Reviewerly/exHarmony huggingface dataset:
GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing

Recently, research communities highlight the neces-sity of formulating a scalability continuum for large-scale graph processing, which gains the scale-out benefits from distributed graph systems, and the scale-up benefits from high-performance accelerators. To this end, we propose a middleware, called the GX-plug, for the ease of integrating the merits of both. As a middleware, the GX-plug is versatile in supporting different runtime environments, computation models, and programming models. More, for improving the middleware performance, we study a series of techniques, including pipeline shuffle, synchro-nization caching and skipping, and workload balancing, for intra-, inter-, and beyond-iteration optimizations, respectively. Exper-iments show that our middleware efficiently plugs accelerators to representative distributed graph systems, e.g., GraphX and Powergraph, with up-to 20x acceleration ratio.
HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks

The attention mechanism is one of the most important priori knowledge to enhance convolutional neural networks.Most attention mechanisms are bound to the convolutional layer and use local or global contextual information to recalibrate the input.This is a popular attention strategy design method.Global contextual information helps the network to consider the overall distribution, while local contextual information is more general.The contextual information makes the network pay attention to the mean or maximum value of a particular receptive field.Different from the most attention mechanism, this article proposes a novel attention mechanism with the heuristic difference attention module (HDAM).HDAM's input recalibration is based on the difference between the local and global contextual information instead of the mean and maximum values.At the same time, to make different layers have a more suitable local receptive field sizes and increase the flexibility of the local receptive field design, we use genetic algorithm to heuristically produce local receptive fields.First, HDAM extracts the mean value of the global and local receptive fields as the corresponding contextual information.Then the difference between the global and local contextual information is calculated.Finally, HDAM uses this difference to recalibrate the input.In addition, we use the heuristic ability of genetic algorithm to search for the local receptive field size of each layer.Our experiments on CIFAR-10 and CIFAR-100 show that HDAM can use fewer parameters than other attention mechanisms to achieve higher accuracy.We implement HDAM with the Python library, Pytorch, and the code and models will be publicly available.
Heralded single-photon source based on an ensemble of Raman-active molecules

Light with high mutual correlations at different frequencies can be used to create heralded single-photon sources, which may serve as the basic elements of existing quantum cryptography and quantum teleportation schemes. One of the important examples in natural systems of light with high mutual correlations is the light produced by spontaneous Raman scattering on an ensemble of molecules. In this paper, we investigate the possibility of using Raman light to create a heralded single-photon source. We show that when using Stokes scattered light for postselection of an anti-Stokes scattered light, the latter may posses the single-photon properties. We analyze the influence of the various negative factors on the characteristics of such a heralded single-photon source, which include a time delay between Stokes and anti-Stokes photons, the finiteness of the correlation radius of an external source, and background radiation. We show that the high purity of the single-photon source is preserved even when the flow of uncorrelated photons exceeds the flow of correlated photons in the scattered Raman light by an order of magnitude.
Learning from Imperfect Demonstrations via Adversarial Confidence Transfer

Existing learning from demonstration algorithms usually assume access to expert demonstrations. However, this assumption is limiting in many real-world applications since the collected demonstrations may be suboptimal or even consist of failure cases. We therefore study the problem of learning from imperfect demonstrations by learning a confidence predictor. Specifically, we rely on demonstrations along with their confidence values from a different correspondent environment (source environment) to learn a confidence predictor for the environment we aim to learn a policy in (target environment-where we only have unlabeled demonstrations). We learn a common latent space through adversarial distribution matching of multi-length partial trajectories to enable the transfer of confidence across source and target environments. The learned confidence reweights the demonstrations to enable learning more from informative demonstrations and discarding the irrelevant ones. Our experiments in three simulated environments and a real robot reaching task demonstrate that our approach learns a policy with the highest expected return. We show the videos of our experiments on our website.
Learning Robust Convolutional Neural Networks with Relevant Feature Focusing Via Explanations

