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The nitrogen-vacancy (NV) center in diamond is a promising candidate for various quantum applications, such as quantum sensing. High sensitivity in NV-based magnetic sensing requires a diamond sample with a high density of NV centers and a long electron spin dephasing time. In this work, we propose a systematic measurement method for determining the electron spin dephasing time of NV center ensembles and analyze the contributions to the dephasing time from various sources, including NV-NV interactions, strain distribution, $^{13}C$ nuclear spin, and P1 electron spin. We demonstrate the effectiveness of our method on a series of high-performance diamond samples and provide a comprehensive understanding of dephasing sources, enabling the optimization of NV-based quantum sensing applications.

Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry and research community have witnessed a large number of new applications, based on those foundation models. Such applications include question and answer, customer services, image and video generation, and code completions, among others. However, as the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. As a result, the demand for cost-effective and fast inference using AI accelerators is ever more higher. To this end, our tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators. Beginning with an overview of basic Transformer architectures and deep learning system frameworks, we deep dive into system optimization techniques for fast and memory-efficient attention computations and discuss how they can be implemented efficiently on AI accelerators. Next, we describe architectural elements that are key for fast transformer inference. Finally, we examine various model compression and fast decoding strategies in the same context., Comment: Tutorial published at KDD 2024. Camera-ready version

Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accelerate imaging and enhance reconstruction quality. Existing networks exhibit some common limitations that constrain further acceleration possibilities, including single-domain learning, reliance on a single regularization term, and equal feature contribution. To address these limitations, we propose to embed information from multiple domains, including low-rank, image, and k-space, in a novel deep learning network for MRI reconstruction, which we denote as A-LIKNet. A-LIKNet adopts a parallel-branch structure, enabling independent learning in the k-space and image domain. Coupled information sharing layers realize the information exchange between domains. Furthermore, we introduce attention mechanisms into the network to assign greater weights to more critical coils or important temporal frames. Training and testing were conducted on an in-house dataset, including 91 cardiovascular patients and 38 healthy subjects scanned with 2D cardiac Cine using retrospective undersampling. Additionally, we evaluated A-LIKNet on the real-time 8x prospectively undersampled data from the OCMR dataset. The results demonstrate that our proposed A-LIKNet outperforms existing methods and provides high-quality reconstructions. The network can effectively reconstruct highly retrospectively undersampled dynamic MR images up to 24x accelerations, indicating its potential for single breath-hold imaging.

The functional replication and actuation of complex structures inspired by nature is a longstanding goal for humanity. Creating such complex structures combining soft and rigid features and actuating them with artificial muscles would further our understanding of natural kinematic structures. We printed a biomimetic hand in a single print process comprised of a rigid skeleton, soft joint capsules, tendons, and printed touch sensors. We showed it's actuation using electric motors. In this work, we expand on this work by adding a forearm that is also closely modeled after the human anatomy and replacing the hand's motors with 22 independently controlled pneumatic artificial muscles (PAMs). Our thin, high-strain (up to 30.1%) PAMs match the performance of state-of-the-art artificial muscles at a lower cost. The system showcases human-like dexterity with independent finger movements, demonstrating successful grasping of various objects, ranging from a small, lightweight coin to a large can of 272g in weight. The performance evaluation, based on fingertip and grasping forces along with finger joint range of motion, highlights the system's potential.

Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels and additionally leverage long-range contextual information using a transformer-based module to alleviate ambiguities in the presence of artifacts caused by undersampling. We combine local and global dependencies to perform simultaneous coarse and fine motion estimation. The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI. The impact of motion estimation accuracy on the downstream task of motion-compensated reconstruction was analyzed. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories (Cartesian and radial) and acceleration factors of up to 16x for cardiac motion and 30x for respiratory motion and achieves superior image quality in motion-compensated reconstruction qualitatively and quantitatively compared to conventional and recent deep learning-based approaches. The code is publicly available at https://github.com/lab-midas/GMARAFT., Comment: 14 pages, 7 figures

We report on the collisional shift and line broadening of Rydberg states in nitric oxide (NO) with increasing density of a background gas at room temperature. As a background gas we either use NO itself or nitrogen (N$_{2}$). The precision spectroscopy is performed by a sub-Doppler three-photon excitation scheme with a subsequent readout of the Rydberg states realized by the amplification of a current generated by free charges due to collisions. The shift shows a dependence on the rotational quantum state of the ionic core and no dependence on the principle quantum number of the orbiting Rydberg electron. The experiment was performed in the context of developing a trace-gas sensor for breath-gas analysis in a medical application.

