Miracle-dz/UniFAR-FTUs huggingface dataset:
Exhaust Particle Characterization for Lean and Stoichiometric DI Vehicles Operating on Ethanol-Gasoline Blends

Gasoline direct injection (GDI) engines can offer better fuel economy and higher performance over their port fuel-injected (PFI) counterparts, and are now appearing in increasingly more U.S. and European vehicles. Small displacement, turbocharged GDI engines are replacing large displacement engines, particularly in light-duty trucks and sport utility vehicles, in order for manufacturers to meet the U.S. fuel economy standards for 2016. Furthermore, lean-burn GDI engines can offer even higher fuel economy than stoichiometric GDI engines and have overcome challenges associated with cost-effective aftertreatment for NOx control. Along with changes in gasoline engine technology, fuel composition may increase in ethanol content beyond the current 10% due to the recent EPA waiver allowing 15% ethanol. In addition, the Renewable Fuels Standard passed as part of the 2007 Energy Independence and Security Act (EISA) mandates the use of biofuels in upcoming years. GDI engines are of environmental concern due to their high particulate matter (PM) emissions relative to port-fuel injected (PFI) gasoline vehicles; widespread market penetration of GDI vehicles may result in additional PM from mobile sources at a time when the diesel contribution is declining. In this study, we characterized particulate emissions from a European certified lean-burn GDI vehicle operating on ethanol-gasolinemore » blends. Particle mass and particle number concentration emissions were measured for the Federal Test Procedure urban driving cycle (FTP 75) and the more aggressive US06 driving cycle. Particle number-size distributions and organic to elemental carbon ratios (OC/EC) were measured for 30 MPH and 80 MPH steady-state operation. In addition, particle number concentration was measured during wide open throttle accelerations (WOTs) and gradual accelerations representative of the FTP 75. Fuels included certification gasoline and 10% (E10) and 20% (E20) ethanol blends from the same supplier. The particle mass emissions were approximately 3 and 7 mg/mile for the FTP75 and US06, respectively, with lower emissions for the ethanol blends. The data are compared to a previous study on a U.S.-legal stoichiometric GDI vehicle operating on the same ethanol blends. The lean-burn GDI vehicle emitted a higher number of particles, but had an overall smaller average size. Particle number per mile decreased with increasing ethanol content for the transient tests. For the 30 and 80 mph tests, particle number concentration decreased with increasing ethanol content, although the shape of the particle size distribution remained the same. Engine-out OC/EC ratios were highest for the stoichiometric GDI vehicle with E20, but tailpipe OC/EC ratios were similar for all vehicles. « less
Uniﬁed Field Theory for Deep and Recurrent Neural Networks

Understanding capabilities and limitations of diﬀerent network architectures is of fundamental importance to machine learning. Bayesian inference on Gaussian processes has proven to be a viable approach for studying recurrent and deep networks in the limit of inﬁnite layer width, n → ∞ . Here we present a uniﬁed and systematic derivation of the mean-ﬁeld theory for both architectures that starts from ﬁrst principles by employing established methods from statistical physics of disordered systems. The theory elucidates that while the mean-ﬁeld equations are diﬀerent with regard to their temporal structure, they yet yield identical Gaussian kernels when readouts are taken at a single time point or layer, respectively. Bayesian inference applied to classiﬁcation then predicts identical performance and capabilities for the two architectures. Numerically, we ﬁnd that convergence towards the mean-ﬁeld theory is typically slower for recurrent networks than for deep networks and the convergence speed depends non-trivially on the parameters of the weight prior as well as the depth or number of time steps, respectively. Our method exposes that Gaussian processes are but the lowest order of a systematic expansion in 1 /n . The formalism thus paves the way to investigate the fundamental diﬀerences between recurrent and deep architectures at ﬁnite widths n .
Breathing modes of skyrmion strings in a synthetic antiferromagnet multilayer

Skyrmions are small topologically protected magnetic structures that hold promise for applications from data storage to neuromorphic computing and they have been shown to possess internal microwave frequency excitations. Skyrmions in a synthetic antiferromagnet have been predicted to be smaller and faster than their ferromagnetic equivalents and also shown to possess more internal modes. In this work, we consider the breathing modes of skyrmions in a four repetition synthetic antiferromagnetic multilayer by means of micromagnetic simulations and examine the further splitting of the modes into different arrangements of out-of-phase, in-phase, and modes with more complex phase relationships. This results in a lowering of frequencies, which is promising for skyrmion sensing applications in a synthetic antiferromagnet.
Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

