Reviewerly/exHarmony huggingface dataset:
Instance Segmentation with Mask-supervised Polygonal Boundary Transformers

In this paper, we present an end-to-end instance segmentation method that regresses a polygonal boundary for each object instance. This sparse, vectorized boundary representation for objects, while attractive in many downstream computer vision tasks, quickly runs into issues of parity that need to be addressed: parity in supervision and parity in performance when compared to existing pixel-based methods. This is due in part to object instances being annotated with ground-truth in the form of polygonal boundaries or segmentation masks, yet being evaluated in a convenient manner using only segmentation masks. Our method, BoundaryFormer, is a Transformer based architecture that directly predicts polygons yet uses instance mask segmentations as the ground-truth supervision for computing the loss. We achieve this by developing an end-to-end differentiable model that solely relies on supervision within the mask space through differentiable rasterization. Boundary-Former matches or surpasses the Mask R-CNN method in terms of instance segmentation quality on both COCO and Cityscapes while exhibiting significantly better transferability across datasets.
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation

Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation. We find that, directly applying existing methods usually results in performance instability at test time, because multi-modal input is not considered jointly. To design a framework that can take full advantage of multi-modality, where each modality provides regularized self-supervisory signals to other modalities, we propose two complementary modules within and across the modalities. First, Intra-modal Pseudo-label Generation (Intra-PG) is introduced to obtain reliable pseudo labels within each modality by aggregating information from two models that are both pre-trained on source data but updated with target data at different paces. Second, Inter-modal Pseudo-label Refinement (Inter-PR) adaptively selects more reliable pseudo labels from different modalities based on a proposed consistency scheme. Experiments demonstrate that our regularized pseudo labels produce stable self-learning signals in numerous multi-modal test-time adaptation scenarios for 3D semantic segmentation. Visit our project website at https://www.nec-labs.com/~mas/MM-TTA
Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model

Solar radiation is one of the cleanest sources of renewable energy, and it affects the carbon sink functions of terrestrial ecosystems. Although efforts have been made to establish solar radiation observation stations around the world, their coverage remains limited. Hence, the development of a wide variety of models and techniques is indispensable for obtaining effective solar radiation data. The aim of this study is to develop hybrid models with high computational speed and high accuracy to estimate global solar radiation (GSR) and quantify the uncertainty in GSR simulations caused by uncertainty in the measurements of atmospheric and surface parameters. The radiative transfer model (RTM) library for radiative transfer (LibRadtran) was coupled with six machine learning models: extreme gradient boosting (XGBoost), random forest (RF), multivariate adaptive regression splines (MARS), multilayer perceptron (MLP), deep neural networks (DNNs), and light gradient boosting machine (LightGBM). The estimated GSR was first compared to the inversion values of the GSR provided by the Aerosol Robotic Network (AERONET) and then validated using ground-based measurements at three locations in China from 2005 to 2018. The results showed that the RTM-RF is superior in terms of computational efficiency and performance, with a mean absolute errors (MAE) and coefficients of determination (R2) of 15.57 W m−2 and 0.98, respectively. Under clear sky conditions, aerosol optical depth (AOD) contributed the most to the accuracy of GSR estimates, with an average contribution of 57.95%. The measurement uncertainty due to the asymmetry factor, AOD, single-scattering albedo, and land surface albedo (LSA) can explain the differences in GSR between RTM estimates and GSR observations at the Lulin (20.33 vs. 20.91 W m−2), Wuhan (−1.40 vs. 14.58 W m−2), and Xianghe (7.28 vs. 14.32 W m−2) sites. Our study supports the use of physical models combined with machine learning models to estimate GSR and provides valuable scientific information for large-area solar radiation estimations.
Efficient resource management in 6G communication networks using hybrid quantum deep learning model

The sixth generation (6G) wireless communication network is expected to provide wide-ranging coverage, short latency, cost effectiveness, lower power consumption, and a high level of security. The Quality of Service (QoS) can be improved with the help of proper resource management by utilizing Artificial Intelligence and Machine Learning procedures. The proposed Hybrid Quantum Deep Learning (HQDL) model which comprises of Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). The CNN achieves network reconfiguration, resource distribution and slice collection while RNN is utilized for error proportion, load balancing etc. The future model QoS performance is evaluated by utilizing numerous indefinite devices, slice features and congestion environments. The proposed model achieved an overall accuracy of 97.16% that replicates its pertinence.
Experimental Realization of Two Qutrits Gate with Tunable Coupling in Superconducting Circuits

