Reviewerly/exHarmony huggingface dataset:
Distillation of squeezing using an engineered pulsed parametric down-conversion source

Hybrid quantum information processing combines the advantages of discrete and continues variable protocols by realizing protocols consisting of photon counting and homodyne measurements. However, the mode structure of pulsed sources and the properties of the detection schemes often require the use of optical filters in order to combine both detection methods in a common experiment. This limits the efficiency and the overall achievable squeezing of the experiment. In our work, we use photon subtraction to implement the distillation of pulsed squeezed states originating from a genuinely spatially and temporally single-mode parametric down-conversion source in non-linear waveguides. Due to the distillation, we witness an improvement of 0.17 dB from an initial squeezing value of −1.648 ± 0.002 dB, while achieving a purity of 0.58, and confirm the non-Gaussianity of the distilled state via the higher-order cumulants. With this, we demonstrate the source’s suitability for scalable hybrid quantum network applications with pulsed quantum light.
Knowledge Graph Embedding Preserving Soft Logical Regularity

Embedding knowledge graphs (KGs) into continuous vector spaces is currently an active research area. Soft rules, despite their uncertainty, are highly beneficial to KG embedding. However, they have not been studied enough in recent methods. A major challenge here is how to devise a principled framework, which efficiently and effectively integrates such soft logical information into embedding models. This paper proposes a highly scalable and effective method for preserving soft logical regularities by imposing soft rule constraints on relation representations. Specifically, we first represent relations as bilinear forms and map entity representations into a non-negative and bounded space. Then we derive a rule-based regularization that merely enforces relation representations to satisfy constraints introduced by soft rules. The proposed method has the following advantages: 1) it regularizes relations directly with the complexity of rule learning independent of entity set size, improving scalability; 2) it imposes prior logical information upon the structure of the embedding space, and would be beneficial to knowledge reasoning. Evaluation in link prediction on Freebase and DBpedia shows the effectiveness of our approach over many competitive baselines. Code and datasets are available at https://github.com/StudyGroup-lab/SLRE.
Blind Image Deblurring via Local Maximum Difference Prior

Blind image deblurring is a well-known conundrum in the digital image processing field. To get a solid and pleasing deblurred result, reasonable statistical prior of the true image and the blur kernel is required. In this work, a novel and efficient blind image deblurring method which utilizes the Local Maximum Difference Prior ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LMD</i> ) is presented. We find that the maximum value of the sum of the differences between the intensity of one pixel and its surrounding 8 pixels in the local image patch becomes smaller with motion blur. This phenomenon is an intrinsic feature of the motion blur process, we demonstrate it theoretically in this paper. By introducing a linear operator to compute <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LMD</i> and adopting the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1} $ </tex-math></inline-formula> norm constrain to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LMD</i> involved term, an effective optimization scheme which makes use of a half-quadratic splitting strategy is exploited. Experimental results show that the presented method is more robust and outperforms the most advanced deblurring methods on both composite images and ground-truth scenes. Besides, this algorithm is more general because it does not require any heuristic edge selection steps or need too many extreme value pixels in the input image.
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis

Cross-domain sentiment analysis is currently a hot topic in both the research and industrial areas.One of the most popular framework for the task is domain-invariant representation learning (DIRL), which aims to learn a distribution-invariant feature representation across domains.However, in this work, we find out that applying DIRL may degrade the cross-domain performance when the label distribution P(Y) changes across domains.To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework.We show that it is easy to transfer existing models from the DIRL framework to the WDIRL framework.Empirical studies on extensive cross-domain sentiment analysis tasks verified our statements and showed the effectiveness of our proposed solution.
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems

Open-domain dialogue systems aim to generate relevant, informative and engaging responses. Seq2seq neural response generation approaches do not have explicit mechanisms to control the content or style of the generated response, and frequently result in uninformative utterances. In this paper, we propose using a dialogue policy to plan the content and style of target responses in the form of an action plan, which includes knowledge sentences related to the dialogue context, targeted dialogue acts, topic information, etc. The attributes within the action plan are obtained by automatically annotating the publicly released Topical-Chat dataset. We condition neural response generators on the action plan which is then realized as target utterances at the turn and sentence levels. We also investigate different dialogue policy models to predict an action plan given the dialogue context. Through automated and human evaluation, we measure the appropriateness of the generated responses and check if the generation models indeed learn to realize the given action plans. We demonstrate that a basic dialogue policy that operates at the sentence level generates better responses in comparison to turn level generation as well as baseline models with no action plan. Additionally the basic dialogue policy has the added effect of controllability.
A dynamic network traffic classifier using supervised ML for a Docker-based SDN network

