Noise-Free Score Distillation

Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduce a straightforward interpretation that demystifies the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the distillation of an undesired noise term. Building upon our interpretation, we propose a novel Noise-Free Score Distillation (NFSD) process, which requires minimal modifications to the original SDS framework. Through this streamlined design, we achieve more effective distillation of pre-trained text-to-image diffusion models while using a nominal CFG scale. This strategic choice allows us to prevent the over-smoothing of results, ensuring that the generated data is both realistic and complies with the desired prompt. To demonstrate the efficacy of NFSD, we provide qualitative examples that compare NFSD and SDS, as well as several other methods.Comment: Project page at https://orenkatzir.github.io/nfsd

A comprehensive genome variation map of melon identifies multiple domestication events and loci influencing agronomic traits

Melon is an economically important fruit crop that has been cultivated for thousands of years; however, the genetic basis and history of its domestication still remain largely unknown. Here we report a comprehensive map of the genomic variation in melon derived from the resequencing of 1,175 accessions, which represent the global diversity of the species. Our results suggest that three independent domestication events occurred in melon, two in India and one in Africa. We detected two independent sets of domestication sweeps, resulting in diverse characteristics of the two subspecies melo and agrestis during melon breeding. Genome-wide association studies for 16 agronomic traits identified 208 loci significantly associated with fruit mass, quality and morphological characters. This study sheds light on the domestication history of melon and provides a valuable resource for genomics-assisted breeding of this important crop.info:eu-repo/semantics/acceptedVersio

A comprehensive genome variation map of melon identifies multiple domestication events and loci influencing agronomic traits

Melon is an economically important fruit crop that has been cultivated for thousands of years; however, the genetic basis and history of its domestication still remain largely unknown. Here we report a comprehensive map of the genomic variation in melon derived from the resequencing of 1,175 accessions, which represent the global diversity of the species. Our results suggest that three independent domestication events occurred in melon, two in India and one in Africa. We detected two independent sets of domestication sweeps, resulting in diverse characteristics of the two subspecies melo and agrestis during melon breeding. Genome-wide association studies for 16 agronomic traits identified 208 loci significantly associated with fruit mass, quality and morphological characters. This study sheds light on the domestication history of melon and provides a valuable resource for genomics-assisted breeding of this important crop.This work was supported by funding from the Agricultural Science and Technology Innovation Program (to Yongyang Xu, S.H., Z.Z. and H.W.), the China Agriculture Research System (CARS-25 to Yongyang Xu and H.W.), the Leading Talents of Guangdong Province Program (00201515 to S.H.), the Shenzhen Municipal (The Peacock Plan KQTD2016113010482651 to S.H.), the Dapeng district government, National Natural Science Foundation of China (31772304 to Z.Z.), the Science and Technology Program of Guangdong (2018B020202007 to S.H.), the National Natural Science Foundation of China (31530066 to S.H.), the National Key R&D Program of China (2016YFD0101007 to S.H.), USDA National Institute of Food and Agriculture Specialty Crop Research Initiative (2015-51181-24285 to Z.F.), the European Research Council (ERC-SEXYPARTH to A.B.), the Spanish Ministry of Economy and Competitiveness (AGL2015–64625-C2-1-R to J.G.-M.), Severo Ochoa Programme for Centres of Excellence in R&D 2016–2010 (SEV-2015–0533 to J.G.-M.), the CERCA Programme/Generalitat de Catalunya to J.G.-M. and the German Science Foundation (SPP1991 Taxon-OMICS to H.S.)

Estimating Sizes of Social Networks via Biased Sampling

Online social networks have become very popular in recent years and their number of users is already measured in many hundreds of millions. For various commercial and sociological purposes, an independent estimate of their sizes is important. In this work, algorithms for estimating the number of users in such networks are considered. The proposed schemes are also applicable for estimating the sizes of networks’ sub-populations. The suggested algorithms interact with the social networks via their public APIs only, and rely on no other external information. Due to obvious traffic and privacy concerns, the number of such interactions is severely limited. We therefore focus on minimizing the number of API interactions needed for producing good size estimates. We adopt the abstraction of social networks as undirected graphs and use random node sampling. By counting the number of collisions or non-unique nodes in the sample, we produce a size estimate. Then, we show analytically that the estimate error vanishes with high probability for smaller number of samples than those required by prior-art algorithms. Moreover, although our algorithms are provably correct for any graph, theyexcelwhenappliedtosocial network-likegraphs. The proposed algorithms were evaluated on syntheticas well real social networks such as Facebook, IMDB, and DBLP. Our experiments corroborated the theoretical results, and demonstrated the effectiveness of the algorithms

Estimating Sizes of Social Networks via Biased Sampling

Online social networks have become very popular in recent years and their number of users is already measured in many hundreds of millions. For various commercial and sociological purposes, an independent estimate of their sizes is important. In this work, algorithms for estimating the number of users in such networks are considered. The proposed schemes are also applicable for estimating the sizes of networks’ sub-populations. The suggested algorithms interact with the social networks via their public APIs only, and rely on no other external information. Due to obvious traffic and privacy concerns, the number of such interactions is severely limited. We therefore focus on minimizing the number of API interactions needed for producing good size estimates. We adopt the abstraction of social networks as undirected graphs and use random node sampling. By counting the number of collisions or non-unique nodes in the sample, we produce a size estimate. Then, we show analytically that the estimate error vanishes with high probability for smaller number of samples than those required by prior-art algorithms. Moreover, although our algorithms are provably correct for any graph, they excel when applied to social network-like graphs. The proposed algorithms were evaluated on synthetic as well real social networks such as Facebook, IMDB, and DBLP. Our experiments corroborated the theoretical results, and demonstrated the effectiveness of the algorithms