Reviewerly/exHarmony huggingface dataset:
Classical and quantum speed limits

The new bound on quantum speed limit (in terms of relative purity) is derived by applying the original Mandelstam-Tamm one to the evolution in the space of Hilbert-Schmidt operators acting in the initial space of states. It is shown that it provides the quantum counterpart of the classical speed limit derived in Phys. Rev. Lett. 120 (2018), 070402 and the $\hbar\rightarrow 0$ limit of the former yields the latter. The existence of classical limit is related to the degree of mixing of the quantum state.
Could you give me a hint ? Generating inference graphs for defeasible reasoning

Defeasible reasoning is a mode of reasoning where conclusions can be overturned by taking into account new evidence.A commonly used method in cognitive science and logic literature is to handcraft argumentation supporting inference graphs.While humans find inference graphs very useful for reasoning, constructing them at scale is difficult.In this paper, we automatically generate such inference graphs through transfer learning from a related NLP task that shares the kind of reasoning that inference graphs support.Through automated metrics and human evaluation, we find that our method generates meaningful graphs for the defeasible inference task.Human accuracy on this task improves by 20% by consulting the generated graphs.Our findings open up exciting new research avenues for cases where machine reasoning can help human reasoning.
Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation

Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training weights to different tokens, in order to alleviate the token imbalance problem.However, previous approaches only use static word frequency information in the target language without considering the source language, which is insufficient for bilingual tasks like machine translation.In this paper, we propose a novel bilingual mutual information (BMI) based adaptive objective, which measures the learning difficulty for each target token from the perspective of bilingualism, and assigns an adaptive weight accordingly to improve token-level adaptive training.This method assigns larger training weights to tokens with higher BMI, so that easy tokens are updated with coarse granularity while difficult tokens are updated with fine granularity.Experimental results on WMT14 English-to-German and WMT19 Chinese-to-English demonstrate the superiority of our approach compared with the Transformer baseline and previous token-level adaptive training approaches.Further analyses confirm that our method can improve the lexical diversity.
Improving a neural network model by explanation-guided training for glioma classification based on MRI data

Abstract In recent years, artificial intelligence systems have come to the forefront. These systems, mostly based on deep learning, achieve excellent results in areas such as image processing, natural language processing and speech recognition. Despite the statistically high accuracy of deep learning models, their output is often based on ”black box” decisions. Thus, interpretability methods (Reyes et al. in Radiol Artif Intell 2(3):e190043, 2020) have become a popular way to gain insight into the decision-making process of deep learning models (Miller in Artif Intell 267:1–38, 2019). Explanation of deep learning models is desirable in the medical domain since experts have to justify their judgments to the patients. In this work, we proposed a method for explanation-guided training that uses a layer-wise relevance propagation technique to force the model to focus only on the relevant part of the image. We experimentally verified our method on a convolutional neural network model for low-grade and high-grade glioma classification problems. Our experiments produced promising results in the way where we use interpretation techniques in the training process.
Steganography-based voice hiding in medical images of COVID-19 patients

A novel image steganography technique in order to hide the ciphered voice data has been suggested in this work. The doctor's voice comments belonging to a coronavirus disease 2019 (COVID-19) patient are hidden in a medical image in order to protect the patient information. The introduced steganography technique is based on chaos theory. Firstly, the voice comments of the doctor are converted to an image and secondly, they are ciphered utilizing the suggested encryption algorithm based on a chaotic system. Then, they are embedded into the cover medical image. A lung angiography dual-energy computed tomography (CT) scan of a COVID-19 patient is used as a cover object. Numerical and security analyses of steganography method have been performed in MATLAB environment. The similarity metrics are calculated for R, G, B components of cover image and stego image as visual quality analysis metrics to examine the performance of the introduced steganography procedure. For a 512 × 512 pixel cover image, SSIM values are obtained as 0.8337, 0.7926, and 0.9273 for R, G, B components, respectively. Moreover, security analyses which are differential attack, histogram, information entropy, correlation of neighboring pixels and the initial condition sensitivity are carried out. The information entropy is calculated as 7.9993 bits utilizing the suggested steganography scheme. The mean value of the ten UACI and NPCR values are obtained as 33.5688% and 99.8069%, respectively. The results of security analysis have revealed that the presented steganography procedure is able to resist statistical attacks and the chaotic system-based steganography scheme shows the characteristics of the sensitive dependence on the initial condition and the secret key. The proposed steganography method which is based on a chaotic system has superior performance in terms of being robust against differential attack and hiding encrypted voice comments of the doctor. Moreover, the introduced algorithm is also resistant against exhaustive, known plaintext, and chosen plaintext attacks.
CRD-CGAN: category-consistent and relativistic constraints for diverse text-to-image generation