A variety of attention mechanisms have been studied to improve the performance of various computer vision tasks. However, the prior methods overlooked the significance of retaining the information on both channel and spatial aspects to enhance the cross-dimension interactions. Therefore, we propose a global attention mechanism that boosts the performance of deep neural networks by reducing information reduction and magnifying the global interactive representations. We introduce 3D-permutation with multilayer-perceptron for channel attention alongside a convolutional spatial attention submodule. The evaluation of the proposed mechanism for the image classification task on CIFAR-100 and ImageNet-1K indicates that our method stably outperforms several recent attention mechanisms with both ResNet and lightweight MobileNet.
Rapid manufacturing of color-based hemispherical soft tactile fingertips

Tactile sensing can provide access to information about the contact (i.e. slippage, surface feature, friction), which is out of reach of vision but crucial for manipulation. To access this information, a dense measurement of the deformation of soft fingertips is necessary. Recently, tactile sensors that rely on a camera looking at a deformable membrane have demonstrated that a dense measurement of the contact is possible. However, their manufacturing can be time-consuming and labor-intensive. Here, we show a new design method that uses multi-color additive manufacturing and silicone casting to efficiently manufacture soft marker-based tactile sensors that are able to capture with high-resolution the three-dimensional deformation field at the interface. Each marker is composed of two superimposed color filters. The subtractive color mixing encodes the normal deformation of the membrane, and the lateral deformation is found by centroid detection. With this manufacturing method, we can reach a density of 400 markers on a 21 mm radius hemisphere, allowing for regular and dense measurement of the deformation. We calibrated and validated the approach by finding the curvature of objects with a threefold increase in accuracy as compared to previous implementations. The results demonstrate a simple yet effective approach to manufacturing artificial fingertips for capturing a rich image of the tactile interaction at the location of contact.
Eccentric self-forced inspirals into a rotating black hole

We develop the first model for extreme mass-ratio inspirals (EMRIs) into a rotating massive black hole driven by the gravitational self-force (GSF). Our model is based on an action angle formulation of the method of osculating geodesics for eccentric, equatorial (i.e., spin-aligned) motion in Kerr spacetime. The forcing terms are provided by an efficient spectral interpolation of the first-order GSF in the outgoing radiation gauge. We apply a near-identity (averaging) transformation to eliminate all dependence of the orbital phases from the equations of motion, while maintaining all secular effects of the first-order GSF at post-adiabatic order. This implies that the model can be evolved without having to resolve all O(105) orbit cycles of an EMRI, yielding an inspiral model that can be evaluated in less than a second for any mass-ratio. In the case of a non-rotating central black hole, we compare inspirals evolved using self-force data computed in the Lorenz and radiation gauges. We find that the two gauges generally produce differing inspirals with a deviation of comparable magnitude to the conservative self-force correction. This emphasizes the need for including the (currently unknown) dissipative second order self-force to obtain gauge independent, post-adiabatic waveforms.
Road Extraction from Overhead Images with Graph Neural Networks

Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectorization, or on iterative graph construction using next move prediction. Both of these strategies suffer from severe drawbacks, in particular high computing resources and incomplete outputs. By contrast, we propose a method that directly infers the final road graph in a single pass. The key idea consists in combining a Fully Convolutional Network in charge of locating points of interest such as intersections, dead ends and turns, and a Graph Neural Network which predicts links between these points. Such a strategy is more efficient than iterative methods and allows us to streamline the training process by removing the need for generation of starting locations while keeping the training end-to-end. We evaluate our method against existing works on the popular RoadTracer dataset and achieve competitive results. We also benchmark the speed of our method and show that it outperforms existing approaches. This opens the possibility of in-flight processing on embedded devices.
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems

Adversarial examples, or nearly indistinguishable inputs created by an attacker, significantly reduce machine learning accuracy. Theoretical evidence has shown that the high intrinsic dimensionality of datasets facilitates an adversary's ability to develop effective adversarial examples in classification models. Adjacently, the presentation of data to a learning model impacts its performance. For example, we have seen this through dimensionality reduction techniques used to aid with the generalization of features in machine learning applications. Thus, data transformation techniques go hand-in-hand with state-of-the-art learning models in decision-making applications such as intelligent medical or military systems. With this work, we explore how data transformations techniques such as feature selection, dimensionality reduction, or trend extraction techniques may impact an adversary's ability to create effective adversarial samples on a recurrent neural network. Specifically, we analyze it from the perspective of the data manifold and the presentation of its intrinsic features. Our evaluation empirically shows that feature selection and trend extraction techniques may increase the RNN's vulnerability. A data transformation technique reduces the vulnerability to adversarial examples only if it approximates the dataset's intrinsic dimension, minimizes codimension, and maintains higher manifold coverage.
System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks

Integrated access and backhaul (IAB) facilitates cost-effective deployment of millimeter wave (mmWave) cellular networks through multihop self-backhauling. Full-duplex (FD) technology, particularly for mmWave systems, is a potential means to overcome latency and throughput challenges faced by IAB networks. We derive practical and tractable throughput and latency constraints using queueing theory and formulate a network utility maximization problem to evaluate both full-duplex (FD)-IAB and half-duplex (HD)-IAB networks. We use this to characterize the network-level improvements seen when upgrading from conventional HD IAB nodes to FD ones by deriving closed-form expressions for (i) latency gain of FD-IAB over HD-IAB and (ii) the maximum number of hops that a HD- and FD-IAB network can support while satisfying latency and throughput targets. Extensive simulations illustrate that FD-IAB can facilitate reduced latency, higher throughput, deeper networks, and fairer service. Compared to HD-IAB, FD-IAB can improve throughput by $8\times $ and reduce latency by $4\times $ for a fourth-hop user. In fact, upgrading IAB nodes with FD capability can allow the network to support latency and throughput targets that its HD counterpart fundamentally cannot meet. The gains are more profound for users further from the donor and can be achieved even when residual self-interference is significantly above the noise floor.
Compressible Navier-Stokes System with Transport Noise

We consider the barotropic Navier--Stokes system driven by a physically well-motivated transport noise in both continuity as well as momentum equation. We focus on three different situations: (i) the noise is smooth in time and the equations are understood as in the sense of the classical weak deterministic theory, (ii) the noise is rough in time and we interpret the equations in the framework of rough paths with unbounded rough drivers and (iii) we have a Brownian noise of Stratonovich type and study the existence of martingale solutions. The first situation serves as an approximation for (ii) and (iii), while (ii) and (iii) are motivated by recent results on the incompressible Navier--Stokes system concerning the physical modeling as well as regularization by noise.
Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching

The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse weights. This challenge can be resolved by emulating analog behavior with the stochastic switching of the binary states of spin-transfer torque magnetoresistive random-access memory (STT-MRAM). However, previous approaches based on STT-MRAM operate in an asynchronous manner that is difficult to implement experimentally. This paper proposes a synchronous spiking neural network system with clocked circuits that perform unsupervised learning leveraging the stochastic switching of STT-MRAM. The proposed system enables a single-layer network to achieve 90% inference accuracy on the MNIST dataset.
Gluon and valence quark distributions for the pion and kaon in nuclear matter

In this paper we study the gluon and valence quark distributions in the pion and kaon in nuclear medium for various nuclear densities as well as in vacuum within the Nambu–Jona-Lasinio (NJL) model with the help of the proper-time regularization scheme which simulates a confinement of QCD. The nuclear medium effect is also determined in the same model for the symmetric nuclear matter. We then analyze the gluon and valence quark distributions for the bound pion and kaon in symmetric nuclear matter as well as those in vacuum. We find that the valence quark and gluon distributions in vacuum have relatively good agreements with the experimental data, the lattice QCD simulations, and the JAM Monte-Carlo (MC) global fit QCD analysis. Evolving to the higher factorization scale Q = 4 GeV, the in-medium gluon and valence-quark distributions of the pion for various nuclear densities are turned out to be almost unchanged in comparison to the vacuum cases. On the contrary, for the kaon, they increase significantly with respect to the densities. Finally, we find that the vacuum gluon distribution for the kaon is smaller than that for the pion, which is consistent with other theoretical predictions. This feature holds for the in-medium gluon distribution in the nuclear density up to the saturation density.
Relaxing inflation models with nonminimal coupling: A general study

We investigate the predictions of inflation models with a non-minimal coupling to gravity for inflationary observables such as the spectral index and tensor-to-scalar ratio in a general setting. We argue that, depending on the relation between the Jordan frame potential and a function characterizing the non-minimal coupling, one can classify the model into three categories each of which gives distinctive predictions for the inflationary observables. We derive general predictions for each class and also investigate some explicit models to discuss how the general features arise. Our results would be useful to design an inflation model consistent with observational constraints with a non-minimal coupling to gravity. ar X iv :2 11 2. 05 28 3v 1 [ as tr oph . C O ] 1 0 D ec 2 02 1
An Energy-Based Approach to the Generalized Optimal Locations of Viscous Dampers in Two-Way Asymmetrical Buildings

