Reviewerly/exHarmony huggingface dataset:
A GeoSPARQL Compliance Benchmark

GeoSPARQL is an important standard for the geospatial linked data community, given that it defines a vocabulary for representing geospatial data in RDF, defines an extension to SPARQL for processing geospatial data, and provides support for both qualitative and quantitative spatial reasoning. However, what the community is missing is a comprehensive and objective way to measure the extent of GeoSPARQL support in GeoSPARQL-enabled RDF triplestores. To fill this gap, we developed the GeoSPARQL compliance benchmark. We propose a series of tests that check for the compliance of RDF triplestores with the GeoSPARQL standard, in order to test how many of the requirements outlined in the standard a tested system supports. This topic is of concern because the support of GeoSPARQL varies greatly between different triplestore implementations, and the extent of support is of great importance for different users. In order to showcase the benchmark and its applicability, we present a comparison of the benchmark results of several triplestores, providing an insight into their current GeoSPARQL support and the overall GeoSPARQL support in the geospatial linked data domain.
Out of the Box: Embodied Navigation in the Real World

The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achieving nearly perfect PointGoal Navigation. However, such architectures are commonly trained with millions, if not billions, of frames and tested in simulation. Together with great enthusiasm, these results yield a question: how many researchers will effectively benefit from these advances? In this work, we detail how to transfer the knowledge acquired in simulation into the real world. To that end, we describe the architectural discrepancies that damage the Sim2Real adaptation ability of models trained on the Habitat simulator and propose a novel solution tailored towards the deployment in real-world scenarios. We then deploy our models on a LoCoBot, a Low-Cost Robot equipped with a single Intel RealSense camera. Different from previous work, our testing scene is unavailable to the agent in simulation. The environment is also inaccessible to the agent beforehand, so it cannot count on scene-specific semantic priors. In this way, we reproduce a setting in which a research group (potentially from other fields) needs to employ the agent visual navigation capabilities as-a-Service. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world. Our code and models are available at https://github.com/aimagelab/LoCoNav.
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service

It is widely acknowledged that learning joint embeddings of recipes with images is challenging due to the diverse composition and deformation of ingredients in cooking procedures. We present a Multi-modal Semantics enhanced Joint Embedding approach (MSJE) for learning a common feature space between the two modalities (text and image), with the ultimate goal of providing high-performance cross-modal retrieval services. Our MSJE approach has three unique features. First, we extract the TFIDF feature from the title, ingredients and cooking instructions of recipes. By determining the significance of word sequences through combining LSTM learned features with their TFIDF features, we encode a recipe into a TFIDF weighted vector for capturing significant key terms and how such key terms are used in the corresponding cooking instructions. Second, we combine the recipe TFIDF feature with the recipe sequence feature extracted through two-stage LSTM networks, which is effective in capturing the unique relationship between a recipe and its associated image(s). Third, we further incorporate TFIDF enhanced category semantics to improve the mapping of image modality and to regulate the similarity loss function during the iterative learning of cross-modal joint embedding. Experiments on the benchmark dataset Recipe1M show the proposed approach outperforms the state-of-the-art approaches.
Pro-UIGAN: Progressive Face Hallucination From Occluded Thumbnails

In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (8×) the occluded and tiny faces (16×16 pixels). Pro-UIGAN iteratively (1) estimates facial geometry priors for low-resolution (LR) faces and (2) acquires non-occluded HR face images under the guidance of the estimated priors. Our multi-stage hallucination network upsamples and inpaints occluded LR faces via a coarse-to-fine fashion, significantly reducing undesirable artifacts and blurriness. Specifically, we design a novel cross-modal attention module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively. Such a design encourages joint feature learning across the input facial and landmark features, and deep feature correspondences will be discovered by attention. Thus, facial appearance features and facial geometry priors are learned in a mutually beneficial manner. Extensive experiments show that our Pro-UIGAN attains visually pleasing completed HR faces, thus facilitating downstream tasks, i.e., face alignment, face parsing, face recognition as well as expression classification.
Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations

