Reviewerly/exHarmony huggingface dataset:
Accurate brain extraction on MRI using U-Net trained in two stages

Brain extraction is an essential processing step for most brain magnetic resonance imaging (MRI) studies. Due to the inaccuracy of available labels in training dataset, existing methods based on U-Net can only obtain rough brains. In this paper, we propose a new deep-learning-based method for accurate 3D brain extraction, in which a U-Net model is trained in two stages using different loss functions. In the first stage, the binary cross entropy (BCE) loss is used to train the model with original head MRIs and coarse labelled brain masks as usual U-Net models. In the second stage, a composite loss function that integrates active contour model (ACM) and BCE loss is introduced to guide the further training. By this means, the final trained model can not only strip head scalp and skull from head MRI scans, but also remove cerebrospinal fluid around brain tissues. Both quantitative and qualitative test results show that our brain extraction is more accurate than other counterparts. The improvement enables to build better brain model with more details.
TINET: Triangulated Irregular Networks Evolving in Time

chapter Share on TINET: Triangulated Irregular Networks Evolving in Time Authors: Goce Trajcevski View Profile , Prabin Giri View Profile , Xu Teng View Profile , Hooman Hashemi View Profile Authors Info & Claims Spatial Gems, Volume 1August 2022 Pages 131–138https://doi.org/10.1145/3548732.3548747Published:08 August 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Authors’ Biographies/Index

index Share on Authors’ Biographies/IndexSpatial Gems, Volume 1August 2022 Pages 139–166https://doi.org/10.1145/3548732.3548748Published:08 August 2022Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Concept of a Cost-effective Underground Air-propelled Tube-capsule Transport System

In our age, due to the rapid development of electronics, information flows at an amazing speed from the point of creation to the place of use, but in addition to electronic data traffic, we would also require physical location change at a similar rate. Due to our accelerated lifestyle, there is a growing need to implement a door-to-door fast delivery system. Current road freight transport accounts for a significant proportion of surface transport, but its efficiency is greatly impaired by congestion on the roads. The potential for road networks to be developed on the surface is limited, raising it above the surface, as is often seen in fantastic films, is impossible without antigravity knowledge, and highways are expensive to construct, the cityscape will be worse, moreover, it is not an aesthetic view from the windows of the buildings.
English Syntactic Analysis and Word Sense Disambiguation Strategy of Neutral Set from the Perspective of Natural Language Processing

In order to improve the effect of English semantic analysis, under the support of natural language processing, this paper analyzes English syntactic analysis and the word sense strategy of the neutral set and solves the parameters through data training, so as to solve the probability distribution of the maximum entropy model of each order. Moreover, by comparing the prediction probability of the model to the judgment mode with the experimental data, it is found that the first-order maximum entropy model (independent model) is quite different from the data. Therefore, when judging data in English semantics, we cannot only consider the influence of second-order correlations but should also consider higher-order correlations. The research results of the simulation experiment show that the English syntactic analysis and the word sense disambiguation strategy of the neutral set proposed in this paper from the perspective of natural language processing are very effective.
Deep human answer understanding for natural reverse QA

This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers. This procedure exists in many real human–machine interaction applications. However, a crucial problem in human–machine interaction is answer understanding. Existing solutions have relied on mandatory option term selections to avoid automatic answer understanding. However, these solutions have led to unnatural human–computer interaction and negatively affected user experience. Thus, we propose a novel deep answer understanding network, AntNet, for reverse QA. The network consists of three new modules, namely, a skeleton attention for questions, a relevance-aware representation of answers, and a multi-hop-based fusion. Furthermore, to alleviate the negative influences of some quite difficult human answers, an improved self-paced learning strategy is proposed to train the AntNet by assigning different weights to training samples according to their learning difficulties. Given that answer understanding for reverse QA has not been explored, a new data corpus is compiled in this study. Experimental results indicate that our proposed network is significantly better than existing methods and those modified from classical natural language processing deep models. The effectiveness of the three modules and the improved self-paced learning strategy is also verified.
Single-photon Transport in a Waveguide-cavity-emitter System

