Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blueprint For Interoperable Blockchains

Research in blockchain systems has mainly focused on improving security and bridging the performance gaps between blockchains and databases. Despite many promising results, we observe a worrying trend that the blockchain landscape is fragmented in which many systems exist in silos. Apart from a handful of general-purpose blockchains, such as Ethereum or Hyperledger Fabric, there are hundreds of others designed for specific applications and typically do not talk to each other. In this paper, we describe our vision of interoperable blockchains. We argue that supporting interaction among different blockchains requires overcoming challenges that go beyond data standardization. The underlying problem is to allow smart contracts running in different blockchains to communicate. We discuss three open problems: access control, general cross-chain transactions, and cross-chain communication. We describe partial solutions to some of these problems in the literature. Finally, we propose a novel design to overcome these challenges.
Jet latitude regimes and the predictability of the North Atlantic Oscillation

In recent years, numerical weather prediction models have begun to show notable levels of skill at predicting the average winter North Atlantic Oscillation (NAO) when initialised one month ahead. At the same time, these model predictions exhibit unusually low signal‐to‐noise ratios, in what has been dubbed a ‘signal‐to‐noise paradox’. We analyse both the skill and signal‐to‐noise ratio of the Integrated Forecast System (IFS), the European Centre for Medium‐range Weather Forecasts (ECMWF) model, in an ensemble hindcast experiment. Specifically, we examine the contribution to both from the regime dynamics of the North Atlantic eddy‐driven jet. This is done by constructing a statistical model which captures the predictability inherent to the trimodal jet latitude system, and fitting its parameters to reanalysis and IFS data. Predictability in this regime system is driven by interannual variations in the persistence of the jet latitude regimes, which determine the preferred state of the jet. We show that the IFS has skill at predicting such variations in persistence: because the position of the jet strongly influences the NAO, this automatically generates skill at predicting the NAO. We show that all of the skill the IFS has at predicting the winter NAO over the period 1980–2010 can be attributed to its skill at predicting regime persistence in this way. Similarly, the tendency of the IFS to underestimate regime persistence can account for the low signal‐to‐noise ratio, giving a possible explanation for the signal‐to‐noise paradox. Finally, we examine how external forcing drives variability in jet persistence, and highlight the role played by transient baroclinic eddy feedbacks to modulate regime persistence.
A new family of entire functions with no wandering domains

The issue of whether an analytic function has wandering domains has long been of interest in complex dynamics. Sullivan proved in 1985 that rational maps do not have wandering domains. On the other hand, several transcendental entire functions have wandering domains. Using recent results on the relationship between Fatou components and the postsingular set, we find a new family of transcendental entire functions that does not have wandering domains. We also prove that the Julia set of a certain subfamily is the whole plane.
Temporal Multimodal Fusion for Driver Behavior Prediction Tasks using Gated Recurrent Fusion Units

The Tactical Driver Behavior modeling problem requires understanding of driver actions in complicated urban scenarios from a rich multi modal signals including video, LiDAR and CAN bus data streams. However, the majority of deep learning research is focused either on learning the vehicle/environment state (sensor fusion) or the driver policy (from temporal data), but not both. Learning both tasks end-to-end offers the richest distillation of knowledge, but presents challenges in formulation and successful training. In this work, we propose promising first steps in this direction. Inspired by the gating mechanisms in LSTM, we propose gated recurrent fusion units (GRFU) that learn fusion weighting and temporal weighting simultaneously. We demonstrate it's superior performance over multimodal and temporal baselines in supervised regression and classification tasks, all in the realm of autonomous navigation. We note a 10% improvement in the mAP score over state-of-the-art for tactical driver behavior classification in HDD dataset and a 20% drop in overall Mean squared error for steering action regression on TORCS dataset.
Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IMDD) links. We consider an autoencoder based on the recently proposed sliding window bidirectional recurrent neural network (SBRNN) design to realize the transceiver for optical IMDD communication. We show that its performance can be improved by introducing a weighted sequence estimation scheme at the receiver. Moreover, we perform bit-to-symbol mapping optimization to reduce the bit-error rate (BER) of the system. Furthermore, we carry out a detailed comparison with classical schemes based on pulse-amplitude modulation and maximum likelihood sequence detection (MLSD). Our investigation shows that for a reference 42 Gb/s transmission, the SBRNN autoencoder achieves a BER performance comparable to MLSD, when both systems account for the same amount of memory. In contrast to MLSD, the SBRNN performance is achieved without incurring a computational complexity exponentially growing with the processed memory.
CYCLOTOMIC POLYNOMIALS WITH PRESCRIBED HEIGHT AND PRIME NUMBER THEORY

