132 research outputs found
An Investigation of Power Saving and Privacy Protection on Smartphones
With the advancements in mobile technology, smartphones have become ubiquitous in people\u27s daily lives and have greatly facilitated users in many aspects. For a smartphone user, power saving and privacy protection are two important issues that matter and draw serious attentions from research communities. In this dissertation, we present our studies on some specific issues of power saving and privacy protection on a smartphone. Although IEEE 802.11 standards provide Power Save Mode (PSM) to help mobile devices conserve energy, PSM fails to bring expected benefits in many real scenarios. We define an energy conserving model to describe the general PSM traffic contention problem, and propose a solution called HPSM to address one specific case, in which multiple PSM clients associate to a single AP. In HPSM, we first use a basic sociological concept to define the richness of a PSM client based on the link resource it consumes. Then we separate these poor PSM clients from rich PSM clients in terms of link resource consumption, and favor the former to save power when they face PSM transmission contention. Our evaluations show that HPSM can help the poor PSM clients effectively save power while only slightly degrading the rich\u27s performance in comparison to the existing PSM solutions. Traditional user authentication methods using passcode or finger movement on smartphones are vulnerable to shoulder surfing attack, smudge attack, and keylogger attack. These attacks are able to infer a passcode based on the information collection of user\u27s finger movement or tapping input. as an alternative user authentication approach, eye tracking can reduce the risk of suffering those attacks effectively because no hand input is required. We propose a new eye tracking method for user authentication on a smartphone. It utilizes the smartphone\u27s front camera to capture a user\u27s eye movement trajectories which are used as the input of user authentication. No special hardware or calibration process is needed. We develop a prototype and evaluate its effectiveness on an android smartphone. Our evaluation results show that the proposed eye tracking technique achieves very high accuracy in user authentication. While LBS-based apps facilitate users in many application scenarios, they raise concerns on the breach of privacy related to location access. We perform the first measurement of this background action on the Google app market. Our investigation demonstrates that many popular apps conduct location access in background within short intervals. This enables these apps to collect a user\u27s location trace, from which the important personal information, Points of Interest (PoIs), can be recognized. We further extract a user\u27s movement pattern from the PoIs, and utilize it to measure the potential privacy breach. The measurement results also show that using the combination of movement pattern related metrics and the other PoI related metrics can help detect the privacy breach in an earlier manner than using either one of them alone. We then propose a preliminary solution to properly handle these location requests from background
Variational operator learning: A unified paradigm for training neural operators and solving partial differential equations
Based on the variational method, we propose a novel paradigm that provides a
unified framework of training neural operators and solving partial differential
equations (PDEs) with the variational form, which we refer to as the
variational operator learning (VOL). We first derive the functional
approximation of the system from the node solution prediction given by neural
operators, and then conduct the variational operation by automatic
differentiation, constructing a forward-backward propagation loop to derive the
residual of the linear system. One or several update steps of the steepest
decent method (SD) and the conjugate gradient method (CG) are provided in every
iteration as a cheap yet effective update for training the neural operators.
