Reviewerly/exHarmony huggingface dataset:
3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents

Intelligent agents gather information and perceive semantics within the environments before taking on given tasks. The agents store the collected information in the form of environment models that compactly represent the surrounding environments. The agents, however, can only conduct limited tasks without an efficient and effective environment model. Thus, such an environment model takes a crucial role for the autonomy systems of intelligent agents. We claim the following characteristics for a versatile environment model: accuracy, applicability, usability, and scalability. Although a number of researchers have attempted to develop such models that represent environments precisely to a certain degree, they lack broad applicability, intuitive usability, and satisfactory scalability. To tackle these limitations, we propose 3-D scene graph as an environment model and the 3-D scene graph construction framework. The concise and widely used graph structure readily guarantees usability as well as scalability for 3-D scene graph. We demonstrate the accuracy and applicability of the 3-D scene graph by exhibiting the deployment of the 3-D scene graph in practical applications. Moreover, we verify the performance of the proposed 3-D scene graph and the framework by conducting a series of comprehensive experiments under various conditions.
Interference-Induced Quantum Squeezing Enhancement in a Two-beam Phase-Sensitive Amplifier

We experimentally demonstrate a method for realizing quantum squeezing enhancement which is induced by the interference in a two-beam phase-sensitive amplifier (PSA) based on a four-wave mixing process. Compared to the normal phase-insensitive amplifier with an intensity-difference squeezing (IDS) of $8.97\ifmmode\pm\else\textpm\fi{}0.24\text{ }\text{ }\mathrm{dB}$ or $8.76\ifmmode\pm\else\textpm\fi{}0.26\text{ }\text{ }\mathrm{dB}$, the IDS of our two-beam PSA is enhanced to $10.13\ifmmode\pm\else\textpm\fi{}0.21\text{ }\text{ }\mathrm{dB}$ under the same experimental situation. Furthermore, we study how various parameters influence the quantum squeezing enhancement of the PSA. These results clearly show that the physical mechanism inducing the IDS enhancement of the two-beam PSA is its intrinsic interference nature. Our results may find potential applications in improving the fidelity of quantum information processing and the precision of quantum metrology.
A quantum network node with crossed optical fibre cavities

Quantum networks provide unique possibilities for resolving open questions on entanglement1 and promise innovative applications ranging from secure communication to scalable computation2. Although two quantum nodes coupled by a single channel are adequate for basic quantum communication tasks between two parties3, fully functional large-scale quantum networks require a web-like architecture with multiply connected nodes4. Efficient interfaces between network nodes and channels can be implemented with optical cavities5. Using two optical fibre cavities coupled to one atom, we here realize a quantum network node that connects to two quantum channels, one provided by each cavity. It functions as a passive, heralded and high-fidelity quantum memory that requires neither amplitude- and phase-critical control fields6–8 nor error-prone feedback loops9. Our node is robust, fits naturally into larger fibre-based networks and has prospects for extensions including qubit-controlled quantum switches10,11, routers12,13 and repeaters14,15. A passive, heralded and high-fidelity quantum memory network node has been realized, which connects simultaneously to two quantum channels provided by orthogonally aligned optical fibre cavities coupled with a single atom.
Blockchain in education: Opportunities, applications, and challenges

This paper discusses the opportunities and challenges of applying blockchain technologies in the education sector. The key blockchain-in-education applications discussed are the digitalization and decentralization of educational certifications and the enhancement and motivation for lifelong learning. Some of the key challenges explored are data protection laws such as the General Data Protection Regulation and the California Consumer Protection Act, which pose impediments for application developers and scalability challenges that arise because of slow-speed blockchain transactions and the Scaling Trilemma. Additionally, market adoption and innovation challenges highlight that blockchain-in-education is a relatively immature innovation that governance bodies within educational institutions often disregard or perceive cautiously.
Error correction in quantum cryptography based on artificial neural networks

Intensive work on quantum computing has increased interest in quantum cryptography in recent years. Although this technique is characterized by a very high level of security, there are still challenges that limit the widespread use of quantum key distribution. One of the most important problems remains secure and effective mechanisms for the key distillation process. This article presents a new idea for a key reconciliation method in quantum cryptography. This proposal assumes the use of mutual synchronization of artificial neural networks to correct errors occurring during transmission in the quantum channel. Users can build neural networks based on their own string of bits. The typical value of the quantum bit error rate does not exceed a few percent; therefore, the strings are similar and also users’ neural networks are very similar at the beginning of the learning process. It has been shown that the synchronization process in the new solution is much faster than in the analogous scenario used in neural cryptography. This feature significantly increases the level of security because a potential eavesdropper cannot effectively synchronize their own artificial neural networks in order to obtain information about the key. Therefore, the key reconciliation based on the new idea can be a secure and efficient solution.
Learning to Recommend With Multiple Cascading Behaviors

