Physical Sciences and Mathematics Commons^™

Soil Nitrate Prediction Using Ftir-Atr, Vis-Nir, And Raman Spectroscopy, Sabiha Ferdous

Department of Biological Systems Engineering: Dissertations and Theses

Fourier transform infrared spectroscopy (FTIR) combined with Attenuated total reflectance (ATR), Visible-Near Infrared spectroscopy (Vis-NIR), and Raman spectroscopy (RS) are non-destructive techniques for rapid determination of nitrogen compounds in soil. Leveraging FTIR-ATR and Vis-NIR spectra using partial least squares regression (PLSR) modeling, the study aims to predict soil nitrate content and explored the feasibility of Raman spectroscopy to detect nitrate (NO₃^-), nitrite (NO₂^-), and ammonium (NH₄⁺) in soil. Soil samples were collected from four different fields, dried, sieved (2mm), and then used for collecting spectra (FTIR-ATR and Vis-NIR). Laboratory analysis was …

Why Linear And Sigmoid Last Layers Work Better In Classification, Lehel Dénes-Fazakas, Lásló Szilágyi, Vladik Kreinovich

Departmental Technical Reports (CS)

Usually, when a deep neural network is used to classify objects, its last layer computes the softmax. Our empirical results show we can improve the classification results if instead, we have linear or sigmoid last layer. In this paper, we provide an explanation for this empirical phenomenon.

Basecol2023 Scientific Content, Dmitri Babikov

Chemistry Faculty Research and Publications

Context. The global context of making numerous data produced by researchers available requires collecting and organising the data, assigning meaningful metadata, and presenting the data in a meaningful and homogeneous way. The BASECOL database, which collects inelastic rate coefficients for application to the interstellar medium and to circumstellar and cometary atmospheres, meets those requirements.

Aims. We aim to present the scientific content of the BASECOL2023 edition.

Methods. While the previous versions relied on finding rate coefficients in the literature, the current version is populated with published results sent by the producers of data. The paper presents the …

Singular Cr Structures Of Constant Webster Curvature And Applications, Chiara Guidi, Ali Maalaoui, Vittorio Martino

Mathematics

We consider the sphere (Formula presented.) equipped with its standard contact form. In this paper, we construct explicit contact forms on (Formula presented.), which are conformal to the standard one and whose related Webster metrics have constant Webster curvature; in particular, it is positive if (Formula presented.). As main applications, we provide two perturbative results. In the first one, we prove the existence of infinitely many contact forms on (Formula presented.) conformal to the standard one and having constant Webster curvature, where (Formula presented.) is a small perturbation of (Formula presented.). In the second application, we show that there exist …

Trapping And Scattering Of A Multiflagellated Bacterium By A Hard Surface, Alexander P. Petroff, Schuyler Mcdonough

Physics

Thiovulum majus, which is one of the fastest known bacteria, swims using hundreds of flagella. Unlike typical pusher cells, which swim in circular paths over hard surfaces, T. majus localize near hard boundaries by turning their flagella to exert a net force normal to the surface. To probe the torques that stabilize this hydrodynamically bound state, the trajectories of several thousand collisions between a T. majus cell and a wall of a quasi-two-dimensional microfluidic chamber are analyzed. Measuring the fraction of cells escaping the wall either to the left or to the right of the point of contact - and …

The Effect Of Internet Firms’ Data Analytics Capability On Their Innovation Speed And Innovation Quality: A Dynamic Capability Perspective, Yeyu Hua

Dissertations and Theses Collection (Open Access)

With the advent of big data era, data plays a pivotal role in sustainingfirms’ competitive advantages. Although a few studies have shown that data analytics capability contributes to firms’ innovative performance, these studies either focus on general innovative performance or specific types of innovation, such as incremental innovation, radical innovation, and supply chaininnovation. In this thesis, I enrich this stream of literature by conducting twostudies to further examine the relationship between data analytics capabilityand innovation speed as well as innovation quality. This thesis consists of twostudies. Study 1 is a survey study, in which I investigate the relationshipbetween data analytics …

A Social-Aware Gaussian Pre-Trained Model For Effective Cold-Start Recommendation, Siwei Liu, Xi Wang, Craig Macdonald, Iadh Ounis

Machine Learning Faculty Publications

The use of pre-training is an emerging technique to enhance a neural model's performance, which has been shown to be effective for many neural language models such as BERT. This technique has also been used to enhance the performance of recommender systems. In such recommender systems, pre-training models are used to learn a better initialisation for both users and items. However, recent existing pre-trained recommender systems tend to only incorporate the user interaction data at the pre-training stage, making it difficult to deliver good recommendations, especially when the interaction data is sparse. To alleviate this rcommon data sparsity issue, we …