Generating photo-realistic images from a text description is a challenging problem in computer vision. Previous works have shown promising performance to generate synthetic images conditional on text by Generative Adversarial Networks (GANs). In this paper, we focus on the category-consistent and relativistic diverse constraints to optimize the diversity of synthetic images. Based on those constraints, a category-consistent and relativistic diverse conditional GAN (CRD-CGAN) is proposed to synthesize K photo-realistic images simultaneously. We use the attention loss and diversity loss to improve the sensitivity of the GAN to word attention and noises. Then, we employ the relativistic conditional loss to estimate the probability of relatively real or fake for synthetic images, which can improve the performance of basic conditional loss. Finally, we introduce a category-consistent loss to alleviate the over-category issues between K synthetic images. We evaluate our approach using the Caltech-UCSD Birds-200-2011, Oxford 102 flower and MS COCO 2014 datasets, and the extensive experiments demonstrate superiority of the proposed method in comparison with state-of-the-art methods in terms of photorealistic and diversity of the generated synthetic images.
PRGFlow: Unified SWAP‐aware deep global optical flow for aerial robot navigation

Global optical flow estimation is the foundation stone for obtaining odometry which is used to enable aerial robot navigation. However, such a method has to be of low latency and high robustness whilst also respecting the size, weight, area and power (SWAP) constraints of the robot. A combination of cameras coupled with inertial measurement units (IMUs) has proven to be the best combination in order to obtain such low latency odometry on resource-constrained aerial robots. Recently, deep learning approaches for visual inertial fusion have gained momentum due to their high accuracy and robustness. However, an equally noteworthy benefit for robotics of these techniques are their inherent scalability (adaptation to different sized aerial robots) and unification (same method works on different sized aerial robots). To this end, we present a deep learning approach called PRGFlow for obtaining global optical flow and then loosely fuse it with an IMU for full 6-DoF (Degrees of Freedom) relative pose estimation (which is then integrated to obtain odometry). The network is evaluated on the MSCOCO dataset and the dead-reckoned odometry on multiple real-flight trajectories without any fine-tuning or re-training. A detailed benchmark comparing different network architectures and loss functions to enable scalability is also presented. It is shown that the method outperforms classical feature matching methods by 2 × under noisy data. The supplementary material and code can be found at http://prg.cs.umd.edu/PRGFlow.
Comparison of data augmentation methods for legal document classification

Sorting out the legal documents by their subject matter is an essential and time-consuming task due to the large amount of data. Many machine learning-based text categorization methods exist, which can resolve this problem. However, these algorithms can not perform well if they do not have enough training data for every category. Text augmentation can resolve this problem. Data augmentation is a widely used technique in machine learning applications, especially in computer vision. Textual data has different characteristics than images, so different solutions must be applied when the need for data augmentation arises. However, the type and different characteristics of the textual data or the task itself may reduce the number of methods that could be applied in a certain scenario. This paper focuses on text augmentation methods that could be applied to legal documents when classifying them into specific groups of subject matters.
Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.
A general protocol for distributed quantum gates

