Reviewerly/exHarmony huggingface dataset:
Long- and short-term self-attention network for sequential recommendation

With great value in real applications, sequential recommendation aims to recommend users the personalized sequential actions. To achieve better performance, it is essential to consider both long-term preferences and sequential patterns (i.e., short-term dynamics). Compared to widely used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), Self-Attention Network (SAN) obtains a surge of interest due to fewer parameters, highly parallelizable computation, and flexibility in modeling dependencies. However, existing SAN-based models are inadequate in characterizing and distinguishing users’ long-term preferences and short-term demands since they do not emphasize the importance of the current interest and temporal order information of sequences. In this paper, we propose a novel multi-layer long- and short-term self-attention network (LSSA) for sequential recommendation. Specifically, we first split the entire sequence of a user into multiple sub-sequences according to the timespan. Then the first self-attention layer learns the user’s short-term dynamics based on the last sub-sequence, while the second one captures the user’s long-term preferences through the previous sub-sequences and the last one. Finally, we integrate the long- and short-term representations together to form the user’s final hybrid representation. We evaluate the proposed model on three real-world datasets, and our experimental results show that LSSA outperforms state-of-the-art methods with a wide margin.
Solving Gauss's law on digital quantum computers with loop-string-hadron digitization

We show that using the loop-string-hadron (LSH) formulation of SU(2) lattice gauge theory (arXiv:1912.06133) as a basis for digital quantum computation easily solves an important problem of fundamental interest: implementing gauge invariance (or Gauss's law) exactly. We first discuss the structure of the LSH Hilbert space in $d$ spatial dimensions, its truncation, and its digitization with qubits. Error detection and mitigation in gauge theory simulations would benefit from physicality "oracles,'"so we decompose circuits that flag gauge invariant wavefunctions. We then analyze the logical qubit costs and entangling gate counts involved with the protocols. The LSH basis could save or cost more qubits than a Kogut-Susskind-type representation basis, depending on how the bases are digitized as well as the spatial dimension. The numerous other clear benefits encourage future studies into applying this framework.
Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring

Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL)-based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the sub-image patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyze every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model outperforms other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images.
Temporal Information Processing on Noisy Quantum Computers

Reservoir computing is a machine learning paradigm that exploits nonlinear dissipative dynamical systems for temporal information processing, and can be combined with quantum computing to form quantum reservoir computers. This study proposes a class of quantum reservoir computers that can be implemented on noisy intermediate-scale quantum (NISQ) computers and possesses the properties required to be reservoir computers, especially universality. Efficient implementation and proof-of-principle demonstration on several cloud-based IBM superconducting quantum devices suggest that the proposed scheme could lead to promising applications of NISQ computers.
Theory of analytical energy derivatives for the variational quantum eigensolver

The variational quantum eigensolver (VQE) and its variants, which is a method for finding eigenstates and eigenenergies of a given Hamiltonian, are appealing applications of near-term quantum computers. Although the eigenenergies are certainly important quantities which determines properties of a given system, their derivatives with respect to parameters of the system, such as positions of nuclei if we target a quantum chemistry problem, are also crucial to analyze the system. Here, we describe methods to evaluate analytical derivatives of the eigenenergy of a given Hamiltonian, including the excited state energy as well as the ground state energy, with respect to the system parameters in the framework of the VQE. We give explicit, low-depth quantum circuits which can measure essential quantities to evaluate energy derivatives, incorporating with proof-of-principle numerical simulations. This work extends the theory of the variational quantum eigensolver, by enabling it to measure more physical properties of a quantum system than before and to explore chemical reactions.
A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

Summary There exist numerous high‐dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high‐dimensional problems. An appropriate approach to solve such problems is to apply multi‐agent systems (MASs) along with the metaheuristic algorithms. The present paper proposes a new approach based on the MASs and the concept of agent, which is named MAS as Metaheuristic (MAMH) method. In the proposed method, several basic and powerful metaheuristic algorithms are considered as separate agents, each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. Altogether, the proposed method was tested on 32 complex benchmark functions, the results of which indicated the effectiveness and powerfulness of the proposed method for solving high‐dimensional optimization problems. In addition, in this paper, the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection. According to the results, the proposed method exhibited a higher degree of precision in the detection of spam emails compared to other metaheuristic algorithms and methods.
Laser-Damage Attack Against Optical Attenuators in Quantum Key Distribution

