Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Challenges for Crowd Density Estimation

Nowadays, many emergency systems and surveillance systems are related to the management of the crowd. The supervision of a crowded area presents a great challenge especially when the size of the crowd is unknown. This issue presents a point of start to the field of the estimation of the crowd based on density or counts. The density of a crowded area is one of the important topics dealt with in many kinds of applications like surveillance, security, biology, traffic. In this paper, we try not only to present a deep review of the different approaches/techniques used in the previous works to estimate the size of the crowd but also to describe the different datasets used. A comparison of some related works based on the weakness and the strength features of each approach is highlighted to show the important research key related to the field of the estimation of the crowded area.
admcycles - a Sage package for calculations in the tautological ring of the moduli space of stable curves

The tautological ring of the moduli space of stable curves has been studied extensively in the last decades. We present a SageMath implementation of many core features of this ring. This includes lists of generators and their products, intersection numbers and verification of tautological relations. Maps between tautological rings induced by functoriality, that is pushforwards and pullbacks under gluing and forgetful maps, are implemented. Furthermore, many interesting cycle classes, such as the double ramification cycles, strata of k-differentials and hyperelliptic or bielliptic cycles are available. In this paper we show how to apply the package, including concrete example computations.
Spatiotemporal scaling of two-dimensional nonequilibrium exciton-polariton systems with weak interactions

We perform a numerical study on the two-dimensional nonequilibrium exciton-polariton systems driven by incoherent pumping based on the stochastic generalized Gross-Pitaevskii equation. We calculate the density fluctuation, coherence function, and scaling function. It is found that the correlations at short range agree with the Bogoliubov linear theory. While at large distance, both static and dynamic correlations are characterized by the nonlinear scaling behaviors of Kardar-Parisi-Zhang (KPZ) universality class, especially when the interaction is weak. In this regime, scaling analyses are crucial to capture the universal KPZ scaling features. In addition, the interaction between vortices is modified in the strong KPZ regime and leads to complex nonequilibrium vortex patterns.
Knowledge Federation: Hierarchy and Unification

With the strengthening of data privacy and security, traditional data centralization for AI faces huge challenges. Moreover, isolated data existing in various industries and institutions is grossly underused and thus retards the advance of AI applications. We propose a possible solution to these problems: knowledge federation. Beyond the concepts of federated learning and secure multi-party computation, we introduce a comprehensive knowledge federation framework, which is a hierarchy with four-level federation. In terms of the occurrence time of federation, knowledge federation can be categorized into information level, model level, cognition level, and knowledge level. To facilitate widespread academic and commercial adoption of this concept, we provide definitions free from ambiguity for the knowledge federation framework. In addition, we clarify the relationship and differentiation between knowledge federation and other related research fields and conclude that knowledge federation is a unified framework for secure multi-party computation and learning.
Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

<p>The Ensemble Adjustment Kalman Filter of the Data Assimilation Research Testbed is implemented to assimilate observations of satellite sea surface temperature, altimeter sea surface height and in-situocean temperature and salinity profiles into an eddy-resolving 4km-Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. We investigate the impact of three different assimilation strategies (1) <em>Iexp</em>&#8211; inflates filter error covariance by 10%, (2) <em>IAexp</em>&#8211; adds ensemble of atmospheric forcing to Iexp, and (3) <em>IAPexp</em>&#8211; adds perturbed model physics toIAexp. The assimilation experiments are run for one year, starting from the same initial ensemble on 1<sup>st</sup>January, 2011 and the data are assimilated every three days. </p><p>Results demonstrate that the <em>Iexp</em> mainly improved the model outputs with respect to assimilation-free MITgcm run in the first few months, before showing signs of dynamical imbalances in the ocean estimates, particularly in the data-sparse subsurface layers. The <em>IAexp</em> yielded substantial improvements throughout the assimilation period with almost no signs of imbalances, including the subsurface layers. It further well preserved the model mesoscales features resulting in an improved forecasts for eddies, both in terms of intensity and location. Perturbing model physics in <em>IAPexp</em> slightly improved the forecast statistics. It further increased smoothness in the ocean forecasts and improved the placement of basin-scale eddies, but caused loss of some high-resolution features. Increasing hydrographic coverage helps recovering the losses and yields more improvements in <em>IAPexp</em> compared to <em>IAexp</em>. Switching off inflation in <em>IAexp</em> and <em>IAPexp</em> leads to further improvements, especially in the subsurface layers. </p>
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.
Optimal deception attack on networked vehicular cyber physical systems

Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observations and messages obtained from neighbouring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation and online stochastic gradient descent. Numerical results demonstrate the efficacy of the attack.
ϵ-shotgun: ϵ-greedy batch bayesian optimisation

