3,068 research outputs found
Sequential Frame-Interpolation and DCT-based Video Compression Framework
Video data is ubiquitous; capturing, transferring, and storing even compressed video data is challenging because it requires substantial resources. With the large amount of video traffic being transmitted on the internet, any improvement in compressing such data, even small, can drastically impact resource consumption. In this paper, we present a hybrid video compression framework that unites the advantages of both DCT-based and interpolation-based video compression methods in a single framework. We show that our work can deliver the same visual quality or, in some cases, improve visual quality while reducing the bandwidth by 10--20%
Essential Features: Reducing the Attack Surface of Adversarial Perturbations with Robust Content-Aware Image Preprocessing
Adversaries are capable of adding perturbations to an image to fool machine
learning models into incorrect predictions. One approach to defending against
such perturbations is to apply image preprocessing functions to remove the
effects of the perturbation. Existing approaches tend to be designed
orthogonally to the content of the image and can be beaten by adaptive attacks.
We propose a novel image preprocessing technique called Essential Features that
transforms the image into a robust feature space that preserves the main
content of the image while significantly reducing the effects of the
perturbations. Specifically, an adaptive blurring strategy that preserves the
main edge features of the original object along with a k-means color reduction
approach is employed to simplify the image to its k most representative colors.
This approach significantly limits the attack surface for adversaries by
limiting the ability to adjust colors while preserving pertinent features of
the original image. We additionally design several adaptive attacks and find
that our approach remains more robust than previous baselines. On CIFAR-10 we
achieve 64% robustness and 58.13% robustness on RESISC45, raising robustness by
over 10% versus state-of-the-art adversarial training techniques against
adaptive white-box and black-box attacks. The results suggest that strategies
that retain essential features in images by adaptive processing of the content
hold promise as a complement to adversarial training for boosting robustness
against adversarial inputs
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
In this paper, a family of ant colony algorithms called DAACA for data
aggregation has been presented which contains three phases: the initialization,
packet transmission and operations on pheromones. After initialization, each
node estimates the remaining energy and the amount of pheromones to compute the
probabilities used for dynamically selecting the next hop. After certain rounds
of transmissions, the pheromones adjustment is performed periodically, which
combines the advantages of both global and local pheromones adjustment for
evaporating or depositing pheromones. Four different pheromones adjustment
strategies are designed to achieve the global optimal network lifetime, namely
Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data
aggregation algorithms, DAACA shows higher superiority on average degree of
nodes, energy efficiency, prolonging the network lifetime, computation
complexity and success ratio of one hop transmission. At last we analyze the
characteristic of DAACA in the aspects of robustness, fault tolerance and
scalability.Comment: To appear in Journal of Computer and System Science
Zoom: A multi-resolution tasking framework for crowdsourced geo-spatial sensing
Abstract—As sensor networking technologies continue to de-velop, the notion of adding large-scale mobility into sensor networks is becoming feasible by crowd-sourcing data collection to personal mobile devices. However, tasking such networks at fine granularity becomes problematic because the sensors are heterogeneous, owned by the crowd and not the network operators. In this paper, we present Zoom, a multi-resolution tasking framework for crowdsourced geo-spatial sensor networks. Zoom allows users to define arbitrary sensor groupings over heterogeneous, unstructured and mobile networks and assign different sensing tasks to each group. The key idea is the separation of the task information ( what task a particular sensor should perform) from the task implementation ( code). Zoom consists of (i) a map, an overlay on top of a geographic region, to represent both the sensor groups and the task information, and (ii) adaptive encoding of the map at multiple resolutions and region-of-interest cropping for resource-constrained devices, allowing sensors to zoom in quickly to a specific region to determine their task. Simulation of a realistic traffic application over an area of 1 sq. km with a task map of size 1.5 KB shows that more than 90 % of nodes are tasked correctly. Zoom also outperforms Logical Neighborhoods, the state-of-the-art tasking protocol in task information size for similar tasks. Its encoded map size is always less than 50 % of Logical Neighborhood’s predicate size. I
