Symmetry-protected spin gaps in quantum wires

This work shows that a strongly correlated phase which is gapped to collective spin excitations but gapless to charge fluctuations emerges as a universal feature in one-dimensional fermionic systems obeying certain symmetries. Namely, nanowires interacting via Coulomb repulsion which are symmetric under time-reversal and spatial inversion symmetry exhibit spin gaps whenever one pair of spin-degenerate subbands is occupied and an arbitrarily weak spin-orbit interaction is present. This general result is independent of the details of the one-dimensional confinement, the fermionic spin or nature of the spin-orbit interaction. In narrow-gap semiconductors, this gap may be of order 10 \textmu eV. This strongly correlated phase may be identified both via an anomalous $h/2e$ flux periodicity in Aharonov-Bohm oscillations and $2e$ periodic Coulomb blockade, features which reflect the existence of fermionic pairing despite the absence of superconductivity and the repulsive nature of the interaction

Model Extraction and Adversarial Attacks on Neural Networks Using Side-Channel Information

Artificial neural networks (ANNs) have gained significant popularity in the last decade for solving narrow AI problems in domains such as healthcare, transportation, and defense. As ANNs become more ubiquitous, it is imperative to understand their associated safety, security, and privacy vulnerabilities. Recently, it has been shown that ANNs are susceptible to a number of adversarial evasion attacks - inputs that cause the ANN to make high-confidence misclassifications despite being almost indistinguishable from the data used to train and test the network. This thesis explores to what degree finding these examples may be aided by using side-channel information, specifically power consumption, of hardware implementations of ANNs. A blackbox threat scenario is assumed, where an attacker has access to the ANN hardware’s input, outputs, and topology, but the trained model parameters are unknown. The extraction of the ANN parameters is performed by training a surrogate model using a dataset derived from querying the blackbox (oracle) model. The effect of the surrogate’s training set size on the accuracy of the extracted parameters was examined. It was found that the distance between the surrogate and oracle parameters increased with larger training set sizes, while the angle between the two parameter vectors held approximately constant at 90 degrees. However, it was found that the transferability of attacks from the surrogate to the oracle improved linearly with increased training set size with lower attack strength. Next, a novel method was developed to incorporate power consumption side-channel information from the oracle model into the surrogate training based on a Siamese neural network structure and a simplified power model. Comparison between surrogate models trained with and without power consumption data indicated that incorporation of the side channel information increases the fidelity of the model extraction by up to 30%. However, no improvement of transferability of adversarial examples was found, indicating behavior dissimilarity of the models despite them being closer in weight space

Separation and extraction of bridge dynamic strain data (in Chinese)

Through comparing the measured data of dynamic strains due to loading and temperature by the strain gauge and temperature sensor at the same location, the information in the strain data was divided into three parts in the frequency domain by using the defined index named PSD (power spectra density)- ratio. The three parts are dominated respectively by temperature varying, stresses and noises and can be distinguished from the determined values of the separatirix frequencies. Then a simple algorithm was developed to separate the three types of information, and to extract the strain caused mainly by structural stresses. As an application of the proposed method, the influence of strain deformation and noises. As an application of the proposed method, the influence of strain deformation and noises on the fatigue assessment was investigated based on the separated data. The results show that, the determined values of separatrix frequencies are valuable for the monitoring data from other bridges. The algorithm is a multi resolution and hierarchical method, which has been validated as a simple and effective method for data analyses, and is suitable for the compression and pre-processing of the great amount monitoring data and easy to be integrated in the SHM's (structural health monitoring)software system. The strain due to temperature varying attributes only a little to the errors of fatigue assessment. However, the noises or random disturbance existed in the monitoring data have much responsibility for the errors, the main reason is that the random disturbance shifts the real strain/stress amplitude picked up by real structural stress or strain

Four-Majorana qubit with charge readout: dynamics and decoherence

We present a theoretical analysis of a Majorana-based qubit consisting of two topological superconducting islands connected via a Josephson junction. The qubit is operated by electrostatic gates which control the coupling of two of the four Majorana zero modes. At the end of the operation, readout is performed in the charge basis. Even though the operations are not topologically protected, the proposed experiment can potentially shed light on the coherence of the parity degree of freedom in Majorana devices and serve as a first step towards topological Majorana qubits. We discuss in detail the charge-stability diagram and its use for characterizing the parameters of the devices, including the overlap of the Majorana edge states. We describe the multi-level spectral properties of the system and present a detailed study of its controlled coherent oscillations, as well as decoherence resulting from coupling to a non-Markovian environment. In particular, we study a gate-controlled protocol where conversion between Coulomb-blockade and transmon regimes generates coherent oscillations of the qubit state due to the overlap of Majorana modes. We show that, in addition to fluctuations of the Majorana coupling, considerable measurement errors may be accumulated during the conversion intervals when electrostatic fluctuations in the superconducting islands are present. These results are also relevant for several proposed implementations of topological qubits which rely on readout based on charge detection

Joint Scheduling for Multi-Service in Coordinated Multi-Point OFDMA Networks

In this paper, the issues upon user scheduling in the downlink packet transmission for multiple services are addressed for coordinated multi-point (CoMP) OFDMA networks. We consider mixed traffic with voice over IP (VOIP) and best effort (BE) services. In order to improve cell-edge performance and guarantee diverse quality of service (QoS), a utility-based joint scheduling algorithm is proposed, which consists of two steps: ant colony optimization (ACO) based joint user selection and greedy subchannel assignment. We compare the proposed algorithm with the greedy user selection (GUC) based scheme. Via simulation results, we show that 95% of BE users are satsified with average cell-edge data rate greater than 200kbps by using either of the two algorithms. Whereas, our proposed algorithm ensures that more than 95% of VoIP users are satisfied with packet drop ratio less than 2%, compared to 78% by the GUC based algorithm