Reviewerly/exHarmony huggingface dataset:
On-demand entanglement of molecules in a reconfigurable optical tweezer array

Entanglement is crucial to many quantum applications, including quantum information processing, quantum simulation, and quantum-enhanced sensing. Because of their rich internal structure and interactions, molecules have been proposed as a promising platform for quantum science. Deterministic entanglement of individually controlled molecules has nevertheless been a long-standing experimental challenge. We demonstrate on-demand entanglement of individually prepared molecules. Using the electric dipolar interaction between pairs of molecules prepared by using a reconfigurable optical tweezer array, we deterministically created Bell pairs of molecules. Our results demonstrate the key building blocks needed for quantum applications and may advance quantum-enhanced fundamental physics tests that use trapped molecules.
Python for Machine Learning

Anticipating that the user has a good knowledge of the core elements of Python we now explore the applicative aspect of Python. In this chapter, we will look at Python, especially from the Machine Learning (ML) point of view. We will discuss the various libraries and their utility in ML and then lay hands over the programming demonstrations.
Can Large Language Models Transform Computational Social Science?

Abstract Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the Computational Social Science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 25 representative English CSS benchmarks. On taxonomic labeling tasks (classification), LLMs fail to outperform the best fine-tuned models but still achieve fair levels of agreement with humans. On free-form coding tasks (generation), LLMs produce explanations that often exceed the quality of crowdworkers' gold references.We conclude that the performance of today’s LLMs can augment the CSS research pipeline in two ways: (1) serving as zeroshot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e.g., explaining the underlying attributes of a text). In summary, LLMs are posed to meaningfully participate in social science analysis in partnership with humans.
Robust Hybrid Visual Servoing Using Reinforcement Learning and Finite-Time Adaptive FOSMC

In this paper, we present and implement a hybrid approach to robust visual servoing for autonomous ground vehicles. A vision integrated model for nonholonomic mobile robots is derived that obviates the need for actual depth data for image-based visual servoing by assuming planar motion. A fractional order sliding-mode controller has been designed where the parameters of the sliding surface are adapted in real time. These adaptive laws are so derived that ensure finite-time convergence. An optical flow based heading restoration law is designed to handle the severe external perturbations, i.e., when the visual marker disappears from the field of view of the camera. The heading restoration law is combined with the reinforcement learning to solve this visibility problem. The proposed algorithm enables the robot to reach the home location even when the visual marker momentarily disappears from the camera field of view due to external disturbances. The proposed algorithm is validated in real time using Pioneer P3-DX robots through perturbation studies. Both simulation and experimental results prove the efficacy of the proposed scheme.
A fast algorithm for material image sequential stitching

In material research, it is often highly desirable to observe images of whole microscopic sections with high resolution. So that micrograph stitching is an important technology to produce a panorama or larger image by combining multiple images with overlapping areas, while retaining microscopic resolution. However, due to high complexity and variety of microstructure, most traditional methods could not balance speed and accuracy of stitching strategy. To overcome this problem, we develop a method named very fast sequential micrograph stitching (VFSMS), which employ incremental searching strategy and GPU acceleration to guarantee the accuracy and the speed of stitching results. Experimental results demonstrate that the VFSMS achieve state-of-art performance on three types’ microscopic datasets on both accuracy and speed aspects. Besides, it significantly outperforms the most famous and commonly used software, such as ImageJ, Photoshop and Autostitch. The software is available at https://www.mgedata.cn/app_entrance/microscope.
An Integral Tag Recommendation Model for Textual Content

