We present the rational synthesis of colloidal copper(I) sulfide nanocrystals and demonstrate their application as an active light absorbing component in combination with CdS nanorods to make a solution-processed solar cell with 1.6percent power conversion efficiency on both conventional glass substrates and flexible plastic substrates with stability over a 4 month testing period.

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Energy Sciences (2) Department of Computer Science & Engineering (1) Department of Mathematics (1) Scripps Institution of Oceanography (1) Integrative Oceanography Division (1)

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## Scholarly Works (28 results)

Abstract

From Bloodsucker to Disease-carrier: Mosquito and Chinese Hygienic Modernity

by Yue Wu

Mosquito has been frequently adopted as a metaphor indicating bureaucratic corruption by Chinese literati throughout the imperial history. The representation of mosquitoes is therefore implicated with Confucian moral discourse that lays the cornerstone of socio-political structure in premodern China. However, the literary portrayal of mosquitoes was confronted by a new scientific rendering as the disease-carrier by the end of 19th century with the introduction of Western science in Chinese intelligentsia. While mosquito as a moral vehicle still secured a voice during the Republican era, it disappeared almost entirely after the establishment of People’s Republic of China in 1950s. Through an extensive investigation on classical anthologies, newspapers, journal articles, and Communist Party propagandas, this project traces the shifted image of mosquitoes from late Qing to Maoist era in sight of the rise of scientific discourse. It argues that the transformed perception of mosquitoes was entangled with modern state-building in 20th century China, centralizing on the goal of “hygienic modernity” that connects personal well-being with public welfare. Moreover, while the discourse of science appropriated that of morality in China’s modernization cause, it was exploited in Communist China to justify ideological struggle against class enemies, which eventually extended the violence towards nature’s menace to human sphere.

This dissertation investigates several challenges in artificial intelligence (AI) alignment and reinforcement learning (RL), particularly focusing on applications when only preference feedback is available. Learning from preference feedback has been one central problem across different fields such as ranking, recommendation systems, and social choice theory. Recently, reinforcement learning from human feedback (RLHF) has also shown its strong potential in utilizing weakly supervised human data (preference feedback) and its ability to encode human values into machine learning models accurately. This dissertation aims to comprehensively characterize preference-based statistical learning, focusing on the sample complexity of ranking and preference model estimation and fine-tuning large language models.

The first part of the dissertation explores novel methods for the learning-to-rank problem. I studied learning to rank under the strong stochastic transitivity (SST) condition, a prevalent model without assuming a score for each option. SST assumes that the accuracy of the comparison between two items increases as the disparity in their quality widens. I proposed one of the first adaptive approaches that can effectively aggregate the feedback from different human labelers and illustrated how the relationship between the number of human queries and resulting performance depends on the properties of the human labelers. I further follow up in this direction with my collaborators and provide active ranking algorithms that can work without strong stochastic transitivity. We developed novel algorithms under this practical yet harder setting. Our efficient algorithm requires fewer human queries compared with algorithms designed with stronger assumptions. The algorithm is provably optimal.

The second part focuses on preference learning without transitivity assumption. In reality, humans rarely make consistent comparisons and often demonstrate contradicting preferences such as a loop within the preference relations. I considered the most general setting where there is no transitivity at all. I proposed algorithms that identify the Borda winner, an optimal choice even when a true underlying rank does not exist. I showed the algorithm enjoys minimum regret, a notion that trades between exploration and exploitation. This result sheds light on the fundamental difficulty and cost of recovering human preferences under the fewest assumptions.

The third part also focuses on preference learning without transitivity assumption, but instead considers an alternative definition of learning objective, the von Neumann winner. I first formulate the general preference as a game environment where two players aim to win over each other and then present an algorithmic framework that can solve this game in a self-play manner asymptotically. The algorithm is then extended to the task of fine-tuning large language models and shows remarkable empirical performance.

The methods and techniques discussed in this dissertation cover a full spectrum of different assumptions and settings of preference learning. In each setting, the new algorithms are presented along with theoretical analysis ensuring a tight performance guarantee. Additionally, during the exploration of different settings, new research directions and open questions are identified, which could help promote the research of preference learning in terms of sample efficiency in the future.

Geometry plays a crucial role in machine learning. We study the geometric properties of machine learning problems and use that information to develop new algorithms that are accurate and efficient. We present three works that lie at the intersection of machine learning and geometry, and we hope to promote more research on geometry-inspired machine learning methods.

We first introduce \textit{geometric policy iteration}~(GPI), a new dynamic programming approach for finite Markov decision process. Recently discovered polyhedral structures of the value function for finite discounted Markov decision processes (MDP) shed light on understanding the success of reinforcement learning. We investigate the value function polytope in greater detail and characterize the polytope boundary using a hyperplane arrangement. We further show that the value space is a union of finitely many cells of the same hyperplane arrangement, and relate it to the polytope of the classical linear programming formulation for MDPs. Inspired by these geometric properties, we propose GPI to solve discounted MDPs. GPI updates the policy of a single state by switching to an action that is mapped to the boundary of the value function polytope, followed by an immediate update of the value function. This new update rule aims at a faster value improvement without compromising computational efficiency. Moreover, our algorithm allows asynchronous updates of state values which is more flexible and advantageous compared to traditional policy iteration when the state set is large. We prove that the complexity of GPI achieves the best known bound $\bigO{\frac{|\actions|}{1 - \gamma}\log \frac{1}{1-\gamma}}$ of policy iteration and empirically demonstrate the strength of GPI on MDPs of various sizes.

