Reviewerly/exHarmony huggingface dataset:
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems

Graph diffusion problems such as the propagation of rumors, computer viruses, or smart grid failures are ubiquitous and societal. Hence it is usually crucial to identify diffusion sources according to the current graph diffusion observations. Despite its tremendous necessity and significance in practice, source localization, as the inverse problem of graph diffusion, is extremely challenging as it is ill-posed: different sources may lead to the same graph diffusion patterns. Different from most traditional source localization methods, this paper focuses on a probabilistic manner to account for the uncertainty of different candidate sources. Such endeavors require to overcome significant challenges along the way including: 1) the uncertainty in graph diffusion source localization is hard to be quantified; 2) the complex patterns of the graph diffusion sources are difficult to be probabilistically characterized; 3) the generalization under any underlying diffusion patterns is hard to be imposed. To solve the above challenges, this paper presents a generic framework: Source Localization Variational AutoEncoder (SL-VAE) for locating the diffusion sources under arbitrary diffusion patterns. Particularly, we propose a probabilistic model that leverages the forward diffusion estimation model along with deep generative models to approximate the diffusion source distribution for quantifying the uncertainty. SL-VAE further utilizes prior knowledge of the source-observation pairs to characterize the complex patterns of diffusion sources by a learned generative prior. Lastly, a unified objective that integrates the forward diffusion estimation model is derived to enforce the model to generalize under arbitrary diffusion patterns. Extensive experiments are conducted on $7$ real-world datasets to demonstrate the superiority of SL-VAE in reconstructing the diffusion sources by excelling the state-of-the-arts on average 20% in AUC score. The code and data are available at: https://github.com/triplej0079/SLVAE.
Automatic depth optimization for a quantum approximate optimization algorithm

Quantum approximate optimization algorithm (QAOA) is a hybrid algorithm whose control parameters are classically optimized. In addition to the variational parameters, the right choice of hyperparameter is crucial for improving the performance of any optimization model. Control depth, or the number of variational parameters, is considered as the most important hyperparameter for QAOA. In this paper we investigate the control depth selection with an automatic algorithm based on proximal gradient descent. The performances of the automatic algorithm are demonstrated on seven-node and ten-node max-cut problems, which show that the control depth can be significantly reduced during the iteration whereas achieving an sufficient level of optimization accuracy. With theoretical convergence guarantee, the proposed algorithm can be used as an efficient tool for choosing the appropriate control depth as a replacement of random search or empirical rules. Moreover, the reduction of control depth will induce a significant reduction in the number of quantum gates in circuit, which improves the applicability of QAOA on noisy intermediate-scale quantum devices.
Solving Quaternion Linear System Based on Semi-Tensor Product of Quaternion Matrices

In this paper, we use semi-tensor product of quaternion matrices, L-representation of quaternion matrices, and GH-representation of special quaternion matrices such as quaternion (anti)-centrosymmetric matrices to solve the special solutions of quaternion matrix equation. Based on semi-tensor product of quaternion matrices and the structure matrix of the multiplication of quaternion, we propose the vector representation operation conclusion of quaternion matrices, and study the different matrix representations of quaternion matrices. Then the problem of the quaternion matrix equation is transformed into the corresponding problem in the real number fields by using vector representation and L-representation of quaternion matrices, combined with the special structure of (anti)-centrosymmetric matrices, the independent elements are extracted by GH-representation method, so as to reduce the number of variables to be calculated and improve the calculation accuracy. Finally, the effectiveness of the method is verified by numerical examples, and the time comparison with the two existing algorithms is carried out. The algorithm in this paper is also applied in a centrosymmetric color digital image restoration model.
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation

The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving. Deep learning-based instance segmentation enables real-time object identification from the radar detection points. In the conventional training process, accurate annotation is the key. However, high-quality annotations of radar detection points are challenging to achieve due to their ambiguity and sparsity. To address this issue, we propose a contrastive learning approach for implementing radar detection points-based instance segmentation. We define the positive and negative samples according to the ground-truth label, apply the contrastive loss to train the model first, and then perform fine-tuning for the following downstream task. In addition, these two steps can be merged into one, and pseudo labels can be generated for the unlabeled data to improve the performance further. Thus, there are four different training settings for our method. Experiments show that when the ground-truth information is only available for a small proportion of the training data, our method still achieves a comparable performance to the approach trained in a supervised manner with 100% ground-truth information.
A Heteromorphous Deep CNN Framework for Medical Image Segmentation Using Local Binary Pattern