Recommending suitable tags for online textual content is a key building block for better content organization and consumption. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped. However, none of the existing methods consider all these three aspects, leading to a suboptimal tag recommendation. In this paper, we propose an integral model to encode all the three aspects in a coherent encoder-decoder framework. In particular, (1) the encoder models the semantics of the textual content via Recurrent Neural Networks with the attention mechanism, (2) the decoder tackles the tag correlation with a prediction path, and (3) a shared embedding layer and an indicator function across encoder-decoder address the content-tag overlapping. Experimental results on three realworld datasets demonstrate that the proposed method significantly outperforms the existing methods in terms of recommendation accuracy.
Design of Synchronized Large-Scale Chaos Random Number Generators and Its Application to Secure Communication

This paper is concerned with the design of synchronized large-scale chaos random number generators (CRNGs) and its application to secure communication. In order to increase the diversity of chaotic signals, we firstly introduce additional modulation parameters in the original chaotic Duffing map system to modulate the amplitude and DC offset of the chaotic states. Then according to the butterfly effect, we implement modulated Duffing map systems with different initial values by using the microcontroller and complete the design of large-scale CRNGs. Next, a discrete sliding mode scheme is proposed to solve the synchronization problem of the master-slave large-scale CRNGs. Finally, we integrate the aforementioned results to implement an innovative secure communication system.
Taking a Studio Course in Distributed Software Engineering from a Large Local Cohort to a Small Global Cohort

One of the challenges of global software engineering courses is to bring the practices and experience of large geographically distributed teams into the local and time-limited environment of a classroom. Over the last 6 years, an on-campus studio course for software engineering has been developed at the University of Queensland (UQ) that places small teams of students on different features of a common product. This creates two layers of collaboration, as students work within their teams on individual features, and the teams must interoperate with many other teams on the common product. The class uses continuous integration practices and predominantly asynchronous communication channels (Slack and GitHub) to facilitate this collaboration. The original goal of this design was to ensure that students would authentically experience issues associated with realistically sized software projects, and learn to apply appropriate software engineering and collaboration practices to overcome them, in a course without significant extra staffing. Data from the development logs showed that most commits take place outside synchronous class hours, and the project operates as a temporally distributed team even though the students are geographically co-located. Since 2015, a course adapted from this format has also been taught at the University of New England (UNE), an Australian regional university that is also a longstanding provider of distance education. In this course, most students study online, and the class has to be able to work globally, because as well as students taking part from around Australia, there are also typically a small number of students taking part from overseas. Transferring the course to a smaller but predominantly online institution has allowed us to evaluate the distributed nature of the course, by considering what aspects of the course needed to change to support students who are geographically distributed, and comparing how the two cohorts behave. This has produced an overall course design, to teach professional distributed software engineering practices, that is adaptable from large classes to small, and from local to global.
Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.
Chaotic Encryption Applied to Optical Ethernet in Industrial Control Systems

In the past decades, Ethernet has become an alternative technology for the field buses traditionally used in industrial control systems and distributed measurement systems. Among different transmission media in Ethernet standards, optical fiber provides the best bandwidth, excellent immunity to electromagnetic interference, and less signal loses than other wired media. Due to the absence of a standard that provides security at the physical layer of optical Ethernet links, the main motivation of this paper is to propose and implement the necessary modifications to introduce encryption in Ethernet 1000Base-X standard. This has consisted of symmetric streaming encryption of the 8b10b symbols flow at physical coding sublayer level, thanks to a keystream generator based on chaotic algorithm. The overall system has been implemented and tested in an field programmable gate array and Ethernet traffic has been encrypted and transmitted over an optical link. The experimental results show that it is possible to cipher traffic at this level and hide the complete Ethernet traffic pattern from passive eavesdroppers. In addition, no space overhead is introduced in data frames during encryption, achieving the maximum throughput.
Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.
CMBPR: Category-Aided Multi-Channel Bayesian Personalized Ranking for Short Video Recommendation