The Interplay Of Spin, Charge, And Heat: From Metal/Insulator Heterostructures To Perovskite Bilayers, Sam M. Bleser

Electronic Theses and Dissertations

In this dissertation begin with an investigation of non-local spin transport in an amorphous germanium (a-Ge) sample via the inverse spin Hall effect (ISHE). In that study we show that commonly used techniques such as differential conductance and delta mode of a paired Keithley 6221/2182a for non-local resistance measurements can lead to false indicators of spin transport. Next, we turn out attention to a thickness dependent study in thermally-evaporated chromium (Cr) thin films on a bulk polycrystalline yttrium-iron-garnet (YIG) substrate. This project analyzed the spin transport in the Cr films versus thickness via the longitudinal spin Seebeck effect (LSSE). This …

Universality Class Of A Spinor Bose–Einstein Condensate Far From Equilibrium, Seung Jung Huh, Koushik Mukherjee, Kiryang Kwon, Jihoon Seo, Junhyeok Hur, Simeon I. Mistakidis, H. R. Sadeghpour, Jae Yoon Choi

Physics Faculty Research & Creative Works

Scale Invariance And Self-Similarity In Physics Provide A Unified Framework For Classifying Phases Of Matter And Dynamical Properties Near Equilibrium In Both Classical And Quantum Systems. This Paradigm Has Been Further Extended To Isolated Many-Body Quantum Systems Driven Far From Equilibrium, For Which The Physical Observables Exhibit Dynamical Scaling With Universal Scaling Exponents. Universal Dynamics Appear In A Wide Range Of Scenarios, Including Cosmology, Quark–gluon Matter, Ultracold Atoms And Quantum Spin Magnets. However, How The Universal Dynamics Depend On The Symmetry Of The Underlying Hamiltonian In Non-Equilibrium Systems Remains An Outstanding Challenge. Here We Report On The Classification Of Universal …

Considering The Impact Framework To Understand The Ai-Well-Being-Complex From An Interdisciplinary Perspective, Christian Montag, Preslav Nakov, Raian Ali

Natural Language Processing Faculty Publications

Artificial intelligence (AI) is built into many products and has the potential to dramatically impact societies around the world. This short theoretical paper aims to provide a simple framework that might help us understand how the introduction and/or use of products with AI might influence the well-being of humans. It is proposed that considering the dynamic Interplay between variables stemming from Modality, Person, Area, Culture and Transparency categories will help to understand the influence of AI on well-being. The Modality category encompasses areas such as the degree of AI being interactive, informational versus actualizing, or autonomous. The Person variable contains …

Pollinator Communities And Their Ecosystem Services At Conservation Grasslands And Adjacent Croplands, Araceli Gomez Villegas

Department of Entomology: Dissertations, Theses, and Student Research

Pollinators are intrinsically linked to the success of unmanaged and managed ecosystems by providing pollination services that aid in the reproduction of wildflowers and many crops. Land use change, habitat loss, fragmentation, and related landscape-level phenomena (for example, increased pesticide exposure) threaten pollinators and have been associated with population declines. In the Midwestern region of the United States, land conversion of native prairies and grasslands to row-crop agriculture has been one of the largest contributors to pollinator habitat loss. Conservation programs, such as the Conservation Reserve Program, have worked towards removing environmentally sensitive lands from agriculture production and enrolling them …

2024 March - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

Using Flipped Classroom Modules To Facilitate Higher Order Learning In Undergraduate Organic Chemistry, Lauren R. Holloway, Tabitha Miller, Bryce Da Camara, Paul M. Bogie, Briana L. Hickey, Jack Barbera, Angie L. Lopez, Multiple Additional Authors

Chemistry Faculty Publications and Presentations

In an ongoing effort to incorporate active learning and promote higher order learning outcomes in undergraduate organic chemistry, a hybrid (“flipped”) classroom structure has been used to facilitate a series of collaborative activities in the first two courses of the lower division organic chemistry sequence. An observational study of seven classes over a five-year period reveals there is a strong correlation between performance on the in-class activities and performance on the final exam across all classes; however, a significant number of students in these courses continue to struggle on both the in-class activities and final exam. The Activity Engagement Survey …

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 …

Dna-Based Assay For Calorimetric Determination Of Protein Concentrations In Pure Or Mixed Solutions, Matthew W. Eskew, Patrick Reardon, Albert S. Benight

