Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data

The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of non-expert stakeholders a challenge. Here we explore the applicability of deep learning approaches by adapting a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exists to construct a balanced training data set, and the deformation signals are slower and more localised than in previous applications. We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of real UK velocity map, and iii) enhanced over-wrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.
Adaptive Dialog Policy Learning with Hindsight and User Modeling

Reinforcement learning (RL) methods have been widely used for learning dialog policies. Sample efficiency, i.e., the efficiency of learning from limited dialog experience, is particularly important in RL-based dialog policy learning, because interacting with people is costly and low-quality dialog policies produce very poor user experience. In this paper, we develop LHUA (Learning with Hindsight, User modeling, and Adaptation) that, for the first time, enables dialog agents to adaptively learn with hindsight from both simulated and real users. Simulation and hindsight provide the dialog agent with more experience and more (positive) reinforcement respectively. Experimental results suggest that LHUA outperforms competitive baselines from the literature, including its no-simulation, no-adaptation, and no-hindsight counterparts.
Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting

Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when images are not available at the testing time.
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation

We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep neural networks (DNNs). We develop a novel algorithm to enforce a specially favorable DNN weight structure, where each layerwise weight matrix can be stored as the product of a small basis matrix and a large sparse coefficient matrix whose non-zero elements are all power-of-2. To our best knowledge, this algorithm is the first formulation that integrates three mainstream model compression ideas: sparsification or pruning, decomposition, and quantization, into one unified framework. The resulting sparse and readily-quantized DNN thus enjoys greatly reduced energy consumption in data movement as well as weight storage. On top of that, we further design a dedicated accelerator to fully utilize the SmartExchange-enforced weights to improve both energy efficiency and latency performance. Extensive experiments show that 1) on the algorithm level, SmartExchange outperforms state-of-the-art compression techniques, including merely sparsification or pruning, decomposition, and quantization, in various ablation studies based on nine models and four datasets; and 2) on the hardware level, SmartExchange can boost the energy efficiency by up to $6.7 \times$ and reduce the latency by up to $19.2 \times$ over four state-of-the-art DNN accelerators, when benchmarked on seven DNN models (including four standard DNNs, two compact DNN models, and one segmentation model) and three datasets.
A 700 pc Extended Coronal Gas Emission in the Circinus Galaxy

We report the first characterization of an extended outflow of high ionized gas in the Circinus Galaxy by means of the coronal line (CL) [Fe vii] λ6087. This emission is located within the ionization cone already detected in the [O iii] λ5007 line and is found to extend up to a distance of ∼700 pc from the active galactic nucleus. The gas distribution appears clumpy, with several knots of emission. Its kinematics is complex, with split profiles and line centroids shifted from the systemic velocity. The physical conditions of the gas show that the extended coronal emission is likely the remnant of shells inflated by the passage of a radio jet. This scenario is supported by extended X-ray emission, which is spatially coincident with the morphology and extension of the [Fe vii] λ6087 gas in the NW side of the galaxy. The extension of the coronal gas in the Circinus galaxy is unique among active galaxies and demonstrates the usefulness of CLs for tracing the shock ionization component in these objects.
Maximum of exponential random variables,
Hurwitz’s zeta function, and the partition function

A natural problem in the context of the coupon collector's problem is the behavior of the maximum of independent geometrically distributed random variables (with distinct parameters). This question has been addressed by Brennan et al. (British J. of Math. & CS. 8 (2015), 330-336). Here we provide explicit asymptotic expressions for the moments of that maximum, as well as of the maximum of exponential random variables with corresponding parameters. We also deal with the probability of each of the variables being the maximal one. The calculations lead to expressions involving Hurwitz's zeta function at certain special points. We find here explicitly the values of the function at these points. Also, the distribution function of the maximum we deal with is closely related to the generating function of the partition function. Thus, our results (and proofs) rely on classical results pertaining to the partition function.
Shared Autonomy with Learned Latent Actions