Due to the viewing convenience for social media users' fragmented time, the short video has become a new carrier for users' network demands on information spread, news reading, social contact, entertainment, and leisure. Therefore, short video recommendation is one of the most important research topics in social media. Current short video recommendation algorithms mainly focus on detecting user's social attributes, developing cross-domain information and so on, few researchers combine video category information and multi-behavior information together. This paper proposes a content-based recommendation algorithm Category-aided Multi-channel Bayesian Personalized Ranking (CMBPR) for short video recommendation, which integrates users' rich preference information by considering the difference among both different video categories and different user interactions. The experimental results demonstrate the effectiveness of the CMBPR video recommendation algorithm, which achieves a significantly higher recommendation accuracy than the traditional video recommendation algorithms and solves the influence of the “Long Tail” effect.
Optimization and deployment of CNNs at the edge

Deep learning (DL) algorithms have already proved their effectiveness on a wide variety of application domains, including speech recognition, natural language processing, and image classification. To foster their pervasive adoption in applications where low latency, privacy issues and data bandwidth are paramount, the current trend is to perform inference tasks at the edge. This requires deployment of DL algorithms on low-energy and resource-constrained computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage without adequate support and experience. In this paper, we present ALOHA, an integrated tool flow that tries to facilitate the design of DL applications and their porting on embedded heterogenous architectures. The proposed tool flow aims at automating different design steps and reducing development costs. ALOHA considers hardware-related variables and security, power efficiency, and adaptivity aspects during the whole development process, from pre-training hyperparameter optimization and algorithm configuration to deployment.
A Contrastive Divergence for Combining Variational Inference and MCMC

We develop a method to combine Markov chain Monte Carlo (MCMC) and variational inference (VI), leveraging the advantages of both inference approaches. Specifically, we improve the variational distribution by running a few MCMC steps. To make inference tractable, we introduce the variational contrastive divergence (VCD), a new divergence that replaces the standard Kullback-Leibler (KL) divergence used in VI. The VCD captures a notion of discrepancy between the initial variational distribution and its improved version (obtained after running the MCMC steps), and it converges asymptotically to the symmetrized KL divergence between the variational distribution and the posterior of interest. The VCD objective can be optimized efficiently with respect to the variational parameters via stochastic optimization. We show experimentally that optimizing the VCD leads to better predictive performance on two latent variable models: logistic matrix factorization and variational autoencoders (VAEs).
Segmentation of Brain MR Images by Using Fully Convolutional Network and Gaussian Mixture Model with Spatial Constraints

Accurate segmentation of brain tissue from magnetic resonance images (MRIs) is a critical task for diagnosis, treatment, and clinical research. In this paper, a novel algorithm (GMMD-U) that incorporates the modified full convolutional neural network U-net and Gaussian-Dirichlet mixture model (GMMD) with spatial constraints is presented. The proposed GMMD-U considers the local spatial relationships by assuming that the prior probability obeys the Dirichlet distribution. Specifically, GMMD is applied for extracting brain tissue that has a distinct intensity region and modified U-net is exploited to correct the wrong-classification areas caused by GMMD or other conventional approaches. The proposed GMMD-U is designed to take advantage of the statistical model-based segmentation techniques and deep neural network. We evaluate the performance of GMMD-U on a publicly available brain MRI dataset by comparing it with several existing algorithms, and the results reported reveal that the proposed framework can accurately detect the brain tissue from MRIs. The proposed learning-based integrated framework could be effective for brain tissue segmentation, which will be helpful for surgeons in brain disease diagnosis.
Mixed Reality for Learning Programming

We present our ongoing investigation into leveraging mixed reality (MR) to help students learn coding more easily and with more fun. We have developed an MR coding learning platform using Apple's ARKit 2 on iOS, with a physical user-configurable coding game board. Our approach could provide major benefits over conventional augmented reality (AR) approaches for learning coding and debugging: (1) allowing teachers to tailor the platform to their instructional needs, and spark creativity and engagement among students in designing programming problems that interest them; (2) enabling students to physically interact with a program, concretizing coding errors and providing real-time visual feedback to aid students' program understanding and reduce cognitive load. We present our preliminary results that uses ARKit's image tracking and object detection to enable core mixed-reality interaction capabilities on our platform.
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent

This paper is concerned with minimizing the average of $n$ cost functions over a network, in which agents may communicate and exchange information with their peers in the network. Specifically, we consider the setting where only noisy gradient information is available. To solve the problem, we study the standard distributed stochastic gradient descent (DSGD) method and perform a non-asymptotic convergence analysis. For strongly convex and smooth objective functions, we not only show that DSGD asymptotically achieves the optimal network independent convergence rate compared to centralized stochastic gradient descent (SGD), but also explicitly identify the non-asymptotic convergence rate as a function of characteristics of the objective functions and the network. Furthermore, we derive the time needed for DSGD to approach the asymptotic convergence rate, which behaves as $K_T=\mathcal{O}(\frac{n}{(1-\rho_w)^2})$, where $(1-\rho_w)$ denotes the spectral gap of the mixing matrix of communicating agents.
Deep Position-Sensitive Tracking

Classification-based tracking strategies often face more challenges from intra-class discrimination than from inter-class separability. Even for deep convolutional neural networks that have been widely proven to be effective in various vision tasks, their intra-class discriminative capability is still limited by the weakness of softmax loss, especially for targets not seen in the training dataset. By taking intrinsic attributes of training samples into account, in this paper, we propose a position-sensitive loss coupled with softmax loss to achieve intra-class compactness and inter-class explicitness. Particularly, two additive margins are introduced to encode the position attribute for decision boundary maximization, which is also utilized with the proposed loss to supervise the fine-tuned features on the pre-trained model. With the nearest neighbor ranking measurement in the feature embedding domain, the whole scheme is able to reach an optimized balance between the feature-level inter-class semantic separability and instance-level intra-class relative distance ranking. We evaluate the proposed work on different popular benchmarks, and experimental results demonstrate that our tracking strategy performs favorably against most of the state-of-the-art trackers in the comparison of accuracy and robustness.
Trajectory Optimization in a Cooperative Aerial Reconnaissance Model

In recent years, the use of modern technology in military operations has become standard practice. Unmanned systems play an important role in operations such as reconnaissance and surveillance. This article examines a model for planning aerial reconnaissance using a fleet of mutually cooperating unmanned aerial vehicles to increase the effectiveness of the task. The model deploys a number of waypoints such that, when every waypoint is visited by any vehicle in the fleet, the area of interest is fully explored. The deployment of waypoints must meet the conditions arising from the technical parameters of the sensory systems used and tactical requirements of the task at hand. This paper proposes an improvement of the model by optimizing the number and position of waypoints deployed in the area of interest, the effect of which is to improve the trajectories of individual unmanned systems, and thus increase the efficiency of the operation. To achieve this optimization, a modified simulated annealing algorithm is proposed. The improvement of the model is verified by several experiments. Two sets of benchmark problems were designed: (a) benchmark problems for verifying the proposed algorithm for optimizing waypoints, and (b) benchmark problems based on typical reconnaissance scenarios in the real environment to prove the increased effectiveness of the reconnaissance operation. Moreover, an experiment in the SteelBeast simulation system was also conducted.
Big Data Visualization Tools

Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities.
Towards Robust, Locally Linear Deep Networks

Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest. For example, we use the derivative of the mapping with respect to its inputs for sensitivity analysis, or to explain (obtain coordinate relevance for) a prediction. One key challenge is that such derivatives are themselves inherently unstable. In this paper, we propose a new learning problem to encourage deep networks to have stable derivatives over larger regions. While the problem is challenging in general, we focus on networks with piecewise linear activation functions. Our algorithm consists of an inference step that identifies a region around a point where linear approximation is provably stable, and an optimization step to expand such regions. We propose a novel relaxation to scale the algorithm to realistic models. We illustrate our method with residual and recurrent networks on image and sequence datasets.