Reviewerly/exHarmony huggingface dataset:
Mumford–Shah Loss Functional for Image Segmentation With Deep Learning

Recent state-of-the-art image segmentation algorithms are mostly based on deep neural networks, thanks to their high performance and fast computation time. However, these methods are usually trained in a supervised manner, which requires large number of high quality ground-truth segmentation masks. On the other hand, classical image segmentation approaches such as level-set methods are formulated in a self-supervised manner by minimizing energy functions such as Mumford-Shah functional, so they are still useful to help generate segmentation masks without labels. Unfortunately, these algorithms are usually computationally expensive and often have limitation in semantic segmentation. In this paper, we propose a novel loss function based on Mumford-Shah functional that can be used in deep-learning based image segmentation without or with small labeled data. This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the Mumford-Shah functional. We show that the new loss function enables semi-supervised and unsupervised segmentation. In addition, our loss function can also be used as a regularized function to enhance supervised semantic segmentation algorithms. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.
Wireless Communications for Collaborative Federated Learning

To facilitate the deployment of machine learning in resource and privacy-constrained systems such as the Internet of Things, federated learning (FL) has been proposed as a means for enabling edge devices to train a shared learning model while promoting privacy. However, Google's seminal FL algorithm requires all devices to be directly connected with a central controller, which limits its applications. In contrast, this article introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller. The fundamentals of this framework are developed and a number of communication techniques are proposed so as to improve CFL performance. An overview of centralized learning, Google's FL, and CFL is presented. For each type of learning, the basic architecture as well as its advantages, drawbacks, and operating conditions are introduced. Then four CFL performance metrics are presented, and a suite of communication techniques ranging from network formation, device scheduling, mobility management, to coding are introduced to optimize the performance of CFL. For each technique, future research opportunities are discussed. In a nutshell, this article showcases how CFL can be effectively implemented at the edge of large-scale wireless systems.
Symmetry-resolved entanglement detection using partial transpose moments

We propose an ordered set of experimentally accessible conditions for detecting entanglement in mixed states. The $k$-th condition involves comparing moments of the partially transposed density operator up to order $k$. Remarkably, the union of all moment inequalities reproduces the Peres-Horodecki criterion for detecting entanglement. Our empirical studies highlight that the first four conditions already detect mixed state entanglement reliably in a variety of quantum architectures. Exploiting symmetries can help to further improve their detection capabilities. We also show how to estimate moment inequalities based on local random measurements of single state copies (classical shadows) and derive statistically sound confidence intervals as a function of the number of performed measurements. Our analysis includes the experimentally relevant situation of drifting sources, i.e. non-identical, but independent, state copies.
High-dimensional optical quantum logic in large operational spaces

The probabilistic nature of single-photon sources and photon-photon interactions encourages encoding as much quantum information as possible in every photon for the purpose of photonic quantum information processing. Here, by encoding high-dimensional units of information (qudits) in time and frequency degrees of freedom using on-chip sources, we report deterministic two-qudit gates in a single photon with fidelities exceeding 0.90 in the computational basis. Constructing a two-qudit modulo SUM gate, we generate and measure a single-photon state with non-separability between time and frequency qudits. We then employ this SUM operation on two frequency-bin entangled photons, each carrying two 32-dimensional qudits, to realize a four-party high-dimensional Greenberger-Horne-Zeilinger state, occupying a Hilbert space equivalent to that of 20 qubits. Although high-dimensional coding alone is ultimately not scalable for universal quantum computing, our design shows the potential of deterministic optical quantum operations in large encoding spaces for practical and compact quantum information processing protocols.
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis

In multimodal sentiment analysis (MSA), the performance of a model highly depends on the quality of synthesized embeddings.These embeddings are generated from the upstream process called multimodal fusion, which aims to extract and combine the input unimodal raw data to produce a richer multimodal representation.Previous work either back-propagates the task loss or manipulates the geometric property of feature spaces to produce favorable fusion results, which neglects the preservation of critical task-related information that flows from input to the fusion results.In this work, we propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimodal input in order to maintain taskrelated information through multimodal fusion.The framework is jointly trained with the main task (MSA) to improve the performance of the downstream MSA task.To address the intractable issue of MI bounds, we further formulate a set of computationally simple parametric and non-parametric methods to approximate their truth value.Experimental results on the two widely used datasets demonstrate the efficacy of our approach.The implementation of this work is publicly available at https://github.com/declare-lab/Multimodal-Infomax.
PPHOPCM: Privacy-Preserving High-Order Possibilistic c-Means Algorithm for Big Data Clustering with Cloud Computing

