Reviewerly/exHarmony huggingface dataset:
A Scale Balanced Loss for Bounding Box Regression

Object detectors typically use bounding box regressors to improve the accuracy of object localization. Currently, the two types of bounding box regression loss are ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -norm-based and intersection over union (IoU)-based. However, we found that these two types of losses have their drawbacks. First, for ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -norm-based loss, large-scale objects are more likely to obtain a larger penalty than the smaller ones when calculating localization errors, which will cause regression loss imbalance. Second, ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -norm-based loss has symmetry so that when the predicted bounding boxes are in some unique symmetrical relationships (i.e., Symmetric Trap), the regression loss remains unchanged. Third, for IoU-based loss, the overlap area and the union area do not change as the shape or relative position of two bounding boxes changes in some cases(i.e., Area Maze). To address these problems, we propose the scale balanced loss(L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SB</sub> ), which is asymmetric, position-sensitive, and scale-invariant. First, in order to obtain the property of scale invariance, it is designed as a fraction to eliminate the scale information contained in the numerator and denominator. Second, by incorporating the Euclidean distance between different corner points instead of the area, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SB</sub> is sensitive to the changes of coordinates of any corner point, so as to solve the area maze problem. Finally, by incorporating the diagonals of the overlap and the smallest enclosing rectangle, this fraction is no longer symmetric, thus solving the symmetry trap problem. To validate the proposed algorithm, we have replaced the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -norm-based loss of YOLOv3 and SSD with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GIoU</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SB</sub> and evaluate their performance on Pascal Visual Object Classes and Microsoft Common Objects in Context benchmarks. The final results show that L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SB</sub> has improved their average precisions at different IoU thresholds and scales. We envision that this regression loss can also improve the performance of other visual tasks.
Beyond Network Pruning: a Joint Search-and-Training Approach

Network pruning has been proposed as a remedy for alleviating the over-parameterization problem of deep neural networks. However, its value has been recently challenged especially from the perspective of neural architecture search (NAS). We challenge the conventional wisdom of pruning-after-training by proposing a joint search-and-training approach that directly learns a compact network from the scratch. By treating pruning as a search strategy, we present two new insights in this paper: 1) it is possible to expand the search space of networking pruning by associating each filter with a learnable weight; 2) joint search-and-training can be conducted iteratively to maximize the learning efficiency. More specifically, we propose a coarse-to-fine tuning strategy to iteratively sample and update compact sub-network to approximate the target network. The weights associated with network filters will be accordingly updated by joint search-and-training to reflect learned knowledge in NAS space. Moreover, we introduce strategies of random perturbation (inspired by Monte Carlo) and flexible thresholding (inspired by Reinforcement Learning) to adjust the weight and size of each layer. Extensive experiments on ResNet and VGGNet demonstrate the superior performance of our proposed method on popular datasets including CIFAR10, CIFAR100 and ImageNet.
Learning Robust Models for e-Commerce Product Search

Showing items that do not match search query intent degrades customer experience in ecommerce.These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs.Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain.In this paper, we develop a deep, endto-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier.We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples.This not only makes the classifier more robust but also boosts the overall ranking performance.Our model achieves a relative gain compared to baselines by over 26% in F-score, and over 17% in Area Under PR curve.On live search traffic, our model gains significant improvement in multiple countries.
Gender issues in machine translation

Recent years have seen significant improvements in machine translation thanks to the enhanced capacity of computers and advanced technologies in the field of Natural Language Processing (NLP). Our contribution focuses on one of the most frequent problems in the state-of-the-art MT systems: translation of gender. It represents a recurrent source of mistranslation: incorrect gender attribution to proforms (personal pronouns, relative pronouns, etc.), the reproduction of gender stereotypes and the overuse of masculine pronouns are among the most frequent problems. In this chapter we briefly outline the different approaches to MT, we address historical perspectives and recent developments concerning gender issues in MT. The final part of the chapter is devoted to a brief discussion of outstanding issues in this field, namely the social and ethical impact of an unbalanced representation of gender in MT output and finally the need of i) fine-grained evaluation metrics which help focus on specific critical areas and ii) benchmark datasets specifically devoted to gender handling.
Towards a Hierarchical Bayesian Model of Multi-View Anomaly Detection

Traditional anomaly detectors examine a single view of instances and cannot discover multi-view anomalies, i.e., instances that exhibit inconsistent behaviors across different views. To tackle the problem, several multi-view anomaly detectors have been developed recently, but they are all transductive and unsupervised thus may suffer some challenges. In this paper, we propose a novel inductive semi-supervised Bayesian multi-view anomaly detector. Specifically, we first present a generative model for normal data. Then, we build a hierarchical Bayesian model, by first assigning priors to all parameters and latent variables, and then assigning priors over the priors. Finally, we employ variational inference to approximate the posterior of the model and evaluate anomalous scores of multi-view instances. In the experiment, we show the proposed Bayesian detector consistently outperforms state-of-the-art counterparts across several public data sets and three well-known types of multi-view anomalies. In theory, we prove the inferred Bayesian estimator is consistent and derive a proximate sample complexity for the proposed anomaly detector.
An Accurate Segmentation-Based Scene Text Detector with Context Attention and Repulsive Text Border

