Miracle-dz/UniFAR-FTUs huggingface dataset:
Bootstrap Your Flow

Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current flow-based approaches are limited on challenging targets where they either suffer from mode seeking behaviour or high variance in the training loss, or rely on samples from the target distribution, which may not be available. To address these challenges, we combine flows with annealed importance sampling (AIS), while using the $\alpha$-divergence as our objective, in a novel training procedure, FAB (Flow AIS Bootstrap). Thereby, the flow and AIS improve each other in a bootstrapping manner. We demonstrate that FAB can be used to produce accurate approximations to complex target distributions, including Boltzmann distributions, in problems where previous flow-based methods fail.
On the spread of outerplanar graphs

Abstract The spread of a graph is the difference between the largest and most negative eigenvalue of its adjacency matrix. We show that for sufficiently large nn, the nn-vertex outerplanar graph with maximum spread is a vertex joined to a linear forest with Ω(n)\Omega \left(n) edges. We conjecture that the extremal graph is a vertex joined to a path on n−1n-1 vertices.
A unified diffuse interface method for the interaction of rigid bodies with elastoplastic solids and multi-phase mixtures

This work outlines a new multi-physics-compatible immersed rigid body method for Eulerian finite-volume simulations. To achieve this, rigid bodies are represented as a diffuse scalar field, and an interface seeding method is employed to mediate the interface boundary conditions. The method is based on an existing multi-material diffuse interface method that is capable of handling an arbitrary mixture of fluids and elastoplastic solids. The underlying method is general and can be extended to a range of different applications including high-strain rate deformation in elastoplastic solids and reactive fluid mixtures. As such, the new method presented here is thoroughly tested through a variety of problems, including fluid–rigid body interaction, elastoplastic–rigid body interaction, and detonation–structure interaction. Comparison is drawn between both experimental work and previous numerical results, with excellent agreement in both cases. The new method is straightforward to implement, highly local, and parallelizable. This allows the method to be employed in three dimensions with multiple levels of adaptive mesh refinement using complex immersed geometries. The rigid body field can be static or dynamic, with the tangent of hyperbola interface capturing reconstruction method being used to keep the interface sharp in the dynamic case.
Experimental studies on the aerodynamic characteristics of a pantograph suitable for a high-speed train

The aerodynamic properties of a pantograph that is under consideration for application on Korean high-speed trains are experimentally investigated. The selected experimental models were one-quarter scale pantographs (a double-arm pantograph, single-arm pantograph and a periscope pantograph) with either a rectangular panhead or a panhead with an optimized streamlined shape. To increase the performance and the robustness of the pantograph, the drag coefficient and the fluctuation of the lift coefficient along the angle of attack are simultaneously minimized. In order to confirm the aerodynamic enhancement of the pantograph due to the optimized panhead, the aerodynamic forces are compared with those of a pantograph with a rectangular panhead. To investigate the aerodynamic force distribution of a pantograph, the aerodynamic forces of the lift and drag of the pantograph are measured in wind tunnel tests and analyzed in terms of the pantograph’s components. In addition, wake flows are examined based on variations of the shape of the panhead.
Instantaneous normal modes of a glass-forming liquid during the relaxation process

Understanding glass formation by quenching remains a challenge in soft condensed matter physics. Recent numerical studies on steepest descent dynamics, which is one of the simplest models of quenching, revealed that quenched liquids undergo slow relaxation with a power law towards mechanical equilibrium and that local rearrangements of particles govern the late stage of the process. These advances motivate the detailed study of instantaneous normal modes during the relaxation process because they are significant in the dynamics governed by stationary points of the potential energy landscape. Here, we performed a normal mode analysis of configurations during relaxation and found that unstable localized modes dominate the dynamics. We also observed power-law relations between several fundamental observables and a stretched exponential law in the most unstable mode of a configuration. These findings substantiate our naive expectation about the relaxation dynamics based on quantitative analysis.
Camera Measurement of Physiological Vital Signs

The need for remote tools for healthcare monitoring has never been more apparent. Camera measurement of vital signs leverages imaging devices to compute physiological changes by analyzing images of the human body. Building on advances in optics, machine learning, computer vision, and medicine, these techniques have progressed significantly since the invention of digital cameras. This article presents a comprehensive survey of camera measurement of physiological vital signs, describing the vital signs that can be measured and the computational techniques for doing so. I cover both clinical and non-clinical applications and the challenges that need to be overcome for these applications to advance from proofs of concept. Finally, I describe the current resources (datasets and code) available to the research community and provide a comprehensive webpage (https://cameravitals.github.io/) with links to these resource and a categorized list of all papers referenced in this article.
Appliance Level Short-term Load Forecasting via Recurrent Neural Network