Abstract In distributed quantum computation, quantum remote-controlled gates are used frequently and applied on separate nodes or subsystems of a network. One of the universal and well-known controlled gates is the n-qubit controlled-NOT gate, especially Toffoli gate for the case of three qubits, which are frequently used to synthesize quantum circuits. In this paper, we considered a more general case, an n-qubit controlled-U gate, and present a general protocol for implementing these gates remotely with minimum required resources. Then, the proposed method is applied to implement a Toffoli gate in bipartite and tripartite systems. In this method, we considered cases in which a group of qubits belongs to one subsystem of the network. Then, we improved its consumption resources.
Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China

As the builder and maintainer of the city appearance and environment, the city appearance and environmental service industry provides urban residents with a beautiful, livable, and comfortable living environment and is also responsible for providing city appearance and environmental public health services for the economic and social functions carried by the global cities task. Based on this problem, this study has carried out the work of government agencies' public health environmental management and influencing factors based on the Internet of Things and edge computing. This study analyzes the influencing factors of public health environmental management of government agencies based on traditional research and conducts data analysis through the Naive Bayes algorithm. On this basis, the study combines the Internet of Things technology to construct system architecture for public health environmental management. The proposed management system is used to manage government agencies, which meets security, privacy and efficiency, and data security requirements. Experimental results prove that after algorithm optimization and architecture design, the evaluation level of the public health environment is significantly improved.
Neural Architecture Search for Portrait Parsing

This work proposes a neural architecture search (NAS) method for portrait parsing, which is a novel up-level task based on portrait segmentation and face labeling. Recently, NAS has become an effective method in terms of automatic machine learning. However, remarkable achievements have been made only in image classification and natural language processing (NLP) areas. Meanwhile, state-of-the-art portrait segmentation and face labeling approaches are all manually designed, but few models reach a tradeoff between efficiency and performance. Thus, we are extremely interested in improving existing NAS methods for dense-per-pixel prediction tasks on portrait datasets. To achieve that, we resort to a cell-based encoder–decoder architecture with an elaborate design of connectivity structure and searching space. As a result, we achieve state-of-the-art performance on three portrait tasks, including 96.8% MIOU on EG1800 (portrait segmentation), 91.2% overall <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score on HELEN (face labeling), and 95.1% overall <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score on CelebAMask-HQ (portrait parsing) with only 2.29M model parameters. That is, our approach compares favorably with all previous works on portrait datasets. More crucially, we empirically prove that even a fundamental encoder–decoder architecture may reach an outstanding result on the aforementioned tasks with the help of the innovative approach of NAS. To the best of our knowledge, our work is also the first to report the success of applying NAS on these portrait tasks.
DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning

Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes average rewards, which may disregard rare but critical situations and hence lack local robustness; (ii) optimization objectives targeting safety typically yield degenerated reward structures which for DRL to work must be replaced with proxy objectives. Here we introduce methodology that can help to address both deficiencies. We incorporate evaluation stages (ES) into DRL, leveraging recent work on deep statistical model checking (DSMC) which verifies NN policies in MDPs. Our ES apply DSMC at regular intervals to determine state space regions with weak performance. We adapt the subsequent DRL training priorities based on the outcome, (i) focusing DRL on critical situations, and (ii) allowing to foster arbitrary objectives. We run case studies in Racetrack, an abstraction of autonomous driving that requires navigating a map without crashing into a wall. Our results show that DSMC-based ES can significantly improve both (i) and (ii).
ℤ₂ℤ₄-Additive Quasi-Cyclic Codes

We study the codes of the title by the CRT method, that decomposes such codes into constituent codes, which are shorter codes over larger alphabets. Criteria on these constituent codes for self-duality and linear complementary duality of the decomposed codes are derived. The special class of the one-generator codes is given a polynomial representation and exactly enumerated. In particular, we present some illustrative examples of binary optimal linear codes with respect to the Griesmer bound derived from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{2} \mathbb {Z}_{4}$ </tex-math></inline-formula> -additive quasi-cyclic codes.
The limits of multiplexing quantum and classical channels: Case study of a 2.5 GHz discrete variable quantum key distribution system

