Search

We present the optical discovery and multiwavelength follow-up observations of AT2024kmq, a likely tidal disruption event (TDE) associated with a supermassive ($M_{\rm BH}\sim 10^{8} M_\odot$) black hole in a massive galaxy at $z=0.192$. The optical light curve of AT2024kmq exhibits two distinct peaks: an early fast (timescale 1 d) and luminous ($M\approx-20$ mag) red peak, then a slower (timescale 1 month) blue peak with a higher optical luminosity ($M\approx-22$ mag) and featureless optical spectra. The second component is similar to the spectroscopic class of "featureless TDEs" in the literature, and during this second component we detect highly variable, luminous ($L_X\approx 10^{44}$ erg s$^{-1}$), and hard ($f_\nu \propto \nu^{-1.5}$) X-ray emission. Luminous ($10^{29} $erg s$^{-1}$ Hz$^{-1}$ at 10 GHz) but unchanging radio emission likely arises from an underlying active galactic nucleus. The luminosity, timescale, and color of the early red optical peak can be explained by synchrotron emission, or alternatively by thermal emission from material at a large radius ($R\approx\mathrm{few}\times10^{15}$ cm). Possible physical origins for this early red component include an off-axis relativistic jet, and shocks from self-intersecting debris leading to the formation of the accretion disk. Late-time radio observations will help distinguish between the two possibilities., Comment: 23 pages, 7 figures, 6 tables. Submitted to journal on 11 Feb 2025. Comments welcome

This study presents the Adaptive Minimum-Variance Portfolio (AMVP) framework and the Adaptive Minimum-Risk Rate (AMRR) metric, innovative tools designed to optimize portfolios dynamically in volatile and nonstationary financial markets. Unlike traditional minimum-variance approaches, the AMVP framework incorporates real-time adaptability through advanced econometric models, including ARFIMA-FIGARCH processes and non-Gaussian innovations. Empirical applications on cryptocurrency and equity markets demonstrate the proposed framework's superior performance in risk reduction and portfolio stability, particularly during periods of structural market breaks and heightened volatility. The findings highlight the practical implications of using the AMVP and AMRR methodologies to address modern investment challenges, offering actionable insights for portfolio managers navigating uncertain and rapidly changing market conditions.

This study examines the investment landscape of Pakistan as an emerging and frontier market, focusing on implications for international investors, particularly those in the United States, through exchange-traded funds (ETFs) with exposure to Pakistan. The analysis encompasses 30 ETFs with varying degrees of exposure to Pakistan, covering the period from January 1, 2016, to February 2024. This research highlights the potential benefits and risks associated with investing in these ETFs, emphasizing the importance of thorough risk assessments and portfolio performance comparisons. By providing descriptive statistics and performance metrics based on historical optimization, this paper aims to equip investors with the necessary insights to make informed decisions when optimizing their portfolios with Pakistan-exposed ETFs. The second part of the paper introduces and assesses dynamic optimization methodologies. This section is designed to explore the adaptability and performance metrics of dynamic optimization techniques in comparison with conventional historical optimization methods. By integrating dynamic optimization into the investigation, this research aims to offer insights into the efficacy of these contrasting methodologies in the context of Pakistan-exposed ETFs. The findings underscore the significance of Pakistan's market dynamics within the broader context of emerging markets, offering a pathway for diversification and potential growth in investment strategies.

This study evaluates the performance of random forest regression models enhanced with technical indicators for high-frequency stock price prediction. Using minute-level SPY data, we assessed 13 models that incorporate technical indicators such as Bollinger bands, exponential moving average, and Fibonacci retracement. While these models improved risk-adjusted performance metrics, they struggled with out-of-sample generalization, highlighting significant overfitting challenges. Feature importance analysis revealed that primary price-based features consistently outperformed technical indicators, suggesting their limited utility in high-frequency trading contexts. These findings challenge the weak form of the efficient market hypothesis, identifying short-lived inefficiencies during volatile periods but its limited persistence across market regimes. The study emphasizes the need for selective feature engineering, adaptive modeling, and a stronger focus on risk-adjusted performance metrics to navigate the complexities of high-frequency trading environments., Comment: 27 pages, 7 figures

