Miracle-dz/UniFAR-FTUs huggingface dataset:
A Consistent Representation of Cloud Overlap and Cloud Subgrid Vertical Heterogeneity

Many global climate models underestimate the cloud cover and overestimate the cloud albedo, especially for low‐level clouds. We determine how a correct representation of the vertical structure of clouds can fix part of this bias. We use the 1D McICA framework and focus on low‐level clouds. Using Large Eddy Simulations results as reference, we propose a method based on exponential‐random overlap that represents the cloud overlap between layers and the subgrid cloud properties over several vertical scales, with a single value of the overlap parameter. Starting from a coarse vertical grid, representative of atmospheric models, this algorithm is used to generate the vertical profile of the cloud fraction with a finer vertical resolution, or to generate it on the coarse grid but with subgrid heterogeneity and cloud overlap that ensures a correct cloud cover. Doing so we find decorrelation lengths are dependent on the vertical resolution, except if the vertical subgrid heterogeneity and interlayer overlap are taken into account coherently. We confirm that the frequently used maximum‐random overlap leads to a significant error by underestimating the low‐level cloud cover with a relative error of about 50%, that can lead to an error of SW cloud albedo as big as 70%. Not taking into account the subgrid vertical heterogeneity of clouds can cause a relative error of 20% in brightness, assuming the cloud cover is correct.
Computational learning algorithms for geometric and algebraic objects

The goal of this thesis is to study the efficient learnability of basic geometric and algebraic objects in on-line learning models. Computational learning algorithms for rectangles and unions of rectangles over the domain $\{0,\. ,n-1\}\sp{d}$ are designed and analyzed. We develop a bounded version of the powerful and dominant design technique in recursion theory, the finite injury priority method, and use it to construct several concrete algorithms for learning unions of rectangles. We design an algorithm for properly learning rectangles over the domain (0, n $-$ 1) $\sp{d}$ with $O(d\sp2\ \log\ n)$ equivalence queries. For the problem of learning unions of two rectangles, we design three efficient algorithms. The first algorithm learns unions of two disjoint rectangles over the domain (0, n $-$ 1) $\sp{d}$ with equivalence queries and uses unions of two rectangles as hypotheses. The second algorithm properly learns unions of two rectangles over the domain (0, n $-$ 1) $\sp2$ with $O(\log\sp2\ n)$ equivalence queries. The third algorithm properly learns unions of two rectangles over the domain (0, n $-$ 1) $\sp{d}$ with both equivalence and membership queries. The query complexity of this algorithm is optimal. We also design an efficient algorithm for learning unions of rectangles whose projections on some unknown dimension are pairwise disjoint. When d is a constant, we show that unions of rectangles over the domain (0, n $-$ 1) $\sp{d}$ are efficiently learnable with equivalence queries. Next we study the problem of learning DNF formulas with equivalence queries and incomplete membership queries. We show that there is a subclass of k-term DNF formulas for nonconstant k such that one can learn any formula in this class with high probability. Finally we study the problem of learning disjunctions of counting functions with modulus q over the domain $Z\sbsp{q}{n}$. We show that for any prime q, one can learn disjunctions of counting functions over the domain $Z\sbsp{q}{n}$ with at most n + 1 queries. We prove that the problem of learning disjunctions of negated counting functions is in general harder than learning DNF formulas. However, we also show that efficient algorithms exist for this problem when the modulus is a constant prime, or when the modulus is a prime and the number of negated functions is bounded.
A Mixed‐Flux‐Based Nodal Discontinuous Galerkin Method for 3D Dynamic Rupture Modeling

Numerical simulation of rupture dynamics provides critical insights for understanding earthquake physics, while the complex geometry and heterogeneous material properties of natural faults make numerical method development challenging. The discontinuous Galerkin (DG) method is suitable for handling these complexities. In the DG method, the fault boundary conditions can be conveniently imposed through the upwind flux by solving a Riemann problem based on a velocity‐strain elastodynamic equation. However, the universal adoption of upwind flux can cause spatial oscillations in cases where elements on adjacent sides of the fault surface are not nearly symmetric. Here we propose a nodal DG method with an upwind/central mixed‐flux scheme to solve the spatial oscillation problem and thus reduce the dependence on mesh quality. Our method can achieve high scalability in parallel computing under different orders of accuracy. We verify the new method by comparing simulation results with those from other methods on a series of published benchmark problems with complex fault geometries, heterogeneous materials, off‐fault plasticity, roughness, thermal pressurization, and various versions of fault friction laws. Finally, we demonstrate that our method can be applied to simulate the dynamic rupture process of the 2008 Mw 7.9 Wenchuan earthquake along realistic fault geometry, including branches, step‐overs and a previously neglected sub‐parallel fault segment, offering a high potential to systematically explore the influences of fault zone properties on earthquake rupture dynamics.
State‐of‐the‐art in Large‐Scale Volume Visualization Beyond Structured Data

Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state‐of‐the‐art report, we review works focusing on large‐scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out‐of‐core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever‐increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large‐scale volume rendering systems and also include a review of tools that support the various volume data types discussed.
Probing the Skill of Random Forest Emulators for Physical Parameterizations Via a Hierarchy of Simple CAM6 Configurations

Machine learning approaches, such as random forests (RF), have been used to effectively emulate various aspects of climate and weather models in recent years. The limitations to these approaches are not yet known, particularly with regards to varying complexity of the underlying physical parameterization scheme within the climate model. Utilizing a hierarchy of model configurations, we explore the limits of random forest emulator skill using simplified model frameworks within NCAR's Community Atmosphere Model, version 6 (CAM6). These include a dry CAM6 configuration, a moist extension of the dry model, and an extension of the moist case that includes an additional convection scheme. Each model configuration is run with identical resolution and over the same time period. With unique RF being optimized for each tendency or precipitation rate across the hierarchy, we create a variety of “best case” emulators. The random forest emulators are then evaluated against the CAM6 output as well as a baseline neural network emulator for completeness. All emulators show significant skill when compared to the “truth” (CAM6), often in line with or exceeding similar approaches within the literature. In addition, as the CAM6 complexity is increased, the random forest skill noticeably decreases, regardless of the extensive tuning and training process each random forest goes through. This indicates a limit on the feasibility of RF to act as physics emulators in climate models and encourages further exploration in order to identify ideal uses in the context of state‐of‐the‐art climate model configurations.
Magnetopause Standoff Position Changes and Geosynchronous Orbit Crossings: Models and Observations

This research examines the ability of current physics‐based models to predict the magnetopause location. We use the Run‐On‐Request capabilities at the Community Coordinated Modeling Center at NASA GSFC with 4 magnetohydrodynamic (MHD) models. The magnetopause position prediction and response time to the solar wind changes is then compared to extreme solar wind conditions where magnetopause crossing of geosynchronous orbit have been observed by the Geostationary Operational Environmental Satellite (GOES) satellites. Rigorous analysis/comparison of observations and models is critical in determining magnetosphere dynamics for model validation. This paper is a preliminary effort defining the metrics necessary to understand the current magnetosphere model capabilities and challenges. Results show that there are discrepancies between the MHD models' standoff positions of the dayside magnetopause for the same solar wind conditions on events that included (a) an increase in solar wind dynamic pressure and a step function in the Interplanetary Magnetic Field Bz component; (b) nominal solar wind conditions (values of approximated 400 km/s for solar wind speed and 5 nT for the magnetic field magnitude) with a northward IMF; and (c) compression caused by several coronal mass ejections impacting the near Earth environment. Overall, the models predicted different magnetopause subsolar locations sometimes in the order of 3 RE. Contingency tables were calculated to show model performance in comparison with the data observed with the GOES 13/15 geosynchronous orbit for extreme events and skill scores were calculated.
Land‐Locked Convection as a Barrier to MJO Propagation Across the Maritime Continent

Large‐scale convection associated with the Madden‐Julian Oscillation (MJO) initiates over the Indian Ocean and propagates eastward across the Maritime Continent (MC). Over the MC, MJO events are generally weakened due to complex interactions between the large‐scale MJO and the MC landmass. The MC barrier effect is responsible for the dissipation of 40%–50% of observed MJO events and is often exaggerated in weather and climate models. We examine how MJO propagation over the MC is affected by two aspects of the MC—its land‐sea contrast and its terrain. To isolate the effects of mountains and land‐sea contrast on MJO propagation, we conduct three high‐resolution coupled atmosphere‐ocean model experiments: (a) control simulation (CTRL) of the 2011 November–December MJO event, (b) flattened terrain without MC mountains (FLAT), and (c) no‐land simulation (WATER) in which the MC islands are replaced with 50 m deep ocean. CTRL captures the general properties of the diurnal cycle of precipitation and MJO propagation across the MC. The WATER simulation produces a more intense and smoother‐propagating MJO compared with that of CTRL. In contrast, the convergence of sea breezes in the FLAT simulation produces much more organized convection and precipitation far inland than in CTRL, which results in a stronger barrier effect to MJO propagation. The land‐sea contrast induced land‐locked convection weakens the MJO's convective organization. The land‐locked convective systems over land in FLAT are more intense, grow larger, and last longer, which is more detrimental to MJO propagation over the MC, than the mountains that are present in CTRL.
Individualized estimated years from onset of Alzheimer's disease– related decline for adults with Down syndrome

