Miracle-dz/UniFAR-FTUs huggingface dataset:
Pilot Distributions for Joint-Channel Carrier-Phase Estimation in Multichannel Optical Communications

Joint-channel carrier-phase estimation can improve the performance of multichannel optical communication systems. In the case of pilot-aided estimation, the pilots are distributed over a two-dimensional channel–time symbol block that is transmitted through multiple channels. However, suboptimal pilot distributions reduce the effectiveness of the carrier-phase estimation and thus result in unnecessary pilot overhead, which reduces the overall information rate of the system. It is shown that placing pilots identically in all channels is suboptimal in general. By instead optimizing the pilot distribution, the mean squared error of the phase-noise estimates can be decreased by over 90% in some cases. Moreover, it is shown that the achievable information rate can be increased by up to 0.05, 0.16, and 0.41 bits per complex symbol for dual-polarization 20 GBd transmission of 64-ary, 256-ary, and 1024-ary quadrature amplitude modulation over 20 four-dimensional channels, respectively, assuming a total laser linewidth of 200 kHz.
Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

Background: the COVID-19 has been successfully contained in China but is spreading all over the world. We use phenomenological models to dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We use the experience from China to analyze the calibration results on Japan, South Korea, Iran, Italy and Europe, and make future scenario projections. Methods: we calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries and Europe as a whole. The different models provide upper and lower bounds of our scenario predictions. Results: We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogenous situations across provinces. Based on Chinese experience, we identify a high risk in Japan with estimated total confirmed cases as of March 25 being 1574 (95% CI: [880, 2372]), and 5669 (95% CI: [988, 11340]) by June. For South Korea, we expect the number of infected cases to approach the ceiling, 7928 (95% CI: [6341, 9754]), in 20 days. We estimate 0.15% (95% CI: [0.03%, 0.30%]) of Italian population to be infected in a positive scenario. We would expect 114867 people infected in Europe in 10 days, in a negative but probable scenario, corresponding to 0.015% European population. Conclusions: The extreme containment measures implemented by China were very effective with some instructive variations across provinces. For other countries, it is almost inevitable to see the continuation of the outbreak in the coming months. Japan and Italy are in serious situations with no short-term end to the outbreak to be expected. There is a significant risk concerning the upcoming July 2020 Summer Olympics in Tokyo. Iran's situation is highly uncertain with unclear and negative future scenarios, while South Korea is approaching the end of the outbreak. Both Europe and the USA are at early stages of the outbreak, posing significant health and economic risks to the world in absence of serious measures.
Convergence of a Cahn-Hilliard type system to a parabolic-elliptic chemotaxis system with nonlinear diffusion

This paper deals with a parabolic-elliptic chemotaxis system with nonlinear diffusion. It was proved that there exists a solution of a Cahn-Hilliard system as an approximation of a nonlinear diffusion equation by applying an abstract theory by Colli-Visintin [Comm. Partial Differential Equations 15 (1990), 737-756] for a doubly nonlinear evolution inclusion with some bounded monotone operator and subdifferential operator of a proper lower semicontinuous convex function (cf. Colli-Fukao [J. Math. Anal. Appl. 429 (2015), 1190-1213]). Moreover, Colli-Fukao [J. Differential Equations 260 (2016), 6930-6959] established existence of solutions to the nonlinear diffusion equation by passing to the limit in the Cahn-Hilliard equation. However, Cahn-Hilliard approaches to chemotaxis systems with nonlinear diffusions seem not to be studied yet. This paper will try to derive existence of solutions to a parabolic-elliptic chemotaxis system with nonlinear diffusion by passing to the limit in a Cahn-Hilliard type chemotaxis system.
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning

Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two recent continual-learning scenarios have opened new avenues of research. In meta-continual learning, the model is pre-trained to minimize catastrophic forgetting of previous tasks. In continual-meta learning, the aim is to train agents for faster remembering of previous tasks through adaptation. In their original formulations, both methods have limitations. We stand on their shoulders to propose a more general scenario, OSAKA, where an agent must quickly solve new (out-of-distribution) tasks, while also requiring fast remembering. We show that current continual learning, meta-learning, meta-continual learning, and continual-meta learning techniques fail in this new scenario. We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario. We empirically show that Continual-MAML is better suited to the new scenario than the aforementioned methodologies, as well as standard continual learning and meta-learning approaches.
A cow structural model for video analytics of cow health

