Membership Inference Attacks Against Recommender Systems

Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly sensitive user data, thus potential data leakage from recommender systems may lead to severe privacy problems. In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference. In contrast with traditional membership inference against machine learning classifiers, our attack faces two main differences. First, our attack is on the user-level but not on the data sample-level. Second, the adversary can only observe the ordered recommended items from a recommender system instead of prediction results in the form of posterior probabilities. To address the above challenges, we propose a novel method by representing users from relevant items. Moreover, a shadow recommender is established to derive the labeled training data for training the attack model. Extensive experimental results show that our attack framework achieves a strong performance. In addition, we design a defense mechanism to effectively mitigate the membership inference threat of recommender systems.

Drive-induced nonlinearities of cavity modes coupled to a transmon ancilla

High-Q microwave cavity modes coupled to transmon ancillas provide a hardware-efficient platform for quantum computing. Due to their coupling, the cavity modes inherit finite nonlinearity from the transmons. In this work, we theoretically and experimentally investigate how an off-resonant drive on the transmon ancilla modifies the nonlinearities of cavity modes in qualitatively different ways, depending on the interrelation among cavity-transmon detuning, drive-transmon detuning and transmon anharmonicity. For a cavity-transmon detuning that is smaller than or comparable to the drive-transmon detuning and transmon anharmonicity, the off-resonant transmon drive can induce multiphoton resonances among cavity and transmon excitations that strongly modify cavity nonlinearities as drive parameters vary. For a large cavity-transmon detuning, the drive induces cavity-photon-number-dependent ac Stark shifts of transmon levels that translate into effective cavity nonlinearities. In the regime of weak transmon-cavity coupling, the cavity Kerr nonlinearity relates to the third-order nonlinear susceptibility function $chi^{(3)}$ of the driven ancilla. This susceptibility function provides a numerically efficient way of computing the cavity Kerr particularly for systems with many cavity modes controlled by a single transmon. It also serves as a diagnostic tool for identifying undesired drive-induced multiphoton resonance processes. Lastly, we show that by judiciously choosing the drive amplitude, a single off-resonant transmon drive can be used to cancel the cavity self-Kerr nonlinearity or the inter-cavity cross-Kerr. This provides a way of dynamically correcting the cavity Kerr nonlinearity during bosonic operations or quantum error correction protocols that rely on the cavity modes being linear.

VideoLT: Large-scale Long-tailed Video Recognition

Label distributions in real-world are oftentimes long-tailed and imbalanced, resulting in biased models towards dominant labels. While long-tailed recognition has been extensively studied for image classification tasks, limited effort has been made for video domain. In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition. Our VideoLT contains 256,218 untrimmed videos, annotated into 1,004 classes with a long-tailed distribution. Through extensive studies, we demonstrate that state-of-the-art methods used for long-tailed image recognition do not perform well in the video domain due to the additional temporal dimension in video data. This motivates us to propose FrameStack, a simple yet effective method for long-tailed video recognition task. In particular, FrameStack performs sampling at the frame-level in order to balance class distributions, and the sampling ratio is dynamically determined using knowledge derived from the network during training. Experimental results demonstrate that FrameStack can improve classification performance without sacrificing overall accuracy. Code and dataset are available at: https://github.com/17Skye17/VideoLT.

Rapid and Unconditional Parametric Reset Protocol for Tunable Superconducting Qubits

Qubit initialization is critical for many quantum algorithms and error correction schemes, and extensive efforts have been made to achieve this with high speed and efficiency. Here we experimentally demonstrate a fast and high fidelity reset scheme for tunable superconducting qubits. A rapid decay channel is constructed by modulating the flux through a transmon qubit and realizing a swap between the qubit and its readout resonator. The residual excited population can be suppressed to 0.08% $pm$ 0.08% within 34 ns, and the scheme requires no additional chip architecture, projective measurements, or feedback loops. In addition, the scheme has negligible effects on neighboring qubits, and is therefore suitable for large-scale multi-qubit systems. Our method also offers a way of entangling the qubit state with an itinerant single photon, particularly useful in quantum communication and quantum network applications.

Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples

The Gaussian-smoothed optimal transport (GOT) framework, recently proposed by Goldfeld et al., scales to high dimensions in estimation and provides an alternative to entropy regularization. This paper provides convergence guarantees for estimating the GOT distance under more general settings. For the Gaussian-smoothed $p$-Wasserstein distance in $d$ dimensions, our results require only the existence of a moment greater than $d + 2p$. For the special case of sub-gamma distributions, we quantify the dependence on the dimension $d$ and establish a phase transition with respect to the scale parameter. We also prove convergence for dependent samples, only requiring a condition on the pairwise dependence of the samples measured by the covariance of the feature map of a kernel space. A key step in our analysis is to show that the GOT distance is dominated by a family of kernel maximum mean discrepancy (MMD) distances with a kernel that depends on the cost function as well as the amount of Gaussian smoothing. This insight provides further interpretability for the GOT framework and also introduces a class of kernel MMD distances with desirable properties. The theoretical results are supported by numerical experiments.

