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Ridesharing has experienced significant global growth over the past decade and is becoming integral to future transportation networks. These services offer alternative mobility options in many urban areas, promoting car-light or car-free lifestyles, with their market share rapidly expanding due to the convenience they offer. However, alongside these benefits, concerns have arisen about the environmental impact of ridesharing, particularly its contribution to carbon emissions. A major source of these emissions is deadhead miles that are driven without passengers between trips. This issue is especially pronounced during high-demand periods when the number of ride requests exceeds platform capacity, leading to longer deadhead miles and higher emissions. While reducing these unproductive miles can lower emissions, it may also result in longer wait times for passengers as they wait for a nearby driver, potentially diminishing the overall user experience. In this paper, we propose LARA, an online algorithm for rider-to-driver assignment that dynamically adjusts the maximum allowed deadhead miles for drivers and assigns ride requests accordingly. While LARA can be applied under any conditions, it is particularly more effective during high-demand hours, aiming to reduce both carbon emissions and rider wait times. We prove that LARA achieves near-optimal performance in online settings compared to the optimal offline algorithm. Furthermore, we evaluate LARA using both synthetic and real-world datasets, demonstrating up to 34.2% reduction in emissions and up to 42.9% reduction in rider wait times compared to state-of-the-art algorithms. While recent studies have introduced the problem of emission-aware ride assignment, LARA is the first algorithm to provide both theoretical and empirical guarantees on performance., Comment: 20 pages

This paper proposes a novel metamaterial-inspired absorber designed to enhance the data rate in 52% RF to DC conversion simultaneous wireless information and power transfer system (SWIPT) through biological tissue. The proposed absorber includes split-ring resonators(SRRs) and demonstrates significant permeability characteristics, with both the real and imaginary parts being negative and close to -1. It also improves isolation by around 5dB in a WPT distance of 9mm. A 5mm thick phantom is used for biological tissue in this study. Experimental results exhibits that the SWIPT systems including a rectifier that converts 52% RF to DC efficiency in a WPT distance of 9mm embedding this absorber between power and signal ports at Tx side results in a 5dB improvement in isolation performance. By using proposed absorber, it enables a 7MB/s improvement of data rate and allows signals to be transmitted with 5dBm weaker power than without absorber SWIPT system.

The rapid adoption of machine learning (ML) has underscored the importance of serving ML models with high throughput and resource efficiency. Traditional approaches to managing increasing query demands have predominantly focused on hardware scaling, which involves increasing server count or computing power. However, this strategy can often be impractical due to limitations in the available budget or compute resources. As an alternative, accuracy scaling offers a promising solution by adjusting the accuracy of ML models to accommodate fluctuating query demands. Yet, existing accuracy scaling techniques target independent ML models and tend to underperform while managing inference pipelines. Furthermore, they lack integration with hardware scaling, leading to potential resource inefficiencies during low-demand periods. To address the limitations, this paper introduces Loki, a system designed for serving inference pipelines effectively with both hardware and accuracy scaling. Loki incorporates an innovative theoretical framework for optimal resource allocation and an effective query routing algorithm, aimed at improving system accuracy and minimizing latency deadline violations. Our empirical evaluation demonstrates that through accuracy scaling, the effective capacity of a fixed-size cluster can be enhanced by more than $2.7\times$ compared to relying solely on hardware scaling. When compared with state-of-the-art inference-serving systems, Loki achieves up to a $10\times$ reduction in Service Level Objective (SLO) violations, with minimal compromises on accuracy and while fulfilling throughput demands.

