Physical Sciences and Mathematics Commons^™

Advanced Progress In Metal Halide Perovskite Solar Cells: A Review, Judy Fan

Physics Faculty Research

Organic and inorganic hybrid perovskite solar cells (PSCs) have attracted intense attention in the past decades due to their fantastic performance in stability, durability, rapid processing, and high efficiency competitive to Si-based counterparts. The lab-fabricated solar cells based on organolead halide perovskites have reached a record efficiency of 26.1 %, which implies that halide perovskite materials may open a new paradigm for future solar energy innovation. Organometal/inorganic halide perovskites take advantage of tunable bandgaps, engineerable composites/interfaces, and solution-processible methods that render easy integrations of subcells to form tandem and multijunction modules with higher efficiencies potentially beyond the Shockley-Queisser (SQ) limit. …

Building Squares With Optimal State Complexity In Restricted Active Self-Assembly, Robert M. Alaniz, David Caballero, Sonya C. Cirlos, Timothy Gomez, Elize Grizzell, Andrew Rodriguez, Robert Schweller, Armando Tenorio, Tim Wylie

Computer Science Faculty Publications

Tile Automata is a recently defined model of self-assembly that borrows many concepts from cellular automata to create active self-assembling systems where changes may be occurring within an assembly without requiring attachment. This model has been shown to be powerful even with limited assembly size, but many fundamental questions have yet to be explored. Here, we study the state complexity of assembling n×n squares in seeded Tile Automata systems where growth starts from a seed and tiles attach one at a time, similar to the abstract Tile Assembly Model. We provide optimal bounds for three classes of seeded Tile Automata …

(R2054) Convergence Of Lagrange-Hermite Interpolation Using Non-Uniform Nodes On The Unit Circle, Swarnima Bahadur, Sameera Iqram, Varun .

Applications and Applied Mathematics: An International Journal (AAM)

In this research article, we brought into consideration the set of non-uniformly distributed nodes on the unit circle to investigate a Lagrange-Hermite interpolation problem. These nodes are obtained by projecting vertically the zeros of Jacobi polynomial onto the unit circle along with the boundary points of the unit circle on the real line. Explicitly representing the interpolatory polynomial as well as establishment of convergence theorem are the key highlights of this manuscript. The result proved are of interest to approximation theory.

(R2056) Convergence Criteria For Solutions Of A System Of Second Order Nonlinear Differential Equations, Akinwale Olutimo

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we investigate the convergence of solutions of certain nonlinear system of two differential equations using a suitable Lyapunov functional with sufficient conditions to establish our new result. An example is given to demonstrate the effectiveness of the result obtained and geometric argument to show that the solutions of the system are better rapidly converging under the criteria obtained.

(R2064) Analytical Approximations In Short Times Of Exact Operational Solutions To Reaction-Diffusion Problems On Bounded Intervals, Kwassi Anani

Applications and Applied Mathematics: An International Journal (AAM)

This paper aims to provide an exact solution in the Laplace domain and related analytic approximations in short time limits for the class of boundary value problems of the one-dimensional linear parabolic equation with constant coefficients. The problem’s most general form involves a parameterized equation on a bounded interval, with unified specification of the three classical types of boundary conditions: Dirichlet, Neumann, and Robin. Under certain integrability assumptions, we have proven that a unique solution exists in the Laplace domain. This operational solution can be obtained in a closed form by using classical integral transforms. Four distinct cases have been …

Are Sheep And Plants The Future Of Solar? Oberlin College Agrivoltaic Project, Sydney Rosensaft

The Synapse: Intercollegiate science magazine

No abstract provided.

Aurora Borealis — Not Bore-Alis! The Northern Lights’ Ancient Legends And Scientific Wonders, Anadi Purewal-Legha

The Synapse: Intercollegiate science magazine

No abstract provided.

Finding Peace With Puberty: The Importance Of Increasing Puberty-Related Dialogue In Athletics, Amber Borofsky

The Synapse: Intercollegiate science magazine

No abstract provided.

Invasion Control Tangled In Knots: How An Oberlin Student And The Outside World Tackled The Knotweed Problem, Michael E. Harvey

The Synapse: Intercollegiate science magazine

No abstract provided.

The Neural Manifold: Unfolding The Matrix Of Our Brain, James Lee

The Synapse: Intercollegiate science magazine

No abstract provided.

Inside Aphasia: A Deeper Dive Into Brain Injuries' Impact On Language, Keesha Joseph

The Synapse: Intercollegiate science magazine

No abstract provided.

Issue 38

The Synapse: Intercollegiate science magazine

No abstract provided.

Meet The Staff

The Synapse: Intercollegiate science magazine

No abstract provided.

Front Matter

The Synapse: Intercollegiate science magazine

No abstract provided.

Dancing With Dopamine: How Raves Enhance Focus And Increase Wellbeing, Ania Ocasio

The Synapse: Intercollegiate science magazine

No abstract provided.

