Reviewerly/exHarmony huggingface dataset:
Universal limitations on implementing resourceful unitary evolutions

We derive a trade-off relation between the accuracy of implementing a desired unitary evolution using a restricted set of free unitaries and the size of the assisting system, in terms of the resource generating/losing capacity of the target unitary. In particular, this relation implies that, for any theory equipped with a resource measure satisfying lenient conditions, any resource changing unitary cannot be perfectly implemented by a free unitary applied to a system and an environment if the environment has finite dimensions. Our results are applicable to a wide class of resources including energy, asymmetry, coherence, entanglement, and magic, imposing ultimate limitations inherent in such important physical settings, as well as providing insights into operational restrictions in general resource theories.
Unsupervised Multi-source Domain Adaptation Driven by Deep Adversarial Ensemble Learning

We address the problem of multi-source unsupervised domain adaptation (MS-UDA) for the purpose of visual recognition. As opposed to single source UDA, MS-UDA deals with multiple labeled source domains and a single unlabeled target domain. Notice that the conventional MS-UDA training is based on formalizing independent mappings between the target and the individual source domains without explicitly assessing the need for aligning the source domains among themselves. We argue that such a paradigm invariably overlooks the inherent category-level correlation among the source domains which, on the contrary, is deemed to bring meaningful complementarity in the learned shared feature space. In this regard, we propose a novel approach which simultaneously (i) aligns the source domains at the class-level in a shared feature space, and (ii) maps the target domain data in the same space through an adversarially trained ensemble of source domain classifiers. Experimental results obtained on the Office-31, ImageCLEF-DA, and Office-CalTech dataset validate that our approach achieves a superior accuracy compared to state-of-the-art methods .
SFA: Small Faces Attention Face Detector

In recent year, tremendous strides have been made in face detection thanks to deep learning. However, most published face detectors deteriorate dramatically as the faces become smaller. In this paper, we present the Small Faces Attention (SFA) face detector to better detect faces with small scale. First, we propose a new scale-invariant face detection architecture which pays more attention to small faces, including 4-branch detection architecture and small faces sensitive anchor design. Second, feature maps fusion strategy is applied in SFA by partially combining high-level features into low-level features to further improve the ability of finding hard faces. Third, we use multi-scale training and testing strategy to enhance face detection performance in practice. Comprehensive experiments show that SFA significantly improves face detection performance, especially on small faces. Our real-time SFA face detector can run at 5 FPS on a single GPU as well as maintain high performance. Besides, our final SFA face detector achieves state-of-the-art detection performance on challenging face detection benchmarks, including WIDER FACE and FDDB datasets, with competitive runtime speed. Both our code and models will be available to the research community.
Monitoring ICU Mortality Risk with A Long Short-Term Memory Recurrent Neural Network

In intensive care units (ICU), mortality prediction is a critical factor not only for effective medical intervention but also for allocation of clinical resources. Structured electronic health records (EHR) contain valuable information for assessing mortality risk in ICU patients, but current mortality prediction models usually require laborious human-engineered features. Furthermore, substantial missing data in EHR is a common problem for both the construction and implementation of a prediction model.Inspired by language-related models, we design a new framework for dynamic monitoring of patients’ mortality risk. Our framework uses the bag-of-words representation for all relevant medical events based on most recent history as inputs. By design, it is robust to missing data in EHR and can be easily implemented as an instant scoring system to monitor the medical development of all ICU patients. Specifically, our model uses latent semantic analysis (LSA) to encode the patients’ states into low-dimensional embeddings, which are further fed to long short-term memory networks for mortality risk prediction. Our results show that the deep learning based framework performs better than the existing severity scoring system, SAPS-II. We observe that bidirectional long short-term memory demonstrates superior performance, probably due to the successful capture of both forward and backward temporal dependencies.
Evidence of the entanglement constraint on wave-particle duality using the IBM Q quantum computer

