Ramifications of Projectile Velocity on the Ballistic Dart Penetration of Sand

With the advent of novel in-situ experimental measurement techniques, highly resolved quantitative observations of dynamic events within granular media can now be made. In particular, high speed imagery and digital analysis now allow for the ballistic behaviors of sand to be examined not only across a range of event velocities but across multiple length scales. In an attempt to further understand the dynamic behavior of granular media, these new experimental developments were implemented utilizing high speed photography coupled with piezo-electric stress gauges to observe visually accessible ballistic events of a dart penetrating Ottawa sand. Projectile velocities ranged from 100 to over 300 meters per second with two distinct chosen fields of view to capture bulk and grain-scale behaviors. Each event was analyzed using the digital image correlation technique, particle image velocimetry from which two dimensional, temporally resolved, velocity fields were extracted, from which bulk granular flow and compaction wave propagation were observed and quantified. By comparing bulk, in situ, velocity field behavior resultant from dart penetration, momentum transfer could be quantified measuring radius of influence or dilatant fluid approximations from which a positive correlation was found across the explored velocity regime, including self similar tendencies. This was, however, not absolute as persistent scatter was observed attributed to granular heterogeneous effects. These were tentatively measured in terms of an irreversible energy amount calculated via energy balance. Grain scale analysis reveals analogous behavior to the bulk response with more chaotic structure, though conclusions were limited by the image processing method to qualitative observations. Even so, critical granular behaviors could be seen, such as densification, pore collapse, and grain fracture from which basic heterogeneous phenomena could be examined. These particularly dominated near nose interactions at high projectile velocities. Resulting empirical models and observations from all approaches provide a baseline from which other studies across may be compared, a metric against which penetrator effectiveness may be evaluated, and an alternative way to validate computationally based analyses. Velocity analysis was further contrasted with piezo-resistive stress gauge data in an effort to pair heterogeneous mechanisms in the bulk stress response. Phenomena such as grain fracture and densification were successfully observed in conjunction with a unique stress signature. Comparing stress responses across the tested velocity spectrum confirm conditional similitude with deviations a low projectile velocities attributed to domination by heterogeneous mechanisms

Exploiting Host Availability in Distributed Systems.

As distributed systems become more decentralized, fluctuating host availability is an increasingly disruptive phenomenon. Older systems such as AFS used a small number of well-maintained, highly available machines to coordinate access to shared client state; server uptime (and thus service availability) were expected to be high. Newer services scale to larger number of clients by increasing the number of servers. In these systems, the responsibility for maintaining the service abstraction is spread amongst thousands of machines. In the extreme, each client is also a server who must respond to requests from its peers, and each host can opt in or out of the system at any time. In these operating environments, a non-trivial fraction of servers will be unavailable at any give time. This diffusion of responsibility from a few dedicated hosts to many unreliable ones has a dramatic impact on distributed system design, since it is difficult to build robust applications atop a partially available, potentially untrusted substrate. This dissertation explores one aspect of this challenge: how can a distributed system measure the fluctuating availability of its constituent hosts, and how can it use an understanding of this churn to improve performance and security? This dissertation extends the previous literature in three ways. First, it introduces new analytical techniques for characterizing availability data, applying these techniques to several real networks and explaining the distinct uptime patterns found within. Second, this dissertation introduces new methods for predicting future availability, both at the granularity of individual hosts and clusters of hosts. Third, my dissertation describes how to use these new techniques to improve the performance and security of distributed systems.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58445/1/jmickens_1.pd

POPULATION DYNAMICS AND ENVIRONMENTAL CHANGE: WHICH FACTORS COMPLICATE PREDICTION AND INFERENCE?