Bayesian optimisation is a popular surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to-query acquisition function. We present an ϵ-greedy procedure for Bayesian optimisation in batch settings in which the black-box function can be evaluated multiple times in parallel. Our ϵ-shotgun algorithm leverages the model's prediction, uncertainty, and the approximated rate of change of the landscape to determine the spread of batch solutions to be distributed around a putative location. The initial target location is selected either in an exploitative fashion on the mean prediction, or - with probability ϵ - from elsewhere in the design space. This results in locations that are more densely sampled in regions where the function is changing rapidly and in locations predicted to be good (i.e. close to predicted optima), with more scattered samples in regions where the function is flatter and/or of poorer quality. We empirically evaluate the ϵ-shotgun methods on a range of synthetic functions and two real-world problems, finding that they perform at least as well as state-of-the-art batch methods and in many cases exceed their performance.
Deep RBF Value Functions for Continuous Control

A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned state-action value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep RBF value functions: state-action value functions learned using a deep neural network with a radial-basis function (RBF) output layer. We show that the optimal action with respect to a deep RBF value function can be easily approximated up to any desired accuracy. Moreover, deep RBF value functions can represent any true value function up to any desired accuracy owing to their support for universal function approximation. By learning a deep RBF value function, we extend the standard DQN algorithm to continuous control, and demonstrate that the resultant agent, RBF-DQN, outperforms standard baselines on a set of continuous-action RL problems.
Online Passive-Aggressive Total-Error-Rate Minimization

We provide a new online learning algorithm which utilizes online passive-aggressive learning (PA) and total-error-rate minimization (TER) for binary classification. The PA learning establishes not only large margin training but also the capacity to handle non-separable data. The TER learning on the other hand minimizes an approximated classification error based objective function. We propose an online PATER algorithm which combines those useful properties. In addition, we also present a weighted PATER algorithm to improve the ability to cope with data imbalance problems. Experimental results demonstrate that the proposed PATER algorithms achieves better performances in terms of efficiency and effectiveness than the existing state-of-the-art online learning algorithms in real-world data sets.
Low-Frequency Noise Parameter Extraction Method for Single-Layer Graphene FETs

In this article, a detailed parameter extraction methodology is proposed for low-frequency noise (LFN) in single-layer (SL) graphene transistors (GFETs) based on a recently established compact LFN model. The drain current and LFN of two short channel back-gated GFETs (<inline-formula> <tex-math notation="LaTeX">${L} = {300}$ </tex-math></inline-formula> and 100 nm) were measured at lower and higher drain voltages, for a wide range of gate voltages covering the region away from charge neutrality point (CNP) up to CNP at p-type operation region. Current–voltage (<italic>IV</italic>) and LFN data were also available from a long-channel SL top solution-gated (SG) GFET (<inline-formula> <tex-math notation="LaTeX">${L} = {5}\,\,\mu \text{m}$ </tex-math></inline-formula>), for both p- and n-type regions near and away CNP. At each of these regimes, the appropriate <italic>IV</italic> and LFN parameters can be accurately extracted. Regarding LFN, mobility fluctuation effect is dominant at CNP, and from there, the Hooge parameter <inline-formula> <tex-math notation="LaTeX">$\alpha _{H}$ </tex-math></inline-formula> can be extracted, whereas the carrier number fluctuation contribution which is responsible for the well-known M-shape bias dependence of output noise divided by squared drain current, also observed in our data, makes possible the extraction of the <inline-formula> <tex-math notation="LaTeX">${N}_{T}$ </tex-math></inline-formula> parameter related to the number of traps. In the less possible case of a <inline-formula> <tex-math notation="LaTeX">$\Lambda $ </tex-math></inline-formula>-shape trend, <inline-formula> <tex-math notation="LaTeX">${N}_{T}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha _{H}$ </tex-math></inline-formula> can be extracted simultaneously from the region near CNP. Away from CNP, contact resistance can have a significant contribution to LFN, and from there, the relevant parameter <inline-formula> <tex-math notation="LaTeX">${S}_{\Delta {R}}^{{2}}$ </tex-math></inline-formula> is defined. The LFN parameters described above can be estimated from the low drain voltage region of operation where the effect of velocity saturation (VS) mechanism is negligible. VS effect results in the reduction of LFN at higher drain voltages, and from there, the <italic>IV</italic> parameter <inline-formula> <tex-math notation="LaTeX">$h\Omega $ </tex-math></inline-formula> which represents the phonon energy and is related to VS effect can be derived both from drain current and LFN data.
DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction

Maps of brain microarchitecture are important for understanding neurological function and behavior, including alterations caused by chronic conditions such as neurodegenerative disease. Techniques such as knife-edge scanning microscopy (KESM) provide the potential for whole organ imaging at sub-cellular resolution. However, multi-terabyte data sizes make manual annotation impractical and automatic segmentation challenging. Densely packed cells combined with interconnected microvascular networks are a challenge for current segmentation algorithms. The massive size of high-throughput microscopy data necessitates fast and largely unsupervised algorithms. In this paper, we investigate a fully-convolutional, deep, and densely-connected encoder-decoder for pixel-wise semantic segmentation. The excessive memory complexity often encountered with deep and dense networks is mitigated using skip connections, resulting in fewer parameters and enabling a significant performance increase over prior architectures. The proposed network provides superior performance for semantic segmentation problems applied to open-source benchmarks. We finally demonstrate our network for cellular and microvascular segmentation, enabling quantitative metrics for organ-scale neurovascular analysis.
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making