We investigate the single-photon transport properties in a hybrid waveguide quantum electrodynamics system, in which a one-dimensional waveguide is simultaneously coupled to a cavity and a driven Λ-type three-level atom. The cavity and the atom are also coupled to each other. We show that when the waveguide is coupled to the cavity and the atom at the same point, double electromagnetically induced transparency (EIT) can be observed from the system. The physical mechanism of the double EIT effect has been given by setting up the eigenstate structure for the whole system. When the waveguide is coupled to the cavity and the atom at different points, we demonstrate that the controllable nonreciprocal scattering effect can be achieved by modulating the non-zero cavity-emitter separation and the phase associated with the cavity-emitter coupling strength. These results have potential applications in realization of photonic coherent control.
Quantum information processing of spatially entangled photons with a programmable light converter

We demonstrate a reconfigurable and scalable processor of entangled photons based on multi-plane light conversion and perform key tasks of quantum information processing including high-dimensional entanglement certification, arbitrary state transformations, and mode conversion.
Demonstration of Generalised Multi-path Wave-particle Duality on a Quantum Photonic Chip

We report the multi-path delayed-choice experiment on a large-scale integrated silicon photonic chip. Wave- and particle-nature in generalised form are characterised experimentally and the generalisation of Bohr’s multi-path duality relation is demonstrated.
Photonics-enabled Continuous and Gapless Time-frequency Analysis of Fast Transient Events

We experimentally demonstrate the gapless operation of lens-based energy conserving time-frequency analysis by real-time spectrum acquisition of events as short as ~160 ps, without any loss of information and regardless of the time of arrival.
The RATTLE Motion Planning Algorithm for Robust Online Parametric Model Improvement With On-Orbit Validation

Certain forms of uncertainty that robotic systems encounter can be explicitly learned within the context of a known model, like parametric model uncertainties such as mass and moments of inertia. Quantifying such parametric uncertainty is important for more accurate prediction of the system behavior, leading to safe and precise task execution. In tandem, providing a form of robustness guarantee against prevailing uncertainty levels like environmental disturbances and current model knowledge is also desirable. To that end, the authors' previously proposed RATTLE algorithm, a framework for online information-aware motion planning, is outlined and extended to enhance its applicability to real robotic systems. RATTLE provides a clear tradeoff between information-seeking motion and traditional goal-achieving motion and features online-adjustable models. Additionally, online-adjustable low level control robustness guarantees and a new method for automatic adjustment of information content down to a specified estimation precision is proposed. Results of extensive experimentation in microgravity using the Astrobee robots aboard the International Space Station and practical implementation details are presented, demonstrating RATTLE's capabilities for real-time, robust, online-adjustable, and model information-seeking motion planning capabilities under parametric uncertainty.
Perception of professors regarding the transition to emergency remote teaching in a large public university in Mexico during the pandemic

Abstract This paper presents the most relevant results from a follow-up questionnaire applied to a sample of professors from the largest public university in Mexico — the National Autonomous University of Mexico (UNAM). It further investigates the issues identified by a questionnaire applied at the start of the pandemic (whose results were also published), sending a follow-up questionnaire to the university faculty months later. The research sought to identify and describe the opinions, experiences, characteristics, and conditions in which these professors had to switch to remote teaching using technological tools during the pandemic. Data were collected by an exploratory online survey with Likert-type multiple choice and open questions applied to a non-random sample of 513 professors at UNAM. Results show that 43% of the participants considered the quality of remote teaching to be equal to classroom teaching, 23% reported an improvement, and 34% stated that it was worse. Most respondents said they spent an average of 1 to 10 hours per week on teaching activities, with assessing and providing feedback on student assignments being the most time-consuming. Clearly, the experiences and teaching activities of university professors are changing as the pandemic continues. Post-pandemic times will require a more compelling answer from higher education institutions regarding the social inequalities generated by the pandemic.
Interface Design for Embedded and Real-Time Systems