We investigate the efficacy of a systematic planetary nebula (PN) search in the Census of the Local Universe (CLU) narrowband (H$\alpha$) survey that covers a considerably larger sky region of above declination $-20^\circ$ than most previous surveys. Using PNe observed by the Isaac Newton Telescope Photometric H$\alpha$ Survey (IPHAS) as validation, we are able to visually recover 432 out of 441 cataloged PNe (98\%) within the CLU dataset, with 5 sources having unusable CLU images and 4 missed due to limitations of imaging quality. Moreover, the reference PNe are conventionally divided into three PN classes in decreasing order of identification confidence given their spectra and morphologies. We record consistently high recovery rate across all classes: 95\% of True, 71\% of Likely, and 81\% of Possible sources are readily recovered. To further demonstrate the ability of CLU to find new PNe, we undertake a preliminary search of compact PNe within a sub-region of the validation catalog, mainly utilizing the significance of narrow-band colors ($\Sigma$) as a metric for identification. In a $200\,\rm deg^2$ region, we search the CLU source catalog and find 31 PN candidates after automated and visual scrutiny, of which 12 are new sources not appearing in previous studies. As a demonstration of our ongoing follow-up campaign, we present medium-resolution optical spectra of six candidates and notice that four of them show emission signatures characteristic of confirmed PNe. As we refine our selection methods, CLU promises to provide a systematic catalog of PNe spanning $2/3$ of the sky., Comment: 40 pages, 12 figures, 3 tables; submitted to the Publications of the Astronomical Society of the Pacific

Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions. Self-attention offers a compelling mechanism for capturing these global effects, but its quadratic complexity presents a significant practical limitation. This problem is particularly pronounced in computational chemistry, where the stringent efficiency requirements of machine learning force fields (MLFFs) often preclude accurately modeling long-range interactions. To address this, we introduce Euclidean fast attention (EFA), a linear-scaling attention-like mechanism designed for Euclidean data, which can be easily incorporated into existing model architectures. A core component of EFA are novel Euclidean rotary positional encodings (ERoPE), which enable efficient encoding of spatial information while respecting essential physical symmetries. We empirically demonstrate that EFA effectively captures diverse long-range effects, enabling EFA-equipped MLFFs to describe challenging chemical interactions for which conventional MLFFs yield incorrect results.

We develop a new approach to estimate the uncertainty due to missing higher orders in perturbative predictions (the perturbative "theory uncertainty"), which overcomes many inherent limitations of the currently prevalent methods based on varying unphysical renormalization scales. In our approach, the true underlying sources of the theory uncertainty, namely the missing higher-order terms, are identified and parameterized in terms of mutually independent theory nuisance parameters (TNPs). The TNPs are true parameters of the calculation, i.e., they have a well-defined true value that is not or only imprecisely known. This approach affords the theory uncertainty all benefits of a truly parametric uncertainty: It provides correct correlations and allows for consistent error propagation and combination. Furthermore, the TNPs can be profiled in fits, allowing the data to reduce the theory uncertainties. On the theory side, it allows maximally exploiting all available higher-order information to reduce the theory uncertainty, such as partial higher-order results or any nontrivial knowledge of the higher-order or all-order structure. We first discuss the method in general as it can be applied across the board of perturbative calculations. As a concrete application, we then discuss the resummed transverse momentum ($q_T$) spectrum in Drell-Yan production, and how TNP-based uncertainties can correctly capture the correlations across the $q_T$ spectrum and between $Z$ and $W$ production. This application is the basis of the theory model enabling the recent precise measurement of the $W$-boson mass by the CMS experiment. In a forthcoming paper, we use it to study the theory uncertainties in extracting the strong coupling constant $\alpha_s$ from the $Z$ $q_T$ spectrum., Comment: 64 pages + references, 8 figures

We provide state-of-the-art precision QCD predictions for the fiducial $W$ and $Z$ boson transverse momentum spectra at the LHC at N$^3$LL$'$ and approximate N$^4$LL in resummed perturbation theory, matched to available $\mathcal{O}(\alpha_s^3)$ fixed-order results. Our predictions consistently combine all information from across the spectrum in a unified way, ranging from the nonperturbative region of small transverse momenta to the fixed-order tail, with an emphasis on estimating the magnitude of residual perturbative uncertainties, and in particular of those related to the matching. Parametric uncertainties related to the strong coupling, the collinear PDFs, and the nonperturbative transverse momentum-dependent (TMD) dynamics are studied in detail. To assess the latter, we explicitly demonstrate how the full complexity of flavor and Bjorken $x$-dependent TMD dynamics can be captured by a single, effective nonperturbative function for the resonant production of any given vector boson at a given collider. We point out that the cumulative $p_T^Z$ cross section at the level of precision enabled by our predictions provides strong constraining power for PDF determinations at full N$^3$LO., Comment: 51 pages + appendices and references, 19 figures, 6 tables