Adults with Down syndrome (DS) are at increased risk for Alzheimer's disease (AD) and vary in their age of transition from AD preclinical to prodromal or more advanced clinical stages. An empirically based method is needed to determine individual “estimated years from symptom onset (EYO),” the same construct used in studies of autosomal dominant AD .
Microbial Adaptations Within Fine‐Scale Soil Structure Alleviate Phosphorus Limitation on Carbon Sequestration Following Afforestation

Afforestation effectively sequesters atmospheric carbon (C) into soil, with the magnitude of C sequestration being constrained by soil nutrient availability, particularly phosphorus (P) derived from parent material. While microbial communities inhabited within soil aggregates can adjust their composition and metabolic activities in response to afforestation, their effects on soil P availability remain uncertain. Here, we investigated how microbial adaptations within different soil aggregate‐size fractions (fine‐scale soil structure) were upscaled to influence P availability for soil C sequestration following long‐term (around 32 years) afforestation in subtropical China. We found that afforestation generally led to lower accumulation of total P relative to C and nitrogen (N) in soil aggregates, but induced higher P‐ versus C‐ and N‐acquiring enzyme activities (i.e., lower ecoenzymatic C:P and N:P ratios) compared with the croplands. Structural equation modeling analysis further showed that ecoenzymatic C:P and N:P ratios were primarily associated with the abundance of fungi in the macroaggregates (>250 μm), but showed significant correlations with the abundance of bacteria in the <250 μm finer soil particles. These results revealed that microbial community within different soil aggregate‐size fractions developed divergent strategies to overcome P limitation on soil C sequestration and cope with soil C:N:P imbalance following afforestation. Taken together, our work underpins that fine‐scale soil structural effects are important for the mechanistic understanding of soil P availability and their impacts on C sequestration following afforestation.
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Large Language Models (LLMs), despite their recent impressive accomplishments, are notably cost-prohibitive to deploy, particularly for applications involving long-content generation, such as dialogue systems and story writing. Often, a large amount of transient state information, referred to as the KV cache, is stored in GPU memory in addition to model parameters, scaling linearly with the sequence length and batch size. In this paper, we introduce a novel approach for implementing the KV cache which significantly reduces its memory footprint. Our approach is based on the noteworthy observation that a small portion of tokens contributes most of the value when computing attention scores. We call these tokens Heavy Hitters (H$_2$). Through a comprehensive investigation, we find that (i) the emergence of H$_2$ is natural and strongly correlates with the frequent co-occurrence of tokens in the text, and (ii) removing them results in significant performance degradation. Based on these insights, we propose Heavy Hitter Oracle (H$_2$O), a KV cache eviction policy that dynamically retains a balance of recent and H$_2$ tokens. We formulate the KV cache eviction as a dynamic submodular problem and prove (under mild assumptions) a theoretical guarantee for our novel eviction algorithm which could help guide future work. We validate the accuracy of our algorithm with OPT, LLaMA, and GPT-NeoX across a wide range of tasks. Our implementation of H$_2$O with 20% heavy hitters improves the throughput over three leading inference systems DeepSpeed Zero-Inference, Hugging Face Accelerate, and FlexGen by up to 29$\times$, 29$\times$, and 3$\times$ on OPT-6.7B and OPT-30B. With the same batch size, H2O can reduce the latency by up to 1.9$\times$. The code is available at https://github.com/FMInference/H2O.
Pore‐Scale Imaging of Multiphase Flow Fluctuations in Continuum‐Scale Samples

