5,336 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
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A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical example of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Single-file pedestrian dynamics: a review of agent-following models
Single-file dynamics has been studied intensively, both experimentally and
theoretically. It shows interesting collective effects, such as stop-and-go
waves, which are validation cornerstones for any agent-based modeling approach
of traffic systems. Many models have been proposed, e.g. in the form of
car-following models for vehicular traffic. These approaches can be adapted for
pedestrian streams. In this study, we delve deeper into these models, with
particular attention on their interconnections. We do this by scrutinizing the
influence of different parameters, including relaxation times, anticipation
time, and reaction time. Specifically, we analyze the inherent fundamental
problems with force-based models, a classical approach in pedestrian dynamics.
Furthermore, we categorize car-following models into stimulus-response and
optimal velocity models, highlighting their historical and conceptual
differences. These classes can further be subdivided considering the conceptual
definitions of the models, e.g. first-order vs. second-order models, or
stochastic vs. deterministic models with and without noise. Our analysis shows
how car-following models originally developed for vehicular traffic can provide
new insights into pedestrian behavior. The focus on single-file motion, which
is similar to single-lane vehicular traffic, allows for a detailed examination
of the relevant interactions between pedestrians.Comment: 35 pages, 10 Figures; chapter accepted for publication in Crowd
Dynamics (vol. 4
Reversible Quantum-Dot Cellular Automata-Based Arithmetic Logic Unit
Quantum-dot cellular automata (QCA) are a promising nanoscale computing technology that exploits the quantum mechanical tunneling of electrons between quantum dots in a cell andelectrostatic interaction between dots in neighboring cells. QCA can achieve higher speed, lowerpower, and smaller areas than conventional, complementary metal-oxide semiconductor (CMOS)
technology. Developing QCA circuits in a logically and physically reversible manner can provide exceptional reductions in energy dissipation. The main challenge is to maintain reversibility down to the physical level. A crucial component of a computerâs central processing unit (CPU) is the arithmetic logic unit (ALU), which executes multiple logical and arithmetic functions on the data processed by the CPU. Current QCA ALU designs are either irreversible or logically reversible; however, they lack physical reversibility, a crucial requirement to increase energy efficiency. This paper shows a new multilayer design for a QCA ALU that can carry out 16 different operations and is both logically and physically reversible. The design is based on reversible majority gates, which are the key building blocks. We use QCA Designer-E software to simulate and evaluate energy dissipation.
The proposed logically and physically reversible QCA ALU offers an improvement of 88.8% in energy efficiency. Compared to the next most efficient 16-operation QCA ALU, this ALU uses 51% fewer QCA cells and 47% less area
Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results
Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are short. By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterize behavioral complexity. Next, we approximate structural (Bennettâs Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioral string. We apply our toolbox to three landmark studies of animal behavior of increasing sophistication and degree of environmental influence, including studies of foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition (e.g., predator-prey) strategies. We find that ants harness the environmental condition in their internal decision process, modulating their behavioral complexity accordingly. Our analysis of flight (fruit flies) invalidated the common hypothesis that animals navigating in an environment devoid of stimuli adopt a random strategy. Fruit flies exposed to a featureless environment deviated the most from Levy flight, suggesting an algorithmic bias in their attempt to devise a useful (navigation) strategy. Similarly, a logical depth analysis of rats revealed that the structural complexity of the rat always ends up matching the structural complexity of the competitor, with the ratsâ behavior simulating algorithmic randomness. Finally, we discuss how experiments on how humans perceive randomness suggest the existence of an algorithmic bias in our reasoning and decision processes, in line with our analysis of the animal experiments. This contrasts with the view of the mind as performing faulty computations when presented with randomized items. In summary, our formal toolbox objectively characterizes external constraints on putative models of the âinternalâ decision process in humans and animals
Layered Cellular Automata
Layered Cellular Automata (LCA) extends the concept of traditional cellular
automata (CA) to model complex systems and phenomena. In LCA, each cell's next
state is determined by the interaction of two layers of computation, allowing
for more dynamic and realistic simulations. This thesis explores the design,
dynamics, and applications of LCA, with a focus on its potential in pattern
recognition and classification. The research begins by introducing the
limitations of traditional CA in capturing the complexity of real-world
systems. It then presents the concept of LCA, where layer 0 corresponds to a
predefined model, and layer 1 represents the proposed model with additional
influence. The interlayer rules, denoted as f and g, enable interactions not
only from adjacent neighboring cells but also from some far-away neighboring
cells, capturing long-range dependencies. The thesis explores various LCA
models, including those based on averaging, maximization, minimization, and
modified ECA neighborhoods. Additionally, the implementation of LCA on the 2-D
cellular automaton Game of Life is discussed, showcasing intriguing patterns
and behaviors. Through extensive experiments, the dynamics of different LCA
models are analyzed, revealing their sensitivity to rule changes and block size
variations. Convergent LCAs, which converge to fixed points from any initial
configuration, are identified and used to design a two-class pattern
classifier. Comparative evaluations demonstrate the competitive performance of
the LCA-based classifier against existing algorithms. Theoretical analysis of
LCA properties contributes to a deeper understanding of its computational
capabilities and behaviors. The research also suggests potential future
directions, such as exploring advanced LCA models, higher-dimensional
simulations, and hybrid approaches integrating LCA with other computational
models.Comment: This thesis represents the culmination of my M.Tech research,
conducted under the guidance of Dr. Sukanta Das, Associate Professor at the
Department of Information Technology, Indian Institute of Engineering Science
and Technology, Shibpur, West Bengal, India. arXiv admin note: substantial
text overlap with arXiv:2210.13971 by other author
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