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Conceptual design is the foundational stage of a design process that translates ill-defined design problems into low-fidelity design concepts and prototypes through design search, creation, and integration. In this stage, product shape design is one of the most paramount aspects. When applying deep learning-based methods to product shape design, two major challenges exist: (1) design data exhibit in multiple modalities and (2) an increasing demand for creativity. With recent advances in deep learning of cross-modal tasks (DLCMTs), which can transfer one design modality to another, we see opportunities to develop artificial intelligence (AI) to assist the design of product shapes in a new paradigm. In this paper, we conduct a systematic review of the retrieval, generation, and manipulation methods for DLCMT that involve three cross-modal types: text-to-3D shape, text-to-sketch, and sketch-to-3D shape. The review identifies 50 articles from a pool of 1341 papers in the fields of computer graphics, computer vision, and engineering design. We review (1) state-of-the-art DLCMT methods that can be applied to product shape design and (2) identify the key challenges, such as lack of consideration of engineering performance in the early design phase that need to be addressed when applying DLCMT methods. In the end, we discuss the potential solutions to these challenges and propose a list of research questions that point to future directions of data-driven conceptual design. 

Vicinal diamines are privileged scaffolds in medicine, agrochemicals, catalysis, and other fields. While significant advancements have been made in diamination of olefins, diamination of allenes is only sporadically explored. Furthermore, direct incorporation of acyclic and cyclic alkyl amines onto unsaturated π systems is highly desirable and important, but problematic for many previously reported amination reactions including the diamination of olefins. Herein, we report a modular and practical diamination of allenes, which offers efficient syntheses of β,γ-diamino carboxylates and sulfones. This reaction features broad substrate scope, excellent functional group tolerability, and scalability. Experimental and computational studies support an ionic reaction pathway initiated with a nucleophilic addition of the in situ formed iodoamine to the electron deficient allene substrate. An iodoamine activation mode via a halogen bond with a chloride ion was revealed to substantially increase the nucleophilicity of the iodoamine and lower the activation energy barrier for the nucleophilic addition step.

 

Short tandem repeats (STRs) represent an important class of genetic variation that can contribute to phenotypic differences. Although millions of single nucleotide variants (SNVs) and short indels have been identified among wild Caenorhabditis elegans strains, the natural diversity in STRs remains unknown. Here, we characterized the distribution of 31,991 STRs with motif lengths of 1–6 bp in the reference genome of C. elegans . Of these STRs, 27,667 harbored polymorphisms across 540 wild strains and only 9691 polymorphic STRs (pSTRs) had complete genotype data for more than 90% of the strains. Compared with the reference genome, the pSTRs showed more contraction than expansion. We found that STRs with different motif lengths were enriched in different genomic features, among which coding regions showed the lowest STR diversity and constrained STR mutations. STR diversity also showed similar genetic divergence and selection signatures among wild strains as in previous studies using SNVs. We further identified STR variation in two mutation accumulation line panels that were derived from two wild strains and found background-dependent and fitness-dependent STR mutations. We also performed the first genome-wide association analyses between natural variation in STRs and organismal phenotypic variation among wild C. elegans strains. Overall, our results delineate the first large-scale characterization of STR variation in wild C. elegans strains and highlight the effects of selection on STR mutations. 

Abstract We introduce a notion of ‘cross-section continuity’ as a criterion for the viability of definitions of angular momentum, J , at null infinity: If a sequence of cross-sections, , of null infinity converges uniformly to a cross-section , then the angular momentum, J n , on should converge to the angular momentum, J , on . The Dray–Streubel (DS) definition of angular momentum automatically satisfies this criterion by virtue of the existence of a well defined flux associated with this definition. However, we show that the one-parameter modification of the DS definition proposed by Compere and Nichols—which encompasses numerous other alternative definitions—does not satisfy cross-section continuity. On the other hand, we prove that the Chen–Wang–Yau definition does satisfy the cross-section continuity criterion. 

Communities have first-hand knowledge about community issues. This study aims to improve the efficiency of social-technical problem-solving by proposing the concept of "artificial process intelligence," based on the theories of socio-technical decision-making. The technical challenges addressed were channeling the communication between the internal-facing and external-facing 311 categorizations. Accordingly, deep learning models were trained on data from Kansas City's 311 system: (1) Bidirectional Encoder Representations from Transformers (BERT) based classification models that can predict the internal-facing 311 service categories and the city departments that handle the issue; (2) the Balanced Latent Dirichlet Allocation (LDA) and BERT clustering (BLBC) model that inductively summarizes residents' complaints and maps the main themes to the internal-facing 311 service categories; (3) a regression time series model that can predict response and completion time. Our case study demonstrated that these models could provide the information needed for reciprocal communication, city service planning, and community envisioning. Future studies should explore interface design like a chatbot and conduct more research on the acceptance and diffusion of AI-assisted 311 systems. 

Conceptual design is the foundational stage of a design process, translating ill-defined design problems to low-fidelity design concepts and prototypes. While deep learning approaches are widely applied in later design stages for design automation, we see fewer attempts in conceptual design for three reasons: 1) the data in this stage exhibit multiple modalities: natural language, sketches, and 3D shapes, and these modalities are challenging to represent in deep learning methods; 2) it requires knowledge from a larger source of inspiration instead of focusing on a single design task; and 3) it requires translating designers’ intent and feedback, and hence needs more interaction with designers and/or users. With recent advances in deep learning of cross-modal tasks (DLCMT) and the availability of large cross-modal datasets, we see opportunities to apply these learning methods to the conceptual design of product shapes. In this paper, we review 30 recent journal articles and conference papers across computer graphics, computer vision, and engineering design fields that involve DLCMT of three modalities: natural language, sketches, and 3D shapes. Based on the review, we identify the challenges and opportunities of utilizing DLCMT in 3D shape concepts generation, from which we propose a list of research questions pointing to future research directions.