Miracle-dz/UniFAR-FTUs huggingface dataset:
Incremental Updates of Generalized Hypertree Decompositions

Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard. Unfortunately, current methods need to compute a completely new decomposition, even if the scopes change only slightly. In this article, we make the first steps toward solving the problem of updating the decomposition of a CSP P so that it becomes a valid decomposition of a new CSP P' produced by some modification of P. Even though the problem is hard in theory, we propose and implement a framework for effectively updating generalized hypertree decompositions. The experimental evaluation of our algorithm strongly suggests practical applicability.
Are Attention Networks More Robust? Towards Exact Robustness Veriﬁcation for Attention Networks *

—As an emerging type of Neural Networks (NNs), Attention Networks (ATNs) such as Transformers have been shown effective, in terms of accuracy, in many applications. This paper further considers their robustness. More speciﬁcally, we are curious about their maximum resilience against local input perturbations compared to the more conventional Multi-Layer Perceptrons (MLPs). Thus, we formulate the veriﬁcation task into an optimization problem, from which exact robustness values can be obtained. One major challenge, however, is the non-convexity and non-linearity of NNs. While the existing literature has handled the challenge to some extent with methods such as Branch-and-Bound, the additional level of difﬁculty introduced by the quadratic and exponential functions in the ATNs has not been tackled. Our work reduces this gap by focusing on sparsemax-based ATNs, encoding them into Mixed Integer Quadratically Constrained Programming problems, and proposing two powerful heuristics for a speedup of one order of magnitude. Finally, we train and evaluate several sparsemax-based ATNs and similar-sized ReLU-based MLPs for a lane departure warning task and show that the former is surprisingly less robust despite generally higher accuracy.
BASS. XXXIII. Swift-BAT Blazars and Their Jets through Cosmic Time

We derive the most up-to-date Swift-Burst Alert Telescope (BAT) blazar luminosity function in the 14–195 keV range, making use of a clean sample of 118 blazars detected in the BAT 105 month survey catalog, with newly obtained redshifts from the BAT Active Galatic Nucleus Spectroscopic Survey. We determine the best-fit X-ray luminosity function for the whole blazar population, as well as for flat-spectrum radio quasars (FSRQs) alone. The main results are: (1) at any redshift, BAT detects the most luminous blazars, above any possible break in their luminosity distribution, which means we cannot differentiate between density and luminosity evolution; (2) the whole blazar population, dominated by FSRQs, evolves positively up to redshift z ∼ 4.3, confirming earlier results and implying lower number densities of blazars at higher redshifts than previously estimated. The contribution of this source class to the cosmic X-ray background at 14–195 keV can range from 5%–18%, while possibly accounting for 100% of the MeV background. We also derived the average 14 keV–10 GeV spectral energy distribution for BAT blazars, which allows us to predict the number counts of sources in the MeV range, as well as the expected number of high-energy (>100 TeV) neutrinos. A mission like COSI will detect 40 MeV blazars, of which two may have coincident neutrino detections. Finally, taking into account beaming selection effects, the distribution and properties of the parent population of these extragalactic jets are derived. We find that the distribution of viewing angles is quite narrow, with most sources aligned within <5° of the line of sight. Moreover, the average Lorentz factor, 〈Γ〉 = 8–12, is lower than previously suggested for these powerful sources.
Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations

Considering two decision-making tasks $A$ and $B$, each of which wishes to compute an effective \textit{decision} $Y$ for a given \textit{query} $X$, {can we solve task $B$ by using query-decision pairs $(X, Y)$ of $A$ without knowing the latent decision-making model? } Such problems, called \textit{inverse decision-making with task migrations}, are of interest in that the complex and stochastic nature of real-world applications often prevents the agent from completely knowing the underlying system. In this paper, we introduce such a new problem with formal formulations and present a generic framework for addressing decision-making tasks in social contagion management. On the theory side, we present a generalization analysis for justifying the learning performance of our framework. In empirical studies, we perform a sanity check and compare the presented method with other possible learning-based and graph-based methods. We have acquired promising experimental results, confirming for the first time that it is possible to solve one decision-making task by using the solutions associated with another one.
Deep Double Descent via Smooth Interpolation