Chemistry Faculty Publications and Presentations

It was recently reported that values of the transition heat capacities, as measured by differential scanning calorimetry, for two globular proteins and a short DNA hairpin in NaCl buffer are essentially equivalent, at equal concentrations (mg/mL). To validate the broad applicability of this phenomenon, additional evidence for this equivalence is presented that reveals it does not depend on DNA sequence, buffer salt, or transition temperature (Tm). Based on the equivalence of transition heat capacities, a calorimetric method was devised to determine protein concentrations in pure and complex solutions. The scheme uses direct comparisons between the thermodynamic stability of a short …

Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows, Vincent F. Yu, Nabila Y. Salsabila, Shih-W Lin, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research investigates the Set Team Orienteering Problem with Time Windows (STOPTW), a new variant of the well-known Team Orienteering Problem with Time Windows and Set Orienteering Problem. In the STOPTW, customers are grouped into clusters. Each cluster is associated with a profit attainable when a customer in the cluster is visited within the customer's time window. A Mixed Integer Linear Programming model is formulated for STOPTW to maximizing total profit while adhering to time window constraints. Since STOPTW is an NP-hard problem, a Simulated Annealing with Reinforcement Learning (SARL) algorithm is developed. The proposed SARL incorporates the core concepts …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Technical Memorandum Grove Gulch Earth Volumetric Studio Model Inputs, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Stopguess: A Framework For Public-Key Authenticated Encryption With Keyword Search, Tao Xiang, Zhongming Wang, Biwen Chen, Xiaoguo Li, Peng Wang, Fei Chen

Research Collection School Of Computing and Information Systems

Public key encryption with keyword search (PEKS) allows users to search on encrypted data without leaking the keyword information from the ciphertexts. But it does not preserve keyword privacy within the trapdoors, because an adversary (e.g., untrusted server) might launch inside keyword-guessing attacks (IKGA) to guess keywords from the trapdoors. In recent years, public key authenticated encryption with keyword search (PAEKS) has become a promising primitive to counter the IKGA. However, existing PAEKS schemes focus on the concrete construction of PAEKS, making them unable to support modular construction, intuitive proof, or flexible extension. In this paper, our proposal called “StopGuess” …

Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao

Research Collection School Of Computing and Information Systems

Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …

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 …

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 …

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 …

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 …

Application Of Collaborative Learning Paradigms Within Software Engineering Education: A Systematic Mapping Study, Rita Garcia, Christoph Treude, Andrew Valentine

Research Collection School Of Computing and Information Systems

Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help students develop collaboration skills. This paper uses a Systematic Mapping Study (SMS) to examine the application of the CL educational theory in SE Education. The SMS identified 14 papers published between 2011 and 2022. We used qualitative analysis to classify the papers into four CL paradigms: Conditions, Effect, Interactions, and Computer-Supported Collaborative Learning (CSCL). We found a high interest in CSCL, with a shift in student interaction research …

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 …

Pa2blo: Low-Power, Personalized Audio Badge, Hemanth Sabbella, Dulaj Sanjaya Weerakoon, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We present the hardware design and software pipeline for an ultra-low power device, in the form factor of a wearable badge, that supports energy efficient sensing, processing and wireless transfer of human voice commands and interactions. The proposed system, called PA2BLO, is envisioned to support both: (a) real-time, scalable, authorized voice based interaction and control of devices and appliances, and (b) longitudinal, low-power logging of natural voice interactions. PA2BLO in-troduces two key novel capabilities. First, it includes a low power, low-complexity voice authentication module that is able to reliably authenticate an authorized user only using low sampling rate (500 Hz) …

Representation Learning For Stack Overflow Posts: How Far Are We?, Junda He, Xin Zhou, Bowen Xu, Ting Zhang, Kisub Kim, Zhou Yang, Thung Ferdian, Ivana Clairine Irsan, David Lo

Research Collection School Of Computing and Information Systems

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for Stack Overflow posts. As the volume of literature on Stack Overflow continues to burgeon, it highlights the need for a powerful Stack Overflow post representation model and drives researchers’ interest in developing specialized representation models that can adeptly capture the intricacies of Stack Overflow posts. The state-of-the-art (SOTA) Stack Overflow post representation models are Post2Vec and BERTOverflow, which are built …

Demystifying Faulty Code: Step-By-Step Reasoning For Explainable Fault Localization, Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen, Neil Sharma, David Lo

Research Collection School Of Computing and Information Systems

Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault is laborious and time-consuming. To overcome this challenge, various fault localization tools have been developed. These tools typically generate a ranked list of suspicious program elements. However, this information alone is insufficient. A prior study emphasized that automated fault localization should offer a rationale. In this study, we investigate the step-by-step reasoning for explainable fault localization. We explore the potential of Large Language Models (LLM) in assisting developers …