Search

We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this problem, most existing methods attempt to find proper valid instrumental variables (IVs) for the target causal effect by expert knowledge or by assuming that the causal model is a one-directional MR model. As such, in this paper, we first theoretically investigate the identification of the bi-directional MR from observational data. In particular, we provide necessary and sufficient conditions under which valid IV sets are correctly identified such that the bi-directional MR model is identifiable, including the causal directions of a pair of phenotypes (i.e., the treatment and outcome). Moreover, based on the identification theory, we develop a cluster fusion-like method to discover valid IV sets and estimate the causal effects of interest. We theoretically demonstrate the correctness of the proposed algorithm. Experimental results show the effectiveness of our method for estimating causal effects in bi-directional MR., Comment: 27 pages, 6 tables, 7 figures

Non-Hermitian dynamics in quantum systems have unveiled novel phenomena, yet the implementation of valid non-Hermitian quantum measurement remains a challenge, because a universal quantum projective mechanism on the complete but skewed non-Hermitian eigenstates is not explicit in experiment. This limitation hinders the direct acquisition of non-Hermitian observable statistics (e.g., non-Hermitian population dynamics), also constrains investigations of non-Hermitian quantum measurement properties such as uncertainty relation. Here, we address these challenges by presenting a non-Hermitian projective protocol and investigating the non-Hermitian uncertainty relation. We derive the uncertainty relation for pseudo-Hermitian (PH) observables that is generalized beyond the Hermitian ones. We then investigate the projective properties of general quantum states onto complete non-Hermitian eigenvectors, and present a quantum simulating method to apply the valid non-Hermitian projective measurement on a direct-sum dilated space. Subsequently, we experimentally construct a quantum simulator in the quantum optical circuit and realize the 3-dimensional non-Hermitian quantum measurement on the single-photon qutrit. Employing this platform, we explore the uncertainty relation experimentally with different PH metrics. Our non-Hermitian quantum measurement method is state-independent and outputs directly the non-Hermitian quantum projective statistics, paving the way for studies of extensive non-Hermitian observable in quantum domain., Comment: 6 pages, 4 figures

In the era of digital commerce, the surge in online shopping and the expectation for rapid delivery have placed unprecedented demands on warehouse operations. The traditional method of order fulfilment, where human order pickers traverse large storage areas to pick items, has become a bottleneck, consuming valuable time and resources. Robotic Mobile Fulfilment Systems (RMFS) offer a solution by using robots to transport storage racks directly to human-operated picking stations, eliminating the need for pickers to travel. This paper introduces combi-stations, a novel type of station that enables both item picking and replenishment, as opposed to traditional separate stations. We analyse the efficiency of combi-stations using queueing theory and demonstrate their potential to streamline warehouse operations. Our results suggest that combi-stations can reduce the number of robots required for stability and significantly reduce order turnover time, indicating a promising direction for future warehouse automation., Comment: 15 pages, 7 figures. arXiv admin note: text overlap with arXiv:1912.01782

In the context of Industry 4.0, the physical and digital worlds are closely connected, and robots are widely used to achieve system automation. Digital twin solutions have contributed significantly to the growth of Industry 4.0. Combining various technologies is a trend that aims to improve system performance. For example, digital twinning can be combined with virtual reality in automated systems. This paper proposes a new concept to articulate this combination, which has mainly been implemented in engineering research projects. However, there are currently no guidelines, plans, or concepts to articulate this combination. The concept will be implemented in data centers, which are crucial for enabling virtual tasks in our daily lives. Due to the COVID-19 pandemic, there has been a surge in demand for services such as e-commerce and videoconferencing. Regular maintenance is necessary to ensure uninterrupted and reliable services. Manual maintenance strategies may not be sufficient to meet the current high demand, and innovative approaches are needed to address the problem. This paper presents a novel approach to data center maintenance: real-time monitoring by an autonomous robot. The robot is integrated with digital twins of assets and a virtual reality interface that allows human personnel to control it and respond to alarms. This methodology enables faster, more cost-effective, and higher quality data center maintenance. It has been validated in a real data centre and can be used for intelligent monitoring and management through joint data sources. The method has potential applications in other automated systems.