For the analysis of medical images, one of the most basic methods is to diagnose diseases by examining blood smears through a microscope to check the morphology, number, and ratio of red blood cells and white blood cells. Therefore, accurate segmentation of blood cell images is essential for cell counting and identification. The aim of this paper is to perform blood smear image segmentation by combining neural ordinary differential equations (NODEs) with U-Net networks to improve the accuracy of image segmentation. In order to study the effect of ODE-solve on the speed and accuracy of the network, the ODE-block module was added to the nine convolutional layers in the U-Net network. Firstly, blood cell images are preprocessed to enhance the contrast between the regions to be segmented; secondly, the same dataset was used for the training set and testing set to test segmentation results. According to the experimental results, we select the location where the ordinary differential equation block (ODE-block) module is added, select the appropriate error tolerance, and balance the calculation time and the segmentation accuracy, in order to exert the best performance; finally, the error tolerance of the ODE-block is adjusted to increase the network depth, and the training NODEs-UNet network model is used for cell image segmentation. Using our proposed network model to segment blood cell images in the testing set, it can achieve 95.3% pixel accuracy and 90.61% mean intersection over union. By comparing the U-Net and ResNet networks, the pixel accuracy of our network model is increased by 0.88% and 0.46%, respectively, and the mean intersection over union is increased by 2.18% and 1.13%, respectively. Our proposed network model improves the accuracy of blood cell image segmentation and reduces the computational cost of the network.
Classification of pulsars using Extreme Deconvolution

We carry out a classification of the observed pulsar dataset into distinct clusters, based on the P−Ṗ diagram, using Extreme Deconvolution based Gaussian Mixture Model. We then use the Bayesian Information Criterion to select the optimum number of clusters. We find in accord with previous works, that the pulsar dataset can be optimally classified into six clusters, with two for the millisecond pulsar population, and four for the ordinary pulsar population. Beyond that, however we do not glean any additional insight into the pulsar population based on this classification. Using numerical experiments, we confirm that Extreme Deconvolution-based classification is less sensitive to variations in the dataset compared to ordinary Gaussian Mixture Models. All our analysis codes used for this work have been made publicly available.
Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing

The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing.To capture COVID-19 information from multiple sources, a new data mart and a new natural language processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a Web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information.The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81% to 92% and to enable pooled testing with a negative predictive value of 90% to 91%, reducing the required tests to about 63%.SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing.
Morphology and word order in Slavic languages: Insights from annotated corpora

Slavic languages are generally assumed to possess rich morphological features with free syntactic word order. Exploring this complexity trade-off can help us better understand the relationship between morphology and syntax within natural languages. However, few quantitative investigations have been carried out into this relationship within Slavic languages. Based on 34 annotated corpora from Universal Dependencies, this paper paid special attention to the correlations between morphology and syntax within Slavic languages by applying two metrics of morphological richness and two of word order freedom, respectively. Our findings are as follows. First, the quantitative metrics adopted can well capture the distributions of morphological richness and word order freedom of languages. Second, the metrics can corroborate the correlation between morphological richness and word order freedom. Within Slavic languages, this correlation is moderate and statistically significant. Precisely, the richer the morphology, the less strict the word order. Third, Slavic languages can be clustered into three subgroups based on classification models. Most importantly, ancient Slavic languages are characterized by richer morphology and more flexible word order than modern ones. Fourth, as two possible disturbing factors, corpus size does not greatly affect the results of the metrics, whereas corpus genre does play an important part in the measurements of word order freedom. Specifically, the word order of formal written genres tends to be more rigid than that of informal written and spoken ones. Overall, based on annotated corpora, the results verify the negative correlation between morphological richness and word order rigidity within Slavic languages, which might shed light on the dynamic relations between morphology and syntax of natural languages and provide quantitative instantiations of how languages encode lexical and syntactic information for the purpose of efficient communication.
Modelling GDPR-Compliant Explanations for Trustworthy AI

