Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical, flow, and thermal analysis of a phototherapy extracorporeal membrane oxygenator for treating carbon monoxide poisoning

Extracorporeal membrane oxygenators (ECMO) are currently utilized to mechanically ventilate blood when lung or lung and heart function are impaired, like in cases of acute respiratory distress syndrome (ARDS). ARDS can be caused by severe cases of carbon monoxide (CO) inhalation, which is the leading cause of poison‐related deaths in the United States. ECMOs can be further optimized for severe CO inhalation using visible light to photo‐dissociate CO from hemoglobin (Hb). In previous studies, we combined phototherapy with an ECMO to design a photo‐ECMO device, which significantly increased CO elimination and improved survival in CO‐poisoned animal models using light at 460, 523, and 620 nm wavelengths. Light at 620 nm was the most effective in removing CO.
Pattern translation and rotation in uncorrelated source distributions for multiple beam antenna design

In the uncorrelated scenario model, a continuous source distribution illuminates a receiver with waves bearing signals that are uncorrelated with respect to their angle of arrival. This model is used for multipath situations such as scintillating atmospheres, but also can be applied to optical beams for indoor communications, acoustic beamforming, and, of particular interest here, mobile communications. For diversity antennas operating in such a scenario, patterns that produce uncorrelated signals are required. An angular separation of directive beams in multipath scenarios acts to decorrelate the received signals. It is of interest to quantify the minimum angular spacing required of beams in order to provide a framework for the design and configuration of the antennas. The approach is to consider only the main lobe of the antenna pattern and to take it as a real function. This is reasonable as long as most of the energy is conveyed via the main beam. In practice, the sidelobe structure and nonuniformity in the phase of an actual pattern act to improve the situation in the sense that the decorrelation angles become smaller. The conditions for angular diversity result in a simple rule-of-thumb for the minimum beam separation requirement, which is essentially independent of the directivity. Finally, both scalar and vector elliptic beams rotated about their boresight axes are analyzed for the decorrelation rotation angle as a function of the ratio of the ellipticity. The resulting design curves offer a guideline to beam configuration for multipath scenarios.
A Bayesian approach for simultaneous spike/LFP separation and spike sorting

Objective. The aim of this paper is to present a novel method for simultaneous spike waveforms extraction and sorting from the raw recorded signal. The objective is twofold: on the one hand, to enhance spike sorting performance by extracting the spike waveforms of each spike and, on the other hand, to improve the analysis of the multi-scale relationships between spikes and local field potentials (LFP) by offering an accurate separation of these two components constitutive of the raw micro recordings. Approach. The method, based on a Bayesian approach, is fully automated and provides a mean spike shape for each cluster, but also an estimate for each singular spike waveform, as well as the LFP signal cleaned of spiking activity. Main results. The performance of the algorithm is evaluated on simulated and real data, for which both the clustering and spike removal aspects are analyzed. Clustering performance significantly increases when compared to state-of-the-art methods, taking benefit from the separation of the spikes from the LFP handled by our model. Our method also performs better in removing the spikes from the LFP when compared to previously proposed methodologies, especially in the high frequency bands. The method is finally applied on real data (ClinicalTrials.gov Identifier: NCT02877576) and confirm the results obtained on benchmark signals. Significance. By separating more efficiently the spikes from the LFP background, our method allows both a better spike sorting and a more accurate estimate of the LFP, facilitating further analysis such as spike-LFP relationships.
Well-aligned Mn-doped ZnO nanowires synthesized by a chemical vapor deposition method

Well-aligned diluted magnetic semiconductor Zn1−xMnxO nanowires have been fabricated at 850°C from a self-formed ZnO substrate using a chemical vapor deposition method. The as-synthesized Mn-doped ZnO nanowires were characterized by field emission scanning electron microscopy and transmission electron microscopy (TEM). The well-aligned nanowires are single crystalline and are perpendicularly grown along the c axis. Electron energy x-ray dispersive analysis, x-ray diffraction spectrometry, and TEM analysis clearly showed that Mn was doped both in the ZnO nanowires and substrate. Ferromagnetic ordering of the as-synthesized Zn1−xMnxO nanowire arrays was observed at 5K with Curie temperature of 44K by superconducting quantum interference device measurement.
On the strong law of large numbers and the central limit theorem for martingales

