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Abstract How can the NGO–NGO interactions of Global South organizations be better understood and improved? Global South nongovernmental organizations (NGOs) have been frequently missing from the overall advocacy network. When they are included, global South organizations are less likely to take on brokerage roles that may help them build connections and resources across communities. Instead, if they are included, Global South NGOs often are relegated to less powerful, intra-community brokerage roles. Drawing on Deloffre and Quack’s framework (Chapter 1, this volume), Chapter 6 examines the top Global South NGOs that have been able to overcome exclusionary structures and forge inter-community brokerage connections to other NGOs. A deeper look at these organizations and the structures where they are embedded can help to gain insights into the transformative nature of NGO–NGO interactions. The chapter finds that certain country, community, and organizational factors help some Global South NGOs develop connections outside of their immediate community. A focus on these factors may help innovation and protect against a civil society backlash. 

Loan behavior modeling is crucial in financial engineering. In particular, predicting loan prepayment based on large-scale historical time series data of massive customers is challenging. Existing approaches, such as logistic regression or nonparametric regression, could only model the direct relationship between the features and the prepayments. Motivated by extracting the hidden states of loan behavior, we propose the smoothing spline state space (QuadS) model based on a hidden Markov model with varying transition and emission matrices modeled by smoothing splines. In contrast to existing methods, our method benefits from capturing the loans’ unobserved state transitions, which not only increases prediction performances but also provides more interpretability. The overall model is learned by EM algorithm iterations, and within each iteration, smoothing splines are fitted with penalized least squares. Simulation studies demonstrate the effectiveness of the proposed method. Furthermore, a real-world case study using loan data from the Federal National Mortgage Association illustrates the practical applicability of our model. The QuadS model not only provides reliable predictions but also uncovers meaningful, hidden behavior patterns that can offer valuable insights for the financial industry. 

Conical intersections often control the reaction products of photochemical processes and occur when two electronic potential energy surfaces intersect. Theory predicts that the conical intersection will result in a geometric phase for a wavepacket on the ground potential energy surface, and although conical intersections have been observed experimentally, the geometric phase has not been directly observed in a molecular system. Here we use a trapped atomic ion system to perform a quantum simulation of a conical intersection. The ion’s internal state serves as the electronic state, and the motion of the atomic nuclei is encoded into the motion of the ions. The simulated electronic potential is constructed by applying state-dependent optical forces to the ion. We experimentally observe a clear manifestation of the geometric phase using adiabatic state preparation followed by motional state measurement. Our experiment shows the advantage of combining spin and motion degrees for quantum simulation of chemical reactions. 

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. 

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 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.