Through the General Data Protection Regulation (GDPR), the European Union has set out its vision for Automated Decision- Making (ADM) and AI, which must be reliable and human-centred. In particular we are interested on the Right to Explanation, that requires industry to produce explanations of ADM. The High-Level Expert Group on Artificial Intelligence (AI-HLEG), set up to support the implementation of this vision, has produced guidelines discussing the types of explanations that are appropriate for user-centred (interactive) Explanatory Tools. In this paper we propose our version of Explanatory Narratives (EN), based on user-centred concepts drawn from ISO 9241, as a model for user-centred explanations aligned with the GDPR and the AI-HLEG guidelines. Through the use of ENs we convert the problem of generating explanations for ADM into the identification of an appropriate path over an Explanatory Space, allowing explainees to interactively explore it and produce the explanation best suited to their needs. To this end we list suitable exploration heuristics, we study the properties and structure of explanations, and discuss the proposed model identifying its weaknesses and strengths.
Appraisal of a Contact Tracing Training Program for COVID-19 in Greece Focusing on Vulnerable Populations

Background: Contact tracing as an epidemiological strategy has repeatedly contributed to the containment of various past epidemics and succeeded in controlling the spread of disease in the community. Systematic training of contact tracers is crucial in ensuring the effectiveness of epidemic containment. Methods: An intensive training course was offered to 216 health and other professionals who work with vulnerable population groups, such as Roma, refugees, and migrants in Greece, by the scientific team of the postgraduate programme “Global Health-Disaster Medicine” of the Medical School, National and Kapodistrian University of Athens, with the support of the Swiss embassy in Greece. The course was delivered online due to the pandemic restriction measures and was comprised of 16 h over 2 days. The course curriculum was adapted in Greek using, upon agreement, a similar training course to what was developed by the Johns Hopkins University Bloomberg School of Public Health. Evaluation of the course was conducted in order to determine the short term satisfaction from participating in this training course. Results: A total of 70% of the course participants completed the evaluation questionnaires and all trainers gave feedback on the course. The training modules were ranked as extremely useful by the majority of the participants and over 50% of the participants specifically stated that the course content was directly related to their work with vulnerable groups. Content about the ethics of contact tracing and the effective communication skills presented were deemed most useful. Conclusion: The course was well organised and provided the required skills for effective contact tracing. Many course participants intend to use some components in their work with vulnerable populations groups. Contact tracing efforts work best in a systematic and coordinated way and the provision of systematic and organised training can greatly increase its effectiveness.
How to Train Your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning

We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance, custom-built quadrotor platform. A common but often under-studied problem in developing RL agents for continuous control is that the control policies developed are not always smooth. This lack of smoothness can be a major problem when learning controllers %intended for deployment on real hardware as it can result in control instability and hardware failure. Issues of noisy control are further accentuated when training RL agents in simulation due to simulators ultimately being imperfect representations of reality - what is known as the reality gap. To combat issues of instability in RL agents, we propose a systematic framework, `REinforcement-based transferable Agents through Learning' (RE+AL), for designing simulated training environments which preserve the quality of trained agents when transferred to real platforms. RE+AL is an evolution of the Neuroflight infrastructure detailed in technical reports prepared by members of our research group. Neuroflight is a state-of-the-art framework for training RL agents for low-level attitude control. RE+AL improves and completes Neuroflight by solving a number of important limitations that hindered the deployment of Neuroflight to real hardware. We benchmark RE+AL on the NF1 racing quadrotor developed as part of Neuroflight. We demonstrate that RE+AL significantly mitigates the previously observed issues of smoothness in RL agents. Additionally, RE+AL is shown to consistently train agents that are flight-capable and with minimal degradation in controller quality upon transfer. RE+AL agents also learn to perform better than a tuned PID controller, with better tracking errors, smoother control and reduced power consumption.
Effect of Initial Configuration of Weights on Training and Function of Artificial Neural Networks

The function and performance of neural networks are largely determined by the evolution of their weights and biases in the process of training, starting from the initial configuration of these parameters to one of the local minima of the loss function. We perform the quantitative statistical characterization of the deviation of the weights of two-hidden-layer feedforward ReLU networks of various sizes trained via Stochastic Gradient Descent (SGD) from their initial random configuration. We compare the evolution of the distribution function of this deviation with the evolution of the loss during training. We observed that successful training via SGD leaves the network in the close neighborhood of the initial configuration of its weights. For each initial weight of a link we measured the distribution function of the deviation from this value after training and found how the moments of this distribution and its peak depend on the initial weight. We explored the evolution of these deviations during training and observed an abrupt increase within the overfitting region. This jump occurs simultaneously with a similarly abrupt increase recorded in the evolution of the loss function. Our results suggest that SGD’s ability to efficiently find local minima is restricted to the vicinity of the random initial configuration of weights.
General connection between time-local and time-nonlocal perturbation expansions