(1.3) E(Sn\Sx,.,Sn.x) = Sn.x, mi (S0=0), with probability 1. In fact, a sequence of partial sums of random variables whose expectations exist is a martingale if and only if (1.1) is true (see e.g. VI.12 of Feller's book [5], or 11.7 of Doob's book [4]), and then we have E(Xn)=0 for all «. Also, if E(S2)<co for all «, the sequence of squares of partial sums of an absolutely fair sequence of random variables is a submartingale, that is
Proprioception and Tail Control Enable Extreme Terrain Traversal by Quadruped Robots

Legged robots leverage ground contacts and the reaction forces they provide to achieve agile locomotion. However, uncertainty coupled with contact discontinuities can lead to failure, especially in real-world environments with unexpected height variations such as rocky hills or curbs. To enable dynamic traversal of extreme terrain, this work introduces 1) a proprioception-based gait planner for estimating unknown hybrid events due to elevation changes and responding by modifying contact schedules and planned footholds online, and 2) a two-degree-of-freedom tail for improving contact-independent control and a corresponding decoupled control scheme for better versatility and efficiency. Simulation results show that the gait planner significantly improves stability under unforeseen terrain height changes compared to methods that assume fixed contact schedules and footholds. Further, tests have shown that the tail is particularly effective at maintaining stability when encountering a terrain change with an initial angular disturbance. The results show that these approaches work synergistically to stabilize locomotion with elevation changes up to 1.5 times the leg length and tilted initial states.
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models

We study the problem of privately estimating the parameters of $d$-dimensional Gaussian Mixture Models (GMMs) with $k$ components. For this, we develop a technique to reduce the problem to its non-private counterpart. This allows us to privatize existing non-private algorithms in a blackbox manner, while incurring only a small overhead in the sample complexity and running time. As the main application of our framework, we develop an $(\varepsilon, \delta)$-differentially private algorithm to learn GMMs using the non-private algorithm of Moitra and Valiant [MV10] as a blackbox. Consequently, this gives the first sample complexity upper bound and first polynomial time algorithm for privately learning GMMs without any boundedness assumptions on the parameters. As part of our analysis, we prove a tight (up to a constant factor) lower bound on the total variation distance of high-dimensional Gaussians which can be of independent interest.
Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

One of the main paths towards the reduction of traffic accidents is the increase in vehicle safety through driver assistance systems or even systems with a complete level of autonomy. In these types of systems, tasks such as obstacle detection and segmentation, especially the Deep Learning-based ones, play a fundamental role in scene understanding for correct and safe navigation. Besides that, the wide variety of sensors in vehicles nowadays provides a rich set of alternatives for improvement in the robustness of perception in challenging situations, such as navigation under lighting and weather adverse conditions. Despite the current focus given to the subject, the literature lacks studies on radar-based and radar-camera fusion-based perception. Hence, this work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. Additionally, we give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
SMaLL: A Software Framework for portable Machine Learning Libraries

Interest in deploying Deep Neural Network (DNN) inference on edge devices has resulted in an explosion of the number and types of hardware platforms to use. While the high-level programming interface, such as TensorFlow, can be readily ported across different devices, high-performance inference implementations rely on a good mapping of the high-level interface to the target hardware platform. Commonly, this mapping may use optimizing compilers to generate code at compile time or high-performance vendor libraries that have been specialized to the target platform. Both approaches rely on expert knowledge to produce the mapping, which may be time-consuming and difficult to extend to new architectures. In this work, we present a DNN library framework, SMaLL, that is easily extensible to new architectures. The framework uses a unified loop structure and shared, cache-friendly data format across all intermediate layers, eliminating the time and memory overheads incurred by data transformation between layers. Layers are implemented by simply specifying the layer's dimensions and a kernel -- the key computing operations of each layer. The unified loop structure and kernel abstraction allows us to reuse code across layers and computing platforms. New architectures only require the 100s of lines in the kernel to be redesigned. To show the benefits of our approach, we have developed software that supports a range of layer types and computing platforms, which is easily extensible for rapidly instantiating high performance DNN libraries. We evaluate our software by instantiating networks from the TinyMLPerf benchmark suite on 5 ARM platforms and 1 x86 platform ( an AMD Zen 2). Our framework shows end-to-end performance that is comparable to or better than ML Frameworks such as TensorFlow, TVM and LibTorch.
Exploring Kinodynamic Fabrics for Reactive Whole-Body Control of Underactuated Humanoid Robots

