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How Criminal Offenders Offend Society’s Equality Expectations
No full textThe public’s concern over safety and rising crime rates dominated opinion polls and played a significant role in the 2024 presidential election. This article argues that, although much has been written on the offender’s state of mind and the concrete harms offenders impose, what is missing is a more victim-centric understanding of the full scope of crime’s consequences.
Criminal offenders’ selfish, self-directed conduct subjugates the victims’ legally protected interests and conveys that they do not consider their victims’ rights sufficiently important or deserving of conduct-guiding respect. Offenders, in short, differentiate themselves from the social group by adopting the harmful mindset that the rules applicable to everyone else do not equally apply to them.
In so doing, offenders morally betray both their victim and society and, therefore, cannot expect equal respect from the community. The article traces out this dynamic and highlights the distinct injury the offender’s self-elevation-through-subjugation inflicts. It concludes that we, for both evaluative and descriptive reasons, must make this more victim-centered understanding of criminal culpability a part of our criminal justice vocabulary
Advanced Mathematical and Computational Methods in Quantum Chemistry
No full textWave-function based ab initio calculation methods have been gaining significant atten- tion from chemists due to their ability to provide insights into experimental results. However, their application to large systems is limited by the considerable computational cost involved, especially when dealing with high-dimensional tensors inherent to these calculations. To address these challenges, two primary strategies have been explored in this study. First, advancements in hardware architecture and algorithmic development. Graphics Processing Units (GPUs) have facilitated parallel computation, leading to a significant acceleration of these calculations. In this work, we propose a method for evaluating two-electron repulsion integrals (ERIs)—a key component of quantum chemistry—on GPUs. Regarding to the algorithm development, we propose a series of new algorithms for skew symmetric matrix LTLT decomposition,andimplementedhighperformanceCPUimplementationtoaccelerate these matrix operations in Quantum Monte Carlo (QMC) simulation./= / \u3eSecond, the development of approximation methods, such as tensor hypercontraction (THC), specifically least-squares tensor (LS-THC), has been introduced to reduce the high- dimensional tensors commonly encountered in computational chemistry, which allows wave- function methods to be applied to larger systems. However, the accuracy of LS-THC depends on the grid points. In this work, we proposed a pair of grid point schemes to account for missing interactions for grid points. In addition, we propose a novel energy pivoting algorithm to select grid points to prune unnecessary ones, which demonstrates good performance over the traditional pruning algorithm
Looking Forward to It: Investigating the Role of Anhedonia in Episodic future Thinking
No full textEpisodic future thinking, or our ability to simulate personal future events, is implicated in a range of important functioning from problem solving to emotion regulation. However, individuals with clinical diagnoses like depression and schizophrenia struggle to produce future thinking that is vivid and detailed. While these deficits mirror well-document autobiographical memory deficits, they remain poorly understood. The current study aimed to examine the role of anhedonia in episodic future thinking vividness using a within-subjects dimensional approach. A mixed sample of sixty-five undergraduate students and community adults completed two future thinking tasks and one autobiographical memory across two study visits, during which participants were prompted to generate a total of twelve specific future narratives and six specific memory narratives across positive, negative, and neutral valances. Participants completed self-reported clinical status measures including an anhedonia scale at each study visit. Multilevel models demonstrated that anhedonia symptom severity predicted subjective vividness across in future thinking narratives. This remained true even after controlling for depression. Our experimental manipulation of preceding a future thinking task with a memory task (as opposed to a control task) did not prompt participants to generate future thinking that was rated as more vivid. Finally, anhedonia severity predicted self-reported positive, but not negative, affect during our future thinking task, providing further evidence for the delineation of the positive and negative affect systems While future research is warranted, result provides preliminary evidence for the unique role of anhedonia in observed future thinking deficits related to mental imagery observed across diagnoses
Revisiting Dr. King\u27s Beloved Community as an Ecclesial Response to Systemic Injustice in the 21st Century