In livestock farming, animal health directly influences productivity. For dairy cows, many health conditions can be evaluated by trained observers based on visual appearance and movement. However, to manually evaluate every cow in a commercial farm is expensive and impractical. This paper introduces a video-analytic system which automatically detects the cow structure from captured video sequences. A side-view cow structural model is designed to describe the spatial positions of the joints (keypoints) of the cow, and we develop a system using deep learning to automatically extract the structural model from videos. The proposed detection system can detect multiple cows in the same frame and provide robust performance under practical challenges like obstacles (fences) and poor illumination. Compared to other object detection methods, this system provides better detection results and successfully isolates the keypoints of each cow even when they are close to each other.
Disentangling the formation history of galaxies via population-orbit superposition: method validation

We present population-orbit superposition models for external galaxies based on Schwarzschild’s orbit-superposition method, by tagging the orbits with age and metallicity. The models fit the density distributions, kinematic, and age and metallicity maps from integral field unit (IFU) spectroscopy observations. We validate the method and demonstrate its power by applying it to mock data, similar to those obtained by the Multi-Unit Spectroscopic Explorer (MUSE) IFU on the Very Large Telescope (VLT). These mock data are created from Auriga galaxy simulations, viewed at three different inclination angles (ϑ = 40°, 60°, 80°). Constrained by MUSE-like mock data, our model can recover the galaxy’s stellar orbit distribution projected in orbital circularity λz versus radius r, the intrinsic stellar population distribution in age t versus metallicity Z, and the correlation between orbits’ circularity λz and stellar age t. A physically motivated age–metallicity relation improves the recovering of intrinsic stellar population distributions. We decompose galaxies into cold, warm, and hot+counter-rotating components based on their orbit circularity distribution, and find that the surface density, velocity, velocity dispersion, and age and metallicity maps of each component from our models well reproduce those from simulation, especially for projections close to edge-on. These galaxies exhibit strong global age versus σz relation, which is well recovered by our model. The method has the power to reveal the detailed build-up of stellar structures in galaxies, and offers a complement to local resolved, and high-redshift studies of galaxy evolution.
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

Scaling adaptive traffic signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning attempts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-network architectures—dominating in the multi-agent setting—do not offer the flexibility to handle an arbitrary number of entities which changes both between road networks, and over time as vehicles traverse the network. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks which adapts to the structure of any road network, to learn detailed representations of traffic signal controllers and their surroundings. Our decentralized approach enables learning of a transferable-adaptive-traffic-signal-control policy. After being trained on an arbitrary set of road networks, our model can generalize to new road networks and traffic distributions, with no additional training and a constant number of parameters, enabling greater scalability compared to prior methods. Furthermore, our approach can exploit the granularity of available data by capturing the (dynamic) demand at both the lane level and the vehicle level. The proposed method is tested on both road networks and traffic settings never experienced during training. We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines. In both synthetic road networks and in a larger experiment involving the control of the 3,971 traffic signals of Manhattan, we show that different instantiations of IG-RL outperform baselines.
Extended Batch Normalization

Batch normalization (BN) has become a standard technique for training the modern deep networks. However, its effectiveness diminishes when the batch size becomes smaller, since the batch statistics estimation becomes inaccurate. That hinders batch normalization's usage for 1) training larger model which requires small batches constrained by memory consumption, 2) training on mobile or embedded devices of which the memory resource is limited. In this paper, we propose a simple but effective method, called extended batch normalization (EBN). For NCHW format feature maps, extended batch normalization computes the mean along the (N, H, W) dimensions, as the same as batch normalization, to maintain the advantage of batch normalization. To alleviate the problem caused by small batch size, extended batch normalization computes the standard deviation along the (N, C, H, W) dimensions, thus enlarges the number of samples from which the standard deviation is computed. We compare extended batch normalization with batch normalization and group normalization on the datasets of MNIST, CIFAR-10/100, STL-10, and ImageNet, respectively. The experiments show that extended batch normalization alleviates the problem of batch normalization with small batch size while achieving close performances to batch normalization with large batch size.
A general linear-time inference method for Gaussian Processes on one dimension

Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues. Here we investigate data sampled on one dimension (e.g., a scalar or vector time series sampled at arbitrarily-spaced intervals), for which state-space models are popular due to their linearly-scaling computational costs. It has long been conjectured that state-space models are general, able to approximate any one-dimensional GP. We provide the first general proof of this conjecture, showing that any stationary GP on one dimension with vector-valued observations governed by a Lebesgue-integrable continuous kernel can be approximated to any desired precision using a specifically-chosen state-space model: the Latent Exponentially Generated (LEG) family. This new family offers several advantages compared to the general state-space model: it is always stable (no unbounded growth), the covariance can be computed in closed form, and its parameter space is unconstrained (allowing straightforward estimation via gradient descent). The theorem's proof also draws connections to Spectral Mixture Kernels, providing insight about this popular family of kernels. We develop parallelized algorithms for performing inference and learning in the LEG model, test the algorithm on real and synthetic data, and demonstrate scaling to datasets with billions of samples.
Quasi-symmetric designs on 56 points