Experimental results show the proposed VOL can learn a variety of solution
operators in PDEs of the steady heat transfer and the variable stiffness
elasticity with satisfactory results and small error. The proposed VOL achieves
nearly label-free training. Only five to ten labels are used for the output
distribution-shift session in all experiments. Generalization benefits of the
VOL are investigated and discussed.Comment: 35 pages, 22 figure
Arbitrating Traffic Contention for Power Saving with Multiple PSM Clients
Data transmission over WiFi quickly drains the batteries of mobile devices. Although the IEEE 802.11 standards provide power save mode (PSM) to help mobile devices conserve energy, PSM fails to bring expected benefits in many real scenarios. In particular, when multiple PSM mobile devices associate to a single access point (AP), PSM does not work well under transmission contention. Optimizing power saving of multiple PSM clients is a challenging task, because each PSM client expects to complete data transmission early so that it can turn to low power mode. In this paper, we define an energy conserving model to describe the general PSM traffic contention problem. We prove that the optimization of energy saving for multiple PSM clients under constraint is an NPcomplete problem. Following this direction, we propose a solution called harmonious power saving mechanism (HPSM) to address one specific case, in which multiple PSM clients associate to a single AP. In HPSM, we first use a basic sociological concept to define the richness of a PSM client based on the link resource it consumes. Then, we separate these poor PSM clients from rich PSM clients in terms of link resource consumption and favor the former to save power when they face PSM transmission contention. We implement prototypes of HPSM based on the open source projects Mad-wifi and NS-2. Our evaluations show that HPSM can help the poor PSM clients effectively save power while only slightly degrading the rich PSM clients\u27 performance in comparison with the existing PSM solutions
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient
Second-order optimization methods have the ability to accelerate convergence
by modifying the gradient through the curvature matrix. There have been many
attempts to use second-order optimization methods for training deep neural
networks. Inspired by diagonal approximations and factored approximations such
as Kronecker-Factored Approximate Curvature (KFAC), we propose a new
approximation to the Fisher information matrix (FIM) called Trace-restricted
Kronecker-factored Approximate Curvature (TKFAC) in this work, which can hold
the certain trace relationship between the exact and the approximate FIM. In
TKFAC, we decompose each block of the approximate FIM as a Kronecker product of
two smaller matrices and scaled by a coefficient related to trace. We
theoretically analyze TKFAC's approximation error and give an upper bound of
it. We also propose a new damping technique for TKFAC on convolutional neural
networks to maintain the superiority of second-order optimization methods
during training. Experiments show that our method has better performance
compared with several state-of-the-art algorithms on some deep network
architectures
Effect of low-intensity transcranial ultrasound stimulation on theta and gamma oscillations in the mouse hippocampal CA1
Previous studies have demonstrated that low-intensity transcranial ultrasound stimulation (TUS) can eliminate hippocampal neural activity. However, until now, it has remained unclear how ultrasound modulates theta and gamma oscillations in the hippocampus under different behavioral states. In this study, we used ultrasound to stimulate the CA1 in mice in anesthesia, awake and running states, and we simultaneously recorded the local field potential of the stimulation location. We analyzed the power spectrum, phase-amplitude coupling (PAC) of theta and gamma oscillations, and their relationship with ultrasound intensity. The results showed that (i) TUS significantly enhanced the absolute power of theta and gamma oscillations under anesthesia and in the awake state. (ii) The PAC strength between theta and gamma oscillations is significantly enhanced under the anesthesia and awake states but is weakened under the running state with TUS. (iii) Under anesthesia, the relative power of theta decreases and that of gamma increases as ultrasound intensity increases, and the result under the awake state is opposite that under the anesthesia state. (iv) The PAC index between theta and gamma increases as ultrasound intensity increases under the anesthesia and awake states. The above results demonstrate that TUS can modulate theta and gamma oscillations in the CA1 and that the modulation effect depends on behavioral states. Our study provides guidance for the application of ultrasound in modulating hippocampal function
Safe motion planning for autonomous vehicles by quantifying uncertainties of deep learning-enabled environment perception
Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep learning (DL) techniques that typically generate deterministic outputs without explicitly evaluating their uncertainties and trustworthiness. Therefore, the downstream decision-making components may generate unsafe outputs leading to system failure or accidents, if the preceding perception component provides highly uncertain information. To mitigate this issue, this paper proposes a coherent safe perception-planning framework that quantifies and transfers DL-based perception uncertainties. Following the Bayesian Deep Learning paradigm, we design a probabilistic 3D object detector that extracts objects from LiDAR point clouds while quantifying the corresponding aleatoric and epistemic uncertainty. A chance-constrained motion planner is designed to formulate an explicit link between DL-based perception uncertainties and operation risk and generate safe and risk-bounding trajectories. The proposed framework is validated through various challenging scenarios in the CARLA simulator. Experiment results demonstrate that our framework can effectively capture the uncertainties in DL, and generate trajectories that bound the risk under DL perception uncertainties. It also outperforms counterpart approaches without explicitly evaluating the uncertainties of DL-based perception
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