Scene text detection is one of the most challenging problems in computer vision and has attracted great interest. In general, scene text detection methods are divided into two categories: detection-based and segmentation-based methods. Recently, the segmentation-based methods are more and more popular due to their superior performances and the advantages of detecting arbitrary-shape texts. However, there still exist the following problems: (a) the misclassfication of the unexpected texts, (b) the split of long text lines, (c) the failure of separating very close text instances. In this paper, we propose an accurate segmentation-based detector, which is equipped with context attention and repulsive text border. The context attention incorporates global channel attention, non-local self-attention and spatial attention to better exploit the global and local context, which can greatly increase the discriminative ability for pixels. Due to the enhancement of pixel-level features, false positives and the misdetections of long texts are reduced. Besides, for the purpose of solving very close text instance, a repulsive pixel link, which focuses on the relationships between pixels at the border, is proposed. Experiments on several standard benchmarks, including MSRA-TD500, ICDAR2015, ICDAR2017-MLT and CTW1500, validate the superiority of the proposed method.
Critical theory for the breakdown of photon blockade

Photon blockade is the result of the interplay between the quantized nature of light and strong optical nonlinearities, whereby strong photon-photon repulsion prevents a quantum optical system from absorbing multiple photons. We theoretically study a single atom coupled to the light field, described by the resonantly driven Jaynes--Cummings model, in which case the photon blockade breaks down in a second order phase transition at a critical drive strength. We show that this transition is associated to the spontaneous breaking of an anti-unitary PT-symmetry. Within a semiclassical approximation we calculate the expectation values of observables in the steady state. We then move beyond the semiclassical approximation and approach the critical point from the disordered (blockaded) phase by reducing the Lindblad quantum master equation to a classical rate equation that we solve. The width of the steady-state distribution in Fock space is found to diverge as we approach the critical point with a simple power-law, allowing us to calculate the critical scaling of steady state observables without invoking mean-field theory. We propose a simple physical toy model for biased diffusion in the space of occupation numbers, which captures the universal properties of the steady state. We list several experimental platforms where this phenomenon may be observed.
Accelerating Generative Neural Networks on Unmodified Deep Learning Processors - A Software Approach

Generative neural network is a new category of neural networks and it has been widely utilized in many applications such as content generation, unsupervised learning, segmentation, and pose estimation. It typically involves massive computing-intensive deconvolution operations that cannot be fitted to conventional neural network processors directly. However, prior works mainly investigated specialized hardware architectures through intensive hardware modifications to the existing deep learning processors to accelerate deconvolution together with the convolution. In contrast, this article proposes a novel deconvolution implementation with a software approach and enables fast and efficient deconvolution execution on the existing deep learning processors. Our proposed method reorganizes the computation of deconvolution and allows the deep learning processors to treat it as the standard convolution by splitting the original deconvolution filters into multiple small filters. Compared to prior acceleration schemes, the implemented acceleration scheme achieves 2.4× -4.3× performance speedup and reduces the energy consumption by 27.7 -54.5 percent on a set of realistic benchmarks. In addition, we have also applied the deconvolution computing approach to the off-the-shelf commodity deep learning processors. The performance of deconvolution also exhibits significant performance speedup over prior deconvolution implementations.
Location Attention for Extrapolation to Longer Sequences

Neural networks are surprisingly good at interpolating and perform remarkably well when the training set examples resemble those in the test set.However, they are often unable to extrapolate patterns beyond the seen data, even when the abstractions required for such patterns are simple.In this paper, we first review the notion of extrapolation, why it is important, and how one could hope to tackle it.We then focus on a specific type of extrapolation, which is especially useful for natural language processing: generalization to sequences longer than those seen during training.We hypothesize that models with a separate contentand location-based attention are more likely to extrapolate than those with common attention mechanisms.We empirically support our claim for recurrent seq2seq models with our proposed attention on variants of the Lookup Table task.This sheds light on some striking failures of neural models for sequences and on possible methods to approaching such issues.
Modeling Long Context for Task-Oriented Dialogue State Generation

Based on the recently proposed transferable dialogue state generator (TRADE) (Wu et al., 2019) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation.By enabling the model to learn a better representation of the long dialogue context, our approaches attempt to solve the problem that the performance of the baseline significantly drops when the input dialogue context sequence is long.In our experiments, our proposed model achieves a 7.03% relative improvement over the baseline, establishing a new state-of-the-art joint goal accuracy of 52.04% on the MultiWOZ 2.0 dataset.
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)

We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graph-theoretic notion of ``cube-like graphs'', thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.
Fatality Killed the Cat or: BabelPic, a Multimodal Dataset for Non-Concrete Concepts