Estimating mitotic nuclei in breast cancer samples can aid in determining the tumor's aggressiveness and grading system.Because of their strong resemblance to non-mitotic nuclei and heteromorphic form, automated evaluation of mitotic nuclei is difficult.This study presents the BreastUNet, a new heteromorphous Deep Convolutional Neural Network (CNN) with feature grafting approach for analysing mitotic nuclei in breast histopathology images.In the first stage, the proposed method identifies probable mitotic patches in histopathological imaging regions, and in the second stage, the proposed model classifies these patches into mitotic and non-mitotic nuclei.For the building of a heteromorphous deep CNN, four distinct deep CNNs are developed and used as the basis CNN model.Deep CNNs with various architectural designs capture the structural, textural, and morphological aspects of mitotic nuclei.The performance of the proposed BreastUNet model is compared to those of state-of-the-art CNNs.The proposed model looks to be superior on the test set, with an F1 score of 0.95, Sensitivity and Specificity is 0.95 and area under the precision curve of 0.95.The recommended hybrid high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist's aid tool.
Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model

A user-generated text on social media enables health workers to keep track of information, identify possible outbreaks, forecast disease trends, monitor emergency cases, and ascertain disease awareness and response to official health correspondence.This exchange of health information on social media has been regarded as an attempt to enhance public health surveillance (PHS).Despite its potential, the technology is still in its early stages and is not ready for widespread application.Advancements in pretrained language models (PLMs) have facilitated the development of several domainspecific PLMs and a variety of downstream applications.However, there are no PLMs for social media tasks involving PHS.We present and release PHS-BERT, a transformer-based PLM, to identify tasks related to public health surveillance on social media.We compared and benchmarked the performance of PHS-BERT on 25 datasets from different social medial platforms related to 7 different PHS tasks.Compared with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT achieved state-ofthe-art performance on all 25 tested datasets, showing that our PLM is robust and generalizable in the common PHS tasks.By making PHS-BERT available 1 , we aim to facilitate the community to reduce the computational cost and introduce new baselines for future works across various PHS-related tasks.
Open or Sneaky? Fast or Slow? Light or Heavy?: Investigating Security Releases of Open Source Packages

Vulnerabilities in open source packages can be a security risk for the downstream client projects. When a new vulnerability is discovered, a package should quickly release a fix in a new version, referred to as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">security release</i> in this study. The security release should be well-documented and require minimal migration effort to facilitate fast adoption by the clients. However, to what extent the open source packages follow these recommendations is not known. In this paper, we study (1) the time lag between fix and release; (2) how security fixes are documented in the release notes; (3) code change characteristics (size and semantic versioning) of the release; and (4) the time lag between the release and an advisory publication for security releases over a dataset of 4,377 security advisories across seven package ecosystems. We find that the median security release becomes available within 4 days of the corresponding fix and contains 131 lines of code (LOC) change. However, one-fourth of the releases in our data set still came at least 20 days after the fix was made.Further, we find that 61.5% of the security releases come with a release note that documents the corresponding security fix. Still, Snyk and NVD, two popular databases, take a median of 17 days (from the release) to publish a security advisory, possibly resulting in delayed notifications to the client projects. We also find that security releases may contain breaking change(s) as 13.2% indicated backward incompatibility through semantic versioning, while 6.4% mentioned breaking change(s) in the release notes. Based on our findings, we point out areas for future work, such as private fork for security fixes and standardized practice for announcing security releases.
Revisiting Automatic Evaluation of Extractive Summarization Task: Can We Do Better than ROUGE?