Assistive robots enable people with disabilities to conduct everyday tasks on their own. However, these tasks can be complex, containing both coarse reaching motions and fine-grained manipulation. For example, when eating, not only does one need to move to the correct food item, but they must also precisely manipulate the food in different ways (e.g., cutting, stabbing, scooping). Shared autonomy methods make robot teleoperation safer and more precise by arbitrating user inputs with robot controls. However, these works have focused mainly on the high-level task of reaching a goal from a discrete set, while largely ignoring manipulation of objects at that goal. Meanwhile, dimensionality reduction techniques for teleoperation map useful high-dimensional robot actions into an intuitive low-dimensional controller, but it is unclear if these methods can achieve the requisite precision for tasks like eating. Our insight is that---by combining intuitive embeddings from learned latent actions with robotic assistance from shared autonomy---we can enable precise assistive manipulation. In this work, we adopt learned latent actions for shared autonomy by proposing a new model structure that changes the meaning of the human's input based on the robot's confidence of the goal. We show convergence bounds on the robot's distance to the most likely goal, and develop a training procedure to learn a controller that is able to move between goals even in the presence of shared autonomy. We evaluate our method in simulations and an eating user study. See videos of our experiments here: this https URL
Robust location estimators in regression models with covariates and responses missing at random

This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some covariates. The target is a marginal location parameter given through an M-functional. To obtain robust Fisher-consistent estimators, properly defined marginal distribution function estimators are considered. These estimators avoid the bias due to missing values assuming a missing at random condition. Three methods are considered to estimate the marginal distribution which allows to obtain the M-location of interest: the well-known inverse probability weighting, a convolution-based method that makes use of the regression model and an augmented inverse probability weighting procedure that prevents against misspecification. Different aspects of their asymptotic behaviour are derived under regularity conditions. The robust studied estimators and their classical relatives are compared through numerical experiments under different missing data models, including clean and contaminated samples. The methodology is illustrated through a real data set.
Phase retrieval of complex-valued objects via a randomized Kaczmarz method

This paper investigates the convergence of the randomized Kaczmarz algorithm for the problem of phase retrieval of complex-valued objects. Although this algorithm has been studied for the real-valued case in [ 28], its generalization to the complex-valued case is nontrivial and has been left as a conjecture. This paper applies a different approach by establishing the connection between the convergence of the algorithm and the convexity of an objective function. Based on the connection, it demonstrates that when the sensing vectors are sampled uniformly from a unit sphere in ${\mathcal{C}}^n$ and the number of sensing vectors $m$ satisfies $m>O(n\log n)$ as $n, m\rightarrow \infty $, then this algorithm with a good initialization achieves linear convergence to the solution with high probability. The method can be applied to other statistical models of sensing vectors as well. A similar convergence result is established for the unitary model, where the sensing vectors are from the columns of random orthogonal matrices. 2000 Math Subject Classification: 68W20, 68W27, 92D25.
Computing with bricks and mortar: Classification of waveforms with a doped concrete blocks

We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir Computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for "reservoir of states" necessary for simple tuning of the readout layer. In that perspective, buildings constructed from computing concrete could function as a highly parallel information processor for smart architecture. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.
Nonlinear 3D cosmic web simulation with heavy-tailed generative adversarial networks

Fast and accurate simulations of the non-linear evolution of the cosmic density field are a major component of many cosmological analyses, but the computational time and storage required to run them can be exceedingly large. For this reason, we use generative adversarial networks (GANs) to learn a compressed representation of the 3D matter density field that is fast and easy to sample, and for the first time show that GANs are capable of generating samples at the level of accuracy of other conventional methods. Using sub-volumes from a suite of GADGET-2 N-body simulations, we demonstrate that a deep-convolutional GAN can generate samples that capture both large- and small-scale features of the matter density field, as validated through a variety of n-point statistics. The use of a data scaling that preserves high-density features and a heavy-tailed latent space prior allow us to obtain state of the art results for fast 3D cosmic web generation. In particular, the mean power spectra from generated samples agree to within 5% up to k=3 and within 10% for k<5 when compared with N-body simulations, and similar accuracy is obtained for a variety of bispectra. By modeling the latent space with a heavy-tailed prior rather than a standard Gaussian, we better capture sample variance in the high-density voxel PDF and reduce errors in power spectrum and bispectrum covariance on all scales. Furthermore, we show that a conditional GAN can smoothly interpolate between samples conditioned on redshift. Deep generative models, such as the ones described in this work, provide great promise as fast, low-memory, high-fidelity forward models of large-scale structure.
Scyclone: High-Quality and Parallel-Data-Free Voice Conversion Using Spectrogram and Cycle-Consistent Adversarial Networks