Project TAIPAN has been carried out jointly by Trinity Research Lab and the Frequency and Quantum Metrology Research Group located at the School of Physics, Mathematics and Computing of the University of Western Australia (UWA). Lockheed Martin Corporation (USA) has also been a partner in this joint collaboration providing financial backing to the project and other support including advanced modelling, assessment of laboratory tests and data analysis. The project aim was to develop a miniaturised gravity gradiometer to measure horizontal mixed gradient components of the Earth gravity in a small, lightweight package that can be deployed in a fixed 4D mode, in a borehole, or on moving exploration platforms including ground-based, airborne and submersible. The gradiometer design has evolved through a few prototypes combining the design of its sensing element with ultra low noise microwave and capacitive read out. The most recent prototype of the gradiometer using novel ultra sensitive capacitive pick off metrology has been trialled in the harsh environment of Outback Western Australia over a known gravity anomaly displaying steep gradients. Despite adverse weather conditions, results of the trial indicate that the gradiometer operated as expected, closely replicating the gravity gradient profile extrapolated from a regional gravity survey., Comment: 7 pages, 8 figures, supplementary materials (6 pages, 11 figures)

We report the discovery of three ultracompact binary white dwarf systems hosting accretion disks, with orbital periods of 7.95, 8.68, and 13.15 minutes. This significantly augments the population of mass-transferring binaries at the shortest periods, and provides the first evidence that accretors in ultracompacts can be dense enough to host accretion disks even below 10 minutes (where previously only direct-impact accretors were known). In the two shortest-period systems, we measured changes in the orbital periods driven by the combined effect of gravitational wave emission and mass transfer; we find $\dot{P}$ is negative in one case, and positive in the other. This is only the second system measured with a positive $\dot{P}$, and it the most compact binary known that has survived a period minimum. Using these systems as examples, we show how the measurement of $\dot{P}$ is a powerful tool in constraining the physical properties of binaries, e.g. the mass and mass-radius relation of the donor stars. We find that the chirp masses of ultracompact binaries at these periods seem to cluster around $\mathcal{M}_c \sim 0.3 M_\odot$, perhaps suggesting a common origin for these systems or a selection bias in electromagnetic discoveries. Our new systems are among the highest-amplitude known gravitational wave sources in the millihertz regime, providing exquisite opportunity for multi-messenger study with future space-based observatories such as \textit{LISA} and TianQin; we discuss how such systems provide fascinating laboratories to study the unique regime where the accretion process is mediated by gravitational waves., Comment: Accepted for publication in ApJ

In the past few years, the improved sensitivity and cadence of wide-field optical surveys have enabled the discovery of several afterglows without associated detected gamma-ray bursts (GRBs). We present the identification, observations, and multiwavelength modeling of a recent such afterglow (AT2023lcr), and model three literature events (AT2020blt, AT2021any, and AT2021lfa) in a consistent fashion. For each event, we consider the following possibilities as to why a GRB was not observed: 1) the jet was off-axis; 2) the jet had a low initial Lorentz factor; and 3) the afterglow was the result of an on-axis classical GRB (on-axis jet with physical parameters typical of the GRB population), but the emission was undetected by gamma-ray satellites. We estimate all physical parameters using afterglowpy and Markov Chain Monte Carlo methods from emcee. We find that AT2023lcr, AT2020blt, and AT2021any are consistent with on-axis classical GRBs, and AT2021lfa is consistent with both on-axis low Lorentz factor ($\Gamma_0 \approx 5 - 13$) and off-axis ($\theta_\text{obs}=2\theta_\text{jet}$) high Lorentz factor ($\Gamma_0 \approx 100$) jets., Comment: 40 pages, 18 figures, 20 tables

We address the problem of asset pricing in a market where there is no risky asset. Previous work developed a theoretical model for a shadow riskless rate (SRR) for such a market in terms of the drift component of the state-price deflator for that asset universe. Assuming asset prices are modeled by correlated geometric Brownian motion, in this work we develop a computational approach to estimate the SRR from empirical datasets. The approach employs: principal component analysis to model the effects of the individual Brownian motions; singular value decomposition to capture the abrupt changes in condition number of the linear system whose solution provides the SRR values; and a regularization to control the rate of change of the condition number. Among other uses (e.g., for option pricing, developing a term structure of interest rate), the SRR can be employed as an investment discriminator between asset classes. We apply the computational procedure to markets consisting of groups of stocks, varying asset type and number. The theoretical and computational analysis provides not only the drift, but also the total volatility of the state-price deflator. We investigate the time trajectory of these two descriptive components of the state-price deflator for the empirical datasets., Comment: 23 pages, 14 figures

The study of van der Waals heterostructures with an interlayer twist, known as "twistronics", has been instrumental in advancing contemporary condensed matter research. Most importantly, it has underpinned the emergence of a multitude of strongly-correlated phases, many of which derive from the topology of the physical system. Here, we explore the application of the twistronics paradigm in plasmonic systems with nontrivial topology, by creating a moir\'e skyrmion superlattice using two superimposed plasmonic skyrmion lattices, twisted at a "magic" angle. The complex electric field distribution of the moir\'e skyrmion superlattice is measured using time-resolved vector microscopy, revealing that each super-cell possesses very large topological invariants and harbors a "skyrmion bag", the size of which is controllable by the twist angle and center of rotation. Our work shows how twistronics leads to a diversity of topological features in optical fields, providing a new route to locally manipulate electromagnetic field distributions, which is crucial for future structured light-matter interaction.

