Energy-efficient Amortized Inference with Cascaded Deep Classifiers

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing. We address this problem by proposing a novel framework that optimizes the prediction accuracy and energy cost simultaneously, thus enabling effective cost-accuracy trade-off at test time. In our framework, each data instance is pushed into a cascade of deep neural networks with increasing sizes, and a selection module is used to sequentially determine when a sufficiently accurate classifier can be used for this data instance. The cascade of neural networks and the selection module are jointly trained in an end-to-end fashion by the REINFORCE algorithm to optimize a trade-off between the computational cost and the predictive accuracy. Our method is able to simultaneously improve the accuracy and efficiency by learning to assign easy instances to fast yet sufficiently accurate classifiers to save computation and energy cost, while assigning harder instances to deeper and more powerful classifiers to ensure satisfiable accuracy. With extensive experiments on several image classification datasets using cascaded ResNet classifiers, we demonstrate that our method outperforms the standard well-trained ResNets in accuracy but only requires less than 20% and 50% FLOPs cost on the CIFAR-10/100 datasets and 66% on the ImageNet dataset, respectively

The effects of mid-air adjustments on knee joint loading when landing from a jump

Introduction. One of the most common injuries involving the knee joint is the Anterior Cruciate Ligament (ACL) tear. Female basketball and soccer players have about 3 times higher risk of ACL injury versus male athletes (Prodromos et al., 2007). Female athletes alter their motion pattern during the landing phase differently than male athletes. That is one of the potential reasons why females have a higher risk of ACL injury during sports (Chappell et al., 2002). Possible mechanisms of ACL injury are the deceleration of the knee in an extended position (Boden et al., 2000), shear forces applied to the leg during landing (Pflum et al., 2004) and large knee varus and internal rotation moments (McLean et al., 2007). The purpose of this study was to determine if mid-air postural adjustments affect the potential for ACL injury during the landing.;Methods. Eleven healthy college female students (age 21.8 +/- 1.6 yrs, height 1.7 +/- 0.5 m, mass 64.1 +/- 11.7 kg) participated in the study. Three tennis balls were suspended from the ceiling. After maximum jump height was established, subjects were instructed to jump from and land on two force platforms (AMTI). Approximately 100 ms after leaving the force platform an LED positioned near one of the balls was illuminated. Subjects were asked to tap this ball using both hands. They were told to jump as high as possible for each of 21 randomly selected trials. A total of 23 retroreflective markers on the right and left lower extremities were used to determine the 3D orientation of the segments (Peak Motus). Inverse dynamics were used to calculate joint moments and reaction forces at the knee joint. Kinematics were imported to a scaled SIMM (MusculoGraphics, Inc.) model to obtain the maximal muscles forces, muscle moment arms and muscle orientations for 88 lower extremity muscles. Static optimization using a cost function that minimizes the sum of the muscle stress squared was used to estimate the individual muscle forces. The knee joint contact forces were then calculated as the sum of the muscle forces and the joint reaction forces. The peak anterior shear force, peak varus moment, and peak internal rotation moment on the knee joint were used to assess the potential for ACI. injury. Two 3 x 2 (reaching direction by right vs left leg and reaching direction by ipsilateral vs contralateral leg) repeated measures ANOVAs were used to determine statistical significance.;Results. The peak anterior shear force was significantly greater on the right knee compared to the left and the peak external rotation moment was significantly greater on the left knee compared to the right. However, data were also analyzed without reference to which leg had the highest peak values. The average peak difference between the middle ball condition and the greater value of the ipsilateral and contralateral legs from the side reaching conditions is 0.12BW, 0.03BWm, and 0.04BWm for peak anterior shear force, peak varus moment and peak external rotation moment respectively. This shows that peak anterior shear forces, peak vans moment and peak external rotation moments all increased in one of the legs when reaching to the side.;Discussion. The results suggest that reaching to the side balls had a higher risk of ACL injury than reaching to the middle ball. This result was not apparent when looking at right/left leg effects or ipsilateral/contralateral leg effects because subjects adopted different strategies to deal with the mid-air adjustments that are necessary when reaching to a side ball. Some subjects always landed on their dominant leg while others always landed on the ipsilateral leg

Exact results for a tunnel-coupled pair of trapped Bose-Einstein condensates

A model describing coherent quantum tunneling between two trapped Bose-Einstein condensates is shown to admit an exact solution. The spectrum is obtained by the algebraic Bethe ansatz. An asymptotic analysis of the Bethe ansatz equations leads us to explicit expressions for the energies of the ground and first excited states in the limit of {\it weak} tunneling and all energies for {\it strong} tunneling. The results are used to extract the asymptotic limits of the quantum fluctuations of the boson number difference between the two Bose-Einstein condensates and to characterize the degree of coherence in the system.Comment: 5 pages, RevTex, No figure

A Novel Data Association Algorithm for Unequal Length Fluctuant Sequence

AbstractThere are quantities of such sensors as radar, ESM, navigator in aerospace areas and the sequence data is the most ordinary data in sensor domain. How to mine the information of these data has attracted a great interest in data mining. But sequence data is easily interfered and produces some fluctuant points. When dealing with these sequences, traditional sequence similarity measurement such as Euclidean distance arises large error, especially for unequal length fluctuant sequence. A novel average weight 1-norm unequal length fluctuant sequence similarity measurement algorithm based on dynamic time warping (DTW) is proposed to solve this problem. It constructs an absolute distance matrix based on DTW firstly, then weight average weight 1-norm and modify it with modifying factor to measure the distance of unequal length fluctuant sequence. It solves the fluctuation sensitivity of maximum distance measurement algorithm. Finally transform distance to similarity as the index of the association, associate the sequence data according to the maximum similarity association rule. Simulation results show the effectiveness of the proposed algorithm when associating unequal length fluctuant sequence, association rate is above 70% and simulate the effect of variation of the sequence length, fluctuant rate and processing time to the proposed algorithm

Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network

Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instability) can easily collapse the performance of error-aware designs. What's worse, users can even not be aware of the performance degradation caused by the change in noise. To address both issues, in this paper we use Quantum Neural Network (QNN) as a vehicle to present a novel compression-aided framework, namely QuCAD, which will adapt a trained QNN to fluctuating quantum noise. In addition, with the historical calibration (noise) data, our framework will build a model repository offline, which will significantly reduce the optimization time in the online adaption process. Emulation results on an earthquake detection dataset show that QuCAD can achieve 14.91% accuracy gain on average in 146 days over a noise-aware training approach. For the execution on a 7-qubit IBM quantum processor, IBM-Jakarta, QuCAD can consistently achieve 12.52% accuracy gain on earthquake detection