With the rapid technological growth in the last decades, the number of devices and users has drastically increased. Software-defined networking (SDN) with machine learning (ML) has become an emerging solution for network scheduling, quality of service (QoS), resource allocations, and security. This paper focuses on the implementation of a network traffic classifier using a novel Docker-based SDN network. ML offers good performance to real-time traffic solutions without depending on well-known TCP or UDP port numbers, IP addresses, or encrypted payloads. In this paper, using three ML techniques, we first classify network flows with 3, 5, and 7 parameters giving up to 97.14% accuracy. Additionally, we present a new performance accelerator algorithm (PAA), which incorporates these three ML classifiers and accelerates the overall performance significantly. We then propose a dynamic network classifier (DNC) generated from PAA over a novel Docker-based SDN network. Finally, we propose a new controller algorithm for Ryu platforms, which integrates the DNC and classifies both TCP and UDP flows in real-time. Based on the evaluations, an improvement in latency performance has been demonstrated, where analysing a packet, controller processing time takes on an average of 10 µs. This study will certainly serve to further research on optimising SDN and QoS.
Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features

For data sets with similar features, for example highly correlated features, most existing stability measures behave in an undesired way: They consider features that are almost identical but have different identifiers as different features. Existing adjusted stability measures, that is, stability measures that take into account the similarities between features, have major theoretical drawbacks. We introduce new adjusted stability measures that overcome these drawbacks. We compare them to each other and to existing stability measures based on both artificial and real sets of selected features. Based on the results, we suggest using one new stability measure that considers highly similar features as exchangeable.
Opinion Mining on Integrated Social Networks and E-Commerce Blog

The escalation of online shopping trends, social networks, and blogs makes shopping customers to know premier and quality product from customer reviews posted on social networks. Also, the business enterprise can perceive customers like opinion, demand, and expectation in real time. Customers are expressing their opinion, feedback, and emotions of online products through social networks and blogs. The problem focused here is, from customer point of view, identifying trendy and quality product for online purchasing, enterprise seeking customer’s opinion, brand reputation based on the reviews in social networks and blogs. The main problem is, for a single product, customer’s opinions are vast and vary in different social networks (Twitter, Facebook) and blogs (Amazon, Flipkart). This problem is addressed in this work for extracting useful hidden information by integrating the reviews of social networks and reviews in blogs. The proposed work on opinion mining has developed a new technique with a set of rules for combining social network reviews (Twitter reviews, Facebook reviews) and blog reviews (Amazon reviews). Then the reviews are mined to provide crisp information of reviewer opinion on products to new customers and organization. The new information will assist customer to make a wise decision for buying products. With the extracted information, the enterprises can read the customer's mind to improve their business by offering to customer trending products. The proposed model has achieved more than 80% accuracy and f1-score in sentiment prediction. The result has efficiently proved the performance of the model.
Mining heterogeneous clinical notes by multi-modal latent topic model

Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient’s underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient’s clinical notes as “documents” and the words in the notes as “tokens”. However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients’ topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr .
Machine Learning Models to Improve the Differentiation Between Benign and Malignant Breast Lesions on Ultrasound: A Multicenter External Validation Study

