Reviewerly/exHarmony huggingface dataset:
Compact adaptive interferometer for unknown freeform surfaces with large departure

The newest experimental validation report of the coverage for the rotationally non-symmetric departure of a freeform surface in adaptive interferometry is about 20 µm. A compact adaptive interferometer is introduced to test unknown freeform surfaces with larger departure. The cascaded DMs (woofer and tweeter) can effectively double the measurable rotationally non-symmetric departure, to ∼80 µm using current DM technology. With a constrained decoupling control algorithm, the woofer and tweeter can averagely share the aberrations without coupling. DM surface monitoring is addressed by a time-division-monitoring (TDM) technique, which avoids separate monitoring devices and configurations and thus makes a compact configuration. Measurements of two different surfaces are presented: a nearly flat freeform with ∼40 um departure, and an off-axis paraboloid with ∼50 um of asymmetric departure.
Effectful applicative similarity for call-by-name lambda calculi

We introduce a notion of applicative similarity in which not terms but monadic values arising from the evaluation of effectful terms, can be compared. We prove this notion to be fully abstract whenever terms are evaluated in call-by-name order. This is the first full-abstraction result for such a generic, coinductive methodology for program equivalence.
Shotgun assembly of random jigsaw puzzles

We consider the shotgun assembly problem for a random jigsaw puzzle, introduced by Mossel and Ross (2015). Their model consists of a puzzle—an n × n grid, where each vertex is viewed as a center of a piece. Each of the four edges adjacent to a vertex is assigned one of q colors (corresponding to “jigs,” or cut shapes) uniformly at random. Unique assembly refers to there being only one puzzle (the original one) that is consistent with the collection of individual pieces. We show that for any ε &gt;0, if q ≥ n 1+ ε , then unique assembly holds with high probability. The proof uses an algorithm that assembles the puzzle in time n Θ(1/ ε ) .22
FAIR data for prehistoric mining archaeology

This paper presents an approach how to create FAIR data for prehistoric mining archaeology, based on the CIDOC CRM ontology and semantic web standards. The interdisciplinary Research Centre HiMAT (History of mining activities in the Tyrol and adjacent areas, University of Innsbruck) investigates mining history from prehistoric to modern times with an interdisciplinary approach. One of the projects carried out at the research centre is the multinational DACH project "Prehistoric copper production in the eastern and central Alps". For a specific geographical region of the project, the data transformation to open and re-usable data is investigated in a separate Open Research Data pilot project. The methodological approach will use the FAIR principles to make data Findable, Accessible, Interoperable and Re-usable. Every archaeological investigation in Austria has to be documented according to the requirements of the Austrian Federal Monuments Office. This documentation is deposited in the CERN-based EU supported research data repository ZENODO. For each deposited file, metadata are created through the application of the conceptual metadata schema CIDOC CRM, an ISO standard for Cultural Heritage Information, which was adopted by ARIADNE, the European Union Research Infrastructure for archaeological resources. Concepts specific to mining archaeology research are organized with the DARIAH Back Bone Thesaurus, a model for sustainable interoperable thesauri maintenance, developed in the European Union Digital Research Infrastructure for the Arts and Humanities. Metadata are created through the extraction of information from the documentation and the transformation to a knowledge graph using semantic web standards. To facilitate usage, graph data are exported to hierarchical and tabular formats representing sites and objects with their geographic locations, temporal and typological assignments and links to the research activities and documents. Metadata are deposited together with the documentation into the repository.
A Neural Approach for Text Extraction from Scholarly Figures

In recent years, the problem of scene text extraction from images has received extensive attention and significant progress. However, text extraction from scholarly figures such as plots and charts remains an open problem, in part due to the difficulty of locating irregularly placed text lines. To the best of our knowledge, literature has not described the implementation of a text extraction system for scholarly figures that adapts deep convolutional neural networks used for scene text detection. In this paper, we propose a text extraction approach for scholarly figures that forgoes preprocessing in favor of using a deep convolutional neural network for text line localization. Our system uses a publicly available scene text detection approach whose network architecture is well suited to text extraction from scholarly figures. Training data are derived from charts in arXiv papers which are extracted using Allen Institute's pdffigures tool. Since this tool analyzes PDF data as a container format in order to extract text location through the mechanisms which render it, we were able to gather a large set of labeled training samples. We show significant improvement from methods in the literature, and discuss the structural changes of the text extraction pipeline.
Satellite imagery and machine learning for aridity disaster classification using vegetation indices