Representative elementary volumes (REVs) are an important concept in studying subsurface multiphase flow at the continuum scale. However, fluctuations in multiphase flow are currently not represented in continuum scale models, and their impact at the REV‐scale is unknown. Previous pore‐scale imaging studies on these fluctuations were limited to small samples with mm‐scale diameters and volumes on the order of ∼0.5 cm3. Here, we image steady‐state co‐injection experiments on a one‐inch diameter core plug sample, with nearly two orders of magnitude larger volume (21 cm3), while maintaining a pore‐scale resolution with X‐ray micro‐computed tomography. This was done for three total flow rates in a series of drainage fractional flow steps. Our observations differ markedly from those reported for mm‐scale samples in two ways: the macroscopic fluid distribution was less ramified at low capillary numbers (Ca) of 10−7; and the volume fraction of intermittency initially increased with increasing Ca (similar to mm‐scale observations), but then decreased at Ca of 10−7. Our results suggest that viscous forces may play a role in the cm‐scale fluid distribution, even at such low Ca, dampening intermittent pathway flow. A representative elementary volume study of the fluid saturation showed that this may be missed in smaller‐scale samples. Pressure drop measurements suggest that the observed pore‐scale fluctuations resulted in non‐Darcy like upscaled behavior. Overall, we show the usefulness of large field‐of‐view, high‐resolution imaging to bridge the gap between pore‐ and continuum‐scale multiphase flow studies.
Semen quality is affected by HLA class I alleles together with sexually transmitted diseases

The human leukocyte antigen (HLA) locus includes several genes with key roles in antigen presentation and immune response, some of them inclusively found to be associated with non‐obstructive azoospermia. Still, HLA connections to other infertility phenotypes such as semen hyperviscosity (SHV), asthenozoospermia (AST), and oligozoospermia (OLI) have been often neglected.
Extensive profiling of transcription factors in postmortem brains defines genomic occupancy in disease-relevant cell types and links TF activities to neuropsychiatric disorders

Transcription factors (TFs) orchestrate gene expression programs crucial for cell physiology, but our knowledge of their function in the brain is limited. Using bulk tissues and sorted nuclei from multiple human post-mortem brain regions, we generated a multi-omic resource (1121 total experiments) that includes binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of regions of high TF occupancy. Further, we find that predictive TF binding models demonstrate a bias for these high occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Several TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. These data are a powerful resource for future studies seeking to understand the role of TFs in epigenetic regulation in the human brain.
Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.
Structural properties of the HNF-1A transactivation domain

Hepatocyte nuclear factor 1α (HNF-1A) is a transcription factor with important gene regulatory roles in pancreatic β-cells. HNF1A gene variants are associated with a monogenic form of diabetes (HNF1A-MODY) or an increased risk for type 2 diabetes. While several pancreatic target genes of HNF-1A have been described, a lack of knowledge regarding the structure-function relationships in HNF-1A prohibits a detailed understanding of HNF-1A-mediated gene transcription, which is important for precision medicine and improved patient care. Therefore, we aimed to characterize the understudied transactivation domain (TAD) of HNF-1A in vitro. We present a bioinformatic approach to dissect the TAD sequence, analyzing protein structure, sequence composition, sequence conservation, and the existence of protein interaction motifs. Moreover, we developed the first protocol for the recombinant expression and purification of the HNF-1A TAD. Small-angle X-ray scattering and synchrotron radiation circular dichroism suggested a disordered conformation for the TAD. Furthermore, we present functional data on HNF-1A undergoing liquid-liquid phase separation, which is in line with in silico predictions and may be of biological relevance for gene transcriptional processes in pancreatic β-cells.
Genome-wide identification of silencers in human cells

Transcriptional regulation is a complex process that is controlled by a variety of factors, including enhancers and silencers. Silencers, also known as repressor elements, play a crucial role in the fine-tuning of gene expression by inhibiting or suppressing transcription in the human genome. Although significant progresses have been made, genome-wide silencer research is still in its early stages. Here, we used a genome-wide method called massively parallel reporter assays (MPRAs) to identify silencers in three human cell lines: K562, LNCap, and HEK293T. We identified 739,434, 643,484, and 491,952 silencer regions in these cell lines, respectively. We found that most of the silencers we identified had inhibitory activity and significantly enriched inhibitory motifs. These results confirm that silencers are ubiquitous in the human genome and play an important role in regulating gene expression. Therefore, our study provides a general strategy for genome-wide functional identification of silencer elements. This information could be used to better understand the mechanisms of gene regulation and to develop new therapeutic strategies for diseases that are caused by dysregulation of gene expression.
Entropy predicts early MEG, EEG and fMRI responses to natural images

To reduce the redundancy in the input, the human visual system employs efficient coding. Therefore, images with varying entropy (amount of information) should elicit distinct brain responses. Here, we show that a simple entropy model outperforms all current models, including many deep neural networks, in predicting early MEG/EEG and fMRI responses to visual objects. This suggests that the neural populations in the early visual cortex adapt to the information in natural images.