Theorems and Application of Local Activity of CNN with Five State Variables and One Port

Coupled nonlinear dynamical systems have been widely studied recently. However, the dynamical properties of these systems are difficult to deal with. The local activity of cellular neural network (CNN) has provided a powerful tool for studying the emergence of complex patterns in a homogeneous lattice, which is composed of coupled cells. In this paper, the analytical criteria for the local activity in reaction-diffusion CNN with five state variables and one port are presented, which consists of four theorems, including a serial of inequalities involving CNN parameters. These theorems can be used for calculating the bifurcation diagram to determine or analyze the emergence of complex dynamic patterns, such as chaos. As a case study, a reaction-diffusion CNN of hepatitis B Virus (HBV) mutation-selection model is analyzed and simulated, the bifurcation diagram is calculated. Using the diagram, numerical simulations of this CNN model provide reasonable explanations of complex mutant phenomena during therapy. Therefore, it is demonstrated that the local activity of CNN provides a practical tool for the complex dynamics study of some coupled nonlinear systems

Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks

Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and mistrust, and pose serious concerns to governments around the world. Convolutional Neural Networks (CNNs) demonstrate encouraging results when detecting fake images that arise from the specific type of manipulation they are trained on. However, this success has not transitioned to unseen manipulation types, resulting in a significant gap in the line-of-defense. We propose a Hierarchical Memory Network (HMN) architecture, which is able to successfully detect faked faces by utilising knowledge stored in neural memories as well as visual cues to reason about the perceived face and anticipate its future semantic embeddings. This renders a generalisable face tampering detection framework. Experimental results demonstrate the proposed approach achieves superior performance for fake and fraudulent face detection compared to the state-of-the-art

Connectome-Constrained Artificial Neural Networks

In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural networks (ANNs), which are, unlike their organic counterparts, practically unbounded, and in many cases, initialized with random weights or arbitrary structural elements. In this dissertation, we consider an inductive base case for imposing BNN constraints onto ANNs. We select explicit connectome topologies from the fruit fly (one of the smallest BNNs) and impose these onto a multilayer perceptron (MLP) and a reservoir computer (RC), in order to craft “fruit fly neural networks” (FFNNs). We study the impact on performance, variance, and prediction dynamics from using FFNNs compared to non-FFNN models on odour classification, chaotic time-series prediction, and multifunctionality tasks. From a series of four experimental studies, we observe that the fly olfactory brain is aligned towards recalling and making predictions from chaotic input data, with a capacity for executing two mutually exclusive tasks from distinct initial conditions, and with low sensitivity to hyperparameter fluctuations that can lead to chaotic behaviour. We also observe that the clustering coefficient of the fly network, and its particular non-zero weight positions, are important for reducing model variance. These findings suggest that BNNs have distinct advantages over arbitrarily-weighted ANNs; notably, from their structure alone. More work with connectomes drawn across species will be useful in finding shared topological features which can further enhance ANNs, and Machine Learning overall

Exploring The Circumstances Of Juvenile Victims Of Officer-Involved Shootings

Officer involved shooting (OIS) incidents and racial/ethnic disparities are not new, however, academic rigor and media attention are. OIS research suggests Blacks and Latinos are more often killed by officers than Whites. Situational characteristics like being armed, mental illness, attacking officers and/or fleeing officers and environmental characteristics like economic status, racial composition, and/or violent crime have been found to correlate, predict, and/or explain these racial/ethnic disparities in OIS incidents. Media-sourced databases that catalog OIS incidents estimate there are about 1,000 OIS incidents per year in the United States. Related research suggests that media disparately portrays racial/ethnic minorities in media articles about OIS incidents. Female victims of OIS incidents do not fare any better. One noticeable gap in the extant literature and media, however, is the age of OIS victims. Utilizing OIS data from Mapping Police Violence from 2013 to 2023 (n = 156) and other incident details collected from media articles, this dissertation explored juvenile OIS victims in three ways. Chapter One is a quantitative study that asked whether racial/ethnic disparities exist in OIS incidents that involve juvenile victims and whether those expected disparities were exacerbated by situational and/or environmental factors. Findings include that Black victims were 3.52 times more often and Latino victims were 1.37 times more often killed by officers compared to Whites. Situational and/or environmental factors regressed onto Victim Race/Ethnicity exacerbated expected racial/ethnic disparities, however, fell short of significance. Chapter Two is a mixed-method study that asked whether racial/ethnic disparities exist in OIS incidents that involve juvenile victims, whether these victims were portrayed in media articles using frameworks like Syntax, Rationalization, Characterization, Visibility, Context, and Criminalblackman, and whether these frameworks varied by Victim Race/Ethnicity. Findings include articles more often blamed victims, justified officer conduct, demonized victims, visualized victims, situated specific OIS incidents within the broader context of police violence, and used Criminalblackman for minority juvenile OIS victims (albeit short of significance). Chapter Three is a qualitative study that explored (1) the characteristics of female juvenile OIS victims and whether racial/ethnic disparities exist in said incidents, (2) the circumstances of these incidents and whether said incidents fit the traditional definition of OIS incidents, and (3) whether these victims were portrayed in media articles using frameworks like Good Girl/Bad Girl, Girl In Peril, Environment Type, and Adultification Bias and whether these frameworks varied by Victim Race/Ethnicity. Findings include articles more often used “bad girl” language for minority victims, more often portrayed White victims as “girls in peril,” more often portrayed minority victims as from “bad environments,” and more often “adultified” minority victims