This study aimed to establish and evaluate the usefulness of a simple, practical, and easy-to-promote machine learning model based on ultrasound imaging features for diagnosing breast cancer (BC).Logistic regression, random forest, extra trees, support vector, multilayer perceptron, and XG Boost models were developed. The modeling data set of 1345 cases was from a tertiary class A hospital in China. The external validation data set of 1965 cases were from 3 tertiary class A hospitals and 2 primary hospitals. The area under the receiver operating characteristic curve (AUC) was used as the main evaluation index, and pathological biopsy was used as the gold standard for evaluating each model. Diagnostic capability was also compared with that of clinicians.Among the six models, the logistic model showed superior diagnostic efficiency, with an AUC of 0.771 and 0.906 and Brier scores of 0.181 and 0.165 in the test and validation sets, respectively. The AUCs of the clinician diagnosis and the logistic model were 0.913 and 0.906. Their AUCs in the tertiary class A hospitals were 0.915 and 0.915, respectively, and were 0.894 and 0.873 in primary hospitals, respectively.The externally validated logical model can be used to distinguish between malignant and benign breast lesions in ultrasound images. Compared with clinician diagnosis, the logistic model has better diagnostic efficiency, making it potentially useful to assist in screening, particularly in lower level medical institutions.http://www.clinicaltrials.gov. ClinicalTrials.gov ID: NCT03080623.
Deriving invariant checkers for critical infrastructure using axiomatic design principles

Abstract Cyber-physical systems (CPSs) in critical infrastructure face serious threats of attack, motivating research into a wide variety of defence mechanisms such as those that monitor for violations of invariants , i.e. logical properties over sensor and actuator states that should always be true. Many approaches for identifying invariants attempt to do so automatically, typically using data logs, but these can miss valid system properties if relevant behaviours are not well-represented in the data. Furthermore, as the CPS is already built, resolving any design flaws or weak points identified through this process is costly. In this paper, we propose a systematic method for deriving invariants from an analysis of a CPS design , based on principles of the axiomatic design methodology from design science. Our method iteratively decomposes a high-level CPS design to identify sets of dependent design parameters (i.e. sensors and actuators), allowing for invariants and invariant checkers to be derived in parallel to the implementation of the system. We apply our method to the designs of two CPS testbeds, SWaT and WADI, deriving a suite of invariant checkers that are able to detect a variety of single- and multi-stage attacks without any false positives. Finally, we reflect on the strengths and weaknesses of our approach, how it can be complemented by other defence mechanisms, and how it could help engineers to identify and resolve weak points in a design before the controllers of a CPS are implemented.
Dynamic crow search algorithm based on adaptive parameters for large-scale global optimization

Despite the good performance of Crow Search Algorithm (CSA) in dealing with global optimization problems, unfortunately it is not the case with respect to the convergence performance. Conventional CSA exploration and exploitation are strongly dependent on the proper setting of awareness probability (AP) and flight length (FL) parameters. In each optimization problem, AP and FL parameters are set in an ad hoc manner and their values do not change over the optimization process. To this date, there is no analytical approach to adjust their best values. This presents a major drawback to apply CSA in complex practical problems. Hence, the conventional CSA is used only for limited problems due to fact that CSA with fixed AP and FL is frequently trapped into local optimum. In this present paper, an enhanced version of CSA called dynamic crow search algorithm (DCSA) is proposed to overcome the drawbacks of the conventional CSA. In the proposed DCSA, two modifications of the basic algorithm are made. The first modification concerns the continuous adjustment of the CSA parameters leading to a DCSA, where AP will be adjusting linearly over optimization process and FL will be adjusting according to the generalized Pareto probability density function. This dynamic adjustment will provide more global search capability as well as more exploitation of the pre-final solutions. The second modification concerns the improvement of CSA’s swarm diversity in the search process. This will lead to a high convergence accuracy, and fast convergence rate. The effectiveness of the proposed algorithm is validated using a set of experimental series using 13 complex benchmark functions. Experimental results highly proved the modified algorithm effectiveness compared to the basic algorithm in terms of convergence rate, global search capability and final solutions. In addition, a comparison with conventional and recent similar algorithms revealed that DCSA gives superior results in terms of performance and efficiency.
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification

Hierarchical Text Classification (HTC) is a challenging task that categorizes a textual description within a taxonomic hierarchy.Most of the existing methods focus on modeling the text.Recently, researchers attempt to model the class representations with some resources (e.g., external dictionaries).However, the concept shared among classes which is a kind of domain-specific and fine-grained information has been ignored in previous work.In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification.Experimental results on two widely used datasets prove that the proposed model outperforms several state-of-theart methods.We release our complementary resources (concepts and definitions of classes) for these two datasets to benefit the research on HTC.
SA-BNN: State-Aware Binary Neural Network