The ability of overparameterized deep networks to interpolate noisy data, while at the same time showing good generalization performance, has been recently characterized in terms of the double descent curve for the test error. Common intuition from polynomial regression suggests that overparameterized networks are able to sharply interpolate noisy data, without considerably deviating from the ground-truth signal, thus preserving generalization ability. At present, a precise characterization of the relationship between interpolation and generalization for deep networks is missing. In this work, we quantify sharpness of fit of the training data interpolated by neural network functions, by studying the loss landscape w.r. t. to the input variable locally to each training point, over volumes around cleanly- and noisily-labelled training samples, as we systematically increase the number of model parameters and training epochs. Our findings show that loss sharpness in the input space follows both model- and epoch-wise double descent, with worse peaks observed around noisy labels. While small interpolating models sharply fit both clean and noisy data, large interpolating models express a smooth loss landscape, where noisy targets are predicted over large volumes around training data points, in contrast to existing intuition.
Global Small Solutions of Mhd Boundary Layer Equations in Gevrey Function Space

In this paper, we obtain global small solutions and decay estimates for the MHD boundary layer in Gevrey space without any structural assumptions, generalizing the results of \cite{NL} in analytic space. The proof method is mainly inspired by \cite{WXLY} and \cite{CW}, using new auxiliary functions and finer structural analysis to overcome the difficulty of the loss of derivatives and then we obtain the global well-posedness of the MHD boundary layer in the Gevrey $\frac{3}{2}$ space.
Learning-Based Radiomic Prediction of Type 2 Diabetes Mellitus Using Image-Derived Phenotypes

Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As medical imaging data become more widely available for many patient populations, we sought to investigate whether image-derived phenotypic data could be leveraged in tabular learning classifier models to predict T2DM incidence without the use of invasive blood lab measurements. We show that both neural network and decision tree models that use image-derived phenotypes can predict patient T2DM status with recall scores as high as 87.6%. We also propose the novel use of these same architectures as ‘SynthA1c encoders’ that are able to output interpretable values mimicking blood hemoglobin A1C empirical lab measurements. Finally, we demonstrate that T2DM risk prediction model sensitivity to small perturbations in input vector components can be used to predict performance on covariates sampled from previously unseen patient populations.
Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms

Significance Cryoelectron microscopy is an imaging technique for determining molecular structures from randomly oriented projection images, with important applications in basic science and drug design. A large number of images is needed for accurate reconstruction due to low signal-to-noise ratio and unknown image orientations. We prove that asymptotically fewer images are required for reconstruction if the structure admits a sparse representation. This may reduce the experimental cost and allow reconstruction from limited datasets. This might also help in the related technology of X-ray free-electron lasers, where throughput is a bottleneck. We introduce sparse representations to the computational pipeline and build projection-based algorithms for low-resolution ab initio modeling that would be used to initialize existing iterative refinement procedures and for model validation.
Quantum Melting of Spin-1 Dimer Solid Induced by Inter-chain Couplings

Dimerized valence bond solids appear naturally in spin-1/2 systems on bipartite lattices, with the geometric frustrations playing a key role both in their stability and the eventual `melting' due to quantum fluctuations. Here, we ask the question of the stability of such dimerized solids in spin-1 systems, taking the anisotropic square lattice with bilinear and biquadratic spin-spin interactions as a paradigmatic model. The lattice can be viewed as a set of coupled spin-1 chains, which in the limit of vanishing inter-chain coupling are known to possess a stable dimer phase. We study this model using the density matrix renormalization group (DMRG) and infinite projected entangled-pair states (iPEPS) techniques, supplemented by the analytical mean-field and linear flavor wave theory calculations. While the latter predicts the dimer phase to remain stable up to a reasonably large interchain-to-intrachain coupling ratio $r \lesssim 0.6$, the DMRG and iPEPS find that the dimer solid melts for much weaker interchain coupling not exceeding $r\lesssim 0.15$. We find the transition into a magnetically ordered state to be first order, manifested by a hysteresis and order parameter jump, precluding the deconfined quantum critical scenario. The apparent lack of stability of dimerized phases in 2D spin-1 systems is indicative of strong quantum fluctuations that melt the dimer solid.
The functional organization of retinal ganglion cell receptive fields across light levels

Two major functions performed by the retina are to establish the parallel processing of visual information and to adapt visual encoding to the trillion-fold range of light intensities encountered in the environment. Previous work has highlighted many specialized cell types and circuits that instantiate parallel processing and light adaptation. However, fully understanding either process requires identifying how light adaptation and parallel processing interact. One possibility is that light adaptation causes uniform or proportional scaling to the receptive fields (RFs) of different retinal ganglion cell (RGC) types, the output neurons of the retina. Alternatively, light adaptation could cause a reorganization of RF structures across RGC types. A third possibility is that RFs across different RGC types are more similar under some conditions (e.g., low light levels) and more divergent under other conditions. To resolve these possibilities, we examined how the spatiotemporal RF structure of six simultaneously measured RGC types in the rat retina change from rod-to cone-mediated light levels. While light adaptation altered the RF properties of all six RGC types, we found that the relative structure across different RGC types was largely preserved across light levels. However, in both the spatial and temporal domains, one of the six RGC types exhibited adaptation distinct from the other types, resulting in a partial reorganization of RF properties across RGC types. These measurements identify how parallel visual processing interacts with light adaptation and highlights the challenges to stably encode visual scenes across light levels.
High-order upwind methods based on C2-continuous two-node integrated-RBF elements for viscous flows