Gate-based quantum computation has been extensively investigated using quantum circuits based on qubits. In many cases, such qubits are actually made out of multilevel systems but with only two states being used for computational purpose. While such a strategy has the advantage of being in line with the common binary logic, it in some sense wastes the ready-for-use resources in the large Hilbert space of these intrinsic multi-dimensional systems. Quantum computation beyond qubits (e.g., using qutrits or qudits) has thus been discussed and argued to be more efficient than its qubit counterpart in certain scenarios. However, one of the essential elements for qutrit-based quantum computation, two-qutrit quantum gate, remains a major challenge. In this work, we propose and demonstrate a highly efficient and scalable two-qutrit quantum gate in superconducting quantum circuits. Using a tunable coupler to control the cross-Kerr coupling between two qutrits, our scheme realizes a two-qutrit conditional phase gate with fidelity 89.3% by combining simple pulses applied to the coupler with single-qutrit operations. We further use such a two-qutrit gate to prepare an EPR state of two qutrits with a fidelity of 95.5%. Our scheme takes advantage of a tunable qutrit-qutrit coupling with a large on:off ratio. It therefore offers both high efficiency and low cross talk between qutrits, thus being friendly for scaling up. Our work constitutes an important step towards scalable qutrit-based quantum computation.
Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.
A multistep short-term solar radiation forecasting model using fully convolutional neural networks and chaotic aquila optimization combining WRF-Solar model results

Solar energy is one of the most promising new energy sources, and making full use of it is the main way to reduce carbon emissions. The prediction of short-term solar radiation is of great significance to the stable operation of grid-connected photovoltaic power stations and the efficient conversion of solar energy. In this paper, a multistep short-term solar radiation prediction method based on the WRF-Solar model, deep fully convolution networks and a chaotic aquila optimization algorithm is proposed. First, the WRF-Solar model is used to predict solar radiation, and the results are spliced with historical satellite observations. Then, the spliced data are fed into five fully convolution networks for separate prediction, and each network has multilayer convolution networks to extract spatial features of different scales. Finally, the final solar radiation prediction is obtained using a chaotic aquila optimization algorithm and combining the results of the five networks. Experiments in Northwest China show that although the prediction performance varies from month to month, on the whole, the proposed method is better than other models, making it easier for the optimizer to jump out of the local optimal solution. The accuracy and robustness of the proposed model can better guide power grid dispatching.
100-Gbit/s Integrated Quantum Random Number Generator Based on Vacuum Fluctuations

Emerging communication and cryptography applications call for reliable fast unpredictable random number generators. Quantum random number generation allows for the creation of truly unpredictable numbers due to the inherent randomness available in quantum mechanics. A popular approach is to use the quantum vacuum state to generate random numbers. While convenient, this approach has been generally limited in speed compared to other schemes. Here, through custom codesign of optoelectronic integrated circuits and side-information reduction by digital filtering, we experimentally demonstrate an ultrafast generation rate of 100 Gbit/s, setting a new record for vacuum-based quantum random number generation by one order of magnitude. Furthermore, our experimental demonstrations are well supported by an upgraded device-dependent framework that is secure against both classical and quantum side information and that also properly considers the nonlinearity in the digitization process. This ultrafast secure random number generator in the chip-scale platform holds promise for next-generation communication and cryptography applications.1 MoreReceived 14 November 2022Accepted 3 February 2023DOI:https://doi.org/10.1103/PRXQuantum.4.010330Published 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 communicationQuantum cryptographyQuantum information processing with continuous variablesQuantum information theoryQuantum Information
A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer

The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive annotation burden. To aid in computational pathology, synthetic data generation, curation, and annotation present a cost-effective means to quickly enable data diversity that is required to boost model performance at different stages. In this study, we introduce a large-scale synthetic pathological image dataset paired with the annotation for nuclei semantic segmentation, termed as Synthetic Nuclei and annOtation Wizard (SNOW). The proposed SNOW is developed via a standardized workflow by applying the off-the-shelf image generator and nuclei annotator. The dataset contains overall 20k image tiles and 1,448,522 annotated nuclei with the CC-BY license. We show that SNOW can be used in both supervised and semi-supervised training scenarios. Extensive results suggest that synthetic-data-trained models are competitive under a variety of model training settings, expanding the scope of better using synthetic images for enhancing downstream data-driven clinical tasks.
Random Contrastive Interaction for Particle Swarm Optimization in High-Dimensional Environment

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.
Gaussian Boson Sampling with Pseudo-Photon-Number-Resolving Detectors and Quantum Computational Advantage

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical spoofing mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ∼600 yr using exact methods, whereas our quantum computer, Jiǔzhāng 3.0, takes only 1.27 μs to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier∼3.1×10^{10} yr.
Querying Linked Ontological Data through Distributed Summarization

As the semantic web expands, ontological data becomes distributed over a large network of data sources on the Web. Consequently, evaluating queries that aim to tap into this distributed semantic database necessitates the ability to consult multiple data sources efficiently. In this paper, we propose methods and heuristics to efficiently query distributed ontological data based on a series of properties of summarized data. In our approach, each source summarizes its data as another RDF graph, and relevant section of these summaries are merged and analyzed at query evaluation time. We show how the analysis of these summaries enables more efficient source selection, query pruning and transformation of expensive distributed joins into local joins.
Faster Optimal and Suboptimal Hierarchical Search

