dyfan/halluhard-seed-questions huggingface dataset:
We present a multiphase mathematical model for tumor growth which incorporates the remodeling of the extracellular matrix and describes the formation of fibrotic tissue by tumor cells. We also detail a full qualitative analysis of the spatially homogeneous problem, and study the equilibria of the system in order to characterize the conditions under which fibrosis may occur.

Research question: What are the main biological mechanisms by which tumor cells remodel the extracellular matrix to produce fibrotic tissue, and how can such fibrosis influence tumor progression and response to treatment?
Abstract We present optical light curves, redshifts, and classifications for spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail improvements to the PS1 SN photometry, astrometry, and calibration that reduce the systematic uncertainties in the PS1 SN Ia distances. We combine the subset of PS1 SNe Ia (0.03 &lt; z &lt; 0.68) with useful distance estimates of SNe Ia from the Sloan Digital Sky Survey (SDSS), SNLS, and various low- z and Hubble Space Telescope samples to form the largest combined sample of SNe Ia, consisting of a total of SNe Ia in the range of 0.01 &lt; z &lt; 2.3, which we call the “Pantheon Sample.” When combining Planck 2015 cosmic microwave background (CMB) measurements with the Pantheon SN sample, we find and for the w CDM model. When the SN and CMB constraints are combined with constraints from BAO and local H 0 measurements, the analysis yields the most precise measurement of dark energy to date: and for the CDM model. Tension with a cosmological constant previously seen in an analysis of PS1 and low- z SNe has diminished after an increase of 2× in the statistics of the PS1 sample, improved calibration and photometry, and stricter light-curve quality cuts. We find that the systematic uncertainties in our measurements of dark energy are almost as large as the statistical uncertainties, primarily due to limitations of modeling the low-redshift sample. This must be addressed for future progress in using SNe Ia to measure dark energy.

Research question: What are the main sources of systematic uncertainty when using Type Ia supernovae as standardized distance indicators—particularly for low-redshift samples—and what strategies can reduce those systematics to improve dark energy measurements?
Energetic ion irradiation is an effective method for studying how single and multi-shelled carbon nanotubes break apart. The energy from ions is dissipated through both linear and nonlinear processes in the nanotubes, leading to defect formation. Fragmentation occurs via atomic collision cascades and thermal spikes, each described by different theoretical models. Experiments with Cs-irradiated nanotubes support these models, and an information-theoretic approach further explains the fragmentation mechanisms. Sputtered species yield probability distributions, which are analyzed using Shannon entropy and fractal dimension to assess spatial characteristics. Kullback-Leibler divergence helps identify the diversity of emission mechanisms. Together, thermal and information-theoretic models clarify and distinguish the roles of collision cascades and thermal spikes in nanotube fragmentation.

Research question: How do collision cascades and thermal spikes differ in the physical mechanisms by which energetic ions cause defects and fragmentation in carbon nanotubes, and what experimental signatures can be used to distinguish between these two processes?
We introduce the Computational 2D Materials Database (C2DB), which organises\na variety of structural, thermodynamic, elastic, electronic, magnetic, and\noptical properties of around 1500 two-dimensional materials distributed over\nmore than 30 different crystal structures. Material properties are\nsystematically calculated by state-of-the art density functional theory and\nmany-body perturbation theory (G$_0\\!$W$\\!_0$ and the Bethe-Salpeter Equation\nfor $\\sim$200 materials) following a semi-automated workflow for maximal\nconsistency and transparency. The C2DB is fully open and can be browsed online\nor downloaded in its entirety. In this paper, we describe the workflow behind\nthe database, present an overview of the properties and materials currently\navailable, and explore trends and correlations in the data. Moreover, we\nidentify a large number of new potentially synthesisable 2D materials with\ninteresting properties targeting applications within spintronics,\n(opto-)electronics, and plasmonics. The C2DB offers a comprehensive and easily\naccessible overview of the rapidly expanding family of 2D materials and forms\nan ideal platform for computational modeling and design of new 2D materials and\nvan der Waals heterostructures.\n

Research question: How can computational databases of two-dimensional materials be used effectively to guide experimental synthesis and validation of promising candidate materials?
Automated segmentation of kidneys and kidney tumors is an important step in quantifying the tumor's morphometrical details to monitor the progression of the disease and accurately compare decisions regarding the kidney tumor treatment. Manual delineation techniques are often tedious, error-prone and require expert knowledge for creating unambiguous representation of kidneys and kidney tumors segmentation. In this work, we propose an end-to-end boundary aware fully Convolutional Neural Networks (CNNs) for reliable kidney and kidney tumor semantic segmentation from arterial phase abdominal 3D CT scans. We propose a segmentation network consisting of an encoder-decoder architecture that specifically accounts for organ and tumor edge information by devising a dedicated boundary branch supervised by edge-aware loss terms. We have evaluated our model on 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge dataset and our method has achieved dice scores of 0.9742 and 0.8103 for kidney and tumor repetitively and an overall composite dice score of 0.8923.

