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Plants have evolved a unique and conserved developmental program that enables the conversion of leaves into floral organs. Elegant genetic and molecular work has identified key regulators of flower meristem identity. However, further understanding of flower meristem specification has been hampered by redundancy and by pleiotropic effects. The KNOXI transcription factor SHOOT MERISTEMLESS (STM) is a well-characterized regulator of shoot apical meristem maintenance. Arabidopsis thaliana stm loss-of-function mutants arrest shortly after germination; therefore, the knowledge on later roles of STM in later processes, including flower development, is limited. Here, we uncover a role for STM in the specification of flower meristem identity. Silencing STM in the APETALA1 (AP1) expression domain in the ap1-4 mutant background resulted in a leafy-flower phenotype, and an intermediate stm-2 allele enhanced the flower meristem identity phenotype of ap1-4. Transcriptional profiling of STM perturbation suggested that STM activity affects multiple floral fate genes, among them the F-box protein-encoding gene UNUSUAL FLORAL ORGANS (UFO). In agreement with this notion, stm-2 enhanced the ufo-2 floral fate phenotype, and ectopic UFO expression rescued the leafy flowers in genetic backgrounds with compromised AP1 and STM activities. This work suggests a genetic mechanism that underlies the activity of STM in the specification of flower meristem identity.

VERNALIZATION INSENSITIVE 3-LIKE (VIL) proteins are PHD-finger proteins that recruit the repressor complex Polycomb Repressive Complex 2 (PRC2) to the promoters of target genes. Most known VIL targets are flowering repressor genes. Here, we show that the tomato VIL gene CRAWLING ELEPHANT ( CREL ) promotes differentiation throughout plant development by facilitating the trimethylation of Histone H3 on lysine 27 (H3K27me3). We identified the crel mutant in a screen for suppressors of the simple-leaf phenotype of entire ( e ), a mutant in the AUX/IAA gene ENTIRE/SlIAA9, involved in compound-leaf development in tomato. crel mutants have increased leaf complexity, and suppress the ectopic blade growth of e mutants. In addition, crel mutants are late flowering, and have delayed and aberrant stem, root and flower development. Consistent with a role for CREL in recruiting PRC2, crel mutants show drastically reduced H3K27me3 enrichment at approximately half of the 14,789 sites enriched in wild-type plants, along with upregulation of many underlying genes. Interestingly, this reduction in H3K27me3 across the genome in crel is also associated with gains in H3K27me3 at a smaller number of sites that normally have modest levels of the mark in wild-type plants, suggesting that PRC2 activity is no longer limiting in the absence of CREL. Our results uncover a wide role for CREL in plant and organ differentiation in tomato and suggest that CREL is required for targeting PRC2 activity to, and thus silencing, a specific subset of polycomb targets.

The glaucous appearance of wheat (Triticum aestivum) and barley (Hordeum vulgare) plants, that is the light bluish-gray look of flag leaf, stem, and spike surfaces, results from deposition of cuticular β-diketone wax on their surfaces; this phenotype is associated with high yield, especially under drought conditions. Despite extensive genetic and biochemical characterization, the molecular genetic basis underlying the biosynthesis of β-diketones remains unclear. Here, we discovered that the wheat W1 locus contains a metabolic gene cluster mediating β-diketone biosynthesis. The cluster comprises genes encoding proteins of several families including type-III polyketide synthases, hydrolases, and cytochrome P450s related to known fatty acid hydroxylases. The cluster region was identified in both genetic and physical maps of glaucous and glossy tetraploid wheat, demonstrating entirely different haplotypes in these accessions. Complementary evidence obtained through gene silencing in planta and heterologous expression in bacteria supports a model for a β-diketone biosynthesis pathway involving members of these three protein families. Mutations in homologous genes were identified in the barley eceriferum mutants defective in β-diketone biosynthesis, demonstrating a gene cluster also in the β-diketone biosynthesis Cer-cqu locus in barley. Hence, our findings open new opportunities to breed major cereal crops for surface features that impact yield and stress response.

Leaf morphogenesis and differentiation are highly flexible processes, resulting in a large diversity of leaf forms. The development of compound leaves involves an extended morphogenesis stage compared with that of simple leaves, and the tomato (Solanum lycopersicum) mutant clausa (clau) exposes a potential for extended morphogenesis in tomato leaves. Here, we report that the CLAU gene encodes a MYB transcription factor that has evolved a unique role in compound-leaf species to promote an exit from the morphogenetic phase of tomato leaf development. We show that CLAU attenuates cytokinin signaling, and that clau plants have increased cytokinin sensitivity. The results suggest that flexible leaf patterning involves a coordinated interplay between transcription factors and hormones.