The Study Of The Polarization Domains Of Mbe-Grown Barium Titanate Thin Films And Nanodots Using Pfm, Mohammad Zamani-Alavijeh

Graduate Theses and Dissertations

Polarization domains are the fundamental elements in ferroelectric materials that contribute to their properties. A natural polarization domain, which possesses a specific polarization direction or state, typically ranges in size from one unit cell to a few micrometers. Therefore, studying polarization domains requires the fabrication of materials and the measurement of ferroelectric properties at the nanoscale. In this study, molecular beam epitaxy (MBE) is employed to fabricate barium titanate (BTO) crystals, and piezoresponse force microscopy (PFM) is used to measure the properties of polarization domains at the nanoscale. In the beginning, this study identifies the reflection of high-energy electron diffraction …

Analysis Of Mitochondrial Dna Sequence Data Demonstrates That Monophyly Of Myotis Occultus Is Complicated By Greater Sampling Of Myotis Lucifugus, Jeffrey M. Lorch, Daniel R. Taylor, Antoinette J. Piaggio

United States Department of Agriculture Wildlife Services: Staff Publications

Abstract

The validity of Myotis occultus as a species unique from Myotis lucifugus has been a source of debate. Most recently, many authorities treat M. occultus as a distinct species, at least in part because a previous study showed that M. occultus and M. l. carissima (the subspecies that occurs in closest geographic proximity to M. occultus) form separate monophyletic clades based on sequences of two mitochondrial genes (cytochrome-b [cytb] and cytochrome oxidase subunit II [COII]). We re-evaluated the phylogenetic relationship between M. occultus and M. lucifugus based on mitochondrial sequences …

Covariance-Based Causal Debiasing For Entity And Relation Extraction, Lin Ren, Yongbin Liu, Yixin Cao, Chunping Ouyang

Research Collection School Of Computing and Information Systems

Joint entity and relation extraction tasks aim to recognize named entities and extract relations simultaneously. Suffering from a variety of data biases, such as data selection bias, and distribution bias (out of distribution, long-tail distribution), serious concerns can be witnessed to threaten the model’s transferability, robustness, and generalization. In this work, we address the above problems from a causality perspective. We propose a novel causal framework called covariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. In particular, the proposed covariance optimizing (COP) minimizes characterizing features’ covariance for alleviating the selection and distribution bias and …

Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Learning-based approaches that learn code representations for software vulnerability detection have been proven to produce inspiring results. However, they still fail to capture complete and precise vulnerability semantics for code representations. To address the limitations, in this work, we propose a learning-based approach namely SnapVuln, which first utilizes multiple vulnerability-specific inter-procedural slicing algorithms to capture vulnerability semantics of various types and then employs a Gated Graph Neural Network (GGNN) with an attention mechanism to learn vulnerability semantics. We compare SnapVuln with state-of-the-art learning-based approaches on two public datasets, and confirm that SnapVuln outperforms them. We further perform an ablation study …

Do Contributing Files Provide Information About Oss Newcomers' Onboarding Barriers?, Felipe Fronchetti, David Shepherd, Igor Wiese, Christoph Treude, Marco Gerosa, Igor Steinmacher

Research Collection School Of Computing and Information Systems

Effectively onboarding newcomers is essential for the success of open source projects. These projects often provide onboarding guidelines in their ‘CONTRIBUTING’ files (e.g., CONTRIBUTING.md on GitHub). These files explain, for example, how to find open tasks, implement solutions, and submit code for review. However, these files often do not follow a standard structure, can be too large, and miss barriers commonly found by newcomers. In this paper, we propose an automated approach to parse these CONTRIBUTING files and assess how they address onboarding barriers. We manually classified a sample of files according to a model of onboarding barriers from the …

Evaluating Transfer Learning For Simplifying Github Readmes, Haoyu Gao, Christoph Treude, Mansooreh Zahedi

Research Collection School Of Computing and Information Systems

Software documentation captures detailed knowledge about a software product, e.g., code, technologies, and design. It plays an important role in the coordination of development teams and in conveying ideas to various stakeholders. However, software documentation can be hard to comprehend if it is written with jargon and complicated sentence structure. In this study, we explored the potential of text simplification techniques in the domain of software engineering to automatically simplify GitHub README files. We collected software-related pairs of GitHub README files consisting of 14,588 entries, aligned difficult sentences with their simplified counterparts, and trained a Transformer-based model to automatically simplify …

Flowpg: Action-Constrained Policy Gradient With Normalizing Flows, Brahmanage Janaka Chathuranga Thilakarathna, Jiajing Ling, Akshat Kumar

Research Collection School Of Computing and Information Systems

Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-critical and resource-allocation related decision making problems. A major challenge in ACRL is to ensure agent taking a valid action satisfying constraints in each RL step. Commonly used approach of using a projection layer on top of the policy network requires solving an optimization program which can result in longer training time, slow convergence, and zero gradient problem. To address this, first we use a normalizing flow model to learn an invertible, differentiable mapping between the feasible action space and the support of a simple distribution on a latent variable, …

A Black-Box Attack On Code Models Via Representation Nearest Neighbor Search, Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu, Xiaofei Xie, Yves Le Traon, Yang Liu

