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Related Articles from SNS
Self-Learning Expression Deformations for Data-Efficient Gaussian Avatars
arXiv:2606.05912v1 Announce Type: new Abstract: Modeling dynamic facial expressions using 3D Gaussian representations remains challenging due to their unstructured nature. Conventional Gaussian avatar pipelines require extensive multiview and sequential expression data, limiting scalability and accessibility. In this work, we introduce Self-Adaptive Gaussian Expression (SAGE), a framework for self-learning expression-induced Gaussian deformations that enables high-fidelity, animatable...
Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression
arXiv:2606.02236v1 Announce Type: new Abstract: Genetic programming (GP) approaches are among the state-of-the-art for symbolic regression, the task of constructing symbolic expressions that fit well with data. To find highly accurate symbolic expressions, both the expression structure and any contained real-valued constants, are important. GP-GOMEA, a modern model-based evolutionary algorithm, is one of the leading algorithms for finding accurate, yet compact expressions.
Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
arXiv:2511.02986v2 Announce Type: replace-cross Abstract: Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression profiles remains a major challenge. This difficulty arises from the count nature of gene expression data and complex latent dependencies among genes. Existing generative models often impose artificial gene orderings or rely on shallow neural network architectures.
Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models
Announce Type: replace Abstract: Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during training, and (2) prompted expression, elicited by explicit prompts. Given their widespread use in value alignment, it is paramount to clearly understand their underlying mechanisms, particularly whether they mostly overlap (as one might expect) or rely on distinct mechanisms. We analyze this largely understudied problem...
Revisiting the expressiveness of metric temporal logic : A tale of "Je t'aime, moi non plus."
Announce Type: replace Abstract: The expressiveness of Metric Temporal Logic (MTL) has been extensively studied throughout the last two decades. In particular, it has been shown that the \emph{interval-based} semantics of MTL is strictly more expressive than the \emph{pointwise} one. These results may suggest that enabling the evaluation of formulae at arbitrary time points \emph{instead of} positions of timed events increases the expressive power of MTL.
Characterization of expression elements for an AAV delivered antibody in nonhuman primates when co-delivered with PD-L1
Successful AAV-expressed antibody therapy for HIV-1 requires broadly neutralizing antibody (bNAbs) concentrations and reduced immune responses to sustain viral suppression without ART. We have previously demonstrated that co-delivery of AAV-expressed PD-L1 reduces immune responses against HIV-1 bNAbs in rhesus macaques. Here we systematically evaluated six AAV9 transgene cassettes encoding 10-1074 with different promoter/intron combinations (CMV, CMV/R, CBA, CASI, CB7, EF1a) across in vitro...
Fat body driver expression report across Drosophila melanogaster tissues and sex
Drosophila melanogaster serves as a valuable model system for advancing our understanding of adipose tissue given its analogous organ systems, conserved metabolic, endocrine, and nutrient-sensing functions, and well-established genetic tools. Among the most widely used genetic tools is the Gal4/UAS system. Several Gal4 driver lines are reported to control expression in the D. melanogaster fat body, but secondary expression sites and responses to physiological changes have not been fully...
On the Recoverability of Causal Relations from Bulk Gene Expression Data
arXiv:2606.00568v2 Announce Type: replace Abstract: Bulk gene expression profiling, which aggregates pooled RNA across cells within a biological sample, remains important in the single-cell era because it is typically less noisy, more sensitive, and more cost-effective than single-cell assays. Accordingly, a growing body of computational methods seeks to recover causal relations among genes from bulk expression data. However, aggregation is a lossy, non-invertible coarsening of the...
Beyond Universality: The GCC-FER Dataset and Culture-Aware Adaptation for Dynamic Facial Expression Recognition
arXiv:2606.07063v1 Announce Type: cross Abstract: Dynamic Facial Expression Recognition (DFER) is a key enabling technology in affective computing, human-computer interaction, and intelligent multimedia systems. Despite the significant influence of cultural nuances on FER performance, most existing FER systems assume that emotional expressions are universally consistent across populations. This variation can be attributed to systematic differences in facial muscle activation patterns across...
Human Genome-Scale Models of Metabolism and Gene Expression Reveal Resource Constraints of Cancer Cell Lines
Genome-scale metabolic models (M-models) provide mechanistic insight into intracellular metabolism by simulating fluxes subject to nutrient and energy resource constraints. However, they cannot account for a major component of resource allocation, since they do not explicitly account for the cost of producing and maintaining enzymes. Genome-scale models of metabolism and gene expression (ME-Models) address this by including gene expression reactions, but these have only been developed for...