Central Java Province is one of provinces in Indonesia that has a high aridity risk index. Aridity disaster risk monitoring and detection can be done more accurately in larger areas and with lower costs if the vegetation index is extracted from the remote sensing imagery. This study aims to provide accurate aridity risk index information using spectral vegetation index data obtained from LANDSAT 8 OLI satellite. The classification of drought risk areas was carried out using k-nn with the Spatial Autocorrelation method. The spectral vegetation indices used in the study are NDVI, SAVI, VHI, TCI and VCI. The results show a positive correlation and trend between the spectral vegetation index influenced by seasonal dynamics and the characteristics of the High R.A. and Middle R.A. drought risk areas. The highest correlation coefficient is SAVI with a High R.A. amounted to 0.967 and Middle R.A. amounted to 0.951. The results of the Kappa accuracy test comparison show that SVM and k-nn have the same accuracy of 88.30. The result of spatial prediction using the IDW method shows that spectral vegetation index data that initially as an outlier, using the k-nn method, the spectral vegetation index data can be identified as data in the aridity classification. The spatial connectivity test among sub-districts that experience drought was done using Moran’s I Analysis.
Self-supervised Multi-view Person Association and Its Applications

Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams. To solve this problem, reliable association of the same person across distant viewpoints and temporal instances is essential. We present a self-supervised framework to adapt a generic person appearance descriptor to the unlabeled videos by exploiting motion tracking, mutual exclusion constraints, and multi-view geometry. The adapted discriminative descriptor is used in a tracking-by-clustering formulation. We validate the effectiveness of our descriptor learning on WILDTRACK T. Chavdarova et al., "WILDTRACK: A multi-camera HD dataset for dense unscripted pedestrian detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018, pp. 5030-5039. and three new complex social scenes captured by multiple cameras with up to 60 people "in the wild". We report significant improvement in association accuracy (up to 18 percent) and stable and coherent 3D human skeleton tracking (5 to 10 times) over the baseline. Using the reconstructed 3D skeletons, we cut the input videos into a multi-angle video where the image of a specified person is shown from the best visible front-facing camera. Our algorithm detects inter-human occlusion to determine the camera switching moment while still maintaining the flow of the action well. Website: http://www.cs.cmu.edu/~ILIM/projects/IM/Association4Tracking.
Single-Net Continual Learning with Progressive Segmented Training

There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference. Different from previous approaches with dynamic structures, this work focuses on a single network and model segmentation to prevent catastrophic forgetting. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and secondary group to be saved (not pruned) for a future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of Progressive Segmented Training (PST) successfully incorporates multiple tasks and achieves the state-of-the-art accuracy in the single-head evaluation on CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning at the edge.
Multi-directional shearography based on multiplexed Mach–Zehnder interference system

Shearography is only sensitive to deformation in the shearing direction, and the deformation of object defects after loading may occur in multiple directions. This work reports a multi-direction shearography system that uses spatial phase-shift methods to detect the object from multi-shearing directions, effectively avoiding the missed detection of directional defects. A single laser is utilized to illuminate an object, and a single CCD camera records images in the multiplexed Mach–Zehnder interference system. First, the aperture stops in suitable size and location are set to produce different spatial carrier frequencies. Second, the shearing amount is independently adjusted by using different devices. Finally, the Fourier transform method is used to extract the phase information from the frequency domain. This system can be used for nondestructive testing of multi-directional defects and the feasibility of the method is verified by theoretical analysis and experiment.
A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions

The successful application of fuzzy control depends to a large extent on the parameters of some subjective decisions, such as fuzzy membership function (MF). Fuzzy logic controller (FLC) implementing augmented output MFs as compare to input MFs is presented to improve the accuracy, robustness, and performance of the system. The best possible combination of input and output MFs is introduced to distribute the uniform input MFs and augmented output MFs in the treatise. The simulation of the 2-Inputs 1-Output Fuzzy Control System is performed in many nonlinear processes. Then, the experimental outcomes of the uniformly and augmented distributed output MFs are compared under similar circumstances. The experimental outcomes are in a virtuous covenant with the simulation outcomes. The experimental outcomes show that the root mean square error (RMSE) is reduced around 75.3% and bringing down the relative error to the acceptable range (≤±10%). The control accuracy is improved and the robustness is boosted by reducing the RMSE through the FLC with augmented-distributed output MFs. Moreover, the cost and energy efficiency in any fuzzy system will be improved by implementing the augmented-distributed output MFs using the best possible combination of input and output MFs.
Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization

Airborne gamma-ray surveys are useful for many applications, ranging from geology and mining to public health and nuclear security. In all these contexts, the ability to decompose a measured spectrum into a linear combination of background source terms can provide useful insights into the data and lead to improvements in the techniques that use spectral energy windows. Multiple methods for the linear decomposition of spectra exist but are subject to various drawbacks, such as allowing negative photon fluxes or requiring detailed Monte Carlo modeling. We propose using non-negative matrix factorization (NMF) as a data-driven approach to spectral decomposition. Using aerial surveys that include flights over water, we demonstrate that the mathematical approach of NMF finds physically relevant structure in the aerial gamma-ray background, namely, that measured spectra can be expressed as the sum of nearby terrestrial emission, distant terrestrial emission, and radon and cosmic emission. These NMF background components are compared with the background components obtained by noise-adjusted singular value decomposition (NASVD), which contain negative photon fluxes and, thus, do not represent the emission spectra in as straightforward a way. Finally, we comment on the potential areas of research that are enabled by NMF decompositions, such as new approaches to spectral anomaly detection and data fusion.
Particle Detector Simulation using Generative Adversarial Networks with Domain Related Constraints

Generative Adversarial Networks have been used extensively to generate high resolution images in different fields. The present work explores the adversarial paradigm to train a deep neural network as a tool for scientific simulation, more specifically, simulation of High Energy Physics detectors. At the core of this approach lies the possibility to interpret the detector response as an image: the main challenges being represented by the large pixel intensity dynamic range (spanning over more than ten orders of magnitude) together with a high level of image sparsity. An initial prototype [1] simulated electrons travelling through an example electromagnetic calorimeter and depositing energy in its volume. It achieved very good results using an adversarial approach to reproduce a simplified physics use case. The present work extends the model to a more complex and realistic scenario: the conditioning approach is modified to include additional physics variables, the network architecture is adjusted to take into account larger image size and more complex pixel dynamic features, the cost function is re-designed in order to include physics based constraints. We ntroduce a multi-step training process based on transfer learning, by initially optimizing the network performance on a restricted particle energy range (therefore, a simplified pixel intensity distribution). Further training the network on the full energy range yields very good results, without the need of additional optimization. Beyond a qualitative visual inspection, the validation of GAN performance is based on a detailed comparison to standard Monte Carlo simulation in terms of several physics quantities: results show a remarkable agreement, ranging from a few percent up to 10% across a large particle energy range.
The Bias-Expressivity Trade-off

Learning algorithms need bias to generalize and perform better than random guessing. We examine the flexibility (expressivity) of biased algorithms. An expressive algorithm can adapt to changing training data, altering its outcome based on changes in its input. We measure expressivity by using an information-theoretic notion of entropy on algorithm outcome distributions, demonstrating a trade-off between bias and expressivity. To the degree an algorithm is biased is the degree to which it can outperform uniform random sampling, but is also the degree to which is becomes inflexible. We derive bounds relating bias to expressivity, proving the necessary trade-offs inherent in trying to create strongly performing yet flexible algorithms.
Salp Swarm Algorithm: A Comprehensive Review

Swarm Intelligence (SI) is referred to the social conduct emerging within decentralized and self-organization of swarms. These swarms are summarized as the well-known examples such as bird groups, fish schools, and the most social in insects species for instance bees, termites, and ants. Among those, Salp Swarm Algorithm (SSA), that has been successfully utilized and held in different fields of optimization, engineering practice, and real-world problems, so far. This review carries out a extensive study for the present status of publications, advances, applications, variants with SSA including its modifications, population topology, hybridization, extensions, theoretical analysis, and parallel implementation in order to show its potential to show its potential to overcome many practical optimization issues. Further, this review will be greatly useful for the researchers and algorithm developers analyzing at Swarm Intelligence, especially SSA to use this simple and yet very efficient approach for several tough optimization issues.
Planar maximally entangled states

We construct a large family of planar maximally entangled (PME) states, which are a wider class of multipartite entangled states than absolutely maximally entangled (AME) states. These are states in which any half of the qudits are in a maximally mixed state, provided that they form a connected subset. We show that in contrast to AMEs, PMEs are easier to find and there are various PMEs for any even number of qudits. In particular, while it is known that no AME state of four qubits exists, we show that there are two distinct multiparameter classes of four-qubit PMEs. We also give explicit families of PMEs for any even number of particles and for any dimension. We also briefly mention the relevance of connectivity and underlying geometry to definition of a PME state. Here we have considered the simplest case of a one-dimensional graph.
Romanized Tunisian dialect transliteration using sequence labelling techniques