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Performance Indicator) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shop carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.
Flag Fault-Tolerant Error Correction for any Stabilizer Code

Conventional fault-tolerant quantum error-correction schemes require a number of extra qubits that grow linearly with the code’s maximum stabilizer generator weight. For some common distance-three codes, the recent “flag paradigm” uses just two extra qubits. Chamberland and Beverland [Quantum 2, 53 (2018)] provide a framework for flag error correction of arbitrary-distance codes. However, their construction requires conditions that only some code families are known to satisfy. We give a flag error-correction scheme that works for any stabilizer code, unconditionally. With fast qubit measurement and reset, it uses ≤d+1 extra qubits for a distance-d code.Received 24 January 2020Accepted 29 June 2020DOI:https://doi.org/10.1103/PRXQuantum.1.010302Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionQuantum memoriesQuantum Information
Learned Fast HEVC Intra Coding

In High Efficiency Video Coding (HEVC), excellent rate-distortion (RD) performance is achieved in part by having a flexible quadtree coding unit (CU) partition and a large number of intra-prediction modes. Such an excellent RD performance is achieved at the expense of much higher computational complexity. In this paper, we propose a learned fast HEVC intra coding (LFHI) framework taking into account the comprehensive factors of fast intra coding to reach an improved configurable tradeoff between coding performance and computational complexity. First, we design a low-complex shallow asymmetric-kernel CNN (AK-CNN) to efficiently extract the local directional texture features of each block for both fast CU partition and fast intra-mode decision. Second, we introduce the concept of the minimum number of RDO candidates (MNRC) into fast mode decision, which utilizes AK-CNN to predict the minimum number of best candidates for RDO calculation to further reduce the computation of intra-mode selection. Third, an evolution optimized threshold decision (EOTD) scheme is designed to achieve configurable complexity-efficiency tradeoffs. Finally, we propose an interpolation-based prediction scheme that allows for our framework to be generalized to all quantization parameters (QPs) without the need for training the network on each QP. The experimental results demonstrate that the LFHI framework has a high degree of parallelism and achieves a much better complexity-efficiency tradeoff, achieving up to 75.2% intra-mode encoding complexity reduction with negligible rate-distortion performance degradation, superior to the existing fast intra-coding schemes.
Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Modern big data applications tend to prefer a cluster computing approach as they are linked to the distributed computing framework that serves users jobs as per demand. It performs rapid processing of tasks by subdividing them into tasks that execute in parallel. Because of the complex environment, hardware and software issues, tasks might run slowly leading to delayed job completion, and such phenomena are also known as stragglers. The performance improvement of distributed computing framework is a bottleneck by straggling nodes due to various factors like shared resources, heavy system load, or hardware issues leading to the prolonged job execution time. Many state-of-the-art approaches use independent models per node and workload. With increased nodes and workloads, the number of models would increase, and even with large numbers of nodes. Not every node would be able to capture the stragglers as there might not be sufficient training data available of straggler patterns, yielding suboptimal straggler prediction. To alleviate such problems, we propose a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating stragglers without moving data to a centralized location. The proposed framework shares information among the various models, allowing us to use larger training data and bring training time down by avoiding data transfer. We rigorously evaluate the proposed method on various datasets with high accuracy results.
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&amp;E whole-slide images in colorectal and breast cancer

We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneity-aware deep Multiple Instance LEarning (DeepSMILE). We apply DeepSMILE to the task of Homologous recombination deficiency (HRD) and microsatellite instability (MSI) prediction. We utilize contrastive self-supervised learning to pre-train a feature extractor on histopathology tiles of cancer tissue. Additionally, we use variability-aware deep multiple instance learning to learn the tile feature aggregation function while modeling tumor heterogeneity. For MSI prediction in a tumor-annotated and color normalized subset of TCGA-CRC (n=360 patients), contrastive self-supervised learning improves the tile supervision baseline from 0.77 to 0.87 AUROC, on par with our proposed DeepSMILE method. On TCGA-BC (n=1041 patients) without any manual annotations, DeepSMILE improves HRD classification performance from 0.77 to 0.81 AUROC compared to tile supervision with either a self-supervised or ImageNet pre-trained feature extractor. Our proposed methods reach the baseline performance using only 40% of the labeled data on both datasets. These improvements suggest we can use standard self-supervised learning techniques combined with multiple instance learning in the histopathology domain to improve genomic label classification performance with fewer labeled data.
Towards artificial general intelligence via a multimodal foundation model

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".
Hybrid quantum linear equation algorithm and its experimental test on IBM Quantum Experience

