Launching a Robust Backdoor Attack under Capability Constrained Scenarios

As deep neural networks continue to be used in critical domains, concerns over their security have emerged. Deep learning models are vulnerable to backdoor attacks due to the lack of transparency. A poisoned backdoor model may perform normally in routine environments, but exhibit malicious behavior when the input contains a trigger. Current research on backdoor attacks focuses on improving the stealthiness of triggers, and most approaches require strong attacker capabilities, such as knowledge of the model structure or control over the training process. These attacks are impractical since in most cases the attacker's capabilities are limited. Additionally, the issue of model robustness has not received adequate attention. For instance, model distillation is commonly used to streamline model size as the number of parameters grows exponentially, and most of previous backdoor attacks failed after model distillation; the image augmentation operations can destroy the trigger and thus disable the backdoor. This study explores the implementation of black-box backdoor attacks within capability constraints. An attacker can carry out such attacks by acting as either an image annotator or an image provider, without involvement in the training process or knowledge of the target model's structure. Through the design of a backdoor trigger, our attack remains effective after model distillation and image augmentation, making it more threatening and practical. Our experimental results demonstrate that our method achieves a high attack success rate in black-box scenarios and evades state-of-the-art backdoor defenses.Comment: 9 pages, 6 figure

Non-Markovian dynamics of mixed-state geometric phase of dissipative qubits

We investigate the geometric phase of a two-level atom (qubit) coupled to a bosonic reservoir with Lorentzian spectral density, and find that for the non-Markovian dynamics in which rotating-wave approximation (RWA) is performed, geometric phase has a $\pi$-phase jump at the nodal point. However, the exact result without RWA given by hierarchy equation of motion method shows that there is no such a phase jump or nodal structure in geometric phase. Thus our results demonstrate that the counter-rotating terms significantly contribute to the geometric phase in multi-mode Hamiltonian under certain circumstances.Comment: 9 pages, 6 figure

Spin Squeezing under Non-Markovian Channels by Hierarchy Equation Method

We study spin squeezing under non-Markovian channels, and consider an ensemble of $N$ independent spin-1/2 particles with exchange symmetry. Each spin interacts with its own bath, and the baths are independent and identical. For this kind of open system, the spin squeezing under decoherence can be investigated from the dynamics of the local expectations, and the multi-qubit dynamics can be reduced into the two-qubit one. The reduced dynamics is obtained by the hierarchy equation method, which is a exact without rotating-wave and Born-Markov approximation. The numerical results show that the spin squeezing displays multiple sudden vanishing and revival with lower bath temperature, and it can also vanish asymptotically.Comment: 7 pages, 4 figure

Comparative genomic analysis of pleurotus species reveals insights into the evolution and coniferous utilization of Pleurotus placentodes

Pleurotus placentodes (PPL) and Pleurotus cystidiosus (PCY) are economically valuable species. PPL grows on conifers, while PCY grows on broad-leaved trees. To reveal the genetic mechanism behind PPL’s adaptability to conifers, we performed de novo genome sequencing and comparative analysis of PPL and PCY. We determined the size of the genomes for PPL and PCY to be 36.12 and 42.74 Mb, respectively, and found that they contain 10,851 and 15,673 protein-coding genes, accounting for 59.34% and 53.70% of their respective genome sizes. Evolution analysis showed PPL was closely related to P. ostreatus with the divergence time of 62.7 MYA, while PCY was distantly related to other Pleurotus species with the divergence time of 111.7 MYA. Comparative analysis of carbohydrate-active enzymes (CAZYmes) in PPL and PCY showed that the increase number of CAZYmes related to pectin and cellulose degradation (e.g., AA9, PL1) in PPL may be important for the degradation and colonization of conifers. In addition, geraniol degradation and peroxisome pathways identified by comparative genomes should be another factors for PPL’s tolerance to conifer substrate. Our research provides valuable genomes for Pleurotus species and sheds light on the genetic mechanism of PPL’s conifer adaptability, which could aid in breeding new Pleurotus varieties for coniferous utilization

Identifying and analyzing pointer misuses for sophisticated memory-corruption exploit diagnosis

Software exploits are one of the major threats to internet security. To quickly respond to these attacks, it is critical to automatically diagnose such exploits and find out how they circumvent existing defense mechanisms

Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations

Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. Meanwhile, state-of-the-art statistical methods were developed to make the best use of high marker density genotype data. In this study, 14 traits from four data sets of three species (maize, cattle, and pig) and five influential factors that affect the prediction accuracy were evaluated, including marker density (from 1 to ~600 k), statistical method (GBLUP-A, GBLUP-AD, and BayesR), minor allele frequency (MAF), heritability, and genetic architecture. Results indicate that in the GBLUP method, higher marker density leads to a higher prediction accuracy. In contrast, BayesR method needs more Monte Carlo Markov Chain (MCMC) iterations to reach the convergence and get reliable prediction values. BayesR outperforms GBLUP in predicting high or medium heritability trait that affected by one or several genes with large effects, while GBLUP performs similarly or slightly better than BayesR in predicting low heritability trait that controlled by a large amount of genes with minor effects. Prediction accuracy of trait with complex genetic architecture can be improved by increasing the marker density. Interestingly, for simple traits that controlled by one or several genes with large effects, higher marker density can cause a lower prediction accuracy if the QTN is included, but leads to a higher prediction accuracy if the QTN is excluded. The quantity of genetic markers with low MAF would not significantly affect the prediction accuracy of GBLUP, but results in a bad prediction accuracy performance of BayesR method. Compared with GBLUP-A, GBLUP-AD didn't show any advantages in capturing the non-additive variance for the traits with high heritability. The factors that affected prediction accuracy are discussed in this study and indicate that a combination of either GBLUP or BayesR method with moderate marker density and favorable polymorphism single nucleotide polymorphisms (SNPs) (~25 k SNPs) would always produce a good and stable prediction accuracy with acceptable breeding and computational costs

Clonal Expansion of Lgr5-Positive Cells from Mammalian Cochlea and High-Purity Generation of Sensory Hair Cells

Death of cochlear hair cells, which do not regenerate, is a cause of hearing loss in a high percentage of the population. Currently, no approach exists to obtain large numbers of cochlear hair cells. Here, using a small-molecule approach, we show significant expansion (>2,000-fold) of cochlear supporting cells expressing and maintaining Lgr5, an epithelial stem cell marker, in response to stimulation of Wnt signaling by a GSK3β inhibitor and transcriptional activation by a histone deacetylase inhibitor. The Lgr5-expressing cells differentiate into hair cells in high yield. From a single mouse cochlea, we obtained over 11,500 hair cells, compared to less than 200 in the absence of induction. The newly generated hair cells have bundles and molecular machinery for transduction, synapse formation, and specialized hair cell activity. Targeting supporting cells capable of proliferation and cochlear hair cell replacement could lead to the discovery of hearing loss treatments.United States. National Institutes of Health (DE-013023)United States. National Institutes of Health (DC-007174)United States. National Institutes of Health (DC-013909)United States. National Institutes of Health (RR-00168

Genome-Wide Association Study Reveals Candidate Genes for Growth Relevant Traits in Pigs

Improvement of the growth rate is a challenge in the pig industry, the Average Daily Gain (ADG) and Days (AGE) to 100 kg are directly related to growth performance. We performed genome-wide association study (GWAS) and genetic parameters estimation for ADG and AGE using the genomic and phonemic from four breed (Duroc, Yorkshire, Landrace, and Pietrain) populations. All analyses were performed by a multi-loci GWAS model, FarmCPU. The GWAS results of all four breeds indicate that five genome-wide significant SNPs were associated with ADG, and the nearby genomic regions explained 4.08% of the genetic variance and 1.90% of the phenotypic variance, respectively. For AGE, six genome-wide significant SNPs were detected, and the nearby genomic regions explained 8.09% of the genetic variance and 3.52% of phenotypic variance, respectively. In total, nine candidate genes were identified to be associated with growth and metabolism. Among them, TRIB3 was reported to associate with pig growth, GRP, TTR, CNR1, GLP1R, BRD2, HCRTR2, SEC11C, and ssc-mir-122 were reported to associate with growth traits in human and mouse. The newly detected candidate genes will advance the understanding of growth related traits and the identification of the novel variants will suggest a potential use in pig genomic breeding programs