Open set segmentation is a relatively new and unexploredtask, with just a handful of methods proposed to model this http URL propose a novel method called CoReSeg thattackles the issue using class conditional reconstruction ofthe input images according to their pixelwise mask. Ourmethod conditions each input pixel to all known classes,expecting higher errors for pixels of unknown classes. Itwas observed that the proposed method produces better se-mantic consistency in its predictions, resulting in cleanersegmentation maps that better fit object boundaries. CoRe-Seg outperforms state-of-the-art methods on the Vaihin-gen and Potsdam ISPRS datasets, while also being com-petitive on the Houston 2018 IEEE GRSS Data Fusiondataset. Official implementation for CoReSeg is availableat:this https URL.

We present new results on average causal effects in settings with unmeasured exposure-outcome confounding. Our results are motivated by a class of estimands, e.g., frequently of interest in medicine and public health, that are currently not targeted by standard approaches for average causal effects. We recognize these estimands as queries about the average causal effect of an intervening variable. We anchor our introduction of these estimands in an investigation of the role of chronic pain and opioid prescription patterns in the opioid epidemic, and illustrate how conventional approaches will lead unreplicable estimates with ambiguous policy implications. We argue that our altenative effects are replicable and have clear policy implications, and furthermore are non-parametrically identified by the classical frontdoor formula. As an independent contribution, we derive a new semiparametric efficient estimator of the frontdoor formula with a uniform sample boundedness guarantee. This property is unique among previously-described estimators in its class, and we demonstrate superior performance in finite-sample settings. Theoretical results are applied with data from the National Health and Nutrition Examination Survey.

The migration of growing protoplanets depends on the thermodynamics of the ambient disc. Standard modelling, using locally isothermal discs, indicate in the low planet mass regime an inward (type-I) migration. Taking into account non-isothermal effects, recent studies have shown that the direction of the type-I migration can change from inward to outward. In this paper we extend previous two-dimensional studies, and investigate the planet-disc interaction in viscous, radiative discs using fully three-dimensional radiation hydrodynamical simulations of protoplanetary accretion discs with embedded planets, for a range of planetary masses. We use an explicit three-dimensional (3D) hydrodynamical code NIRVANA that includes full tensor viscosity. We have added implicit radiation transport in the flux-limited diffusion approximation, and to speed up the simulations significantly we have newly adapted and implemented the FARGO-algorithm in a 3D context. First, we present results of test simulations that demonstrate the accuracy of the newly implemented FARGO-method in 3D. For a planet mass of 20 M_earth we then show that the inclusion of radiative effects yields a torque reversal also in full 3D. For the same opacity law used the effect is even stronger in 3D than in the corresponding 2D simulations, due to a slightly thinner disc. Finally, we demonstrate the extent of the torque reversal by calculating a sequence of planet masses. Through full 3D simulations of embedded planets in viscous, radiative discs we confirm that the migration can be directed outwards up to planet masses of about 33 M_earth. Hence, the effect may help to resolve the problem of too rapid inward migration of planets during their type-I phase.

The integrated luminosity from the features of the polycyclic aromatic hydrocarbons (PAHs) exceeds the luminosity from atomic and molecular emission lines in the star-forming regions in galaxies and is a potential tracer of galaxy-scale star formation and molecular gas content of the high-redshift universe. We simulate the observable PAH spectra using the PRobe far-Infrared Mission for Astrophysics far-infrared enhanced survey spectrometer (FIRESS) and investigate the capability of the FIRESS low-resolution spectroscopy for observing PAH emission spectrum from high-redshift galaxies. Our investigation suggests that (1) PRIMA observations of PAH emission are $\gtrsim10$ times more efficient at detecting galaxies than the VLA observations of CO(1-0) for galaxies with the same infrared luminosity, (2) PRIMA/FIRESS can detect the PAH emission from galaxies with $L_{IR}\sim10^{12}L_{\odot}$ up to the end of reionization (and possibly beyond, if $L_{IR}\sim10^{13}L_{\odot}$), (3) the PAH band ratios measured from a full spectral fitting and from a simple flux "clipping" method are different and vary depending on the interstellar radiation field strength, and (4) PRIMA/FIRESS can also be used as the PAH mapping instrument to measure star formation and redshift of the galaxies in high-redshift protoclusters.

In the research on FANETs (Flying Ad-Hoc Networks) and distributed coordination of UAVs (Unmanned Aerial Vehicles), also known as drones, there are many studies that validate their proposals through simulations. Simulations are important, but beyond them, there is also a need for real-world tests to validate the proposals and enhance results. However, field experiments involving drones and FANETs are not trivial, and this work aims to share experiences and results obtained during the construction of a testbed actively used in comparing simulations and field tests.

In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents. However, current prompt-based agents often heavily rely on large-scale LLMs. Meanwhile, although fine-tuning methods significantly enhance the capabilities of smaller LLMs, the fine-tuned agents often lack the potential for self-reflection and self-improvement. To address these challenges, we introduce a novel agent framework named RetroAct, which is a framework that jointly optimizes both task-planning and self-reflective evolution capabilities in language agents. Specifically, we develop a two-stage joint optimization process that integrates imitation learning and reinforcement learning, and design an off-policy joint policy gradient optimization algorithm with imitation learning regularization to enhance the data efficiency and training stability in agent tasks. RetroAct significantly improves the performance of open-source models, reduces dependency on closed-source LLMs, and enables fine-tuned agents to learn and evolve continuously. We conduct extensive experiments across various testing environments, demonstrating RetroAct has substantial improvements in task performance and decision-making processes.