In recent years, there has been an increased emphasis on reducing the carbon emissions from electricity consumption. Many organizations have set ambitious targets to reduce the carbon footprint of their operations as a part of their sustainability goals. The carbon footprint of any consumer of electricity is computed as the product of the total energy consumption and the carbon intensity of electricity. Third-party carbon information services provide information on carbon intensity across regions that consumers can leverage to modulate their energy consumption patterns to reduce their overall carbon footprint. In addition, to accelerate their decarbonization process, large electricity consumers increasingly acquire power purchase agreements (PPAs) from renewable power plants to obtain renewable energy credits that offset their "brown" energy consumption. There are primarily two methods for attributing carbon-free energy, or renewable energy credits, to electricity consumers: location-based and market-based. These two methods yield significantly different carbon intensity values for various consumers. As there is a lack of consensus which method to use for carbon-free attribution, a concurrent application of both approaches is observed in practice. In this paper, we show that such concurrent applications can cause discrepancies in the carbon savings reported by carbon optimization techniques. Our analysis across three state-of-the-art carbon optimization techniques shows possible overestimation of up to 55.1% in the carbon reductions reported by the consumers and even increased emissions for consumers in some cases. We also find that carbon optimization techniques make different decisions under the market-based method and location-based method, and the market-based method can yield up to 28.2% less carbon savings than those claimed by the location-based method for consumers without PPAs.

GRAPES-3 is a mid-altitude (2200 m) and near equatorial ($11.4^{\circ}$ North) air shower array, overlapping in its field of view for cosmic ray observations with experiments that are located in Northern and Southern hemispheres. We analyze a sample of $3.7\times10^9$ cosmic ray events collected by the GRAPES-3 experiment between 1 January 2013 and 31 December 2016 with a median energy of $\sim16$ TeV for study of small-scale ($<60^{\circ}$) angular scale anisotropies. We observed two structures labeled as A and B, deviate from the expected isotropic distribution of cosmic rays in a statistically significant manner. Structure `A' spans $50^{\circ}$ to $80^{\circ}$ in the right ascension and $-15^{\circ}$ to $30^{\circ}$ in the declination coordinate. The relative excess observed in the structure A is at the level of $(6.5\pm1.3)\times10^{-4}$ with a statistical significance of 6.8 standard deviations. Structure `B' is observed in the right ascension range of $110^{\circ}$ to $140^{\circ}$. The relative excess observed in this region is at the level of $(4.9\pm1.4)\times10^{-4}$ with a statistical significance of 4.7 standard deviations. These structures are consistent with those reported by Milagro, ARGO-YBJ, and HAWC. These observations could provide a better understanding of the cosmic ray sources, propagation and the magnetic structures in our Galaxy.

Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in improving the quality of degraded videos through deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach into video streaming, allowing users to download low-quality video segments and then enhance them to obtain high-quality content without violating the playback of the video stream. We introduce BONES, an NES control algorithm that jointly manages the network and computational resources to maximize the quality of experience (QoE) of the user. BONES formulates NES as a Lyapunov optimization problem and solves it in an online manner with near-optimal performance, making it the first NES algorithm to provide a theoretical performance guarantee. Comprehensive experimental results indicate that BONES increases QoE by 5\% to 20\% over state-of-the-art algorithms with minimal overhead. Our code is available at https://github.com/UMass-LIDS/bones.

In this work, a highly customizable and scalable vision based system for automation of mechanical assembly lines is described. The proposed system calculates the features that are required to classify and identify the different kinds of bolts that are used in the assembly line. The system describes a novel method of calculating the pitch of the bolt in addition to bolt identification and calculating the dimensions of the bolts. This identification and classification system is extremely lightweight and can be run on bare minimum hardware. The system is very fast in the order of milliseconds, hence the system can be used successfully even if the components are steadily moving on a conveyor. The results show that our system can correctly identify the parts in our dataset with 98% accuracy using the calculated features., Comment: 8 pages

