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A model of downward abusive communication: exploring relationships between cognitive complexity, downward communicative adaptability, and downward abusive communication
Thesis (M.A.) University of Alaska Fairbanks, 2013A model was proposed to understand the antecedents of abusive supervision. Relationships were explored between cognitive complexity, downward communicative adaptability, and downward abusive communication. Superiors from various organizations were asked to take an online survey which measured superiors' cognitive complexity, downward communicative adaptability and abusive supervision. There was no evidence to support H1, which linked cognitive complexity to downward communicative adaptability, but there was evidence for H2, which stated that downward communicative adaptability was negatively correlated with downward abusive communication. The RCQ proved to be reliable but its validity was questioned in the present study which is why H1 may not have been supported.Chapter 1. Theory and research -- 1.1. Abusive supervision -- 1.2. Cognitive complexity -- 1.2.1. Constructs -- 1.2.2. Cognitive complexity -- 1.2.3. Effects of cognitive complexity -- 1.2.3.1. Relational compatibility -- 1.2.3.2. Interpersonal problem solving -- 1.2.3.3. Perceptual differentiation -- 1.3. Communicative adaptability -- 1.3.1. Effects of communicative adaptability -- 1.3.1.1. Interpersonal attraction -- 1.3.1.2. Friendship formation -- 1.3.1.3. Conflict management -- 1.4. Linking cognitive complexity to communicative adaptability -- 1.5. Abusive supervision -- 1.5.1. Individual difference variables as causes of abusive supervision -- 1.5.1.1. Personality characteristics -- 1.5.1.2. Demographic characteristics -- 1.5.1.3. Supervisors' beliefs -- 1.6. Linking communicative adaptability to abusive communication -- 1.7. Hypotheses -- 2. Research methodology -- 2.1. Participants -- 2.2. Procedures -- 2.3. Measures -- 2.3.1. Cognitive complexity -- 2.3.2. Downward commicative adaptability -- 2.3.3. Downward abusive communication -- Chapter 3. Results -- 3.1.1. Linking cognitive complexity with downward communicative adaptability -- 3.1.2. Linking downward communicative adaptability and downward abusive commication -- Chapter 4. Discussion -- References
Multi-consensus Decentralized Accelerated Gradient Descent
This paper considers the decentralized optimization problem, which has
applications in large scale machine learning, sensor networks, and control
theory. We propose a novel algorithm that can achieve near optimal
communication complexity, matching the known lower bound up to a logarithmic
factor of the condition number of the problem. Our theoretical results give
affirmative answers to the open problem on whether there exists an algorithm
that can achieve a communication complexity (nearly) matching the lower bound
depending on the global condition number instead of the local one. Moreover,
the proposed algorithm achieves the optimal computation complexity matching the
lower bound up to universal constants. Furthermore, to achieve a linear
convergence rate, our algorithm \emph{doesn't} require the individual functions
to be (strongly) convex. Our method relies on a novel combination of known
techniques including Nesterov's accelerated gradient descent, multi-consensus
and gradient-tracking. The analysis is new, and may be applied to other related
problems. Empirical studies demonstrate the effectiveness of our method for
machine learning applications
A general purpose programming framework for ubiquitous computing environments
It is important to note that the need to support ad-hoc and potentially mobile arrangements of devices in ubiquitous environments does not fit well within the traditional client/server architecture. We believe peer-to-peer communication offers a preferable alternative due to its decentralised nature, removing dependence on individual nodes. However, this choice adds to the complexity of the developers task. In this paper, we describe a two-tiered approach to address this problem: A lower tier employing peer-to-peer interactions for managing the network infrastructure and an upper tier providing a mobile agent based programming framework. The result is a general purpose framework for developing ubiquitous applications and services, where the underlying complexity is hidden from the developer. This paper discusses our on-going work; presenting our design decisions, features supported by our framework, and some of the challenges still to be addressed in a complex programming environment
Parameterized Verification of Safety Properties in Ad Hoc Network Protocols
We summarize the main results proved in recent work on the parameterized
verification of safety properties for ad hoc network protocols. We consider a
model in which the communication topology of a network is represented as a
graph. Nodes represent states of individual processes. Adjacent nodes represent
single-hop neighbors. Processes are finite state automata that communicate via
selective broadcast messages. Reception of a broadcast is restricted to
single-hop neighbors. For this model we consider a decision problem that can be
expressed as the verification of the existence of an initial topology in which
the execution of the protocol can lead to a configuration with at least one
node in a certain state. The decision problem is parametric both on the size
and on the form of the communication topology of the initial configurations. We
draw a complete picture of the decidability and complexity boundaries of this
problem according to various assumptions on the possible topologies.Comment: In Proceedings PACO 2011, arXiv:1108.145
Tribes Is Hard in the Message Passing Model
We consider the point-to-point message passing model of communication in
which there are processors with individual private inputs, each -bit
long. Each processor is located at the node of an underlying undirected graph
and has access to private random coins. An edge of the graph is a private
channel of communication between its endpoints. The processors have to compute
a given function of all their inputs by communicating along these channels.
While this model has been widely used in distributed computing, strong lower
bounds on the amount of communication needed to compute simple functions have
just begun to appear. In this work, we prove a tight lower bound of
on the communication needed for computing the Tribes function,
when the underlying graph is a star of nodes that has leaves with
inputs and a center with no input. Lower bound on this topology easily implies
comparable bounds for others. Our lower bounds are obtained by building upon
the recent information theoretic techniques of Braverman et.al (FOCS'13) and
combining it with the earlier work of Jayram, Kumar and Sivakumar (STOC'03).
This approach yields information complexity bounds that is of independent
interest
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