In recent years, social web users in Arabic countries have been resorting to the dialects as a written language in their social exchanges. Arabic dialects derive from modern standard Arabic (MSA) and differ significantly from one country to another and one region to another. The use of these dialects has led to an increase of interest in the specificities of such informal languages and their automatic processing within the NLP community. In this work, we deal with the Tunisian dialect (TD) in particular. We address the issue of the automatic Latin to Arabic transliteration of TD language productions on the social web and propose an approach that models the transliteration as a sequence labeling task. At a word level, several techniques, based on machine and deep learning, have been tested for this study, using real word messages extracted from social networks. We experiment and compare three transliteration models: A Conditional Random Fields-based model (CRF), a Bidirectional Long Short-Term Memory based model (BLSTM), and a BLSTM based model with CRF decoding (BLSTM-CRF). The obtained results show that BLSTM-CRF, leads to the best performance, reaching 96.78% of correctly transliterated words. We also evaluate the BLSTM-CRF transliteration approach in context on a set of random TD messages extracted from the social web. We obtained a total error rate of 2.7%. 25% of which are context errors.
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task. Variants of GCN are used in multiple state-of-the-art approaches and therefore it is important to understand the specifics and limitations of GCN-based models. Despite serious efforts, we were not able to fully reproduce the results from the original paper and after a thorough audit of the code provided by authors, we concluded, that their implementation is different from the architecture described in the paper. In addition, several tricks are required to make the model work and some of them are not very intuitive. We provide an extensive ablation study to quantify the effects these tricks and changes of architecture have on final performance. Furthermore, we examine current evaluation approaches and systematize available benchmark datasets. We believe that people interested in KG matching might profit from our work, as well as novices entering the field
Non-Bayesian Estimation Framework for Signal Recovery on Graphs

Graph signals arise from physical networks, such as power and communication systems, or as a result of a convenient representation of data with complex structure, such as social networks. We consider the problem of general graph signal recovery from noisy, corrupted, or incomplete measurements and under structural parametric constraints, such as smoothness in the graph frequency domain. In this paper, we formulate the graph signal recovery as a non-Bayesian estimation problem under a weighted mean-squared-error (WMSE) criterion, which is based on a quadratic form of the Laplacian matrix of the graph and its trace WMSE is the Dirichlet energy of the estimation error w.r.t. the graph. The Laplacian-based WMSE penalizes estimation errors according to their graph spectral content and is a difference-based cost function which accounts for the fact that in many cases signal recovery on graphs can only be achieved up to a constant addend. We develop a new Cram\'er-Rao bound (CRB) on the Laplacian-based WMSE and present the associated Lehmann unbiasedness condition w.r.t. the graph. We discuss the graph CRB and estimation methods for the fundamental problems of 1) A linear Gaussian model with relative measurements; and 2) Bandlimited graph signal recovery. We develop sampling allocation policies that optimize sensor locations in a network for these problems based on the proposed graph CRB. Numerical simulations on random graphs and on electrical network data are used to validate the performance of the graph CRB and sampling policies.
Detecting Unsigned Physical Road Incidents From Driver-View Images

Safety on roads is of uttermost importance, especially in the context of autonomous vehicles. A critical need is to detect and communicate disruptive incidents early and effectively. In this paper we propose a system based on an off-the-shelf deep neural network architecture that is able to detect and recognize types of unsigned (non-placarded, such as traffic signs), physical (visible in images) road incidents. We develop a taxonomy for unsigned physical incidents to provide a means of organizing and grouping related incidents. After selecting eight target types of incidents, we collect a dataset of twelve thousand images gathered from publicly-available web sources. We subsequently fine-tune a convolutional neural network to recognize the eight types of road incidents. The proposed model is able to recognize incidents with a high level of accuracy (higher than 90%). We further show that while our system generalizes well across spatial context by training a classifier on geostratified data in the United Kingdom (with an accuracy of over 90%), the translation to visually less similar environments requires spatially distributed data collection. Note: this is a pre-print version of work accepted in IEEE Transactions on Intelligent Vehicles (T-IV;in press). The paper is currently in production, and the DOI link will be added soon.