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures market expectations of future volatility, but traditional methods based on second-moment shocks and time-varying volatility of the VIX often fail to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we construct a revised VIX by fitting a double-subordinated Normal Inverse Gaussian Levy process to S&P 500 option prices, providing a more comprehensive measure of volatility that reflects the extreme movements and heavy tails observed in financial data. Using an axiomatic approach, we introduce a general family of risk-reward ratios, computed with our revised VIX and fitted over a fractional time series to more accurately identify uncertainty shocks in financial markets.

We measure the growth of cosmic density fluctuations on large scales and across the redshift range $0.3

The kinematic Sunyaev--Zel'dovich (kSZ) effect induces a non-zero density-density-temperature bispectrum, which we can use to reconstruct the large-scale velocity field from a combination of cosmic microwave background (CMB) and galaxy density measurements, in a procedure known as ``kSZ velocity reconstruction''. This method has been forecast to constrain large-scale modes with future galaxy and CMB surveys, improving their measurement beyond what is possible with the galaxy surveys alone. Such measurements will enable tighter constraints on large-scale signals such as primordial non-Gaussianity, deviations from homogeneity, and modified gravity. In this work, we demonstrate a statistically significant measurement of kSZ velocity reconstruction for the first time, by applying quadratic estimators to the combination of the ACT DR6 CMB+kSZ map and the DESI LRG galaxies (with photometric redshifts) in order to reconstruct the velocity field. We do so using a formalism appropriate for the 2-dimensional projected galaxy fields that we use, which naturally incorporates the curved-sky effects important on the largest scales. We find evidence for the signal by cross-correlating with an external estimate of the velocity field from the spectroscopic BOSS survey and rejecting the null (no-kSZ) hypothesis at $3.8\sigma$. Our work presents a first step towards the use of this observable for cosmological analyses., Comment: 16 pages (main)+5 pages (Appendix); 13 figures (main) + 8 figures (appendix)

Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine requires reliable evaluation, which remains a challenge. In this narrative review, we assess the current evaluation state for clinical summarization tasks and propose future directions to address the resource constraints of expert human evaluation.

We present a joint analysis of the CMB lensing power spectra measured from the Data Release 6 of the Atacama Cosmology Telescope and Planck PR4, cross-correlations between the ACT and Planck lensing reconstruction and galaxy clustering from unWISE, and the unWISE clustering auto-spectrum. We obtain 1.5% constraints on the matter density fluctuations at late times parametrised by the best constrained parameter combination $S_8^{\rm 3x2pt}\equiv\sigma_8 (\Omega_m/0.3)^{0.4}=0.815\pm0.012$. The commonly used $S_8\equiv\sigma_8 (\Omega_m/0.3)^{0.5}$ parameter is constrained to $S_8=0.816\pm0.015$. In combination with baryon acoustic oscillation (BAO) measurements we find $\sigma_8=0.815\pm 0.012$. We also present sound-horizon-independent estimates of the present day Hubble rate of $H_0=66.4^{+3.2}_{-3.7} \,\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$ from our large scale structure data alone and $H_0=64.3^{+2.1}_{-2.4}\,\mathrm{km}\,\mathrm{s}^{-1}\mathrm{Mpc}^{-1}$ in combination with uncalibrated supernovae from Pantheon+. Using parametric estimates of the evolution of matter density fluctuations, we place constraints on cosmic structure in a range of high redshifts typically inaccessible with cross-correlation analyses. Combining lensing cross- and auto-correlations, we derive a 3.3% constraint on the integrated matter density fluctuations above $z=2.4$, one of the tightest constraints in this redshift range and fully consistent with a $\Lambda$CDM model fit to the primary CMB from Planck. Combining with primary CMB observations and using the extended low redshift coverage of these combined data sets we derive constraints on a variety of extensions to the $\Lambda$CDM model including massive neutrinos, spatial curvature, and dark energy. We find in flat $\Lambda$CDM $\sum m_\nu<0.12$ eV at 95% confidence using the LSS data, BAO measurements from SDSS and primary CMB observations., Comment: 30 pages, 15 figures, accepted for publication in PRD, data available under https://portal.nersc.gov/project/act/act_x_unWISE_xcorr+3x2pt/