Given any positive integer n, let A(n) denote the height of the nth cyclotomic polynomial, that is its maximum coefficient in absolute value. It is well known that A(n) is unbounded. We conjecture that every natural number can arise as value of A(n) and prove this assuming that for every pair of consecutive primes p and p′ with p⩾127 , we have p′−p
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping

Deep learning has enabled remarkable improvements in grasp synthesis for previously unseen objects from partial object views. However, existing approaches lack the ability to explicitly reason about the full 3D geometry of the object when selecting a grasp, relying on indirect geometric reasoning derived when learning grasp success networks. This abandons explicit geometric reasoning, such as avoiding undesired robot object collisions. We propose to utilize a novel, learned 3D reconstruction to enable geometric awareness in a grasping system. We leverage the structure of the reconstruction network to learn a grasp success classifier which serves as the objective function for a continuous grasp optimization. We additionally explicitly constrain the optimization to avoid undesired contact, directly using the reconstruction. We examine the role of geometry in grasping both in the training of grasp metrics and through 96 robot grasping trials. Our results can be found on https://sites.google. com/view/reconstruction-grasp/.
E-services evaluation criteria: the case of Azerbaijan

[full article in English] The development of evaluation and effective public administration mechanisms are crucial for providing effective e-government services. The article explores the international expertise in the field of the e-service evaluation process. The organization and provision of e-services in government entities, as well as the evaluation regarding the organization and functioning of information systems, are carried out by the “ASAN service” system in Azerbaijan. The evaluation of the organization and functioning of e-services is conducted by considering three determined criteria: the level of digitalization and relevance, the level of information openness and accessibility, and the level of convenience of use. The evaluation process of e-services allows for improving the level of services and the development of feedback mechanisms; currently, much attention is attributed to the development of citizen-centered services.
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering. Graph AE, VAE and most of their extensions rely on graph convolutional networks (GCN) to learn vector space representations of nodes. In this paper, we propose to replace the GCN encoder by a simple linear model w.r. t. the adjacency matrix of the graph. For the two aforementioned tasks, we empirically show that this approach consistently reaches competitive performances w.r. t. GCN-based models for numerous real-world graphs, including the widely used Cora, Citeseer and Pubmed citation networks that became the de facto benchmark datasets for evaluating graph AE and VAE. This result questions the relevance of repeatedly using these three datasets to compare complex graph AE and VAE models. It also emphasizes the effectiveness of simple node encoding schemes for many real-world applications.
Testing the Radius Scaling Relation with Gaia DR2 in the Kepler Field

We compare radii based on Gaia parallaxes to radii based on asteroseismic scaling relations for ∼300 dwarfs and subgiants and ∼3600 first-ascent giants from the Kepler mission. Systematics due to temperature, bolometric correction, extinction, asteroseismic radius, and the spatially correlated Gaia parallax zero-point contribute to a 2% systematic uncertainty on the agreement in Gaia–asteroseismic radius. We find that dwarf and giant scaling radii are on a parallactic scale at the level of −2.1% ± 0.5% (rand.) ± 2.0% (syst.) (dwarfs) and +1.7% ± 0.3% (rand.) ± 2.0% (syst.) (giants), supporting the accuracy and precision of scaling relations. In total, the 2% agreement that we find holds for stars spanning radii between 0.8 R ⊙ and 30 R ⊙ . We do, however, see evidence for relative errors in scaling radii between dwarfs and giants at the level of 4% ± 0.6%, and find evidence of departures from simple scaling relations for radii above 30 R ⊙ . Asteroseismic masses for very metal-poor stars are still overestimated relative to astrophysical priors, but at a reduced level. We see no trend with metallicity in radius agreement for stars with −0.5 < [Fe/H] < +0.5. We quantify the spatially correlated parallax errors in the Kepler field, which globally agree with the Gaia team’s published covariance model. We provide Gaia radii, corrected for extinction and the Gaia parallax zero-point, for our full sample of ∼3900 stars, including dwarfs, subgiants, and first-ascent giants.
Area Graph: Generation of Topological Maps using the Voronoi Diagram

Representing a scanned map of the real environment as a topological structure is an important research topic in robotics. Since topological representations of maps save a huge amount of map storage space and online computing time, they are widely used in fields such as path planning, map matching, and semantic mapping. We use a topological map representation, the Area Graph, in which the vertices represent areas and edges represent passages. The Area Graph is developed from a pruned Voronoi Graph, the Topology Graph. We also employ a simple room detection algorithm to compensate the fact that the Voronoi Graph gets unstable in open areas. We claim that our area segmentation method is superior to state-of-the-art approaches in complex indoor environments and support this claim with a number of experiments.
Full-Dimension Massive MIMO Technology for Fifth Generation Cellular Networks