This paper proposes Scyclone, a high-quality voice conversion (VC) technique without parallel data training. Scyclone improves speech naturalness and speaker similarity of the converted speech by introducing CycleGAN-based spectrogram conversion with a simplified WaveRNN-based vocoder. In Scyclone, a linear spectrogram is used as the conversion features instead of vocoder parameters, which avoids quality degradation due to extraction errors in fundamental frequency and voiced/unvoiced parameters. The spectrogram of source and target speakers are modeled by modified CycleGAN networks, and the waveform is reconstructed using the simplified WaveRNN with a single Gaussian probability density function. The subjective experiments with completely unpaired training data show that Scyclone is significantly better than CycleGAN-VC2, one of the existing state-of-the-art parallel-data-free VC techniques.
Observability of dark matter substructure with pulsar timing correlations

Dark matter substructure on small scales is currently weakly constrained, and its study may shed light on the nature of the dark matter. In this work we study the gravitational effects of dark matter substructure on measured pulsar phases in pulsar timing arrays (PTAs). Due to the stability of pulse phases observed over several years, dark matter substructure around the Earth-pulsar system can imprint discernible signatures in gravitational Doppler and Shapiro delays. We compute pulsar phase correlations induced by general dark matter substructure, and project constraints for a few models such as monochromatic primordial black holes (PBHs), and Cold Dark Matter (CDM)-like NFW subhalos. This work extends our previous analysis, which focused on static or single transiting events, to a stochastic analysis of multiple transiting events. We find that stochastic correlations, in a PTA similar to the Square Kilometer Array (SKA), are uniquely powerful to constrain subhalos as light as ∼ 10−13 M⊙, with concentrations as low as that predicted by standard CDM.
A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains

We present a LiDAR-based and real-time capable 3D perception system for automated driving in urban domains. The hierarchical system design is able to model stationary and movable parts of the environment simultaneously and under real-time conditions. Our approach extends the state of the art by innovative in-detail enhancements for perceiving road users and drivable corridors even in case of non-flat ground surfaces and overhanging or protruding elements. We describe a runtime-efficient pointcloud processing pipeline, consisting of adaptive ground surface estimation, 3D clustering and motion classification stages. Based on the pipeline's output, the stationary environment is represented in a multi-feature mapping and fusion approach. Movable elements are represented in an object tracking system capable of using multiple reference points to account for viewpoint changes. We further enhance the tracking system by explicit consideration of occlusion and ambiguity cases. Our system is evaluated using a subset of the TUBS Road User Dataset. We enhance common performance metrics by considering application-driven aspects of real-world traffic scenarios. The perception system shows impressive results and is able to cope with the addressed scenarios while still preserving real-time capability.
The twisted dark matter halo of the Milky Way

We analyse systems analogous to the Milky Way (MW) in the eagle cosmological hydrodynamics simulation in order to deduce the likely structure of the MW’s dark matter (DM) halo. We identify MW mass haloes in the simulation whose satellite galaxies have similar kinematics and spatial distribution to those of the bright satellites of the MW, specifically systems in which the majority of the satellites (8 out of 11) have nearly coplanar orbits that are also perpendicular to the central stellar disc. We find that the normal to the common orbital plane of the coplanar satellites is well aligned with the minor axis of the host DM halo, with a median misalignment angle of only 17.3°. Based on this result, we infer that the minor axis of the Galactic DM halo points towards (l, b) = (182°, −2°), with an angular uncertainty at the 68 and 95 percentile confidence levels of 22° and 43°, respectively. Thus, the inferred minor axis of the MW halo lies in the plane of the stellar disc. The halo, however, is not homologous and its flattening and orientation vary with radius. The inner parts of the halo are rounder than the outer parts and well aligned with the stellar disc (that is the minor axis of the halo is perpendicular to the disc). Further out, the halo twists and the minor axis changes direction by 90°. This twist occurs over a very narrow radial range and reflects variations in the filamentary network along which mass was accreted into the MW.
Theoretical framework for physical unclonable functions, including quantum readout