A Comparative Study on Spin-Orbit Torque Efficiencies from W/ferromagnetic and W/ferrimagnetic Heterostructures
It has been shown that W in its resistive form possesses the largest
spin-Hall ratio among all heavy transition metals, which makes it a good
candidate for generating efficient dampinglike spin-orbit torque (DL-SOT)
acting upon adjacent ferromagnetic or ferrimagnetic (FM) layer. Here we provide
a systematic study on the spin transport properties of W/FM magnetic
heterostructures with the FM layer being ferromagnetic
CoFeB or ferrimagnetic CoTb with
perpendicular magnetic anisotropy. The DL-SOT efficiency , which is
characterized by a current-induced hysteresis loop shift method, is found to be
correlated to the microstructure of W buffer layer in both
W/CoFeB and W/CoTb systems. Maximum values
of and are achieved when
the W layer is partially amorphous in the W/CoFeB and
W/CoTb heterostructures, respectively. Our results suggest that
the spin Hall effect from resistive phase of W can be utilized to effectively
control both ferromagnetic and ferrimagnetic layers through a DL-SOT mechanism
PVW: Designing Virtual World Server Infrastructure
This paper presents a high level overview of PVW (Partitioned Virtual Worlds), a distributed system architecture for the management of virtual worlds. PVW is designed to support arbitrarily large and complex virtual worlds while accommodating dynamic and highly variable user population and content distribution density. The PVW approach enables the task of simulating and managing the virtual world to be distributed over many servers by spatially partitioning the environment into a hierarchical structure. This structure is useful both for balancing the simulation load across many nodes, as well as features such as geometric simplification and distribution of dynamic content
MetaGCD: Learning to Continually Learn in Generalized Category Discovery
In this paper, we consider a real-world scenario where a model that is
trained on pre-defined classes continually encounters unlabeled data that
contains both known and novel classes. The goal is to continually discover
novel classes while maintaining the performance in known classes. We name the
setting Continual Generalized Category Discovery (C-GCD). Existing methods for
novel class discovery cannot directly handle the C-GCD setting due to some
unrealistic assumptions, such as the unlabeled data only containing novel
classes. Furthermore, they fail to discover novel classes in a continual
fashion. In this work, we lift all these assumptions and propose an approach,
called MetaGCD, to learn how to incrementally discover with less forgetting.
Our proposed method uses a meta-learning framework and leverages the offline
labeled data to simulate the testing incremental learning process. A
meta-objective is defined to revolve around two conflicting learning objectives
to achieve novel class discovery without forgetting. Furthermore, a soft
neighborhood-based contrastive network is proposed to discriminate uncorrelated
images while attracting correlated images. We build strong baselines and
conduct extensive experiments on three widely used benchmarks to demonstrate
the superiority of our method.Comment: This paper has been accepted by ICCV202
An adaptive-order rational Arnoldi method for model-order reductions of linear time-invariant systems
AbstractThis work proposes a model reduction method, the adaptive-order rational Arnoldi (AORA) method, to be applied to large-scale linear systems. It is based on an extension of the classical multi-point Padé approximation (or the so-called multi-point moment matching), using the rational Arnoldi iteration approach. Given a set of predetermined expansion points, an exact expression for the error between the output moment of the original system and that of the reduced-order system, related to each expansion point, is derived first. In each iteration of the proposed adaptive-order rational Arnoldi algorithm, the expansion frequency corresponding to the maximum output moment error will be chosen. Hence, the corresponding reduced-order model yields the greatest improvement in output moments among all reduced-order models of the same order. A detailed theoretical study is described. The proposed method is very appropriate for large-scale electronic systems, including VLSI interconnect models and digital filter designs. Several examples are considered to demonstrate the effectiveness and efficiency of the proposed method
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