88 research outputs found
SoK: Play-to-Earn Projects
Play-to-earn is one of the prospective categories of decentralized
applications. The play-to-earn projects combine blockchain technology with
entertaining games and finance, attracting various participants. While huge
amounts of capital have been poured into these projects, the new crypto niche
is considered controversial, and the traditional gaming industry is hesitant to
embrace blockchain technology. In addition, there is little systematic research
on these projects. In this paper, we delineate play-to-earn projects in terms
of economic & governance models and implementation and analyze how blockchain
technology can benefit these projects by providing system robustness,
transparency, composability, and decentralized governance. We begin by
identifying the participants and characterizing the tokens, which are products
of composability. We then summarize the roadmap and governance model to exposit
there is a transition from centralized governance to decentralized governance.
We also classify the implementation of the play-to-earn projects with different
extents of robustness and transparency. Finally, we discuss the security &
societal challenges for future research in terms of possible attacks, the
economics of tokens, and governance
Temperature-Sensitive Point Selection and Thermal Error Model Adaptive Update Method of CNC Machine Tools
The thermal error of CNC machine tools can be reduced by compensation, where a thermal error model is required to provide compensation values. The thermal error model adaptive update method can correct the thermal error model by supplementing new data, which fundamentally solves the problem of model robustness. Certain problems associated with this method in temperature-sensitive point (TSP) selection and model update algorithms are investigated in this study. It was found that when the TSPs were selected frequently, the selection results may be different, that is, there was a variability problem in TSPs. Further, it was found that the variability of TSPs is mainly due to some problems with the TSP selection method, (1) the conflict between the collinearity among TSPs and the correlation of TSPs with thermal error is ignored, (2) the stability of the correlation is not considered. Then, a stable TSP selection method that can choose more stable TSPs with less variability was proposed. For the model update algorithm, this study proposed a novel regression algorithm which could effectively combine the new data with the old model. It has advantages for a model update, (1) fewer data are needed for the model update, (2) the model accuracy is greatly improved. The effectiveness of the proposed method was verified by 20 batches of thermal error measurement experiments in the real cutting state of the machine tool
Convex searches for discrete-time Zames-Falb multipliers
In this paper we develop and analyse convex searches for Zames--Falb
multipliers. We present two different approaches: Infinite Impulse Response
(IIR) and Finite Impulse Response (FIR) multipliers. The set of FIR multipliers
is complete in that any IIR multipliers can be phase-substituted by an
arbitrarily large order FIR multiplier. We show that searches in discrete-time
for FIR multipliers are effective even for large orders. As expected, the
numerical results provide the best -stability results in the
literature for slope-restricted nonlinearities. Finally, we demonstrate that
the discrete-time search can provide an effective method to find suitable
continuous-time multipliers.Comment: 12 page
Prompt-based All-in-One Image Restoration using CNNs and Transformer
Image restoration aims to recover the high-quality images from their degraded
observations. Since most existing methods have been dedicated into single
degradation removal, they may not yield optimal results on other types of
degradations, which do not satisfy the applications in real world scenarios. In
this paper, we propose a novel data ingredient-oriented approach that leverages
prompt-based learning to enable a single model to efficiently tackle multiple
image degradation tasks. Specifically, we utilize a encoder to capture features
and introduce prompts with degradation-specific information to guide the
decoder in adaptively recovering images affected by various degradations. In
order to model the local invariant properties and non-local information for
high-quality image restoration, we combined CNNs operations and Transformers.
