Reviewerly/exHarmony huggingface dataset:
Model-Based Policy Search Using Monte Carlo Gradient Estimation With Real Systems Application

In this paper, we present a Model-Based Reinforcement Learning (MBRL) algorithm named \emph{Monte Carlo Probabilistic Inference for Learning COntrol} (MC-PILCO). The algorithm relies on Gaussian Processes (GPs) to model the system dynamics and on a Monte Carlo approach to estimate the policy gradient. This defines a framework in which we ablate the choice of the following components: (i) the selection of the cost function, (ii) the optimization of policies using dropout, (iii) an improved data efficiency through the use of structured kernels in the GP models. The combination of the aforementioned aspects affects dramatically the performance of MC-PILCO. Numerical comparisons in a simulated cart-pole environment show that MC-PILCO exhibits better data efficiency and control performance w.r.t. state-of-the-art GP-based MBRL algorithms. Finally, we apply MC-PILCO to real systems, considering in particular systems with partially measurable states. We discuss the importance of modeling both the measurement system and the state estimators during policy optimization. The effectiveness of the proposed solutions has been tested in simulation and on two real systems, a Furuta pendulum and a ball-and-plate rig.
What Makes a UI Simple? Difficulty and Complexity in Tasks Engaging Visual-Spatial Working Memory

Tasks that imply engagement of visual-spatial working memory (VSWM) are common in interaction with two-dimensional graphical user interfaces. In our paper, we consider two groups of factors as predictors of user performance in such tasks: (1) the metrics based on compression algorithms (RLE and Deflate) plus the Hick’s law, which are known to be characteristic of visual complexity, and (2) metrics based on Gestalt groping principle of proximity, operationalized as von Neumann and Moore range 1 neighborhoods from the cellular automata theory. We involved 88 subjects who performed about 5000 VSWM-engaging tasks and 78 participants who assessed the complexity of the tasks’ configurations. We found that the Gestalt-based predictors had a notable advantage over the visual complexity-based ones, as the memorized chunks best corresponded to von Neumann neighborhood groping. The latter was further used in the formulation of index of difficulty and throughput for VSWM-engaging tasks, which we proposed by analogy with the infamous Fitts’ law. In our experimental study, throughput amounted to 3.75 bit/s, and we believe that it can be utilized for comparing and assessing UI designs.
Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation

Domain adaptation is an important task to enable learning when labels are scarce. While most works focus only on the image modality, there are many important multi-modal datasets. In order to leverage multi-modality for domain adaptation, we propose cross-modal learning, where we enforce consistency between the predictions of two modalities via mutual mimicking. We constrain our network to make correct predictions on labeled data and consistent predictions across modalities on unlabeled target-domain data. Experiments in unsupervised and semi-supervised domain adaptation settings prove the effectiveness of this novel domain adaptation strategy. Specifically, we evaluate on the task of 3D semantic segmentation from either the 2D image, the 3D point cloud or from both. We leverage recent driving datasets to produce a wide variety of domain adaptation scenarios including changes in scene layout, lighting, sensor setup and weather, as well as the synthetic-to-real setup. Our method significantly improves over previous uni-modal adaptation baselines on all adaption scenarios. Our code is publicly available at https://github.com/valeoai/xmuda_journal
Unambiguous quantum state elimination for qubit sequences

Quantum state elimination measurements tell us what states a quantum system does not have. This is different from state discrimination, where one tries to determine what the state of a quantum system is rather than what it is not. Apart from being of fundamental interest, quantum state elimination may find uses in quantum communication and quantum cryptography. We consider unambiguous quantum state elimination for two or more qubits, where each qubit can be in one of two possible states. Optimal measurements for eliminating one and two states out of four two-qubit states are given. We also prove that if we want to maximize the average number of eliminated overall N-qubit states, then individual measurements on each qubit are optimal.Received 21 August 2019Accepted 10 December 2019DOI:https://doi.org/10.1103/PhysRevResearch.2.013256Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum measurementsQuantum Information
Superresolution Reconstruction of Severely Undersampled Point-spread Functions Using Point-source Stacking and Deconvolution