Public transport is vital for meeting people's mobility needs. Providers need to plan their services well to offer high quality and low cost. Optimized planning can benefit providers, customers, and municipalities. The planning process for public transport involves various decision problems, such as vehicle and crew planning. These problems are usually solved by providers. More and more studies suggest that integrated solution approaches for these problems are better than sequential and iterative ones. Integrated optimization of multiple planning phases allows more flexibility in planning, which can reduce operational costs and improve service quality. This paper reviews solution approaches for integrated optimization using operations research techniques for the vehicle scheduling, crew scheduling, and crew rostering problems. It also covers some relevant related approaches from other industries. The paper analyzes existing optimization approaches based on different aspects such as mathematical modeling, optimization objective and method, and data source and scope. Moreover, the paper examines the problem dimensions that are often required in practical applications. The paper identifies some directions for future research, such as focusing more on objectives other than cost-minimization like robustness, schedule regularity, or fairness.

Deep reinforcement learning (RL) has recently shown significant benefits in solving combinatorial optimization (CO) problems, reducing reliance on domain expertise, and improving computational efficiency. However, the field lacks a unified benchmark for easy development and standardized comparison of algorithms across diverse CO problems. To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configuration of diverse RL algorithms, neural network architectures, inference techniques, and environments. RL4CO allows researchers to seamlessly navigate existing successes and develop their unique designs, facilitating the entire research process by decoupling science from heavy engineering. We also provide extensive benchmark studies to inspire new insights and future work. RL4CO has attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co., Comment: A previous version was presented as a workshop paper at the NeurIPS 2023 GLFrontiers Workshop (Oral)

Among the various kinds of spin defects in hBN, the negatively charged boron vacancy ($\rm V_B^-$) spin defect that can be deterministically generated is undoubtedly a potential candidate for quantum sensing, but its low quantum efficiency restricts its %use in practical applications. Here, we demonstrate a robust enhancement structure with advantages including easy on-chip integration, convenient processing, low cost and suitable broad-spectrum enhancement for $\rm V_B^-$ defects. %Improved photoluminescence (PL) intensity and optically detected magnetic resonance (ODMR) contrast of $\rm V_B^-$ defect arrays. In the experiment, we used a metal reflective layer under the hBN flakes, filled with a transition dielectric layer in the middle, and adjusted the thickness of the dielectric layer to achieve the best coupling between the reflective dielectric cavity and the hBN spin defect. Using a reflective dielectric cavity, we achieved a PL enhancement of approximately 7-fold, and the corresponding ODMR contrast achieved 18\%. Additionally, the oxide layer of the reflective dielectric cavity can be used as an integrated material for micro-nano photonic devices for secondary processing, which means that it can be combined with other enhancement structures to achieve stronger enhancement. This work has guiding significance for realizing the on-chip integration of spin defects in two-dimensional materials.

Optically addressable spins in two-dimensional hexagonal boron nitride (hBN) attract widespread attention for their potential advantage in on-chip quantum devices, such as quantum sensors and quantum network. A variety of spin defects have been found in hBN, but no convenient and deterministic generation methods have been reported for other defects except negatively charged boron vacancy ($V_B^-$). Here we report that by using femtosecond laser direct writing technology, we can deterministically create spin defect ensembles with spectra range from 550 nm to 800 nm on nanoscale hBN flakes. Positive single-peak optically detected magnetic resonance (ODMR) signals are detected in the presence of magnetic field perpendicular to the substrate, and the contrast can reach 0.8%. With the appropriate thickness of hBN flakes, substrate and femtosecond laser pulse energy, we can deterministically and efficiently generate bright spin defect array. Our results provide a convenient deterministic method to create spin defects in hBN, which will motivate more endeavors for future researches and applications of spin-based technologies such as quantum magnetometer array.

Hexagonal boron nitride (hBN) is a remarkable two-dimensional (2D) material that hosts solid-state spins and has great potential to be used in quantum information applications, including quantum networks. However, in this application, both the optical and spin properties are crucial for single spins but have not yet been discovered simultaneously for hBN spins. Here, we realize an efficient method for arraying and isolating the single defects of hBN and use this method to discover a new spin defect with a high probability of 85%. This single defect exhibits outstanding optical properties and an optically controllable spin, as indicated by the observed significant Rabi oscillation and Hahn echo experiments at room temperature. First principles calculations indicate that complexes of carbon and oxygen dopants may be the origin of the single spin defects. This provides a possibility for further addressing spins that can be optically controlled., Comment: 10 pages, 5 figures

This paper introduces a new set of video analytics plugins developed for the FFmpeg framework. Multimedia applications that increasingly utilize the FFmpeg media features for its comprehensive media encoding, decoding, muxing, and demuxing capabilities can now additionally analyze the video content based on AI models. The plugins are thread optimized for best performance overcoming certain FFmpeg threading limitations. The plugins utilize the Intel OpenVINO Toolkit inference engine as the backend. The analytics workloads are accelerated on different platforms such as CPU, GPU, FPGA or specialized analytics accelerators. With our reference implementation, the feature of OpenVINO as inference backend has been pushed into FFmpeg mainstream repository. We plan to submit more patches later.

