Reviewerly/exHarmony huggingface dataset:
A Midbrain Inspired Recurrent Neural Network Model for Robust Change Detection

We present a biologically inspired recurrent neural network (RNN) that efficiently detects changes in natural images. The model features sparse, topographic connectivity (st-RNN), closely modeled on the circuit architecture of a “midbrain attention network.” We deployed the st-RNN in a challenging change blindness task, in which changes must be detected in a discontinuous sequence of images. Compared with a conventional RNN, the st-RNN learned 9x faster and achieved state-of-the-art performance with 15x fewer connections. An analysis of low-dimensional dynamics revealed putative circuit mechanisms, including a critical role for a global inhibitory (GI) motif, for successful change detection. The model reproduced key experimental phenomena, including midbrain neurons' sensitivity to dynamic stimuli, neural signatures of stimulus competition, as well as hallmark behavioral effects of midbrain microstimulation. Finally, the model accurately predicted human gaze fixations in a change blindness experiment, surpassing state-of-the-art saliency-based methods. The st-RNN provides a novel deep learning model for linking neural computations underlying change detection with psychophysical mechanisms. SIGNIFICANCE STATEMENT For adaptive survival, our brains must be able to accurately and rapidly detect changing aspects of our visual world. We present a novel deep learning model, a sparse, topographic recurrent neural network (st-RNN), that mimics the neuroanatomy of an evolutionarily conserved “midbrain attention network.” The st-RNN achieved robust change detection in challenging change blindness tasks, outperforming conventional RNN architectures. The model also reproduced hallmark experimental phenomena, both neural and behavioral, reported in seminal midbrain studies. Lastly, the st-RNN outperformed state-of-the-art models at predicting human gaze fixations in a laboratory change blindness experiment. Our deep learning model may provide important clues about key mechanisms by which the brain efficiently detects changes.
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user. However, most of these methods treat all the training triplets equally, which ignores the subtle difference between different positive or negative items. On the other hand, even though some other works make use of the auxiliary information (e.g., dwell time) of user behaviors to capture this subtle difference, such auxiliary information is hard to obtain. To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets. We devise two strategies for the importance score generation and formulate the whole procedure as a bilevel optimization, which does not require any rule-based design. We integrate the proposed training procedure with several Matrix Factorization (MF)- and Graph Neural Network (GNN)-based recommendation models, demonstrating the compatibility of our framework. Via a comparison using three real-world datasets with many state-of-the-art methods, we show that our proposed method outperforms the best existing models by 3-21\% in terms of Recall@k for the top-k recommendation.
A time-dependent offset field approach to simulating realistic interactions between beating hearts and surgical devices in virtual interventional radiology

Endovascular interventional radiology (IR) is a minimally invasive procedure for the treatment of vascular diseases. This procedure requires physicians to be highly skilled at manipulating interventional devices under the guidance of two-dimensional X-ray imaging. By offering a non-error-sensitive and radiation-free environment, a virtual reality-based simulator provides a promising alternative for surgical skills training and surgery planning. Building a realistic and interactive simulator is a challenging task. To achieve better realism, this paper proposes a novel method of simulating the heartbeat for both standard and patient-specific anatomical data. A time-dependent offset field approach is proposed to efficiently and stably simulate the interactive behavior between the dynamic heart mesh and surgical devices. For medical imaging simulation, we propose a GPU-based linear depth subtraction method to approximate fluoroscopic images based on the attenuation of the X-ray. On this basis, a topology-based flow map method is proposed to simulate the propagation of the contrast medium in angiography. Experimental results show that the proposed algorithm can simulate heartbeat stably for meshes with varying geometrical shapes and complexities. In efficiency, the dynamic heart mesh can interact with surgical devices stably at 60 frames/s. Under the simulated fluoroscopic imaging effect, the injected contrast medium can realistically visualize both dynamic and static vessels. In a face validity by medical students and clinicians, the category of effectiveness score 8.35 out of 10 on average, demonstrating that our simulator is useful in surgical skills training and surgery planning.
Entanglement properties of random invariant quantum states

