Miracle-dz/UniFAR-FTUs huggingface dataset:
Γ-convergence of Nonlocal Dirichlet Energies With Penalty Formulations of Dirichlet Boundary Data

We study nonlocal Dirichlet energies associated with a class of nonlocal diffusion models on a bounded domain subject to the conventional local Dirichlet boundary condition. The Dirichlet boundary condition is imposed through a specifically designed penalty formulation. We prove that the nonlocal Dirichlet energies with the penalty terms converge to local Dirichlet energies with Dirichlet boundary conditions in the sense of $\varGamma$-convergence.
Don't miss the forest for the trees: the Lyman alpha forest power spectrum in effective field theory

We derive an effective field theory (EFT) for cosmological Lyman alpha forest fluctuations valid for the power spectrum at the one-loop order. The ``bottom-up'' EFT expansion at the level of the transmitted flux is identical to the line-of-sight dependent bias model first derived by Desjacques et al. We confirm this result by a ``top-down'' derivation based on the exponential map of the optical depth field. Specifically, we show that the combination of the exponential map and conditions of renormalizability generates the same EFT expansion as the ``bottom-up'' approach. In passing, we point out inconsistencies of the tree-level perturbative expansion of the exponential map without counterterms. To facilitate practical applications, we generalize the FFTLog method for efficient calculations of one-loop integrals from new line-of-sight dependent operators. Finally, we compare the one-loop EFT model against data from the Sherwood hydrodynamic simulation. The theory fits the data with sub percent accuracy up to $k= $ 3 $h$Mpc$^{-1}$ at $z= 2.8$ for both 3D and 1D correlations. Our model can be readily used for cosmological full-shape analyses of Lyman alpha forest data.
First X-Ray Polarization Measurement Confirms the Low Black Hole Spin in LMC X-3

X-ray polarization is a powerful tool to investigate the geometry of accreting material around black holes, allowing independent measurements of the black hole spin and orientation of the innermost parts of the accretion disk. We perform X-ray spectropolarimetric analysis of an X-ray binary system in the Large Magellanic Cloud, LMC X-3, that hosts a stellar-mass black hole, known to be persistently accreting since its discovery. We report the first detection of the X-ray polarization in LMC X-3 with the Imaging X-ray Polarimetry Explorer, and find the average polarization degree (PD) of 3.2% ± 0.6% and a constant polarization angle of −42° ± 6° over the 2–8 keV range. Using accompanying spectroscopic observations by NICER, NuSTAR, and the Neil Gehrels Swift observatories, we confirm previous measurements of the black hole spin via the X-ray continuum method, a ≈ 0.2. From polarization analysis only, we found consistent results with low black hole spin, with an upper limit of a < 0.7 at a 90% confidence level. A slight increase in the PD with energy, similar to other black hole X-ray binaries in the soft state, is suggested from the data but with a low statistical significance.
Context-Aware Neural Video Compression on Solar Dynamics Observatory

NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLa Win), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off.
Flash-LLM: Enabling Low-Cost and Highly-Efficient Large Generative Model Inference With Unstructured Sparsity

With the fast growth of parameter size, it becomes increasingly challenging to deploy large generative models as they typically require large GPU memory consumption and massive computation. Unstructured model pruning has been a common approach to reduce both GPU memory footprint and the overall computation while retaining good model accuracy. However, the existing solutions do not provide an efficient support for handling unstructured sparsity on modern GPUs, especially on the highly-structured tensor core hardware. Therefore, we propose Flash-LLM for enabling low-cost and highly efficient large generative model inference with the sophisticated support of unstructured sparsity on high-performance but highly restrictive tensor cores. Based on our key observation that the main bottleneck of generative model inference is the several skinny matrix multiplications for which tensor cores would be significantly under-utilized due to low computational intensity, we propose a general Load-as-Sparse and Compute-as-Dense methodology for unstructured sparse matrix multiplication (SpMM). The basic insight is to address the significant memory bandwidth bottleneck while tolerating redundant computations that are not critical for end-to-end performance on tensor cores. Based on this, we design an effective software framework for tensor core based unstructured SpMM, leveraging on-chip resources for efficient sparse data extraction and computation/memory-access overlapping. Extensive evaluations demonstrate that (1) at SpMM kernel level, Flash-LLM significantly outperforms the state-of-the-art library, i.e., Sputnik and SparTA by an average of 2.9X and 1.5X, respectively. (2) At end-to-end framework level on OPT-30B/66B/175B models, for tokens per GPU-second , Flash-LLM achieves up to 3.8X and 3.6X improvement over DeepSpeed and FasterTransformer, respectively, with significantly lower inference cost.
Uncertainty Estimation in Instance Segmentation with Star-convex Shapes