Many quantum key distribution systems employ a laser followed by an optical attenuator to prepare weak coherent states in the source. Their mean photon number must be pre-calibrated to guarantee the security of key distribution. Here we experimentally show that this calibration can be broken with a high-power laser attack. We have tested four fiber-optic attenuator types used in quantum key distribution systems, and found that two of them exhibit a permanent decrease in attenuation after laser damage. This results in higher mean photon numbers in the prepared states and may allow an eavesdropper to compromise the key.
Fault-Tolerant Resource Estimation of Quantum Random-Access Memories

Quantum random-access look-up of a string of classical bits is a necessary ingredient in several important quantum algorithms. In some cases, the cost of such quantum random-access memory (qRAM) is the limiting factor in the implementation of the algorithm. In this paper we study the cost of fault-tolerantly implementing a qRAM. We construct and analyze generic families of circuits that function as a qRAM, discuss opportunities for qubit-time tradeoffs, and estimate their resource costs when embedded in a surface code.
A fitness-based adaptive differential evolution algorithm

The performance of differential evolution (DE) mainly depends on its breeding offspring strategy (i.e., trial vector generation strategies and associated control parameters). To take full advantage of several effective breeding offspring strategies proposed in recent years, a fitness-based adaptive differential evolution algorithm (FADE) is proposed in this paper. In FADE, the entire population is split into multiple small-sized swarms, and three popular breeding strategies are saved in an archive which can be utilized by the multiple swarms. In each generation, different individuals in a same swarm adaptively select their own breeding strategy from the archive based on their fitness. With the adaptive breeding strategy, the individuals in a same swarm can exhibit distinct search behaviors. Moreover, the population size can be adaptively adjusted during the evolutionary process according to the performance of the best individual. Based on the adaptive population size, computational resources can be rationally assigned in different evolutionary stages, and then to satisfy diverse requirements of different fitness landscapes. The comprehensive performance of FADE is extensively evaluated by comparisons between it and other eight state-of-art DE variants based on CEC2013 and CEC2017 test suites as well as seven real applications. In addition, the effectiveness and efficiency of the newly introduced adaptive strategies are further confirmed by a set of experiments.
Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.
Comprehensive Image Captioning via Scene Graph Decomposition

We address the challenging problem of image captioning by revisiting the representation of image scene graph. At the core of our method lies the decomposition of a scene graph into a set of sub-graphs, with each sub-graph capturing a semantic component of the input image. We design a deep model to select important sub-graphs, and to decode each selected sub-graph into a single target sentence. By using sub-graphs, our model is able to attend to different components of the image. Our method thus accounts for accurate, diverse, grounded and controllable captioning at the same time. We present extensive experiments to demonstrate the benefits of our comprehensive captioning model. Our method establishes new state-of-the-art results in caption diversity, grounding, and controllability, and compares favourably to latest methods in caption quality. Our project website can be found at http://pages.cs.wisc.edu/~yiwuzhong/Sub-GC.html .
Human evaluation of automatically generated text: Current trends and best practice guidelines

Currently, there is little agreement as to how Natural Language Generation (NLG) systems should be evaluated, with a particularly high degree of variation in the way that human evaluation is carried out. This paper provides an overview of how (mostly intrinsic) human evaluation is currently conducted and presents a set of best practices, grounded in the literature. These best practices are also linked to the stages that researchers go through when conducting an evaluation research (planning stage; execution and release stage), and the specific steps in these stages. With this paper, we hope to contribute to the quality and consistency of human evaluations in NLG.
Adversarial Continual Learning

Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demonstrate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at https://github.com/facebookresearch/Adversarial-Continual-Learning .
Dynamic path planning for unmanned surface vehicle in complex offshore areas based on hybrid algorithm