Accurate load forecasting is critical for electricity market operations and other real-time decision-making tasks in power systems. This paper considers the short-term load forecasting (STLF) problem for residential customers within a community. Existing STLF work mainly focuses on forecasting the aggregated load for either a feeder system or a single customer, but few efforts have been made on forecasting the load at individual appliance level. In this work, we present an STLF algorithm for efficiently predicting the power consumption of individual electrical appliances. The proposed method builds upon a powerful recurrent neural network (RNN) architecture in deep learning, termed as long short-term memory (LSTM). As each appliance has uniquely repetitive consumption patterns, the patterns of prediction error will be tracked such that past prediction errors can be used for improving the final prediction performance. Numerical tests on real-world load datasets demonstrate the improvement of the proposed method over existing LSTM-based method and other benchmark approaches.
Threat Modeling and Security Analysis of Containers: A Survey

Traditionally, applications that are used in large and small enterprises were deployed on “bare metal” servers installed with operating systems. Recently, the use of multiple virtual machines (VMs) on the same physical server was adopted due to cost reduction and flexibility. Nowadays, containers have become popular for application deployment due to smaller footprints than the VMs, their ability to start and stop more quickly, and their capability to pack the application binaries and their dependencies/libraries in standalone units for seamless portability. A typical container ecosystem includes a code repository (e.g., GitHub) where the container images are built from the codes and libraries and then pushed to the image registry (e.g., Docker Hub) for subsequent deployment as application containers. However, the pervasive use of containers also leads to a wide-range of security breaches such as attackers stealing credentials, source codes and sensitive data from image registry and code repository, carrying out DoS attacks on application containers, and gaining root access to misuse the underlying host resources, among others. In this paper, we first perform threat modeling on the containers ecosystem using the popular threat modeling framework, called STRIDE. Using STRIDE, we identify the vulnerabilities in each system component, and investigate potential security threats and their consequences. Then, we conduct a comprehensive survey on the existing countermeasures designed against the identified threats and vulnerabilities in containers. In particular, we assess the strengths and weaknesses of the existing mitigation strategies designed against such threats. We believe that this work will help researchers and practitioners to gain a deeper understanding of the threat landscape in containers and the state-of-the-art countermeasures. We also discuss open research problems, the research gaps and future research directions in containers security, which may ignite further research to be done in this area.
Innovative Electroacoustic resonator Control enforcing Duffing dynamics at moderate excitation levels: conception and experimental validation

The electroacoustic resonator is an efficient electro-active device for noise attenuation in enclosed cavities or acoustic waveguides. It is made of a loudspeaker (the actuator) and one or more microphones (the sensors). So far, the desired acoustic behaviour, expressed in terms of a linear relationship between sound pressure and vibrational motion, has been more efficiently achieved by a model-inversion strategy which is implemented by driving the electrical current in the loudspeaker coil, based upon the measured pressure. The corrector transfer function is hence digitally executed by the classical infinite-impulseresponse technique. In order to enforce non-linear, instead of linear, operators between the pressure and vibrational motion of the speaker diaphragm, in the same pressure-based, current-driven architecture, the transfer-function-based control strategies must be abandoned. In this manuscript, we present a novel technique based upon real-time integration of potentially any target dynamics (either linear or nonlinear). Then, we experimentally validate such control strategy by enforcing a Duffing type dynamics at excitation levels far below the linearity limit of the mechano-acoustical dynamics of the resonator. Such control strategy demonstrates to efficiently reproduce nonlinear operators, opening the doors for the experimental investigation of noise-control by a potentially vast spectrum of programmable non-linear boundaries, under the ordinary sound excitation levels.
Is Shapley Explanation for a model unique?

