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Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. In the past, datasets were of limited scale and could be easily analyzed by hand or with rudimentary methods to identify a very limited set of traffic waves present within the data. This paper introduces an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph-based representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. New insights revealed from this demonstration with emerging phenomena include: (a) we demonstrate that waves do generate, propagate, and dissipate at a scale (miles and hours) and ubiquity never observed before; (b) wave fronts and tails travels at a consistent speed for a critical speed between 10-20 mph, with propagation variation across lanes, where wave speed on the outer lane are less consistent compared to those on the inner lane; (c) wave fronts and tails propagate at different speeds; (d) wave boundaries capture rich and non-trivial wave topologies, highlighting the complexity of waves.

In quantum mechanics, a quantum wavepacket may acquire a geometrical phase as it evolves along a cyclic trajectory in parameter space. In condensed matter systems, the Berry phase plays a crucial role in fundamental phenomena such as the Hall effect, orbital magnetism, and polarization. Resolving the quantum nature of these processes commonly requires sensitive quantum techniques, as tunneling, being the dominant mechanism in STM microscopy and tunneling transport devices. In this study, we integrate these two phenomena - geometrical phases and tunneling - and observe a complex-valued Berry phase via strong field light matter interactions in condensed matter systems. By manipulating the tunneling barrier, with attoseconds precision, we measure the imaginary Berry phase accumulated as the electron tunnels during a fraction of the optical cycle. Our work opens new theoretical and experimental directions in geometrical phases physics and their realization in condensed matter systems, expanding solid state strong field light metrology to study topological quantum phenomena., Comment: 17 pages, 4 figures

Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct fine-grained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, I24-WaveX, comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic measurement enhancement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic data enhancement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-sourced our trained model and code to facilitate further research and applications.

The current over-provisioned heterogeneous multi-cores require effective run-time optimization strategies, and the run-time power monitoring subsystem is paramount for their success. Several state-of-the-art methodologies address the design of a run-time power monitoring infrastructure for generic computing platforms. However, the power model's training requires time-consuming gate-level simulations that, coupled with the ever-increasing complexity of the modern heterogeneous platforms, dramatically hinder the usability of such solutions. This paper introduces Blink, a scalable framework for the fast and automated design of run-time power monitoring infrastructures targeting computing platforms implemented on FPGA. Blink optimizes the time-to-solution to deliver the run-time power monitoring infrastructure by replacing traditional methodologies' gate-level simulations and power trace computations with behavioral simulations and direct power trace measurements. Applying Blink to multiple designs mixing a set of HLS-generated accelerators from a state-of-the-art benchmark suite demonstrates an average time-to-solution speedup of 18 times without affecting the quality of the run-time power estimates., Comment: Accepted for lecture presentation at 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Nancy, France, Nov. 18-20, 2024

The recently installed internal gas target at LHCb presents exceptional opportunities for an extensive physics program for heavy-ion, hadron, spin, and astroparticle physics. A storage cell placed in the LHC primary vacuum, an advanced Gas Feed System, the availability of multi-TeV proton and ion beams and the recent upgrade of the LHCb detector make this project unique worldwide. In this paper, we outline the main components of the system, the physics prospects it offers and the hardware challenges encountered during its implementation. The commissioning phase has yielded promising results, demonstrating that fixed-target collisions can occur concurrently with the collider mode without compromising efficient data acquisition and high-quality reconstruction of beam-gas and beam-beam interactions.

Multi-Task Reinforcement Learning aims at developing agents that are able to continually evolve and adapt to new scenarios. However, this goal is challenging to achieve due to the phenomenon of catastrophic forgetting and the high demand of computational resources. Learning from scratch for each new task is not a viable or sustainable option, and thus agents should be able to collect and exploit prior knowledge while facing new problems. While several methodologies have attempted to address the problem from different perspectives, they lack a common structure. In this work, we propose a new framework, I Know How (IKH), which provides a common formalization. Our methodology focuses on modularity and compositionality of knowledge in order to achieve and enhance agent's ability to learn and adapt efficiently to dynamic environments. To support our framework definition, we present a simple application of it in a simulated driving environment and compare its performance with that of state-of-the-art approaches., Comment: 7 pages, Conference on Games (CoG) 2024

We propose a new model for multi-lane traffic with moving bottlenecks, e.g., autonomous vehicles (AV). It consists of a system of balance laws for traffic in each lane, coupled in the source terms for lane changing, and fully coupled to ODEs for the AVs' trajectories.More precisely, each AV solves a controlled equation depending on the traffic density, while the PDE on the corresponding lane has a flux constraint at the AV's location. We prove existence of entropy weak solutions, and we characterize the limiting behavior for the source term converging to zero (without AVs), corresponding to a scalar conservation law for the total density.The convergence in the presence of AVs is more delicate and we show that the limit does not satisfy an entropic equation for the total density as in the original coupled ODE-PDE model. Finally, we illustrate our results via numerical simulations.