The cores of stars are the cosmic furnaces where light elements are fused into heavier nuclei. The fusion of hydrogen to helium initially powers all stars. The ashes of the fusion reactions are then predicted to serve as fuel in a series of stages, eventually transforming massive stars into a structure of concentric shells. These are composed of natal hydrogen on the outside, and consecutively heavier compositions inside, predicted to be dominated by helium, carbon/oxygen, oxygen/neon/magnesium, and oxygen/silicon/sulphur. Silicon and sulphur are fused into inert iron, leading to the collapse of the core and either a supernova explosion or the direct formation of a black hole. Stripped stars, where the outer hydrogen layer has been removed and the internal He-rich layer (in Wolf-Rayet WN stars) or even the C/O layer below it (in Wolf-Rayet WC/WO stars) are exposed, provide evidence for this shell structure, and the cosmic element production mechanism it reflects. The types of supernova explosions that arise from stripped stars embedded in shells of circumstellar material (most notably Type Ibn supernovae from stars with outer He layers, and Type Icn supernovae from stars with outer C/O layers) confirm this scenario. However, direct evidence for the most interior shells, which are responsible for the production of elements heavier than oxygen, is lacking. Here, we report the discovery of the first-of-its-kind supernova arising from a star peculiarly stripped all the way to the silicon and sulphur-rich internal layer. Whereas the concentric shell structure of massive stars is not under debate, it is the first time that such a thick, massive silicon and sulphur-rich shell, expelled by the progenitor shortly before the SN explosion, has been directly revealed., Comment: 48 pages, 12 figures and 10 tables. Submitted to a high-impact journal. The reduced spectra and photometry will be made available via the journal webpage and the WISeREP archive after the acceptance of the paper

In this paper, we introduce a temporal logic-based safety filter for Autonomous Intersection Management (AIM), an emerging infrastructure technology for connected vehicles to coordinate traffic flow through intersections. Despite substantial work on AIM systems, the balance between intersection safety and efficiency persists as a significant challenge. Building on recent developments in formal methods that now have become computationally feasible for AIM applications, we introduce an approach that starts with a temporal logic specification for the intersection and then uses reachability analysis to compute safe time-state corridors for the connected vehicles that pass through the intersection. By analyzing these corridors, in contrast to single trajectories, we can make explicit design decisions regarding safety-efficiency trade-offs while taking each vehicle's decision uncertainty into account. Additionally, we compute safe driving limits to ensure that vehicles remain within their designated safe corridors. Combining these elements, we develop a service that provides safety filters for AIM coordination of connected vehicles. We evaluate the practical feasibility of our safety framework using a simulated 4-way intersection, showing that our approach performs in real-time for multiple scenarios., Comment: To be published in 27th IEEE International Conference on Intelligent Transportation Systems

We study a magnitude-limited sample of 36 Broad-lined Type Ic Supernovae (SNe Ic-BL) from the Zwicky Transient Facility Bright Transient Survey (detected between March 2018 and August 2021), which is the largest systematic study of SNe Ic-BL done in literature thus far. We present the light curves (LCs) for each of the SNe, and analyze the shape of the LCs to derive empirical parameters, along with the explosion epochs for every event. The sample has an average absolute peak magnitude in the r band of $M_r^{max}$ = -18.51 $\pm$ 0.15 mag. Using spectra obtained around peak light, we compute expansion velocities from the Fe II 5169 Angstrom line for each event with high enough signal-to-noise ratio spectra, and find an average value of $v_{ph}$ = 16,100 $\pm$ 1,100 km $s^{-1}$. We also compute bolometric LCs, study the blackbody temperature and radii evolution over time, and derive the explosion properties of the SNe. The explosion properties of the sample have average values of $M_{Ni}$ = $0.37_{-0.06}^{+0.08}$ solar masses, $M_{ej}$ = $2.45_{-0.41}^{+0.47}$ solar masses, and $E_K$= $4.02_{-1.00}^{+1.37} \times 10^{51}$ erg. Thirteen events have radio observations from the Very Large Array, with 8 detections and 5 non-detections. We find that the populations that have radio detections and radio non-detections are indistinct from one another with respect to their optically-inferred explosion properties, and there are no statistically significant correlations present between the events' radio luminosities and optically-inferred explosion properties. This provides evidence that the explosion properties derived from optical data alone cannot give inferences about the radio properties of SNe Ic-BL, and likely their relativistic jet formation mechanisms., Comment: 52 pages, 34 Figures, 8 Tables; Accepted to ApJ, Revised Title from Proofs