Existing image recognition techniques based on convolutional neural networks (CNNs) basically assume that the training and test datasets are sampled from i.i.d distributions. However, this assumption is easily broken in the real world because of the distribution shift that occurs when the co-occurrence relations between objects and backgrounds in input images change. Under this type of distribution shift, CNNs learn to focus on features that are not task-relevant, such as backgrounds from the training data, and degrade their accuracy on the test data. To tackle this problem, we propose relevant feature focusing (ReFF). ReFF detects task-relevant features and regularizes CNNs via explanation outputs (e.g., Grad-CAM). Since ReFF is composed of post-hoc explanation modules, it can be easily applied to off-the-shelf CNNs. Furthermore, ReFF requires no additional inference cost at test time because it is only used for regularization while training. We demonstrate that CNNs trained with ReFF focus on features relevant to the target task and that ReFF improves the test-time accuracy.
Lessons from the AdKDD’21 Privacy-Preserving ML Challenge

Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed under the Improving Web Advertising Business Group at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD’21, a premier workshop on Advertising Science with data provided by advertising company Criteo. In this paper, we describe the challenge tasks, the structure of the available datasets, report the challenge results, and enable its full reproducibility. A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap. We also run additional experiments to observe the sensitivity of winning methods to different parameters such as privacy budget or quantity of available privileged side information. We conclude that the industry needs either alternate designs for private data sharing or a breakthrough in learning with aggregated data only to keep ad relevance at a reasonable level.
Medication Error Detection Using Contextual Language Models

Medication errors most commonly occur at the ordering or prescribing stage, potentially leading to medical complications and poor health outcomes. While it is possible to catch these errors using different techniques; the focus of this work is on textual and contextual analysis of prescription information to detect and prevent potential medication errors. In this paper, we demonstrate how to use BERT-based contextual language models to detect anomalies in written or spoken text based on a data set extracted from real-world medical data of thousands of patient records. The proposed models are able to learn patterns of text dependency and predict erroneous output based on contextual information such as patient data. The experimental results yield accuracy up to 96.63% for text input and up to 79.55% for speech input, which is satisfactory for most real-world applications.
OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas

Immersive maps such as Google Street View and Bing Streetside provide true-to-life views with a massive collection of panoramas. However, these panoramas are only available at sparse intervals along the path they are taken, resulting in visual discontinuities during navigation. Prior art in view synthesis is usually built upon a set of perspective images, a pair of stereoscopic images, or a monocular image, but barely examines wide-baseline panoramas, which are widely adopted in commercial platforms to optimize bandwidth and storage usage. In this paper, we leverage the unique characteristics of wide-baseline panoramas and present OmniSyn, a novel pipeline for 360&#x00B0; view synthesis between wide-baseline panoramas. OmniSyn predicts omnidirectional depth maps using a spherical cost volume and a monocular skip connection, renders meshes to 360&#x00B0; images, and synthesizes intermediate views with a fusion network. We envision our work may inspire future research for this unheeded real-world task and eventually produce a smoother experience for navigating immersive maps.
On Monocular Depth Estimation and Uncertainty Quantification Using Classification Approaches for Regression

Monocular depth is important in many tasks, such as 3D reconstruction and autonomous driving. Deep learning based models achieve state-of-the-art performance in this field. A set of novel approaches for estimating monocular depth consists of transforming the regression task into a classification one. However, there is a lack of detailed descriptions and comparisons for Classification Approaches for Regression (CAR) in the community and no in-depth exploration of their potential for uncertainty estimation. To this end, this paper will introduce a taxonomy and summary of CAR approaches, a new uncertainty estimation solution for CAR, and a set of experiments on depth accuracy and uncertainty quantification for CAR-based models on KITTI dataset. The experiments reflect the differences in the portability of various CAR methods on two backbones. Meanwhile, the newly proposed method for uncertainty estimation can outperform the ensembling method with only one forward propagation.
Partially latent factors based multi‐view subspace learning

Abstract Multi‐view subspace clustering always performs well in high‐dimensional data analysis, but is sensitive to the quality of data representation. To this end, a two‐stage fusion strategy is proposed to embed representation learning into the process of multi‐view subspace clustering. This article first proposes a novel matrix factorization method that can separate the coupling consistent and complementary information from observations of multiple views. Based on the obtained latent representations, we further propose two subspace clustering strategies: feature‐level fusion and subspace‐level hierarchical strategy. The feature‐level method concatenates all kinds of latent representations from multiple views, and the original problem therefore degenerates to a single‐view subspace clustering process. The subspace‐level hierarchical method performs different self‐expressive reconstruction processes on the corresponding complementary and consistent latent representations coming from each view, that is, the prior constraints imposed on different types of subspace representations are related to the relevant input factors. Finally, extensive experimental results on real‐world datasets demonstrate the superiority of our proposed methods by comparing them against some state‐of‐the‐art subspace clustering algorithms.
Partition Ghz Slocc Class of Three Qubits into Ten Families Under Lu