In problem domains for which an informed admissible heuristic function is not available, one attractive approach is hierarchical search. Hierarchical search uses search in an abstracted version of the problem to dynamically generate heuristic values. This paper makes two contributions to hierarchical search. First, we propose a simple modification to the state-of-the-art algorithm Switchback that reduces the number of expansions (and hence the running time) by approximately half, while maintaining its guarantee of optimality. Second, we propose a new algorithm for suboptimal hierarchical search, called Switch. Empirical results suggest that Switch yields faster search than straightforward modifications of Switchback, such as weighting the heuristic or greedy search. The success of Switch illustrates the potential for further research on specifically suboptimal hierarchical search.
A Bootstrapping Approach to Identifying Relevant Tweets for Social TV

Manufacturers of TV sets have recently started adding social media features to their products. Some of these products display microblogging messages relevant to the TV show which the user is currently watching. However, such systems suffer from low precision and recall when they use the title of the show to search for relevant messages. Titles of some popular shows such as Lost or Survivor are highly ambiguous, resulting in messages unrelated to the show. Thus, there is a need to develop filtering algorithms that can achieve both high precision and recall. Filtering microblogging messages for Social TV poses several challenges, including lack of training data, lack of proper grammar and capitalization, lack of context due to text sparsity, etc. We describe a bootstrapping algorithm which uses a small manually labeled dataset, a large dataset of unlabeled messages, and some domain knowledge to derive a high precision classifier that can successfully filter microblogging messages which discuss television shows. The classifier is designed to generalize to TV shows which were not part of the training set. The algorithm achieves high precision on our two test datasets and successfully generalizes to unseen television shows. Furthermore, it compares favorably to a text classifier specifically trained on the television shows used for testing.
Non-Optimal Multi-Agent Pathfinding is Solved (Since 1984)

Optimal solutions for multi-agent pathfinding problems are often too expensive to compute. For this reason, suboptimal approaches have been widely studied in the literature. Specifically, in recent years a number of efficient suboptimal algorithms that are complete for certain subclasses have been proposed at highly-rated robotics and AI conferences, all mentioning that it is an open problem which subclasses of non-optimal multi-agent pathfinding are tractable. However, it turns out that this problem has already been completely solved in another research community in the 1980s by a constructive proof that provides a polynomial algorithm that is complete for the entire class of problems. In this paper, we would like to bring this earlier related work to the attention of the robotics and AI communities.
Targeting Horror via Level and Soundscape Generation

Horror games form a peculiar niche within game design paradigms, as they entertain by eliciting negative emotions such as fear and unease to their audience during play. This genre often follows a specific progression of tension culminating at a metaphorical peak, which is defined by the designer. A player's tension is elicited by several facets of the game, including its mechanics, its sounds, and the placement of enemies in its levels. This paper investigates how designers can control and guide the automated generation of levels and their soundscapes by authoring the intended tension of a player traversing them.
An empirical study of professional translators’ attitudes, use and awareness of Web 2.0 technologies, and implications for the adoption of emerging technologies and trends

This questionnaire-based study was conducted as a part of an MA Dissertation in the summer of 2010 (Gough, 2010a). It examines the trends within the translation industry which have developed in response to the evolution of the Web from Web 1.0 (the information web) to Web 2.0 (the social web) and places professional translators against the backdrop of these trends. The developments based on the principles of sharing, openness and collaboration associated with Web 2.0 can be seen as affecting the tools used by translators and the processes in which they engage. This study examines professional translators’ awareness and perception of the new open, collaborative tools and processes and the degree of tools usage and process participation. The key findings of this study highlight translators’ vague awareness and insufficient understanding of these trends, marginal use of the open tools and little engagement in the collaborative processes. The underlying factor determining translators’ awareness, perception and the use of these tools and processes is their attitude towards adopting new technologies, with an indication that professionals with innovative attitudes are more inclined to embrace the new trends and developments.
An overview of assessing the quality of peer review reports of scientific articles

Assuring the quality control of publications in the scientific literature is one of the main challenges of the peer review process. Consequently, there has been an increasing demand for computing solutions that will help to maintain the quality of this process. Recently, the use of Artificial Intelligence techniques has been highlighted, applied in the detection of plagiarism, bias, among other functions. The assessment of the reviewer’s review has also been considered as important in the process, but, little is known about it, for instance, which techniques have been applied in this assessment or which criteria have been assessed. Therefore, this systematic literature review aims to find evidence regarding the computational approaches that have been used to evaluate reviewers' reports. In order to achieve this, five online databases were selected, from which 72 articles were identified that met the inclusion criteria of this review, all of which have been published since 2000. The result returned 10 relevant studies meeting the evaluation requirements of scientific article reviews. The review revealed that mechanisms to rank review reports according to a score, as well as the word analysis, are the most common tools, and that there is no consensus on quality criteria. The systematic literature review has shown that reviewers’ report assessment is a valid tool for maintaining quality throughout the process. However, it still needs to be further developed if it is to be used as a resource which surpass a single conference or journal, making the peer review process more rigorous and less based on random choice.