Research Collection School Of Computing and Information Systems

Existing methods for generating adversarial code examples face several challenges: limted availability of substitute variables, high verification costs for these substitutes, and the creation of adversarial samples with noticeable perturbations. To address these concerns, our proposed approach, RNNS, uses a search seed based on historical attacks to find potential adversarial substitutes. Rather than directly using the discrete substitutes, they are mapped to a continuous vector space using a pre-trained variable name encoder. Based on the vector representation, RNNS predicts and selects better substitutes for attacks. We evaluated the performance of RNNS across six coding tasks encompassing three programming languages: Java, …

Kape: Knn-Based Performance Testing For Deep Code Search, Yuejun Guo, Qiang Hu, Xiaofei Xie, Cordy Maxime, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

Code search is a common yet important activity of software developers. An efficient code search model can largely facilitate the development process and improve the programming quality. Given the superb performance of learning the contextual representations, deep learning models, especially pre-trained language models, have been widely explored for the code search task. However, studies mainly focus on proposing new architectures for ever-better performance on designed test sets but ignore the performance on unseen test data where only natural language queries are available. The same problem in other domains, e.g., CV and NLP, is usually solved by test input selection that …

Llm4vis: Explainable Visualization Recommendation Using Chatgpt, Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, Yong Wang

Research Collection School Of Computing and Information Systems

Data visualization is a powerful tool for exploring and communicating insights in various domains. To automate visualization choice for datasets, a task known as visualization recommendation has been proposed. Various machine-learning-based approaches have been developed for this purpose, but they often require a large corpus of dataset-visualization pairs for training and lack natural explanations for their results. To address this research gap, we propose LLM4Vis, a novel ChatGPT-based prompting approach to perform visualization recommendation and return human-like explanations using very few demonstration examples. Our approach involves feature description, demonstration example selection, explanation generation, demonstration example construction, and inference steps. To …

A Comprehensive Evaluation Of Large Language Models On Legal Judgment Prediction, Ruihao Shui, Yixin Cao, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. However, recent disputes over GPT-4’s law evaluation raise questions concerning their performance in real-world legal tasks. To systematically investigate their competency in the law, we design practical baseline solutions based on LLMs and test on the task of legal judgment prediction. In our solutions, LLMs can work alone to answer open questions or coordinate with an information retrieval (IR) system to learn from similar cases or solve simplified multi-choice questions. We show that similar cases and multi-choice options, namely label candidates, included in prompts …

Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

ARINC 653 as the de facto standard of partitioning operating systems has been applied in many safety-critical domains. The multi-core version of ARINC 653, ARINC 653 Part 1-4 (Version 4), provides support for services to be utilized with a module that contains multiple processor cores. Formal specification and analysis of this standard document could provide a rigorous specification and uncover concealed errors in the textual description of service requirements. This article proposes a specification method for concurrency on a multi-core platform using Event-B, and a refinement structure for the complicated ARINC 653 Part 1-4 provides a comprehensive, stepwise refinement-based Event-B …

(Un)Likelihood Training For Interpretable Embedding, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan, Zhijian Hou

Research Collection School Of Computing and Information Systems

Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data. Learning modality agnostic representations in a continuous latent space, however, is often treated as a black-box data-driven training process. It is well known that the effectiveness of representation learning depends heavily on the quality and scale of training data. For video representation learning, having a complete set of labels that annotate the full spectrum of video content for training is highly difficult, if not impossible. These issues, black-box training and dataset bias, make representation learning practically challenging to be deployed for video …

C³: Code Clone-Based Identification Of Duplicated Components, Yanming Yang, Ying Zou, Xing Hu, David Lo, Chao Ni, John C. Grundy, Xin: Xia

Research Collection School Of Computing and Information Systems

Reinventing the wheel is a detrimental programming practice in software development that frequently results in the introduction of duplicated components. This practice not only leads to increased maintenance and labor costs but also poses a higher risk of propagating bugs throughout the system. Despite numerous issues introduced by duplicated components in software, the identification of component-level clones remains a significant challenge that existing studies struggle to effectively tackle. Specifically, existing methods face two primary limitations that are challenging to overcome: 1) Measuring the similarity between different components presents a challenge due to the significant size differences among them; 2) Identifying …

Enhancing Urban Water Quality Through Biological-Chemical Treatment: Aquatic Macroinvertebrate Community And Temporal Chlorophyll-A Response, Matthew Chaffee

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

With a growing human population, urbanization is impeding a plethora of natural waterways. Of these, urban ponds play a vital role in nutrient sequestration, flood prevention, and habitat sanctuaries. However, nutrient loading can reduce habitat effectiveness and promote harmful algae blooms. To reduce internal nutrient loads, a biological-chemical treatment strategy consisting of floating treatment wetlands (FTWs) and lanthanum were applied to two urban retention ponds, Densmore and Wilderness Ridge Ponds. To measure effectiveness, chlorophyll-a samples were collected and correlated with Sentinel-2. A novel band algorithm termed 3BR1 produced a strong correlation (R² = 0.72) to physical chlorophyll-a …