The stress intensity factor is important for understanding crack initiation and propagation. Because it cannot be measured directly, the characterization of the stress intensity factor relies on the measurement of deformation around a crack tip. Such measurements are challenging for dynamic fracture of brittle materials where the deformation is small and the crack tip velocity can be high (>1 km/s). Digital gradient sensing (DGS) is capable of full-field measurement of surface deformation with sub-microsecond temporal resolution, but it is limited to centimeter-scale specimens and has a spatial resolution of only $\sim 1$mm. This limits its ability to measure deformations close to the crack tip. Here, we demonstrate the potential of Shack-Hartmann wavefront sensing (SHWFS), as an alternative to DGS, for measuring surface deformation during dynamic brittle fracture of millimeter-scale specimens. Using an commercial glass ceramic as an example material, we demonstrate the capability of SHWFS to measure the surface slope evolution induced by a propagating crack on millimeter-scale specimens with a micrometer-scale spatial resolution and a sub-microsecond temporal resolution. The SHWFS apparatus has the additional advantage of being physically more compact than a typical DGS apparatus. We verify our SHWFS measurements by comparing them with analytical predictions and phase-field simulations of the surface slope around a crack tip. Then, fitting the surface slope measurements to the asymptotic crack-tip field solution, we extract the evolution of the apparent stress intensity factor associated with the propagating crack tip. We conclude by discussing potential future enhancements of this technique and how its compactness could enable the integration with other characterization techniques including x-ray phase-contrast imaging (XPCI) toward a multi-modal characterization.

The response of metals and their microstructures under extreme dynamic conditions can be markedly different from that under quasistatic conditions. Traditionally, high strain rates and shock stresses are measured using cumbersome and expensive methods such as the Kolsky bar or large spall experiments. These methods are low throughput and do not facilitate high-fidelity microstructure-property linkages. In this work, we combine two powerful small-scale testing methods, custom nanoindentation, and laser-driven micro-flyer shock, to measure the dynamic and spall strength of metals. The nanoindentation system is configured to test samples from quasistatic to dynamic strain rate regimes (10$^{-3}$ s$^{-1}$ to 10$^{+4}$ s$^{-1}$). The laser-driven micro-flyer shock system can test samples through impact loading between 10$^{+5}$ s$^{-1}$ to 10$^{+7}$ s$^{-1}$ strain rates, triggering spall failure. The model material used for testing is Magnesium alloys, which are lightweight, possess high-specific strengths and have historically been challenging to design and strengthen due to their mechanical anisotropy. Here, we modulate their microstructure by adding or removing precipitates to demonstrate interesting upticks in strain rate sensitivity and evolution of dynamic strength. At high shock loading rates, we unravel an interesting paradigm where the spall strength of these materials converges, but the failure mechanisms are markedly different. Peak aging, considered to be a standard method to strengthen metallic alloys, causes catastrophic failure, faring much worse than solutionized alloys. Our high throughput testing framework not only quantifies strength but also teases out unexplored failure mechanisms at extreme strain rates, providing valuable insights for the rapid design and improvement of metals for extreme environments.

Recent advances in omnidirectional cameras and AR/VR headsets have spurred the adoption of 360-degree videos that are widely believed to be the future of online video streaming. 360-degree videos allow users to wear a head-mounted display (HMD) and experience the video as if they are physically present in the scene. Streaming high-quality 360-degree videos at scale is an unsolved problem that is more challenging than traditional (2D) video delivery. The data rate required to stream 360-degree videos is an order of magnitude more than traditional videos. Further, the penalty for rebuffering events where the video freezes or displays a blank screen is more severe as it may cause cybersickness. We propose an online adaptive bitrate (ABR) algorithm for 360-degree videos called BOLA360 that runs inside the client's video player and orchestrates the download of video segments from the server so as to maximize the quality-of-experience (QoE) of the user. BOLA360 conserves bandwidth by downloading only those video segments that are likely to fall within the field-of-view (FOV) of the user. In addition, BOLA360 continually adapts the bitrate of the downloaded video segments so as to enable a smooth playback without rebuffering. We prove that BOLA360 is near-optimal with respect to an optimal offline algorithm that maximizes QoE. Further, we evaluate BOLA360 on a wide range of network and user head movement profiles and show that it provides $13.6\%$ to $372.5\%$ more QoE than state-of-the-art algorithms. While ABR algorithms for traditional (2D) videos have been well-studied over the last decade, our work is the first ABR algorithm for 360-degree videos with both theoretical and empirical guarantees on its performance., Comment: 27 pages