. Moreover, we employ striped window for SA (SWSA) to
gain an efficient global dependency modelling by utilizing image anisotropy.
Experimental results show that EMT outperforms the existing methods on
benchmark dataset and achieved state-of-the-art performance. The Code is
available at https://github. com/Fried-Rice-Lab/EMT.git.Comment: Super-resolution, Long-range attention, Transformer, Localit
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images
Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with
computed tomography (CT) is considered the primary solution for detecting some
cancers, such as lung cancer and melanoma. Automatic segmentation of tumors in
PET/CT images can help reduce doctors' workload, thereby improving diagnostic
quality. However, precise tumor segmentation is challenging due to the small
size of many tumors and the similarity of high-uptake normal areas to the tumor
regions. To address these issues, this paper proposes a
localization-to-segmentation framework (L2SNet) for precise tumor segmentation.
L2SNet first localizes the possible lesions in the lesion localization phase
and then uses the location cues to shape the segmentation results in the lesion
segmentation phase. To further improve the segmentation performance of L2SNet,
we design an adaptive threshold scheme that takes the segmentation results of
the two phases into consideration. The experiments with the MICCAI 2023
Automated Lesion Segmentation in Whole-Body FDG-PET/CT challenge dataset show
that our method achieved a competitive result and was ranked in the top 7
methods on the preliminary test set. Our work is available at:
https://github.com/MedCAI/L2SNet.Comment: 7 pages,3 figure
A new result on observer-based sliding mode control design for a class of uncertain Ito^ stochastic delay systems
© 2017 The Franklin Institute This paper develops a new observer-based sliding mode control (SMC) scheme for a general class of Ito^ stochastic delay systems (SDS). The key merit of the presented scheme lies in its simplicity and integrity in design process of the traditional sliding mode observer (SMO) strategy, i.e., the state observer and sliding surface design as well as the associated sliding mode controller synthesis. For guaranteeing to use the scheme, a new LMIs-based criterion is established to ensure the exponential stability of the underlying sliding mode dynamics (SMDs) in mean-square sense with H∞ performance. A bench test example is provided to numerically demonstrate the efficacy of the scheme and illustrate the application procedure for potential readers/users with interest in their ad hoc applications and methodology expansion
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Collaborative filtering (CF) is a widely studied research topic in
recommender systems. The learning of a CF model generally depends on three
major components, namely interaction encoder, loss function, and negative
sampling. While many existing studies focus on the design of more powerful
interaction encoders, the impacts of loss functions and negative sampling
ratios have not yet been well explored. In this work, we show that the choice
of loss function as well as negative sampling ratio is equivalently important.
More specifically, we propose the cosine contrastive loss (CCL) and further
incorporate it to a simple unified CF model, dubbed SimpleX. Extensive
experiments have been conducted on 11 benchmark datasets and compared with 29
existing CF models in total. Surprisingly, the results show that, under our CCL
loss and a large negative sampling ratio, SimpleX can surpass most
sophisticated state-of-the-art models by a large margin (e.g., max 48.5%
improvement in NDCG@20 over LightGCN). We believe that SimpleX could not only
serve as a simple strong baseline to foster future research on CF, but also
shed light on the potential research direction towards improving loss function
and negative sampling. Our source code will be available at
https://reczoo.github.io/SimpleX.Comment: Accepted by CIKM 2021. Code available at
https://reczoo.github.io/Simple
Image Super-Resolution using Efficient Striped Window Transformer
Transformers have achieved remarkable results in single-image
super-resolution (SR). However, the challenge of balancing model performance
and complexity has hindered their application in lightweight SR (LSR). To
tackle this challenge, we propose an efficient striped window transformer
(ESWT). We revisit the normalization layer in the transformer and design a
concise and efficient transformer structure to build the ESWT. Furthermore, we
introduce a striped window mechanism to model long-term dependencies more
efficiently. To fully exploit the potential of the ESWT, we propose a novel
flexible window training strategy that can improve the performance of the ESWT
without additional cost. Extensive experiments show that ESWT outperforms
state-of-the-art LSR transformers, and achieves a better trade-off between
model performance and complexity. The ESWT requires fewer parameters, incurs
faster inference, smaller FLOPs, and less memory consumption, making it a
promising solution for LSR.Comment: SOTA lightweight super-resolution transformer. 8 pages, 9 figures and
6 tables. The Code is available at
https://github.com/Fried-Rice-Lab/FriedRiceLa
Ground clutter mitigation for slow-time MIMO radar using independent component analysis
The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets
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