Neural networks have shown remarkable performance in various tasks, yet they remain susceptible to subtle changes in their input or model parameters. One particularly impactful vulnerability arises through the Bit-Flip Attack (BFA), where flipping a small number of critical bits in a model's parameters can severely degrade its performance. A common technique for inducing bit flips in DRAM is the Row-Hammer attack, which exploits frequent uncached memory accesses to alter data. Identifying susceptible bits can be achieved through exhaustive search or progressive layer-by-layer analysis, especially in quantized networks. In this work, we introduce Impactful Bit-Flip Search (IBS), a novel method for efficiently pinpointing and flipping critical bits in full-precision networks. Additionally, we propose a Weight-Stealth technique that strategically modifies the model's parameters in a way that maintains the float values within the original distribution, thereby bypassing simple range checks often used in tamper detection.

Error correction codes (ECC) are crucial for ensuring reliable information transmission in communication systems. Choukroun & Wolf (2022b) recently introduced the Error Correction Code Transformer (ECCT), which has demonstrated promising performance across various transmission channels and families of codes. However, its high computational and memory demands limit its practical applications compared to traditional decoding algorithms. Achieving effective quantization of the ECCT presents significant challenges due to its inherently small architecture, since existing, very low-precision quantization techniques often lead to performance degradation in compact neural networks. In this paper, we introduce a novel acceleration method for transformer-based decoders. We first propose a ternary weight quantization method specifically designed for the ECCT, inducing a decoder with multiplication-free linear layers. We present an optimized self-attention mechanism to reduce computational complexity via codeaware multi-heads processing. Finally, we provide positional encoding via the Tanner graph eigendecomposition, enabling a richer representation of the graph connectivity. The approach not only matches or surpasses ECCT's performance but also significantly reduces energy consumption, memory footprint, and computational complexity. Our method brings transformer-based error correction closer to practical implementation in resource-constrained environments, achieving a 90% compression ratio and reducing arithmetic operation energy consumption by at least 224 times on modern hardware.

Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the relationships between the textual and the visual components of a chart, in order to answer general questions or infer numerical values. Most existing CQA datasets and models are based on simplifying assumptions that often enable surpassing human performance. In this work, we address this outcome and propose a new model that jointly learns classification and regression. Our language-vision setup uses co-attention transformers to capture the complex real-world interactions between the question and the textual elements. We validate our design with extensive experiments on the realistic PlotQA dataset, outperforming previous approaches by a large margin, while showing competitive performance on FigureQA. Our model is particularly well suited for realistic questions with out-of-vocabulary answers that require regression.

The task of Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. However, current CoIR datasets are orders of magnitude smaller compared to other vision and language (V&L) datasets. Additionally, some of these datasets have noticeable issues, such as queries containing redundant modalities. To address these shortcomings, we introduce the Large Scale Composed Image Retrieval (LaSCo) dataset, a new CoIR dataset which is ten times larger than existing ones. Pre-training on our LaSCo, shows a noteworthy improvement in performance, even in zero-shot. Furthermore, we propose a new approach for analyzing CoIR datasets and methods, which detects modality redundancy or necessity, in queries. We also introduce a new CoIR baseline, the Cross-Attention driven Shift Encoder (CASE). This baseline allows for early fusion of modalities using a cross-attention module and employs an additional auxiliary task during training. Our experiments demonstrate that this new baseline outperforms the current state-of-the-art methods on established benchmarks like FashionIQ and CIRR.

Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. However, existing image retrieval approaches typically address the case of a single query-to-image round, and the use of chats for image retrieval has been mostly overlooked. In this work, we introduce ChatIR: a chat-based image retrieval system that engages in a conversation with the user to elicit information, in addition to an initial query, in order to clarify the user's search intent. Motivated by the capabilities of today's foundation models, we leverage Large Language Models to generate follow-up questions to an initial image description. These questions form a dialog with the user in order to retrieve the desired image from a large corpus. In this study, we explore the capabilities of such a system tested on a large dataset and reveal that engaging in a dialog yields significant gains in image retrieval. We start by building an evaluation pipeline from an existing manually generated dataset and explore different modules and training strategies for ChatIR. Our comparison includes strong baselines derived from related applications trained with Reinforcement Learning. Our system is capable of retrieving the target image from a pool of 50K images with over 78% success rate after 5 dialogue rounds, compared to 75% when questions are asked by humans, and 64% for a single shot text-to-image retrieval. Extensive evaluations reveal the strong capabilities and examine the limitations of CharIR under different settings. Project repository is available at https://github.com/levymsn/ChatIR.