TabDDPM: Modelling Tabular Data with Diffusion Models

Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some attention in other domains, including speech, NLP, and graph-like data. In this work, we investigate if the framework of diffusion models can be advantageous for general tabular problems, where datapoints are typically represented by vectors of heterogeneous features. The inherent heterogeneity of tabular data makes it quite challenging for accurate modeling, since the individual features can be of completely different nature, i.e., some of them can be continuous and some of them can be discrete. To address such data types, we introduce TabDDPM -- a diffusion model that can be universally applied to any tabular dataset and handles any type of feature. We extensively evaluate TabDDPM on a wide set of benchmarks and demonstrate its superiority over existing GAN/VAE alternatives, which is consistent with the advantage of diffusion models in other fields. Additionally, we show that TabDDPM is eligible for privacy-oriented setups, where the original datapoints cannot be publicly shared.Comment: code https://github.com/rotot0/tab-ddp

Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models

Knowledge distillation methods have recently shown to be a promising direction to speedup the synthesis of large-scale diffusion models by requiring only a few inference steps. While several powerful distillation methods were recently proposed, the overall quality of student samples is typically lower compared to the teacher ones, which hinders their practical usage. In this work, we investigate the relative quality of samples produced by the teacher text-to-image diffusion model and its distilled student version. As our main empirical finding, we discover that a noticeable portion of student samples exhibit superior fidelity compared to the teacher ones, despite the "approximate" nature of the student. Based on this finding, we propose an adaptive collaboration between student and teacher diffusion models for effective text-to-image synthesis. Specifically, the distilled model produces the initial sample, and then an oracle decides whether it needs further improvements with a slow teacher model. Extensive experiments demonstrate that the designed pipeline surpasses state-of-the-art text-to-image alternatives for various inference budgets in terms of human preference. Furthermore, the proposed approach can be naturally used in popular applications such as text-guided image editing and controllable generation.Comment: CVPR2024 camera ready v

Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models

Recent advances in diffusion models enable many powerful instruments for image editing. One of these instruments is text-driven image manipulations: editing semantic attributes of an image according to the provided text description. % Popular text-conditional diffusion models offer various high-quality image manipulation methods for a broad range of text prompts. Existing diffusion-based methods already achieve high-quality image manipulations for a broad range of text prompts. However, in practice, these methods require high computation costs even with a high-end GPU. This greatly limits potential real-world applications of diffusion-based image editing, especially when running on user devices. In this paper, we address efficiency of the recent text-driven editing methods based on unconditional diffusion models and develop a novel algorithm that learns image manipulations 4.5-10 times faster and applies them 8 times faster. We carefully evaluate the visual quality and expressiveness of our approach on multiple datasets using human annotators. Our experiments demonstrate that our algorithm achieves the quality of much more expensive methods. Finally, we show that our approach can adapt the pretrained model to the user-specified image and text description on the fly just for 4 seconds. In this setting, we notice that more compact unconditional diffusion models can be considered as a rational alternative to the popular text-conditional counterparts

USAGE OF INTERVAL CAUSE-EFFECT RELATIONSHIP COEFFICIENTS IN THE QUANTITATIVE MODEL OF STRATEGIC PERFORMANCE

This paper proposes the method to obtain values of the coefficients of cause-effect relationships between strategic objectives in the form of intervals and use them in solving the problem of the optimal allocation of organization’s resources. We suggest taking advantage of the interval analytical hierarchy process for obtaining the ntervals. The quantitative model of strategic performance developed by M. Hell, S. Vidučić and Ž. Garača is employed for finding the optimal resource allocation. The uncertainty originated in the optimization problem as a result of interval character of the cause-effect relationship coefficients is eliminated through the application of maximax and maximin criteria. It is shown that the problem of finding the optimal maximin, maximax, and compromise resource allocation can be represented as a mixed 0-1 linear programming problem. Finally, numerical example and directions for further research are given

Haplotype analysis of APOE intragenic SNPs

BACKGROUND: APOE epsilon4 allele is most common genetic risk factor for Alzheimer\u27s disease (AD) and cognitive decline. However, it remains poorly understood why only some carriers of APOE epsilon4 develop AD and how ethnic variabilities in APOE locus contribute to AD risk. Here, to address the role of APOE haplotypes, we reassessed the diversity of APOE locus in major ethnic groups and in Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) dataset on patients with AD, and subjects with mild cognitive impairment (MCI), and control non-demented individuals. RESULTS: We performed APOE gene haplotype analysis for a short block of five SNPs across the gene using the ADNI whole genome sequencing dataset. The compilation of ADNI data with 1000 Genomes identified the APOE epsilon4 linked haplotypes, which appeared to be distant for the Asian, African and European populations. The common European epsilon4-bearing haplotype is associated with AD but not with MCI, and the Africans lack this haplotype. Haplotypic inference revealed alleles that may confer protection against AD. By assessing the DNA methylation profile of the APOE haplotypes, we found that the AD-associated haplotype features elevated APOE CpG content, implying that this locus can also be regulated by genetic-epigenetic interactions. CONCLUSIONS: We showed that SNP frequency profiles within APOE locus are highly skewed to population-specific haplotypes, suggesting that the ancestral background within different sites at APOE gene may shape the disease phenotype. We propose that our results can be utilized for more specific risk assessment based on population descent of the individuals and on higher specificity of five site haplotypes associated with AD

Studying the effect of modifying additives on the hydration and hardening of cement composites for 3D printing

The development and application of multicomponent multifunctional additives for cement composites is an important research area since the use of such additives allows controlling both the rheological properties of fresh mixtures and the physical and mechanical properties of the hardened composite. In our study, we used several additives, including metakaolin and xanthan gum together with tetrapotassium pyrophosphate and a SiO2 based complex additive, to modify cementitious sand-based materials. We studied the peculiarities of the influence of these additives on the technological characteristics of mixtures (plasticity and shape retention) and the processes of setting, hydration, and hardening of the composite materials. The optimal values of plasticity, for stability, acceleration of hardening were demonstrated by sand-based systems modified with a complex nanosized additive and metakaolin. The hydration products in the such systems are mainly formed from low basic hydroxides. Metakaolin also results in the formation of ettringite. These systems demonstrate the optimal time of the beginning of setting and the maximum strength gain of the modified cementitious sand-based materials at 28 days. The optimal ratio of indicators of plasticity and shape retention of cement mixtures and the strength of composites based on them obtained by using the studied additives allows us to recommend using these additives in the innovative technologies for 3D-build printing