Reviewerly/exHarmony huggingface dataset:
Meta-Agent Conflict-Based Search For Optimal Multi-Agent Path Finding

The task in the multi-agent path finding problem (MAPF) isto find paths for multiple agents, each with a different startand goal position, such that agents do not collide. It is possibleto solve this problem optimally with algorithms that arebased on the A* algorithm. Recently, we proposed an alternativealgorithm called Conflict-Based Search (CBS) (Sharonet al. 2012), which was shown to outperform the A*-basedalgorithms in some cases. CBS is a two-level algorithm. Atthe high level, a search is performed on a tree based on conflictsbetween agents. At the low level, a search is performedonly for a single agent at a time. While in some cases CBSis very efficient, in other cases it is worse than A*-based algorithms.This paper focuses on the latter case by generalizingCBS to Meta-Agent CBS (MA-CBS). The main idea isto couple groups of agents into meta-agents if the number ofinternal conflicts between them exceeds a given bound. MACBSacts as a framework that can run on top of any completeMAPF solver. We analyze our new approach and provideexperimental results demonstrating that it outperforms basicCBS and other A*-based optimal solvers in many cases.
Toward a Context-Aware Human–Robot Interaction Framework Based on Cognitive Development

The purpose of this paper was to understand how an agent's performance is affected when interaction workflows are incorporated in its information model and decision-making process. Our expectation was that this incorporation could reduce errors and faults on agent's operation, improving its interaction performance. We based this expectation on the existing challenges in designing and implementing artificial social agents, where an approach based on predefined user scenarios and action scripts is insufficient to account for uncertainty in perception or unclear expectations from the user. Therefore, we developed a framework that captures the expected behavior of the agent into descriptive scenarios and then translated these into the agent's information model and used the resulting representation in probabilistic planning and decision making to control interaction. Our results indicated an improvement in terms of specificity while maintaining precision and recall, suggesting that the hypothesis being proposed in our approach is plausible. We believe the presented framework will contribute to the field of cognitive robotics, e.g., by improving the usability of artificial social companions, thus overcoming the limitations imposed by approaches that use predefined static models for an agent's behavior resulting in non-natural interaction.
Fast Single Image Super-Resolution Via Dilated Residual Networks

Recently, deep convolutional neural networks (CNNs) have been attracting considerable attention in single image super-resolution. Some CNN-based methods, such as VDSR verified that residual learning can speed up the training and significantly improve the performance of accuracy. However, with very deep networks, convergence speed is still a critical issue in training due to the cost of requiring enormous parameters. In order to deal with this issue, we redesign the residual networks based on dilated networks. In this paper, we propose symmetrical dilated residual convolution networks (FDSR) to tackle image super-resolution problems. Our network is on the basis of the dilated convolutions supported exponential expansion of the receptive field without loss of resolution and coverage. This means that FDSR can speed up the training and improve the performance of accuracy without increasing the model's depth or complexity. Meanwhile, we attempt to combine the image pre-processing approach of VGG-net with mean squared error (MSE) to enhance the performance. The experimental results demonstrate that the training time-consuming proposed model achieves nearly a half with even superior restoration quality. Further, we present a novel network with less layers and parameters can achieve real-time performance on a generic CPU and still maintain superior performance.
Axially Symmetric Data Clustering Through Dirichlet Process Mixture Models of Watson Distributions

This paper proposes a Bayesian nonparametric framework for clustering axially symmetric data. Our approach is based on a Dirichlet processes mixture model with Watson distributions, which can also be considered as the infinite Watson mixture model. In this paper, first, we extend the finite Watson mixture model into its infinite counterpart based on the framework of truncated Dirichlet process mixture model with a stick-breaking representation. Second, we propose a coordinate ascent mean-field variational inference algorithm that can effectively learn the parameters of our model with closed-form solutions; Third, to cope with a massive data set, we develop a stochastic variational inference algorithm to learn the proposed model through the method of stochastic gradient ascent; Finally, the proposed nonparametric Bayesian model is evaluated through simulated axially symmetric data sets and a real-world application, namely, gene expression data clustering.
Elimination of systematic error in digital image correlation caused by intensity interpolation by introducing position randomness to subset points

A strategy called random subset is presented to reduce systematic error in digital image correlation caused by intensity interpolation. In classical digital image correlation techniques, the subset points fall at integer locations; by contrast, the proposed random subset strategy imposes random offsets on the conventional locations, and consequently the subset points are located at non-integer positions. The introduction of position randomness to subset points makes the random subset strategy distinctly different from the classical digital image correlation technique. The random offsets can follow various probability distributions. In this work, two types of random offset were studied as examples, and the maximum of the random offsets was adjusted to modify performance. Using the random subset strategy, random-subset-based subpixel registration algorithms were developed in this study, including the random-subset-based forward additive Gaussian–Newton algorithm and the random-subset-based inverse compositional Gauss–Newton algorithm. The measurement accuracy of the random subset-based subpixel registration algorithms was investigated. Numerical simulations and real-world experiments demonstrated that as the maximum of the random offsets increases, the amplitude of the sinusoidal-shaped systematic error changes from positive to negative. Hence, if an appropriate random offset is selected, systematic error can be considerably reduced. In fact, the accuracy enhancement is greater than one order of magnitude, and systematic error is virtually eliminated.
Graph isomorphism and Gaussian boson sampling