In this paper, 2-node integrated radial basis function elements (IRBFEs)[CMES, vol.72, no.4, pp.299-334, 2011] are further developed for the simulation of incompressible viscous flows in two dimensions. Emphasis is placed on (i) the incorporation of C2-continuous 2-node IRBFEs into the subregion and point collocation frameworks for the discretisation of the stream function-vorticity formulation on Cartesian grids; and (ii) the development of high order upwind schemes based on 2-node IRBFEs for the case of convection-dominant flows. High levels of accuracy and efficiency of the present methods are demonstrated by solutions of several benchmark problems defined on rectangular and non-rectangular domains.
Hippocampal damage causes retrograde amnesia for objects’ visual, but not odour, properties in male rats

Damage to the hippocampus produces profound retrograde amnesia, but odour and object discrimination memories can be spared in the retrograde direction. Prior lesion studies testing retrograde amnesia for object/odour discriminations are problematic due to sparing of large parts of the hippocampus, which may support memory recall, and/or the presence of uncontrolled, distinctive odours that may support object discrimination. To address these issues, we used a simple object discrimination test to assess memory in male rats. Two visually distinct objects, paired with distinct odour cues, were presented. One object was associated with a reward. Following training, neurotoxic hippocampal lesions were made using N-methyl-D-aspartate (NMDA). The rats were then tested on the preoperatively learned object discrimination problem, with and without the availability of odour or visual cues during testing. The rats were also postoperatively trained on a new object discrimination problem. Lesion sizes ranged from 67-97% of the hippocampus (average of 87%). On the preoperatively learned discrimination problem, the rats with hippocampal lesions showed preserved object discrimination memory when tested in the dark (i.e., without visual cues) but not when the explicit odour cues were removed from the objects. Hippocampal lesions increased the number of trials required to reach criterion but did not prevent rats from solving the postoperatively learned discrimination problem. Our results support the idea that long-term memories for odours, unlike recall of visual properties of objects, does not depend on the hippocampus in rats, consistent with previous observations that hippocampal damage does not cause retrograde amnesia for odour memories.
Topographically driven microclimatic gradients shape patterns of forest structure, diversity, and composition at a forest-grassland transition zone

Globally, forests provide important ecosystem services, but anthropogenic change may shift the boundaries of forested biomes, because small-scale environmental changes govern biome transitions. This is especially true in semi-arid forests, where minor topographic and microclimatic changes influence forest functioning and transitions to open biomes such as grasslands. However, we lack quantitative descriptions of topographically driven microclimate variation and how it shapes forest structure, diversity, and composition in these transition zones. Leveraging a 20.2-ha forest inventory plot (Niobrara plot) at a semi-arid forest-grassland transition zone in the Niobrara River valley (Nebraska, USA), we paired data on abundances and distributions of seedlings, saplings, and adults of woody species with topographic and microclimate data to test the hypothesis that if topographic variation causes variation in microclimate that affects forest function, then forest structure, diversity, and composition should vary significantly with topography and microclimate. Microclimatic variation within the Niobrara plot strongly corresponded with topography, creating a sharp water availability and exposure gradient from the river floodplain to the forest-grassland transition zone. The magnitude of microclimate variation corresponded to that of regional macroclimate variation. Mean soil moisture was 10.2% lower along the higher-elevation transition zone than in the canyon bottoms, corresponding to variation across approximately 2.5 degrees of longitude. Mean air temperature increased by 2.2 °C from the canyon bottoms to upper canyon, corresponding to variation across approximately 3 degrees of latitude. Forest structure, diversity, and composition correlated strongly with topographic and microclimatic gradients. More complex forest structure and higher species richness of adults and saplings occurred in moister, less exposed habitats with steeper slopes and lower elevations, whereas seedling stem density and richness were higher in higher-light, moister habitats at lower elevations. Species occupied well-defined topographic niches, promoting high beta diversity along topographic and microclimatic gradients and high species turnover from the floodplain to the transition zone. Synthesis: Microclimatic and topographic variation drive patterns of structure, diversity, and composition in the forests at this forest-grassland transition zone. As the macroclimate becomes warmer and drier, topographically mediated microclimatic refuges supporting diverse, structurally complex forested ecosystems may shrink in semi-arid regions.
The assessment of cortical hemodynamic responses induced by tubuloglomerular feedback using in vivo imaging