We experimentally verify the link existing between entanglement and the amount of wave-particle duality in a bipartite quantum system with superconducting qubits in the IBM Q quantum computer. We consider both pure and mixed states, and study the influence of state purity on the observation of the complementarity triality relation of Jakob and Bergou [arXiv:quant-ph/0302075; Opt. Commun. 283, 827 (2010)]. This work confirms the quantitative completion of local Bohr's complementarity principle by the nonlocal quantum entanglement typical of a truly bipartite quantum system.
X-BERT: eXtreme Multi-label Text Classification with using Bidirectional Encoder Representations from Transformers

We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input text could be a product description on this http URL and the labels could be product categories. XMC is an important yet challenging problem in the NLP community. Recently, deep pretrained transformer models have achieved state-of-the-art performance on many NLP tasks including sentence classification, albeit with small label sets. However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue. In this paper, we propose X-Transformer, the first scalable approach to fine-tuning deep transformer models for the XMC problem. The proposed method achieves new state-of-the-art results on four XMC benchmark datasets. In particular, on a Wiki dataset with around 0.5 million labels, the prec@1 of X-Transformer is 77.28%, a substantial improvement over state-of-the-art XMC approaches Parabel (linear) and AttentionXML (neural), which achieve 68.70% and 76.95% precision@1, respectively. We further apply X-Transformer to a product2query dataset from Amazon and gained 10.7% relative improvement on prec@1 over Parabel.
Integrating Non-monotonic Logical Reasoning and Inductive Learning With Deep Learning for Explainable Visual Question Answering

State of the art algorithms for many pattern recognition problems rely on data-driven deep network models. Training these models requires a large labeled dataset and considerable computational resources. Also, it is difficult to understand the working of these learned models, limiting their use in some critical applications. Toward addressing these limitations, our architecture draws inspiration from research in cognitive systems, and integrates the principles of commonsense logical reasoning, inductive learning, and deep learning. As a motivating example of a task that requires explainable reasoning and learning, we consider Visual Question Answering in which, given an image of a scene, the objective is to answer explanatory questions about objects in the scene, their relationships, or the outcome of executing actions on these objects. In this context, our architecture uses deep networks for extracting features from images and for generating answers to queries. Between these deep networks, it embeds components for non-monotonic logical reasoning with incomplete commonsense domain knowledge, and for decision tree induction. It also incrementally learns and reasons with previously unknown constraints governing the domain's states. We evaluated the architecture in the context of datasets of simulated and real-world images, and a simulated robot computing, executing, and providing explanatory descriptions of plans and experiences during plan execution. Experimental results indicate that in comparison with an "end to end" architecture of deep networks, our architecture provides better accuracy on classification problems when the training dataset is small, comparable accuracy with larger datasets, and more accurate answers to explanatory questions. Furthermore, incremental acquisition of previously unknown constraints improves the ability to answer explanatory questions, and extending non-monotonic logical reasoning to support planning and diagnostics improves the reliability and efficiency of computing and executing plans on a simulated robot.
Retrieving Quantum Information with Active Learning

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies.
Hypernym Detection Using Strict Partial Order Networks

This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.
Video Surveillance for Violence Detection Using Deep Learning

In order to detect violence through surveillance cameras, we provide a neural architecture which can sense violence and can be a measure to prevent any chaos. This architecture uses a pre-trained ResNet-50 model to extract features from the video frames and then feeds them further into a ConvLSTM block. We use a short-term difference of video frames to provide more robustness in order to get rid of occlusions and discrepancies. Convolutional neural networks allow us to get more concentrated spatio-temporal features in the frames, which aids the sequential nature of videos to be fed in LSTMs. The model incorporates a pre-trained convolutional neural network connected to convolutional LSTM layer. The model takes raw videos as an input, converts it into frames, and outputs a binary classification of violence or non-violence label. We have pre-processed the video frames using cropping, dark-edge removal, and other data augmentation techniques to make data get rid of unnecessary details. For evaluation of the performance of our proposed method, three standard public datasets were used, and accuracy as the metric evaluation is used.
A Pilot Study on Electroencephalogram-based Evaluation of Visually Induced Motion Sickness