Abstract We introduce a connection between a near-term quantum computing device, specifically a Gaussian boson sampler, and the graph isomorphism problem. We propose a scheme where graphs are encoded into quantum states of light, whose properties are then probed with photon-number-resolving detectors. We prove that the probabilities of different photon-detection events in this setup can be combined to give a complete set of graph invariants. Two graphs are isomorphic if and only if their detection probabilities are equivalent. We present additional ways that the measurement probabilities can be combined or coarse-grained to make experimental tests more amenable. We benchmark these methods with numerical simulations on the Titan supercomputer for several graph families: pairs of isospectral nonisomorphic graphs, isospectral regular graphs, and strongly regular graphs.
High temperature modal analysis of a non-uniformly heated rectangular plate: Experiments and simulations

Structures in demanding environments where high-temperatures and high-frequency vibratory loads are combined often experience fatigue which shortens their lifecycle. A limited amount of experimental data is available on the mechanical behaviour of plates under such thermo-acoustic loading. In this work, on Hastelloy-X plates, non-contact techniques were used to simultaneously acquire full-field temperature and out-of-plane displacement data for a thin plate. The plate was heated using halogen quartz lamps arranged in two different configurations and mechanically loaded using a commercially-available shaker. The centre of the plate was mounted to the shaker through a stinger with a bolted connection. The structure's resonant frequencies were determined using experimental modal analysis when mechanically loading the plate using a randomly varying signal from 0 to 800 Hz. Modal shapes were studied by exciting the plate to its first eleven resonant frequencies and acquiring displacement data using a PL-DIC (Pulse Laser Digital Image Correlation) method. Infra-red imaging was used to acquire temperature maps across the specimen. A finite element model was developed to include temperature-dependent material properties in the prediction of the plate's resonant frequencies and mode shapes. For the first time, experimental results showed the resonant response of the plate to strongly depend on the temperature distribution across the structure, correlating well with past predictive work in the literature. This was supported by the results from the finite element model, which were validated against experimental data and found to yield reliable predictions.
Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach

With the increasing data dimensionality, feature selection has become a fundamental task to deal with high-dimensional data. Semi-supervised feature selection focuses on the problem of how to learn a relevant feature subset in the case of abundant unlabeled data with few labeled data. In recent years, many semi-supervised feature selection algorithms have been proposed. However, these algorithms are implemented by separating the processes of feature selection and classifier training, such that they cannot simultaneously select features and learn a classifier with the selected features. Moreover, they ignore the difference of reliability inside unlabeled samples and directly use them in the training stage, which might cause performance degradation. In this paper, we propose a joint semi-supervised feature selection and classification algorithm (JSFS) which adopts a Bayesian approach to automatically select the relevant features and simultaneously learn a classifier. Instead of using all unlabeled samples indiscriminately, JSFS associates each unlabeled sample with a self-adjusting weight to distinguish the difference between them, which can effectively eliminate the irrelevant unlabeled samples via introducing a left-truncated Gaussian prior. Experiments on various datasets demonstrate the effectiveness and superiority of JSFS.
DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.
State Abstraction as Compression in Apprenticeship Learning

State abstraction can give rise to models of environments that are both compressed and useful, thereby enabling efficient sequential decision making. In this work, we offer the first formalism and analysis of the trade-off between compression and performance made in the context of state abstraction for Apprenticeship Learning. We build on Rate-Distortion theory, the classic Blahut-Arimoto algorithm, and the Information Bottleneck method to develop an algorithm for computing state abstractions that approximate the optimal tradeoff between compression and performance. We illustrate the power of this algorithmic structure to offer insights into effective abstraction, compression, and reinforcement learning through a mixture of analysis, visuals, and experimentation.
Syntax-Aware Neural Semantic Role Labeling

Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic structures, traditional discrete-feature-based SRL approaches make heavy use of syntactic features. In contrast, deep-neural-network-based approaches usually encode the input sentence as a word sequence without considering the syntactic structures. In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are beneficial for neural SRL models. Experiments on the benchmark CoNLL-2005 dataset show that syntax-aware SRL approaches can effectively improve performance over a strong baseline with external word representations from ELMo. With the extra syntax-aware representations, our approaches achieve new state-of-the-art 85.6 F1 (single model) and 86.6 F1 (ensemble) on the test data, outperforming the corresponding strong baselines with ELMo by 0.8 and 1.0, respectively. Detailed error analysis are conducted to gain more insights on the investigated approaches.
A Comparative Analysis of Expected and Distributional Reinforcement Learning

Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which models expected values (expected RL). However, aside from convergence guarantees, there have been few theoretical results investigating the reasons behind the improvements distributional RL provides. In this paper we begin the investigation into this fundamental question by analyzing the differences in the tabular, linear approximation, and non-linear approximation settings. We prove that in many realizations of the tabular and linear approximation settings, distributional RL behaves exactly the same as expected RL. In cases where the two methods behave differently, distributional RL can in fact hurt performance when it does not induce identical behaviour. We then continue with an empirical analysis comparing distributional and expected RL methods in control settings with non-linear approximators to tease apart where the improvements from distributional RL methods are coming from.
&lt;p&gt;Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China&lt;/p&gt;