We propose a theoretical framework to quantitatively describe physical unclonable functions (PUFs), including extensions to quantum protocols, so-called quantum readout PUFs (QR-PUFs). (QR-) PUFs are physical systems with challenge-response behavior intended to be hard to clone or simulate. Their use has been proposed in several cryptographic protocols, with particular emphasis on authentication. Here, we provide theoretical assumptions and definitions behind the intuitive ideas of (QR-) PUFs. This allows us to quantitatively characterize the security of such devices in cryptographic protocols. First, by generalizing previous ideas, we design a general authentication scheme which is applicable to different physical implementations of both classical PUFs and (QR-) PUFs. Then, we define the robustness and the unclonability, which allows us to derive security thresholds for (QR-) PUF authentication and paves the way to develop further new authentication protocols.
Lyra’s cosmology of homogeneous and isotropic universe in Brans–Dicke theory

In this paper, we investigate a scalar field Brans–Dicke cosmological model in Lyra’s geometry which is based on the modifications in a geometrical term as well as energy term of Einstein’s field equations. We have examined the validity of the proposed cosmological model on the observational scale by performing statistical analysis from the latest [Formula: see text] and SN Ia observational data. We find that the estimated values of Hubble’s constant and matter energy density parameter is in agreement with their corresponding values, obtained from recent observations of Wilkinson Microwave Anisotropy Probe (WMAP) and Plank collaboration. We also derived the deceleration parameter, age of the universe and jerk parameter in terms of red-shift and computed its present values. The dynamics of the deceleration parameter in the derived model of the universe show a signature flipping from positive to a negative value and also indicate that the present universe is in the accelerating phase.
Affect-Aware Word Clouds

Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and color palettes are powerful typographical signals that may determine this impact. Typically, these signals are assigned randomly, or expected to be chosen by the users. We present an affect-aware font and color palette selection methodology that aims to facilitate more informed choices. We infer associations of fonts with a set of eight affects, and evaluate the resulting data in a series of user studies both on individual words as well as in word clouds. Relying on a recent study to procure affective color palettes, we carry out a similar user study to understand the impact of color choices on word clouds. Our findings suggest that both fonts and color palettes are powerful tools contributing to the affects evoked by a word cloud. The experiments further confirm that the novel datasets we propose are successful in enabling this. We also find that, for the majority of the affects, both signals need to be congruent to create a stronger impact. Based on this data, we implement a prototype that allows users to specify a desired affect and recommends congruent fonts and color palettes for the word.
Biomass distribution among trophic levels in lakes lacking vertebrate predators

The relative importance of nutrient supply and food chain composition for the structure and function of ''two-level'' South Georgian (Sub-Antarctica) freshwater ecosystems was assessed. Along a productivity gradient of 19 lakes, we determined the abundance of bacteria, bacterivorous flagellates, and herbivorous macrozooplankters, as well as the concentrations of nutrients (phosphorus and nitrogen) and chlorophyll. Our aims were to test predictions from ''classical'' food chain theory on these simple ecosystems. and to include microbial components in the analysis. The abundance of all organisms increased with increasing productivity, although chlorophyll concentration showed a damped increase compared with lower latitude lakes with similar productivity. Moreover, the amount of chlorophyll per zooplankton grazer showed no increase with increasing productivity, indicating that grazing is a major structuring factor for algal biomass development. Similarly, there was no increase in the ratio between the abundances of flagellates (an alternative prey for zooplankters) and zooplankton with increasing productivity. Conversely, the number of bacteria per flagellate increased with increasing productivity, suggesting that bacteria are not regulated by flagellate grazing. We conclude that food chain theory may explain a considerable part of the variation in abundance of organisms of all size classes, including microorganisms.