Research question: How do boundary-aware or edge-sensitive segmentation techniques improve the accuracy of automated kidney and tumor delineation, especially for small or irregularly shaped tumors, compared to standard segmentation approaches?
Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier–Stokes (RANS) equations. In the past few years, with the availability of large and diverse data sets, researchers have begun to explore methods to systematically inform turbulence models with data, with the goal of quantifying and reducing model uncertainties. This review surveys recent developments in bounding uncertainties in RANS models via physical constraints, in adopting statistical inference to characterize model coefficients and estimate discrepancy, and in using machine learning to improve turbulence models. Key principles, achievements, and challenges are discussed. A central perspective advocated in this review is that by exploiting foundational knowledge in turbulence modeling and physical constraints, researchers can use data-driven approaches to yield useful predictive models.

Research question: How can physical constraints (like conservation laws and realizability) be incorporated into machine‑learning approaches for turbulence modeling to ensure physically consistent and reliable predictions?
Hydrophobic interfaces have unique physicochemical properties and are used in various chemical products such as food, cosmetics, soap, and medicine and technologies such as pan coating and ski wax. In this chapter, we describe the fundamental concept of hydrophobic interfaces and explain their ion adsorption and zeta potential by using experimental data from the literature. Thus far, these electrical properties are considered universal for solid/water, liquid/water, and gas/water interfaces; however, a careful comparison in this chapter will reveal significant differences among them. To confirm that the affinity of H$^+$ ions for all hydrophobic interfaces is stronger than that of OH$^-$ ions, more experimental data on hydrophobic liquid/water and solid/water interfaces are required.

Research question: What experimental techniques and measurement approaches are best suited to determine and compare ion adsorption and zeta potential at solid/water, liquid/water, and gas/water hydrophobic interfaces, and what are the main limitations of those methods?
In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections. However, detection performance tends to degrade with increasing the IoU thresholds. Two main factors are responsible for this: 1) overfitting during training, due to exponentially vanishing positive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these problems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. The detectors are trained stage by stage, leveraging the observation that the output of a detector is a good distribution for training the next higher quality detector. The resampling of progressively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reducing the overfitting problem. The same cascade procedure is applied at inference, enabling a closer match between the hypotheses and the detector quality of each stage. A simple implementation of the Cascade R-CNN is shown to surpass all single-model object detectors on the challenging COCO dataset. Experiments also show that the Cascade R-CNN is widely applicable across detector architectures, achieving consistent gains independently of the baseline detector strength. The code is available at https://github.com/zhaoweicai/cascade-rcnn.

Research question: How can using a sequence of detectors trained with progressively higher IoU thresholds improve detection quality and reduce overfitting compared to training a single detector with a fixed IoU threshold?
We aim to characterize the U-band variability of young brown dwarfs in the Taurus Molecular Cloud and discuss its origin. We used the XMM-Newton Extended Survey of the Taurus Molecular Cloud, where a sample of 11 young bona fide brown dwarfs (spectral type later than M6) were observed simultaneously in X-rays with XMM-Newton and in the U-band with the XMM-Newton Optical/UV Monitor (OM). We obtained upper limits to the U-band emission of 10 brown dwarfs (U>19.6-20.6 mag), whereas 2MASSJ04141188+2811535 was detected in the U-band. Remarkably, the magnitude of this brown dwarf increased regularly from U~19.5 mag at the beginning of the observation, peaked 6h later at U~18.4 mag, and then decreased to U~18.65 mag in the next 2h. The first OM U-band measurement is consistent with the quiescent level observed about one year later thanks to ground follow-up observations. This brown dwarf was not detected in X-rays by XMM-Newton during the OM observation. We discuss the possible sources of U-band variability for this young brown dwarf, namely a magnetic flare, non-steady accretion onto the substellar surface, and rotational modulation of a hot spot. We conclude that this event is related to accretion from a circumsubstellar disk, where the mass accretion rate was about a factor of 3 higher than during the quiescent level.

Research question: How can astronomers distinguish whether U-band/UV variability in young brown dwarfs is caused by magnetic flares, changes in accretion rate, or rotational modulation of hot spots, using observational data?
The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.