Full-Dimension Massive MIMO Technology for Fifth Generation Cellular Networks Qurrat-Ul-Ain Nadeem Full dimension (FD) multiple-input multiple-output (MIMO) technology has recently attracted substantial research attention in the 3rd Generation Partnership Project (3GPP) as a promising technique for the next-generation of wireless communication networks. FD-MIMO scenarios utilize a planar two-dimensional (2D) active antenna system (AAS) that not only allows a large number of antenna elements to be placed within feasible base station (BS) form factors, but also provides the ability of elevation beamforming. This dissertation presents the elevation beamforming analysis for cellular networks utilizing FD massive MIMO antenna arrays. In particular, two architectures are proposed for the AAS the uniform linear array (ULA) and the uniform circular array (UCA) of antenna ports, where each port is mapped to a group of vertically arranged antenna elements with a corresponding downtilt weight vector. To support FD-MIMO techniques, this dissertation presents two different 3D ray-tracing channel modeling approaches, the ITU based ‘antenna port approach’ and the 3GPP technical report (TR) 36.873 based ‘antenna element approach’. The spatial correlation functions (SCF)s for both FD-MIMO arrays are characterized based on the antenna port approach. The resulting expressions depend on the underlying angular distributions and antenna patterns through the Fourier series coefficients of the power spectra and are therefore valid for any 3D propagation environment. Simulation results investigate the performance patterns of the two arrays as a function of several channel and array parameters. 5 The SCF for the ULA of antenna ports is then characterized in terms of the downtilt weight vectors, based on the more recent antenna element approach. The derived SCFs are used to form the Rayleigh correlated 3D channel model. All these aspects are put together to provide a mathematical framework for the design of elevation beamforming schemes in single-cell and multi-cell scenarios. Finally, this dissertation proposes to use the double scattering channel to model limited scattering in realistic propagation environments and derives deterministic equivalents of the signal-to-interference-plus-noise ratio (SINR) and ergodic rate with regularized zeroforcing (RZF) precoding. The performance of a massive MIMO system is shown to be limited by the number of scatterers. To this end, this dissertation points out future research directions.
Robust Few-Shot Learning with Adversarially Queried Meta-Learners

Previous work on adversarially robust neural networks requires large training sets and computationally expensive training procedures. On the other hand, few-shot learning methods are highly vulnerable to adversarial examples. The goal of our work is to produce networks which both perform well at few-shot tasks and are simultaneously robust to adversarial examples. We adapt adversarial training for meta-learning, we adapt robust architectural features to small networks for meta-learning, we test pre-processing defenses as an alternative to adversarial training for meta-learning, and we investigate the advantages of robust meta-learning over robust transfer-learning for few-shot tasks. This work provides a thorough analysis of adversarially robust methods in the context of meta-learning, and we lay the foundation for future work on defenses for few-shot tasks.
Quantized Reinforcement Learning (QUARL)

Recent work has shown that quantization can help reduce the memory, compute, and energy demands of deep neural networks without significantly harming their quality. However, whether these prior techniques, applied traditionally to image-based models, work with the same efficacy to the sequential decision making process in reinforcement learning remains an unanswered question. To address this void, we conduct the first comprehensive empirical study that quantifies the effects of quantization on various deep reinforcement learning policies with the intent to reduce their computational resource demands. We apply techniques such as post-training quantization and quantization aware training to a spectrum of reinforcement learning tasks (such as Pong, Breakout, BeamRider and more) and training algorithms (such as PPO, A2C, DDPG, and DQN). Across this spectrum of tasks and learning algorithms, we show that policies can be quantized to 6-8 bits of precision without loss of accuracy. We also show that certain tasks and reinforcement learning algorithms yield policies that are more difficult to quantize due to their effect of widening the models' distribution of weights and that quantization aware training consistently improves results over post-training quantization and oftentimes even over the full precision baseline. Finally, we demonstrate real-world applications of quantization for reinforcement learning. We use half-precision training to train a Pong model 50% faster, and we deploy a quantized reinforcement learning based navigation policy to an embedded system, achieving an 18$\times$ speedup and a 4$\times$ reduction in memory usage over an unquantized policy.
Parametrising non-linear dark energy perturbations

In this paper, we quantify the non-linear effects from k-essence dark energy through an effective parameter μ that encodes the additional contribution of a dark energy fluid or a modification of gravity to the Poisson equation. This is a first step toward quantifying non-linear effects of dark energy/modified gravity models in a more general approach. We compare our N-body simulation results from k-evolution with predictions from the linear Boltzmann code \texttt{CLASS}, and we show that for the k-essence model one can safely neglect the difference between the two potentials, Φ −Ψ, and short wave corrections appearing as higher order terms in the Poisson equation, which allows us to use single parameter μ for characterizing this model. We also show that for a large k-essence speed of sound the CLASS results are sufficiently accurate, while for a low speed of sound non-linearities in matter and in the k-essence field are non-negligible. We propose a tanh-based parameterisation for μ, motivated by the results for two cases with low (cs2=10−7) and high (cs2=10−4) speed of sound, to include the non-linear effects based on the simulation results. This parametric form of μ can be used to improve Fisher forecasts or Newtonian N-body simulations for k-essence models.
ChainSplitter: Towards Blockchain-Based Industrial IoT Architecture for Supporting Hierarchical Storage