This overview for the handbook provides insights on design aspects, approaches, and alternatives for monitoring and controlling real-time and embedded devices and systems. Making these interface designs usable and effective is central to successful product introductions and differentiation. The detailed review of interface design techniques includes developing for both user interfaces and message-based communications, enabling key interactions. A wide range of user interface options are reviewed, including direct physical controls, interactive graphical displays, and voice user interfaces, along with industry-standard user interface design tools such as Qt and HTML. Design for these elements is supported by a framework of processes used in phases of iterative UX design like analysis and planning, user research, and user experience design and test. Each UX design phase visited includes sets of UX design methods regularly employed at that stage, with common and useful UX methods such as personas, usability heuristics, sketching, prototyping, surveys, and microinteraction design. Device control via messaging and application programming interfaces (APIs) are also considered, including protocol alternatives to enable low level, machine-to-machine (M2M), internet of things (IoT), and low-power wide area networks (LPWANs) communications. Methods for analysis, selection, and implementation of communication protocols in designs are reviewed, including application of message queueing tools, cloud-based IoT frameworks, messaging and microservice architectures, and API design best practices. Design examples include architectures for a wide variety of systems such as single-board computer-based prototypes, wireless sensor networks and IoT gateways, devices using web- and mobile-based device interfaces, and connected wearable devices.
Adaptively Center-Shape Sensitive Sample Selection for Ship Detection in SAR Images

With the wide application of synthetic aperture radar in maritime surveillance, a ship detection method has been rapidly developed. However, there is still a key problem common in most methods, i.e., how to select positive and negative samples. The mainstream MaxIoUAssign has inherent problems, such as a fixed threshold and rough classification, resulting in the low quality of the positive samples. To solve these problems, we propose a new sample selection method called adaptively center-shape sensitive sample selection (AC4S). The proposed method introduces shape similarity between proposal boxes and ground truth as one of the evaluation criteria and collaborates with Intersection of Union(IoU) to measure the quality of the proposal boxes. Meanwhile, the center distance between proposal boxes and ground truth is used to control the influence degree of IoU and shape similarity. In this way, the quality score of the proposal boxes can be determined through IoU, shape similarity, and center position, making sample selection more comprehensive. Additionally, to avoid a fixed threshold, the standard deviation of the quality score is used as a variable to form the adaptive threshold. Finally, we conducted extensive experiments on the benchmark SSDD and HRSID datasets. The experimental results demonstrated the superiority of our method.
AI-Enabled Experience-Driven Networking: Vision, State-of-the-Art and Future Directions

Modern networks have become immensely complicated, while future networks are expected to be more highly dynamic and sophisticated. In such a complex network environment, it is challenging to design effective control schemes to allocate network resources and manage network systems. Recently, Artificial Intelligence (AI) has made tremendous successes and breakthroughs. Particularly, as a branch of AI technology, the model-free experience-based machine learning (ML) technology has attracted widespread attention in the field of Network Control Problems (NCPs). Motivated by recent breakthroughs in AI technology, we share our vision of deploying ML technology to the field of solving NCPs to achieve experience-driven networking. Compared with the domain knowledge-based heuristic algorithms and fixed policies, which face mounting challenges in achieving desirable performance in highly dynamic and complicated networks, the ML-based methods can accumulate experiences by constantly collecting system feedback, and finally learn desirable/optimal control policies. In this article, we first summarize the differences between the traditional solutions and the ML-based methods and highlight the advantages of experience-driven networking with ML. Then we introduce several state-of-the-art AI-enabled experience-driven works for NCPs. In the end, we shed light on the future directions of adapting ML to more networking problems and the emerging opportunities for experience-driven networking. We hope that our work can help and encourage researchers to propose more innovative experience-driven solutions for the NCPs of modern network systems.
Scalable Ultralow Latency Photonic Tensor Processor

We propose a photonic processor with the potential to compute deep neural network layer outputs in ∼ 10 ns. Our proof-of-concept experiments show low loss of classification accuracy on the MNIST dataset; we investigate fundamental throughput limits.
AFSD: Adaptive Feature Space Distillation for Distributed Deep Learning

We propose a novel and adaptive feature space distillation method (AFSD) to reduce the communication overhead among distributed computers.The proposed method improves the Codistillation process by supporting longer update interval rates.AFSD performs knowledge distillates across the models infrequently and provides flexibility to the models in terms of exploring diverse variations in the training process.We perform knowledge distillation in terms of sharing the feature space instead of output only.Therefore, we also propose a new loss function for the Codistillation technique in AFSD.Using the feature space leads to more efficient knowledge transfer between models with a longer update interval rates.In our method, the models can achieve the same accuracy as Allreduce and Codistillation with fewer epochs.
Toward an Analysis Method of Software Evolution Triggered by Place Change in Mobile Computing