Entanglement properties of random multipartite quantum states which are invariant under global $\textnormal{SU}(d)$ action are investigated. The random states live in the tensor power of an irreducible representation of $\textnormal{SU}(d)$. We calculate and analyze the expectation and fluctuation of the second-order R\'enyi entanglement measure of the random invariant and near-invariant states in high dimension, and reveal the phenomenon of concentration of measure the random states exhibit. We show that with high probability a random SU($d$)-invariant state is close to being maximally entangled with respect to any bipartite cut as the dimension of individual system goes to infinity. We also show that this generic entanglement property of random SU(2)-invariant state is robust to arbitrarily finite disturbation.
A New Way to Obtain Climate Files in Areas with the Presence of Microclimates by Applying the Sandia Method: A Galician Case Study

In order to obtain reliable energy simulation results, it is essential to have accurate climate files corresponding to specific geographical locations. The present work describes a selection process of the Typical Meteorological Months (TMM) that will generate the Typical Meteorological Years (TMY) in eight locations of the Community of Galicia for an analysis period between 2008 and 2017 (10 years). The region of Galicia, located in the northwest of the Iberian Peninsula, due to its particular orography, is prone to the generation of differentiated microclimates in relatively close locations. The process of selecting the typical meteorological months has been carried out following the Sandia Laboratories method. In the present work, data from terrestrial meteorological stations have been combined with solar radiation data obtained from satellite images. Finally, for the validation and comparative study of results, files have been generated in Energy Plus Weather (epw) format. Trends have been checked and typical statistics have been used to analyse the correlations between the files generated with the Sandia method, and the usual reference files (LT, WY, BY). It is observed that with the eight files generated, new differentiated climates are detected, which will affect the improvement of the precision of the energy simulations of buildings that are going to be carried out. For example, in the case of the Campus Lugo and Pedro Murias stations, located in the same climatic zone according to Spanish regulations, differences are observed in the annual averages: DTm (13.7%), WV (41%) or GHI (9%).
Perfect teleportation with a partially entangled quantum channel

Quantum teleportation provides a way to transfer unknown quantum states from one system to another via an entangled state as a quantum channel without physical transmission of the object itself. The entangled channel, measurement performed by the sender (Alice), and classical information sent to the receiver (Bob) are three key ingredients in the procedure, which need to cooperate with each other. To study the relationship among the three parts, we propose a scheme for perfect teleportation of a qubit through a high-dimensional quantum channel in a pure state with two equal largest Schmidt coefficients. The scheme requires less entanglement of Alice's measurement but more classical bits than the original scheme via a Bell state. The two quantities increase with the entanglement of the quantum channel when its dimension is fixed and thereby can be regard as Alice's necessary capabilities to use the quantum channel. And the two capabilities appear complementary to each other.
Comparative Analysis of Breast and Prostate Cancer Prediction Using Machine Learning Techniques

Around the whole world, cancer is the most life-threatening disease. Basically, cancer can arise in any tissue of the body, and while each variety of cancer has unique characteristics, the fundamental processes that might cause cancer are highly common in all disease types. Breast cancer is one of the most ubiquitous types of cancer in females. In males, prostate cancer is the most dangerous during recent years. This study focuses on breast cancer as well as on prostate cancer in the direction of their early predictions. For early prediction, eight classification models had been used such as logistic regression (LR), Naïve Bayes (NB), decision tree (DT), stochastic gradient descent (SGD), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). This work includes three different datasets for research analysis of breast and prostate cancer predictions. Two datasets for breast cancer (Coimbra and Wisconsin) and one for prostate cancer are taken from UCI and Kaggle repository, respectively. For improving the results of prediction, the normalization technique and feature selection method had been used in this paper. Performance in terms of accuracy, precision, recall, F1-score, and curves of each classifier are analyzed in this study. Most of the classifiers did well after using the feature selection method (ANOVA). In the case of Breast Cancer Coimbra, KNN give good results with 80% accuracy in both the cases with or without using feature selection. Logistic regression with feature selection doing the best work on Wisconsin Breast Cancer with 99% accuracy. There are four classifiers (SVM, RF, DT, and SGD) which gives highest accuracy (97%) on prostate cancer.
Re_Trans: Combined Retrieval and Transformer Model for Source Code Summarization