We propose a hybrid quantum algorithm based on the Harrow-Hassidim-Lloyd (HHL) algorithm for solving a system of linear equations. In our hybrid scheme, a classical information feed-forward is required from the quantum phase estimation algorithm to reduce a circuit depth from the original HHL algorithm. In this paper, we show that this hybrid algorithm is functionally identical to the HHL algorithm under the assumption that the number of qubits used in algorithms is large enough. In addition, it is experimentally examined with four qubits in the IBM Quantum Experience setups, and the experimental results of our algorithm show higher accurate performance on specific systems of linear equations than that of the HHL algorithm.
An efficient AES implementation using FPGA with enhanced security features

Data transferred in an electronic way is vulnerable to attacks. With an aim to protect data for secure communication, a new Hybrid non pipelined Advanced Encryption Standard (AES) algorithm based on traditional AES algorithm with enhanced security features is proposed in this work. Abysmal analysis of the AES algorithm implies that the security of AES lies in the S-box operations. This paper presents a new approach for generating S-box values (modified S-box) and initial key required for encryption/encryption (improved key generation) using PN Sequence Generator. The AES algorithm with proposed modifications shows significant improvement in the encryption quality as compared to traditional AES algorithm. The traditional AES algorithm equipped with proposed novel modified S-box technique and improved key generation technique gives an avalanche effect of 60% making it invulnerable to attacks. The proposed design is synthesized on various Field Programmable Gate Array (FPGA) devices and compared to the existing designs resulting in significant improvement in throughput. The proposed design is implemented on Spartan6 FPGA device.
POBA-GA: Perturbation optimized black-box adversarial attacks via genetic algorithm

Most deep learning models are easily vulnerable to adversarial attacks. Various adversarial attacks are designed to evaluate the robustness of models and develop defense model. Currently, adversarial attacks are brought up to attack their own target model with their own evaluation metrics. And most of the black-box adversarial attack algorithms cannot achieve the expected success rate compared with white-box attacks. In this paper, comprehensive evaluation metrics are brought up for different adversarial attack methods. A novel perturbation optimized black-box adversarial attack based on genetic algorithm (POBA-GA) is proposed for achieving white-box comparable attack performances. Approximate optimal adversarial examples are evolved through evolutionary operations including initialization, selection, crossover and mutation. Fitness function is specifically designed to evaluate the example individual in both aspects of attack ability and perturbation control. Population diversity strategy is brought up in evolutionary process to promise the approximate optimal perturbations obtained. Comprehensive experiments are carried out to testify POBA-GA’s performances. Both simulation and application results prove that our method is better than current state-of-art black-box attack methods in aspects of attack capability and perturbation control.
Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification

Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used is very limited in scale. In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900,000 and 13,000,000, respectively, and with categories of 3,143 and 5,157 respectively. We use the same Inception-Residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. Experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets.
Needles in a Haystack: Tracking City-Scale Moving Vehicles From Continuously Moving Satellite

In recent years, the satellite videos have been captured by moving satellite platforms. In contrast to consumers, movies, and common surveillance videos, satellite videos can record the snapshots of city-scale scenes. In a broad fieldof-view of satellite videos, each moving target would be very tiny and usually composed of several pixels in frames. Even worse, the noise signals also exist in the video frames, and the background of the video frames subpixel-level and uneven moving thanks to the motion of satellites. We argue that it is a novel type of computer vision task since previous technologies are unable to detect such tiny moving vehicles efficiently. This paper proposes a novel framework that can identify small moving vehicles in satellite videos. In particular, we offer a novel detecting algorithm based on the local noise modeling. We differentiate the potential vehicle targets from noise patterns by an exponential probability distribution. Subsequently, a multi-morphologicalcue based discrimination strategy is designed to distinguish correct vehicle targets from the existing noises further. Another significant contribution is to introduce a series of evaluation protocols to measure the performance of tiny moving vehicle detection systematically. We annotate satellite videos manually to test our algorithms under different evaluation criterions. The proposed algorithm is also compared with the state-of-the-art baselines, which demonstrates the advantages of our framework over the benchmarks. Besides, the dataset would be downloaded from http://first.authour.github.com.
Reverse quantum annealing of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> -spin model with relaxation

In reverse quantum annealing, the initial state is an eigenstate of the final problem Hamiltonian and the transverse field is cycled rather than strictly decreased as in standard (forward) quantum annealing. We present a numerical study of the reverse quantum annealing protocol applied to the $p$-spin model ($p=3$), including pausing, in an open-system setting accounting for dephasing in the energy eigenbasis, which results in thermal relaxation. We consider both independent and collective dephasing and demonstrate that in both cases the open-system dynamics substantially enhances the performance of reverse annealing. Namely, including dephasing overcomes the failure of purely closed-system reverse annealing to converge to the ground state of the $p$-spin model. We demonstrate that pausing further improves the success probability. The collective dephasing model leads to somewhat better performance than independent dephasing. The protocol we consider corresponds closely to the one implemented in the current generation of commercial quantum annealers, and our results help to explain why recent experiments demonstrated enhanced success probabilities under reverse annealing and pausing.