Abstract Point-spread function (PSF) estimation in spatially undersampled images is challenging because large pixels average fine-scale spatial information. This is problematic when fine-resolution details are necessary, as in optimal photometry where knowledge of the illumination pattern beyond the native spatial resolution of the image may be required. Here, we introduce a method of PSF reconstruction where point sources are artificially sampled beyond the native resolution of an image and combined together via stacking to return a finely sampled estimate of the PSF. This estimate is then deconvolved from the pixel-gridding function to return a superresolution kernel that can be used for optimally weighted photometry. We benchmark against the &lt;1% photometric error requirement of the upcoming SPHEREx mission to assess performance in a concrete example. We find that standard methods like Richardson–Lucy deconvolution are not sufficient to achieve this stringent requirement. We investigate a more advanced method with significant heritage in image analysis called iterative back-projection (IBP) and demonstrate it using idealized Gaussian cases and simulated SPHEREx images. In testing this method on real images recorded by the LORRI instrument on New Horizons, we are able to identify systematic pointing drift. Our IBP-derived PSF kernels allow photometric accuracy significantly better than the requirement in individual SPHEREx exposures. This PSF reconstruction method is broadly applicable to a variety of problems and combines computationally simple techniques in a way that is robust to complicating factors such as severe undersampling, spatially complex PSFs, noise, crowded fields, or limited source numbers.
PathoFusion: An Open-Source AI Framework for Recognition of Pathomorphological Features and Mapping of Immunohistochemical Data

We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is designed to simultaneously capture both index and contextual feature information from shorter and longer image tiles, respectively. This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal. Adjacent tissue sections obtained from glioblastoma cases were processed for hematoxylin and eosin (H&amp;E) and immunohistochemical (CD276) staining. Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN. PathoFusion demonstrated its ability to recognize malignant neuropathological features autonomously and map immunohistochemical data simultaneously. Our experiments show that PathoFusion achieved areas under the curve (AUCs) of 0.985 ± 0.011 and 0.988 ± 0.001 in patch-level recognition of six typical pathomorphological features and detection of associated immunoreactivity, respectively. On this basis, the system further correlated CD276 immunoreactivity to abnormal tumor vasculature. Corresponding feature distributions and overlaps were visualized by heatmaps, permitting high-resolution qualitative as well as quantitative morphological analyses for entire histological slides. Recognition of more user-defined pathomorphological features can be added to the system and included in future tissue analyses. Integration of PathoFusion with the day-to-day service workflow of a (neuro)pathology department is a goal. The software code for PathoFusion is made publicly available.
EL PASSO: Efficient and Lightweight Privacy-preserving Single Sign On

Abstract Anonymous credentials are a solid foundation for privacy-preserving Single Sign-On (SSO). They enable unlinkable authentication across domains and allow users to prove their identity without revealing more than necessary. Unfortunately, anonymous credentials schemes remain difficult to use and complex to deploy. They require installation and use of complex software at the user side, suffer from poor performance, and do not support security features that are now common, such as two-factor authentication, secret recovery, or support for multiple devices. In contrast, Open ID Connect (OIDC), the de facto standard for SSO is widely deployed and used despite its lack of concern for users’ privacy. We present EL PASSO, a privacy-preserving SSO system based on anonymous credentials that does not trade security for usability, and can be incrementally deployed at scale alongside Open ID Connect with no significant changes to end-user operations. EL PASSO client-side operations leverage a WebAssembly module that can be downloaded on the fly and cached by users’ browsers, requiring no prior software installation or specific hardware. We develop automated procedures for managing cryptographic material, supporting multi-device support, secret recovery, and privacy-preserving two-factor authentication using only the built-in features of common Web browsers. Our implementation using PS Signatures achieves 39x to 180x lower computational cost than previous anonymous credentials schemes, similar or lower sign-on latency than Open ID Connect and is amenable for use on mobile devices.
When Word Embeddings Become Endangered

Big languages such as English and Finnish have many natural language processing (NLP) resources and models, but this is not the case for low-resourced and endangered languages as such resources are so scarce despite the great advantages they would provide for the language communities. The most common types of resources available for low-resourced and endangered languages are translation dictionaries and universal dependencies. In this paper, we present a method for constructing word embeddings for endangered languages using existing word embeddings of different resource-rich languages and the translation dictionaries of resource-poor languages. Thereafter, the embeddings are fine-tuned using the sentences in the universal dependencies and aligned to match the semantic spaces of the big languages; resulting in cross-lingual embeddings. The endangered languages we work with here are Erzya, Moksha, Komi-Zyrian and Skolt Sami. Furthermore, we build a universal sentiment analysis model for all the languages that are part of this study, whether endangered or not, by utilizing cross-lingual word embeddings. The evaluation conducted shows that our word embeddings for endangered languages are well-aligned with the resource-rich languages, and they are suitable for training task-specific models as demonstrated by our sentiment analysis model which achieved a high accuracy. All our cross-lingual word embeddings and the sentiment analysis model have been released openly via an easy-to-use Python library.
A Scalable and Efficient Multi-Agent Architecture for Malware Protection in Data Sharing Over Mobile Cloud