In [A. Ac´ın et al., Phys. Rev. Lett. 85, 1560 (2000)], via the general Schmidt decomposition of three qubits, pure states of three qubits were partitioned into five types under LU, where the type-number indicates how many of the five Local Bases Product States (LBPS) characterize the states of that type. The fundamental result has had a significant impact on QIC. In this paper, we indicate that the number of LBPS is not LU invariant. It means that it is impossible to partition pure states of three qubits into the five types. We present a LU invariant and an entanglement measures for the GHZ SLOCC class of three qubits, and partition states of the GHZ SLOCC class of three qubits into ten families and each family into two subfamilies under LU. We give a necessary and sufficient condition for the LU equivalence of two complex conjugate states.
Progressive Continual Learning for Spoken Keyword Spotting

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. To tackle such challenges, we propose a progressive continual learning strategy for small-footprint spoken keyword spotting (PCL-KWS). Specifically, the proposed PCL-KWS framework introduces a network instantiator to generate the task-specific sub-networks for remembering previously learned keywords. As a result, the PCL-KWS approach incrementally learns new keywords without forgetting prior knowledge. Besides, the proposed keyword-aware network scaling mechanism of PCL-KWS constrains the growth of model parameters while achieving high performance. Experimental results show that after learning five new tasks sequentially, our proposed PCL-KWS approach archives the new state-of-the-art performance of 92.8% average accuracy for all the tasks on Google Speech Command dataset compared with other baselines.
Real-Time Scene Text Detection Based on Global Level and Word Level Features

It is an extremely challenging task to detect arbitrary shape text in natural scenes on high accuracy and efficiency. In this paper, we propose a scene text detection framework, namely GWNet, which mainly includes two modules: Global module and RCNN module. Specifically, Global module improves the adaptive performance of the DB (Differentiable Binarization) module by adding k submodule and shift submodule. Two submodules enhance the adaptability of amplifying factor k, accelerate the convergence of models and help to produce more accurate detection results. RCNN module fuses global-level and word-level features. The word-level label is generated by obtaining the minimum axis-aligned rectangle boxes of the shrunk polygon. In the inference period, GWNet only uses global-level features to output simple polygon detections. Experiments on four benchmark datasets, including the MSRA-TD500, Total-Text, ICDAR2015 and CTW-1500, demonstrate that our GWNet outperforms the state-of-the-art detectors. Specifically, with a backbone of ResNet-50, we achieve an F-measure of 88.6% on MSRATD500, 87.9% on Total-Text, 89.2% on ICDAR2015 and 87.5% on CTW-1500.
Robust Deepfake on Unrestricted Media: Generation and Detection

Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance. Although deepfake media have potential application in a wide range of areas and are drawing much attention from both the academic and industrial communities, it also leads to serious social and criminal concerns. This chapter explores the evolution of and challenges in deepfake generation and detection. It also discusses possible ways to improve the robustness of deepfake detection for a wide variety of media (e.g., in-the-wild images and videos). Finally, it suggests a focus for future fake media research.
Robust Reinforcement Learning via Genetic Curriculum

Achieving robust performance is crucial when applying deep reinforcement learning (RL) in safety critical systems. Some of the state of the art approaches try to address the problem with adversarial agents, but these agents often require expert supervision to fine tune and prevent the adversary from becoming too challenging to the trainee agent. While other approaches involve automatically adjusting environment setups during training, they have been limited to simple environments where low-dimensional encodings can be used. Inspired by these approaches, we propose genetic curriculum, an algorithm that automatically identifies scenarios in which the agent currently fails and generates an associated curriculum to help the agent learn to solve the scenarios and acquire more robust behaviors. As a non-parametric optimizer, our approach uses a raw, non-fixed encoding of scenarios, reducing the need for expert supervision and allowing our algorithm to adapt to the changing performance of the agent. Our empirical studies show improvement in robustness over the existing state of the art algorithms, providing training curricula that result in agents being 2 - 8x times less likely to fail without sacrificing cumulative reward. We include an ablation study and share insights on why our algorithm outperforms prior approaches.
Scheduling Policies for Stability and Optimal Server Running Cost in Cloud Computing Platforms