Source code summarization (SCS) is a natural language description of source code functionality. It can help developers understand programs and maintain software efficiently. Retrieval-based methods generate SCS by reorganizing terms selected from source code or use SCS of similar code snippets. Generative methods generate SCS via attentional encoder–decoder architecture. However, a generative method can generate SCS for any code, but sometimes the accuracy is still far from expectation (due to the lack of numerous high-quality training sets). A retrieval-based method is considered to have a higher accurac, but usually fails to generate SCS for a source code in the absence of a similar candidate in the database. In order to effectively combine the advantages of retrieval-based methods and generative methods, we propose a new method: Re_Trans. For a given code, we first utilize the retrieval-based method to obtain its most similar code with regard to sematic and corresponding SCS (S_RM). Then, we input the given code and similar code into the trained discriminator. If the discriminator outputs onr, we take S_RM as the result; otherwise, we utilize the generate model, transformer, to generate the given code’ SCS. Particularly, we use AST-augmented (AbstractSyntax Tree) and code sequence-augmented information to make the source code semantic extraction more complete. Furthermore, we build a new SCS retrieval library through the public dataset. We evaluate our method on a dataset of 2.1 million Java code-comment pairs, and experimental results show improvement over the state-of-the-art (SOTA) benchmarks, which demonstrates the effectiveness and efficiency of our method.
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer

In recent years, the standard hybrid DNN-HMM speech recognizers are outperformed by the end-to-end speech recognition systems. One of the very promising approaches is the grapheme Wav2Vec 2.0 model, which uses the self-supervised pretraining approach combined with transfer learning of the fine-tuned speech recognizer. Since it lacks the pronunciation vocabulary and language model, the approach is suitable for tasks where obtaining such models is not easy or almost impossible. In this paper, we use the Wav2Vec speech recognizer in the task of spoken term detection over a large set of spoken documents. The method employs a deep LSTM network which maps the recognized hypothesis and the searched term into a shared pronunciation embedding space in which the term occurrences and the assigned scores are easily computed. The paper describes a bootstrapping approach that allows the transfer of the knowledge contained in traditional pronunciation vocabulary of DNN-HMM hybrid ASR into the context of grapheme-based Wav2Vec. The proposed method outperforms the previously published system based on the combination of the DNN-HMM hybrid ASR and phoneme recognizer by a large margin on the MALACH data in both English and Czech languages.
Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction

Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.
Enhancing the Aggregation of the Federated Learning for the Industrial Cyber Physical Systems

Industrial Cyber-Physical Systems (ICPS) have gained considerable interest in the last decade from both industry and academia. Such systems have proven particularly complex and provide considerable challenges to master their design and ensure their functionalities. In this paper, we intend to tackle some of these challenges related to the performance and decision supports of ICPS by proposing a federated learning based framework, called FedGA-ICPS. First, we initiate a ICPS modeling formalism to specify such systems structure and behaviors. Then, based on the ICPS generated data from the industrial sensors, FedGA-ICPS analyzes their performance by proposing locally embedded learning models. Then, federated learning is powered by genetic algorithm to accelerate and improve the aggregation. Finally, transfer learning is applied to broadcast the performed parameters of the leaning models over different constrained entities. FedGA-ICPS has been applied on MNIST and showed prominent results.
MonoDCN: Monocular 3D object detection based on dynamic convolution