Measurements of Higgs boson processes by the ATLAS and CMS experiments at the LHC use Simplified Template Cross Sections (STXS) as a common framework for the combination of measurements in different decay channels and their further interpretation, e.g. to measure Higgs couplings. The different Higgs production processes are measured in predefined kinematic regions -- the STXS bins -- requiring precise theory predictions for each individual bin. In gluon-fusion Higgs production a main division is into 0-jet, 1-jet, and $\geq 2$-jet bins, which are further subdivided in bins of the Higgs transverse momentum $p_T^H$. Requiring a fixed number of jets induces logarithms $\ln p_T^{\mathrm{cut}}/Q$ in the cross section where $p_T^{\mathrm{cut}}$ is the jet-$p_T$ threshold and $Q\sim p_T^H\sim m_H$ the hard-interaction scale. These jet-veto logarithms can be resummed to all orders in perturbation theory to achieve the highest possible perturbative precision. We provide state-of-the art predictions for the $p_T^H$ spectrum in exclusive $H+$1-jet production and the corresponding $H+$1-jet STXS bins in the kinematic regime $p_T^{\mathrm{cut}} \ll p_T^H\sim m_H$. We carry out the resummation at NNLL$'$ accuracy, using theory nuisance parameters to account for the few unknown ingredients at this order, and match to full NNLO. We revisit the jet-veto factorization for this process and find that it requires refactorizing the total soft function into a global and soft-collinear contribution in order to fully account for logarithms of the signal jet radius. The leading nonglobal logarithms are also included, though they are numerically small for the region of phenomenological interest., Comment: 52 pages, 13 figures

In this work, we present a transformer-based framework for predicting future pedestrian states based on clustered historical trajectory data. In previous studies, researchers propose enhancing pedestrian trajectory predictions by using manually crafted labels to categorize pedestrian behaviors and intentions. However, these approaches often only capture a limited range of pedestrian behaviors and introduce human bias into the predictions. To alleviate the dependency on manually crafted labels, we utilize a transformer encoder coupled with hierarchical density-based clustering to automatically identify diverse behavior patterns, and use these clusters in data-driven reachability analysis. By using a transformer-based approach, we seek to enhance the representation of pedestrian trajectories and uncover characteristics or features that are subsequently used to group trajectories into different "behavior" clusters. We show that these behavior clusters can be used with data-driven reachability analysis, yielding an end-to-end data-driven approach to predicting the future motion of pedestrians. We train and evaluate our approach on a real pedestrian dataset, showcasing its effectiveness in forecasting pedestrian movements.

We infer the growth of large scale structure over the redshift range $0.4\lesssim z \lesssim 1$ from the cross-correlation of spectroscopically calibrated Luminous Red Galaxies (LRGs) selected from the Dark Energy Spectroscopic Instrument (DESI) legacy imaging survey with CMB lensing maps reconstructed from the latest Planck and ACT data. We adopt a hybrid effective field theory (HEFT) model that robustly regulates the cosmological information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime. We perform an extensive set of bandpower- and parameter-level systematics checks to ensure the robustness of our results and to characterize the uniformity of the LRG sample. We demonstrate that our results are stable to a wide range of modeling assumptions, finding excellent agreement with a linear theory analysis performed on a restricted range of scales. From a tomographic analysis of the four LRG photometric redshift bins we find that the rate of structure growth is consistent with $\Lambda$CDM with an overall amplitude that is $\simeq5-7\%$ lower than predicted by primary CMB measurements with modest $(\sim2\sigma)$ statistical significance. From the combined analysis of all four bins and their cross-correlations with Planck we obtain $S_8 = 0.765\pm0.023$, which is less discrepant with primary CMB measurements than previous DESI LRG cross Planck CMB lensing results. From the cross-correlation with ACT we obtain $S_8 = 0.790^{+0.024}_{-0.027}$, while when jointly analyzing Planck and ACT we find $S_8 = 0.775^{+0.019}_{-0.022}$ from our data alone and $\sigma_8 = 0.772^{+0.020}_{-0.023}$ with the addition of BAO data. These constraints are consistent with the latest Planck primary CMB analyses at the $\simeq 1.6-2.2\sigma$ level, and are in excellent agreement with galaxy lensing surveys., Comment: 60 pages, 26 figures, comments welcome