As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogenous data, since it is initially designed for only small structured dataset. To tackle this problem, the paper proposes a high-order PCM algorithm (HOPCM) for big data clustering by optimizing the objective function in the tensor space. Further, we design a distributed HOPCM method based on MapReduce for very large amounts of heterogeneous data. Finally, we devise a privacy-preserving HOPCM algorithm (PPHOPCM) to protect the private data on cloud by applying the BGV encryption scheme to HOPCM, In PPHOPCM, the functions for updating the membership matrix and clustering centers are approximated as polynomial functions to support the secure computing of the BGV scheme. Experimental results indicate that PPHOPCM can effectively cluster a large number of heterogeneous data using cloud computing without disclosure of private data.
Stigmatizing language in news media coverage of the opioid epidemic: Implications for public health

Public stigma toward people who use illicit drugs impedes advancement of public health solutions to the opioid epidemic and reduces willingness to seek addiction treatment. Experimental studies show that use of certain terms, such as “addict” and “substance abuser,” exacerbate stigma while alternative terms, such as “person with a substance use disorder,” are less stigmatizing. We examine the frequency with which stigmatizing terms and less-stigmatizing alternatives are used in U.S. news media coverage of the opioid epidemic. We analyzed 6399 news stories about the opioid epidemic published/aired by high-circulation and high-viewership U.S. national and regional print and television news outlets from July 2008 through June 2018. We calculated the proportion of news stories mentioning terms shown to be stigmatizing, as well as terms shown to be less-stigmatizing alternatives, in randomized experiments. Data was collected during May through August 2018 and analyzed in September 2018. Over the 10-year study period, 49% of news stories about the opioid epidemic mentioned any stigmatizing term and 2% mentioned any less-stigmatizing alternative. The proportion of news stories mentioning stigmatizing terms over the 10-year study period increased from 37% in July 2008–June 2009 to 45% in July 2017–June 2018. The language included in U.S. news media coverage of the opioid epidemic may contribute to and reinforce widespread public stigma toward people with opioid use disorders. This stigma may be a barrier to implementation of evidence-based interventions to prevent opioid overdose deaths. Establishing journalistic standards to de-stigmatize the language of addiction is a public health priority.
Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment

Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present TextFooler, a simple but strong baseline to generate natural adversarial text. By applying it to two fundamental natural language tasks, text classification and textual entailment, we successfully attacked three target models, including the powerful pre-trained BERT, and the widely used convolutional and recurrent neural networks. We demonstrate the advantages of this framework in three ways: (1) effective---it outperforms state-of-the-art attacks in terms of success rate and perturbation rate, (2) utility-preserving---it preserves semantic content and grammaticality, and remains correctly classified by humans, and (3) efficient---it generates adversarial text with computational complexity linear to the text length. *The code, pre-trained target models, and test examples are available at this https URL.
What is the State of Neural Network Pruning

Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years. We provide a meta-analysis of the literature, including an overview of approaches to pruning and consistent findings in the literature. After aggregating results across 81 papers and pruning hundreds of models in controlled conditions, our clearest finding is that the community suffers from a lack of standardized benchmarks and metrics. This deficiency is substantial enough that it is hard to compare pruning techniques to one another or determine how much progress the field has made over the past three decades. To address this situation, we identify issues with current practices, suggest concrete remedies, and introduce ShrinkBench, an open-source framework to facilitate standardized evaluations of pruning methods. We use ShrinkBench to compare various pruning techniques and show that its comprehensive evaluation can prevent common pitfalls when comparing pruning methods.
Molecular docking with Gaussian Boson Sampling

Photonic quantum devices called Gaussian Boson Samplers can be programmed to predict molecular docking configurations.
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation

Brian Thompson, Jeremy Gwinnup, Huda Khayrallah, Kevin Duh, Philipp Koehn. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Unsupervised feature selection via latent representation learning and manifold regularization

With the rapid development of multimedia technology, massive unlabelled data with high dimensionality need to be processed. As a means of dimensionality reduction, unsupervised feature selection has been widely recognized as an important and challenging pre-step for many machine learning and data mining tasks. Traditional unsupervised feature selection algorithms usually assume that the data instances are identically distributed and there is no dependency between them. However, the data instances are not only associated with high dimensional features but also inherently interconnected with each other. Furthermore, the inevitable noises mixed in data could degenerate the performances of previous methods which perform feature selection in original data space. Without label information, the connection information between data instances can be exploited and could help select relevant features. In this work, we propose a robust unsupervised feature selection method which embeds the latent representation learning into feature selection. Instead of measuring the feature importances in original data space, the feature selection is carried out in the learned latent representation space which is more robust to noises. The latent representation is modelled by non-negative matrix factorization of the affinity matrix which explicitly reflects the relationships of data instances. Meanwhile, the local manifold structure of original data space is preserved by a graph based manifold regularization term in the transformed feature space. An efficient alternating algorithm is developed to optimize the proposed model. Experimental results on eight benchmark datasets demonstrate the effectiveness of the proposed method.
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation

Social biases present in data are often directly reflected in the predictions of models trained on that data. We analyze gender bias in dialogue data, and examine how this bias is not only replicated, but is also amplified in subsequent generative chit-chat dialogue models. We measure gender bias in six existing dialogue datasets before selecting the most biased one, the multi-player text-based fantasy adventure dataset LIGHT, as a testbed for bias mitigation techniques. We consider three techniques to mitigate gender bias: counterfactual data augmentation, targeted data collection, and bias controlled training. We show that our proposed techniques mitigate gender bias by balancing the genderedness of generated dialogue utterances, and find that they are particularly effective in combination. We evaluate model performance with a variety of quantitative methods—including the quantity of gendered words, a dialogue safety classifier, and human assessments—all of which show that our models generate less gendered, but equally engaging chit-chat responses.
A Capsule Network for Recommendation and Explaining What You Like and Dislike

User reviews contain rich semantics towards the preference of users to features of items. Recently, many deep learning based solutions have been proposed by exploiting reviews for recommendation. The attention mechanism is mainly adopted in these works to identify words or aspects that are important for rating prediction. However, it is still hard to understand whether a user likes or dislikes an aspect of an item according to what viewpoint the user holds and to what extent, without examining the review details. Here, we consider a pair of a viewpoint held by a user and an aspect of an item as a logic unit. Reasoning a rating behavior by discovering the informative logic units from the reviews and resolving their corresponding sentiments could enable a better rating prediction with explanation.
Reinforced Negative Sampling for Recommendation with Exposure Data

In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we improve the negative sampler by integrating the exposure data. We propose to generate high-quality negative instances by adversarial training to favour the difﬁcult instances, and by optimizing additional objective to favour the real negatives in exposure data. However, this idea is non-trivial to implement since the distribution of exposure data is latent and the item space is discrete. To this end, we design a novel RNS method (short for Reinforced Negative Sampler) that generates exposure-alike negative instances through feature matching technique instead of directly choosing from exposure data. Optimized under the reinforcement learning framework, RNS is able to integrate user preference signals in exposure data and hard negatives. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our RNS method. Our implementation is available at: https://github. com/dingjingtao/ReinforceNS.
Your classifier is secretly an energy based model and you should treat it like one

We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x, y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures may be used and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, and out-of-distribution detection while also enabling our models to generate samples rivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and present an approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-art in both generative and discriminative learning within one hybrid model.
Two-photon interference: the Hong–Ou–Mandel effect

Abstract Nearly 30 years ago, two-photon interference was observed, marking the beginning of a new quantum era. Indeed, two-photon interference has no classical analogue, giving it a distinct advantage for a range of applications. The peculiarities of quantum physics may now be used to our advantage to outperform classical computations, securely communicate information, simulate highly complex physical systems and increase the sensitivity of precise measurements. This separation from classical to quantum physics has motivated physicists to study two-particle interference for both fermionic and bosonic quantum objects. So far, two-particle interference has been observed with massive particles, among others, such as electrons and atoms, in addition to plasmons, demonstrating the extent of this effect to larger and more complex quantum systems. A wide array of novel applications to this quantum effect is to be expected in the future. This review will thus cover the progress and applications of two-photon (two-particle) interference over the last three decades.
A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models

With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and flexible sense – we demand to attack various kinds of graph embedding model with black-box driven. To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter. As such, a generalized adversarial attacker: GF-Attack is constructed by the graph filter and feature matrix. Instead of accessing any knowledge of the target classifiers used in graph embedding, GF-Attack performs the attack only on the graph filter in a black-box attack fashion. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experimental results validate the effectiveness of our attacker on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different graph embedding models.
Blockchain technology in the supply chain: An integrated theoretical perspective of organizational adoption

Blockchain technology has been growing in importance and acceptability over the past few years. Yet, there is limited empirical research on the organizational and technology specific factors that play a critical role in driving its adoption in the supply chain. The purpose of this paper is to develop a comprehensive framework for blockchain adoption in the supply chain by identifying the enablers and empirically evaluating their interdependencies and impact on adoption. 20 enablers of blockchain adoption in the supply chain are identified using an extensive literature review and theoretical lenses from the Diffusion of Innovation (DOI) theory and the business technology adoption model developed by Iacovou, Benbassat and Dexter (1995). In the confirmatory phase, we employ the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to extract logic from data collected from 37 French experts about the impact of the enablers and their interdependencies. Our paper extends the multi-theoretic empirical studies to blockchain technology and identifies the enablers of blockchain adoption from technological, organizational, supply chain and external environment perspectives. Regarding the importance of the categories of enablers, we find that the relative advantage of the technology and the external pressure are the most prominent categories of enablers that impact blockchain adoption in the supply chain. Our analysis also shows the important causal role on adoption of the potential of blockchain to reduce transaction cost, the consumer interest in traceability data and the establishment of a regulatory framework for blockchain usage.