Self-Assembly of DNA-Coated Particles: Experiment, Simulation and Theory

The bottom-up assembly of material architectures with tunable complexity, function, composition, and structure is a long sought goal in rational materials design. One promising approach aims to harnesses the programmability and specificity of DNA hybridization in order to direct the assembly of oligonucleotide-functionalized nano- and micro-particles by tailoring, in part, interparticle interactions. DNA-programmable assembly into three-dimensionally ordered structures has attracted extensive research interest owing to emergent applications in photonics, plasmonics and catalysis and potentially many other areas. Progress on the rational design of DNA-mediated interactions to create useful two-dimensional structures (e.g., structured films), on the other hand, has been rather slow. In this thesis, we establish strategies to engineer a diversity of 2D crystalline arrangements by designing and exploiting DNA-programmable interparticle interactions. We employ a combination of simulation, theory and experiments to predict and confirm accessibility of 2D structural diversity in an effort to establish a rational approach to 2D DNA-mediated particle assembly.We start with the experimental realization of 2D DNA-mediated assembly by decorating micron-sized silica particles with covalently attached single-stranded DNA through a two-step reaction. Subsequently, we elucidate sensitivity and ultimate controllability of DNA-mediated assembly—specifically the melting transition from dispersed singlet particles to aggregated or assembled structures—through control of the concentration of commonly employed nonionic surfactants. We relate the observed tunability to an apparent coupling with the critical micelle temperature in these systems. Also, both square and hexagonal 2D ordered particle arrangements are shown to evolve from disordered aggregates under appropriate annealing conditions defined based upon pre-established melting profiles. Subsequently, the controlled mixing of complementary ssDNA functionality on individual particles (‘multi-flavoring’) as opposed to functionalization of particles with the same type of ssDNA (‘uni-flavoring’) is explored as a possible design handle for tuning interparticle interactions and, thereby, accessing diverse structures. We employ a combination of simulations, theory, and experimental validation toward establishing ‘multi-flavoring’ as a rational design strategy. Firstly, MD simulations are carried out using effective pair potentials to describe interparticle interactions that are representative of different degrees of ssDNA ‘multi-flavoring’. These simulations reveal the template-free assembly of a diversity of 2D crystal polymorphs that is apparently tunable by controlling the relative attractive strengths between like and unlike functionalized particles. The resulting phase diagrams predict conditions (i.e., strengths of relative interparticle interactions) for obtaining crystalline phases with lattice symmetries ranging among square, alternating string hexagonal, random hexagonal, rhombic, honeycomb, and even kagome.Finally, these model findings are translated to experiments, in which binary microparticles are decorated with a tailored mixture of two different complementary ssDNA strands as a straight-forward means to realize tunable particle interactions. Guided by simple statistical mechanics and the detailed MD simulations, ‘multi-flavoring’ and control of solution phase particle stoichiometry resulted in experimental realization of structurally diverse 2D microparticle assemblies consistent with predictions, such as square, pentagonal and hexagonal lattices (honeycomb, kagome). The combined simulation, theory, and experimental findings reveal how control of interparticle interactions via DNA-functionalized particle “multi-flavoring” can lead to an even wider range of accessible colloidal crystal structures. The 2D experiments coupled with the model predictions may be used to provide new fundamental insight into nano- or microparticle assembly in three dimensions

Binary superlattice design by controlling DNA-mediated interactions

Most binary superlattices created using DNA functionalization or other approaches rely on particle size differences to achieve compositional order and structural diversity. Here we study two-dimensional (2D) assembly of DNA-functionalized micron-sized particles (DFPs), and employ a strategy that leverages the tunable disparity in interparticle interactions, and thus enthalpic driving forces, to open new avenues for design of binary superlattices that do not rely on the ability to tune particle size (i.e., entropic driving forces). Our strategy employs tailored blends of complementary strands of ssDNA to control interparticle interactions between micron-sized silica particles in a binary mixture to create compositionally diverse 2D lattices. We show that the particle arrangement can be further controlled by changing the stoichiometry of the binary mixture in certain cases. With this approach, we demonstrate the abil- ity to program the particle assembly into square, pentagonal, and hexagonal lattices. In addition, different particle types can be compositionally ordered in square checkerboard and hexagonal - alternating string, honeycomb, and Kagome arrangements.Comment: 4 figures in the main text. 5 figures in the supplementary informatio

Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise

Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two samplers for imbalanced labels, generating minority-augmented instances with high diversity. It also refines multi-labels at the label-wise granularity, categorizing noisy labels as clean, re-labeled, or ambiguous for robust optimization. Extensive experiments on three benchmark datasets demonstrate that BalanceMix outperforms existing state-of-the-art methods. We release the code at https://github.com/DISL-Lab/BalanceMix.Comment: This paper was accepted at AAAI 2024. We upload the full version of our paper on arXiv due to the page limit of AAA

A Survey of Large Language Models in Finance (FinLLMs)

Large Language Models (LLMs) have shown remarkable capabilities across a wide variety of Natural Language Processing (NLP) tasks and have attracted attention from multiple domains, including financial services. Despite the extensive research into general-domain LLMs, and their immense potential in finance, Financial LLM (FinLLM) research remains limited. This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges. Firstly, we present a chronological overview of general-domain Pre-trained Language Models (PLMs) through to current FinLLMs, including the GPT-series, selected open-source LLMs, and financial LMs. Secondly, we compare five techniques used across financial PLMs and FinLLMs, including training methods, training data, and fine-tuning methods. Thirdly, we summarize the performance evaluations of six benchmark tasks and datasets. In addition, we provide eight advanced financial NLP tasks and datasets for developing more sophisticated FinLLMs. Finally, we discuss the opportunities and the challenges facing FinLLMs, such as hallucination, privacy, and efficiency. To support AI research in finance, we compile a collection of accessible datasets and evaluation benchmarks on GitHub.Comment: More information on https://github.com/adlnlp/FinLLM

An Evidence-Based Decision Support Framework for Clinician Medical Scheduling

In healthcare management, waiting time for consultation is an important measure that has strong associations with patient's satisfaction (i.e., the longer patients wait for consultation, the less satisfied they are). To this end, it is required to optimize medical scheduling for clinicians. A typical approach for deriving the optimized schedules is to perform experiments using discrete event simulation. The existing work has developed how to build a simulation model based on process mining techniques. However, applying this method for outpatient processes straightforwardly, in particular medical scheduling, is challenging: 1) the collected data from electronic health record system requires a series of processes to acquire simulation parameters from the raw data; and 2) even if the derived simulation model fully reflects the reality, there is no systematic approach to deriving effective improvements for simulation analysis, i.e., experimental scenarios. To overcome these challenges, this paper proposes a novel decision support framework for a clinician's schedule using simulation analysis. In the proposed framework, a data-driven simulation model is constructed based on process mining analysis, which includes process discovery, patient arrival rate analysis, and service time analysis. Also, a series of steps to derive the optimal improvement method from the simulation analysis is included in the framework. To demonstrate the usefulness of our approach, we present the case study results with real-world data in a hospital.11Ysciescopu

Customization of IBM Intu’s Voice by Connecting Text-to-Speech Services and a Voice Conversion Network

IBM has recently launched Project Intu, which extends the existing web-based cognitive service Watson with the Internet of Things to provide an intelligent personal assistant service. We propose a voice customization service that allows a user to directly customize the voice of Intu. The method for voice customization is based on IBM Watson’s text-to-speech service and voice conversion model. A user can train the voice conversion model by providing a minimum of approximately 100 speech samples in the preferred voice (target voice). The output voice of Intu (source voice) is then converted into the target voice. Furthermore, the user does not need to offer parallel data for the target voice since the transcriptions of the source speech and target speech are the same. We also suggest methods to maximize the efficiency of voice conversion and determine the proper amount of target speech based on several experiments. When we measured the elapsed time for each process, we observed that feature extraction accounts for 59.7% of voice conversion time, which implies that fixing inefficiencies in feature extraction should be prioritized. We used the mel-cepstral distortion between the target speech and reconstructed speech as an index for conversion accuracy and found that, when the number of target speech samples for training is less than 100, the general performance of the model degrades