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The online bipartite matching problem, extensively studied in the literature, deals with the allocation of online arriving vertices (items) to a predetermined set of offline vertices (agents). However, little attention has been given to the concept of class fairness, where agents are categorized into different classes, and the matching algorithm must ensure equitable distribution across these classes. We here focus on randomized algorithms for the fair matching of indivisible items, subject to various definitions of fairness. Our main contribution is the first (randomized) non-wasteful algorithm that simultaneously achieves a $1/2$ approximation to class envy-freeness (CEF) while simultaneously ensuring an equivalent approximation to the class proportionality (CPROP) and utilitarian social welfare (USW) objectives. We supplement this result by demonstrating that no non-wasteful algorithm can achieve an $\alpha$-CEF guarantee for $\alpha > 0.761$. In a similar vein, we provide a novel input instance for deterministic divisible matching that demonstrates a nearly tight CEF approximation. Lastly, we define the ``price of fairness,'' which represents the trade-off between optimal and fair matching. We demonstrate that increasing the level of fairness in the approximation of the solution leads to a decrease in the objective of maximizing USW, following an inverse proportionality relationship., Comment: Accepted to NeurIPS 2024

We present algorithms for classifying rational polygons with fixed denominator and number of interior lattice points. Our approach is to first describe maximal polygons and then compute all subpolygons, where we eliminate redundancy by a suitable normal form. Executing our classification, we obtained a dataset of several billions of polygons covering a wide variety of cases., Comment: 23 pages, 10 figures

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension of the spatial morphology of a microquasar, SS 433, to TeV gamma rays \cite{abeysekara2018very} localizes the acceleration of electrons at shocks in the jet far from the black hole \cite{hess2024ss433}. Here we report TeV gamma-ray emission from another microquasar, V4641~Sgr, which reveals particle acceleration at similar distances from the black hole as SS~433. Additionally, the gamma-ray spectrum of V4641 is among the hardest TeV spectra observed from any known gamma-ray source and is detected up to 200 TeV. Gamma rays are produced by particles, either electrons or hadrons, of higher energies. Because electrons lose energy more quickly the higher their energy, such a spectrum either very strongly constrains the electron production mechanism or points to the acceleration of high-energy hadrons. This observation suggests that large-scale jets from microquasars could be more common than previously expected and that microquasars could be a significant source of Galactic cosmic rays. high energy gamma-rays also provide unique constraints on the acceleration mechanisms of extra-Galactic cosmic rays postulated to be produced by the supermassive black holes and relativistic jets of quasars. The distance to quasars limits imaging studies due to insufficient angular resolution of gamma-rays and due to attenuation of the highest energy gamma-rays by the extragalactic background light.

This paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike traditional approaches, which address these problems using parameter-sharing, we consider an alternative methodology based on irreducible representations and Schur's lemma. Using this methodology, we obtain an alternative derivation for existing models like DeepSets, 2-IGN graph equivariant networks, and Deep Weight Space (DWS) networks. The derivation for DWS networks is significantly simpler than that of previous results. Next, we extend our approach to unaligned symmetric sets, where equivariance to the wreath product of groups is required. Previous works have addressed this problem in a rather restrictive setting, in which almost all wreath equivariant layers are Siamese. In contrast, we give a full characterization of layers in this case and show that there is a vast number of additional non-Siamese layers in some settings. We also show empirically that these additional non-Siamese layers can improve performance in tasks like graph anomaly detection, weight space alignment, and learning Wasserstein distances. Our code is available at \href{https://github.com/yonatansverdlov/Irreducible-Representations-of-Deep-Weight-Spaces}{GitHub}.