Future low-noise cosmic microwave background (CMB) lensing measurements from e.g., CMB-S4 will be polarization dominated, rather than temperature dominated. In this new regime, statistically optimal lensing reconstructions outperform the standard quadratic estimator, but their sensitivity to extragalactic polarized foregrounds has not been quantified. Using realistic simulations of polarized radio and infrared point sources, we show for the first time that optimal Bayesian lensing from a CMB-S4-like experiment is insensitive to the expected level of polarized extragalactic foregrounds after masking, as long as an accurate foreground power spectrum is included in the analysis. For more futuristic experiments where these foregrounds could cause a detectable bias, we propose a new method to jointly fit for lensing and the Poisson foregrounds, generalizing the bias hardening from the standard quadratic estimator to Bayesian lensing., Comment: 14+5 pages, 16 figures, comments welcome

We derive new constraints on the $E_G$ statistic as a test of gravity, combining the CMB lensing map estimated from Data Release 6 (DR6) of the Atacama Cosmology Telescope with SDSS BOSS CMASS and LOWZ galaxy data. We develop an analysis pipeline to measure the cross-correlation between CMB lensing maps and galaxy data, following a blinding policy and testing the approach through null and consistency checks. By testing the equivalence of the spatial and temporal gravitational potentials, the $E_G$ statistic can distinguish $\Lambda$CDM from alternative models of gravity. We find $E_G= 0.31^{+0.06}_{-0.05}$ for ACT and CMASS data at 68.28\% confidence level, and $E_G = 0.49^{+0.14}_{-0.11}$ for ACT and LOWZ. Systematic errors are estimated to be 3\% and 4\% respectively. Including CMB lensing information from Planck PR4 results in $E_G = 0.34^{+0.05}_{-0.05}$ with CMASS and $E_G= 0.43^{+0.11}_{-0.09}$ with LOWZ. These are consistent with predictions for the $\Lambda$CDM model that best fits the Planck CMB anisotropy and SDSS BOSS BAO, where $E_G^{\rm GR} (z_{\rm eff} = 0.555) = 0.401\pm 0.005$ for CMB lensing combined with CMASS and $E_G^{\rm GR} (z_{\rm eff} = 0.316) = 0.452\pm0.005$ combined with LOWZ. We also find $E_G$ to be scale independent, with PTE $>5\%$, as predicted by general relativity. The methods developed in this work are also applicable to improved future analyses with upcoming spectroscopic galaxy samples and CMB lensing measurements., Comment: 31 pages, 18 figures, Accepted for publication in PRD

We present a unified, market-complete model that integrates both the Bachelier and Black-Scholes-Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. In contrast to classical Black-Scholes-Merton, we show that option pricing in the unified model displays a difference depending on whether the replicating, self-financing portfolio uses riskless bonds or a single riskless bank account. We derive option price formulas and extend our analysis to the term structure of interest rates by deriving the pricing of zero-coupon bonds, forward contracts, and futures contracts. We identify a necessary condition for the unified model to support a perpetual derivative. Discrete binomial pricing under the unified model is also developed. In every scenario analyzed, we show that the unified model simplifies to the standard Black-Scholes-Merton pricing under specific limits and provides pricing in the Bachelier model limit. We note that the Bachelier limit within the unified model allows for positive riskless rates. The unified model prompts us to speculate on the possibility of a mixed multiplicative and additive deflator model for risk-neutral option pricing., Comment: 38 pages

In this work, we propose an approach for ensuring the safety of vehicles passing through an intelligent intersection. There are many proposals for the design of intelligent intersections that introduce central decision-makers to intersections for enhancing the efficiency and safety of the vehicles. To guarantee the safety of such designs, we develop a safety framework for intersections based on temporal logic and reachability analysis. We start by specifying the required behavior for all the vehicles that need to pass through the intersection as linear temporal logic formula. Then, using temporal logic trees, we break down the linear temporal logic specification into a series of Hamilton-Jacobi reachability analyses in an automated fashion. By successfully constructing the temporal logic tree through reachability analysis, we verify the feasibility of the intersection specification. By taking this approach, we enable a safety framework that is able to automatically provide safety guarantees on new intersection behavior specifications. To evaluate our approach, we implement the framework on a simulated T-intersection, where we show that we can check and guarantee the safety of vehicles with potentially conflicting paths.

In this work, we present a small-scale testbed for evaluating the real-life performance of cellular V2X (C-V2X) applications on 5G cellular networks. Despite the growing interest and rapid technology development for V2X applications, researchers still struggle to prototype V2X applications with real wireless networks, hardware, and software in the loop in a controlled environment. To help alleviate this challenge, we present a testbed designed to accelerate development and evaluation of C-V2X applications on 5G cellular networks. By including a small-scale vehicle platform into the testbed design, we significantly reduce the time and effort required to test new C-V2X applications on 5G cellular networks. With a focus around the integration of small-scale vehicle platforms, we detail the design decisions behind the full software and hardware setup of commonly needed intelligent transport system agents (e.g. sensors, servers, vehicles). Moreover, to showcase the testbed's capability to produce industrially-relevant, real world performance evaluations, we present an evaluation of a simple test case inspired from shared situational awareness. Finally, we discuss the upcoming use of the testbed for evaluating 5G cellular network-based shared situational awareness and other C-V2X applications.