The most prominent problem in virtual reality (VR) technology is that users may experience motion sickness-like symptoms when they immerse into a VR environment. These symptoms are recognized as visually induced motion sickness (VIMS) or virtual reality motion sickness (VRMS). The objectives of this study were to investigate the association between the electroencephalogram (EEG) and subjectively rated VIMS level (VIMSL) and find the EEG markers for VIMS evaluation. In this study, a VR-based vehicle-driving simulator (VDS) was used to induce VIMS symptoms, and a wearable EEG device with four electrodes, the Muse, was used to collect EEG data of subjects. Our results suggest that individual tolerance, susceptibility, and recoverability to VIMS varied largely among subjects; the following markers were shown to be significantly different from no-VIMS and VIMS states (P < 0.05): (1) Means of gravity frequency (GF) for theta@FP1, alpha@TP9, alpha@FP2, alpha@TP10, and beta@FP1; (2) Standard deviation of GF for alpha@TP9, alpha@FP1, alpha@FP2, alpha@TP10, and alpha@(FP2-FP1); (3) Standard deviation of power spectral entropy (PSE) for FP1; (4) Means of Kolmogorov complexity (KC) for TP9, FP1, and FP2. These results also demonstrate that it is feasible to perform VIMS evaluation using an EEG device with a small number of electrodes.
A Decryptable Attribute-Based Keyword Search Scheme on eHealth Cloud in Internet of Things Platforms

Recently, attribute-based keyword search (ABKS) schemes have been used to provide fine-grained search over encrypted data on eHealth cloud in the Internet of Things (IoT) platforms. As compared to conventional public key encryption with keyword search (PEKS) schemes, ABKS schemes provide more powerful and flexible search operations which allow encrypted data to be retrieved by multiple users that satisfy set of attributes. However, there are still some limitations and security issues on the existing ABKS schemes. Many of the existing ABKS schemes only support for the encryption of keyword and require a separate cryptographic primitive to encrypt the message. Also, most of the schemes cannot resist offline keyword guessing attacks by inside attackers (i.e., the honest-but-curious servers). A secure-channel is needed for most of the ABKS schemes to transmit the trapdoors between the server and receivers. To solve these problems, we propose a secure-channel free ciphertext-policy decryptable attribute-based keyword search (CP-DABKS) scheme. The proposed scheme allows the authorised user who satisfy the access structure to decrypt the ciphertext. Our scheme not only resists the insider keyword guessing attack, but also eliminates the secure channel for trapdoor transmission. We formally define and prove the security of the proposed CP-DABKS scheme. We also demonstrate its application on an eHealth cloud platform.
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming

Renewable energy sources have shown remarkable growth in recent times in terms of their contribution to sustainable societies. However, integrating them into the national power grids is usually hindered because of their weather-dependent nature and variability. The combination of different sources to profit from their beneficial complementarity has often been proposed as a partial solution to overcome these issues. Thus, efficient planning for optimizing the exploitation of these energy resources requires different types of decision support tools. A mathematical index for assessing energetic complementarity between multiple energy sources constitutes an important tool for this purpose, allowing a comparison of complementarity between existing facilities at different planning stages and also allowing a dynamic assessment of complementarity between variable energy sources throughout the operation, assisting in the dispatch of power supplies. This article presents a method for quantifying and spatially representing the total temporal energetic complementarity between three different variable renewable sources, through an index created from correlation coefficients and compromise programming. The method is employed to study the complementarity of wind speed, solar radiation and surface runoff on a monthly scale using continental Colombia as a case study during the year of 2015.•This paper describes a method for quantifying and spatially representing energetic complementarity between three renewable energy sources.•The method quantifies energetic complementarity by combining known metrics: correlations and compromise programming.•The proposed index for energetic complementarity assessment is sensitive to the time scale adopted.
Estimation of Video Streaming KQIs for Radio Access Negotiation in Network Slicing Scenarios

The use of multimedia content has hugely increased in recent times, becoming one of the most important services for the users of mobile networks. Consequently, network operators struggle to optimize their infrastructure to support the best video service-provision. As an additional challenge, 5G introduces the concept of network slicing as a new paradigm that presents a completely different view of the network configuration and optimization. A main challenge of this scheme is to establish which specific resources would provide the necessary quality of service for the users using the slice. To address this, the present work presents a complete framework for this support of the slice negotiation process through the estimation of the provided Video Streaming Key Quality Indicators (KQIs), which are calculated from network low-layer configuration parameters and metrics. The proposed estimator is then evaluated in a real cellular scenario.
UTxO- vs Account-Based Smart Contract Blockchain Programming Paradigms

