Radiative Neutrino Mass with Z3Z_3 Dark matter: From Relic Density to LHC Signatures

In this work we give a comprehensive analysis on the phenomenology of a specific $\mathbb{Z}_3$ dark matter (DM) model in which neutrino mass is induced at two loops by interactions with a DM particle that can be a complex scalar or a Dirac fermion. Both the DM properties in relic density and direct detection and the LHC signatures are examined in great detail, and indirect detection for gamma-ray excess from the Galactic Center is also discussed briefly. On the DM side, both semi-annihilation and co-annihilation processes play a crucial role in alleviating the tension of parameter space between relic density and direct detection. On the collider side, new decay channels resulting from $\mathbb{Z}_3$ particles lead to distinct signals at LHC. Currently the trilepton signal is expected to give the most stringent bound for both scalar and fermion DM candidates, and the signatures of fermion DM are very similar to those of electroweakinos in simplified supersymmetric models.Comment: 40 pages, 24 figure

The contribution of Alu exons to the human proteome.

BackgroundAlu elements are major contributors to lineage-specific new exons in primate and human genomes. Recent studies indicate that some Alu exons have high transcript inclusion levels or tissue-specific splicing profiles, and may play important regulatory roles in modulating mRNA degradation or translational efficiency. However, the contribution of Alu exons to the human proteome remains unclear and controversial. The prevailing view is that exons derived from young repetitive elements, such as Alu elements, are restricted to regulatory functions and have not had adequate evolutionary time to be incorporated into stable, functional proteins.ResultsWe adopt a proteotranscriptomics approach to systematically assess the contribution of Alu exons to the human proteome. Using RNA sequencing, ribosome profiling, and proteomics data from human tissues and cell lines, we provide evidence for the translational activities of Alu exons and the presence of Alu exon derived peptides in human proteins. These Alu exon peptides represent species-specific protein differences between primates and other mammals, and in certain instances between humans and closely related primates. In the case of the RNA editing enzyme ADARB1, which contains an Alu exon peptide in its catalytic domain, RNA sequencing analyses of A-to-I editing demonstrate that both the Alu exon skipping and inclusion isoforms encode active enzymes. The Alu exon derived peptide may fine tune the overall editing activity and, in limited cases, the site selectivity of ADARB1 protein products.ConclusionsOur data indicate that Alu elements have contributed to the acquisition of novel protein sequences during primate and human evolution

Requirements-driven self-repairing against environmental failures

Self-repairing approaches have been proposed to alleviate the runtime requirements satisfaction problem by switching to appropriate alternative solutions according to the feedback monitored. However, little has been done formally on analyzing the relations between specific environmental failures and corresponding repairing decisions, making it a challenge to derive a set of alternative solutions to withstand possible environmental failures at runtime. To address these challenges, we propose a requirements-driven self-repairing approach against environmental failures, which combines both development-time and runtime techniques. At the development phase, in a stepwise manner, we formally analyze the issue of self-repairing against environmental failures with the support of the model checking technique, and then design a sufficient and necessary set of alternative solutions to withstand possible environmental failures. The runtime part is a runtime self-repairing mechanism that monitors the operating environment for unsatisfiable situations, and makes self-repairing decisions among alternative solutions in response to the detected environmental failures

RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto physical scattering model, we rethink the degradation of real hazy images and propose a phenomenological pipeline considering diverse degradation types. (2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is pre-trained on a large-scale high-quality dataset to obtain the discrete codebook, encapsulating high-quality priors (HQPs). After replacing the negative effects brought by haze with HQPs, the decoder equipped with a novel normalized feature alignment module can effectively utilize high-quality features and produce clean results. However, although our degradation pipeline drastically mitigates the domain gap between synthetic and real data, it is still intractable to avoid it, which challenges HQPs matching in the wild. Thus, we re-calculate the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts. We provide a recommendation to control the matching based on an explainable solution. Users can also flexibly adjust the enhancement degree as per their preference. Extensive experiments verify the effectiveness of our data synthesis pipeline and the superior performance of RIDCP in real image dehazing.Comment: Acceptted by CVPR 202