Simultaneously, we made several key designs in the Transformer blocks
(multi-head rearranged attention with prompts and simple-gate feed-forward
network) to reduce computational requirements and selectively determines what
information should be persevered to facilitate efficient recovery of
potentially sharp images. Furthermore, we incorporate a feature fusion
mechanism further explores the multi-scale information to improve the
aggregated features. The resulting tightly interlinked hierarchy architecture,
named as CAPTNet, despite being designed to handle different types of
degradations, extensive experiments demonstrate that our method performs
competitively to the task-specific algorithms
Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer
By hiding the front-facing camera below the display panel, Under-Display
Camera (UDC) provides users with a full-screen experience. However, due to the
characteristics of the display, images taken by UDC suffer from significant
quality degradation. Methods have been proposed to tackle UDC image restoration
and advances have been achieved. There are still no specialized methods and
datasets for restoring UDC face images, which may be the most common problem in
the UDC scene. To this end, considering color filtering, brightness
attenuation, and diffraction in the imaging process of UDC, we propose a
two-stage network UDC Degradation Model Network named UDC-DMNet to synthesize
UDC images by modeling the processes of UDC imaging. Then we use UDC-DMNet and
high-quality face images from FFHQ and CelebA-Test to create UDC face training
datasets FFHQ-P/T and testing datasets CelebA-Test-P/T for UDC face
restoration. We propose a novel dictionary-guided transformer network named
DGFormer. Introducing the facial component dictionary and the characteristics
of the UDC image in the restoration makes DGFormer capable of addressing blind
face restoration in UDC scenarios. Experiments show that our DGFormer and
UDC-DMNet achieve state-of-the-art performance
Fairly Adaptive Negative Sampling for Recommendations
Pairwise learning strategies are prevalent for optimizing recommendation
models on implicit feedback data, which usually learns user preference by
discriminating between positive (i.e., clicked by a user) and negative items
(i.e., obtained by negative sampling). However, the size of different item
groups (specified by item attribute) is usually unevenly distributed. We
empirically find that the commonly used uniform negative sampling strategy for
pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the
majority item group as negative instances, severely countering group fairness
on the item side. In this paper, we propose a Fairly adaptive Negative sampling
approach (FairNeg), which improves item group fairness via adaptively adjusting
the group-level negative sampling distribution in the training process. In
particular, it first perceives the model's unfairness status at each step and
then adjusts the group-wise sampling distribution with an adaptive momentum
update strategy for better facilitating fairness optimization. Moreover, a
negative sampling distribution Mixup mechanism is proposed, which gracefully
incorporates existing importance-aware sampling techniques intended for mining
informative negative samples, thus allowing for achieving multiple optimization
purposes. Extensive experiments on four public datasets show our proposed
method's superiority in group fairness enhancement and fairness-utility
tradeoff.Comment: Accepted by TheWebConf202
RNA-seq liver transcriptome analysis reveals an activated MHC-I pathway and an inhibited MHC-II pathway at the early stage of vaccine immunization in zebrafish
BACKGROUND: Zebrafish (Danio rerio) is a prominent vertebrate model of human development and pathogenic disease and has recently been utilized to study teleost immune responses to infectious agents threatening the aquaculture industry. In this work, to clarify the host immune mechanisms underlying the protective effects of a putative vaccine and improve its immunogenicity in the future efforts, high-throughput RNA sequencing technology was used to investigate the immunization-related gene expression patterns of zebrafish immunized with Edwardsiella tarda live attenuated vaccine. RESULTS: Average reads of 18.13 million and 14.27 million were obtained from livers of zebrafish immunized with phosphate buffered saline (mock) and E. tarda vaccine (WED), respectively. The reads were annotated with the Ensembl zebrafish database before differential expressed genes sequencing (DESeq) comparative analysis, which identified 4565 significantly differentially expressed genes (2186 up-regulated and 2379 down-regulated in WED; p<0.05). Among those, functional classifications were found in the Gene Ontology database for 3891 and in the Kyoto Encyclopedia of Genes and Genomes database for 3467. Several pathways involved in acute phase response, complement activation, immune/defense response, and antigen processing and presentation were remarkably affected at the early stage of WED immunization. Further qPCR analysis confirmed that the genes encoding the factors involved in major histocompatibility complex (MHC)-I processing pathway were up-regulated, while those involved in MHC-II pathway were down-regulated. CONCLUSION: These data provided insights into the molecular mechanisms underlying zebrafish immune response to WED immunization and might aid future studies to develop a highly immunogenic vaccine against gram-negative bacteria in teleosts
DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
The Class Incremental Semantic Segmentation (CISS) extends the traditional
segmentation task by incrementally learning newly added classes. Previous work
has introduced generative replay, which involves replaying old class samples
generated from a pre-trained GAN, to address the issues of catastrophic
forgetting and privacy concerns. However, the generated images lack semantic
precision and exhibit out-of-distribution characteristics, resulting in
inaccurate masks that further degrade the segmentation performance. To tackle
these challenges, we propose DiffusePast, a novel framework featuring a
diffusion-based generative replay module that generates semantically accurate
images with more reliable masks guided by different instructions (e.g., text
prompts or edge maps). Specifically, DiffusePast introduces a dual-generator
paradigm, which focuses on generating old class images that align with the
distribution of downstream datasets while preserving the structure and layout
of the original images, enabling more precise masks. To adapt to the novel
visual concepts of newly added classes continuously, we incorporate class-wise
token embedding when updating the dual-generator. Moreover, we assign adequate
pseudo-labels of old classes to the background pixels in the new step images,
further mitigating the forgetting of previously learned knowledge. Through
comprehensive experiments, our method demonstrates competitive performance
across mainstream benchmarks, striking a better balance between the performance
of old and novel classes.Comment: e.g.: 13 pages, 7 figure
- …