13,530 research outputs found
Learning joint feature adaptation for zero-shot recognition
Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR is the relative scarcity of source-domain features (e.g. one feature vector per class), which do not fully account for wide variability in target-domain instances. In this paper we propose a novel framework of learning data-dependent feature transforms for scoring similarity between an arbitrary pair of source and target data instances to account for the wide variability in target domain. Our proposed approach is based on optimizing over a parameterized family of local feature displacements that maximize the source-target adaptive similarity functions. Accordingly we propose formulating zero-shot learning (ZSL) using latent structural SVMs to learn our similarity functions from training data. As demonstration we design a specific algorithm under the proposed framework involving bilinear similarity functions and regularized least squares as penalties for feature displacement. We test our approach on several benchmark datasets for ZSR and show significant improvement over the state-of-the-art. For instance, on aP&Y dataset we can achieve 80.89% in terms of recognition accuracy, outperforming the state-of-the-art by 11.15%
Early wildfire detection by air quality sensors on unmanned aerial vehicles: Optimization and feasibility
“Millions of acres of forests are destroyed by wildfires every year, causing ecological, environmental, and economical losses. The recent wildfires in Australia and the Western U.S. smothered multiple states with more than fifty million acres charred by the blazes. The warmer and drier climate makes scientists expect increases in the severity and frequency of wildfires and the associated risks in the future. These inescapable crises highlight the urgent need for early detection and prevention of wildfires. This work proposed an energy management framework that integrated unmanned aerial vehicle (UAV) with air quality sensors for early wildfire detection and forest monitoring. An autonomous patrol solution that effectively detects wildfire events, while preserving the UAV battery for a larger area of coverage was developed. The UAV can send real-time data (e.g., sensor readings, thermal pictures, videos, etc) to nearby communications base stations (BSs) when a wildfire is detected. An optimization problem that minimized the total UAV’s consumed energy and satisfied a certain quality-of-service (QoS) data rate were formulated and solved. More specifically, this study optimized the flight track of a UAV and the transmit power between the UAV and BSs. Finally, selected simulation results that illustrate the advantages of the proposed model were proposed”--Abstract, page iii
Learning joint feature adaptation for zero-shot recognition
Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR is the relative scarcity of source-domain features (e.g. one feature vector per class), which do not fully account for wide variability in target-domain instances. In this paper we propose a novel framework of learning data-dependent feature transforms for scoring similarity between an arbitrary pair of source and target data instances to account for the wide variability in target domain. Our proposed approach is based on optimizing over a parameterized family of local feature displacements that maximize the source-target adaptive similarity functions. Accordingly we propose formulating zero-shot learning (ZSL) using latent structural SVMs to learn our similarity functions from training data. As demonstration we design a specific algorithm under the proposed framework involving bilinear similarity functions and regularized least squares as penalties for feature displacement. We test our approach on several benchmark datasets for ZSR and show significant improvement over the state-of-the-art. For instance, on aP&Y dataset we can achieve 80.89% in terms of recognition accuracy, outperforming the state-of-the-art by 11.15%
SNAP: Stateful Network-Wide Abstractions for Packet Processing
Early programming languages for software-defined networking (SDN) were built
on top of the simple match-action paradigm offered by OpenFlow 1.0. However,
emerging hardware and software switches offer much more sophisticated support
for persistent state in the data plane, without involving a central controller.
Nevertheless, managing stateful, distributed systems efficiently and correctly
is known to be one of the most challenging programming problems. To simplify
this new SDN problem, we introduce SNAP.
SNAP offers a simpler "centralized" stateful programming model, by allowing
programmers to develop programs on top of one big switch rather than many.
These programs may contain reads and writes to global, persistent arrays, and
as a result, programmers can implement a broad range of applications, from
stateful firewalls to fine-grained traffic monitoring. The SNAP compiler
relieves programmers of having to worry about how to distribute, place, and
optimize access to these stateful arrays by doing it all for them. More
specifically, the compiler discovers read/write dependencies between arrays and
translates one-big-switch programs into an efficient internal representation
based on a novel variant of binary decision diagrams. This internal
representation is used to construct a mixed-integer linear program, which
jointly optimizes the placement of state and the routing of traffic across the
underlying physical topology. We have implemented a prototype compiler and
applied it to about 20 SNAP programs over various topologies to demonstrate our
techniques' scalability
Applying Dynamic Co-occurrence in Story Link Detection
Story link detection is part of a broader initiative called Topic
Detection and Tracking, which is defined to be the task of
determining whether two stories, such as news articles or radio
broadcasts, are about the same event, or linked. In order to mine
more information from the contents of the stories being compared and
achieve a more high-powered system, motivated by the idea of the
word co-occurrence analysis, we propose our dynamic co-occurrence,
which is defined to be a pair of words that satisfy certain relation
restriction. In this paper, relation restriction refers to a set of
features. This paper evaluates three features: capital, location and
distance. We use dynamic co-occurrence in the similarity computation
when we apply it in the story link detection system. Experimental
results show that the story link detection systems based on the
dynamic co-occurrence perform very well, which testify the great
capabilities of the dynamic co-occurrence. At the same time, we also
find that relation restriction is critical to the performance of
dynamic co-occurrence
Event-Driven Network Programming
Software-defined networking (SDN) programs must simultaneously describe
static forwarding behavior and dynamic updates in response to events.
Event-driven updates are critical to get right, but difficult to implement
correctly due to the high degree of concurrency in networks. Existing SDN
platforms offer weak guarantees that can break application invariants, leading
to problems such as dropped packets, degraded performance, security violations,
etc. This paper introduces EVENT-DRIVEN CONSISTENT UPDATES that are guaranteed
to preserve well-defined behaviors when transitioning between configurations in
response to events. We propose NETWORK EVENT STRUCTURES (NESs) to model
constraints on updates, such as which events can be enabled simultaneously and
causal dependencies between events. We define an extension of the NetKAT
language with mutable state, give semantics to stateful programs using NESs,
and discuss provably-correct strategies for implementing NESs in SDNs. Finally,
we evaluate our approach empirically, demonstrating that it gives well-defined
consistency guarantees while avoiding expensive synchronization and packet
buffering
Remedial training: Will CRM work for everyone
The subject of those pilots who seem unresponsive to Cockpit Resource Management (CRM) training is addressed. Attention is directed to the need and opportunity for remedial action. Emphasis is given to the requirement for new perspectives and additional training resources. It is also argued that, contrary to conventional training wisdom, such individuals do not represent a hard core which is beyond assistance. Some evidence is offered that such a new perspective will lend itself to a wider appreciation of certain specific training needs. The role of appropriately trained specialists is briefly outlined, and a selected bibliography is attached. The combined experiences of several Pilot Advisory Groups (PAG's) within IFALPA member association form the basis for this discussion. It does not purport to desribe the activities of any one PAG. While much of the activities of PAG's have no relevance to CRM, there are clearly some very important points of intersection. The relevance of these points to diagnostic skills, and remedial training in the general domain of CRM is made obvious
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