The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning. In this paper, we propose a novel Forward-Backward Learning procedure to test MA in sequential decision making. The proposed test does not assume any parametric form on the joint distribution of the observed data and plays an important role for identifying the optimal policy in high-order Markov decision processes and partially observable MDPs. We apply our test to both synthetic datasets and a real data example from mobile health studies to illustrate its usefulness.
MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: ad loading content and ad clicking content. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem.
FastGAE: Fast, Scalable and Effective Graph Autoencoders with Stochastic Subgraph Decoding

Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of nodes and edges. Our strategy, based on node sampling and subgraph decoding, significantly speeds up the training of graph AE and VAE while preserving or even improving performances. We demonstrate the effectiveness of FastGAE on numerous real-world graphs, outperforming the few existing approaches to scale graph AE and VAE by a wide margin.
Detecting and Characterizing Young Quasars. I. Systemic Redshifts and Proximity Zone Measurements

In a multiwavelength survey of 13 quasars at 5.8 ≲ z ≲ 6.5, which were preselected to be potentially young, we find five objects with extremely small proximity zone sizes that may imply UV-luminous quasar lifetimes of ≲100,000 yr. Proximity zones are regions of enhanced transmitted flux in the vicinity of quasars that are sensitive to the quasars’ lifetimes because the intergalactic gas has a finite response time to their radiation. We combine submillimeter observations from the Atacama Large Millimetre Array and the NOrthern Extended Millimeter Array, as well as deep optical and near-infrared spectra from the medium-resolution spectrograph on the Very Large Telescope and on the Keck telescopes, in order to identify and characterize these new young quasars, which provide valuable clues about the accretion behavior of supermassive black holes in the early universe and pose challenges on current black hole formation models to explain the rapid formation of billion-solar-mass black holes. We measure the quasars’ systemic redshifts, black hole masses, Eddington ratios, emission-line luminosities, and star formation rates of their host galaxies. Combined with previous results, we estimate the fraction of young objects within the high-redshift quasar population at large to be 5% ≲ f young ≲ 10%. One of the young objects, PSO J158–14, shows a very bright dust continuum flux (F cont = 3.46 ± 0.02 mJy), indicating a highly starbursting host galaxy with a star formation rate of approximately 1420 M ⊙ yr−1.
Topology Identification in Distribution Networks using Harmonic Synchrophasor Measurements

Topology identification (TI) in distribution networks is a challenging task due to the limited measurement resources and therefore the inevitable need to use pseudo-measurements that are often inaccurate. To address this issue, a new method is proposed in this paper to integrate harmonic synchrophasors into the TI problem in order to enhance TI accuracy in distribution networks. In this method, topology identification is done jointly based on both fundamental synchrophasor measurements and harmonic synchrophasor measurements. This is done by formulating and then solving a mixed-integer linear programming (MILP) problem. Furthermore, an analysis is provided to capture the number of and the location of harmonic sources and sensors that are needed to ensure full observability. The benefits of the proposed TI scheme are compared against those of the traditional scheme that utilizes only the fundamental measurements. Finally, through numerical simulations on the IEEE 33-Bus power system, it is shown that the proposed scheme is considerably accurate compared to the traditional scheme in topology identification.
Data-driven high-fidelity prediction of the equivalent sand-grain height of rough surfaces

This work addresses a long standing question about roughness: what is the equivalent sand-grain height, given the roughness topography? Deep Neural Network (DNN) and Gaussian Process Regression (GPR) machine learning approaches are used to develop a high-fidelity prediction approach of the Nikuradse (1933) equivalent sand-grain height $k_s$ for turbulent flows over a wide variety of different rough walls. To this end, 45 surface geometries are generated and simulated at $Re_\tau=1000$. The surfaces geometry differ widely in moments of surface height fluctuations, effective slope, average inclination, porosity and degree of randomness. When combined with 15 fully rough experimental data sets, courtesy of Flack et al (2016, 2018 and 2019), the DNN and GPR methods predict $k_s$ with an rms error of less than 10\% and a maximum error of less than 30\%, which appears to be significantly more accurate than existing correlations applied to the present database.
The fundamental performance of FAST with 19-beam receiver at L band

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has passed national acceptance and finished one pilot cycle of ‘Shared-Risk’ observations. It will start formal operation soon. In this context, this paper describes testing results of key fundamental parameters for FAST, aiming to provide basic support for observation and data reduction of FAST for scientific researchers. The 19-beam receiver covering 1.05–1.45 GHz was utilized for most of these observations. The fluctuation in electronic gain of the system is better than 1% over 3.5 hours, enabling enough stability for observations. Pointing accuracy, aperture efficiency and system temperature are three key parameters for FAST. The measured standard deviation of pointing accuracy is 7.9″, which satisfies the initial design of FAST. When zenith angle is less than 26.4°, the aperture efficiency and system temperature around 1.4 GHz are ∼0.63 and less than 24 K for central beam, respectively. The sensitivity and stability of the 19-beam backend are confirmed to satisfy expectation by spectral Hi observations toward NGC 672 and polarization observations toward 3C 286. The performance allows FAST to take sensitive observations for various scientific goals, from studies of pulsars to galaxy evolution.