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report various information needs regarding buprenorphine treatment on social media platforms like Reddit. However, the key challenge is to characterize these needs. In this study, we propose a theme-based framework to curate and analyze large-scale data from social media to characterize self-reported treatment information needs (TINs). Methods: We collected 15,253 posts from r/Suboxone, one of the largest Reddit sub-community for buprenorphine products. Following the standard protocol, we first identified and defined five main themes from the data and then coded 6,000 posts based on these themes, where one post can be labeled with applicable one to three themes. Finally, we determined the most frequently appearing sub-themes (topics) for each theme by analyzing samples from each group. Results: Among the 6,000 posts, 40.3% contained a single theme, 36% two themes, and 13.9% three themes. The most frequent topics for each theme or theme combination came with several key findings - prevalent reporting of psychological and physical effects during recovery, complexities in accessing buprenorphine, and significant information gaps regarding medication administration, tapering, and usage of substances during different stages of recovery. Moreover, self-treatment strategies and peer-driven advice reveal valuable insights and potential misconceptions. Conclusions: The findings obtained using our proposed framework can inform better patient education and patient-provider communication, design systematic interventions to address treatment-related misconceptions and rumors, and streamline the generation of hypotheses for future research.

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Trustworthiness assessment is an essential step to assure that interdependent systems perform critical functions as anticipated, even under adverse conditions. In this paper, a holistic trustworthiness assessment framework for ultra-wideband self-localization is proposed, including attributes of reliability, security, privacy, and resilience. Our goal is to provide guidance for evaluating a system's trustworthiness based on objective evidence, so-called trustworthiness indicators. These indicators are carefully selected through the threat analysis of the particular system. Our approach guarantees that the resulting trustworthiness indicators correspond to chosen real-world threats. Moreover, experimental evaluations are conducted to demonstrate the effectiveness of the proposed method. While the framework is tailored for this specific use case, the process itself serves as a versatile template, which can be used in other applications in the domains of the Internet of Things or cyber-physical systems., Comment: 20 pages, accepted for MDPI Sensors, also check associated dataset https://doi.org/10.5281/zenodo.12705892

Supernova remnants (SNRs) are believed to be capable of accelerating cosmic rays (CRs) to PeV energies. SNR G106.3+2.7 is a prime PeVatron candidate. It is formed by a head region, where the pulsar J2229+6114 and its boomerang-shaped pulsar wind nebula are located, and a tail region containing SN ejecta. The lack of observed gamma ray emission from the two regions of this SNR has made it difficult to assess which region would be responsible for the PeV CRs. We aim to characterize the very-high-energy (VHE, 0.1-100 TeV) gamma ray emission from SNR G106.3+2.7 by determining the morphology and spectral energy distribution of the region. This is accomplished using 2565 days of data and improved reconstruction algorithms from the HAWC Observatory. We also explore possible gamma ray production mechanisms for different energy ranges. Using a multi-source fitting procedure based on a maximum-likelihood estimation method, we evaluate the complex nature of this region. We determine the morphology, spectrum, and energy range for the source found in the region. Molecular cloud information is also used to create a template and evaluate the HAWC gamma ray spectral properties at ultra-high-energies (UHE, >56 TeV). This will help probe the hadronic nature of the highest-energy emission from the region. We resolve one extended source coincident with all other gamma ray observations of the region. The emission reaches above 100~TeV and its preferred log-parabola shape in the spectrum shows a flux peak in the TeV range. The molecular cloud template fit on the higher energy data reveals that the SNR's energy budget is fully capable of producing a purely hadronic source for UHE gamma rays.

HESS J1809-193 is an unidentified TeV source, first detected by the High Energy Stereoscopic System (H.E.S.S.) Collaboration. The emission originates in a source-rich region that includes several Supernova Remnants (SNR) and Pulsars (PSR) including SNR G11.1+0.1, SNR G11.0-0.0, and the young radio pulsar J1809-1917. Originally classified as a pulsar wind nebula (PWN) candidate, recent studies show the peak of the TeV region overlapping with a system of molecular clouds. This resulted in the revision of the original leptonic scenario to look for alternate hadronic scenarios. Marked as a potential PeVatron candidate, this region has been studied extensively by H.E.S.S. due to its emission extending up-to several tens of TeV. In this work, we use 2398 days of data from the High Altitude Water Cherenkov (HAWC) observatory to carry out a systematic source search for the HESS J1809-193 region. We were able to resolve emission detected as an extended component (modelled as a Symmetric Gaussian with a 1 $\sigma$ radius of 0.21 $^\circ$) with no clear cutoff at high energies and emitting photons up-to 210 TeV. We model the multi-wavelength observations for the region HESS J1809-193 using a time-dependent leptonic model and a lepto-hadronic model. Our model indicates that both scenarios could explain the observed data within the region of HESS J1809-193.

