A Transmissive X-ray Polarimeter Design For Hard X-ray Focusing Telescopes

The X-ray Timing and Polarization (XTP) is a mission concept for a future space borne X-ray observatory and is currently selected for early phase study. We present a new design of X-ray polarimeter based on the time projection gas chamber. The polarimeter, placed above the focal plane, has an additional rear window that allows hard X-rays to penetrate (a transmission of nearly 80% at 6 keV) through it and reach the detector on the focal plane. Such a design is to compensate the low detection efficiency of gas detectors, at a low cost of sensitivity, and can maximize the science return of multilayer hard X-ray telescopes without the risk of moving focal plane instruments. The sensitivity in terms of minimum detectable polarization, based on current instrument configuration, is expected to be 3% for a 1mCrab source given an observing time of 10^5 s. We present preliminary test results, including photoelectron tracks and modulation curves, using a test chamber and polarized X-ray sources in the lab

Biomass Burning in the Conterminous United States: A Comparison and Fusion of Active Fire Observations from Polar-Orbiting and Geostationary Satellites for Emissions Estimation

Biomass burning is an important source of atmospheric greenhouse gases and aerosol emissions that significantly influence climate and air quality. Estimation of biomassburning emissions (BBE) has been limited to the conventional method in which parameters (i.e., burned area and fuel load) can be challenging to quantify accurately. Recent studies have demonstrated that the rate of biomass combustion is a linear function of fire radiative power (FRP), the instantaneous radiative energy released from actively burning fires, which provides a novel pathway to estimate BBE. To obtain accurate and timely BBE estimates for near real-time applications (i.e., air quality forecast), the satellite FRP-based method first requires a reliable biomass combustion coefficient that converts fire radiative energy (FRE), the temporal integration of FRP, to biomass consumption. The combustion coefficient is often derived in controlled small-scale fire experiments and is assumed a constant, whereas the coefficient based on satellite retrievals of FRP and atmospheric optical depth is suggested varying in a wide range. Undoubtedly, highly variable combustion coefficient results in large uncertainty of BBE estimates. Further, the FRP-based method also depends on high-spatiotemporalresolution FRP retrievals that, however, are not available in any active fire products from current polar-orbiting and geostationary satellites due to their sampling limitations. To address these challenges, this study first investigates the combustion coefficient for landscape-scale wildfires in the Conterminous United States (CONUS) by comparing FRE from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellite system (GOES) with the Landsat-based biomass consumption. The results confirms that biomass consumption is a linear function of FRE for wildfires. The derived combustion coefficient is 0.374 kg · MJ- 1 for GOES FRE, 0.266 kg · MJ-1 for MODIS FRE, and 0.320 kg · MJ-1 considering both GOES and MODIS FRE in the CONUS. Limited sensitivity analyses indicate that the combustion coefficient varies from 0.301 to 0.458 kg · MJ-1, which is similar to the reported values in small fire experiments. Then, this study reconstructs diurnal FRP cycle to derive high-spatiotemporal-resolution FRP by fusing MODIS and GOES FRP retrievals and estimates hourly BBE at a 0.25°×0.3125° grid across the CONUS. The results indicate that the reconstructed diurnal FRP cycle varies significantly in magnitude and shape among 45 CONUS ecosystems. In the CONUS, the biomass burning annually releases approximately 690 Gg particulate matter (smaller than 2.5 μm in diameter, PM2.5). The diurnal-FRP-cycle-based BBE estimates compare well with BBE derived from Landsat burned areas in the western CONUS and with the hourly carbon monoxide emissions simulated using a biogeochemical model over the Rim Fire in California. Moreover, the BBE estimates show a similar seasonal variation to six existing BBE inventories but with variable magnitude. Finally, this study examines potential improvements in fires characterization capability of the Visible Infrared Imaging Radiometer Suite (VIIRS), which is the follow-on sensor of the MODIS sensor, for integrating VIIRS FRP retrievals into the FRP-based method for BBE estimation in future work. The results indicate that the VIIRS fire characterization capability is similar across swath, whereas MODIS is strongly dependent on satellite view zenith angle. VIIRS FRP is generally comparable with contemporaneous MODIS FRP at continental scales and in most fire clusters. At 1-degree grid cells, the FRP difference between the two sensors is, on average, approximately 20% in fire-prone regions but varies significantly in fire-limited regions. In summary, this study attempts to enhance the capability of the FRP-based method by addressing challenges in its two parameters (combustion coefficient and FRP), which should help to improve estimation of BBE and advance our understanding of the effects of BBE on climate and air quality. This research has resulted in two published papers and one paper to be submitted to a peer-reviewed journal so far

Genome-Wide Association Study for Plant Height and Grain Yield in Rice under Contrasting Moisture Regimes