To investigate the feasibility of a CAD system S-detect on a database from a single Chinese medical center.An experienced radiologist performed breast US examinations and made assessments of 266 consecutive breast lesions in 227 patients. S-detect classified the lesions automatically in a dichotomous form. An in-training resident who was blind to both the US diagnostic results and histological results reviewed the images afterward. The final histological results were considered as the diagnostic gold standard. The diagnostic performances and interrater agreements were analyzed.A total of 266 focal breast lesions (161 benign lesions and 105 malignant lesions) were assessed in this study. S-detect had a lower sensitivity (87.07%) and a higher specificity (72.27%) compared with the experienced radiologist (sensitivity 98.1% and specificity 65.43%). The sensitivity and specificity of S-detect were better than that of the resident (sensitivity 82.86% and specificity 68.94%). The AUC value of S-detect (0.807) showed no significant difference with the experienced radiologist (0.817) and was higher than that of the resident (0.758). S-detect had moderate agreement with the experienced radiologist.In this single-center study, a high level of diagnostic performance of S-detect on 266 breast lesions of Chinese women was observed. S-detect had almost equal diagnostic capacity with an experienced radiologist and performed better than a novice reader. S-detect was also distinguished for its high specificity. These results supported the feasibility of S-detect in aiding the diagnosis of breast lesions on an independent database.
Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach

Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies.Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users.Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques.Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers.Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers.We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of mlearning systems.Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users.Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments.
HTTP/2-based Frame Discarding for Low-Latency Adaptive Video Streaming

In this article, we propose video delivery schemes insuring around 1s delivery latency with Dynamic Adaptive Streaming over HTTP (DASH), which is a standard version of HTTP Live Streaming (HLS), so as to benefit from the video representation switching between successive video segments. We also propose HTTP/2-based algorithms to apply video frame discarding policies inside a video segment when a selected DASH representation does not match with the available network resources. The current solutions with small buffer suffer from rebuffering events. Rebuffering not only impacts the Quality of Experience (QoE) but also increases the delivery delay between the displayed and the original video streams. In this work, we completely eliminate rebuffering events by developing optimal and practical video frame discarding algorithms to meet the 1s latency constraint. In all our algorithms, we request the video frames individually through HTTP/2 multiple streams, and we selectively drop the least meaningful video frames thanks to HTTP/2 stream resetting feature. Our simulations show that the proposed algorithms eliminate rebuffering while insuring an acceptable video quality with at least a Peak Signal to Noise Ratio (PSNR) of 35dB compared to 25dB of the basic First In First Out (FIFO) algorithm. We also quantify and qualify the resulting temporal distortion of the video segments per algorithm. An important number of missing video frames results in a temporal fluidity break known as video jitter . The displayed video looks like a series of snapshots. We show that both the optimal Integer Linear Program (ILP) and practical algorithms decrease the frequency and duration of the jitters. For example, practical algorithms reduce the number of crashed displayed videos (presenting one jitter longer than 1,350ms) with 22% compared to the basic FIFO algorithm. We also show that requesting video frames separately with HTTP/2 slightly increases the overhead from 4.34% to 5.76%.
Identifying the Added Value of Virtual Reality for Treatment in Forensic Mental Health: A Scenario-Based, Qualitative Approach

Background: Although literature and practice underline the potential of virtual reality (VR) for forensic mental healthcare, studies that explore why and in what way VR can be of added value for treatment of forensic psychiatric patients is lacking. Goals: This study aimed to identify (1) points of improvements in existing forensic mental health treatment of in- and outpatients, (2) possible ways of using VR that can improve current treatment, and (3) positive and negative aspects of the use of VR for the current treatment according to patients and therapists. Methods: Two scenario-based methods were used. First, semi-structured interviews were conducted with eight therapists and three patients to elicit scenarios from them. Based on these results, six scenarios about possibilities for using VR in treatment were created and presented to 89 therapists and 19 patients in an online questionnaire. The qualitative data from both methods were coded independently by two researchers, using the method of constant comparison. Results: In the interviews, six main codes with accompanying sub codes emerged. Ideas for improvement of treatment were grouped around the unique characteristics of the forensic setting, characteristics of the complex patient population, and characteristics of the type of treatment. For possibilities of VR, main codes were skills training with interaction, observation of situations or stimuli without interaction, and creating insight for others into the patient. The questionnaire resulted in a broad range of insights into potential positive and negative aspects of VR related to the current treatment, the patient, the content of a VR application, and practical matters. Conclusion: VR offers a broad range of possibilities for forensic mental health. Examples are offering training of behavioral and cognitive skills in a realistic context to bridge the gap between a therapy room and the real world, increasing treatment motivation, being able to adapt a VR application to individual patients, and providing therapists with new insights into a patient. These findings can be used to ground the development of new VR applications. Nevertheless, we should remain critical of when in the treatment process and for whom VR could be of added value.