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Price Cycles in Ridesharing Platforms
No full textIn ridesharing platforms such as Uber and Lyft, it is observed that drivers sometimes collaboratively go offline when the price is low, and then return after the price has risen due to the perceived lack of supply. This collective strategy leads to cyclic fluctuations in prices and available drivers, resulting in poor reliability and social welfare. We study a continuous time, non-atomic model and prove that such online/offline strategies may form a Nash equilibrium among drivers, but lead to a lower total driver payoff if the market is sufficiently dense. Further, we show how to set price floors that effectively mitigate the emergence and impact of price cycles
Non-line-of-sight imaging via wavefront shaping
No full textNon-line-of-sight (NLOS) imaging is a rapidly developing research direction that has significant applications in autonomous vehicles, remote sensing, etc. Existing NLOS methods primarily depend on time gated measurements and/or sophisticated signal processing to extract information from the scattered light. Here, we introduce a new method that directly manipulates the light to counter the wall’s scattering. This method operates by actively focusing light onto the target in a NLOS path using wavefront shaping. By raster scanning that focus, we can actively image the occluded object. The focus thus formed is near diffraction limited and can be substantially smaller than the object itself, thereby enabling us to perform NLOS imaging with unprecedented resolution. We demonstrate that a resolution of ∼ 0.6 mm at a distance of 0.55 m is achievable in our experiment
Suprathermal Ion Energy Spectra and Anisotropies near the Heliospheric Current Sheet Crossing Observed by the Parker Solar Probe during Encounter 7
No full textWe present observations of ≳10–100 keV nucleon⁻¹ suprathermal (ST) H, He, O, and Fe ions associated with crossings of the heliospheric current sheet (HCS) at radial distances of <0.1 au from the Sun. Our key findings are as follows: (1) very few heavy ions are detected during the first full crossing, the heavy-ion intensities are reduced during the second partial crossing and peak just after the second crossing; (2) ion arrival times exhibit no velocity dispersion; (3) He pitch-angle distributions track the magnetic field polarity reversal and show up to ∼10:1 anti-sunward, field-aligned flows and beams closer to the HCS that become nearly isotropic farther from the HCS; (4) the He spectrum steepens either side of the HCS, and the He, O, and Fe spectra exhibit power laws of the form ∼E−⁻⁴–E⁶; and (5) maximum energies EX increase with the ion's charge-to-mass (Q/M) ratio as E_X/E_H ∝ Q_X/M_X^δ, where δ ∼ 0.65–0.76, assuming that the average Q states are similar to those measured in gradual and impulsive solar energetic particle events at 1 au. The absence of velocity dispersion in combination with strong field-aligned anisotropies closer to the HCS appears to rule out solar flares and near-Sun coronal-mass-ejection-driven shocks. These new observations present challenges not only for mechanisms that employ direct parallel electric fields and organize maximum energies according to E/Q but also for local diffusive and magnetic-reconnection-driven acceleration models. Reevaluation of our current understanding of the production and transport of energetic ions is necessary to understand this near-solar, current-sheet-associated population of ST ions
Using Hyperoptimized Tensor Networks and First-Principles Electronic Structure to Simulate the Experimental Properties of the Giant {Mn₈₄} Torus
No full textThe single-molecule magnet {Mn₈₄} is a challenge to theory because of its high nuclearity. We directly compute two experimentally accessible observables, the field-dependent magnetization up to 75 T and the temperature-dependent heat capacity, using parameter-free theory. In particular, we use first-principles calculations to derive short- and long-range exchange interactions and compute the exact partition function of the resulting classical Potts and Ising spin models for all 84 Mn S = 2 spins to obtain observables. The latter computation is made possible by using hyperoptimized tensor network contractions, a technique developed to simulate quantum supremacy circuits. We also synthesize the magnet and measure its heat capacity and magnetization, observing qualitative agreement between theory and experiment and identifying an unusual bump in the heat capacity and a plateau in the magnetization. Our work also identifies some limitations of current theoretical modeling in large magnets, such as sensitivity to small, long-range exchange couplings
Integration of photoacoustic computed tomography with multitargeted polymer-based nanoparticles visualizes breast cancer intratumor heterogeneity
No full textOne of the primary challenges in breast cancer diagnosis and treatment is intratumor heterogeneity (ITH), i.e., the coexistence of different genetically and epigenetically distinct malignant cells within the same tumor. Identification of ITH is critical for designing better treatments and hence to increase patient survival rates. Here, we report a non-invasive approach that integrates photoacoustic computed tomography (PACT) with multitargeted and multiplexed patchy polymeric photoacoustic contrast agents (MTMPPPCAs). The target specificity of MTMPPPCAs to distinguish estrogen and progesterone receptor-positive in breast tumors was demonstrated through both fluorescence and photoacoustic measurements and validated by tissue pathology analysis
PHANGS-HST: new methods for star cluster identification in nearby galaxies
