Physical Sciences and Mathematics Commons^™

Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

Alkaline Pretreatment And Air Mixing For Improvement Of Methane Production From Anaerobic Co-Digestion Of Poultry Litter With Wheat Straw, Yuanhang Zhan, Jun Zhu, Yiting Xiao, Leland C. Schrader

Biological and Agricultural Engineering Faculty Publications and Presentations

Alkaline pretreatment (AL) and air mixing (air) both have the potential to improve anaerobic co-digestion (Co-AD) of poultry litter with wheat straw for methane production. In this study, the effects of the combination of AL (pH 12 for 12 h) and air mixing (12 mL·d⁻¹) on the Co-AD process were investigated. The substrate hydrolysis was enhanced by AL, with soluble chemical oxygen demand increased by 4.59 times and volatile fatty acids increased by 5.04 times. The cumulative methane yield in the group of Co-AD by AL integrated with air (Co-(AL + air)), being 287 mL·(g VS_added) …

Lead Exposure Of Mainland Australia's Top Avian Predator, Jordan O. Hampton, Michael T. Lohr, Aaron J. Specht, Damien Nzabanita, Jasmin Hufschmid, Lee Berger, Kate Mcginnis, Jane Melville, Emma Bennett, James M. Pay

Research outputs 2022 to 2026

Lead (Pb) toxicity, through ingestion of lead ammunition in carcasses, is a threat to scavenging birds worldwide, but has received little attention in Australia. We analyzed lead exposure in the wedge-tailed eagle (Aquila audax), the largest raptor species found in mainland Australia and a facultative scavenger. Eagle carcasses were collected opportunistically throughout south-eastern mainland Australia between 1996 and 2022. Lead concentrations were measured in bone samples from 62 animals via portable X-ray fluorescence (XRF). Lead was detected (concentration > 1 ppm) in 84% (n = 52) of the bone samples. The mean lead concentration of birds in which lead was detected …

Non-Contact Optical Spectroscopy For Tumor-Sensitive Diffuse Reflectance And Fluorescence Measurements On Murine Subcutaneous Tissue Models: Monte Carlo Modeling And Experimental Validations, Zahid Hasan, Pranto Soumik Saha, Madison O. Korfhage, Caigang Zhu

Markey Cancer Center Faculty Publications

Fiber-optic probes are commonly used in biomedical optical spectroscopy platforms for light delivery and collection. At the same time, it was reported that the inconsistent probe- sample contact could induce significant distortions in measured optical signals, which consequently cause large analysis errors. To address this challenge, non-contact optical spectroscopy has been explored for tissue characterizations. However, existing non-contact optical spectroscopy platforms primarily focused on diffuse reflectance measurements and may still use a fiber probe in which the probe was imaged onto the tissue surface using a lens, which serves as a non-contact probe for the measurements. Here, we report a …

Approximate Confidence Distribution Computing, Suzanne Thornton, W. Li, M. Xie

Mathematics & Statistics Faculty Works

Approximate confidence distribution computing (ACDC) offers a new take on the rapidly developing field of likelihood-free inference from within a frequentist framework. The appeal of this computational method for statistical inference hinges upon the concept of a confidence distribution, a special type of estimator which is defined with respect to the repeated sampling principle. An ACDC method provides frequentist validation for computational inference in problems with unknown or intractable likelihoods. The main theoretical contribution of this work is the identification of a matching condition necessary for frequentist validity of inference from this method. In addition to providing an example of …

Experimental Data Supporting Co2 Enhanced Oil Recovery And Storage Potential In The Bakken Petroleum System (Bps), University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet on CO₂ enhanced oil recovery (EOR) in the Bakken petroleum system. Highlights experimental data concerning CO₂’s role in oil recovery in the region.

Bpop Prospectus, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Prospectus for Bakken Production Optimization Program (BPOP) 4.0.

Upgraded Carbon Ores To Products, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet on the Energy & Environmental Research Center project to develop domestic battery materials from upgraded carbon ores. Includes information on upgraded carbon ores to products (UCOP) technology and a timeline for development.

Impact Of Pycnonuclear Fusion Uncertainties On The Cooling Of Accreting Neutron Star Crusts, R. Jain, E. F. Brown, H. Schatz, A. V. Afanasjev, M. Beard, L. R. Gasques, S. S. Gupta, G. W. Hitt, W. R. Hix, R. Lau, P. Möller, W. J. Ong, M. Wiescher, Y. Xu

Physics and Engineering Science

The observation of X-rays during quiescence from transiently accreting neutron stars provides unique clues about the nature of dense matter. This, however, requires extensive modeling of the crusts and matching the results to observations. The pycnonuclear fusion reaction rates implemented in these models are theoretically calculated by extending phenomenological expressions and have large uncertainties spanning many orders of magnitude. We present the first sensitivity studies of these pycnonuclear fusion reactions in realistic network calculations. We also couple the reaction network with the thermal evolution code dStar to further study their impact on the neutron star cooling curves in quiescence. Varying …