A systematic review of recent air source heat pump (ASHP) systems assisted by solar thermal, photovoltaic and photovoltaic/thermal sources

The air source heat pump (ASHP) systems assisted by solar energy have drawn great attentions, owing to their great feasibility in buildings for space heating/cooling and hot water purposes. However, there are a variety of configurations, parameters and performance criteria of solar assisted ASHP systems, leading to a major inconsistency that increase the degree of complexity to compare and implement different systems. A comparative literature review is lacking, with the aim to evaluate the performance of various ASHP systems from three main solar sources, such as solar thermal (ST), photovoltaic (PV) and hybrid photovoltaic/thermal (PV/T). This paper thus conducts a systematic review of the prevailing solar assisted ASHP systems, including their boundary conditions, system configurations, performance indicators, research methodologies and system performance. The comparison result indicates that PV-ASHP system has the best techno-economic performance, which performs best in average with coefficient of performance (COP) of around 3.75, but with moderate cost and payback time. While ST-ASHP and PV/T-ASHP systems have lower performance with mean COP of 2.90 and 3.03, respectively. Moreover, PV/T-ASHP system has the highest cost and longest payback time, while ST-ASHP has the lowest ones. Future research are discussed from aspects of methodologies, system optimization and standard evaluation.

A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into electricity prosumers. Existing studies have developed some advanced building side controls that enable renewable energy sharing and that aim to optimise building-cluster-level performance via regulating the energy storage charging/ discharging. However, the flexible demand shifting ability of electric vehicles is not considered in these building side controls. For instance, the electric vehicle charging will usually start once they are plugged into charging stations. But, in such charging period the renewable generation may be insufficient to cover the EV charging load, leading to grid electricity imports. Consequently, the building-cluster-level performance is not optimised. Therefore, this study proposes a coordinated control of building prosumers for improving the cluster-level performance, by making use of energy sharing and storage capability of electricity batteries in both buildings and EVs. An EV charging/discharging model is first developed. Then, based on the predicted future 24h electricity demand and renewable generation data, the coordinated control first considers the whole building cluster as one integrated building and optimises its operation as well as the EV charging/discharging using genetic algorithm. Next, the operation of individual buildings in the future 24h is coordinated using nonlinear programming. For validation, the developed control has been tested on a real building cluster in Ludvika, Sweden. The study results show that the developed control can increase the cluster-level daily renewable self-consumption rate by 19% and meanwhile reduce the daily electricity bills by 36% compared with the conventional controls.

A pulsed lock-in method for ensemble nitrogen-vacancy center magnetometry

This article proposes a scheme for nitrogen-vacancy (NV) center magnetometry that combines the advantages of lock-in detection and pulse-type scheme. The optimal conditions, optimal sensitivity, and noise-suppression capability of the proposed method are compared with those of the conventional methods from both theoretical and simulation points of view. Through experimental measurements, a four-time improvement in sensitivity and 60-times improvement in minimum resolvable magnetic field (MRMF) was obtained. By using a confocal experiment setup, proposed scheme achieves a sensitivity of 3 nT/Hz1/2 and a MRMF of 100 pT.

Intrinsic Time-reversal-invariant Topological Superconductivity in Thin Films of Iron-based Superconductors

We establish quasi-two-dimensional thin films of iron-based superconductors (FeSCs) as a new high-temperature platform for hosting intrinsic time-reversal-invariant helical topological superconductivity (TSC). Based on the combination of Dirac surface state and bulk extended $s$-wave pairing, our theory should be directly applicable to a large class of experimentally established FeSCs, opening a new TSC paradigm. In particular, an applied electric field serves as a topological switch for helical Majorana edge modes in FeSC thin films, allowing for an experimentally feasible design of gate-controlled helical Majorana circuits. Applying an in-plane magnetic field drives the helical TSC phase into a higher-order TSC carrying corner-localized Majorana zero modes. Our proposal should enable the experimental realization of helical Majorana fermions.

Anomalous Floquet Chiral Topological Superconductivity in a Topological Insulator Sandwich Structure

We show that Floquet chiral topological superconductivity arises naturally in Josephson junctions made of magnetic topological insulator-superconductor sandwich structures. The Josephson phase modulation associated with an applied bias voltage across the junction drives the system into the anomalous Floquet chiral topological superconductor hosting chiral Majorana edge modes in the quasienergy spectrum, with the bulk Floquet bands carrying zero Chern numbers. The bias voltage acts as a tuning parameter enabling novel dynamical topological quantum phase transitions driving the system into a myriad of exotic Majorana-carrying Floquet topological superconducting phases. Our theory establishes a new paradigm for realizing Floquet chiral topological superconductivity in solid-state systems, which should be experimentally directly accessible.