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We developed a deep learning classifier of rectal cancer response (tumor vs. no-tumor) to total neoadjuvant treatment (TNT) from endoscopic images acquired before, during, and following TNT. We further evaluated the network's ability in a near out-of-distribution (OOD) problem to identify local regrowth (LR) from follow-up endoscopy images acquired several months to years after completing TNT. We addressed endoscopic image variability by using optimal mass transport-based image harmonization. We evaluated multiple training regularization schemes to study the ResNet-50 network's in-distribution and near-OOD generalization ability. Test time augmentation resulted in the most considerable accuracy improvement. Image harmonization resulted in slight accuracy improvement for the near-OOD cases. Our results suggest that off-the-shelf deep learning classifiers can detect rectal cancer from endoscopic images at various stages of therapy for surveillance.

The ground-based gravitational wave (GW) detectors LIGO and Virgo have enabled the birth of multi-messenger GW astronomy via the detection of GWs from merging stellar-mass black holes (BHs) and neutron stars (NSs). GW170817, the first binary NS merger detected in GWs and all bands of the electromagnetic spectrum, is an outstanding example of the impact that GW discoveries can have on multi-messenger astronomy. Yet, GW170817 is only one of the many and varied multi-messenger sources that can be unveiled using ground-based GW detectors. In this contribution, we summarize key open questions in the astrophysics of stellar-mass BHs and NSs that can be answered using current and future-generation ground-based GW detectors, and highlight the potential for new multi-messenger discoveries ahead., Comment: Review submitted to the Frontiers Research Topic "The Dynamic Universe: Realizing the Potential of Time Domain and Multimessenger Astrophysics"

In many robotic systems, the holding state consumes power, limits operating time, and increases operating costs. Electrostatic clutches have the potential to improve robotic performance by generating holding torques with low power consumption. A key limitation of electrostatic clutches has been their low specific shear stresses which restrict generated holding torque, limiting many applications. Here we show how combining the Johnsen-Rahbek (JR) effect with the exponential tension scaling capstan effect can produce clutches with the highest specific shear stress in the literature. Our system generated 31.3 N/cm^2 sheer stress and a total holding torque of 7.1 Nm while consuming only 2.5 mW/cm^2 at 500 V. We demonstrate a theoretical model of an electrostatic adhesive capstan clutch and demonstrate how large angle (theta > 2pi) designs increase efficiency over planar or small angle (theta < pi) clutch designs. We also report the first unfilled polymeric material, polybenzimidazole (PBI), to exhibit the JR-effect.

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches., Comment: 33 pages, 5 figures, published in European Physical Journal Plus for focus issue on "Citizen science for physics: From Education and Outreach to Crowdsourcing fundamental research"

Gravitational-wave observations by the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A#, given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped Cosmic Explorer observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein Telescope with 10 km arms in Europe. The presence of one or two A# observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy and precision measurement of the Hubble parameter. Two Cosmic Explorer observatories are indispensable for achieving precise localization of binary neutron star events, facilitating detection of electromagnetic counterparts and transforming multimessenger astronomy. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two Cosmic Explorer observatories is critical for accomplishing all the identified science metrics. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A# observatories., Comment: 48 pages, 20 figures, 14 tables

Gravitational-wave astronomy has revolutionized humanity's view of the universe, a revolution driven by observations that no other field can make. This white paper describes an observatory that builds on decades of investment by the National Science Foundation and that will drive discovery for decades to come: Cosmic Explorer. Major discoveries in astronomy are driven by three related improvements: better sensitivity, higher precision, and opening new observational windows. Cosmic Explorer promises all three and will deliver an order-of-magnitude greater sensitivity than LIGO. Cosmic Explorer will push the gravitational-wave frontier to almost the edge of the observable universe using technologies that have been proven by LIGO during its development. With the unprecedented sensitivity that only a new facility can deliver, Cosmic Explorer will make discoveries that cannot yet be anticipated, especially since gravitational waves are both synergistic with electromagnetic observations and can reach into regions of the universe that electromagnetic observations cannot explore. With Cosmic Explorer, scientists can use the universe as a laboratory to test the laws of physics and study the nature of matter. Cosmic Explorer allows the United States to continue its leading role in gravitational-wave science and the international network of next-generation observatories. With its extraordinary discovery potential, Cosmic Explorer will deliver revolutionary observations across astronomy, physics, and cosmology including: Black Holes and Neutron Stars Throughout Cosmic Time, Multi-Messenger Astrophysics and Dynamics of Dense Matter, New Probes of Extreme Astrophysics, Fundamental Physics and Precision Cosmology, Dark Matter and the Early Universe.

Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme. The robotics community has experimented autocurricular training with physically grounded problems, such as robust control and interactive manipulation tasks. However, the asymmetric nature of these tasks makes the generation of sophisticated policies challenging. Indeed, the asymmetry in the environment may implicitly or explicitly provide an advantage to a subset of agents which could, in turn, lead to a low-quality equilibrium. This paper proposes a novel game-theoretic algorithm, Stackelberg Multi-Agent Deep Deterministic Policy Gradient (ST-MADDPG), which formulates a two-player MARL problem as a Stackelberg game with one player as the `leader' and the other as the `follower' in a hierarchical interaction structure wherein the leader has an advantage. We first demonstrate that the leader's advantage from ST-MADDPG can be used to alleviate the inherent asymmetry in the environment. By exploiting the leader's advantage, ST-MADDPG improves the quality of a co-evolution process and results in more sophisticated and complex strategies that work well even against an unseen strong opponent.

We combine the Planck-SZ2 galaxy cluster catalogue with near-infrared photometry of galaxies from the VISTA Hemisphere Survey to identify candidate brightest cluster galaxies (BCGs) in 306 massive clusters in the Southern skies at redshifts of $z>0.1$. We find that 91% of these clusters have at least one candidate BCG within the 95% confidence interval on the cluster centers quoted by the Planck collaboration, providing reassurance that our analyses are statistically compatible, and find 92% to be reasonable candidates following a manual inspection. We make our catalog publicly available to assist colleagues interested in multi-wavelength studies of cluster cores, and the search for gravitationally lensed explosive transients in upcoming surveys including the Legacy Survey of Space and Time by the Vera C. Rubin Observatory., Comment: Published in RNAAS in March 2023. 3 pages, 1 figure

The role of large-scale bars in the fuelling of active galactic nuclei (AGN) is still debated, even as evidence mounts that black hole growth in the absence of galaxy mergers cumulatively dominated and may substantially influence disc (i.e., merger-free) galaxy evolution. We investigate whether large-scale galactic bars are a good candidate for merger-free AGN fuelling. Specifically, we combine slit spectroscopy and Hubble Space Telescope imagery to characterise star formation rates (SFRs) and stellar masses of the unambiguously disc-dominated host galaxies of a sample of luminous, Type-1 AGN with 0.02 < z 0.024. After carefully correcting for AGN signal, we find no clear difference in SFR between AGN hosts and a stellar mass-matched sample of galaxies lacking an AGN (0.013 < z < 0.19), although this could be due to a small sample size (n_AGN = 34). We correct for SFR and stellar mass to minimise selection biases, and compare the bar fraction in the two samples. We find that AGN are marginally (1.7$\sigma$) more likely to host a bar than inactive galaxies, with AGN hosts having a bar fraction, fbar = 0.59^{+0.08}_{-0.09} and inactive galaxies having a bar fraction fbar = 0.44^{+0.08}_{-0.09}. However, we find no further differences between SFR- and mass-matched AGN and inactive samples. While bars could potentially trigger AGN activity, they appear to have no further, unique effect on a galaxy's stellar mass or SFR., Comment: 15 pages (9 figures). Accepted for publication in MNRAS

In this paper, we study the implementation of a model predictive controller (MPC) for the task of object manipulation in a highly uncertain environment (e.g., picking objects from a semi-flexible array of densely packed bins). As a real-time perception-driven feedback controller, MPC is robust to the uncertainties in this environment. However, our experiment shows MPC cannot control a robot to complete a sequence of motions in a heavily occluded environment due to its myopic nature. It will benefit from adding a high-level policy that adaptively adjusts the optimization problem for MPC.