<p style='text-indent:20px;'>Computational techniques for the construction of quasi-symmetric block designs are explored and applied to the case with <inline-formula><tex-math id="M2">\begin{document}$ 56 $\end{document}</tex-math></inline-formula> points. One new <inline-formula><tex-math id="M3">\begin{document}$ (56,16,18) $\end{document}</tex-math></inline-formula> and many new <inline-formula><tex-math id="M4">\begin{document}$ (56,16,6) $\end{document}</tex-math></inline-formula> designs are discovered, and non-existence of <inline-formula><tex-math id="M5">\begin{document}$ (56,12,9) $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M6">\begin{document}$ (56,20,19) $\end{document}</tex-math></inline-formula> designs with certain automorphism groups is proved. The number of known symmetric <inline-formula><tex-math id="M7">\begin{document}$ (78,22,6) $\end{document}</tex-math></inline-formula> designs is also significantly increased. </p>
Factorization of Antenna Efficiency of Aperture-Type Antenna: Beam Coupling and Two Spillovers

Antenna efficiency is one of the most important figures of merit of a radio telescope for observations, especially at millimeter wavelengths or shorter wavelengths, even for a multibeam radio telescope. To analyze a system with a beam waveguide, a lossless antenna consisting of two apertures in series is considered in the frame of the scalar wave approximation. We found that the antenna efficiency can be evaluated with the field distribution over the second aperture, and the antenna efficiency is factorized into three factors: efficiencies of beam coupling, transmission spillover, and reception spillover. The factorization is applicable to general aperture-type antennas with beam waveguides and can relate the aperture efficiency to the pupil function. We numerically confirmed our factorization with an optical simulation. This evaluation enables us to manage the aberrations and is useful in the design of multibeam radio telescopes.
Inline Detection of DGA Domains Using Side Information

Malware applications typically use a command and control (C&C) server to manage bots to perform malicious activities. Domain Generation Algorithms (DGAs) are popular methods for generating pseudo-random domain names that can be used to establish a communication between an infected bot and the C&C server. In recent years, machine learning based systems have been widely used to detect DGAs. There are several well known state-of-the-art classifiers in the literature that can detect DGA domain names in real-time applications with high predictive performance. However, these DGA classifiers are highly vulnerable to adversarial attacks in which adversaries purposely craft domain names to evade DGA detection classifiers. In our work, we focus on hardening DGA classifiers against adversarial attacks. To this end, we train and evaluate state-of-the-art deep learning and random forest (RF) classifiers for DGA detection using side information that is harder for adversaries to manipulate than the domain name itself. Additionally, the side information features are selected such that they are easily obtainable in practice to perform inline DGA detection. The performance and robustness of these models is assessed by exposing them to one day of real-traffic data as well as domains generated by adversarial attack algorithms. We found that the DGA classifiers that rely on both the domain name and side information have high performance and are more robust against adversaries.
Differential Impact of Climate Change on the Hydropower Economics of Two River Basins in High Mountain Asia

Water stored in the form of snow and glaciers in the High Mountain Asia (HMA) region regulates the water supply, and resultant water-based economies, that support the livelihoods of millions of people. Trends in the seasonal and long-term melting of snow and glaciers, governed by initial ice reserves, meteorological factors and geographic features, vary across sub-basins in the HMA region. We examined the economic impacts of climate-led changes in river flow in two drainage basins, one each from the Karakoram and Central Himalaya region. We used an integrated assessment framework to estimate the changes in economic value of the hydropower generation from hydropower plants on rivers fed by snow and glacier melt in the two sub-basins. The framework, developed under a NASA High Mountain Asia project, coupled biophysical models (a suite of climate models, snow/glacier-hydrology, and hydropower model) with economic analysis. We compared the differences in estimated river flow over historic and future time using the water balance model in sixteen scenarios (eight climate models and two emissions scenarios) for rivers upstream of hydropower plants in each sub-basin. Using the hydropower model we developed, we estimated the changes in hydropower generation at the Naltar IV hydropower plant, with an 18 MW capacity, located in Hunza, Karakoram, and the Trishuli hydropower plant, with a 19.6 MW capacity, in Trishuli, Central Himalaya. When compared to their baselines, the estimated impact of climate change and temporal variability were higher for the Naltar plant than for the Trishuli plant. Our sensitivity analysis shows that hydropower plants with water storage facilities help reduce the impact of changes, but the estimated impacts are higher for the higher capacity plants. This study provides an example of the differential impacts of climate change on hydropower plants located in rivers fed by varying amounts of snow and glacier melt at different decades in this century. This type of integrated assessment of climate change impact will support the scientific understanding of hydrologic flow and its impacts on a hydropower economy under various climate scenarios, as well as generate information about water resource management in a changing climate.
2D Convection-Diffusion in Multipolar Flows