Mineral Licks As A Potential Nidus For Parasite Transmission, William J. Severud, Todd M. Kautz, Jerrold L. Belant, Seth A. Moore

Natural Resource Management Faculty Publications

Discrete landscape features can concentrate animals in time and space, leading to non-random interspecific encounters. These encounters have implications for predator-prey interactions, habitat selection, intraspecific competition, and transmission of parasites and other pathogens. The lifecycle of the parasitic nematode Parelaphostrongylus tenuis requires an intermediate host of a terrestrial gastropod. Natural hosts of P. tenuis are whitetailed deer, and an aberrant host of conservation concern is moose, which are susceptible to high levels of mortality as a naive host to the parasite. Intermediate hosts become infected when P. tenuis larvae are shed in deer feces, then consumed or enter the gastropod …

Advances And Perspectives Of Using Stable Isotope Probing (Sip)-Based Technologies In Contaminant Biodegradation, Jinha Kim, Myung Hwangbo, Chih-Hsuan Shih, Kung-Hui Chu

School of Earth, Environmental, & Marine Sciences Faculty Publications

Stable isotope probing (SIP) is a powerful tool to study microbial community structure and function in both nature and engineered environments. Coupling with advanced genomics and other techniques, SIP studies have generated substantial information to allow researchers to draw a clearer picture of what is occurring in complex microbial ecosystems. This review provides an overview of the advances of SIP-based technologies over time, summarizes the status of SIP applications to contaminant biodegradation, provides critical perspectives on ecological interactions within the community, and important factors (controllable and non-controllable) to be considered in SIP experimental designs and data interpretation. Current trend and …

Carbon-Aware Mine Planning With A Novel Multi-Objective Framework, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The logistical complication of long-term mine planning involves deciding the sequential extraction of materials from the mine pit and their subsequent processing steps based on geological, geometrical, and resource constraints. The net present value (NPV) of profit over the mine's lifespan usually forms the sole objective for this problem, which is considered as the NP-hard precedence-constrained production scheduling problem (PCPSP) as well. However, increased pressure for more sustainable and carbon-aware industries also calls for environmental indicators to be considered. In this paper, we enhance the generic PCPSP formulation into a multi-objective optimization (MOO) problem whereby carbon cost forms an additional …

Experimental Comparison Of Features, Analyses, And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Naing Tun Yan, David Lo, Lingxiao Jiang, Christoph Bienert

Research Collection School Of Computing and Information Systems

Android malware detection has been an active area of research. In the past decade, several machine learning-based approaches based on different types of features that may characterize Android malware behaviors have been proposed. The usually-analyzed features include API usages and sequences at various abstraction levels (e.g., class and package), extracted using static or dynamic analysis. Additionally, features that characterize permission uses, native API calls and reflection have also been analyzed. Initial works used conventional classifiers such as Random Forest to learn on those features. In recent years, deep learning-based classifiers such as Recurrent Neural Network have been explored. Considering various …

Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision, Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, Yongjun Wang

Research Collection School Of Computing and Information Systems

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method …

Generative Model-Based Testing On Decision-Making Policies, Zhuo Li, Xiongfei Wu, Derui Zhu, Mingfei Cheng, Siyuan Chen, Fuyuan Zhang, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

The reliability of decision-making policies is urgently important today as they have established the fundamentals of many critical applications, such as autonomous driving and robotics. To ensure reliability, there have been a number of research efforts on testing decision-making policies that solve Markov decision processes (MDPs). However, due to the deep neural network (DNN)-based inherit and infinite state space, developing scalable and effective testing frameworks for decision-making policies still remains open and challenging.In this paper, we present an effective testing framework for decision-making policies. The framework adopts a generative diffusion model-based test case generator that can easily adapt to different …

Edge Distraction-Aware Salient Object Detection, Sucheng Ren, Wenxi Liu, Jianbo Jiao, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Integrating low-level edge features has been proven to be effective in preserving clear boundaries of salient objects. However, the locality of edge features makes it difficult to capture globally salient edges, leading to distraction in the final predictions. To address this problem, we propose to produce distraction-free edge features by incorporating cross-scale holistic interdependencies between high-level features. In particular, we first formulate our edge features extraction process as a boundary-filling problem. In this way, we enforce edge features to focus on closed boundaries instead of those disconnected background edges. Second, we propose to explore cross-scale holistic contextual connections between every …