This research investigates the potential use of a blockchain-based Public Key Infrastructure (PKI) within an organization and compares it to conventional PKI systems. The goal is to assess the advantages and disadvantages of both approaches in order to determine the feasibility of employing blockchain technology for a decentralized PKI. The study will also evaluate the impact of current legal frameworks, such as the Cyber Resilience Act (CRA) and NIS-2 Directive. The study will examine various implementations of blockchain PKIs based on factors such as security, performance, and platform. The results indicate that blockchain-based PKIs can overcome the limitations of conventional PKIs by decentralizing the trust anchor, providing greater security. Blockchain technology allows for the immutable and transparent management of certificates, making tampering significantly more challenging. Additionally, blockchain-based PKIs offer enhanced mechanisms for identifying and addressing certificate misconduct.

We report an observation of ultra-high energy (UHE) gamma rays from the Galactic Center region, using seven years of data collected by the High-Altitude Water Cherenkov (HAWC) Observatory. The HAWC data are best described as a point-like source (HAWC J1746-2856) with a power-law spectrum ($\mathrm{d}N/\mathrm{d}E=\phi(E/26 \,\text{TeV})^{\gamma}$), where $\gamma=-2.88 \pm 0.15_{\text{stat}} - 0.1_{\text{sys}} $ and $\phi=1.5 \times 10^{-15}$ (TeV cm$^{2}$s)$^{-1}$ $\pm\, 0.3_{\text{stat}}\,^{+0.08_{\text{sys}}}_{-0.13_{\text{sys}}}$ extending from 6 to 114 TeV. We find no evidence of a spectral cutoff up to $100$ TeV using HAWC data. Two known point-like gamma-ray sources are spatially coincident with the HAWC gamma-ray excess: Sgr A$^{*}$ (HESS J1745-290) and the Arc (HESS J1746-285). We subtract the known flux contribution of these point sources from the measured flux of HAWC J1746-2856 to exclude their contamination and show that the excess observed by HAWC remains significant ($>$5$\sigma$) with the spectrum extending to $>$100 TeV. Our result supports that these detected UHE gamma rays can originate via hadronic interaction of PeV cosmic-ray protons with the dense ambient gas and confirms the presence of a proton PeVatron at the Galactic Center.

The first TeV gamma-ray source with no lower energy counterparts, TeV J2032+4130, was discovered by HEGRA. It appears in the third HAWC catalog as 3HWC J2031+415 and it is a bright TeV gamma-ray source whose emission has previously been resolved as 2 sources: HAWC J2031+415 and HAWC J2030+409. While HAWC J2030+409 has since been associated with the \emph{Fermi-LAT} Cygnus Cocoon, no such association for HAWC J2031+415 has yet been found. In this work, we investigate the spectrum and energy-dependent morphology of HAWC J2031+415. We associate HAWC J2031+415 with the pulsar PSR J2032+4127 and perform a combined multi-wavelength analysis using radio, X-ray, and $\gamma$-ray emission. We conclude that HAWC J2031+415 and, by extension, TeV J2032+4130 are most probably a pulsar wind nebula (PWN) powered by PSR J2032+4127.