No full textWe present an innovative and widely applicable approach for the detection and classification of stellar clusters, developed for the PHANGS-HST Treasury Program, an NUV-to-I band imaging campaign of 38 spiral galaxies. Our pipeline first generates a unified master source list for stars and candidate clusters, to enable a self-consistent inventory of all star formation products. To distinguish cluster candidates from stars, we introduce the Multiple Concentration Index (MCI) parameter, and measure inner and outer MCIs to probe morphology in more detail than with a single, standard concentration index (CI). We improve upon cluster candidate selection, jointly basing our criteria on expectations for MCI derived from synthetic cluster populations and existing cluster catalogues, yielding model and semi-empirical selection regions (respectively). Selection purity (confirmed clusters versus candidates, assessed via human-based classification) is high (up to 70 per cent) for moderately luminous sources in the semi-empirical selection region, and somewhat lower overall (outside the region or fainter). The number of candidates rises steeply with decreasing luminosity, but pipeline-integrated Machine Learning (ML) classification prevents this from being problematic. We quantify the performance of our PHANGS-HST methods in comparison to LEGUS for a sample of four galaxies in common to both surveys, finding overall agreement with 50–75 per cent of human verified star clusters appearing in both catalogues, but also subtle differences attributable to specific choices adopted by each project. The PHANGS-HST ML-classified Class 1 or 2 catalogues reach ∼1 mag fainter, ∼2 × lower stellar mass, and are 2−5 × larger in number, than attained in the human classified samples
Dynamical orbital evolution scenarios of the wide-orbit eccentric planet HR 5183b
No full textThe recently discovered giant exoplanet HR5183b exists on a wide, highly eccentric orbit (a = 18 au, e = 0.84). Its host star possesses a common proper-motion companion which is likely on a bound orbit. In this paper, we explore scenarios for the excitation of the eccentricity of the planet in binary systems such as this, considering planet–planet scattering, Lidov–Kozai cycles from the binary acting on a single-planet system, or Lidov–Kozai cycles acting on a two-planet system that also undergoes scattering. Planet–planet scattering, in the absence of a binary companion, has a 2.8-7.2 per cent probability of pumping eccentricities to the observed values in our simulations, depending on the relative masses of the two planets. Lidov–Kozai cycles from the binary acting on an initially circular orbit can excite eccentricities to the observed value but require very specific orbital configurations for the binary and overall there is a low probability of catching the orbit at the high observed high eccentricity (0.6 per cent). The best case is provided by planet–planet scattering in the presence of a binary companion: here, the scattering provides the surviving planet with an initial eccentricity boost that is subsequently further increased by Kozai cycles from the binary. We find a success rate of 14.5 per cent for currently observing e ≥ 0.84 in this set-up. The single-planet plus binary and two-planet plus binary cases are potentially distinguishable if the mutual inclination of the binary and the planet can be measured, as the latter permits a broader range of mutual inclinations. The combination of scattering and Lidov–Kozai forcing may also be at work in other wide-orbit eccentric giant planets, which have a high rate of stellar binary companions
Black hole-galaxy scaling relations in FIRE: the importance of black hole location and mergers
No full textThe concurrent growth of supermassive black holes (SMBHs) and their host galaxies remains to be fully explored, especially at high redshift. While often understood as a consequence of self-regulation via AGN feedback, it can also be explained by alternative SMBH accretion models. Here, we expand on previous work by studying the growth of SMBHs with the help of a large suite of cosmological zoom-in simulations (MassiveFIRE) that are part of the Feedback in Realistic Environments (FIRE) project. The growth of SMBHs is modelled in post-processing with different black hole accretion models, placements, and merger treatments, and validated by comparing to on-the-fly calculations. Scaling relations predicted by the gravitational torque-driven accretion (GTDA) model agree with observations at low redshift without the need for AGN feedback, in contrast to models in which the accretion rate depends strongly on SMBH mass. At high redshift, we find deviations from the local scaling relations in line with previous theoretical results. In particular, SMBHs are undermassive, presumably due to stellar feedback, but start to grow efficiently once their host galaxies reach M* ∼ 10¹⁰ M_⊙. We analyse and explain these findings in the context of a simple analytic model. Finally, we show that the predicted scaling relations depend sensitively on the SMBH location and the efficiency of SMBH merging, particularly in low-mass systems. These findings highlight the relevance of understanding the evolution of SMBH-galaxy scaling relations to predict the rate of gravitational wave signals from SMBH mergers across cosmic history
Quenching, bursting, and galaxy shapes: colour transformation as a function of morphology
No full textDifferent mechanisms for quenching star formation in galaxies are commonly invoked in the literature, but the relative impact of each one at different cosmic epochs is still unknown. In particular, the relation between these processes and morphological transformation remains poorly understood. In this work, we measure the effectiveness of changes in star formation rates by analysing a new parameter, the Star Formation Acceleration (SFA), as a function of galaxy morphology. This methodology is capable of identifying both bursting and quenching episodes that occurred in the preceding 300 Myr. We use morphological classification catalogues based on Deep Learning techniques. Our final sample has ∼14 200 spirals and ∼2500 ellipticals. We find that elliptical galaxies in the transition region have median shorter quenching time-scales (τ 10^(11.0) M_⊕) with signs of disturbance. This is in accordance with the scenario where low-mass galaxies are losing their gas in an interaction with a massive galaxy: while the former is quenching, the last is being refueled and going through a burst, showing signs of recent interaction
Perspectives in machine learning for wildlife conservation
No full textInexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation