First introduced by Forman in 1998, discrete Morse theory has become a standard tool in topological combinatorics. The main idea of discrete Morse theory is to pair cells in a cellular complex in a manner that permits cancellation via elementary collapses, reducing the complex under consideration to a homotopy equivalent complex with fewer cells. In chapter 1, we introduce the relevant background for discrete Morse theory. In chapter 2, we define a discrete Morse matching for a family of independence complexes that generalize the matching complexes of suitable "small" grid graphs. Using this matching, we determine the dimensions of the chain spaces for the resulting Morse complexes and derive bounds on the location of non-trivial homology groups. Furthermore, we determine the Euler characteristic for these complexes and prove that several of their homology groups are non-zero. In chapter 3, we introduce the notion of a homomorphism complex for partially ordered sets, placing particular emphasis on maps between chain posets and the Boolean algebras. We extend the notion of folding from general graph homomorphism complexes to the poset case, and we define an iterative discrete Morse matching for these Boolean complexes. We provide formulas for enumerating the number of critical cells arising from this matching as well as for the Euler characteristic. We end with a conjecture on the optimality of our matching derived from connections to 3-equal manifolds

Although gold nanoparticles (GNPs) are promising probes for biological imaging because of their attracting optical properties and bio-friendly nature, properties of the multi-photon (MP) emission from GNP aggregates produced by a short-wave infrared (SWIR) laser have not been examined. In this paper, characterization of MP emission from aggregated 50 nm GNPs excited by a femtosecond (fs) laser at 1560 nm is discussed with respect to aggregate structures. The key technique in this work is single particle spectroscopy. A pattern matching technique is applied to correlate MP emission and SEM images, which includes an optimization processes to maximize cross correlation coefficients between a binary microscope image and a binary SEM image with respect to xy displacement, image rotation angle, and image magnification. Once optimization is completed, emission spots are matched to the SEM image, which clarifies GNP ordering and emission properties of each aggregate. Correlation results showed that GNP aggregates have stronger MP emission than single GNPs. By combining the pattern matching technique with spectroscopy, MP emission spectrum is characterized for each GNP aggregate. A broad spectrum in the visible region and near infrared (NIR) region is obtained from GNP dimers, unlike previously reported surface plasmon enhanced emission spectrum.

Approved for public release, distribution is unlimited / The enlisted personnel assignment process is a major part in the United States Navy's Personnel Distribution system. It ensures warfighters and supporting activities receive the right sailor with the right training to the right billet at the right time (R4) and is a critical element in meeting the challenges of Seapower 21 and Global CONOPS. In order to attain these optimal goals the ways-to-do-it need to be customer-centered and should optimize both, the Navy's needs and the sailor's interests. Recent studies and a detailing pilot in 2002 used a web-based marketplace with two-sided matching mechanisms to accomplish this vision. This research examines the introduction of an Assignment Incentive Pay (AIP) as part of the U.S. Navy's enlisted personnel assignment process in a simulation environment. It uses a previously developed simulation tool, including the Deferred Acceptance (DA) and the Linear Programming (LP) matching algorithm to simulate the assignment process. The results of the sensitivity analysis suggested that the Navy should mainly emphasize sailor quality rather than saving AIP funds in order to maximize utility and the possible matches. When adopting such an introduction policy also the percentage of unstable matches under the LP as the matching algorithm was reduced. / Commander, German Navy

Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers’ effectiveness, i.e., the accuracy and the completeness of the results. In this context (i) the size of the empirical data is often small, (ii) all data is used for the matcher development, and (iii) the validity of the design decisions is not studied. As a result, existing matchers yield a varying and typically low effectiveness when applied to different datasets, as among others demonstrated by the process model matching contests in 2013 and 2015. With this in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In particular, the thesis develops matchers that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced which achieves a high effectiveness in comparison to the state of the art. Second, the Order Preserving Bag-of-Words Technique analyzes temporal dependencies between activities in order to automatically configure the Bag-of-Words Technique and to improve its effectiveness. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections. Here, the Adaptive Bag-of-Words Technique is introduced which outperforms the state-of-the-art matchers and the other matchers from this thesis.