We give the first non-trivial decremental dynamic embedding of a weighted, undirected graph $G$ into $\ell_p$ space. Given a weighted graph $G$ undergoing a sequence of edge weight increases, the goal of this problem is to maintain a (randomized) mapping $\phi: (G,d) \to (X,\ell_p)$ from the set of vertices of the graph to the $\ell_p$ space such that for every pair of vertices $u$ and $v$, the expected distance between $\phi(u)$ and $\phi(v)$ in the $\ell_p$ metric is within a small multiplicative factor, referred to as the \emph{distortion}, of their distance in $G$. Our main result is a dynamic algorithm with expected distortion $O(\log^3 n)$ and total update time $O\left((m^{1+o(1)} \log^2 W + Q \log n)\log(nW) \right)$, where $W$ is the maximum weight of the edges, $Q$ is the total number of updates and $n, m$ denote the number of vertices and edges in $G$ respectively. This is the first result of its kind, extending the seminal result of Bourgain to the growing field of dynamic algorithms. Moreover, we demonstrate that in the fully dynamic regime, where we tolerate edge insertions as well as deletions, no algorithm can explicitly maintain an embedding into $\ell_p$ space that has a low distortion with high probability., Comment: Accepted to ICML 2024 (15 pages, 3 figures)

Sharing and receiving information plays a pivotal role in science and technology. Quantum communication relies on the principles of quantum mechanics to transmit information in a nonclassical manner. Existing quantum communication protocols are commonly based on shared entangled states between sender and receiver, while the transmitting medium is classical. We here demonstrate that information transfer may be enhanced in a quantum correlated medium without entanglement distribution. We concretely show that nonclassical correlations, with nonzero discord, between the first two spins of a spin chain that acts as a quantum wire can increase the information flow and reduce the propagation time. We relate this effect to the breaking of the spatial symmetry of the out-of-time-order correlator that characterizes the spread of information through the medium.

Sharpness-Aware Minimization (SAM) has emerged as a promising alternative optimizer to stochastic gradient descent (SGD). The originally-proposed motivation behind SAM was to bias neural networks towards flatter minima that are believed to generalize better. However, recent studies have shown conflicting evidence on the relationship between flatness and generalization, suggesting that flatness does fully explain SAM's success. Sidestepping this debate, we identify an orthogonal effect of SAM that is beneficial out-of-distribution: we argue that SAM implicitly balances the quality of diverse features. SAM achieves this effect by adaptively suppressing well-learned features which gives remaining features opportunity to be learned. We show that this mechanism is beneficial in datasets that contain redundant or spurious features where SGD falls for the simplicity bias and would not otherwise learn all available features. Our insights are supported by experiments on real data: we demonstrate that SAM improves the quality of features in datasets containing redundant or spurious features, including CelebA, Waterbirds, CIFAR-MNIST, and DomainBed., Comment: 25 pages, 10 figures, 2 tables

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

We need to look at our shoelaces as we first learn to tie them but having mastered this skill, can do it from touch alone. We call this phenomenon "sensory scaffolding": observation streams that are not needed by a master might yet aid a novice learner. We consider such sensory scaffolding setups for training artificial agents. For example, a robot arm may need to be deployed with just a low-cost, robust, general-purpose camera; yet its performance may improve by having privileged training-time-only access to informative albeit expensive and unwieldy motion capture rigs or fragile tactile sensors. For these settings, we propose "Scaffolder", a reinforcement learning approach which effectively exploits privileged sensing in critics, world models, reward estimators, and other such auxiliary components that are only used at training time, to improve the target policy. For evaluating sensory scaffolding agents, we design a new "S3" suite of ten diverse simulated robotic tasks that explore a wide range of practical sensor setups. Agents must use privileged camera sensing to train blind hurdlers, privileged active visual perception to help robot arms overcome visual occlusions, privileged touch sensors to train robot hands, and more. Scaffolder easily outperforms relevant prior baselines and frequently performs comparably even to policies that have test-time access to the privileged sensors. Website: https://penn-pal-lab.github.io/scaffolder/, Comment: ICLR 2024 Spotlight version