Patchy reionization generates kinematic Sunyaev-Zeldovich (kSZ) anisotropies in the cosmic microwave background (CMB). Large-scale velocity perturbations along the line of sight modulate the small-scale kSZ power spectrum, leading to a trispectrum (or four-point function) in the CMB that depends on the physics of reionization. We investigate the challenges in detecting this trispectrum and use tools developed for CMB lensing, such as realization-dependent bias subtraction and cross-correlation based estimators, to counter uncertainties in the instrumental noise and assumed CMB power spectrum. We also find that both lensing and extragalactic foregrounds can impart larger trispectrum contributions than the reionization kSZ signal. We present a range of mitigation methods for both of these sources of contamination, validated on microwave-sky simulations. We use ACT DR6 and Planck data to calculate an upper limit on the reionization kSZ trispectrum from a measurement dominated by foregrounds. The upper limit is about 50 times the signal predicted from recent simulations., Comment: Measurements and covariances will be made public upon publication

The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. The goal of this challenge was to promote the development of deep generative models (DGMs) for medical imaging and to emphasize the need for their domain-relevant assessment via the analysis of relevant image statistics. As part of this Grand Challenge, a training dataset was developed based on 3D anthropomorphic breast phantoms from the VICTRE virtual imaging toolbox. A two-stage evaluation procedure consisting of a preliminary check for memorization and image quality (based on the Frechet Inception distance (FID)), and a second stage evaluating the reproducibility of image statistics corresponding to domain-relevant radiomic features was developed. A summary measure was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, and to identify various artifacts. 58 submissions from 12 unique users were received for this Challenge. The top-ranked submission employed a conditional latent diffusion model, whereas the joint runners-up employed a generative adversarial network, followed by another network for image superresolution. We observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID-based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. This Grand Challenge highlighted the need for domain-specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

The early dark energy (EDE) extension to $\Lambda$CDM has been proposed as a candidate scenario to resolve the "Hubble tension". We present new constraints on the EDE model by incorporating new data from the Dark Energy Spectroscopic Instrument (DESI) Baryon Acoustic Oscillation (BAO) survey and CMB lensing measurements from the Atacama Cosmology Telescope (ACT) DR6 and \textit{Planck} NPIPE data. We do not find evidence for EDE. The maximum fractional contribution of EDE to the total energy density is $f_\mathrm{EDE}< 0.091 \; (95\% \; \mathrm{CL} )$ from our baseline combination of \textit{Planck} CMB, CMB lensing, and DESI BAO. Our strongest constraints on EDE come from the combination of \textit{Planck} CMB and CMB lensing alone, yielding $f_\mathrm{EDE}< 0.070 \; (95\% \; \mathrm{CL} )$. We also explore extensions of $\Lambda$CDM beyond the EDE parameters by treating the total neutrino mass as a free parameter, finding $\sum m_\nu < 0.096 \,\, {\rm eV} \; (95\% \; \mathrm{CL} )$ and $f_\mathrm{EDE}< 0.087 \; (95\% \; \mathrm{CL} )$. For the first time in EDE analyses, we perform Bayesian parameter estimation using neural network emulators of cosmological observables, which are on the order of a hundred times faster than full Boltzmann solutions., Comment: 8+7 pages, 3+6 figures

Asteroids with low orbital perihelion distances experience extreme heating from the Sun that can modify their surfaces and trigger non-typical activity mechanisms. These objects are generally difficult to observe from ground-based telescopes due to their frequent proximity to the Sun. The Near Earth Object Surveyor mission, however, will regularly survey down to Solar elongations of 45 degrees and is well-suited for the detection and characterization of low-perihelion asteroids. Here, we use the survey simulation software tools developed for mission verification to explore the expected sensitivity of NEO Surveyor to these objects. We find that NEO Surveyor is expected to be >90% complete for near-Sun objects larger than D~300 m. Additionally, if the asteroid (3200) Phaethon underwent a disruption event in the past to form the Geminid meteor stream, Surveyor will be >90% complete to any fragments larger than D~200 m. For probable disruption models, NEO Surveyor would be expected to detect dozens of objects on Phaethon-like orbits, compared to a predicted background population of only a handful of asteroids, setting strong constraints on the likelihood of this scenario., Comment: 13 pages, accepted for publication in PSJ