Binary Neural Networks (BNNs) have received significant attention due to the memory and computation efficiency recently. However, the considerable accuracy gap between BNNs and their full-precision counterparts hinders BNNs to be deployed to resource-constrained platforms. One of the main reasons for the performance gap can be attributed to the frequent weight flip, which is caused by the misleading weight update in BNNs. To address this issue, we propose a state-aware binary neural network (SA-BNN) equipped with the well designed state-aware gradient. Our SA-BNN is inspired by the observation that the frequent weight flip is more likely to occur, when the gradient magnitude for all quantization states {-1,1} is identical. Accordingly, we propose to employ independent gradient coefficients for different states when updating the weights. Furthermore, we also analyze the effectiveness of the state-aware gradient on suppressing the frequent weight flip problem. Experiments on ImageNet show that the proposed SA-BNN outperforms the current state-of-the-arts (e.g., Bi-Real Net) by more than 3% when using a ResNet architecture. Specifically, we achieve 61.7%, 65.5% and 68.7% Top-1 accuracy with ResNet-18, ResNet-34 and ResNet-50 on ImageNet, respectively.
Retrieval Enhanced Model for Commonsense Generation

Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts.Its requirement of reasoning over commonsense knowledge and compositional generalization ability even puzzles strong pre-trained language generation models.We propose a novel framework using retrieval methods to enhance both the pre-training and fine-tuning for commonsense generation.We retrieve prototype sentence candidates by concept matching and use them as auxiliary input.For finetuning, we further boost its performance with a trainable sentence retriever.We demonstrate experimentally on the large-scale Common-Gen benchmark that our approach achieves new state-of-the-art results. 1
Conceptualized and Contextualized Gaussian Embedding

Word embedding can represent a word as a point vector or a Gaussian distribution in high-dimensional spaces. Gaussian distribution is innately more expressive than point vector owing to the ability to additionally capture semantic uncertainties of words, and thus can express asymmetric relations among words more naturally (e.g., animal entails cat but not the reverse. However, previous Gaussian embedders neglect inner-word conceptual knowledge and lack tailored Gaussian contextualizer, leading to inferior performance on both intrinsic (context-agnostic) and extrinsic (context-sensitive) tasks. In this paper, we first propose a novel Gaussian embedder which explicitly accounts for inner-word conceptual units (sememes) to represent word semantics more precisely; during learning, we propose Gaussian Distribution Attention over Gaussian representations to adaptively aggregate multiple sememe distributions into a word distribution, which guarantees the Gaussian linear combination property. Additionally, we propose a Gaussian contextualizer to utilize outer-word contexts in a sentence, producing contextualized Gaussian representations for context-sensitive tasks. Extensive experiments on intrinsic and extrinsic tasks demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance with near 5.00% relative improvement.
Impossibility of Quantum Virtual Black-Box Obfuscation of Classical Circuits

Virtual black-box obfuscation is a strong cryptographic primitive: it encrypts a circuit while maintaining its full input/output functionality. A remarkable result by Barak et al. (Crypto 2001) shows that a general obfuscator that obfuscates classical circuits into classical circuits cannot exist. A promising direction that circumvents this impossibility result is to obfuscate classical circuits into quantum states, which would potentially be better capable of hiding information about the obfuscated circuit. We show that, under the assumption that Learning With Errors (LWE) is hard for quantum computers, this quantum variant of virtual black-box obfuscation of classical circuits is generally impossible. On the way, we show that under the presence of dependent classical auxiliary input, even the small class of classical point functions cannot be quantum virtual black-box obfuscated.
Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems

Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response.However, current dialogue models with the seq2seq framework lack the ability to effectively manage concept transitions and can hardly introduce multiple concepts to responses in a sequential decoding manner.To facilitate a controllable and coherent dialogue, in this work, we devise a conceptguided non-autoregressive model (CG-nAR) for open-domain dialogue generation.The proposed model comprises a multi-concept planning module that learns to identify multiple associated concepts from a concept graph and a customized Insertion Transformer that performs concept-guided non-autoregressive generation to complete a response.The experimental results on two public datasets show that CG-nAR can produce diverse and coherent responses, outperforming state-of-the-art baselines in both automatic and human evaluations with substantially faster inference speed.