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model trained jointly on the entire data stream. Given that any CL model will eventually make mistakes, it is of crucial importance to build calibrated CL models: models that can reliably tell their confidence when making a prediction. Model calibration is an active research topic in machine learning, yet to be properly investigated in CL. We provide the first empirical study of the behavior of calibration approaches in CL, showing that CL strategies do not inherently learn calibrated models. To mitigate this issue, we design a continual calibration approach that improves the performance of post-processing calibration methods over a wide range of different benchmarks and CL strategies. CL does not necessarily need perfect predictive models, but rather it can benefit from reliable predictive models. We believe our study on continual calibration represents a first step towards this direction., Comment: Accepted at CLVISION workshop, CVPR 2024

Structured light has gained significant attention in recent years, especially in the generation and application of vector beams. These beams, characterized by a spatially varying polarization state, are a powerful tool to enhance our capacity to control light-matter interactions. In this study, we demonstrate the synthesis of extreme-ultraviolet (EUV) vector beams in a chip through high-order harmonic generation (HHG). Our findings showcase the chip's ability to transfer the laser polarization state into the EUV beam despite the extended interaction length. This approach not only outperforms conventional free-space methods but also paves the way for a multitude of on-chip investigations in the realms of EUV and soft-X-ray spectroscopy.

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on the server side, named Speed Planner, and low-level controllers called vehicle controllers deployed on the vehicle side. The Speed Planner designs and updates target speeds for the vehicle controllers based on real-time Traffic State Estimation (TSE) [1]. The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module. The TSE enhancement module is designed to minimize the effects of inherent latency in the received traffic information and to improve the spatial and temporal resolution of the input traffic data. The target speed design module generates target speed profiles with the goal of improving traffic flow. The vehicle controllers are designed to track the target speed meanwhile responding to the surrounding situation. The numerical simulation indicates the performance of the proposed method: the bottleneck throughput has increased by 5.01%, and the speed standard deviation has been reduced by a significant 34.36%. We further showcase an operational study with a description of how the controller was implemented on a field-test with 100 AVs and its comprehensive effects on the traffic flow.

Silicon oxynitride (SiON) is a low-loss and versatile material for linear and nonlinear photonics applications. Controlling the oxygen-to-nitrogen (O/N) ratio in SiON provides an effective way to engineer its optical and mechanical properties, making it a great platform for the investigation of on-chip optomechanical interactions, especially the stimulated Brillouin scattering (SBS). Here we report the Brillouin nonlinearity characterization of a SiON platform with a specific O/N ratio (characterized by a refractive index of $n=1.65$). First, we introduce this particular SiON platform with fabrication details. Subsequently, we discuss various techniques for the on-chip Brillouin nonlinearity characterizations. In particular, we focus on the intensity-modulated pump-probe lock-in amplifier technique, which enables ultra-sensitive characterization. Finally, we analyze the Brillouin nonlinearities of this SiON platform and compare them with other SiON platforms. This work underscores the potential of SiON for on-chip Brillouin-based applications. Moreover, it paves the way for Brillouin nonlinearity characterization across various material platforms.

Integrated photonics has emerged as one of the most promising platforms for quantum applications. The performances of quantum photonic integrated circuits (QPIC) necessitate a demanding optimization to achieve enhanced properties and tailored characteristics with more stringent requirements with respect to their classical counterparts. In this study, we report on the simulation, fabrication, and characterization of a series of fundamental components for photons manipulation in QPIC based on silicon nitride. These include crossing waveguides, multimode-interferometer-based integrated beam splitters (MMIs), asymmetric integrated Mach-Zehnder interferometers (MZIs) based on MMIs, and micro-ring resonators. Our investigation revolves primarily around the Visible to Near-Infrared spectral region, as these devices are meticulously designed and tailored for optimal operation within this wavelength range. By advancing the development of these elementary building blocks, we aim to pave the way for significant improvements in QPIC in a spectral region only little explored so far., Comment: 13 pages, 10 figures

We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given that the complex dynamics of other drivers and their response to a team of automated vehicles cannot be effectively modeled. This capability has been blocked because there is no existing scalable and affordable platform for experimental control. This paper introduces an extensible open-source hardware and software platform, enabling a team of 100 vehicles to execute several different vehicular control algorithms as a collaborative fleet, composed of three different makes and models, which drove 22752 miles in a combined 1022 hours, over 5 days in Nashville, TN in November 2022.

