Reviewerly/exHarmony huggingface dataset:
Bounding‐box deep calibration for high performance face detection

Modern convolutional neural networks (CNNs)-based face detectors have achieved tremendous strides due to large annotated datasets. However, misaligned results with high detection confidence but low localization accuracy restrict the further improvement of detection performance. In this paper, the authors first predict high confidence detection results on the training set itself. Surprisingly, a considerable part of them exist in the same misalignment problem. Then, the authors carefully examine these cases and point out that annotation misalignment is the main reason. Later, a comprehensive discussion is given for the replacement rationality between predicted and annotated bounding-boxes. Finally, the authors propose a novel Bounding-Box Deep Calibration (BDC) method to reasonably replace misaligned annotations with model predicted bounding-boxes and offer calibrated annotations for the training set. Extensive experiments on multiple detectors and two popular benchmark datasets show the effectiveness of BDC on improving models' precision and recall rate, without adding extra inference time and memory consumption. Our simple and effective method provides a general strategy for improving face detection, especially for light-weight detectors in real-time situations.
User Dynamic Preference Construction Method Based on Behavior Sequence

People’s needs are constantly changing, and the performance of traditional recommendation algorithms is no longer enough to meet the demand. Considering that users’ preferences change with time, the users’ behavior sequence hides the evolution and change law of users’ preferences, so mining the dependence of the users’ behavior sequence is extremely important to predict users’ dynamic preferences. From the perspective of constructing users’ dynamic preferences, this paper proposes a users’ dynamic preference model based on users’ behavior sequences. Firstly, the user’s interest model is divided into short-term and long-term interest models. The short-term interest reflects the user’s current preference, and the long-term interest refers to the user’s interest from all his historical behaviors, representing the user’s consistent and stable preference. Users’ dynamic preference is obtained by integrating short-term interest and long-term interest, which solves the problem that the user’s preference cannot reflect the change in the user’s interest in real-time. We use the public Amazon review dataset to test the model we propose in the paper. Our model achieves the best performance, with a maximum performance improvement of 15.21% compared with the basic model (BPR, NCF) and 2.04% compared with the sequence model (GRU4REC, Caser, etc.), which proves that the user’s dynamic preference model can effectively predict the user’s dynamic preference. Users’ dynamic preferences are helpful in predicting users’ real-time preferences, especially in the field of recommendation.
Computable limits of optical multiple-access communications

Entanglement is a valuable quantum resource in quantum information processing. In classical communication over quantum channels, it is known to boost the communication rate drastically. To generalize such a boost to more general scenarios, we provide computable limits on the communication over optical multiple-access channels (MACs) for both the entanglement-assisted and unassisted communication. For the unassisted case, we generalize the coherent-state achievable rate region and outer bound known for the thermal-loss case [B. J. Yen and J. H. Shapiro, Phys. Rev. A 72, 062312 (2005)] to general bosonic Gaussian MACs. For the assisted case, we generalize the two-mode squeezed vacuum rate region and the outer bound for the thermal-loss case [H. Shi et al., npj Quantum Inf. 7, 74 (2021)] to general bosonic MACs. In terms of the total communication rate of all senders, we prove additivity for general MACs, generalizing the two-sender version in M.-H. Hsieh et al., IEEE Trans. Inf. Theory 54, 3078 (2008). Furthermore, for optical communication modeled as phase-insensitive bosonic Gaussian MACs, we prove that the optimal total rate is achieved by Gaussian entanglement and therefore can be efficiently evaluated. The computable limits confirm entanglement's boosts in optical multiple-access communications. Finally, we formulate an entanglement-assisted version of minimum entropy conjecture, which leads to the additivity of the capacity region of phase-insensitive bosonic Gaussian MACs if it is true.
Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust  Depth Prediction