Research question: What are best practices for creating reliable, culturally sensitive, and reusable annotated datasets for automated hate speech detection across multiple languages?
Primary angle-closure disease (PACD) is a severe retinal disease, which might cause irreversible vision loss. In clinic, accurate identification of angle-closure and localization of the scleral spur's position on anterior segment optical coherence tomography (AS-OCT) is essential for the diagnosis of PACD. However, manual delineation might confine in low accuracy and low efficiency. In this paper, we propose an efficient and accurate end-to-end architecture for angle-closure classification and scleral spur localization. Specifically, we utilize a revised ResNet152 as our backbone to improve the accuracy of the angle-closure identification. For scleral spur localization, we adopt EfficientNet as encoder because of its powerful feature extraction potential. By combining the skip-connect module and pyramid pooling module, the network is able to collect semantic cues in feature maps from multiple dimensions and scales. Afterward, we propose a novel keypoint registration loss to constrain the model's attention to the intensity and location of the scleral spur area. Several experiments are extensively conducted to evaluate our method on the angle-closure glaucoma evaluation (AGE) Challenge dataset. The results show that our proposed architecture ranks the first place of the classification task on the test dataset and achieves the average Euclidean distance error of 12.00 pixels in the scleral spur localization task.

Research question: What are the main challenges in accurately localizing the scleral spur in anterior segment OCT images, and how do those challenges impact the reliability of automated angle-closure diagnosis in clinical practice?
We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together. The end-to-end learning is achieved by directly embedding the atmospheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense network that can maximize the information flow along features from different levels, we propose a new edge-preserving densely connected encoder-decoder structure with multi-level pyramid pooling module for estimating the transmission map. This network is optimized using a newly introduced edge-preserving loss function. To further incorporate the mutual structural information between the estimated transmission map and the dehazed result, we propose a joint-discriminator based on generative adversarial network framework to decide whether the corresponding dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demonstrate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods. Code and dataset is made available at: https://github.com/hezhangsprinter/DCPDN.

Research question: How does jointly estimating the transmission map and atmospheric light within a deep learning framework improve single-image dehazing compared to estimating them separately or using purely data-driven approaches?
Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools.
  Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.

Research question: What are the main sources of uncertainty when producing fine-resolution, multi-decadal maps of forest biomass using combinations of satellite imagery, inventory plots, and LiDAR, and how can those uncertainties be quantified and reduced for use in carbon accounting?
Many democratic nations are experiencing increased levels of false information circulating through social media and political websites that mimic journalism formats. In many cases, this disinformation is associated with the efforts of movements and parties on the radical right to mobilize supporters against centre parties and the mainstream press that carries their messages. The spread of disinformation can be traced to growing legitimacy problems in many democracies. Declining citizen confidence in institutions undermines the credibility of official information in the news and opens publics to alternative information sources. Those sources are often associated with both nationalist (primarily radical right) and foreign (commonly Russian) strategies to undermine institutional legitimacy and destabilize centre parties, governments and elections. The Brexit campaign in the United Kingdom and the election of Donald Trump in the United States are among the most prominent examples of disinformation campaigns intended to disrupt normal democratic order, but many other nations display signs of disinformation and democratic disruption. The origins of these problems and their implications for political communication research are explored.

Research question: What practical strategies can democratic societies use to rebuild public trust in institutions and reduce citizens' susceptibility to disinformation spread via social media and partisan websites?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare neutral B meson decays into muon pairs constrain physics beyond the Standard Model, and which types of new-physics scenarios are most strongly affected by these results?
Abstract An electronic (e‐) skin is expected to experience significant wear and tear over time. Therefore, self‐healing stretchable materials that are simultaneously soft and with high fracture energy, that is high tolerance of damage or small cracks without propagating, are essential requirements for the realization of robust e‐skin. However, previously reported elastomers and especially self‐healing polymers are mostly viscoelastic and lack high mechanical toughness. Here, a new class of polymeric material crosslinked through rationally designed multistrength hydrogen bonding interactions is reported. The resultant supramolecular network in polymer film realizes exceptional mechanical properties such as notch‐insensitive high stretchability (1200%), high toughness of 12 000 J m −2 , and autonomous self‐healing even in artificial sweat. The tough self‐healing materials enable the wafer‐scale fabrication of robust and stretchable self‐healing e‐skin devices, which will provide new directions for future soft robotics and skin prosthetics.

Research question: What material design strategies and molecular mechanisms allow a soft, stretchable polymer to simultaneously achieve very high toughness, extreme extensibility, and autonomous self‑healing performance even in humid or sweaty environments?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare B meson decays to muon pairs constrain theories beyond the Standard Model, and what would a significant deviation from the predicted branching fractions imply for new physics?