Network integration of quantum key distribution is crucial for its future widespread deployment due to the high cost of using optical fibers dedicated for the quantum channel, only. We studied the performance of a system running a simplified BB84 protocol at 2.5 GHz repetition rate, operating in the original wavelength band, short O-band, when multiplexed with communication channels in the conventional wavelength band, short C-band. Our system could successfully generate secret keys over a single-mode fiber with a length of 95.5 km and with co-propagating classical signals at a launch power of 8.9 dBm. Further, we discuss the performance of an ideal system under the same conditions, showing the limits of what is possible with a discrete variable system in the O-band. We also considered a short and lossy link with 51 km optical fiber resembling a real link in a metropolitan area network. In this scenario we could exchange a secret key with a launch power up to 16.7 dBm in the classical channels.
COMPUTER CODES OF GEOCHEMICAL MODELING USED TO WATER-ROCK INTERACTION SIMPLE AND COMPLEX SYSTEMS

The numerical modeling of transport and reaction was used for the understanding of the evolution of the diagenetic processes and their importance in the characterization and prediction of oil reservoir quality. Geochemical models are represented by numerical equations based on the physical-chemical properties of minerals. There are many software’s available in the market to simulate systems and geochemical reactions. The codes are divided into three distinct categories: coupled transport of reaction, modeling speciation, and batch mode according to the numerical method. Simple systems have clear connections between inputs and outputs. Complex systems have multiple factors that provide a probability distribution of data inputs that interact in specific functions. The outputs produced as a result are therefore impossible to predict with complete accuracy. Several research groups tried to develop numerical codes for geochemical modeling. The critical factors for the use of these systems are (i) verification of the simulation results with empirical data set and (ii) sensitivity analysis of these results, for the construction of general models which provide a predictive character. This last factor is particularly important as it establishes the qualitative and quantitative impact of each parameter in the simulations. Thus, with a complete numerical model diagenetic, it is possible to perform various simulations modifying one or the other parameter to test the sensitivity in the construction of these different geological scenarios. This set includes mineral composition and texture, the composition of fluids, paragenetic sequence, and burial history. This work brings fundamental concepts related to this topic as well as an analysis of commercial software available.
Entanglement assisted training algorithm for supervised quantum classifiers

We propose a new training algorithm for supervised quantum classifiers. Here, we have harnessed the property of quantum entanglement to build a model that can simultaneously manipulate multiple training samples along with their labels. Subsequently a Bell-inequality based cost function is constructed, that can encode errors from multiple samples, simultaneously, in a way that is not possible by any classical means. We show that upon minimizing this cost function one can achieve successful classification in benchmark datasets. The results presented in this paper are for binary classification problems. Nevertheless, the analysis can be extended to multi-class classification problems as well.
Extracting Prerequisite Relations Among Wikipedia Concepts Using the Clickstream Data

A prerequisite relation describes a basic dependency relation between concepts in education, cognition and other fields. Especially, prerequisite relations among concepts play a very important role in various intelligent education applications, such as concept map extraction, learning object sequencing, reading order list generation. In this paper, we investigate the problem of extracting prerequisite relations among Wikipedia concepts. We take advantage of Wikipedia clickstream data and related concept sets to discover prerequisite relations among Wikipedia concepts. Evaluations on two datasets that include nine domains show that the proposed method can cover most of the concept pairs, and achieves significant improvements (+1.7–31.0% by Accuracy) comparing with existing methods.
NeMo Inverse Text Normalization: From Development to Production

Inverse text normalization (ITN) converts spoken-domain automatic speech recognition (ASR) output into written-domain text to improve the readability of the ASR output. Many state-of-the-art ITN systems use hand-written weighted finite-state transducer(WFST) grammars since this task has extremely low tolerance to unrecoverable errors. We introduce an open-source Python WFST-based library for ITN which enables a seamless path from development to production. We describe the specification of ITN grammar rules for English, but the library can be adapted for other languages. It can also be used for written-to-spoken text normalization. We evaluate the NeMo ITN library using a modified version of the Google Text normalization dataset.