Handling the Subclassing Anomaly with Object Teams

Java software or libraries can evolve via subclassing. Unfortunately, subclassing may not properly support code adaptation when there are dependencies between classes. More precisely, subclassing in collections of related classes may require reimplementation of otherwise valid classes. This problem is defined as the subclassing anomaly, which is an issue when software evolution or code reuse is a goal of the programmer who is using existing classes. Object Teams offers an implicit fix to this problem and is largely compatible with the existing JVMs. In this paper, we evaluate how well Object Teams succeeds in providing a solution for a complex, real world project. Our results indicate that while Object Teams is a suitable solution for simple examples, it does not meet the requirements for large scale projects. The reasons why Object Teams fails in certain usages may prove useful to those who create linguistic modifications in languages or those who seek new methods for code adaptation

The Java 5 Generics Compromise Orthogonality to Keep Compatibility

In response to a long-lasting anticipation by the Java community, version 1.5 of the Java 2 platform - referred to as Java 5 - introduced generic types and methods to the Java language. The Java 5 generics are a significant enhancement to the language expressivity because they allow straightforward composition of new generic classes from existing ones while reducing the need for a plethora of type casts. While the Java 5 generics are expressive, the chosen implementation method, type erasure, has triggered undesirable orthogonality violations. This paper identifies six cases of orthogonality violations in the Java 5 generics and demonstrates how these violations are mandated by the use of type erasure. The paper also compares the Java 5 cases of orthogonality violations to compatible cases in C# 2 and NextGen 2 and analyzes the trade-offs in the three approaches. The conclusion is that Java 5 users face new challenges: a number of generic type expressions are forbidden, while others that are allowed are left unchecked

Exploiting semantic information in a spiking neural SLAM system

To navigate in new environments, an animal must be able to keep track of its position while simultaneously creating and updating an internal map of features in the environment, a problem formulated as simultaneous localization and mapping (SLAM) in the field of robotics. This requires integrating information from different domains, including self-motion cues, sensory, and semantic information. Several specialized neuron classes have been identified in the mammalian brain as being involved in solving SLAM. While biology has inspired a whole class of SLAM algorithms, the use of semantic information has not been explored in such work. We present a novel, biologically plausible SLAM model called SSP-SLAM—a spiking neural network designed using tools for large scale cognitive modeling. Our model uses a vector representation of continuous spatial maps, which can be encoded via spiking neural activity and bound with other features (continuous and discrete) to create compressed structures containing semantic information from multiple domains (e.g., spatial, temporal, visual, conceptual). We demonstrate that the dynamics of these representations can be implemented with a hybrid oscillatory-interference and continuous attractor network of head direction cells. The estimated self-position from this network is used to learn an associative memory between semantically encoded landmarks and their positions, i.e., an environment map, which is used for loop closure. Our experiments demonstrate that environment maps can be learned accurately and their use greatly improves self-position estimation. Furthermore, grid cells, place cells, and object vector cells are observed by this model. We also run our path integrator network on the NengoLoihi neuromorphic emulator to demonstrate feasibility for a full neuromorphic implementation for energy efficient SLAM

Application of functional genomics to the chimeric mouse model of HCV infection: optimization of microarray protocols and genomics analysis

BACKGROUND: Many model systems of human viral disease involve human-mouse chimeric tissue. One such system is the recently developed SCID-beige/Alb-uPA mouse model of hepatitis C virus (HCV) infection which involves a human-mouse chimeric liver. The use of functional genomics to study HCV infection in these chimeric tissues is complicated by the potential cross-hybridization of mouse mRNA on human oligonucleotide microarrays. To identify genes affected by mouse liver mRNA hybridization, mRNA from identical human liver samples labeled with either Cy3 or Cy5 was compared in the presence and absence of known amounts of mouse liver mRNA labeled in only one dye. RESULTS: The results indicate that hybridization of mouse mRNA to the corresponding human gene probe on Agilent Human 22 K oligonucleotide microarray does occur. The number of genes affected by such cross-hybridization was subsequently reduced to approximately 300 genes both by increasing the hybridization temperature and using liver samples which contain at least 80% human tissue. In addition, Real Time quantitative RT-PCR using human specific probes was shown to be a valid method to verify the expression level in human cells of known cross-hybridizing genes. CONCLUSION: The identification of genes affected by cross-hybridization of mouse liver RNA on human oligonucleotide microarrays makes it feasible to use functional genomics approaches to study the chimeric SCID-beige/Alb-uPA mouse model of HCV infection. This approach used to study cross-species hybridization on oligonucleotide microarrays can be adapted to other chimeric systems of viral disease to facilitate selective analysis of human gene expression