Consumer-grade drones have become effective multimedia collection tools, spring-boarded by rapid development in embedded CPUs, GPUs, and cameras. They are best known for their ability to cheaply collect high-quality aerial video, 3D terrain scans, infrared imagery, etc., with respect to manned aircraft. However, users can also create and attach custom sensors, actuators, or computers, so the drone can collect different data, generate composite data, or interact intelligently with its environment, e.g., autonomously changing behavior to land in a safe way, or choosing further data collection sites. Unfortunately, developing custom payloads is prohibitively difficult for many researchers outside of engineering. We provide guidelines for how to create a sophisticated computational payload that integrates a Raspberry Pi 5 into a DJI Matrice 350. The payload fits into the Matrice's case like a typical DJI payload (but is much cheaper), is easy to build and expand (3D-printed), uses the drone's power and telemetry, can control the drone and its other payloads, can access the drone's sensors and camera feeds, and can process video and stream it to the operator via the controller in real time. We describe the difficulties and proprietary quirks we encountered, how we worked through them, and provide setup scripts and a known-working configuration for others to use., Comment: 5 pages, 3 figures, 1 table, workshop paper, version 1 (preprint)

The High-Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory located on the side of the Sierra Negra volcano in Mexico, has been fully operational since 2015. The HAWC collaboration has recently significantly improved their extensive-air-shower reconstruction algorithms, which has notably advanced the observatory performance. The energy resolution for primary gamma rays with energies below 1~TeV was improved by including a noise-suppression algorithm. Corrections have also been made to systematic errors in direction fitting related to the detector and shower plane inclinations, $\mathcal{O}(0.1^{\circ})$ biases in highly inclined showers, as well as enhancements to the core reconstruction. The angular resolution for gamma rays approaching the HAWC array from large zenith angles ($> 37^{\circ}$) has improved by a factor of four at the highest energies ($> 70$~TeV) as compared to previous reconstructions. The inclusion of a lateral distribution function fit to the extensive air shower footprint on the array to separate gamma-ray primaries from cosmic-ray ones, based on the resulting $\chi^{2}$ values, improved the background rejection performance at all inclinations. At large zenith angles, the improvement in significance is a factor of four compared to previous HAWC publications. These enhancements have been verified by observing the Crab Nebula, which is an overhead source for the HAWC Observatory. We show that the sensitivity to Crab-like point sources ($E^{-2.63}$) with locations overhead to 30$^{\circ}$ zenith is comparable or less than 10\% of the Crab Nebula's flux between 2 and 50~TeV. Thanks to these improvements, HAWC can now detect more sources, including the Galactic Center.

In this work, the design and execution of an experiment with the goal of demonstrating control over the evolution of a copper jet is described. Simulations show that when using simple multi-material buffers placed between a copper target with a conical defect and a cylinder of high-explosive, a variety of jetting behaviors occur based on material placement, including both jet velocity augmentation and mitigation. A parameter sweep was performed to determine optimal buffer designs in two configurations. Experiments using the optimal buffer designs verified the effectiveness of the buffer and validated the modeling.

Detection of moving sources over complicated background is important for several reasons. First is measuring the astrophysical motion of the source. Second is that such motion resulting from atmospheric scintillation, color refraction, or astrophysical reasons is a major source of false alarms for image subtraction methods. We extend the Zackay, Ofek, and Gal-Yam image subtraction formalism to deal with moving sources. The new method, named translient (translational transient) detector, applies hypothesis testing between the hypothesis that the source is stationary and that the source is moving. It can be used to detect source motion or to distinguish between stellar variability and motion. For moving source detection, we show the superiority of translient over the proper image subtraction, using the improvement in the receiver-operating characteristic curve. We show that in the small translation limit, Translient is an optimal detector of point source motion in any direction. Furthermore, it is numerically stable, fast to calculate, and presented in a closed form. Efficient transient detection requires both the proper image subtraction statistics and the translient statistics: when the translient statistic is higher, then the subtraction artifact is likely due to motion. We test our algorithm both on simulated data and on real images obtained by the Large Array Survey Telescope (LAST). We demonstrate the ability of translient to distinguish between motion and variability, which has the potential to reduce the number of false alarms in transients detection. We provide the translient implementation in Python and MATLAB., Comment: 13 pages, 11 figures, submitted to AJ

