Digital Commons Network^™

Finding The Humanity In Policy Work: A Teacher Educators’ Response To The 2023 Amle Policy Agenda, Kristina N. Falbe

Faculty Publications - College of Education

[In the absence of an abstract, the conclusion is presented.] When I first opened the document containing the policy priorities, I was struck by the way that AMLE centered the voices of young adolescent students. By including direct quotes from 6th, 7th, and 8th graders, AMLE positioned the priorities as being about middle school students and not just problem solving to answer calls about teacher retention, class sizes, or other structural issues that need to be addressed. I believe that policy must be in pursuit of improving and meeting the needs of these young peoples’ lives. I am hopeful that …

Technology Assessment For Cybersecurity Organizational Readiness: Case Of Airlines Sector And Electronic Payment, Sultan Ayed Alghamdi, Tugrul Daim, Saeed Mohammed Alzahrani

Engineering and Technology Management Faculty Publications and Presentations

Payment processing systems have advanced significantly in the airline business. Because e-payments are easy, they have captured the attention of many companies in the aviation industry and are quickly becoming the dominant means of payment. However, as technology advances, fraud grows at a comparable rate. Over the years, there has been a surge in payment fraud incidents in the airline sector, reducing the platform's trustworthiness. Despite attempts to eliminate epayment fraud, decision-makers lack the technical expertise required to use the finest fraud detection and prevention assessments. This research recognizes the lack of an established decision model as a hurdle and …

Investigating Communication Of Findings In Environmental Impact Assessment And Developing A Research Agenda For Improvement, Alan Bond, Francois Retief, Angus Morrison-Saunders, Jenny Pope, Reece C. Alberts, Claudine Roos, Dirk Cilliers

Research outputs 2022 to 2026

Environmental Impact Assessment (EIA) aims to embed consideration of the significance of predicted environmental consequences (the findings) of proposed developments into approval decision making. Achieving this aim relies on adequate communication of the findings of the EIA to the stakeholders, especially the decision makers responsible for the approval decision. However, the naïve assumption that this communication of findings can be effectively achieved through the publication of a written report pervades legislation worldwide, despite decades of evidence to the contrary. As a first step towards improving such communication, this research identifies the contingent conditions associated with effectively transferring EIA findings from …

A 2,000-Year Record Of Eelgrass (Zostera Marina L.) Colonization Shows Substantial Gains In Blue Carbon Storage And Nutrient Retention, Martin Dahl, Martin Gullström, Irene Bernabeu, Oscar Serrano, Carmen Leiva-Dueñas, Hans W. Linderholm, Maria E. Asplund, Mats Björk, Tinghai Ou, J. Robin Svensson, Elinor Andrén, Thomas Andrén, Sanne Bergman, Sara Braun, Anneli Eklöf, Zilvinas Ežerinskis, Andrius Garbaras, Petter Hällberg, Elin Löfgren, Malin E. Kylander, Pere Masqué, Justina Šapolaitė, Rienk Smittenberg, Miguel A. Mateo

Research outputs 2022 to 2026

Assessing historical environmental conditions linked to habitat colonization is important for understanding long-term resilience and improving conservation and restoration efforts. Such information is lacking for the seagrass Zostera marina, an important foundation species across cold-temperate coastal areas of the Northern Hemisphere. Here, we reconstructed environmental conditions during the last 14,000 years from sediment cores in two eelgrass (Z. marina) meadows along the Swedish west coast, with the main aims to identify the time frame of seagrass colonization and describe subsequent biogeochemical changes following establishment. Based on vegetation proxies (lipid biomarkers), eelgrass colonization occurred about 2,000 years ago after geomorphological changes …

Introducing Pour Points: Characteristics And Hydrological Significance Of A Rainfall-Concentrating Mechanism In A Water-Limited Woodland Ecosystem, Ashvath S. Kunadi, Tim Lardner, Richard P. Silberstein, Matthias Leopold, Nik Callow, Erik Veneklaas, Aryan Puri, Eleanor Sydney, Sally E. Thompson