Sensitive Data requires encryption before uploading to a public cloud.Access control based on Attribute-Based Encryption (ABE) is an effective technique to ensure data shared security and privacy in the public cloud.Cipher-Text Policy of ABE may suffer from scalability and performance issues as they do not permit for addition or removal of computing nodes at run time.Furthermore, another problem is existing approaches suffer from single-point-of-failure (SPoF).Therefore, we introduce a scalable multi-agent system architecture based on CP-ABE to ensure data sharing on public cloud storage and reliability in our proposed work.We proposed a cloud host as an inter-mediator between the user and the authorized agents without violating the system's privacy and security.We have also proposed a novel methodology to protect the cloud from malware by exploiting the state's efficient power of the art Gemini approach.Gemini is an efficient methodology for binary code-based graph embedding similarity detection.Our proposed study overcomes the deficiencies of scalability and efficiency along with providing the mechanism for malware detection in the cloud.It covers three aspects: scalability, the efficiency with multi-agents, and malware detection capability.Our contributed work is scalable, efficient for cloud data sharing, and protects from malware.Results reveal that our work provides better performance with preserving the security, privacy, and fine granularity features of CP-ABE and malware screening using regress analysis by the graph embedding technique.
The Role of Redundant Bases and Shrinkage Functions in Image Denoising

Wavelet denoising is a classical and effective approach for reducing noise in images and signals.Suggested in 1994 [1], this approach is carried out by rectifying the coefficients of a noisy image in the transform domain, using a set of scalar shrinkage function (SFs).A plethora of papers deals with the optimal shape of the SFs and the transform used, where it is known that applying the SFs in redundant bases provides improved results.This paper provides a complete picture of the interrelations between the transform used, the optimal shrinkage functions, and the domains in which they are optimized.In particular, we show that for subband optimization, where each SF is optimized independently for a particular band, optimizing the SFs in the spatial domain is always better than or equal to optimizing the SFs in the transform domain.For redundant bases, we provide the expected denoising gain we may achieve, relative to the unitary basis, as a function of the redundancy rate.
Balanced Deep Supervised Hashing

Recently, Convolutional Neural Network (CNN) based hashing method has achieved its promising performance for image retrieval task. However, tackling the discrepancy between quantization error minimization and discriminability maximization of network outputs simultaneously still remains unsolved. Motivated by the concern, we propose a novel Balanced Deep Supervised Hashing (BDSH) based on variant posterior probability to learn compact discriminability-preserving binary code for large scale image data. Distinguished from the previous works, BDSH can search an equilibrium point within the discrepancy. Towards the goal, a delicate objective function is utilized to maximize the discriminability of the output space with the variant posterior probability of the pair-wise label. A quantization regularizer is utilized as a relaxation from real-value outputs to the desired discrete values (e.g., -1/+1). Extensive experiments on the benchmark datasets show that our method can yield state-of-the-art image retrieval performance from various perspectives.
A Hybrid MPC for Constrained Deep Reinforcement Learning applied for Planar Robotic Arm

Recently, deep reinforcement learning techniques have achieved tangible results for learning high dimensional control tasks. Due to the trial and error interaction, between the autonomous agent and the environment, the learning phase is unconstrained and limited to the simulator. Such exploration has an additional drawback of consuming unnecessary samples at the beginning of the learning process. Model-based algorithms, on the other hand, handle this issue by learning the dynamics of the environment. However, model-free algorithms have a higher asymptotic performance than model-based ones. The main contribution of this paper is to construct a hybrid structured algorithm from model predictive control (MPC) and deep reinforcement learning (DRL) (MPC-DRL), that makes use of the benefits of both methods, to satisfy constraint conditions throughout the learning process. The validity of the proposed approach is demonstrated by learning a reachability task. The results show complete satisfaction for the constraint condition, represented by a static obstacle, with a smaller number of samples and higher performance compared to state-of-the-art model-free algorithms.
Image segmentation algorithm based on partial differential equation