Monocular depth prediction plays a crucial role in understanding 3D scene geometry. Although recent methods have achieved impressive progress in terms of evaluation metrics such as the pixel-wise relative error, most methods neglect the geometric constraints in the 3D space. In this work, we show the importance of the high-order 3D geometric constraints for depth prediction. By designing a loss term that enforces a simple geometric constraint, namely, virtual normal directions determined by randomly sampled three points in the reconstructed 3D space, we significantly improve the accuracy and robustness of monocular depth estimation. Significantly, the virtual normal loss can not only improve the performance of learning metric depth, but also disentangle the scale information and enrich the model with better shape information. Therefore, when not having access to absolute metric depth training data, we can use virtual normal to learn a robust affine-invariant depth generated on diverse scenes. In experiments, We show state-of-the-art results of learning metric depth on NYU Depth-V2 and KITTI. From the high-quality predicted depth, we are now able to recover good 3D structures of the scene such as the point cloud and surface normal directly, eliminating the necessity of relying on additional models as was previously done. To demonstrate the excellent generalizability of learning affine-invariant depth on diverse data with the virtual normal loss, we construct a large-scale and diverse dataset for training affine-invariant depth, termed Diverse Scene Depth dataset (DiverseDepth), and test on five datasets with the zero-shot test setting. Code is available at: https://git.io/Depth
VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera

Vehicle re-identification is a challenging task due to high intra-class variances and small inter-class variances. In this work, we focus on the failure cases caused by similar background and shape. They pose serve bias on similarity, making it easier to neglect fine-grained information. To reduce the bias, we propose an approach named VOC-ReID, taking the triplet vehicle-orientation-camera as a whole and reforming background/shape similarity as camera/orientation re-identification. At first, we train models for vehicle, orientation and camera re-identification respectively. Then we use orientation and camera similarity as penalty to get final similarity. Besides, we propose a high performance baseline boosted by bag of tricks and weakly supervised data augmentation. Our algorithm achieves the second place in vehicle re-identification at the NVIDIA AI City Challenge 2020.
On Synthetic Data for Back Translation

Back translation (BT) is one of the most significant technologies in NMT research fields.Existing attempts on BT share a common characteristic: they employ either beam search or random sampling to generate synthetic data with a backward model but seldom work studies the role of synthetic data in the performance of BT.This motivates us to ask a fundamental question: what kind of synthetic data contributes to BT performance?Through both theoretical and empirical studies, we identify two key factors on synthetic data controlling the back-translation NMT performance, which are quality and importance.Furthermore, based on our findings, we propose a simple yet effective method to generate synthetic data to better trade off both factors so as to yield a better performance for BT.We run extensive experiments on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks.By employing our proposed method to generate synthetic data, our BT model significantly outperforms the standard BT baselines (i.e., beam and sampling based methods for data generation), which proves the effectiveness of our proposed methods.
Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis

Compositional data arises in a wide variety of research areas when some form of standardization and composition is necessary. Estimating covariance matrices is of fundamental importance for high-dimensional compositional data analysis. However, existing methods require the restrictive Gaussian or sub-Gaussian assumption, which may not hold in practice. We propose a robust composition adjusted thresholding covariance procedure based on Huber-type M-estimation to estimate the sparse covariance structure of high-dimensional compositional data. We introduce a cross-validation procedure to choose the tuning parameters of the proposed method. Theoretically, by assuming a bounded fourth moment condition, we obtain the rates of convergence and signal recovery property for the proposed method and provide the theoretical guarantees for the cross-validation procedure under the high-dimensional setting. Numerically, we demonstrate the effectiveness of the proposed method in simulation studies and also a real application to sales data analysis.
Text Sentiment Analysis of Movie Reviews Based on Word2Vec-LSTM

A hybrid model based on Word2Vec-LSTM is utilized to analyze movie review sentiment in this paper. Word2Vec integrates text context semantics to generate text vector, and LSTM extracts semantic information to classify positive and negative emotions. In order to measure the classification capacity of the Word2Vec-LSTM, Word Index and Hash Trick method are constructed as benchmark models. We combine the word index and Hash Trick with several mainstream machine learning models to obtain the Word Index-Based Classifiers and Hash Trick-Based Classifiers. The experimental results show that Word2Vec-LSTM has the best performance. The accuracy is improved by 29.12% and 18.84% compared with Word Index-Based Classifiers and Hash Trick-Based Classifiers respectively, which shows that the Word2Vec-LSTM hybrid model is more effective for the movie review sentiment analysis.
Analysis on Lightweight Network Methods and Technologies

There are currently two main schools of deep learning. One is academic. They pursue stronger performance through powerful, complex models. The other is the engineering school. Their purpose is to efficiently deploy models to various hardware platforms. Complex models have better performance. However, it also brings unavoidable consumption. With the increasing depth of convolutional neural networks, lightweighting has become a key research direction. There are currently four main methods for designing lightweight networks. This article will first introduce CNN model compression and basic convolution operations.This paper also introduces the model compression based on AutoML and the automatic animation design based on NAS. Finally, according to the above three points, this paper introduces the application of the above methods in artificially designed neural networks.This paper mainly introduces the step-by-step evolution of the existing methods. This paper analyzes aspects of current neural network improvements and emerging problems. The significance of this paper is to summarize and deepen the solved problems and key problems in the lightweight process through past experience.
Tree-Like Justification Systems are Consistent