Instance segmentation has witnessed promising advancements through deep neural network-based algorithms. However, these models often exhibit incorrect predictions with unwarranted confidence levels. Consequently, evaluating prediction uncertainty becomes critical for informed decision-making. Existing methods primarily focus on quantifying uncertainty in classification or regression tasks, lacking emphasis on instance segmentation. Our research addresses the challenge of estimating spatial certainty associated with the location of instances with star-convex shapes. Two distinct clustering approaches are evaluated which compute spatial and fractional certainty per instance employing samples by the Monte-Carlo Dropout or Deep Ensemble technique. Our study demonstrates that combining spatial and fractional certainty scores yields improved calibrated estimation over individual certainty scores. Notably, our experimental results show that the Deep Ensemble technique alongside our novel radial clustering approach proves to be an effective strategy. Our findings emphasize the significance of evaluating the calibration of estimated certainties for model reliability and decision-making.
Computed tomography‐determined skeletal muscle density predicts 3‐year mortality in initial‐dialysis patients in China

Skeletal muscle mass and quality assessed by computed tomography (CT) images of the third lumbar vertebra (L3) level have been established as risk factors for poor clinical outcomes in several illnesses, but the relevance for dialysis patients is unclear. A few studies have suggested a correlation between CT‐determined skeletal muscle mass and quality at the first lumbar vertebra (L1) level and adverse outcomes. Generally, chest CT does not reach beyond L1. We aimed to determine whether opportunistic CT scan (chest CT)‐determined skeletal muscle mass and quality at L1 are associated with mortality in initial‐dialysis patients.
How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study

Data analysis is challenging as analysts must navigate nuanced decisions that may yield divergent conclusions. AI assistants have the potential to support analysts in planning their analyses, enabling more robust decision making. Though AI-based assistants that target code execution (e.g., Github Copilot) have received significant attention, limited research addresses assistance for both analysis execution and planning. In this work, we characterize helpful planning suggestions and their impacts on analysts' workflows. We first review the analysis planning literature and crowd-sourced analysis studies to categorize suggestion content. We then conduct a Wizard-of-Oz study (n=13) to observe analysts' preferences and reactions to planning assistance in a realistic scenario. Our findings highlight subtleties in contextual factors that impact suggestion helpfulness, emphasizing design implications for supporting different abstractions of assistance, forms of initiative, increased engagement, and alignment of goals between analysts and assistants.
The interplay of drought and hurricanes on tree recovery: insights from dynamic and weak functional responses

Identifying the functional traits that enable recovery after extreme events is necessary for assessing forest persistence and functioning. However, the variability of traits mediating responses to disturbances presents a significant limitation, as these relationships may be contingent on the type of disturbance and change over time. This study investigates the effects of traits on tree growth—for short and longer terms—in response to two vastly different extreme climatic events (droughts and hurricanes) in a Puerto Rican forest. I found that trees display a dynamic functional response to extreme climatic events. Leaf traits associated with efficient photosynthesis mediated faster tree growth after hurricanes, while trees with low wood density and high water use efficiency displayed faster growth after drought. In the longer term, over both drought and hurricanes, tree size was the only significant predictor of growth, with faster growth for smaller trees. However, despite finding significant trait–growth relationships, the predictive power of traits was overall low. As the frequency of extreme events increases due to climate change, understanding the dynamic relationships between traits and tree growth is necessary for identifying strategies for recovery.
Resilience under protracted conflict and siege: a new paradigm shift

ABSTRACT This study sought to explore the relationship between resilience as perceived within the Palestinian context, and individual, contextual, and psychosocial factors among adults living in the Gaza Strip. Secondly, this study applies the Connor-Davidson Resilience Scale to the Palestinian population within the context of the challenges they are facing while living under extended sieges, in order to examine the generalizability of the resilience scale. Gender-specific bivariate analyses and multivariate linear regression were conducted to examine links between resilience, and diverse psychosocial and contextual factors. Males scored higher in resilience than females. Consistent predictors of higher resilience included, being male, employed, income, not living in a peaceful area, higher life satisfaction, and the Palestinian idea of resilience (Sumud). For the overall sample and males, hardiness was a positive predictor of higher resilience. For the overall sample and females, post-secondary education was a positive predictor of resilience. Psychosocial factors may act synergistically to foster resilience in the face of adversity, low socioeconomic status, and fear of living in a non-peaceful area. Our findings add to a limited body of literature calling for attention to contextual and psychosocial influences on gender differences in measuring and defining the dynamic nature of resilience.
Cone-Opponent Ganglion Cells in the Primate Fovea Tuned to Non-Cardinal Color Directions