No full textAs a womanist and practical theologian, I am resolute in my conviction of God’s sovereignty and the integrity of His written word. Despite uncertainty and the prevalence of systemic injustice in the world, the Bible has remained a steadfast source from which I have gained insight, guidance, and strength. From a theological perspective, the Bible is the manifestation of my faith seeking understanding. It is the foundation upon which practical theology is built, the embodiment of biblical teachings, beliefs, creeds, traditions, and rituals enacted by individuals within faith institutions and in community. However, it is through a womanist lens that I focus on the intersectionality of issues addressing race, gender, culture, and social justice to make the world a better place. A robust, practical theology that addresses race, gender, culture, and social justice issues can be constructed to impact church congregations and communities when we consider Dr. Martin Luther King’s vison of the Beloved Community as a universal, interfaith model. My proposal of an ecclesial response to systemic injustice in the twenty-first century aligns with the Great Commission (Matthew 28:18-20 NIV). Adhering to the Great Commission requires upholding the greatest of the commandments meaning to love God and neighbor (Matthew 22:36-40 NIV). My spiritual frame of reference is Christocentric. However, it is upheld by a theology that welcomes and advocates for all of humanity according to the Golden Rule (Matthew 7:12 NIV). I have incorporated literary devices such as vignettes, metaphors, aphorisms, scripture, and italicized quotations to emphasize subject matters and context. I have also utilized Kairos time instead of Chronos time throughout this dissertation for the purpose of narrative transitions as well as to demonstrate how events are often reoccurring within cycles of life and the existence of humanity. Humanity has been adversely affected by the systems of the world for centuries. Inequity and inequality continue to persist because of deeply rooted unjust systems that sustain the legacy of racism and overtly discriminatory practices, policies, laws, and ideologies. When addressing systemic injustice, it is imperative to ascertain the role of structures and institutions within society. My quest to research and examine systemic injustice within political, legal, criminal justice, educational, and health care systems was an awakening. My conclusion is that systemic injustice is perpetrated by the integration of multiple systems that are often interrelated and corroborated by individuals and institutions
Load Forecasting And Modeling For Power System
No full textAccurate load forecasting and modeling play a pivotal role in ensuring the stability, reliability, and economic efficiency of modern power systems. With the increasing integration of renewable energy sources, distributed energy resources, and demand-side management strategies, power systems are becoming more dynamic and complex, making traditional load forecasting methods inadequate. This dissertation introduces two novel approaches to address the challenges associated with day-ahead load forecasting and load modeling.
First, a Diffusion Model-Based Probabilistic Day-Ahead Load Forecasting (PDALF) Framework is proposed to enhance the accuracy and robustness of load forecasting. By employing a conditional denoising diffusion probabilistic model (DDPM), the framework, termed DALNet (Diffusion-Augmented Load Network), generates load curves by progressively adding and removing Gaussian noise in a Markov chain. This approach effectively models the complex distribution of load data, avoiding the error accumulation inherent in rolling forecasts and capturing intra-day correlations. Additionally, a Temporal Multi-Scale Attention Block (TMSAB) is integrated into DALNet to extract both positional and temporal information, further improving prediction accuracy. Comparative experiments on real-world datasets, including GEFCom2014 and Arizona State University\u27s load data, demonstrate that DALNet significantly outperforms traditional benchmarks such as LSTM, Transformer, and Bayesian Neural Networks (BNNs), offering superior reliability, sharpness, and overall forecasting performance.
Second, this dissertation presents a Reinforcement Learning-Based Symbolic Regression (SR) Framework for load modeling to address the limitations of fixed-form parametric models and complex machine learning models that lack interpretability. Leveraging the Actor-Critic reinforcement learning architecture, a trainable expression tree structure is designed to discover mathematical expressions that describe the relationship between load characteristics and system variables. To balance model complexity and interpretability, a candidate pool is employed to refine the best-performing expressions using policy gradient optimization and gradient-based fine-tuning. Case studies demonstrate that the proposed symbolic regression framework effectively captures the nonlinear dynamics of load responses under various grid disturbances, outperforming conventional parametric models and artificial neural networks in terms of accuracy, interpretability, and computational efficiency.