Justification theory is an abstract unifying formalism that captures semantics of various non-monotonic logics. One intriguing problem that has received significant attention is the consistency problem: under which conditions are justifications for a fact and justifications for its negation suitably related. Two variants of justification theory exist: one in which justifications are trees and one in which they are graphs. In this work we resolve the consistency problem once and for all for the tree-like setting by showing that all reasonable tree-like justification systems are consistent.
Revealing hidden steering nonlocality in a quantum network

By combining two objects with no quantum effect one can get an object with quantum effect. Such a phenomenon, often referred to as activation has been analyzed for the notion of steering nonlocality. Activation of steering nonlocality is observed for different classes of mixed entangled states in linear network scenarios. Characterization of arbitrary two qubit states, in ambit of steering activation in network scenarios has been provided in this context. Using the notion of reduced steering, instances of steerability activation are also observed in nonlinear network. Present analysis involves three measurement settings scenario(for both trusted and untrusted parties) where steering nonlocality is distinguishable from Bell nonlocality.
The MIT Supercloud Workload Classification Challenge

High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly larger share of the compute workloads, new approaches to optimized resource usage, allocation, and deployment of new AI frameworks are needed. By identifying compute workloads and their utilization characteristics, HPC systems may be able to better match available resources with the application demand. By leveraging datacenter instrumentation, it may be possible to develop AI-based approaches that can identify workloads and provide feedback to researchers and datacenter operators for improving operational efficiency. To enable this research, we released the MIT Supercloud Dataset, which provides de-tailed monitoring logs from the MIT Supercloud cluster. This dataset includes CPU and GPU usage by jobs, memory usage, and file system logs. In this paper, we present a workload classification challenge based on this dataset. We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches. The goal of this challenge is to foster algorithmic innovations in the analysis of compute workloads that can achieve higher accuracy than existing methods. Data and code will be made publicly available via the Datacenter Challenge website: https://dcc.mit.edu.
Temporal Surgical Gesture Segmentation and Classification in Multi-gesture Robotic Surgery using Fine-tuned features and Calibrated MS-TCN

Temporal Gesture Segmentation is an active research problem for many applications such as surgical skill assessment, surgery training, robotic training. In this paper, we propose a novel method for Gesture Segmentation on untrimmed surgical videos of the challenging JIGSAWS dataset by using a two-step methodology. We train and evaluate our method on 39 videos of the Suturing task which has 10 gestures. The length of gestures ranges from 1 second to 75 seconds and full video length varies from 1 minute to 5 minutes. In step one, we extract encoded frame-wise spatio-temporal features on full temporal resolution of the untrimmed videos. In step two, we use these extracted features to identify gesture segments for temporal segmentation and classification. To extract high-quality features from the surgical videos, we also pre-train gesture classification models using transfer learning on the JIGSAWS dataset using two state-of-the-art pretrained backbone architectures. For segmentation, we propose an improved calibrated MS-TCN (CMS-TCN) by introducing a smoothed focal loss as loss function which helps in regularizing our TCN to avoid making over-confident decisions. We achieve a frame-wise accuracy of 89.8% and an Edit Distance score of 91.5%, an improvement of 2.2% from previous works. We also propose a novel evaluation metric that normalizes the effect of correctly classifying the frames of larger segments versus smaller segments in a single score.
On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode

The streaming automatic speech recognition (ASR) models are more popular and suitable for voice-based applications. However, non-streaming models provide better performance as they look at the entire audio context. To leverage the benefits of the non-streaming model in streaming applications like voice search, it is commonly used in second pass re-scoring mode. The candidate hypothesis generated using steaming models is re-scored using a non-streaming model.In this work, we evaluate the non-streaming attention-based end-to-end ASR models on the Flipkart voice search task in both standalone and re-scoring modes. These models are based on Listen-Attend-Spell (LAS) encoder-decoder architecture. We experiment with different encoder variations based on LSTM, Transformer, and Conformer. We compare the latency requirements of these models along with their performance. Overall we show that the Transformer model offers acceptable WER with the lowest latency requirements. We report a relative WER improvement of around 16% with the second pass LAS rescoring with latency overhead under 5ms. We also highlight the importance of CNN front-end with Transformer architecture to achieve comparable word error rates (WER). Moreover, we observe that in the second pass re-scoring mode all the encoders provide similar benefits whereas the difference in performance is prominent in standalone text generation mode.
Ultra-Low-Power Unitary Matrix Multiplier Based on Silicon Photonic MEMS

