On The Origin of Super-Hot Electrons from Intense Laser Interactions with Solid Targets having Moderate Scale Length Preformed Plasmas

We use PIC modeling to identify the acceleration mechanism responsible for the observed generation of super-hot electrons in ultra-intense laser-plasma interactions with solid targets with pre-formed plasma. We identify several features of direct laser acceleration (DLA) that drive the generation of super-hot electrons. We find that, in this regime, electrons that become super-hot are primarily injected by a looping mechanism that we call loop-injected direct acceleration (LIDA)

Graph-Sparse LDA: A Topic Model with Structured Sparsity

Originally designed to model text, topic modeling has become a powerful tool for uncovering latent structure in domains including medicine, finance, and vision. The goals for the model vary depending on the application: in some cases, the discovered topics may be used for prediction or some other downstream task. In other cases, the content of the topic itself may be of intrinsic scientific interest. Unfortunately, even using modern sparse techniques, the discovered topics are often difficult to interpret due to the high dimensionality of the underlying space. To improve topic interpretability, we introduce Graph-Sparse LDA, a hierarchical topic model that leverages knowledge of relationships between words (e.g., as encoded by an ontology). In our model, topics are summarized by a few latent concept-words from the underlying graph that explain the observed words. Graph-Sparse LDA recovers sparse, interpretable summaries on two real-world biomedical datasets while matching state-of-the-art prediction performance

The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition

Applications of Sparse Representations

In this dissertation I explore the properties and uses of sparse representations. Sparse representations use high dimensional binary vectors for representing information. They have many properties which make this representation useful for applications involving pattern recognition in highly noisy and complex environments. Sparse representations have a very high capacity. A typical sparse representation vector has a capacity of 10^84 distinct vectors, which is more than the number of atoms in the universe. Sparse representations are highly noise robust. They can tolerate even up to 50% noise. A very powerful and useful property of sparse representations is that they allow us to easily measure similarity between two things by directly comparing their representations. These properties allow them to have applications in a variety of fields, like Artificial Intelligence and Molecular Biology, that need to encode information that is complex and noisy in nature. In this dissertation, I show how sparse representations can be used for representing complex environments for an agent based on Learning Intelligent Decision Agent (LIDA) model. Sparse representations allowed us to achieve a two-fold goal of producing information rich representations of things in the environment while proposing a method of generating grounded representations for the LIDA model. Sparse representations also allowed us to ground the representations used by LIDA in the sensory apparatus of the agent while still allowing a perfect fidelity communication between the sensory memory of LIDA and the rest of the model. I also show how sparse representations are useful in Molecular Biology for discovering data-driven patterns in heterogeneous and noisy gene expression data. We used a sparse auto-encoder to learn sparse representations of transcriptomics experiments taken from a huge publicly available dataset. These representations were then used to identify biological patterns in the form of gene sets. The representation provided a unique signature for a set of samples originating from the same experimental condition. Applications of our method include the identification of previously undiscovered gene sets as well as supervised classification of samples from different biological classes. Overall, our results show that sparse representations are useful in a variety of fields that involve finding patterns in a complex and noisy environment

QED and Electroweak Corrections to Deep Inelastic Scattering

We describe the state of the art in the field of radiative corrections for deep inelastic scattering. Different methods of calculation of radiative corrections are reviewed. Some new results for QED radiative corrections for polarized deep inelastic scattering at HERA are presented. A comparison of results obtained by the codes POLRAD and HECTOR is given for the kinematic regime of the HERMES experiment. Recent results on radiative corrections to deep inelastic scattering with tagged photons are briefly discussed.Comment: 22 pages Latex, including 6 eps-figures; to appear in the Proceedings of the 3rd International Symposium on Radiative Corrections, Cracow, August 1-5, 1996, Acta Phys. Polonica

Heterogeneous Proxytypes Extended: Integrating Theory-like Representations and Mechanisms with Prototypes and Exemplars

The paper introduces an extension of the proposal according to which conceptual representations in cognitive agents should be intended as heterogeneous proxytypes. The main contribution of this paper is in that it details how to reconcile, under a heterogeneous representational perspective, different theories of typicality about conceptual representation and reasoning. In particular, it provides a novel theoretical hypothesis - as well as a novel categorization algorithm called DELTA - showing how to integrate the representational and reasoning assumptions of the theory-theory of concepts with the those ascribed to the prototype and exemplars-based theories