The combination of these two innovative approaches provides a comprehensive solution to the challenges of probabilistic load forecasting and dynamic load modeling in modern power systems. By bridging the gap between accuracy, interpretability, and scalability, this work contributes to advancing the state-of-the-art in power system analysis and operation
Dynamic Transformation to an Original Creature
No full textThe artifact is a real-time animation of a normal human transforming into an original creature.
About the creature, I designed the creature which is Cthulhu-like, based on the plants and religion. The creature is more organic and human shaped. There is animation for the muscle pulsation and facial change.
Regarding real-time transformation, I used morph targets for the creation. I imported the original model into ZBrush or Blender for secondary sculpting, then utilized morph targets or blend shapes for the transformation
Schwerin Castle: Historical Recreation with Dynamic Materials in Sebastian Luca’s Style
No full textI created a scene in Unreal Engine 5, using in-depth research on Schwerin Castle to faithfully recreate its architecture. The goal was to analyze and replicate Sebastian Luca’s art style within the UE5. Finally, I presented three dynamic material variations of the scene in UE to illustrate seasonal changes through material adjustments
Predictive Modeling of Colorectal Cancer Risk: Leveraging Health, Demographic, and Socioeconomic Factors for Targeted Screening
No full textColorectal cancer (CRC) remains a significant public health concern, affecting millions in the United States and worldwide. This study investigates the risk factors associated with CRC using data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and aims to develop predictive models to identify high-risk individuals for targeted screening and increased awareness. The dataset integrates CRC incidence data from the National Cancer Institute with socioeconomic indicators from U.S. Census Bureau, linked by zip code. We employ Logistic Regression and Neural Network models to predict CRC risk, incorporating health, demographic, and socio-economic features. While the results suggest that factors such as age and address history are significant contributors to CRC risk, the inclusion of Census data had a marginal impact on model performance, likely due to limited geographic diversity. The study further explores the use of a user-facing risk calculator, designed to raise awareness and encourage screening, with a focus on accessibility and simplicity for users. These findings emphasize the importance of incorporating a broad range of factors in CRC risk prediction and the potential for improving outreach through user-friendly tools
Destruction of Castle Savoia (Castle Savoy) with Dynamic Weather Effects
No full textThis thesis project focused on three masteries: Historical Recreation, Static Destruction, and Dynamic Weather. The Castle Savoia is recreated in the original and the destroyed version, to be switched between different weather types. Each weather type would affect the scene dynamically, either making the castle appear wet or covered by accumulated snow
Delights of the Cup: Materiality, Function, and Reception of Late Antique Iranian Silver Vessels
No full textThis dissertation aims to reassert the role of materiality in the interpretation of silver vessels produced in the Iranian world during the Sasanian and early Islamic periods (c. 300–900 CE). It argues that the material qualities of silver vessels, along with the sophisticated processes employed in their making, profoundly contributed to the fascination inspired by these objects throughout history. This research also attends to the questions of function and reception. Particularly, it explores how silver vessels were used and physically engaged with in their original setting. This emphasis on the objects’ practical and tangible dimensions leads to a deeper appreciation of their multi-sensory affordances. Furthermore, this dissertation foregrounds a transcultural and transhistorical approach to the study of Iranian silver vessels. As the objects crossed the temporal and geographical boundaries of late antique Iran, their original iconographic significance was often lost, while they adopted new meanings and, in many cases, new functions. Yet, this process of recontextualization did not impact the allure that stemmed from the objects’ material and technical brilliance. This study argues that the transcultural fascination with the materiality of silver vessels is key to unravelling their long-term currency across the wider Eurasian expanse. This dissertation presents the first comprehensive reevaluation of late antique Iranian silver vessels in over four decades and the first art historical analysis of their material, technical, and sensory dimensions