Changes in both the biotic and abiotic environment influence individuals’ physiology, morphology and behaviour and influence key ecological rates underpinning population dynamics. Environmental change is ubiquitous in natural systems and is often multifaceted, as multiple aspects of the climate often change simultaneously and the abundance and traits of species in the community are constantly fluctuating. In this thesis, we study the ecological consequences of environmental changes. We identify fundamental factors complicating our understanding of population dynamics and develop analytical tools to reliably infer, from data, the impacts of environmental change on key biological processes. We present evidence that the impacts of environmental change on population dynamics can be modified by other concurrent environmental changes. Furthermore, the impacts on a focal species will likely be strongly dependent on how the performance of interacting species are affected. We then show that the addition of predators to an environment can cause prey to become more defended against predation, at a cost of reduced population growth. Such growth-defence trade-offs are expected to drive complex population dynamics. We demonstrate that our understanding of community dynamics can be improved by identifying how consumption rates vary with changes in morphological or behavioural traits. We identify feedbacks between species’ trait and abundance dynamics. We then provide evidence that environmental warming can modify the impacts of trait change on species interactions. We inferred that this likely resulted from a modified life history strategy or altered resources allocation to growth rather than defence. Finally, we use simulation studies to assess the reliability of current methods at inferring climate effects on the demography of wild populations. We demonstrate that commonly used approaches perform poorly and also identify a reliable modelling framework. The findings of this work provided quantitative insights into the impacts of environmental change on the processes driving species’ dynamics. It also highlights the role of combined environmental change, trait change and species interaction in complicating the prediction of population dynamics

Evidence-based Cybersecurity: Data-driven and Abstract Models

Achieving computer security requires both rigorous empirical measurement and models to understand cybersecurity phenomena and the effectiveness of defenses and interventions. To address the growing scale of cyber-insecurity, my approach to protecting users employs principled and rigorous measurements and models. In this dissertation, I examine four cybersecurity phenomena. I show that data-driven and abstract modeling can reveal surprising conclusions about longterm, persistent problems, like spam and malware, and growing threats like data-breaches and cyber conflict. I present two data-driven statistical models and two abstract models. Both of the data-driven models show that the presence of heavy-tailed distributions can make naive analysis of trends and interventions misleading. First, I examine ten years of publicly reported data breaches and find that there has been no increase in size or frequency. I also find that reported and perceived increases can be explained by the heavy-tailed nature of breaches. In the second data-driven model, I examine a large spam dataset, analyzing spam concentrations across Internet Service Providers. Again, I find that the heavy-tailed nature of spam concentrations complicates analysis. Using appropriate statistical methods, I identify unique risk factors with significant impact on local spam levels. I then use the model to estimate the effect of historical botnet takedowns and find they are frequently ineffective at reducing global spam concentrations and have highly variable local effects. Abstract models are an important tool when data are unavailable. Even without data, I evaluate both known and hypothesized interventions used by search providers to protect users from malicious websites. I present a Markov model of malware spread and study the effect of two potential interventions: blacklisting and depreferencing. I find that heavy-tailed traffic distributions obscure the effects of interventions, but with my abstract model, I showed that lowering search rankings is a viable alternative to blacklisting infected pages. Finally, I study how game-theoretic models can help clarify strategic decisions in cyber-conflict. I find that, in some circumstances, improving the attribution ability of adversaries may decrease the likelihood of escalating cyber conflict

The sleeping brain and the neural basis of emotions

In addition to active wake, emotions are generated and experienced in a variety of functionally different states such as those of sleep, during which external stimulation and cognitive control are lacking. The neural basis of emotions can be specified by regarding the multitude of emotion-related brain states, as well as the distinct neuro- and psychodynamic stages (generation and regulation) of emotional experienc

Probabilistically Inferring Attack Ramifications Using Temporal Dependence Network