Software dynamic evolution is a hot topic in software engineering. Traditional evolution is caused by the change in user requirements and running environments. However, in mobile computing systems, there is a new problem that place change leads to the change in software configuration, deployment, and function, which is called the problem of software evolution triggered by place change. Facing this new problem, traditional evolution methods fail to formally describe their evolution process and analyze their evolution performance. Therefore, a new method needs to be developed to ensure the reliability of software evolution triggered by place change. We first address various situations regarding the change of the software function caused by place change in physical space, which are described by a Bigraph model. Then, the triggering conditions for software evolution are defined, and a reaction system corresponding to software evolution is discussed. A theory about software function change caused by place change is developed, including a novel algorithm for identifying software evolution rules that involve place information and the control process for the whole software evolution (CPSE) in a mobile computing environment. Finally, some real case studies illustrate the effectiveness and correctness of our proposed method. Extensive experiments show that our algorithm CPSE outperforms Quiescence and version consistency (VC)-concurrent versions (CV) algorithms in timeliness and average disruption.
HQ2CL: A High-Quality Class Center Learning System for Deep Face Recognition

Benefited from the proposals of function losses margin-based, face recognition has achieved significant improvements in recent years. Those losses aim to increase the margin between the different identities to enhance the discriminability. Ideally, the class center of different identities is far from each other, and face samples are compact around the corresponding class center. Hence, it's very vital to produce a high-quality class center. However, the distribution of training sets determines the class center. With low-quality samples being in the majority, the class center would be close to the samples with little identity information. As a result, it would impair the discriminability of the learned model for those unseen samples. In this work, we propose a High-Quality Class Center Learning system (HQ2CL). This is an effective system and guides the class center to approach the high-quality samples to keep the discriminability. Specifically, HQ2CL introduces a quality-aware scale and margin layer for the identification loss and constructs a new high-quality center loss. We implement the proposed system without additional burden. And we present the experimental evaluation over different face benchmarks. The experimental results show the superiority of our proposed HQ2CL over the state-of-the-arts.
All-optical, ultrafast energy-efficient ReLU function for nanophotonic neural networks

We introduce and experimentally demonstrate an all-optical ReLU nonlinear activation function based on the strong quadratic nonlinearity of lithium niobate nanophotonic waveguides and achieve a record-breaking energy-time product per activation of 1 . 2 × 10 − 27 J · s to overcome the nonlinearity bottleneck in photonic neural networks.
Rejection-Free Monte Carlo Simulation of QUBO and Lechner–Hauke–Zoller Optimization Problems

Many studies have attempted to develop simple and efficient methods for solving global optimization problems. Simulated annealing (SA) has been recognized as a powerful tool for performing this task. In this study, we implemented a rejection-free Monte Carlo (RFMC) algorithm, which is a kernel of rejection-free SA (RFSA). We showed its validity and advantage in obtaining globally optimal solution for quadratic unconstrained binary optimization (QUBO) and spin-glass problem embedded in the Lechner&#x2013;Hauke&#x2013;Zoller (LHZ) architecture. Landscapes of success probabilities of finding the globally optimal solution as well as feasible solutions were evaluated as a function of a set of hyper-parameters used to characterize the weights of cost and penalty functions. We demonstrated that the efficiency of RFMC was greatly enhanced compared to that of standard MC. Furthermore, we found that the landscapes for QUBO and those for LHZ problem show considerably different views. When we focus on a specific class of problems, the smaller the problem size, the more scattered is the success probability. We also show that the success probabilities can be further improved by avoiding inefficiencies due to cycling of state transitions using short-term memory mechanisms. We suggest a reasonable strategy to tune hyper-parameters and find the correct global solution using RFSA.
Time Series Prediction and Classification using Silicon Photonic Neuron with a Self-Connection

We experimentally demonstrated real-time operation of a photonic neuron with a self-connection, a pre-requisite for integrated recurrent neural networks (RNNs). After studying two applications we propose a photonics-assisted platform for time series prediction and classification.