According to its planning scope, path planning for unmanned surface vehicle (USV) can be divided into global and local path planning. Many scholars have improved the classic algorithms, including grids method, visibility graph method, A* algorithm and artificial potential field method (APF), But the global planning algorithm still has outstanding problems such as long calculation time and large computational overhead in large task space, local planning algorithms usually ignore the global optimal constraints. Aiming at the problem of dynamic path planning of environmental monitoring USV under complicated offshore navigation conditions, based on the idea of bi-level planning, a hybrid algorithm which combines global and local path planning is proposed. This paper first proposes an improved Particle Swarm Optimization (PSO) for global path planning according to the given information about marine environment, and introduces Opposition-based Learning (OBL) and improves the inertia weight as well as search step size to effectively avoid the precocity of PSO. Then on the basis of global optimized path and sensor information, the improved Artificial Potential Field (APF) algorithm is adopted for local dynamic obstacle avoidance, so as to solve the local minimum problem. The results of simulation indicated that the improved PSO can effectively avoid the precocity of particles and enhance the optimization capability and stability of the PSO; the improved APF would not be restricted by local minimum point and achieve dynamic obstacle avoidance under the constraints of global optimization path. Therefore, the combination of these two algorithms can effectively solve the problems of path optimization and dynamic obstacle avoidance for environment monitoring USV when it is executing missions in complex offshore areas.
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation

In the feature maps of CNNs, there commonly exists considerable spatial redundancy that leads to much repetitive processing. Towards reducing this superfluous computation, we propose to compute features only at sparsely sampled locations, which are probabilistically chosen according to activation responses, and then densely reconstruct the feature map with an efficient interpolation procedure. With this sampling-interpolation scheme, our network avoids expending computation on spatial locations that can be effectively interpolated, while being robust to activation prediction errors through broadly distributed sampling. A technical challenge of this sampling-based approach is that the binary decision variables for representing discrete sampling locations are non-differentiable, making them incompatible with backpropagation. To circumvent this issue, we make use of a reparameterization trick based on the Gumbel-Softmax distribution, with which backpropagation can iterate these variables towards binary values. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation

Unsupervised domain adaptation is critical in various computer vision tasks, such as object detection, instance segmentation, and semantic segmentation, which aims to alleviate performance degradation caused by domain-shift. Most of previous methods rely on a single-mode distribution of source and target domains to align them with adversarial learning, leading to inferior results in various scenarios. To that end, in this paper, we design a new spatial attention pyramid network for unsupervised domain adaptation. Specifically, we first build the spatial pyramid representation to capture context information of objects at different scales. Guided by the task-specific information, we combine the dense global structure representation and local texture patterns at each spatial location effectively using the spatial attention mechanism. In this way, the network is enforced to focus on the discriminative regions with context information for domain adaptation. We conduct extensive experiments on various challenging datasets for unsupervised domain adaptation on object detection, instance segmentation, and semantic segmentation, which demonstrates that our method performs favorably against the state-of-the-art methods by a large margin. Our source code is available at https://isrc.iscas.ac.cn/gitlab/research/domain-adaption .
Detecting Human-Object Interactions with Action Co-occurrence Priors

A common problem in human-object interaction (HOI) detection task is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution. The lack of positive labels can lead to low classification accuracy for these classes. Towards addressing this issue, we observe that there exist natural correlations and anti-correlations among human-object interactions. In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially on rare classes. The utility of our approach is demonstrated experimentally, where the performance of our approach exceeds the state-of-the-art methods on both of the two leading HOI detection benchmark datasets, HICO-Det and V-COCO.
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management

We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Challenge 1 , judged by medical experts.Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 by answering high priority questions from the community and summarizing salient questionrelated information.It combines information extraction with state-of-the-art QA and queryfocused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query.We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question.We further conduct quantitative experiments that show consistent improvements on various metrics for each module.We have launched our website CAiRE-COVID 2 for broader use by the medical community, and have open-sourced the code 3 for our system, to bootstrap further study by other researches.
Demonstration of non-Markovian process characterisation and control on a quantum processor

Abstract In the scale-up of quantum computers, the framework underpinning fault-tolerance generally relies on the strong assumption that environmental noise affecting qubit logic is uncorrelated (Markovian). However, as physical devices progress well into the complex multi-qubit regime, attention is turning to understanding the appearance and mitigation of correlated — or non-Markovian — noise, which poses a serious challenge to the progression of quantum technology. This error type has previously remained elusive to characterisation techniques. Here, we develop a framework for characterising non-Markovian dynamics in quantum systems and experimentally test it on multi-qubit superconducting quantum devices. Where noisy processes cannot be accounted for using standard Markovian techniques, our reconstruction predicts the behaviour of the devices with an infidelity of 10 −3 . Our results show this characterisation technique leads to superior quantum control and extension of coherence time by effective decoupling from the non-Markovian environment. This framework, validated by our results, is applicable to any controlled quantum device and offers a significant step towards optimal device operation and noise reduction.