It has been the norm for a long time to evaluate automated summarization tasks using the popular ROUGE metric.Although several studies in the past have highlighted the limitations of ROUGE, researchers have struggled to reach a consensus on a better alternative until today.One major limitation of the traditional ROUGE metric is the lack of semantic understanding (relies on direct overlap of n-grams).In this paper, we exclusively focus on the extractive summarization task and propose a semantic-aware nCG (normalized cumulative gain)-based evaluation metric (called Sem-nCG) for evaluating this task.One fundamental contribution of the paper is that it demonstrates how we can generate more reliable semantic-aware ground truths for evaluating extractive summarization tasks without any additional human intervention.To the best of our knowledge, this work is the first of its kind.We have conducted extensive experiments with this new metric using the widely used CNN/DailyMail dataset.Experimental results show that the new Sem-nCG metric is indeed semantic-aware, shows higher correlation with human judgement (more reliable) and yields a large number of disagreements with the original ROUGE metric (suggesting that ROUGE often leads to inaccurate conclusions also verified by humans).
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval

Training dense passage representations via contrastive learning has been shown effective for Open-Domain Passage Retrieval (ODPR).Existing studies focus on further optimizing by improving negative sampling strategy or extra pretraining.However, these studies keep unknown in capturing passage with internal representation conflicts from improper modeling granularity.Specifically, under our observation that a passage can be organized by multiple semantically different sentences, modeling such a passage as a unified dense vector is not optimal.This work thus presents a refined model on the basis of a smaller granularity, contextual sentences, to alleviate the concerned conflicts.In detail, we introduce an in-passage negative sampling strategy to encourage a diverse generation of sentence representations within the same passage.Experiments on three benchmark datasets verify the efficacy of our method, especially on datasets where conflicts are severe.Extensive experiments further present good transferability of our method across datasets.
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations

Recently, various response generation models for two-party conversations have achieved impressive improvements, but less effort has been paid to multi-party conversations (MPCs) which are more practical and complicated.Compared with a two-party conversation where a dialogue context is a sequence of utterances, building a response generation model for MPCs is more challenging, since there exist complicated context structures and the generated responses heavily rely on both interlocutors (i.e., speaker and addressee) and history utterances.To address these challenges, we present HeterMPC, a heterogeneous graph-based neural network for response generation in MPCs which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.Besides, we also design six types of meta relations with node-edge-typedependent parameters to characterize the heterogeneous interactions within the graph.Through multi-hop updating, HeterMPC can adequately utilize the structural knowledge of conversations for response generation.Experimental results on the Ubuntu Internet Relay Chat (IRC) channel benchmark show that HeterMPC outperforms various baseline models for response generation in MPCs.
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots

We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. However, in robotics applications, it is often the case that (potentially very few) examples of anomalies are available. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model, by minimizing, jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We perform quantitative experiments on a novel dataset (which we publish as supplementary material) designed for anomaly detection in an indoor patrolling scenario. On a disjoint test set, our approach outperforms alternatives and shows that exposing even a small number of anomalous frames yields significant performance improvements.
Heralded Multiplexed High-Efficiency Cascaded Source of Dual-Rail Entangled Photon Pairs Using Spontaneous Parametric Down-Conversion

Deterministic sources of high-fidelity entangled qubit pairs encoded in the dual-rail photonic basis, i.e., presence of a single photon in one of two orthogonal modes, are a key enabling technology of many applications of quantum information processing, including high-rate high-fidelity quantum communications over long distances. The most popular and mature sources of such photonic entanglement, e.g., those that leverage spontaneous parametric down-conversion (SPDC) or spontaneous four-wave mixing (sFWM), generate an entangled (so-called, continuous-variable) quantum state that contains contributions from high-order photon terms that lie outside the span of the dual-rail basis, which is detrimental to most applications. One often uses low pump power to mitigate the effects of those high-order terms. However that reduces the pair generation rate, and the source becomes inherently probabilistic. We investigate a cascaded source that performs a linear-optical entanglement swap between two SPDC sources, to generate a heralded photonic entangled state that has a higher fidelity (to the ideal Bell state) compared to a free-running SPDC source. Further, with the Bell swap providing a heralding trigger, we show how to build a multiplexed source, which despite reasonable switching losses and detector loss and noise, yields a Fidelity versus Success Probability trade-off of a high-efficiency source of high-fidelity dual-rail photonic entanglement. We find however that there is a threshold of $1.5$ dB of loss per switch, beyond which multiplexing hurts the Fidelity versus Success Probability trade-off.
Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack

Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we observe that existing methods suffer from catastrophic performance degradation. We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels. To this end, we propose a novel hard-label model stealing method termed black-box dissector, which consists of two erasing-based modules. One is a CAM-driven erasing strategy that is designed to increase the information capacity hidden in hard labels from the victim model. The other is a random-erasing-based self-knowledge distillation module that utilizes soft labels from the substitute model to mitigate overfitting. Extensive experiments on four widely-used datasets consistently demonstrate that our method outperforms state-of-the-art methods, with an improvement of at most $$8.27\%$$ . We also validate the effectiveness and practical potential of our method on real-world APIs and defense methods. Furthermore, our method promotes other related tasks, i.e., transfer adversarial attacks.
Efficient approximations of the multi-sensor labelled multi-Bernoulli filter

In this paper, we propose two efficient, approximate formulations of the multi-sensor labelled multi-Bernoulli (LMB) filter, which both allow the sensors' measurement updates to be computed in parallel. Our first filter is based on the direct mathematical manipulation of the multi-sensor, multi-object Bayes filter's posterior distribution. Unfortunately, it requires the division of probability distributions and its extension beyond linear Gaussian applications is not obvious. Our second filter is based on geometric average fusion and it approximates the multi-sensor, multi-object Bayes filter's posterior distribution using the geometric average of each sensor's measurement-updated distribution. This filter can be used under non-linear conditions; however, it is not as accurate as our first filter. In both cases, we approximate the LMB filter's measurement update using an existing loopy belief propagation algorithm. Both filters have a constant complexity in the number of sensors, and linear complexity in both number of measurements and objects. This is an improvement on an iterated-corrector LMB (IC-LMB) filter, which has linear complexity in the number of sensors. The proposed filters are of interest when tracking many objects using several sensors, where filter run-time is more important than filter accuracy. We evaluate both filters' performances on simulated data and the results indicate that the filters' loss of accuracy compared to the IC-LMB filter is not significant.
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization

Neural abstractive summarization models are prone to generate summaries which are factually inconsistent with their source documents.Previous work has introduced the task of recognizing such factual inconsistency as a downstream application of natural language inference (NLI).However, state-of-the-art NLI models perform poorly in this context due to their inability to generalize to the target task.In this work, we show that NLI models can be effective for this task when the training data is augmented with high-quality task-oriented examples.We introduce Falsesum, a data generation pipeline leveraging a controllable text generation model to perturb human-annotated summaries, introducing varying types of factual inconsistencies.Unlike previously introduced document-level NLI datasets, our generated dataset contains examples that are diverse and inconsistent yet plausible.We show that models trained on a Falsesum-augmented NLI dataset improve the state-of-the-art performance across four benchmarks for detecting factual inconsistency in summarization. 1
Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation

Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown.To quantify how different attributes of AI-based cancer detection and risk prediction tools affect radiologists' intentions to use AI during screening mammography interpretation.Through qualitative interviews with radiologists, we identified five primary attributes for AI-based breast cancer detection and four for breast cancer risk prediction. We developed a discrete choice experiment based on these attributes and invited 150 US-based radiologists to participate. Each respondent made eight choices for each tool between three alternatives: two hypothetical AI-based tools versus screening without AI. We analyzed samplewide preferences using random parameters logit models and identified subgroups with latent class models.Respondents (n = 66; 44% response rate) were from six diverse practice settings across eight states. Radiologists were more interested in AI for cancer detection when sensitivity and specificity were balanced (94% sensitivity with <25% of examinations marked) and AI markup appeared at the end of the hanging protocol after radiologists complete their independent review. For AI-based risk prediction, radiologists preferred AI models using both mammography images and clinical data. Overall, 46% to 60% intended to adopt any of the AI tools presented in the study; 26% to 33% approached AI enthusiastically but were deterred if the features did not align with their preferences.Although most radiologists want to use AI-based decision support, short-term uptake may be maximized by implementing tools that meet the preferences of dissuadable users.