Shapley value has recently become a popular way to explain the predictions of complex and simple machine learning models. This paper is discusses the factors that influence Shapley value. In particular, we explore the relationship between the distribution of a feature and its Shapley value. We extend our analysis by discussing the difference that arises in Shapley explanation for different predicted outcomes from the same model. Our assessment is that Shapley value for particular feature not only depends on its expected mean but on other moments as well such as variance and there are disagreements for baseline prediction, disagreements for signs and most important feature for different outcomes such as probability, log odds, and binary decision generated using same linear probability model (logit/probit). These disagreements not only stay for local explainability but also affect the global feature importance. We conclude that there is no unique Shapley explanation for a given model. It varies with model outcome (Probability/Log-odds/binary decision such as accept vs reject) and hence model application.
IF equation: a feature extractor for high-concentration time-frequency representation of mixed signals

High-concentration time-frequency (TF) representation provides a valuable tool for characterizing multi-component non-stationary signals. In our previous work, we proposed using an instantaneous frequency (IF) equation to sharpen the TF distribution, and experiments verified its effectiveness. In this paper, we systematically discuss why the IF equation-based TF analysis methods work and how to use the IF equation to improve TF sharpness. By the analysis of the properties of the IF equation, we prove that a good IF equation can unify the well-known IF and group delay estimators and provides an effective way to characterize the mixture of time-varying and frequency-varying signals. By discussing the post-processing techniques based on the IF equation, we can prove that many popular TF post-processing methods, such as the synchroextracting transform, the multi-synchrosqueezing transform, and the time extracting transform, fall into the IF equation-based category. We also propose a novel approach to combine different IF equations to minimize energy spreading based on local sparsity. Numerical simulations and practical experiments are presented to illustrate the performance of the proposed IF equation-based TF analysis method.
Different types of voltage instability

In reported incidents, different types of voltage instability have occurred and each has presented in a quite distinct manner. To better acquaint power engineers with the different types, two incidents are described as each commenced with a type of system voltage instability and culminated with radial voltage instability. In neither of these two incidents were operators fully aware of the situation. The analysis of the incidents provided in this paper can give important operational insights. In particular it shows the value of timely load shedding and the need to allow a 20% margin from the radial voltage stability limit. >
Developments in Connected Vehicles and the Requirement for Increased Cybersecurity

The increase in popularity of connected features in intelligent transportation systems, has led to a greater risk of cyber-attacks and subsequently, requires a more robust validation of cybersecurity in vehicle design. This article explores three such cyber-attacks and the weaknesses in the connected networks. A review is carried out on current vulnerabilities and key considerations for future vehicle design and validation are highlighted. This article addresses the vehicle manufactures desire to add unnecessary remote connections without appropriate security analysis and assessment of the risks involved. The modern vehicle is All Connected and only as strong as its weakest link.
Homogenization for a Variational Problem with a Slip Interface Condition

Inspired by applications, we study the effect of interface slip on the effective wave propagation in poroelastic composites. The current literature on the homogenization for the poroelastic wave equations are all based on the no-slip interface condition posed on the micro-scale. However, for certain pore fluids, the no-slip conditions are known to be physically invalid. Even though there are results in a few papers regarding porous media with slip condition on the interface, they are for porous media with rigid solid matrix rather than an elastic one. For the former case, the equations for the micro-scale are posed only in the pore space and the slip on the interface involves only the fluid velocity and the fluid stress. For the latter case, both the fluid equations and the elastic equations are posed in the respective phases and the slip conditions involve the velocities on both sides of the interface, rather than just the fluid side. With this slip condition, a variational boundary value problem governing the small vibrations of a periodic mixture of an elastic solid and a slightly viscous fluid is studied in the paper. The method of two-scale convergence is used to obtain the macroscopic behavior of the solution and to identify the role played by the slip interface condition.
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning

Federated Learning (FL) is an increasingly popular machine learning paradigm in which multiple nodes try to collaboratively learn under privacy, communication and multiple heterogeneity constraints. A persistent problem in federated learning is that it is not clear what the optimization objective should be: the standard average risk minimization of supervised learning is inadequate in handling several major constraints specific to federated learning, such as communication adaptivity and personalization control. We identify several key desiderata in frameworks for federated learning and introduce a new framework, FedMix, that takes into account the unique challenges brought by federated learning. FedMix has a standard finitesum form, which enables practitioners to tap into the immense wealth of existing (potentially non-local) methods for distributed optimization. Through a smart initialization that does not require any communication, FedMix does not require the use of local steps but is still provably capable of performing dissimilarity regularization on par with local methods. We give several algorithms for solving the FedMix formulation efficiently under communication constraints. Finally, we corroborate our theoretical results with extensive experimentation.
On Resonant Scattering States in Graphene Circular Quantum Dots