Rydberg atom-based radio frequency electromagnetic field sensors are drawing wide-spread interest because of their unique properties, such as small size, dielectric construction, and self-calibration. These photonic sensors use lasers to prepare atoms and read out the atomic response to a radio frequency electromagnetic field based on electromagnetically induced transparency, or related phenomena. Much of the theoretical work has focused on the Autler-Townes splitting induced by the radio frequency wave. The amplitude regime, where the change in transmission observed on resonance is measured to determine electric field strength, has received less attention. In this paper, we deliver analytic expressions that are useful for calculating the absorption coefficient and sensitivity in the amplitude regime. We describe the approximations that we applied to obtain the analytic expressions and demonstrate their validity over a large range of the interesting parameter space. The effect of the thermal motion of the atoms is explicitly addressed. Residual Doppler shifts are shown to limit sensitivity. An analytic expression for the amplitude regime of Rydberg atom-based sensing has not, to our knowledge, been obtained previously. The expressions, approximations and descriptions presented in the paper are important for maximizing the sensitivity of Rydberg atom-based sensors and for providing insight into the physics of multi-level interference phenomena.

Soft pneumatic actuators are used to steer soft growing "vine" robots while being flexible enough to undergo the tip eversion required for growth. In this study, we compared the performance of three types of pneumatic actuators in terms of their ability to perform eversion, quasi-static bending, dynamic motion, and force output: the pouch motor, the cylindrical pneumatic artificial muscle (cPAM), and the fabric pneumatic artificial muscle (fPAM). The pouch motor is advantageous for prototyping due to its simple manufacturing process. The cPAM exhibits superior bending behavior and produces the highest forces, while the fPAM actuates fastest and everts at the lowest pressure. We evaluated a range of dimensions for each actuator type. Larger actuators can produce more significant deformations and forces, but smaller actuators inflate faster and can evert at a lower pressure. Because vine robots are lightweight, the effect of gravity on the functionality of different actuators is minimal. We developed a new analytical model that predicts the pressure-to-bending behavior of vine robot actuators. Using the actuator results, we designed and demonstrated a 4.8 m long vine robot equipped with highly maneuverable 60x60 mm cPAMs in a three-dimensional obstacle course. The vine robot was able to move around sharp turns, travel through a passage smaller than its diameter, and lift itself against gravity., Comment: 13 pages, 8 figures, 3 tables

In aggregated variables the impact of interventions is typically ill-defined because different micro-realizations of the same macro-intervention can result in different changes of downstream macro-variables. We show that this ill-definedness of causality on aggregated variables can turn unconfounded causal relations into confounded ones and vice versa, depending on the respective micro-realization. We argue that it is practically infeasible to only use aggregated causal systems when we are free from this ill-definedness. Instead, we need to accept that macro causal relations are typically defined only with reference to the micro states. On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions. We also discuss generalizations of this observation., Comment: CLeaR 2024

We demonstrate Rydberg atom-based radio frequency sensing with a colinear three-photon scheme in a room temperature cesium vapor cell that minimizes residual Doppler broadening of the probe laser absorption feature. A sub-200 kHz spectral linewidth is observed and extends the self-calibrated Autler-Townes sensing regime to weaker fields by a factor of ~18 compared to the theoretical limit of the most commonly used two-photon scheme. The sensitivity of the method to microsecond pulses is shown to be sufficient to detect radio frequency Rabi frequencies of 2$\pi$ x 0.44 MHz using a 480 kHz bandwidth at 108.9 GHz, demonstrating the ability to sense time-dependent signals suitable for radar and communications.

Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible when the arms and the randomness of the rewards of the arms can be queried in superposition. Here we introduce further bandit models where we only have limited access to the randomness of the rewards, but we can still query the arms in superposition. We show that then the query complexity is the same as for classical algorithms. This generalizes the prior result that no speed-up is possible for unstructured search when the oracle has positive failure probability., Comment: 47 pages

Magnetic topological quantum materials are attracting considerable attention owing to their potential technological applications. However, only a small number of these materials have been experimentally realized, thereby giving rise to the need for new stable magnetic topological quantum materials. Magnetism and spin-orbit coupling, two essential ingredients of the oxide materials, lead to various topological transport phenomena such as the anomalous Hall and anomalous Nernst effects, which can be significantly enhanced by designing an electronic structure with a large Berry curvature. In that respect, double perovskites with the general formula A$_2$BB'O$_6$ with an alternating ordered arrangement of two transition metal sites, B(3d) and B'(4d/5d), present attractive possibilities as they are robustly stable against oxidation under ambient conditions and versatile. These double perovskites also offer a high energy scale for magnetism as well as strong spin-orbit coupling with a high magnetic ordering temperature. Here, using first-principles density functional theory calculations, we present a comprehensive study of the intrinsic anomalous transport for 3d-4d/5d based cubic and tetragonal stable double perovskite (DP) compounds. A few of the DPs exhibit a very large anomalous Hall effect with a distinct topological band crossing in the vicinity of the Fermi energy. Our results show the importance of symmetries, particularly the mirror planes, as well as the clean topological band crossing near the Fermi energy, which is primarily contributed by the 5d-t$_{2g}$ for large anomalous Hall and Nernst effects., Comment: 12 pages, 7 figures

Everting, soft growing vine robots benefit from reduced friction with their environment, which allows them to navigate challenging terrain. Vine robots can use air pouches attached to their sides for lateral steering. However, when all pouches are serially connected, the whole robot can only perform one constant curvature in free space. It must contact the environment to navigate through obstacles along paths with multiple turns. This work presents a multi-segment vine robot that can navigate complex paths without interacting with its environment. This is achieved by a new steering method that selectively actuates each single pouch at the tip, providing high degrees of freedom with few control inputs. A small magnetic valve connects each pouch to a pressure supply line. A motorized tip mount uses an interlocking mechanism and motorized rollers on the outer material of the vine robot. As each valve passes through the tip mount, a permanent magnet inside the tip mount opens the valve so the corresponding pouch is connected to the pressure supply line at the same moment. Novel cylindrical pneumatic artificial muscles (cPAMs) are integrated into the vine robot and inflate to a cylindrical shape for improved bending characteristics compared to other state-of-the-art vine robots. The motorized tip mount controls a continuous eversion speed and enables controlled retraction. A final prototype was able to repeatably grow into different shapes and hold these shapes. We predict the path using a model that assumes a piecewise constant curvature along the outside of the multi-segment vine robot. The proposed multi-segment steering method can be extended to other soft continuum robot designs., Comment: Accepted for presentation at the 6th IEEE-RAS International Conference on Soft Robotics (RoboSoft 2023). 7 pages, 12 figures. For associated video, see ancillary files

In this paper, I consider a simple heterogeneous agents model of a production economy with uncertain climate change and examine constrained efficient carbon taxation. If there are frictionless, complete financial markets, the simple model predicts a unique Pareto-optimal level of carbon taxes and abatement. In the presence of financial frictions, however, the optimal level of abatement cannot be defined without taking a stand on how abatement costs are distributed among individuals. I propose a simple linear cost-sharing scheme that has several desirable normative properties. I use calibrated examples of economies with incomplete financial markets and/or limited market participation to demonstrate that different schemes to share abatement costs can have large effects on optimal abatement levels and that the presence of financial frictions can increase optimal abatement by a factor of three relative to the case of frictionless financial market.

Oxide materials exhibit several novel structural, magnetic, and electronic properties. Their stability under ambient conditions, easy synthesis, and high transition temperatures provide such systems with an ideal ground for realizing topological properties and real-life technological applications. However, experimental evidence of topological states in oxide materials is rare. In this study, we have synthesized single crystals of oxide double perovskite Sr2FeMoO6 and revealed its topological nature by investigating its structural, magnetic, and electronic properties. We observed that the system crystallized in the cubic space group Fm-3m, which is a half-metallic ferromagnet. Transport measurements show an anomalous Hall effect, and it is evident that the Hall contribution originates from the Berry curvature. Assuming a shift of the Fermi energy towards the conduction band, the contribution of the anomalous Hall effect is enhanced owing to the presence of a gaped nodal line. This study can be used to explore and realize the topological properties of bulk oxide systems., Comment: Topological quantum material, Oxide topological material