Different retail and e-commerce companies are facing the challenge of assembling large numbers of time-critical picking orders that include both small-line and multi-line orders. To reduce unproductive picker working time as in traditional picker-to-parts warehousing systems, different solutions are proposed in the literature and in practice. For example, in a mixed-shelves storage policy, items of the same stock keeping unit are spread over several shelves in a warehouse; or automated guided vehicles (AGVs) are used to transport the picked items from the storage area to packing stations instead of human pickers. This is the first paper to combine both solutions, creating what we call AGV-assisted mixed-shelves picking systems. We model the new integrated order batching and routing problem in such systems as an extended multi-depot vehicle routing problem with both three-index and two-commodity network flow formulations. Due to the complexity of the integrated problem, we develop a novel variable neighborhood search algorithm to solve the integrated problem more efficiently. We test our methods with different sizes of instances, and conclude that the mixed-shelves storage policy is more suitable than the usual storage policy in AGV-assisted mixed-shelves systems for orders with different sizes of order lines (saving up to 62% on driving distances for AGVs). Our variable neighborhood search algorithm provides optimal solutions within an acceptable computational time.

In the era of big data, a large number of text data generated by the Internet has given birth to a variety of text representation methods. In natural language processing (NLP), text representation transforms text into vectors that can be processed by computer without losing the original semantic information. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in language. Therefore, a text feature representation model based on convolutional neural network (CNN) and variational autoencoder (VAE) is proposed to extract the text features and apply the obtained text feature representation on the text classification tasks. CNN is used to extract the features of text vector to get the semantics among words and VAE is introduced to make the text feature space more consistent with Gaussian distribution. In addition, the output of the improved word2vec model is employed as the input of the proposed model to distinguish different meanings of the same word in different contexts. The experimental results show that the proposed model outperforms in k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) classification algorithms.

Physically vitrifying single-element metallic glass requires ultrahigh cooling rates, which are still unachievable for most of the closest-packed metals. Here, we report a facile synthetic strategy for creating mono-atomic palladium metallic glass nanoparticles with a purity of 99.35 +/- 0.23 at% from palladium-silicon liquid droplets using a cooling rate below 1000 K/s. In-situ environmental transmission electron microscopy directly detected the leaching of silicon. Further hydrogen absorption experiment showed that this palladium metallic glass expanded little upon hydrogen uptake, exhibiting a great potential application for hydrogen separation. Our results provide insight into the formation of mono-atomic metallic glass at nanoscale., Comment: Nano Res. (2022)

Versatile quantum modes emerge for plasmon describing the collective oscillations of free electrons in metallic nanoparticles when the particle sizes are greatly reduced. Rather than traditional nanoscale study, the understanding of quantum plasmon desires extremal atomic control of the nanoparticles, calling for size dependent plasmon measurement over a series of nanoparticles with atomically adjustable atom number over several orders of magnitude. Here we report the N dependent plasmonic evolution of atomically size selected gold particles with N= 100 70000 using electron energy loss (EEL) spectroscopy in a scanning transmission electron microscope. The EEL mapping assigns a feature at 2.7 eV as the bulk plasmon and another at 2.4 eV as surface plasmon, which evolution reveals three regimes. When N decreases from 70000 to 887, the bulk plasmon stays unchanged while the surface plasmon exhibits a slight red shift from 2.4 to 2.3 eV. It can be understood by the dominance of classical plasmon physics and electron boundary scattering induced retardation. When N further decreases from 887 to 300, the bulk plasmon disappears totally and the surface plasmon shows a steady blueshift, which indicates that the quantum confinement emerges and modifies the intraband transition. When N 100 300, the plasmon is split to three fine features, which is attributed to superimposed single electron transitions between the quantized molecular like energy level by the time dependent density functional theory calculations. The surface plasmon's excitation ratio has a scaling law with an exponential dependence on N ( N^0.669), essentially the square of the radius. A unified evolution picture from the classical to quantum, molecular plasmon is thus demonstrated.