Over the last 30 years, the World Wide Web has changed significantly. In this paper, we argue that common practices to prepare web pages for delivery conflict with many efforts to present content with minimal latency, one fundamental goal that pushed changes in the WWW. To bolster our arguments, we revisit reasons that led to changes of HTTP and compare them systematically with techniques to prepare web pages. We found that the structure of many web pages leverages features of HTTP/1.1 but hinders the use of recent HTTP features to present content quickly. To improve the situation in the future, we propose fine-grained content segmentation. This would allow to exploit streaming capabilities of recent HTTP versions and to render content as quickly as possible without changing underlying protocols or web browsers.

We propose a method for autonomous precision drone landing with fiducial markers and a gimbal-mounted, multi-payload camera with wide-angle, zoom, and IR sensors. The method has minimal data requirements; it depends primarily on the direction from the drone to the landing pad, enabling it to switch dynamically between the camera's different sensors and zoom factors, and minimizing auxiliary sensor requirements. It eliminates the need for data such as altitude above ground level, straight-line distance to the landing pad, fiducial marker size, and 6 DoF marker pose (of which the orientation is problematic). We leverage the zoom and wide-angle cameras, as well as visual April Tag fiducial markers to conduct successful precision landings from much longer distances than in previous work (168m horizontal distance, 102m altitude). We use two types of April Tags in the IR spectrum - active and passive - for precision landing both at daytime and nighttime, instead of simple IR beacons used in most previous work. The active IR landing pad is heated; the novel, passive one is unpowered, at ambient temperature, and depends on its high reflectivity and an IR differential between the ground and the sky. Finally, we propose a high-level control policy to manage initial search for the landing pad and subsequent searches if it is lost - not addressed in previous work. The method demonstrates successful landings with the landing skids at least touching the landing pad, achieving an average error of 0.19m. It also demonstrates successful recovery and landing when the landing pad is temporarily obscured., Comment: 7 pages, 6 figures, 2 tables

Recent approaches to improving the extraction of text embeddings from autoregressive large language models (LLMs) have largely focused on improvements to data, backbone pretrained language models, or improving task-differentiation via instructions. In this work, we address an architectural limitation of autoregressive models: token embeddings cannot contain information from tokens that appear later in the input. To address this limitation, we propose a simple approach, "echo embeddings," in which we repeat the input twice in context and extract embeddings from the second occurrence. We show that echo embeddings of early tokens can encode information about later tokens, allowing us to maximally leverage high-quality LLMs for embeddings. On the MTEB leaderboard, echo embeddings improve over classical embeddings by over 9% zero-shot and by around 0.7% when fine-tuned. Echo embeddings with a Mistral-7B model achieve state-of-the-art compared to prior open source models that do not leverage synthetic fine-tuning data., Comment: 36 pages, 11 figures, 16 tables

Informing drivers about the predicted state of upcoming traffic lights is considered a key solution to reduce unneeded energy expenditure and dilemma zones at intersections. However, newer traffic lights can react to traffic demand, resulting in spontaneous switching behavior and poor predictability. To assess whether future traffic light assistance services are viable, it is crucial to understand how strongly predictability is affected by such spontaneous switching behavior. Previous studies have so far only reported percentages of adaptivity-capable traffic lights, but the actual switching behavior has not been measured. Addressing this research gap, we conduct a large-scale predictability evaluation based on 424 million recorded switching cycles over four weeks for 18,009 individual traffic lights in Hamburg. Two characteristics of predictability are studied: cycle discrepancy and wait time diversity. Results indicate that fewer traffic lights exhibit hard-to-predict switching behavior than suggested by previous work, considering a reported number of 90.7% adaptive traffic lights in Hamburg. Contrasting previous work, we find that not all traffic lights capable of adaptiveness may necessarily exhibit low predictability. We critically review these results and derive avenues for future research., Comment: Accepted for publication at IEEE IV 2024