For bipedal humanoid robots to successfully operate in the real world, they must be competent at simultaneously executing multiple motion tasks while reacting to unforeseen external disturbances in real-time. We propose Kinodynamic Fabrics as an approach for the specification, solution and simultaneous execution of multiple motion tasks in real-time while being reactive to dynamism in the environment. Kinodynamic Fabrics allows for the specification of prioritized motion tasks as forced spectral semi-sprays and solves for desired robot joint accelerations at real-time frequencies. We evaluate the capabilities of Kinodynamic fabrics on diverse physically-challenging whole-body control tasks with a bipedal humanoid robot both in simulation and in the real-world. Kinodynamic Fabrics outperforms the state-of-the-art Quadratic Program based whole-body controller on a variety of whole-body control tasks on run-time and reactivity metrics in our experiments. Our open-source implementation of Kinodynamic Fabrics as well as robot demonstration videos can be found at this url: https://adubredu.github. io/kinofabs
Hybrid Finite-Volume Reduced-Order Model Method for Nonlinear Aeroelastic Modeling

A fully coupled partitioned fluid–structure interaction technology is developed for transonic aeroelastic structures undergoing nonlinear displacements. The Euler equations, written in an arbitrary Lagrangian–Eulerian coordinate frame, describe the fluid domain, whereas the structure is represented by a quadratic modal reduced-order model. A Runge–Kutta dual time-stepping method is employed for the fluid solver, where three upwind schemes are considered, viz., Advection Upwind Splitting Method plus-up, Harten-Lax-van Leer with Contact, and Roe schemes. The Harten-Lax-van Leer with Contact implementation is found to offer a superior balance between efficiency and robustness. The developed fluid–structure interaction technology is applied to modeling transonic flutter, and the quadratic reduced-order model is demonstrated to offer dramatic improvements in accuracy over the more conventional linear method.
SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images

General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. However, the binary changes provided by GCD is often not practical enough, while annotating semantic labels for training SCD models is very expensive. Therefore, there is a novel solution that intuitively dividing changes into three trends (``appear'', ``disappear'' and ``transform'') instead of semantic categories, named it trend change detection (TCD) in this paper. It offers more detailed change information than GCD, while requiring less manual annotation cost than SCD. However, there are limited public data sets with specific trend labels to support TCD application. To address this issue, we propose a softmatch distance which is used to construct a weakly-supervised TCD branch in a simple GCD model, using GCD labels instead of TCD label for training. Furthermore, a strategic approach is presented to successfully explore and extract background information, which is crucial for the weakly-supervised TCD task. The experiment results on four public data sets are highly encouraging, which demonstrates the effectiveness of our proposed model.
Convergence analysis of the weighted state space search algorithm for recurrent neural networks

Recurrent neural networks (RNNs) have emerged as a promising tool in modeling nonlinear dynamical systems. The convergence is one of the most important issues of concern among the dynamical properties for the RNNs in practical applications. The reason is that the viability of many applications of RNNs depends on their convergence properties. We study in this paper the convergence properties of the weighted state space search algorithm (WSSSA) -- a derivative-free and non-random learning algorithm which searches the neighborhood of the target trajectory in the state space instead of the parameter space. Because there is no computation of partial derivatives involved, the WSSSA has a couple of salient features such as simple, fast and cost effective. In this study we provide a necessary and sufficient condition that required for the convergence of the WSSSA. Restrictions are offered that may help assure convergence of the of the WSSSA to the desired solution. The asymptotic rate of convergence is also analyzed. Our study gives insights into the problem and provides useful information for the actual design of the RNNs. A numerical example is given to support the theoretical analysis and to demonstrate that it is applicable to many applications.
NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.
Femtosecond laser near-field nanoablation patterning using Mie resonance high dielectric constant particle with small size parameter