Dynamic Chromatin Organization during Foregut Development Mediated by the Organ Selector Gene PHA-4/FoxA

Central regulators of cell fate, or selector genes, establish the identity of cells by direct regulation of large cohorts of genes. In Caenorhabditis elegans, foregut (or pharynx) identity relies on the FoxA transcription factor PHA-4, which activates different sets of target genes at various times and in diverse cellular environments. An outstanding question is how PHA-4 distinguishes between target genes for appropriate transcriptional control. We have used the Nuclear Spot Assay and GFP reporters to examine PHA-4 interactions with target promoters in living embryos and with single cell resolution. While PHA-4 was found throughout the digestive tract, binding and activation of pharyngeally expressed promoters was restricted to a subset of pharyngeal cells and excluded from the intestine. An RNAi screen of candidate nuclear factors identified emerin (emr-1) as a negative regulator of PHA-4 binding within the pharynx, but emr-1 did not modulate PHA-4 binding in the intestine. Upon promoter association, PHA-4 induced large-scale chromatin de-compaction, which, we hypothesize, may facilitate promoter access and productive transcription. Our results reveal two tiers of PHA-4 regulation. PHA-4 binding is prohibited in intestinal cells, preventing target gene expression in that organ. PHA-4 binding within the pharynx is limited by the nuclear lamina component EMR-1/emerin. The data suggest that association of PHA-4 with its targets is a regulated step that contributes to promoter selectivity during organ formation. We speculate that global re-organization of chromatin architecture upon PHA-4 binding promotes competence of pharyngeal gene transcription and, by extension, foregut development

Sewer System Alternatives Evaluation for Potential Creswell Area Expansion in Harford County

Final project for ENCE422: Project Cost Accounting and Economics (Fall 2018). University of Maryland, College Park.This report summarizes the findings of the ENCE422 Fall 2018 class term project. Students were tasked with evaluating sewer system alternatives for the Creswell area expansion in Harford County. Student groups were to consider environmental impacts, community/social impacts, and perform financial analysis for the alternatives they chose to evaluate. This report extracts information from 14 separate team presentations and synthesizes it around the following structure; 1. Systems that Utilize Septic Tanks a. Traditional Septic System b. Orenco Effluent System c. Small Diameter Gravity Sewer System 2. System that Do Not Utilize Septic Tanks a. Traditional Gravity System b. Vacuum System c. Grinder Pump SystemHarford Count

HANDLING THE SUBCLASSING ANOMALY WITH OBJECT TEAMS

Abstract: Java software or libraries can evolve via subclassing. Unfortunately, subclassing may not properly support code adaptation when there are dependencies between classes. More precisely, subclassing in collections of related classes may require reimplementation of otherwise valid classes. This problem is defined as the subclassing anomaly, which is an issue when software evolution or code reuse is a goal of the programmer who is using existing classes. Object Teams offers an implicit fix to this problem and is largely compatible with the existing JVMs. In this paper, we evaluate how well Object Teams succeeds in providing a solution for a complex, real world project. Our results indicate that while Object Teams is a suitable solution for simple examples, it does not meet the requirements for large scale projects. The reasons why Object Teams fails in certain usages may prove useful to those who create linguistic modifications in languages or those who seek new methods for code adaptation

Using Vector Symbolic Architectures for Distributed Action Representations in a Spiking Model of the Basal Ganglia

Existing models of the basal ganglia assume the existence of separate channels of neuron populations for representing each available action. This type of localist mapping limits models to small, discrete action spaces, since additional actions require additional channels, costing neural resources and imposing new connective tracts. In contrast, evidence suggests that the basal ganglia plays a role in the selection of both discrete action units, and continuously-valued action kinematics. In this work, we model the basal ganglia with distributed action representations, using high-dimensional vectors. This method lends itself to representing both discrete and continuous action spaces. Vectors that represent actions are weighted by a scalar value (their salience to the current task), and bundled together to form a single input vector. This paper provides an overview of the encoding method and network structure, as well as a demonstration of the model solving an action selection task using spiking neurons