3D object detection is vital in the environment perception of autonomous driving. The current monocular 3D object detection technology mainly uses RGB images and pseudo radar point clouds as input. The methods of taking RGB images as input need to learn with geometric constraints and ignore the depth information in the picture, leading to the method being too complicated and inefficient. Although some image-based methods use depth map information for post-calibration and correction, such methods usually require a high-precision depth estimation network. The methods of using the pseudo radar point cloud as input easily introduce noise in the conversion process of depth information to the pseudo radar point cloud, which cause a large deviation in the detection process and ignores semantic information simultaneously. We introduce dynamic convolution guided by the depth map into the feature extraction network, the convolution kernel of dynamic convolution automatically learns from the depth map of the image. It solves the problem that depth information and semantic information cannot be used simultaneously and improves the accuracy of monocular 3D object detection. MonoDCN is able to significantly improve the performance of both monocular 3D object detection and Bird's Eye View tasks within the KITTI urban autonomous driving dataset.
Safety and Potential Usability of Immersive Virtual Reality for Brain Rehabilitation: A Pilot Study

Objective: This study was conducted to demonstrate the safety and usability of an immersive virtual reality (VR) game as a rehabilitative training by assessing adverse events (AEs), adherence, and satisfaction in patients with brain injury who had free optional opportunities. Materials and Methods: The results were analyzed retrospectively. Seventy-eight patients with brain injury, undergoing rehabilitation treatment for motor impairment, were recruited. Among them, 51 were available for postintervention survey. The immersive type of VR training was programmed to facilitate use of the paralyzed upper extremity through a fishing simulation game. The Oculus Rift was used as head-mounted display device. Patients were observed for any AEs as defined in the Common Terminology Criteria for AEs during and after each VR training session. A postintervention telephone survey was done to investigate adherence-related factors and safety. Results: The results were analyzed after dividing the patients into nonadherence (patients participated <3 times) and high-adherence (≥3 times) groups. No serious AEs were reported during and after the VR training, and several patients reported other AEs, predominantly dizziness, with one case requiring cessation of VR training. Overall, the satisfaction rate was 54%. Compared with the nonadherence group, the high-adherence group expressed higher satisfaction with VR training, regarded it as effective for recovery from upper limb paralysis, accepted VR as comprehensible, and considered the level of difficulty to be appropriate (P < 0.05). Conclusion: Immersive VR training appeared to be safe for patients with brain injury.
Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution

Color-guided depth super-resolution (DSR) is an encouraging paradigm that enhances a low-resolution (LR) depth map guided by an extra high-resolution (HR) RGB image from the same scene. Existing methods usually use interpolation to upscale the depth maps before feeding them into the network and transfer the high-frequency information extracted from HR RGB images to guide the reconstruction of depth maps. However, the extracted high-frequency information usually contains textures that are not present in depth maps in the existence of the cross-modality gap, and the noises would be fur- ther aggravated by interpolation due to the resolution gap between the RGB and depth images. To tackle these challenges, we propose a novel Symmetric Uncertainty-aware Feature Transmission (SUFT) for color-guided DSR. (1) For the resolution gap, SUFT builds an iterative up-and-down sampling pipeline, which makes depth features and RGB features spatially consistent while suppressing noise amplification and blurring by replacing common interpolated pre-upsampling. (2) For the cross-modality gap, we propose a novel Symmetric Uncertainty scheme to remove parts of RGB information harmful to the recovery of HR depth maps. Extensive experiments on benchmark datasets and challenging real-world settings suggest that our method achieves superior performance compared to state-of-the-art methods. Our code and models are available at https://github.com/ShiWuxuan/SUFT.
Modulation Recognition of Digital Signal Using Graph Feature and Improved K-Means

Automatic modulation recognition (AMR) has been wildly used in both military and civilian fields. Since the recognition of digital signal at low signal-to-noise (SNR) ratio is difficult and complex, in this paper, a clustering analysis algorithm is proposed for its recognition. Firstly, the digital signal constellation is extracted from the received waveform (digital signal + noise) by using the orthogonal decomposition and then, it is denoised by using an algorithm referred to as auto density-based spatial clustering technique in noise (ADBSCAN). The combination of density peak clustering (DPC) algorithm and improved K-means clustering is used to extract the constellation’s graph features, the eigenvalues are input into cascade support vector machine (SVM) multi-classifiers, and the signal modulation mode is obtained. BPSK, QPSK, 8PSK, 16QAM and 32QAM five kinds of digital signals are trained and classified by our proposed method. Compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR (less than 4dB), which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.