Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scaling to large graphs. Sparse attention variants such as Exphormer can help, but may require high-degree augmentations to the input graph for good performance, and do not attempt to sparsify an already-dense input graph. As the learned attention mechanisms tend to use few of these edges, such highdegree connections may be unnecessary. We show (empirically and with theoretical backing) that attention scores on graphs are usually quite consistent across network widths, and use this observation to propose a two-stage procedure, which we call Spexphormer: first, train a narrow network on the full augmented graph. Next, use only the active connections to train a wider network on a much sparser graph. We establish theoretical conditions when a narrow network's attention scores can match those of a wide network, and show that Spexphormer achieves good performance with drastically reduced memory requirements on various graph datasets.

Thank you for acknowledging the key contributions of our paper. R1.2 Generalize to video: As suggested, we conducted additional The top-1 accuracy of JCL pre-trained features is 48.6%, which outperforms MoCo v2 (47.3%). Generalization of JCL for other data modalities (sound, language, video) will be included in our future work. Regarding your concerns of the written quality and typos (e.g., Algorithm 1 The top-1 accuracy on ImageNet100 for vanilla (ResNet-50) is 80.9% while JCL achieves 82.0%. R2.3 SimCLR: The top-5 accuracy we reported (87.3%) for SimCLR was extracted from the Thus, there is no one-one correspondence between the data in Table1 and Figure2.

Figure 1: A dataset informational card for ActionSense, constructed based on [1, 3].

Autonomous agents that accomplish complex computer tasks with minimal human interventions can significantly enhance accessibility and productivity of humancomputer interactions. Existing benchmarks either lack interactive environments or are limited to specific applications/domains, failing to reflect the diversity and complexity of real-world computer use and limiting agent scalability.

ZDNET's pick for the best budget-friendly video doorbell is now getting an upgrade, as Amazon has announced the launch of a new generation of the Blink Video Doorbell. The second-generation Blink Video Doorbell is being released four years after the first; it comes alongside the new Blink Sync Module Core and, unfortunately, sports a price increase. The new Blink Video Doorbell is available now for 70, a 20 price increase compared to the first model, which cost 50 when it was released. Also: I test smart locks for a living, and the most reliable one I've used is on sale for 130 However, the higher price brings some upgrades. The new Blink Video Doorbell has a 150-degree field of view, which gives you a bigger picture of people from head to toe and any packages that may be on your porch.

Amazon's Blink division--the budget-oriented sibling of Ring in the smart home space--has announced an all-new version of its affordable battery-powered Blink Video Doorbell, which is available now for 69.99. While the price has gone up by 10 compared to the previous model, the new version's higher-resolution camera (1440 1440 pixels), 150-degree field of view, and 1:1 aspect ratio will provide a head-to-toe view of visitors. Blink is bundling the new doorbell with its new AC-powered Blink Sync Module Core, which acts as a bridge to the home's Wi-Fi network (it does not have a chime that sounds off when a visitor rings the doorbell). Blink says having the doorbell connect to the Core, instead of directly to a home's router, is what enables the doorbell's remarkable 2-year battery life. The latest Blink Video Doorbell comes bundled with the Blink Sync Core, which serves as a bridge to a home's Wi-Fi network. Since the Sync Module Core does not offer any means for local storage--unlike the Sync Module 2 (with a USB-A socket) and the Sync Module XR (with a microSD card slot)--buyers of the latest Blink Video Doorbell will need to either sign up for a subscription to store video recordings in the cloud, or they'll need to replace the Core with either of Blink's other two Sync Modules.

As industrial models and designs grow increasingly complex, the demand for optimal control of large-scale dynamical systems has significantly increased. However, traditional methods for optimal control incur significant overhead as problem dimensions grow. In this paper, we introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups. Our algorithm, based on a policy gradient method, incorporates a novel quantum subroutine for solving the matrix Lyapunov equation. Specifically, we build a quantum-assisted differentiable simulator for efficient gradient estimation that is more accurate and robust than classical methods relying on stochastic approximation. Compared to the classical approaches, our method achieves a super-quadratic speedup. To the best of our knowledge, this is the first end-to-end quantum application to linear control problems with provable quantum advantage.

This paper considers the problem of finding the(δ, ǫ)-Goldstein stationary point of the Lipschitz continuous objective, which is a rich function class to cover a large number of important applications. We construct a novel zeroth-order quantum estimator for the gradient of the smoothed surrogate.

However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency.

The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth increases expressivity, but also results in a detrimental accumulation of errors. Furthermore, the number of parameters involved in the PQC significantly influences the performance through the necessary number of measurements to evaluate gradients, which scales linearly with the number of parameters. Motivated by this, we look at deep equilibrium models (DEQs), which mimic an infinite-depth, weight-tied network using a fraction of the memory by employing a root solver to find the fixed points of the network. In this work, we present Quantum Deep Equilibrium Models (QDEQs): a training paradigm that learns parameters of a quantum machine learning model given by a PQC using DEQs.