We propose throughput and cost optimal job scheduling algorithms in cloud computing platforms offering Infrastructure as a Service. We first consider online job migration and propose job scheduling algorithms to minimize job migration and server running costs. We consider algorithms that assume knowledge of job-size on the arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically, this algorithm yields a tradeoff between delay and costs. We then relax the job-size knowledge assumption and give an algorithm that uses readily offered service to the jobs. We show that this algorithm gives order-wise identical cost as the job size based algorithm. Later, we consider offline job migration that incurs migration delays. We again present throughput optimal algorithms that minimize server running cost. We illustrate the performance of the proposed algorithms and compare these to the existing algorithms via simulation.
Semantically Proportional Patchmix for Few-Shot Learning

Few-shot learning aims to classify unseen classes with only a limited number of labeled data. Recent works have demonstrated that training models with a simple transfer learning strategy can achieve competitive results in few-shot classification. Although excelling at distinguishing training data, these models are not well generalized to unseen data, probably due to insufficient feature representations on evaluation. To tackle this issue, we propose Semantically Proportional Patchmix (SePPMix), in which patches are cut and pasted among training images and the ground truth labels are mixed proportionally to the semantic information of the patches. In this way, we can improve the generalization ability of the model by regional dropout effect without introducing severe label noise. To learn more robust representations of data, we further take rotate transformation on the mixed images and predict rotations as a rule-based regularizer. Extensive experiments on prevalent few-shot benchmarks have shown the effectiveness of our proposed method.
Should I take a walk? Estimating Energy Expenditure from Video Data

We explore the problem of automatically inferring the amount of kilocalories used by human during physical activity from his/her video observation. To study this under-researched task, we introduce Vid2Burn – an omni-source benchmark for estimating caloric expenditure from video data featuring both, high- and low-intensity activities for which we derive energy expenditure annotations based on models established in medical literature.In practice, a training set would only cover a certain amount of activity types, and it is important to validate, if the model indeed captures the essence of energy expenditure, (e.g., how many and which muscles are involved and how intense they work) instead of memorizing fixed values of specific activity categories seen during training. Ideally, the models should look beyond such category-specific biases and regress the caloric cost in videos depicting activity categories not explicitly present during training. With this property in mind, Vid2Burn is accompanied with a cross-category benchmark, where the task is to regress caloric expenditure for types of physical activities not present during training. An extensive evaluation of state-of-the-art approaches for video recognition modified for the energy expenditure estimation task demonstrates the difficulty of this problem, especially for new activity types at test-time, marking a new research direction. Dataset and code are available at https://github.com/KPeng9510/Vid2Burn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Socialformer: Social Network Inspired Long Document Modeling for Document Ranking

Utilizing pre-trained language models has achieved great success for neural document ranking. Limited by the computational and memory requirements, long document modeling becomes a critical issue. Recent works propose to modify the full attention matrix in Transformer by designing sparse attention patterns. However, most of them only focus on local connections of terms within a fixed-size window. How to build suitable remote connections between terms to better model document representation remains underexplored. In this paper, we propose the model Socialformer, which introduces the characteristics of social networks into designing sparse attention patterns for long document modeling in document ranking. Specifically, we consider several attention patterns to construct a graph like social networks. Endowed with the characteristic of social networks, most pairs of nodes in such a graph can reach with a short path while ensuring the sparsity. To facilitate efficient calculation, we segment the graph into multiple subgraphs to simulate friend circles in social scenarios. Experimental results confirm the effectiveness of our model on long document modeling.
Speaker Adaption with Intuitive Prosodic Features for Statistical Parametric Speech Synthesis

In this paper, we propose a method of speaker adaption with intuitive prosodic features for statistical parametric speech synthesis. The intuitive prosodic features employed in this method include pitch, pitch range, speech rate and energy considering that they are directly related with the overall prosodic characteristics of different speakers. The intuitive prosodic features are extracted at utterance-level or speaker-level, and are further integrated into the existing speaker-encoding-based and speaker-embedding-based adaptation frameworks respectively. The acoustic models are sequence-to-sequence ones based on Tacotron2. Intuitive prosodic features are concatenated with text encoder outputs and speaker vectors for decoding acoustic features. Experimental results have demonstrated that our proposed methods can achieve better objective and subjective performance than the baseline methods without intuitive prosodic features. Besides, the proposed speaker adaption method with utterance-level prosodic features has achieved the best similarity of synthetic speech among all compared methods.