In the second project, we consider the \emph{integer feasibility problem}, the challenge of deciding whether a system of linear equations and inequalities has a solution with integer values. This is a famous NP-complete problem with applications in many areas of Mathematics and Computer Science. We show that the integer feasibility problem can be transformed into a 3-D tensor game which we call the \textit{Feasibility Game}~(FG). To win the game the player must find a path between the initial state and a final terminal winning state, if one exists. Finding such a winning state is equivalent to solving the integer feasibility problem. The key algebraic ingredient is a Gr\"obner basis of the toric ideal for the underlying axial transportation polyhedron. The Gr\"obner basis can be seen as a set of connecting moves (actions) of the game. We then propose a novel RL approach that trains an agent to predict moves in continuous space to cope with the large size of action space. The continuous move is then projected onto the set of legal moves so that the path always leads to valid states. As a proof of concept we demonstrate in experiments that our agent can play well the simplest version of our game for 2-way tables. Our work highlights the potential to train agents to solve non-trivial mathematical queries through contemporary machine learning methods used to train agents to play games.

% In the third project, we applied persistence-based clustering for microglia segmentation which is a critical problem in biology and immunology. Light microscopy methods have continued to advance allowing for unprecedented analysis of various cell types in tissues including the brain. Although the functional state of some cell types such as microglia can be determined by morphometric analysis, techniques to perform robust, quick, and accurate measurements have not kept pace with the amount of imaging data that can now be generated. Here we present PrestoCell, a novel use of persistence-based clustering to segment cells in light microscopy images, as a customized Python-based tool that leverages the free visualization tool Napari. In evaluating and comparing PrestoCell to several existing tools, including a commercial machine-learning implementation, we demonstrate that PrestoCell produces image segmentations that rival or exceed existing solutions. In particular, our use of cell nuclei information resulted in the ability to correctly segment individual cells that were interacting with one another, increasing the accuracy of the segmentation. These benefits are in addition to the simplified graphically based user refinement of cell masks that does not require expensive commercial software licenses. We further demonstrate that PrestoCell can complete image segmentation in large samples from light sheet microscopy, allowing quantitative analysis of these large datasets. As an open-source program that leverages freely available visualization software, with minimum computer requirements, we believe that PrestoCell can significantly increase the ability of users without data or computer science expertise to perform complex image analysis.

In the third work, we develop \textit{PrestoCell} which is a Python-based topological framework for segmenting objects with complex shapes. The main contribution of this work is the use of persistence-based clustering~(PBC) to generate segmentations that are topologically correct. Specifically, we use PBC to segment microglia whose 0-d homology is 1 (one connected component), and higher-order homology is 0. PBC, as an unsupervised method, is able to generate high-quality clusters that can be easily improved by some post-processing steps. Our framework is able to take as input very large 3D light microscopy imaging data where a single input volume can contain hundreds of microglia and nuclei. We use PBC to quickly generate candidate microglia clusters which are later refined by the coupled nuclei information. We present the machine-generated segmentation in the free visualization tool Napari. In evaluating and comparing PrestoCell to several existing tools, including a commercial machine-learning implementation, we demonstrate that PrestoCell produces image segmentations that rival or exceed existing solutions. In particular, our use of cell nuclei information resulted in the ability to correctly segment individual cells that were interacting with one another, increasing the accuracy of the segmentation. These benefits are in addition to the simplified graphically based user refinement of cell masks that does not require expensive commercial software licenses. We further demonstrate that PrestoCell can complete image segmentation in large samples from light sheet microscopy, allowing quantitative analysis of these large datasets. As an open-source program that leverages freely available visualization software, with minimum computer requirements, we believe that PrestoCell can significantly increase the ability of users without data or computer science expertise to perform complex image analysis.

Understanding the genetic architecture of complex traits is a central goal of modern human genetics.Recent efforts focused on building large-scale biobanks, that collect genetic and trait data on large numbers of individuals, present exciting opportunities for understanding genetic architecture. However, these datasets also pose several statistical and computational challenges. In this dissertation, we consider a series of statistical models that allow us to infer aspects of the genetic architecture of single and multiple traits. Inference in these models is computationally challenging due to the size of the genetic data -- consisting of millions of genetic variants measured across hundreds of thousands of individuals.We propose a series of scalable computational methods that can perform efficient inference in these models and apply these methods to data from the UK Biobank to showcase their utility.

Tsunami-generated acoustic-gravity waves propagate in the atmosphere up to the ionosphere, where they have been observed to have an impact on the total electron content. We simulate numerically the propagation of linear acoustic-gravity waves in an atmosphere with vertically varying stratification and horizontal background winds. Our goal is to quantify the effects of background winds and compressibility of the atmosphere on wave transmission and reflection, and how much energy reaches the lower ionosphere before non-neutral and nonlinear effects become significant. We then extend the simulation to three dimensions to include a curved tsunami wavefront and take into account the cases when the wind is not aligned in the same direction as the tsunami propagation. Next, we consider the time-dependent effects to study the time evolution of acoustic-gravity waves in the atmosphere and their first arrival at the lower ionosphere with the long-term goal of real-time tsunami warning. Finally, the effects of stochastic variations in atmospheric parameters and in background winds are considered.