We demonstrate near-field nanohole patterning using a Mie resonance, small size parameter particle for nanofabrication technology regardless of substrate’s refractive index. Maximal enhancement factor and nearly smallest spot diameter among the same size dielectric particles are simultaneously obtainable on both low-refractive-index SiO2 and high-refractive-index Si substrates with a 200 nm particle of magnetic quadrupole resonance scattering mode (n∼2.7) at 400 nm excitation wavelength. Circular nanoholes with approximately 100 nm in diameter were fabricated on both substrates using a 200 nm amorphous TiO2 particle (n=2.66+0.024i) even with lower laser fluences than a half ablation threshold of the bare substrates.
A Strategy-Oriented Bayesian Soft Actor-Critic Model

Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method -- soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks -- Hopper-v2, Walker2d-v2, and Humanoid-v2 -- in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency.
ConBaT: Control Barrier Transformer for Safe Policy Learning

Large-scale self-supervised models have recently revolutionized our ability to perform a variety of tasks within the vision and language domains. However, using such models for autonomous systems is challenging because of safety requirements: besides executing correct actions, an autonomous agent must also avoid the high cost and potentially fatal critical mistakes. Traditionally, self-supervised training mainly focuses on imitating previously observed behaviors, and the training demonstrations carry no notion of which behaviors should be explicitly avoided. In this work, we propose Control Barrier Transformer (ConBaT), an approach that learns safe behaviors from demonstrations in a self-supervised fashion. ConBaT is inspired by the concept of control barrier functions in control theory and uses a causal transformer that learns to predict safe robot actions autoregressively using a critic that requires minimal safety data labeling. During deployment, we employ a lightweight online optimization to find actions that ensure future states lie within the learned safe set. We apply our approach to different simulated control tasks and show that our method results in safer control policies compared to other classical and learning-based methods such as imitation learning, reinforcement learning, and model predictive control.
Dynamic Scenario Representation Learning for Motion Forecasting With Heterogeneous Graph Convolutional Recurrent Networks

Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a challenging problem in autonomous driving. Most existing works exploit static road graphs to characterize scenarios and are limited in modeling evolving spatio-temporal dependencies in dynamic scenarios. In this letter, we resort to dynamic heterogeneous graphs to model the scenario. Various scenario components including vehicles (agents) and lanes, multi-type interactions, and their changes over time are jointly encoded. Furthermore, we design a novel heterogeneous graph convolutional recurrent network, aggregating diverse interaction information and capturing their evolution, to learn to exploit intrinsic spatio-temporal dependencies in dynamic graphs and obtain effective representations of dynamic scenarios. Finally, with a motion forecasting decoder, our model predicts realistic and multi-modal future trajectories of agents and outperforms state-of-the-art published works on several motion forecasting benchmarks.
Aharonov-Bohm magnetism in open Fermi surfaces

Orbital diamagnetism requires closed orbits according to the Liftshiftz-Kosevich theory. Therefore, one might expect that open Fermi surfaces do not have a diamagnetic response. Contrary to this expectation, we show that open orbits in finite systems do contribute a magnetic response which oscillates between diamagnetism and paramagnetism. The oscillations are similar to the Aharonov-Bohm effect, because the oscillation phase is set by the number of flux quanta through the area defined by the width of the sample and the distance between adjacent atomic layers. The magnetic response originates from the closed trajectories formed by counter-propagating open orbits coupled via specular boundary reflections. The phenomenon acts as a probe of the phase coherence of open electron trajectories.
Models for Longitudinal Data with Random Effects and AR(1) Errors

Abstract For longitudinal data on several individuals, linear models that contain both random effects across individuals and autocorrelation in the within-individual errors are studied. A score test for autocorrelation in the within-individual errors for the “conditional independence” random effects model is first developed. An explicit maximum likelihood estimation procedure using the scoring method for the model with random effects and (autoregressive) AR(1) errors is then derived. Empirical Bayes estimation of the random effects and prediction of future responses of an individual based on this random effects with AR(1) errors model are also considered. A numerical example is presented to illustrate these methods.