The fast developing Industrial Internet of Things (IIoT) technologies provide a promising opportunity to build large-scale systems to connect numerous heterogeneous devices into the Internet. Most existing IIoT infrastructures are based on a centralized architecture, which is easier for management but cannot effectively support immutable and verifiable services among multiple parties. Blockchain technology provides many desired features for large-scale IIoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. This paper presents a blockchain-based IIoT architecture to support immutable and verifiable services. However, when applying blockchain technology to the IIoT infrastructure, the required storage space posts a grant challenge to resource-constrained IIoT infrastructures. To address the storage issue, this paper proposes a hierarchical blockchain storage structure, ChainSplitter. Specially, the proposed architecture features a hierarchical storage structure where the majority of the blockchain is stored in the clouds, while the most recent blocks are stored in the overlay network of the individual IIoT networks. The proposed architecture seamlessly binds local IIoT networks, the blockchain overlay network, and the cloud infrastructure together through two connectors, the blockchain connector and the cloud connector, to construct the hierarchical blockchain storage. The blockchain connector in the overlay network builds blocks in blockchain from data generated in IIoT networks, and the cloud connector resolves the blockchain synchronization issues between the overlay network and the clouds. We also provide a case study to show the efficiency of the proposed hierarchical blockchain storage in a practical Industrial IoT case.
Hydrodynamic and phoretic interactions of active particles in Python.

We present a modular Python library for computing many-body hydrodynamic and phoretic interactions between spherical active particles in suspension, when these are given by solutions of the Stokes and Laplace equations. Underpinning the library is a grid-free methodology that combines dimensionality reduction, spectral expansion, and Ritz-Galerkin discretization, thereby reducing the computation to the solution of a linear system. The system can be solved analytically as a series expansion or numerically at a cost quadratic in the number of particles. Suspension-scale quantities like fluid flow, entropy production, and rheological response are obtained at a small additional cost. The library is agnostic to boundary conditions and includes, amongst others, confinement by plane walls or liquid-liquid interfaces. The use of the library is demonstrated with six fully coded examples simulating active phenomena of current experimental interest.
The OpenUAV Swarm Simulation Testbed: a Collaborative DesignStudio for Field Robotics

In this paper, we describe our OpenUAV multi-robot design studio that enables simulations to run as browser accessible Lubuntu desktop containers. Our simulation testbed, based on ROS, Gazebo, and PX4 flight stack has been developed to facilitate collaborative mission planning, and serve as a sandbox for vision-based problems, collision avoidance, and multi-robot coordination for Unmanned Aircraft Systems (UAS). OpenUAV testbed saves students and researchers from the tedious and complex software setup by providing user-friendly Lubuntu containers equipped with PX4 flight controllers and swarm capabilities. The OpenUAV architecture is built around TurboVNC and noVNC WebSockets technology to seamlessly provide real-time web performance for 3D rendering in a collaborative design tool. We have built upon our previous work that leveraged concurrent multi-UAS simulations, and extended it to be useful for underwater, aerial and ground vehicles. The OpenUAV testbed also enables vehicles in gazebo to have visual realistic camera feeds from Unity Game Engine. Three case studies presented in this paper illustrate the scalability, extensibility, and usability of the latest OpenUAV testbed. The first study focuses on interactive performance analysis of Gazebo running in browser sessions with respect to number of consumers. The second case study demonstrates an extended OpenUAV architecture for swarm simulations through multi-host networking using docker swarm. The third study focuses on student engagement of OpenUAV testbed as part of their robotics course work.
An estimate for spherical functions on $\mathrm{SL}(3,\mathbb{R})$

We prove an estimate for spherical functions $\phi_\lambda(a)$ on $\mathrm{SL}(3,\mathbb{R})$, establishing uniform decay in the spectral parameter $\lambda$ when the group parameter $a$ is restricted to a compact subset of the abelian subgroup $\mathrm{A}$. In the case of $\mathrm{SL}(3,\mathbb{R})$, it improves a result by J.J. Duistermaat, J.A. C. Kolk and V.S. Varadarajan by removing the limitation that $a$ should remain regular. As in their work, we estimate the oscillatory integral that appears in the integral formula for spherical functions by the method of stationary phase. However, the major difference is that we investigate the stability of the singularities arising from the linearized phase function by classifying their local normal forms when the parameters $\lambda$ and $a$ vary.