A long-standing question in vision science is how the three cone photoreceptor types – long (L), medium (M) and short (S) wavelength sensitive – combine to generate our perception of color. Hue perception can be described along two opponent axes: red-green and blue-yellow. Psychophysical measurements of color appearance indicate that the cone inputs to the red-green and blue-yellow opponent axes are M vs. L+S and L vs. M+S, respectively. However, the “cardinal directions of color space” revealed by psychophysical measurements of color detection thresholds following adaptation are L vs. M and S vs. L+M. These cardinal directions match the most common cone-opponent retinal ganglion cells (RGCs) in the primate retina. Accordingly, the cone opponency necessary for color appearance is thought to be established in cortex. However, small populations with the appropriate M vs. L+S and L vs. M+S cone-opponency have been reported in large surveys of cone inputs to primate RGCs and their projections to the lateral geniculate nucleus (LGN), yet their existence continues to be debated. Resolving this long-standing open question is necessary because a complete account of the coneopponency in the retinal output is critical for efforts to understand how downstream neural circuits process color. Here, we performed adaptive optics calcium imaging to longitudinally, noninvasively measure foveal RGC light responses in the living macaque eye. We confirm the presence of L vs. M+S and M vs. L+S neurons with non-cardinal coneopponency and demonstrate that cone-opponent signals in the retinal output are more diverse than classically thought.
Opposing roles of RUBCN isoforms in autophagy and memory B cell generation

RUBCN (also known as Rubicon) was originally identified as a negative regulator of autophagy, a process by which cells degrade and recycle damaged components or organelles and that requires the activity of the class III PI3K VPS34 and the mTORC1 protein complex. Here, we characterized the role of a shorter isoform, RUBCN100, as an autophagy-promoting factor in B cells. RUBCN100 was translated from alternative translation initiation sites and lacked the RUN domain of the longer, previously characterized RUBCN130 isoform. Specific deficiency of RUBCN130 in B cells enhanced autophagy, which promoted memory B cell generation. In contrast to RUBCN130, which is localized in late endosomes and lysosomes and suppresses the enzymatic activity of VPS34, an effect thought to mediated by its RUN domain, RUBCN100 was preferentially located in early endosomes and enhanced VPS34 activity, presumably because of the absence of the RUN domain. Furthermore, RUBCN100, but not RUBCN130, enhanced autophagy and suppressed mTORC1 activation. Our findings reveal that the opposing roles of two RUBCN isoforms are critical for autophagy regulation and memory B cell differentiation. Description Two RUBCN isoforms exert distinct effects on autophagy and the differentiation of memory B cells. Editor’s summary Memory B cells generated upon the first encounter with a pathogen persist to enable antibody-based defenses for subsequent encounters. The survival of memory B cells depends on autophagy, a process that eliminates damaged cellular components. The 130–amino acid protein Rubicon (RUBCN130) is thought to inhibit autophagy, and Tsai et al. characterized a shorter Rubicon isoform (RUBCN100) that enhanced autophagy in B cells. Mice with B cells that specifically lacked RUBCN130 generated more memory B cells in an autophagy-dependent manner. The differential effects on autophagy might be due to the ability of RUBCN130 to suppress the activity of autophagy-promoting proteins. The identification and characterization of the shorter Rubicon isoform help to resolve conflicting results regarding the function of Rubicon in autophagy. —Wei Wong
Brain structural covariance network features are robust markers of early heavy alcohol use

Background and Aims: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. Design and Setting: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14–22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17–22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22–37 years). Cases: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. Measurements: Graph theory metrics of segregation and integration were used to summarize SCN. Findings: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = −0.029, P = 0.002], lower modularity (AUC = −0.14, P = 0.004), lower average shortest path length (AUC = −0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = −0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. Conclusion: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.
Expression of ERG11, ERG3, MDR1 and CDR1 genes in Candida tropicalis