There exist two canonical approaches to describe open quantum systems by a time-evolution equation: the Nakajima-Zwanzig quantum master equation, featuring a time-nonlocal memory kernel $\mathcal{K}$, and the time-convolutionless equation with a time-local generator $\mathcal{G}$. These key quantities have recently been shown to be connected by an exact fixed-point relation [Phys. Rev. X 11, 021041 (2021)]. Here we show that this implies a recursive relation between their perturbative expansions, allowing a series for the kernel $\mathcal{K}$ to be translated directly into a corresponding series for the more complicated generator $\mathcal{G}$. This leads to an elegant way of computing the generator using well-developed, standard memory-kernel techniques for strongly interacting open systems. Moreover, it allows for an unbiased comparison of time-local and time-nonlocal approaches independent of the particular technique chosen to calculate expansions of $\mathcal{K}$ and $\mathcal{G}$ (Nakajima-Zwanzig projections, real-time diagrams, etc.). We illustrate this for leading and next-to-leading order calculations of $\mathcal{K}$ and $\mathcal{G}$ for the single impurity Anderson model using both the bare expansion in the system-environment coupling and a more advanced renormalized series. We compare the different expansions obtained, quantify the legitimacy of the generated dynamics (complete positivity) and benchmark with the exact result in the non-interacting limit.
Recurrent neural networks for partially observed dynamical systems

Complex nonlinear dynamics are ubiquitous in many fields. Moreover, we rarely have access to all of the relevant state variables governing the dynamics. Delay embedding allows us, in principle, to account for unobserved state variables. Here we provide an algebraic approach to delay embedding that permits explicit approximation of error. We also provide the asymptotic dependence of the first-order approximation error on the system size. More importantly, this formulation of delay embedding can be directly implemented using a recurrent neural network (RNN). This observation expands the interpretability of both delay embedding and the RNN and facilitates principled incorporation of structure and other constraints into these approaches.
Quantum state truncation using an optical parametric amplifier and a beam splitter

We present a scheme of quantum state truncation in the Fock basis (quantum scissors), based on the combined action of a nondegenerate optical parametric amplifier and a beamsplitter. Differently from previously proposed linear-optics-based quantum scissors devices, which depend on reliable Fock states sources, our scheme requires only readily available Gaussian states, such as coherent states inputs (vacuum state included). A truncated state is generated after performing photodetections in the global state. We find that, depending on which output ports each of the two photodetectors is positioned, different types of truncated states may be produced: i) states having a maximum Fock number of $N$, or ii) states having a minimum Fock number $N$. In order to illustrate our method, we discuss an example having as input states a coherent state in the beamsplitter and vacuum states in the amplifier, and show that the resulting truncated states display nonclassical properties, such as sub-Poissonian statistics and squeezing. We quantify the nonclassicality degree of the generated states using the Wigner-Yanase skew information measure. For complementarity, we discuss the efficiency of the protocol, e.g., generation probability as well as the effects of imperfections such as the detector's quantum efficiency and dark counts rate.
SSCAN: A Spatial–Spectral Cross Attention Network for Hyperspectral Image Denoising

Hyperspectral images (HSIs) have been widely used in a variety of applications thanks to the rich spectral information they are able to provide. Among all HSI processing tasks, HSI denoising is a crucial step. Recently, deep learning-based image denoising methods have made great progress and achieved great performance. However, existing methods tend to ignore the correlations between adjacent spectral bands, leading to problems such as spectral distortion and blurred edges in denoised results. In this study, we propose a novel HSI denoising network, termed SSCAN, that combines group convolutions and attention modules. Specifically, we use a group convolution with a spatial attention module to facilitate feature extraction by directing models' attention to band-wise important features. We propose a spectral-spatial attention block (SSAB) to exploit the spatial and spectral information in hyperspectral images in an effective manner. In addition, we adopt residual learning operations with skip connections to ensure training stability. The experimental results indicate that the proposed SSCAN outperforms several state-of-the-art HSI denoising algorithms.