Fine-Grained In-Context Permission Classification For Android Apps Using Control-Flow Graph Embedding, Vikas Kumar Malviya, Naing Tun Yan, Chee Wei Leow, Ailys Xynyn Tee, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Android is the most popular operating system for mobile devices nowadays. Permissions are a very important part of Android security architecture. Apps frequently need the users’ permission, but many of them only ask for it once—when the user uses the app for the first time—and then they keep and abuse the given permissions. Longing to enhance Android permission security and users’ private data protection is the driving factor behind our approach to explore fine-grained contextsensitive permission usage analysis and thereby identify misuses in Android apps. In this work, we propose an approach for classifying the fine-grained permission uses for each …

Graph-Level Anomaly Detection Via Hierarchical Memory Networks, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules---node and graph memory modules---via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the …

Are We Ready To Embrace Generative Ai For Software Q&A?, Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong, Thong Hoang, Jiakun Liu, Kisub Kim, Chen Gong, Changan Niu, Chenyu Wang, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, …

Real: A Representative Error-Driven Approach For Active Learning, Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du

Research Collection School Of Computing and Information Systems

Given a limited labeling budget, active learning (al) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, al typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose Real, a novel approach to select data instances with Representative Errors for Active Learning. It identifies minority predictions as pseudo errors within a cluster and allocates an adaptive sampling budget for …

Development Of An Enhanced Hybrid Bi-Catalytic Electrode Containing Bimetallic Composite Catalyst And Immobilized Enzyme For Complete Glucose Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Rodrigo Garcia Da Silva, Shelley D. Minteer, Adalgisa R. De Andrade

Chemistry Faculty Research & Creative Works

We describe an enzymatic fuel cell (EFC) that can electrooxidize glucose completely. the EFC contains the hybrid Ni@Pt-CNT/OxOx bioanode, composed of a bimetallic composite catalyst (Ni@Pt-CNT) and the enzyme oxalate oxidase (OxOx), which can cleave carbon-carbon bonds. Ni@Pt-CNT/OxOx displayed 3-fold higher catalytic activity in the presence than in the absence of glucose (1.3 and 0.4 mA cm−2, respectively), indicating that Ni@Pt-CNT and OxOx acted synergistically. Electrochemical impedance spectroscopy showed that Ni@Pt-CNT/OxOx had higher charge transfer resistance and double layer capacitance than Ni@Pt-CNT. Long-term bulk electrolysis (18 h) revealed that the EFC operating with Ni@Pt-CNT/OxOx presented better current density and stability …

A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks, Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari

All Works

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of …

Constrained Quantization For A Uniform Distribution With Respect To A Family Of Constraints, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, with respect to a family of constraints for a uniform probability distribution we determine the optimal sets of n-points and the nth constrained quantization errors for all positive integers n. We also calculate the constrained quantization dimension and the constrained quantization coefficient. The work in this paper shows that the constrained quantization dimension of an absolutely continuous probability measure depends on the family of constraints and is not always equal to the Euclidean dimension of the underlying space where the support of the probability measure is defined.

Bacteroides Fragilis In The Gut Microbiomes Of Alzheimer’S Disease Activates Microglia And Triggers Pathogenesis In Neuronal C/Ebpβ Transgenic Mice, Yiyuan Xia, Yifan Xiao, Zi-Hao Wang, Ashfaqul M. Alam, John P. Haran, Beth A. Mccormick, Xiji Shu, Xiaochuan Wang, Keqiang Ye

Markey Cancer Center Faculty Publications

Gut dysbiosis contributes to Alzheimer’s disease (AD) pathogenesis, and Bacteroides strains are selectively elevated in AD gut microbiota. However, it remains unknown which Bacteroides species and how their metabolites trigger AD pathologies. Here we show that Bacteroides fragilis and their metabolites 12-hydroxy-heptadecatrienoic acid (12-HHTrE) and Prostaglandin E2 (PGE2) activate microglia and induce AD pathogenesis in neuronal C/EBPβ transgenic mice. Recolonization of antibiotics cocktail-pretreated Thy1-C/EBPβ transgenic mice with AD patient fecal samples elicits AD pathologies, associated with C/EBPβ/Asparaginyl endopeptidase (AEP) pathway upregulation, microglia activation, and cognitive disorders compared to mice receiving healthy donors’ fecal microbiota transplantation (FMT). Microbial 16S rRNA sequencing …

Syne1 Mutation Is Associated With Increased Tumor Mutation Burden And Immune Cell Infiltration In Ovarian Cancer, Laura M. Harbin, Nan Lin, Frederick R. Ueland, Jill M. Kolesar

Markey Cancer Center Faculty Publications

SYNE1, a nuclear envelope protein critical for cellular structure and signaling, is downregulated in numerous malignancies. SYNE1 alterations are found in 10% of gynecologic malignancies and 5% of epithelial ovarian cancers. Previous studies demonstrated an association between SYNE1 mutation, increased tumor mutation burden (TMB), and immunotherapy response. This study evaluates the SYNE1 mutation frequency, association with TMB, and downstream effects of SYNE1 mutation in ovarian cancer. Genetic information, including whole-exome sequencing, RNA analysis, and somatic tumor testing, was obtained for consenting ovarian cancer patients at an academic medical center. Mutation frequencies were compared between the institutional cohort and The Cancer …