The Effects of Social Media on Police Recruitment in Massachusetts

There is a decline in individuals seeking to become police officers in the Commonwealth of Massachusetts leading to police departments accepting individuals with lower qualifications than in the recent past. This decrease in qualified officers has taken place simultaneously with the increase in social media posts both for and against police officers and police departments. The theory of reasoned action provided a means to investigate the issue by dividing the research questions into direct social media effects on the individual and societal pressure on the individual. This quantitative study of 221 police academy recruits delved further into the effects by examining if there were differing relationships based on demographic differences amongst the participants. The data showed that the majority of the respondents reported disagreeing at some level that social media influenced their decision to become police officers. Despite believing that social media influenced their decision, there was data to support that the decision was influenced by social media for a small number of participants and by family and friends for a larger portion of the respondents. However, there were no significant differences when age, race, or socioeconomic status was factored into the equation. Police agencies can use this knowledge on social media influence to better promote their departments and the profession in general and to actively work to repair strained police-community relationships

THRIVING THROUGH THE CRACKS: PROMOTING MENTAL HEALTH RESILIENCE IN THE U.S. BORDER PATROL

The U.S. Border Patrol has become the face of immigration in the United States because of the wave of migrants at the southern border who continue to seek the American dream by extreme means. While the literature is replete with accounts of immigrants and their treacherous journeys, this thesis tells a different, yet equally relevant, story—that of the Border Patrol agent whose professional and personal life is challenged every day by such stressors as the threat of violence and danger, political pressures, and moral injury. This thesis seeks to identify alternative programs for building mental-health resilience as suicides among Border Patrol agents remain constant. To this end, this thesis explores mental health challenges and their causes and the barriers to seeking treatment that are unique to law enforcement and the Border Patrol. As the migrant crisis continues, it is essential that Border Patrol agents be provided with the necessary tools to maintain their resilience while protecting America. This thesis conducts a comparative case study to analyze two programs that the U.S. military utilizes (equine assistance therapy and the battle buddy system). This thesis finds that both programs benefit overall mental wellness and thus recommends that the U.S. Border Patrol consider implementing the two programs into its current resiliency plan.Civilian, Department of Homeland SecurityApproved for public release. Distribution is unlimited

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation

Body language (BL) refers to the non-verbal communication expressed through physical movements, gestures, facial expressions, and postures. It is a form of communication that conveys information, emotions, attitudes, and intentions without the use of spoken or written words. It plays a crucial role in interpersonal interactions and can complement or even override verbal communication. Deep multi-modal learning techniques have shown promise in understanding and analyzing these diverse aspects of BL. The survey emphasizes their applications to BL generation and recognition. Several common BLs are considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and Talking Head (TH), and we have conducted an analysis and established the connections among these four BL for the first time. Their generation and recognition often involve multi-modal approaches. Benchmark datasets for BL research are well collected and organized, along with the evaluation of SOTA methods on these datasets. The survey highlights challenges such as limited labeled data, multi-modal learning, and the need for domain adaptation to generalize models to unseen speakers or languages. Future research directions are presented, including exploring self-supervised learning techniques, integrating contextual information from other modalities, and exploiting large-scale pre-trained multi-modal models. In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time. By analyzing advancements, challenges, and future directions, it serves as a valuable resource for researchers and practitioners in advancing this field. n addition, we maintain a continuously updated paper list for deep multi-modal learning for BL recognition and generation: https://github.com/wentaoL86/awesome-body-language

Montana Kaimin, October 29, 1985

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/8811/thumbnail.jp