Research outputs 2022 to 2026

The interception of rainfall by plant canopies alters the depth and spatial distribution of water arriving at the soil surface, and thus the location, volume, and depth of infiltration. Mechanisms like stemflow are known to concentrate rainfall and route it deep into the soil, yet other mechanisms of flow concentration are poorly understood. This study characterizes pour points, formed by the detachment of water flowing under a branch, using a combination of field observations in Western Australian banksia woodlands and rainfall simulation experiments on Banksia menziesii branches. We aim to establish the hydrological significance of pour points in a water-limited …

Traceability Technology Adoption In Supply Chain Networks, Philippe Blaettchen, Andre P. Calmon, Georgina Hall

Research Collection Lee Kong Chian School Of Business

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, and verifying sustainable supplier practices. Initiatives leading the implementation of traceability technologies must choose the least-costly set of firms—or seed set—to target for early adoption. Choosing this seed set is challenging because firms are part of supply chains interlinked in complex networks, yielding an inherent supply chain effect: benefits obtained from traceability are conditional on technology adoption by a subset of firms in a product’s supply chain. We prove that the problem of selecting the least-costly seed set in a supply chain network is hard to …

Face Peace Design Brief #3: Facilitating Training And Education Programs In The Peacebuilding Field, John Porten

Kroc IPJ Research and Resources

Trust-building and social bonding are easier in person — and to some extent in synchronous rather than asynchronous distanced learning. Distance and time differences can make organization of synchronous learning difficult. Differences in access to the internet and other infrastructure issues can exacerbate this problem. Cultural differences and other forms of diversity can also make building trust among students, and between students and teachers, more difficult.

While advances in technology and technique have improved the outcomes of distanced educational programs in recent years, distanced educational models still result in underperformance for the participants when compared to face-to-face programs. Research shows …

Interim Regret Minimization, Wei He, Jiangtao Li, Kexin Wang

Research Collection School Of Economics

We consider a robust version of monopoly pricing when the seller only knows the bound on valuations and the mean of the distribution of the buyer’s value. The seller seeks to minimize interim regret, the forgone expected revenue due to not knowing the distribution of the buyer’s value. The optimal pricing policy randomizes over a range of prices; the support of the pricing policy is bounded away from zero.

Math Word Problem Generation Via Disentangled Memory Retrieval, Wei Qin, Xiaowei Wang, Zhenzhen Hu, Lei Wang, Yunshi Lan, Richang Hong

Research Collection Lee Kong Chian School Of Business

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers’ attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as …

Electronic Relaxation Mechanism Of 9-Methyl-2,6-Diaminopurine And 2,6-Diaminopurine-2′-Deoxyribose In Solution, Luis A. Ortiz-Rodríguez, Naishka E. Caldero-Rodríguez, Sourav Kanti Seth, Karitza Díaz-González, Carlos E. Crespo-Hernández

Faculty Scholarship

Prolonged ultraviolet exposure results in the formation of cyclobutane pyrimidine dimers (CPDs) in RNA. Consequently, prebiotic photolesion repair mechanisms should have played an important role in the maintenance of the structural integrity of primitive nucleic acids. 2,6-Diaminopurine is a prebiotic nucleobase that repairs CPDs with high efficiency when incorporated into polymers. We investigate the electronic deactivation pathways of 2,6-diaminopurine-2′-deoxyribose and 9-methyl-2,6-diaminopurine in acetonitrile and aqueous solution to shed light on the photophysical and excited state properties of the 2,6-diaminopurine chromophore. Evidence is presented that both are photostable compounds exhibiting similar deactivation mechanisms upon the population of the S1(ππ* La) state …

A General Materials Data Science Framework For Quantitative 2d Analysis Of Particle Growth From Image Sequences, Sameera Nalin Venkat, Thomas G. Ciardi, Mingjian Lu, Preston C. Deleo, Jube Augustino, Adam Goodman, Jayvic Cristian Jimenez, Anirban Mondal, Frank Ernst, Yinghui Wu, Roger H. French, Laura S. Bruckman