The rapid increase in mobile malware and deployment of over-privileged applications over the years has been of great concern to the security community. Encroaching on user’s privacy, mobile applications (apps) increasingly exploit various sensitive data on mobile devices. The information gathered by these applications is sufficient to uniquely and accurately profile users and can cause tremendous personal and financial damage. On Android specifically, the security and privacy holes in the operating system and framework code has created a whole new dynamic for malware and privacy exploitation. This research work seeks to develop novel analysis techniques that monitor Android applications for possible unwanted behaviors and then suggest various ways to deal with the privacy leaks associated with them. Current state-of-the-art static malware analysis techniques on Android-focused mainly on detecting known variants without factoring any kind of software obfuscation. The dynamic analysis systems, on the other hand, are heavily dependent on extending the Android OS and/or runtime virtual machine. These methodologies often tied the system to a single Android version and/or kernel making it very difficult to port to a new device. In privacy, accesses to the database system’s objects are not controlled by any security check beyond overly-broad read/write permissions. This flawed model exposes the database contents to abuse by privacy-agnostic apps and malware. This research addresses the problems above in three ways. First, we developed a novel static analysis technique that fingerprints known malware based on three-level similarity matching. It scores similarity as a function of normalized opcode sequences found in sensitive functional modules and application permission requests. Our system has an improved detection ratio over current research tools and top COTS anti-virus products while maintaining a high level of resiliency to both simple and complex obfuscation. Next, we augment the signature-related weaknesses of our static classifier with a hybrid analysis system which incorporates bytecode instrumentation and dynamic runtime monitoring to examine unknown malware samples. Using the concept of Aspect-oriented programming, this technique involves recompiling security checking code into an unknown binary for data flow analysis, resource abuse tracing, and analytics of other suspicious behaviors. Our system logs all the intercepted activities dynamically at runtime without the need for building custom kernels. Finally, we designed a user-level privacy policy enforcement system that gives users more control over their personal data saved in the SQLite database. Using bytecode weaving for query re-writing and enforcing access control, our system forces new policies at the schema, column, and entity levels of databases without rooting or voiding device warranty.

The paper applies a non-parametric propensity score matching approach to evaluate the effects of two types of farm programs (agri-environment (AE) programs and the less favoured area (LFA) scheme) on input use and farm output of individual farms in Germany. The analysis reveals a positive and significant treatment effect of the LFA scheme for farm sales and the area under cultivation. Participants in AE schemes are found to significantly increase the area under cultivation (in particular grassland), resulting in a decrease of livestock densities. Furthermore, participation in AE programs significantly reduced the purchase of farm chemicals (fertilizer, pesticide). We also find substantial differences in the treatment effect between individual farms (heterogeneous treatment effects). Farms which can generate the largest benefit from the program are most likely to participate. (authors' abstract) / Series: Department of Economics Working Paper Series

Indoor tracking and navigation is a fundamental need for pervasive and context-aware applications. However, no practical and reliable indoor positioning solution is available at present. The major challenge of a practical solution lies in the fact that only the existing devices and infrastructure can be utilized to achieve high positioning accuracy. This thesis presents a robust indoor positioning system with the lifelong learning ability. The typical features of the proposed solution is low-cost, accurate, robust, and scalable. This system only takes the floor plan and the existing devices, e.g. phones, pads, etc. and infrastructure such as WiFi/BLE access points for the sake of practicality. This system has four closely correlated components including, non-line-of-sight identification and mitigation (NIMIT), robust pedestrian dead reckoning (R-PDR), lightweight map matching (MapCraft), and lifelong learning. NIMIT projects the received signal strength (RSS) from WiFi/BLE to locations. The R-PDR component converts the data from inertial measurement unit (IMU) sensors ubiquitous in mobile devices and wearables to the trajectories of the user. Then MapCraft fuses trajectories estimated from the R-PDR and the coarse location information from NIMIT with the floor plan and provides accurate location estimations. The lifelong learning component then learns the various parameters used in all other three components in an unsupervised manner, which continuously improves the the positioning accuracy of the system. Extensive real world experiments in multiple sites show how the proposed system outperforms state-of-the art approaches, demonstrating excellent sub-meter positioning accuracy and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position regardless of the users, devices, attachments, and environments. We believe that such an accurate and robust approach will enable always-on background localization, enabling a new era of location-aware applications to be developed.

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The aim of this study was to establish whether the asymmetry of bilateral elements of the skeleton is useful for the reassociation of paired elements in the analysis of commingled skeletal remains; particularly addressing the forensic scenario of Chilean Human Rights cases. The asymmetry of the appendicular skeleton of the modern adult Chilean population was investigated in its morphological aspect, using both traditional anthropometry and geometric morphometrics. The sample was selected from the Colección Subactual de Santiago, housed in the University of Chile, Santiago, Chile, with N= 131 (69 males and 62 females). The traditional metric analysis of size and the geometric morphometric analysis of shape showed that there was a significant difference between sides in both sexes with a strong component of directional asymmetry. Mean metrics and ranges of asymmetry were established, contributing to the characterization of this population. A method to pair match elements from commingled settings, which is a combination of metric ranges of asymmetry and principal component analysis of shape variables, was created resulting in 95% accuracy when pair matching the humerus, radius, femur and tibia. This constitutes an important contribution to the analysis of shape in forensic contexts due to its strong mathematical component, objectivity and repeatability.

The problem of this study was to determine the effect of a modified form of Kephart's perceptual-motor training program on the performance of kindergarten pupils on Metropolitan Readiness Tests. This program was made up of certain perceptual-motor activities which were utilized during the regular school play periods.