Drought is one of the vitally critical environmental stresses affecting both growth and yield potential in rice. Drought resistance is a complicated quantitative trait that is regulated by numerous small effect loci and hundreds of genes controlling various morphological and physiological responses to drought. For this study, 270 rice landraces and cultivars were analyzed for their drought resistance. This was done via determination of changes in plant height and grain yield under contrasting water regimes, followed by detailed identification of the underlying genetic architecture via genome-wide association study (GWAS). We controlled population structure by setting top two eigenvectors and combining kinship matrix for GWAS in this study. Eighteen, five, and six associated loci were identified for plant height, grain yield per plant, and drought resistant coefficient, respectively. Nine known functional genes were identified, including five for plant height (OsGA2ox3, OsGH3-2, sd-1, OsGNA1 and OsSAP11/OsDOG), two for grain yield per plant (OsCYP51G3 and OsRRMh) and two for drought resistant coefficient (OsPYL2 and OsGA2ox9), implying very reliable results. A previous study reported OsGNA1 to regulate root development, but this study reports additional controlling of both plant height and root length. Moreover, OsRLK5 is a new drought resistant candidate gene discovered in this study. OsRLK5 mutants showed faster water loss rates in detached leaves. This gene plays an important role in the positive regulation of yield-related traits under drought conditions. We furthermore discovered several new loci contributing to the three investigated traits (plant height, grain yield, and drought resistance). These associated loci and genes significantly improve our knowledge of the genetic control of these traits in rice. In addition, many drought resistant cultivars screened in this study can be used as parental genotypes to improve drought resistance of rice by molecular breeding

Benchmarking and enhancing spatial reasoning in large language models

Spatial reasoning is essential for both human cognition and machine intelligence in understanding and navigating spatial relationships between objects. Despite significant advances in large language models (LLMs) like ChatGPT, spatial reasoning remains a challenging area. This thesis aims to make a contribution to addressing this issue. Firstly, we analyze the existing benchmarks: bAbI, StepGame, SpartQA, and SpaRTUN, providing initial LLM evaluations and examining their limitations. Results on StepGame demonstrate LLMs' proficiency in mapping natural language to spatial relations, while also highlighting challenges in multi-hop reasoning tasks. As an alternative approach, this thesis also investigates using LLMs to translate the spatial reasoning tasks into a logical format appropriate for an answer set programming reasoner. Experiments demonstrate that this neuro-symbolic approach results in almost perfect accuracy scores on StepGame. Secondly, the thesis investigates advanced prompting strategies, specifically Chain-of-Thought (CoT) and Tree-of-Thought (ToT) methods, to enhance LLMs' spatial reasoning capabilities. These strategies decompose complex reasoning tasks into manageable steps, significantly improving performance on spatial reasoning benchmarks. CoT and ToT approaches show substantial improvements in accuracy, particularly with complex, multi-hop tasks. Thirdly, the thesis introduces a novel benchmark based on realistic 3D simulation data, featuring diverse room layouts with various objects and their spatial relationships. This benchmark encompasses a wide range of qualitative spatial relationships, such as topological, directional, and distance relations, and presents scenarios from different viewpoints to reflect real-world complexities. Alongside the benchmark itself, the code is available online, thus allowing arbitrary further versions to be created. A further contribution of this benchmark is the inclusion of a logic-based consistency-checking tool that evaluates multiple plausible solutions, aligning with real-world scenarios where spatial relationships often have several valid interpretations. This thesis advances the spatial reasoning abilities of LLMs by identifying deficiencies in current benchmarks and proposing practical enhancements. The integrated approach of refining evaluation benchmarks and employing advanced prompting techniques paves the way for future advances in AI spatial reasoning capabilities based on LLMs

Genome-wide Association Study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice

Annotation of candidate genes anchored by associated SNPs. (XLSX 34 kb

Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning

Spatial reasoning plays a vital role in both human cognition and machine intelligence, prompting new research into language models' (LMs) capabilities in this regard. However, existing benchmarks reveal shortcomings in evaluating qualitative spatial reasoning (QSR). These benchmarks typically present oversimplified scenarios or unclear natural language descriptions, hindering effective evaluation. We present a novel benchmark for assessing QSR in LMs, which is grounded in realistic 3D simulation data, offering a series of diverse room layouts with various objects and their spatial relationships. This approach provides a more detailed and context-rich narrative for spatial reasoning evaluation, diverging from traditional, toy-task-oriented scenarios. Our benchmark encompasses a broad spectrum of qualitative spatial relationships, including topological, directional, and distance relations. These are presented with different viewing points, varied granularities, and density of relation constraints to mimic real-world complexities. A key contribution is our logic-based consistency-checking tool, which enables the assessment of multiple plausible solutions, aligning with real-world scenarios where spatial relationships are often open to interpretation. Our benchmark evaluation of advanced LMs reveals their strengths and limitations in spatial reasoning. They face difficulties with multi-hop spatial reasoning and interpreting a mix of different view descriptions, pointing to areas for future improvement.Comment: Camera-Ready version for IJCAI 202

Exploring the GLIDE model for Human Action-effect Prediction

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing this task. GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text. Our idea is to mask-out a region of the input image where the effect of the action is expected to occur. GLIDE is then used to inpaint the masked region conditioned on the required action. In this way, the resulting image has the same background context as the input image, updated to show the effect of the action. We give qualitative results from experiments using the EPIC dataset of ego-centric videos labelled with actions

Dense matter with eXTP

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry (eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Science, the eXTP mission is expected to be launched in the mid 2020s.Comment: Accepted for publication on Sci. China Phys. Mech. Astron. (2019

The convergence rate of the Chebyshev semiiterative method under a perturbation of the foci of an elliptic domain

The Chebyshev semiiterative method (CHSIM) is a powerful method for finding the iterative solution of a nonsymmetric real linear system Ax = b if an ellipse excluding the origin well fits the spectrum of A. The asymptotic rate of convergence of the CHSIM for solving the above system under a perturbation of the foci of the optimal ellipse is studied. Several formulae to approximate the asymptotic rates of convergence, up to the first order of a perturbation, are derived. These generalize the results about the sensitivity of the asymptotic rate of convergence to a perturbation of a real-line segment spectrum by Hageman and Young, and by the first author. A numerical example is given to illustrate the theoretical results