We propose and demonstrate an ultra-low-power 3×3 unitary matrix multiplier with MEMS-based tunable couplers and phase shifters. The static power consumption and tuning energy per tuning element are less than 0.13 µW and 115 pJ, respectively.
Bilateral Dependency Optimization

Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training data. To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i.e., minimizing the dependency between inputs (i.e., features) and outputs (i.e., labels) during training the classifier. However, such a minimization process conflicts with minimizing the supervised loss that aims to maximize the dependency between inputs and outputs, causing an explicit trade-off between model robustness against MI attacks and model utility on classification tasks. In this paper, we aim to minimize the dependency between the latent representations and the inputs while maximizing the dependency between latent representations and the outputs, named a bilateral dependency optimization (BiDO) strategy. In particular, we use the dependency constraints as a universally applicable regularizer in addition to commonly used losses for deep neural networks (e.g., cross-entropy), which can be instantiated with appropriate dependency criteria according to different tasks. To verify the efficacy of our strategy, we propose two implementations of BiDO, by using two different dependency measures: BiDO with constrained covariance (BiDO-COCO) and BiDO with Hilbert-Schmidt Independence Criterion (BiDO-HSIC). Experiments show that BiDO achieves the state-of-the-art defense performance for a variety of datasets, classifiers, and MI attacks while suffering a minor classification-accuracy drop compared to the well-trained classifier with no defense, which lights up a novel road to defend against MI attacks.
A Continuous Optimisation Benchmark Suite from Neural Network Regression

Designing optimisation algorithms that perform well in general requires experimentation on a range of diverse problems. Training neural networks is an optimisation task that has gained prominence with the recent successes of deep learning. Although evolutionary algorithms have been used for training neural networks, gradient descent variants are by far the most common choice with their trusted good performance on large-scale machine learning tasks. With this paper we contribute CORNN (Continuous Optimisation of Regression tasks using Neural Networks), a large suite for benchmarking the performance of any continuous black-box algorithm on neural network training problems. Using a range of regression problems and neural network architectures, problem instances with different dimensions and levels of difficulty can be created. We demonstrate the use of the CORNN Suite by comparing the performance of three evolutionary and swarm-based algorithms on over 300 problem instances, showing evidence of performance complementarity between the algorithms. As a baseline, the performance of the best population-based algorithm is benchmarked against a gradient-based approach. The CORNN suite is shared as a public web repository to facilitate easy integration with existing benchmarking platforms.
Maximal Independent Vertex Set Applied to Graph Pooling

Convolutional neural networks (CNN) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete grid into a reduced grid with the same connectivity and allows reduction functions to take into account all the pixels of an image. However, a pooling satisfying such properties does not exist for graphs. Indeed, some methods are based on a vertex selection step which induces an important loss of information. Other methods learn a fuzzy clustering of vertex sets which induces almost complete reduced graphs. We propose to overcome both problems using a new pooling method, named MIVSPool. This method is based on a selection of vertices called surviving vertices using a Maximal Independent Vertex Set (MIVS) and an assignment of the remaining vertices to the survivors. Consequently, our method does not discard any vertex information nor artificially increase the density of the graph. Experimental results show an increase in accuracy for graph classification on various standard datasets.
Feature Point Matching for Human-Computer Interaction Multi-Feature Gesture Recognition Based on Virtual Reality VR Technology

This paper proposes a system for human-computer interaction using video gesture extraction. The system includes four modules: video acquisition and separation, image preprocessing, gesture extraction and recognition, and virtual reality human-computer interaction. This paper proposes a system for human-computer interaction using video gesture extraction. The system includes four modules: video acquisition and separation, image preprocessing, gesture extraction and recognition, and virtual reality human-computer interaction. Among them, the contour feature should be listed by the LCS description method. This article also uses three methods to describe the characteristics of dynamic gestures, which are static gesture sequence, fingertip movement feature, and palm movement feature. The static gesture sequence refers to a set of ordered static gesture numbers.