Unleashing The Potential Of 1,3-Diketone Analogues As Selective Lh2 Inhibitors, Juhoon Lee, Hou-Fu Guo, Shike Wang, Yazdan Maghsoud, Erik Antonio Vázquez-Montelongo, Zhifeng Jing, Rae M. Sammons, Eun Jeong Cho, Pengyu Ren, G. Andrés Cisneros, Jonathan M. Kurie, Kevin N. Dalby

Markey Cancer Center Faculty Publications

Lysyl hydroxylase 2 (LH2) catalyzes the formation of highly stable hydroxylysine aldehyde-derived collagen cross-links (HLCCs), thus promoting lung cancer metastasis through its capacity to modulate specific types of collagen cross-links within the tumor stroma. Using 1 and 2 from our previous high-throughput screening (HTS) as lead probes, we prepared a series of 1,3-diketone analogues, 1−18, and identified 12 and 13 that inhibit LH2 with IC50’s of approximately 300 and 500 nM, respectively. Compounds 12 and 13 demonstrate selectivity for LH2 over LH1 and LH3. Quantum mechanics/molecular mechanics (QM/MM) modeling indicates that the selectivity of 12 and 13 may stem from …

Factors Affecting California Grassland Biodiversity: Two Case Studies, Nora Bales

Master's Theses

Biodiversity loss over the last century has been significant and is projected to increase. Biodiversity loss is accelerating due to climate change and habitat loss and degradation. Biodiversity supports key ecosystem services including pollination services, climate regulation, and water filtration. Biodiversity conservation is a top priority for land managers and researchers, however many ecological studies with practical applications lack a thorough understanding of the soil environment in their study system. We conducted two case studies integrating soil and plant data to answer management questions. The first case study investigated the relationship between a threatened plant, Hooveria purpurea var. purpurea and …

Geneseo Solar Eclipse Website, Anne Pellerin, Aaron Steinhauer, Sherry Larson-Rhodes, Leah Root

Myth and Science, 2023-24

The Geneseo Solar Eclipse website is a collaboration by Faculty from the SUNY Geneseo Physics & Astronomy Department and the Staff of Fraser Hall Library, highlighting Geneseo campus and local eclipse-related events, and demonstrating the science behind solar eclipses. Learn about the differences between partial and total solar eclipses, how eclipses are used to find new worlds, and find resources and Geneseo campus events celebrating the eclipses of Oct 14, 2023 and April 8, 2024!

This archived copy of eclipse.geneseo.edu, captured on July 16, 2024, can be viewed using The Internet Archive, Conifer, or by loading the WARC …

Chloeia Rozbaczyloi, A New Species Of Polychaete (Archinominae: Amphinomidae) And First Record Of The Family For The Nazca Ridge, Southeastern Pacific Ocean, Juan I. Cañete, María S. Romero, Erin E. Easton, Ariadna Mecho, Javier Sellanes

School of Earth, Environmental, & Marine Sciences Faculty Publications

Highlights

• A new species of amphinomid polychaete, Chloeia rozbaczyloi sp. nov. is described.

• The new species inhabits poorly explored seamounts of the remote Nazca Ridge off Chile.

• This finding constitutes the first report of the genus Chloeia for Chilean waters.

• Genetic data for Chloeia rozbaczyloi sp. nov is provided.

• The new species is reported for seamounts lying within the recently created Nazca Desventuradas Marine Park.

Abstract

The amphinomid polychaete Chloeia rozbaczyloi sp. nov., collected from seamounts of the Nazca Ridge (NR), northwest of Desventuradas islands, southeastern Pacific Ocean, is described. The new species was observed on only two of seven …

Evaluation And Description Of Nature-Based Engineering Stream Restoration Through Dutton Shadyside Park, Julie Wildschut, Andrea Lubberts, David P. Warners, Haley Weesies

Faculty and Professional Research

As land uses change over time, from wetlands and forests to agricultural fields, suburban neighborhoods, and commercial areas, stormwater runoff increases in volume and moves more rapidly into nearby streams. This altered hydrology conveys pollutants directly to local waterways and increases erosion, resulting in deeply entrenched channels with steep banks lacking vegetation. Plaster Creek, located near Grand Rapids, MI, is an example of such an impaired stream, having become channelized and separated from adjacent floodplain habitat along much of its 26-mile length. As part of a Michigan EGLE 319 grant, Plaster Creek Stewards, an initiative of Calvin University, used nature-based …