Two-step Gradient-assisted Phase-shifting Demodulation Algorithm for Fast 3D Reconstruction

The conventional multi-frequency heterodyne method is one of the most widely used methods in non-contact 3D measurement. However, it needs to project different phase-shifting patterns with different frequencies, so a large number of patterns are required. For most conventional methods, the fringe period number of the projected patterns is usually small due to its limited noise tolerance, though the larger fringe period number always means higher accuracy. We propose a two-step phase-shifting demodulation algorithm based on intensity-gradient. In this method, only two patterns for each frequency are required. With the intensity-gradient of the two patterns, we obtain the wrapped phase of each frequency. Next, the absolute phase is retrieved from the three wrapped phases with the heterodyne algorithm. Because only two patterns are required for each frequency, the proposed method is more robust and has higher measuring speed compared with the traditional 3-frequency 4-step heterodyne method. Simulations and experiments prove the feasibility and effectiveness of the method, and demonstrate that the proposed method extends the noise tolerance and achieves high-precision with only a half of the patterns required by the traditional 3-frequency 4-step method.

Enabling Training of Neural Networks on Noisy Hardware

Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog hardware composed of resistive device arrays with non-symmetric conductance modulation characteristics. Recently we proposed a new algorithm, the Tiki-Taka algorithm, that overcomes this stringent symmetry requirement. Here we build on top of Tiki-Taka and describe a more robust algorithm that further relaxes other stringent hardware requirements. This more robust second version of the Tiki-Taka algorithm (referred to as TTv2) 1. decreases the number of device conductance states requirement from 1000s of states to only 10s of states, 2. increases the noise tolerance to the device conductance modulations by about 100x, and 3. increases the noise tolerance to the matrix-vector multiplication performed by the analog arrays by about 10x. Empirical simulation results show that TTv2 can train various neural networks close to their ideal accuracy even at extremely noisy hardware settings. TTv2 achieves these capabilities by complementing the original Tiki-Taka algorithm with lightweight and low computational complexity digital filtering operations performed outside the analog arrays. Therefore, the implementation cost of TTv2 compared to SGD and Tiki-Taka is minimal, and it maintains the usual power and speed benefits of using analog hardware for training workloads. Here we also show how to extract the neural network from the analog hardware once the training is complete for further model deployment. Similar to Bayesian model averaging, we form analog hardware compatible averages over the neural network weights derived from TTv2 iterates. This model average then can be transferred to another analog or digital hardware with notable improvements in test accuracy, transcending the trained model itself. In short, we describe an end-to-end training and model extraction technique for extremely noisy crossbar-based analog hardware that can be used to accelerate DNN training workloads and match the performance of full-precision SGD.

Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation

Recently, abstract argumentation-based models of case-based reasoning (AA-CBR in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios. However, the formal properties of AA-CBR as a reasoning system remain largely unexplored. In this paper, we focus on analysing the non-monotonicity properties of a regular version of AA-CBR (that we call AA-CBR_>). Specifically, we prove that AA-CBR_> is not cautiously monotonic, a property frequently considered desirable in the literature. We then define a variation of AA-CBR_> which is cautiously monotonic. Further, we prove that such variation is equivalent to using AA-CBR_> with a restricted casebase consisting of all "surprising" and "sufficient" cases in the original casebase. As a by-product, we prove that this variation of AA-CBR_> is cumulative, rationally monotonic, and empowers a principled treatment of noise in "incoherent" casebases. Finally, we illustrate AA-CBR and cautious monotonicity questions on a case study on the U.S. Trade Secrets domain, a legal casebase.