This paper proposes a simple approach to the generalized optimal locations of linear viscous dampers in elastic two-way asymmetrical buildings under bi-directional ground excitations. The control target used in this optimization process is to maximize the average dissipation rate of the overall strain energy of the two-way asymmetrical building under the ground excitation of two bi-directional unit impulses. The proposed control target, referred to as the smeared damping ratio, is an intrinsic property of the building system. Two advantages of the proposed approach appeal to engineering practice. First, the proposed approach does not require a complicated optimization algorithm. Second, due to the employment of an intrinsic property rather than a certain response parameter as the target performance index, the optimal damper locations resulting from the proposed approach are generalized, which are independent on the characteristics of input ground motions.
Interpretable Clustering via Multi-Polytope Machines

Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description few state-of-the-art algorithms provide any rationale or description behind the clusters found. We propose a novel approach for interpretable clustering that both clusters data points and constructs polytopes around the discovered clusters to explain them. Our framework allows for additional constraints on the polytopes including ensuring that the hyperplanes constructing the polytope are axis-parallel or sparse with integer coefficients. We formulate the problem of constructing clusters via polytopes as a Mixed-Integer Non-Linear Program (MINLP). To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance. We benchmark our approach on a suite of synthetic and real world clustering problems, where our algorithm outperforms state of the art interpretable and non-interpretable clustering algorithms.
Effect of soil on structural response to wind and earthquake

An approximate analytical approach is presented which makes it possible to consider soil properties and footing embedment in the analysis of the response of structures to external excitation such as wind and earthquake. The approach is based on modal analysis and the definition of stiffness and damping due to soil pertinent to each vibration mode. The approach also facilitates the analysis of coupled motions of a footing alone. The analysis of a tall chimney for the effects of gusting wind, vortex shedding and earthquake is used as an example.
Multivariate double truncated expectation and covariance risk measures for elliptical distributions

The main objective of this work is to calculate the multivariate double truncated expectation (MDTE) and covariance (MDTCov) for elliptical distributions. We also consider double truncated expectation (DTE) and variance (DTV) for univariate elliptical distributions. The exact expressions of MDTE and MDTCov are derived for some special cases of the family, such as normal, student-t, logistic, Laplace and Pearson type VII distributions. As numerical illustration, the DTE, DTV, MDTE and MDTCov for normal distribution are computed in details. Finally, we discuss MDTE and MDTCov of three industry segments’ (Banks, Insurance, Financial and Credit Service) stock return in London stock exchange.
Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs

Energy-Based Models (EBMs) allow for extremely flexible specifications of probability distributions. However, they do not provide a mechanism for obtaining exact samples from these distributions. Monte Carlo techniques can aid us in obtaining samples if some proposal distribution that we can easily sample from is available. For instance, rejection sampling can provide exact samples but is often difficult or impossible to apply due to the need to find a proposal distribution that upper-bounds the target distribution everywhere. Approximate Markov chain Monte Carlo sampling techniques like Metropolis-Hastings are usually easier to design, exploiting a local proposal distribution that performs local edits on an evolving sample. However, these techniques can be inefficient due to the local nature of the proposal distribution and do not provide an estimate of the quality of their samples. In this work, we propose a new approximate sampling technique, Quasi Rejection Sampling (QRS), that allows for a trade-off between sampling efficiency and sampling quality, while providing explicit convergence bounds and diagnostics. QRS capitalizes on the availability of high-quality global proposal distributions obtained from deep learning models. We demonstrate the effectiveness of QRS sampling for discrete EBMs over text for the tasks of controlled text generation with distributional constraints and paraphrase generation. We show that we can sample from such EBMs with arbitrary precision at the cost of sampling efficiency.
Electrical conduction in polyimide-FEP fluoropolymer films

The aromatic polyimide has excellent mechanical, electrical and chemical properties. Absorption and conduction currents are studied over a temperature range of 50 to 200/spl deg/C in Teflon coated film (25.4 /spl mu/m Kapton/spl reg/ base with 12.7 /spl mu/m coating of Teflon/spl reg/ FEP fluoropolymer resin on one side) and the influence of parameters such as field strengths and electrode materials are investigated. The charging current decays with time according to t/sup -n/ (decay constant) with the value of index n in the range of 0.66 to 1.25. The discharging current decreases with the value of n in the range of 1.47 to 1.59 for the temperature range investigated. The dielectric loss factors at low frequency were evaluated from the absorption currents. For temperature up to 90/spl deg/C one broad peak is observed at frequencies around 3/spl times/10/sup -4/ Hz. For temperatures (>120/spl deg/C) the magnitude of the loss factor increases by an order of magnitude and no peaks are observed. Results on conduction currents are also reported for temperatures 90-200 /spl deg/C and fields up to 100 kV/cm. The activation energy of 0.70 eV and the ionic jump distance in the range 19-28 nm have been evaluated.