Due to effect of Klein tunneling two-dimensional graphene quantum dots do not possess genuine bound states but quasi-bound (resonant tunneling) states only. We discuss in detail the attempt to describe these states within the framework of the Dirac pseudo-fermion model for circular dot. We demonstrate explicitly that introduced earlier the so called "resonance condition" corresponds to inconsistent system of linear equations obtained from matching conditions on the boundary of the quantum dot when one try to use it for complex energy values and to the case of total reflection for the energies coincided with the potential well top.
KML: Using Machine Learning to Improve Storage Systems

Operating systems include many heuristic algorithms designed to improve overall storage performance and throughput. Because such heuristics cannot work well for all conditions and workloads, system designers resorted to exposing numerous tunable parameters to users -- thus burdening users with continually optimizing their own storage systems and applications. Storage systems are usually responsible for most latency in I/O-heavy applications, so even a small latency improvement can be significant. Machine learning (ML) techniques promise to learn patterns, generalize from them, and enable optimal solutions that adapt to changing workloads. We propose that ML solutions become a first-class component in OSs and replace manual heuristics to optimize storage systems dynamically. In this paper, we describe our proposed ML architecture, called KML. We developed a prototype KML architecture and applied it to two case studies: optimizing readahead and NFS read-size values. Our experiments show that KML consumes less than 4KB of dynamic kernel memory, has a CPU overhead smaller than 0.2%, and yet can learn patterns and improve I/O throughput by as much as 2.3x and 15x for two case studies -- even for complex, never-seen-before, concurrently running mixed workloads on different storage devices.
Simultaneous face detection and 360 degree head pose estimation

With many practical applications in human life, including manufacturing surveillance cameras, analyzing and processing customer behavior, many researchers are noticing face detection and head pose estimation on digital images. A large number of proposed deep learning models have state-of-the-art accuracy such as YOLO, SSD, MTCNN, solving the problem of face detection or HopeNet, FSA-Net, RankPose model used for head pose estimation problem. According to many state-of-the-art methods, the pipeline of this task consists of two parts, from face detection to head pose estimation. These two steps are completely independent and do not share information. This makes the model clear in setup but does not leverage most of the featured resources extracted in each model. In this paper, we proposed the Multitask-Net model with the motivation to leverage the features extracted from the face detection model, sharing them with the head pose estimation branch to improve accuracy. Also, with the variety of data, the Euler angle domain representing the face is large, our model can predict with results in the 360° Euler angle domain. Applying the multitask learning method, the Multitask-Net model can simultaneously predict the position and direction of the human head. To increase the ability to predict the head direction of the model, we change the representation of the human face from the Euler angle to vectors of the Rotation matrix.
Integrating Imitation Learning with Human Driving Data into Reinforcement Learning to Improve Training Efficiency for Autonomous Driving

Two current methods used to train autonomous cars are reinforcement learning and imitation learning. This research develops a new learning methodology and systematic approach in both a simulated and a smaller real world environment by integrating supervised imitation learning into reinforcement learning to make the RL training data collection process more effective and efficient. By combining the two methods, the proposed research successfully leverages the advantages of both RL and IL methods. First, a real mini-scale robot car was assembled and trained on a 6 feet by 9 feet real world track using imitation learning. During the process, a handle controller was used to control the mini-scale robot car to drive on the track by imitating a human expert driver and manually recorded the actions using Microsoft AirSim's API. 331 accurate human-like reward training samples were able to be generated and collected. Then, an agent was trained in the Microsoft AirSim simulator using reinforcement learning for 6 hours with the initial 331 reward data inputted from imitation learning training. After a 6-hour training period, the mini-scale robot car was able to successfully drive full laps around the 6 feet by 9 feet track autonomously while the mini-scale robot car was unable to complete one full lap round the track even after 30 hour training pure RL training. With 80% less training time, the new methodology produced significantly more average rewards per hour. Thus, the new methodology was able to save a significant amount of training time and can be used to accelerate the adoption of RL in autonomous driving, which would help produce more efficient and better results in the long run when applied to real life scenarios. Key Words: Reinforcement Learning (RL), Imitation Learning (IL), Autonomous Driving, Human Driving Data, CNN