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment., Comment: 21 pages, 1 Table , 9 figures

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding.

In this work, we assess several deep learning strategies for hyperspectral pansharpening. First, we present a new dataset with a greater extent than any other in the state of the art. This dataset, collected using the ASI PRISMA satellite, covers about 262200 km2, and its heterogeneity is granted by randomly sampling the Earth's soil. Second, we adapted several state of the art approaches based on deep learning to fit PRISMA hyperspectral data and then assessed, quantitatively and qualitatively, the performance in this new scenario. The investigation has included two settings: Reduced Resolution (RR) to evaluate the techniques in a supervised environment and Full Resolution (FR) for a real-world evaluation. The main purpose is the evaluation of the reconstruction fidelity of the considered methods. In both scenarios, for the sake of completeness, we also included machine-learning-free approaches. From this extensive analysis has emerged that data-driven neural network methods outperform machine-learning-free approaches and adapt better to the task of hyperspectral pansharpening, both in RR and FR protocols., Comment: Project page: https://thezino.github.io/HSbenchmarkPRISMA/

Background Chronic kidney disease (CKD) induces muscle wasting and a reduction in the maximum voluntary force (MVF). Little is known about the neuromuscular fatigability in CKD patients, defined as the reduction of muscle force capacities during exercise. Neuromuscular fatigability is a crucial physical parameter of the daily living. The quantification of explosive force has been shown to be a sensitive means to assess neuromuscular fatigability. Thus, our study used explosive force estimates to assess neuromuscular fatigability in elderly CKD patients. Methods Inclusion criteria for CKD patients were age $\ge$ 60 years old and glomerular filtration rate (GFR) < 45 mL/ min/1.73 m 2 not on dialysis, and those for controls were GFR > 60 mL/min/1.73 m 2 , age and diabetes matched. The fatigability protocol focused on a handgrip task coupled with surface electromyography (sEMG). Scalars were extracted from the rate of force development (RFD): absolute and normalized time periods (50, 75, 100, 150 and 200 ms, RFD 50 , RFD 75 , RFD 100 , RFD 150 and RFD 200 , respectively), peak RFD (RFD peak in absolute; NRFD peak normalized), timeto-peak RFD (t-RFD peak) and the relative force at RFD peak (MVF-RFD peak). A statistical parametric mapping approach was performed on the force, impulse and RFD-time curves. The integrated sEMG with time at 0-30, 0-50, 0-100 and 0-200 ms time intervals relative to onset of sEMG activity was extracted and groups were compared separately for each sex. Results The cohort of 159 individuals had a median age of 69 (9 IQR) years and body mass index was 27.6 (6.2 IQR) kg/ m 2. Propensity-score-matched groups balanced CKD patients and controls by gender with 66 males and 34 females. In scalar analysis, CKD patients manifested a higher decrement than controls in the early phase of contraction, regarding the NRFD peak (P = 0.009; $\eta$ 2 p = 0.034) and RFD 75 and RFD 100 (for both P < 0.001; $\eta$ 2 p = 0.068 and 0.064). The onedimensional analysis confirmed that CKD males manifest higher and delayed neuromuscular fatigability, especially before 100 ms from onset of contraction. sEMG was lower in CKD patients than controls in the 0-100 ms (at rest: P = 0.049, Cohen's d = 0.458) and 0-200 ms (at rest: P = 0.016, Cohen's d = 0.496; during exercise: P = 0.006, Cohen's d = 0.421) time windows. Controls showed greater decrease of sEMG than CKD patients in the 0-30 ms (P = 0.020, Cohen's d = 0.533) and 0-50 ms (P = 0.010, Cohen's d = 0.640) time windows. As opposite to females, males showed almost the same differences between groups. Conclusions Our study is the first to show that CKD patients have higher fatigability than controls, which may be associated with an impaired motor-unit recruitment, highlighting a neural drive disturbance with CKD. Further studies are needed to confirm these findings., Comment: Journal of Cachexia, Sarcopenia and Muscle, In press