Chosen Ciphertext k-Trace Attacks on Masked CCA2 Secure Kyber

Single-trace attacks are a considerable threat to implementations of classic public-key schemes, and their implications on newer lattice-based schemes are still not well understood. Two recent works have presented successful single-trace attacks targeting the Number Theoretic Transform (NTT), which is at the heart of many lattice-based schemes. However, these attacks either require a quite powerful side-channel adversary or are restricted to specific scenarios such as the encryption of ephemeral secrets. It is still an open question if such attacks can be performed by simpler adversaries while targeting more common public-key scenarios. In this paper, we answer this question positively. First, we present a method for crafting ring/module-LWE ciphertexts that result in sparse polynomials at the input of inverse NTT computations, independent of the used private key. We then demonstrate how this sparseness can be incorporated into a side-channel attack, thereby significantly improving noise resistance of the attack compared to previous works. The effectiveness of our attack is shown on the use-case of CCA2 secure Kyber k-module-LWE, where k ∈ {2, 3, 4}. Our k-trace attack on the long-term secret can handle noise up to a σ ≤ 1.2 in the noisy Hamming weight leakage model, also for masked implementations. A 2k-trace variant for Kyber1024 even allows noise σ ≤ 2.2 also in the masked case, with more traces allowing us to recover keys up to σ ≤ 2.7. Single-trace attack variants have a noise tolerance depending on the Kyber parameter set, ranging from σ ≤ 0.5 to σ ≤ 0.7. As a comparison, similar previous attacks in the masked setting were only successful with σ ≤ 0.5.

Dynamical differential covariance recovers directional network structure in multiscale neural systems

Investigating causal neural interactions are essential to understanding sub- sequent behaviors. Many statistical methods have been used for analyzing neural activity, but efficiently and correctly estimating the direction of net- work interactions remains difficult. Here, we derive dynamical differential covariance (DDC), a new method based on dynamical network models that detects directional interactions with low bias and high noise tolerance with- out the stationary assumption. The method is first validated on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity is known. Then, applying DDC to recordings of resting-state functional magnetic resonance imaging (rs-fMRI) from over 1,000 individual subjects, DDC consistently detected regional interactions with strong structural connectivity. DDC can be generalized to a wide range of dynamical models and recording techniques.

The Role of the Medial Olivocochlear Reflex in Acceptable Noise Level in Adults

Background The acceptable noise level (ANL) is a measurement used to quantify how much noise a person is willing to accept while listening to speech. ANL has been used to predict success with hearing aid use. However, physiological correlates of the ANL are poorly understood. One potential physiological correlate is the medial olivocochlear reflex (MOCR), which decreases the output of the cochlea and is thereby expected to increase noise tolerance. Purpose This study investigates the relationship between contralateral activation of the MOCR and tolerance of background noise. Research Design This study recruited 22 young adult participants with normal hearing. ANL was measured using the Arizona Travelogue recording under headphones presented at the most comfortable level (MCL) with and without multitalker babble noise. The MOCR strength was evaluated in all participants by measuring the cochlear microphonic (CM) with and without 40 dB sound pressure level contralateral broadband noise (CBBN). Data Analysis The CM observed in response to a 500-Hz tone was measured with and without CBBN, and changes in response to fast Fourier transform amplitude at 500 Hz were used as an indicator of the MOCR effect. The ANL was calculated by subtracting the maximum acceptable background noise level from the MCL. Participants were divided into two groups based on their ANL: low-ANL (ANL < 7 dB) and moderate-ANL (ANL ≥ 7 dB). An independent samples t-test was used to compare CM enhancement between low-ANL and moderate-ANL groups. Additionally, Pearson's correlation was used to investigate the relationship between the ANL and the MOCR effect on the CM. Results The results indicated that presentation of CBBN increased the CM amplitude, consistent with eliciting the MOCR. Participants in the low-ANL group had significantly larger CM enhancement than moderate-ANL participants. The results further revealed a significant correlation between the ANL and the MOCR effect on the CM. Conclusion This study suggests that a stronger MOCR, as assessed using CM enhancement, is associated with greater noise tolerance. This research provides a possible objective measure to predict background tolerance in patients and adds to our understanding about the MOCR function in humans.