The tubuloglomerular feedback (TGF) mechanism is crucial in modulating renal hemodynamics and glomerular filtration rate in individual nephrons. Our study aimed to evaluate the TGF-induced vascular responses by comparing the effects of two transport inhibitors with different sites and mechanisms of action. We assessed cortical hemodynamics with high-resolution laser speckle contrast imaging, which enabled the evaluation of blood flow individual micro-vessels and analysis of their dynamical patterns in the time-frequency domain. We demonstrated that a systemic administration of a loop diuretic abolishes TGF-mediated hemodynamic responses. Furthermore, we showed that the local microcirculatory blood flow decreased, and the TGF reset in response to reduced proximal reabsorption elicited by systemic administration of a sodium-glucose co-transporter 2 inhibitor, phlorizin.
Feature similarity gradients detect alterations in the neonatal cortex associated with preterm birth

The early life environment programmes cortical architecture and cognition across the life course. A measure of cortical organisation that integrates information from multi-modal MRI and is unbound by arbitrary parcellations has proven elusive, which hampers efforts to uncover the perinatal origins of cortical health. Here, we use the Vogt-Bailey index to provide a fine-grained description of regional homogeneities and sharp variations in cortical microstructure based on feature gradients, and we investigate the impact of being born preterm on cortical development. Preterm infants have a homogeneous microstructure in temporal and occipital lobes, and the medial parietal, cingulate, and frontal cortices, compared with term infants. These observations replicated across two independent datasets and were robust to differences that remain in the data after matching samples and alignment of processing and quality control strategies. We conclude that cortical microstructural architecture is altered in preterm infants in a spatially distributed rather than localised fashion.
Genome-wide fixation under viability selection

We investigate through simulations the effect of random sweepstakes, recurrent bottlenecks, dominance, and epistasis on selection a diploid population when the phenotype is determined by the genotype at a number of unlinked sites. We assume there are two types at each site, the wild type and a type conferring selective advantage. The focus is on identifying main trends in how the stated elements impact selection when selection acts on at least two unlinked site. We give examples of fixation trajectories under an array of scenarios, and record corresponding numerical estimates of the probability of fixation of the fit type at all the sites in question each time, and the time to fixation conditional on fixation. The results suggest that joint fixation of the fit type at the sites can occur in a number of scenarios, despite starting with the fit type in a single copy at each site. Random sweepstakes generally reduce both the probability and the time to fixation (conditional on fixation). Recurrent strong bottlenecks generally reduce the probability of fixation, but the effect of bottlenecks on the time to fixation (conditional on fixation), depends on the dominance mechanism in question. We identify an example of epistasis where fixation can occur, when in the absence of epistasis fixation does not occur at all or with much lower probability. Our results, coupled with an extensive analysis of genomic data of Atlantic cod(4), is further evidence for pervasive strong positive selection as being a key driving force in the evolution at least of highly fecund populations.
Theory and Construction Methods for Large Regular Resolution IV Designs

Abstract : The author defines 2 (super k-p) fractional factorial designs that use all of their degrees of freedom to estimate main effects and two-factor interactions as second order saturated (SOS) designs. He proves that resolution IV SOS designs project to every other resolution IV design, and shows the details of these projections for every n = 32 and n = 64 run fraction. For k greater than (5/l6)n, all resolution IV designs are a projection from the even SOS design at k = n/2. For k less than or equal to (5/l6)n the minimum aberration design resolution IV designs are projections of SOS designs with both even and odd words in the defining relation. While even resolution IV designs are limited to estimating fewer than n/2 two-factor interactions (in addition to the k main effects), resolution IV designs with odd-length words in the defining relation may devote more than half of their degrees of freedom to two-factor interactions. The author proposes a method to search for good resolution IV designs using naive projections from even/odd SOS designs. He introduces the alias length pattern as a tool to help characterize designs, and he describes how the matrix T = DD for a design D is useful in searching for designs. He lists the resolution IV even/odd minimum aberration designs for n = 128 and provides a catalog of the best resolution IV even/odd designs for n = 128. These results are based on an isomorphic check using a convenient function of T, as well as the set of projections of a design. Finally, the author suggests a new method for finding good regular resolution IV designs for large n (greater than 128) and provides a preliminary table of good resolution IV even/odd designs for n = 256. (19 tables, 7 figures, 26 refs.)