Thanks to the wealth of high-quality annotated images available in popular repositories such as ImageNet, multimodal language-vision research is in full bloom.However, events, feelings and many other kinds of concepts which can be visually grounded are not well represented in current datasets.Nevertheless, we would expect a wide-coverage language understanding system to be able to classify images depicting RECESS and REMORSE, not just CATS, DOGS and BRIDGES.We fill this gap by presenting BabelPic, a hand-labeled dataset built by cleaning the image-synset association found within the BabelNet Lexical Knowledge Base (LKB).BabelPic explicitly targets nonconcrete concepts, thus providing refreshing new data for the community.We also show that pre-trained language-vision systems can be used to further expand the resource by exploiting natural language knowledge available in the LKB.
Visual Tracking via Deep Feature Fusion and Correlation Filters

Visual tracking is a fundamental vision task that tries to figure out instances of several object classes from videos and images. It has attracted much attention for providing the basic semantic information for numerous applications. Over the past 10 years, visual tracking has made a great progress, but huge challenges still exist in many real-world applications. The facade of a target can be transformed significantly by pose changing, occlusion, and sudden movement, which possibly leads to a sudden target loss. This paper builds a hybrid tracker combining the deep feature method and correlation filter to solve this challenge, and verifies its powerful characteristics. Specifically, an effective visual tracking method is proposed to address the problem of low tracking accuracy due to the limitations of traditional artificial feature models, then rich hiearchical features of Convolutional Neural Networks are used to make the multi-layer features fusion improve the tracker learning accuracy. Finally, a large number of experiments are conducted on benchmark data sets OBT-100 and OBT-50, and show that our proposed algorithm is effective.
Extractors for adversarial sources via extremal hypergraphs

Randomness extraction is a fundamental problem that has been studied for over three decades. A well-studied setting assumes that one has access to multiple independent weak random sources, each with some entropy. However, this assumption is often unrealistic in practice. In real life, natural sources of randomness can produce samples with no entropy at all or with unwanted dependence. Motivated by this and applications from cryptography, we initiate a systematic study of randomness extraction for the class of adversarial sources defined as follows.
The 2020 Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries

The 2020 Embedded Deep Learning Object Detection Model Compression Competition for traffic in Asian countries held in IEEE ICME2020 focuses on the object detection technologies in autonomous driving scenarios. This competition targets on realizing the deep learning object detection through embedded systems with low complexity and model size. The dataset, recorded in Asian countries (e.g., Taiwan), contains several harsh driving environments, including crowded scooters, bicycles, pedestrians and vehicles. There are 89,002 annotated images provided for training and 1,000 images for validation. Additional 5,400 testing images are used in the contest evaluation process, in which 2,700 of them is used in the qualification stage, and the rest are used in the final stage. There are in total 133 of registered participating teams joining this competition. We have selected the top 10 teams with the highest detection accuracy entering the final competition, from which 8 teams submitted the final results. The overall best model belongs to team USTC-NELSLIP, followed by team "BUPT_MCPRL" and team "DD_VISION". Two special awards of best bicycle and scooter detections go to teams "IBDO-AIOT" and "Deep Learner", respectively.
Unsupervised Monocular Visual-inertial Odometry Network

Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper. Our method is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visual-inertial motion features from image-IMU stream to achieve long-term odometry task. A novel sliding window optimization strategy, which consists of an intra-window and an inter-window optimization, is introduced for overcoming the error accumulation and scale ambiguity problem. The intra-window optimization restrains the geometric inferences within the window through checking the photometric consistency. And the inter-window optimization checks the 3D geometric consistency and trajectory consistency among predictions of separate windows. Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of UnVIO over other state-of-the-art VO / VIO methods. The codes are open-source.
Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences

The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer.Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs.We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies (UD; Nivre et al., 2016).We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation.We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study.We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.
Improving Employee Well-Being by Means of Virtual Reality – REALEX: An Empirical Case Study

The present paper seeks to analyse how a virtual reality system can foster employee well-being at the workplace. In order to answer the research problem, whether such a system can benefit employees in regards to their well-being, stress perception and relaxation, an empirical case study was conducted. Relevant literature both on the advantages of virtual reality systems in general and on different approaches to fostering employee well-being are displayed and discussed within the paper, leading up to a description of the methodology. Based on a sample of 52 participants from Germany, Austria and Croatia an empirical study with a quantitative, pre- and post-study approach was conducted. Participants were assessed in regards to their initial stress perception, before they used either the REALEX system (a VR based relaxation tool displaying natural landscapes in a virtual reality setting) or a video streaming service on a classical display. The statistical analyses revealed that the usage of the REALEX system was able to significantly improve participants’ stress perception and well-being. In doing so it – also significantly – exceeded the positive effect that the control setting (video streaming) had in these regards. Potential limitations of the study are presented and recommendations both for practical work in the field of human resource management and for scientific research are deducted.