Abstract Introduction. Drug resistance to azoles is a growing problem in the Candida genus. Objective. To analyze molecularly the genes responsible for fluconazole resistance in Candida tropicalis strains. Materials and methods. Nineteen strains, with and without exposure to fluconazole, were selected for this study. The expression of MDR1, CDR1, ERG11, and ERG3 genes was analyzed in sensitive, dose-dependent sensitive, and resistant strains exposed to different concentrations of the antifungal drug. Results. MDR1, ERG11 and ERG3 genes were significantly overexpressed in the different sensitivity groups. CDR1 gene expression was not statistically significant among the studied groups. Seven of the eight fluconazole-resistant strains showed overexpression of one or more of the analyzed genes. In some dose-dependent sensitive strains, we found overexpression of CDR1, ERG11, and ERG3. Conclusion. The frequency of overexpression of ERG11 and ERG3 genes indicates that they are related to resistance. However, the finding of dose-dependent resistant/sensitive strains without overexpression of these genes suggests that they are not exclusive to this phenomenon. More basic research is needed to study other potentially involved genes in the resistance mechanism to fluconazole.
Impact of a Saccharomyces cerevisiae fermentation product during an intestinal barrier challenge in lactating Holstein cows on ileal microbiota and markers of tissue structure and immunity

Abstract Feeding a Saccharomyces cerevisiae fermentation product (SCFP; NutriTek, Diamond V, Cedar Rapids, IA) during periods of metabolic stress is beneficial to the health of dairy cows partially through its effect on the gut microbiota. Whether SCFP alters the ileal microbiota in lactating cows during intestinal challenges induced by feed restriction (FR) is not known. We used 16S rRNA sequencing to assess if feeding SCFP during FR to induce gut barrier dysfunction alters microbiota profiles in the ileum. The mRNA abundance of key genes associated with tissue structures and immunity was also detected. Multiparous cows (97.1 ± 7.6 days in milk (DIM); n = 7 per treatment) fed a control diet or the control plus 19 g/d NutriTek for 9 wk were subjected to an FR challenge for 5 d, during which they were fed 40% of their ad libitum intake from the 7 d before FR. All cows were slaughtered at the end of FR. DNA extracted from ileal digesta was subjected to PacBio Full-Length 16S rRNA gene sequencing. High-quality amplicon sequence analyses were performed with Targeted Amplicon Diversity Analysis and MicrobiomeAnalyst. Functional analysis was performed and analyzed using PICRUSt and STAMP. Feeding SCFP did not (P > 0.05) alter dry matter intake, milk yield, or milk components during FR. In addition, SCFP supplementation tended (P = 0.07) to increase the relative abundance of Proteobacteria and Bifidobacterium animalis. Compared with controls, feeding SCFP increased the relative abundance of Lactobacillales (P = 0.03). Gluconokinase, oligosaccharide reducing-end xylanase, and 3-hydroxy acid dehydrogenase were among the enzymes overrepresented (P < 0.05) in response to feeding SCFP. Cows fed SCFP had a lower representation of adenosylcobalamin biosynthesis I (early cobalt insertion) and pyrimidine deoxyribonucleotides de novo biosynthesis III (P < 0.05). Subsets of the Firmicutes genus, Bacteroidota phylum, and Treponema genus were correlated with the mRNA abundance of genes associated with ileal integrity (GCNT3, GALNT5, B3GNT3, FN1, ITGA2, LAMB2) and inflammation (AOX1, GPX8, CXCL12, CXCL14, CCL4, SAA3). Our data indicated that the moderate FR induced dysfunction of the ileal microbiome, but feeding SCFP increased the abundance of some beneficial gut probiotic bacteria and other species related to tissue structures and immunity.
Effects of Underwater Explosions on Life in the Sea

Abstract : Available literature on the effects of underwater explosions on life in the sea is reviewed. Reported effects on marine plants, invertebrates, fishes, turtles, birds, sea mammals and humans, from both experimental and field observations, are presented, as are theories on the damage process and models developed to predict lethal and safe ranges around explosions of varying configurations.
Partition regularity of Pythagorean pairs

We address a core partition regularity problem in Ramsey theory by proving that every finite coloring of the positive integers contains monochromatic Pythagorean pairs, i.e., $x,y\in \mathbb{N}$ such that $x^2\pm y^2=z^2$ for some $z\in \mathbb{N}$. We also show that partitions generated by level sets of multiplicative functions taking finitely many values always contain Pythagorean triples. Our proofs combine known Gowers uniformity properties of aperiodic multiplicative functions with a novel and rather flexible approach based on concentration estimates of multiplicative functions.