We present a complete analysis of the problem of convection-diffusion in low Re, 2-dimensional flows with distributions of singularities, such as those found in open-space microfluidics and in groundwater flows. Using Boussinesq transformations and solving the problem in streamline coordinates, we obtain concentration profiles in flows with complex arrangements of sources and sinks for both high and low Pe. These yield the complete analytical concentration profile at every point in applications that previously relied on material surface tracking, local lump models or numerical analysis such as microfluidic probes, groundwater heat pumps, or diffusive flows in porous media. Using conformal transforms, we generate families of symmetrical solutions from simple ones, and provide a general methodology that can be used to analyze any arrangement of source and sinks. The solutions obtained that contain the explicit dependence on the various parameters of the problems, such as Pe, the spacing of the apertures and their relative injection and aspiration rates. In particular, we show that the high Pe models can model problems with Pe as low as 1 with a maximum error committed of under $10\%$, and that this error decreases approximately as $Pe^{-1.5}$.
Collision-geometry-based 3D initial condition for relativistic heavy-ion collisions

We present a simple way to construct three-dimensional initial conditions for relativistic heavy-ion collisions based on the Glauber collision geometry. Local energy and momentum conservation conditions are imposed to set non-trivial constraints on our parametrizations of longitudinal profiles for the system's initial energy density and flow velocity. After calibrating parameters with charged hadron rapidity distributions in central $\mathrm{Au}+\mathrm{Au}$ collisions, we test model predictions for particle rapidity distributions in $d+\text{Au}$ and peripheral $\mathrm{Au}+\mathrm{Au}$ collisions in the Beam Energy Scan program at the Relativistic Heavy-Ion Collider. Simulations and comparisons with measurements are also made for $\mathrm{Pb}+\mathrm{Pb}$ collisions at Super Proton Synchrotron energies. We demonstrate that elliptic flow measurements in heavy-ion collisions at $\sqrt{s}\ensuremath{\sim}10$ GeV can set strong constraints on the dependence of quark-gluon plasma shear viscosity on temperature and net baryon chemical potential.
Investigating the influence Brexit had on Financial Markets, in particular the GBP/EUR exchange rate

On 23rd June 2016, 51.9% of British voters voted to leave the European Union, triggering a process and events that have led to the United Kingdom leaving the EU, an event that has become known as 'Brexit'. In this piece of research, we investigate the effects of this entire process on the currency markets, specifically the GBP/EUR exchange rate. Financial markets are known to be sensitive to news articles and media, and the aim of this research is to evaluate the magnitude of impact of relevant events, as well as whether the impact was positive or negative for the GBP.
Securing Autonomous Service Robots through Fuzzing, Detection, and Mitigation

Autonomous service robots share social spaces with humans, usually working together for domestic or professional tasks. Cyber security breaches in such robots undermine the trust between humans and robots. In this paper, we investigate how to apprehend and inflict security threats at the design and implementation stage of a movable autonomous service robot. To this end, we leverage the idea of directed fuzzing and design RoboFuzz that systematically tests an autonomous service robot in line with the robot's states and the surrounding environment. The methodology of RoboFuzz is to study critical environmental parameters affecting the robot's state transitions and subject the robot control program with rational but harmful sensor values so as to compromise the robot. Furthermore, we develop detection and mitigation algorithms to counteract the impact of RoboFuzz. The difficulties mainly lie in the trade-off among limited computation resources, timely detection and the retention of work efficiency in mitigation. In particular, we propose detection and mitigation methods that take advantage of historical records of obstacles to detect inconsistent obstacle appearances regarding untrustworthy sensor values and navigate the movable robot to continue moving so as to carry on a planned task. By doing so, we manage to maintain a low cost for detection and mitigation but also retain the robot's work efficacy. We have prototyped the bundle of RoboFuzz, detection and mitigation algorithms in a real-world movable robot. Experimental results confirm that RoboFuzz makes a success rate of up to 93.3% in imposing concrete threats to the robot while the overall loss of work efficacy is merely 4.1% at the mitigation mode.