Image segmentation is very important for various fields. With the development of computer technology, computer technology has become more and more effective for image segmentation, and it is studied on the basis of partial differential equations. The curve representation method in plane differential geometry is expounded, with the SegNet-v2 segmentation model analyzed and tested in medical image segmentation. The test results show that the partial differential equation image segmentation algorithm can achieve more accurate segmentation, especially in medical image segmentation, which can achieve good results, and it is worth in practice to further promote.
Perspective-Corrected Spatial Referring Expression Generation for Human–Robot Interaction

Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation, the ambiguity is unavoidable due to the diversity of reference frames, which will lead to an understanding gap between humans and robots. To narrow this gap, in this paper, we propose a novel perspective-corrected spatial referring expression generation (PcSREG) approach for human-robot interaction by considering the selection of reference frames. The task of referring expression generation is simplified into the process of generating diverse spatial relation units. First, we pick out all landmarks in these spatial relation units according to the entropy of preference and allow its updating through a stack model. Then all possible referring expressions are generated according to different reference frame strategies. Finally, we evaluate every expression using a probabilistic referring expression resolution model and find the best expression that satisfies both of the appropriateness and effectiveness. We implement the proposed approach on a robot system and empirical experiments show that our approach can generate more effective spatial referring expressions for practical applications.
“Aarogya Setu”: The mobile application that monitors and mitigates the risks of COVID-19 pandemic spread in India

The case study demonstrates the journey of the ‘Aarogya Setu’ app - A contact tracing the mechanism developed in the form of a mobile application to monitor and mitigate the risk of COVID-19 pandemic spread in India. The case describes how Aarogya Setu mobile app works, how it identifies the hotspot areas, its features and privacy policies, and the reasons why the app had been in the news due to positive and negative perceptions of the citizens related to the app. However, the case can be used as a pedagogical tool for the various subjects in the discipline of Information Systems and Digital Technologies. The case helps to understand how to design and develop mobile application features and functions that protect individual privacy. In the end, the case throws challenging questions to the users on different aspects of gamification and m-governance.
Very Lightweight Photo Retouching Network With Conditional Sequential Modulation

Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects, especially for poor contrast, over/under exposure, and inharmonious saturation. In practice, photo retouching can be accomplished by a series of image processing operations. As most commonly-used retouching operations are pixel-independent, i.e., the manipulation on one pixel is uncorrelated with its neighboring pixels, we can take advantage of this property and design a specialized algorithm for efficient global photo retouching. We analyze these global operations and find that they can be mathematically formulated by a Multi-Layer Perceptron (MLP). Based on this observation, we propose an extremely lightweight framework – Conditional Sequential Retouching Network (CSRNet). Benefiting from the utilization of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1\times 1$</tex-math></inline-formula> convolution, CSRNet only contains less than 37 K trainable parameters, which are orders of magnitude smaller than existing learning-based methods. Experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. In addition to achieve global photo retouching, the proposed framework can be easily extended to learn local enhancement effects. The extended model, namely CSRNet-L, also achieves competitive results in various local enhancement tasks.
Deep Constraint-Based Propagation in Graph Neural Networks

The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs). We focus our attention on the originally proposed GNN model of Scarselli et al. 2009, which encodes the state of the nodes of the graph by means of an iterative diffusion procedure that, during the learning stage, must be computed at every epoch, until the fixed point of a learnable state transition function is reached, propagating the information among the neighbouring nodes. We propose a novel approach to learning in GNNs, based on constrained optimization in the Lagrangian framework. Learning both the transition function and the node states is the outcome of a joint process, in which the state convergence procedure is implicitly expressed by a constraint satisfaction mechanism, avoiding iterative epoch-wise procedures and the network unfolding. Our computational structure searches for saddle points of the Lagrangian in the adjoint space composed of weights, nodes state variables and Lagrange multipliers. This process is further enhanced by multiple layers of constraints that accelerate the diffusion process. An experimental analysis shows that the proposed approach compares favourably with popular models on several benchmarks.
Monte Carlo elites

A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions. Quality-diversity search has explicitly walked this tightrope between a population's diversity and its quality. This paper extends a popular quality-diversity search algorithm, MAP-Elites, by treating the selection of parents as a multi-armed bandit problem. Using variations of the upper-confidence bound to select parents from under-explored but potentially rewarding areas of the search space can accelerate the discovery of new regions as well as improve its archive's total quality. The paper tests an indirect measure of quality for parent selection: the survival rate of a parent's offspring. Results show that maintaining a balance between exploration and exploitation leads to the most diverse and high-quality set of solutions in three different testbeds.