We implement two versions of a simple but illustrative smart contract: one in Solidity on the Ethereum blockchain platform, and one in Plutus on the Cardano platform, with annotated code excerpts and with source code attached. We get a clearer view of the Cardano programming model in particular by introducing a novel mathematical abstraction which we call Idealised EUTxO. For each version of the contract, we trace how the architectures of the underlying platforms and their mathematics affects the natural programming styles and natural classes of errors. We prove some simple but novel results about alpha-conversion and observational equivalence for Cardano, and explain why Ethereum does not have them. We conclude with a wide-ranging and detailed discussion in the light of the examples, mathematical model, and mathematical results so far.
Scalable quantum computing stabilised by optical tweezers on an ion crystal

Abstract As it has been demonstrated that trapped ion systems have unmatched long-lived quantum-bit (qubit) coherence and can support high-fidelity quantum manipulations, how to scale up the system size becomes an inevitable task for practical purposes. In this work, we theoretically analyse the physical limitation of scalability with a trapped ion array, and propose a feasible scheme of architecture that in principle allows an arbitrary number of ion qubits, for which the overhead only scales linearly with the system size. This scheme relies on the combined ideas of a trap architecture of tunable size, stabilisation of an ion crystal by optical tweezers, and continuous sympathetic cooling without touching the stored information. We demonstrate that illumination of optical tweezers modifies the motional spectrum by effectively pinning the ions, lifting the frequencies of the motional ground modes. By doing so, we make the structure of the array less vulnerable from thermal excitations, and suppress the position fluctuations to insure faithful gate operations. Finally, we also explore the local behaviour of cooling when a sub-array is isolated by optical tweezers from other parts of the crystal.
Interpretation of machine learning predictions for patient outcomes in electronic health records.

Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcomes using three state-of-the-art machine learning methods. Our primary goal is to validate the models by interpreting the importance of predictors in the final models. Central to interpretation is the use of feature importance scores, which vary depending on the underlying methodology. In order to assess feature importance, we compared univariate statistical tests, information-theoretic measures, permutation testing, and normalized coefficients from multivariate logistic regression models. In general we found poor correlation between methods in their assessment of feature importance, even when their performance is comparable and relatively good. However, permutation tests applied to random forest and gradient boosting models showed the most agreement, and the importance scores matched the clinical interpretation most frequently.
YouTube celebrity endorsement: audience evaluation of source attributes and response to sponsored content. A case study of influencer Verdeliss

This article analyses follower response to the growing number of product endorsements present in YouTube videos published under the username “Verdeliss” by Estefanía Unzu Ripoll, Spain’s most popular YouTube influencer on the topic of maternity and childcare. Results of a self-administered online survey of 949 Verdeliss followers focused on their individual evaluations of source attributes indicate that Unzu Ripoll’s YouTube fans tend to buy products she endorses on the basis of her perceived likeability and expertise, and that the overall influence she exerts on their purchasing decisions is slight. In closing, the authors offer insights into how social media influencers can enhance the effectiveness of their online endorsements and identify tactics brands can employ to ensure that the influencers they collaborate with are optimally suited to promote their products.
Acute myeloid leukemia and artificial intelligence, algorithms and new scores

Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex datasets. Advances in computer hardware and software have led to breakthroughs in multiple sectors of our society, including a burgeoning role in medical research and clinical practice. As the volume of medical data grows at an apparently exponential rate, particularly since the human genome project laid the foundation for modern genetic inquiry, informatics tools like machine learning are becoming crucial in analyzing these data to provide meaningful tools for diagnostic, prognostic, and therapeutic purposes. Within medicine, hematologic diseases can be particularly challenging to understand and treat given the increasingly complex and intercalated genetic, epigenetic, immunologic, and regulatory pathways that must be understood to optimize patient outcomes. In acute myeloid leukemia (AML), new developments in machine learning algorithms have enabled a deeper understanding of disease biology and the development of better prognostic and predictive tools. Ongoing work in the field brings these developments incrementally closer to clinical implementation.