Faculty Scholarship

Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network …

Measurement Of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning, Seung Jun Choi, Junfeng Jiao

Research Collection College of Integrative Studies

This study explores the socioeconomic disparities observed in the early adoption of Electric Vehicles (EVs) in the United States. A multiagent deep reinforcement learning-based policy simulator was developed to address the disparities. The model, tested using data from Austin, Texas, indicates that neighborhoods with higher incomes and a predominantly White demographic are leading in EV adoption. To help low-income communities keep pace, we introduced tiered subsidies and incrementally increased their amounts. In our environment, with the reward and policy design implemented, the adoption gap began to narrow when the incentive was equivalent to an increase in promotion from 20% to …

Health And Safety Plan (Hasp) Butte Priority Soils Operable Unit, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao

Research Collection School Of Computing and Information Systems

Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …

T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng

Research Collection School Of Computing and Information Systems

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory …

Harnessing The Advances Of Meda To Optimize Multi-Puf For Enhancing Ip Security Of Biochips, Chen Dong, Xiaodong Guo, Sihuang Lian, Yinan Yao, Zhenyi Chen, Yang Yang, Zhanghui Liu

Research Collection School Of Computing and Information Systems

Digital microfluidic biochips (DMFBs) have a significant stride in the applications of medicine and the biochemistry in recent years. DMFBs based on micro-electrode-dot-array (MEDA) architecture, as the next-generation DMFBs, aim to overcome drawbacks of conventional DMFBs, such as droplet size restriction, low accuracy, and poor sensing ability. Since the potential market value of MEDA biochips is vast, it is of paramount importance to explore approaches to protect the intellectual property (IP) of MEDA biochips during the development process. In this paper, an IP authentication strategy based on the multi-PUF applied to MEDA biochips is presented, called bioMPUF, consisting of Delay …

Knowledge Generation For Zero-Shot Knowledge-Based Vqa, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Previous solutions to knowledge-based visual question answering (K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results. However, these recent methods do not explicitly show the knowledge needed to answer the questions and thus lack interpretability. Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated …

Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan

Research Collection School Of Computing and Information Systems

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number …

Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu

Research Collection School Of Computing and Information Systems

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly …

Temporal Implicit Multimodal Networks For Investment And Risk Management, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a …

T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with …

2024 March, Morehead State University. Office Of Communications & Marketing.

Morehead State Press Release Archive, 1961 to the Present

Press releases for March of 2024.

Strongly Magnetized Accretion In Two Ultracompact Binary Systems, Thomas J. Maccarone, Thomas Kupfer, Edgar Najera Casarrubias, Liliana E. Rivera Sandoval, Aarran W. Shaw, Christoper T. Britt, Jan Van Roestel, David R. Zurek

Physics & Astronomy Faculty Publications

We present the discoveries of two of AM CVn systems, Gaia14aae and SDSS J080449.49+161624.8, which show X-ray pulsations at their orbital periods, indicative of magnetically collimated accretion. Both also show indications of higher rates of mass transfer relative to the expectations from binary evolution driven purely by gravitational radiation, based on existing optical data for Gaia14aae, which show a hotter white dwarf temperature than expected from standard evolutionary models, and X-ray data for SDSS J080449.49+161624.8 which show a luminosity 10−100 times higher than those for other AM CVn at similar orbital periods. The higher mass transfer rates could be driven …

Single Domain Soft Ferromagnetic Ferrofluid Suitable For Intratumoural Magnetic Hyperthermia, Karthickraja Duraisamy, Muthu Devaraj, Ajithkumar Gangadharan, Karen S. Martirosyan, Niroj Kumar Sahu, Prasath Manogaran, Girija Easwaradas Kreedapathy