There is an increasing need of assessing and mitigating the effects of successful attacks. Uncovering malicious and contaminated objects in an attacked computing system is referred to as identification of attack ramifications. Previous methods identify the attack ramifications by directly tracking information flows (or dependences) from the intrusion root (i.e., the entry point of an attack). They face challenges such as undetermined intrusion root and dependence explosion. In this paper, we present a novel, light-weight method capable of identifying attack ramifications without the knowledge of intrusion root and less subject to dependency explosion. The method utilizes a probabilistic reasoning approach to fuse evidence derived from a subset of objects whose security states are known. It first splits the lifetime of an object into consecutive time slices (object-slices) to profile how the security state of this object changes over time. Then, a temporal dependence network (TDN) is constructed from system call traces to correlate object-slices according to information flows between them. Based on that, a Bayesian network (BN) model is built to characterize the uncertainties of infection propagations in the TDN. Finally, the method adopts loopy belief propagation on the BN model to infer the security state of an object. We evaluate the proposed method using a large data set of 389 attacks launched by the real-world malware samples including sophisticated ones such as Stuxnet. Extensive experiments demonstrate that our method is able to identify attack ramifications with a 97.47% precision at 97.21% recall without the knowledge of intrusion root

Appraisal of Cashless Policy on the Nigerian Financial System

The Central Bank of Nigeria (CBN) has been active in the inauguration of policies and schemes to foster the implementation of the cashless policy in Nigeria. However the current transition to cashless economy raises a lot of concerns with no substantial evidence yet to justify its implementation. This study was carried out in order to appraise the implementation of the cashless policy since its introduction into the Nigerian financial system in 2012 and also to examine the persistent challenges facing its implementation. In view of the above stated objective, primary data were collected with the aid of the questionnaire, which was randomly administered to 120 respondents ranging from First Bank, Zenith Bank and United Bank for Africa. The banks were selected based on their total assets and the information collected covered the activities of the CBN and that of these banks towards implementation of the cashless policy from 2012 till date.The data collected were presented and analyzed with the aid of the Statistical Package for Social Sciences (SPSS) using descriptive statistics and one-sample t-test. The results led to the conclusion that despite the need to operate cashless transactions dominating the modern Nigerian economy, the cashless policy will have the desired impact only if a lot is done to ensure the implementation of an effective cashless system

Causation and the Objectification of Agency

This dissertation defends the so-called 'agency-approach' to causation, which attempts to ground the causal relation in the cause's role of being a means to bring about its effect. The defence is confined to a conceptual interpretation of this theory, pertaining to the concept of causation as it appears in a causal judgement. However, causal judgements are not seen as limited to specific domains, and they are not exclusively attributed to human agents alone. As a methodological framework to describe the different perspectives of causal judgments, a method taken from the philosophy of information is made use of – the so-called 'method of abstraction'. According to this method, levels of abstraction are devised for the subjective perspective of the acting agent, for the agent as observer during the observation of other agents’ actions, and for the agent that judges efficient causation. As a further piece of propaedeutic work, a class of similar (yet not agency-centred) approaches to causation is considered, and their modelling paradigms – Bayesian networks and interventions objectively construed – will be criticised. The dissertation then proceeds to the defence of the agency-approach, the first part of which is a defence against the objection of conceptual circularity, which holds that agency analyses causation in causal terms. While the circularity-objection is rebutted, I rely at that stage on a set of subjective concepts, i.e. concepts that are eligible to the description of the agent’s own experience while performing actions. In order to give a further, positive corroboration of the agency-approach, an investigation into the natural origins and constraints of the concept of agency is made in the central chapter six of the dissertation. The thermodynamic account developed in that part affords a third-person perspective on actions, which has as its core element a cybernetic feedback cycle. At that point, the stage is set to analyse the relation between the first- and the third-person perspectives on actions previously assumed. A dual-aspect interpretation of the cybernetic-thermodynamic picture developed in chapter six will be directly applied to the levels of abstraction proposed earlier. The level of abstraction that underpins judgments of efficient causation, the kind of causation seemingly devoid of agency, will appear as a derived scheme produced by and dependent on the concept of agency. This account of efficient causation, the ‘objectification of agency’, affords the rebuttal of a second objection against the agency-approach, which claims that the approach is inappropriately anthropomorphic. The dissertation concludes with an account of single-case, or token level, causation, and with an examination of the impact of the causal concept on the validity of causal models