Physics & Astronomy Faculty Publications

Magnetic hyperthermia is an effective modality for treating cancerous cells selectively at the intracellular level via proper administration, surface tagging and appropriate choice of magnetic nanoparticle. Also, achieving the therapeutic temperature with as much low concentration of nanoparticles, as possible, is desirable. Recently, ferromagnetic nanoparticles or ferrofluid are gaining attention as effective magnetic hyperthermia agents due to their enhanced heating potential under in vivo conditions. In the present work, we have fabricated citrate capped cobalt ferrite ferrofluid via a simple two-step process and investigated the physicochemical, magnetic and induction heating performance. The average size of the synthesized nanoparticle …

The Α-Crystallin Chaperones Undergo A Quasi-Ordered Co-Aggregation Process In Response To Saturating Client Interaction, Kirsten Lampi, Adam P. Miller, Susan E. O'Neill, Steve L. Reichow

Chemistry Faculty Publications and Presentations

Small heat shock proteins (sHSPs) are ATP-independent chaperones vital to cellular proteostasis, preventing protein aggregation events linked to various human diseases including cataract. The α-crystallins, αA-crystallin (αAc) and αB-crystallin (αBc), represent archetypal sHSPs that exhibit complex polydispersed oligomeric assemblies and rapid subunit exchange dynamics. Yet, our understanding of how this plasticity contributes to chaperone function remains poorly understood. This study investigates structural changes in αAc and αBc during client sequestration under varying degree of chaperone saturation. Using biochemical and biophysical analyses combined with single-particle electron microscopy (EM), we examined αAc and αBc in their apo-states and at various stages of …

Sigmadiff: Semantics-Aware Deep Graph Matching For Pseudocode Diffing, Lian Gao, Yu Qu, Sheng Yu, Yue Duan, Heng Yin

Research Collection School Of Computing and Information Systems

Pseudocode diffing precisely locates similar parts and captures differences between the decompiled pseudocode of two given binaries. It is particularly useful in many security scenarios such as code plagiarism detection, lineage analysis, patch, vulnerability analysis, etc. However, existing pseudocode diffing and binary diffing tools suffer from low accuracy and poor scalability, since they either rely on manually-designed heuristics (e.g., Diaphora) or heavy computations like matrix factorization (e.g., DeepBinDiff). To address the limitations, in this paper, we propose a semantics-aware, deep neural network-based model called SIGMADIFF. SIGMADIFF first constructs IR (Intermediate Representation) level interprocedural program dependency graphs (IPDGs). Then it uses …

Ditmos: Delving Into Diverse Tiny-Model Selection On Microcontrollers, Xiao Ma, Shengfeng He, Hezhe Qiao, Dong Ma

Research Collection School Of Computing and Information Systems

Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model accuracy. In this paper, we rethink the problem from the inverse perspective by constructing small/weak models directly and improving their accuracy. Thus, we introduce DiTMoS, a novel DNN training and inference framework with a selectorclassifiers architecture, where the selector routes each input sample to the appropriate classifier for classification. DiTMoS is grounded on a key insight: a composition of weak models can exhibit high diversity and the …

Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer (MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.

Win: Weight-Decay-Integrated Nesterov Acceleration For Faster Network Training, Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Training deep networks on large-scale datasets is computationally challenging. This work explores the problem of “how to accelerate adaptive gradient algorithms in a general manner", and proposes an effective Weight-decay-Integrated Nesterov acceleration (Win) to accelerate adaptive algorithms. Taking AdamW and Adam as examples, per iteration, we construct a dynamical loss that combines the vanilla training loss and a dynamic regularizer inspired by proximal point method, and respectively minimize the first- and second-order Taylor approximations of dynamical loss to update variable. This yields our Win acceleration that uses a conservative step and an aggressive step to update, and linearly combines these …

Stability Verification In Stochastic Control Systems Via Neural Network Supermartingales, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger

Research Collection School Of Computing and Information Systems

We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control systems is extensively studied in the literature, verifying stability in stochastic control systems is an open problem. The few existing works on this topic either consider only specialized forms of stochasticity or make restrictive assumptions on the system, rendering them inapplicable to learning algorithms with neural network policies. In this work, we present an approach for general nonlinear stochastic control problems with two novel aspects: (a) instead of classical stochastic extensions of Lyapunov functions, we use ranking …