Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow

Three‐dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high‐performance computing cluster. One‐dimensional (1D) and lumped‐parameter zero‐dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient‐specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open‐source software platform SimVascular. We demonstrate the reduced‐order approximation quality against rigid‐wall 3D solutions in a comprehensive comparison with N = 72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high‐performance machines to personal computers.
Improved log-concavity for rotationally invariant measures of symmetric convex sets

We prove that the (B) conjecture and the Gardner-Zvavitch conjecture are true for all log-concave measures that are rotationally invariant, extending previous results known for Gaussian measures. Actually, our result apply beyond the case of log-concave measures, for instance to Cauchy measures as well. For the proof, new sharp weighted Poincar\'e inequalities are obtained for even probability measures that are log-concave with respect to a rotationally invariant measure.
New complementary pairs of sequences

A general method for construction of polyphase complementary pairs of sequences of length 2/sup N/ is presented. This method is based on the recursive algorithms introduced by Golay for binary sequences and by Sivaswamy for polyphase sequences. A modification of the proposed algorithm can generate multilevel and complex sequences of magnitude different from unity. The proposed sequences are suitable for radar applications where complementary sequences different from the known binary sequences are required.
Comparison of linear Brill and Teukolsky waves

Motivated by studies of critical phenomena in the gravitational collapse of vacuum gravitational waves we compare, at the linear level, two common approaches to constructing gravitational-wave initial data. Specifically, we construct analytical, linear Brill wave initial data and compare these with Teukolsky waves in an attempt to understand the different numerical behavior observed in dynamical (nonlinear) evolutions of these two different sets of data. In general, the Brill waves indeed feature higher multipole moments than the quadrupolar Teukolsky waves, which might have provided an explanation for the differences observed in the dynamical evolution of the two types of waves. However, we also find that, for a common choice of the Brill-wave seed function, all higherorder moments vanish identically, rendering the (linear) Brill initial data surprisingly similar to the Teukolsky data for a similarly common choice of its seed function.
Structural parameters of 389 local open clusters

Context. The distribution of member stars in the surroundings of an open cluster (OC) can shed light on the process of its formation, evolution, and dissolution. The analysis of structural parameters of OCs as a function of their age and position in the Galaxy constrains theoretical models of cluster evolution. The Gaia catalog is very appropriate for ﬁnding members of OCs at large distance from their centers. Aims. We revisit the membership lists of OCs from the solar vicinity, in particular, by extending these membership lists to the peripheral areas through Gaia EDR3. We then take advantage of these new member lists to study the morphological properties and the mass segregation levels of the clusters. Methods. We used the clustering algorithm HDBSCAN on Gaia parallaxes and proper motions to systematically search for members up to 50pc from the cluster centers. We ﬁt a King’s function on the radial density proﬁle of these clusters and a Gaussian mixture model (GMM) on their two-dimensional member distribution to study their shape. We also evaluated the degree of mass segregation of the clusters and the correlations of these parameters with the age and Galactic position of the clusters. Results. Our method performs well on 389 clusters out of the 467 clusters we selected, including several recently discovered clusters that were poorly studied until now. We report the detection of vast coronae around almost all the clusters and report the detection of 71 OCs with tidal tails. This multiplies the number of these structures that are identiﬁed by more than four. The size of the cores is smaller for old clusters than for young ones on average. Moreover, the overall size of the clusters seems to increase slightly with age, but the fraction of stars in the halo seems to decrease. As expected, the mass segregation is more pronounced in the oldest clusters, but no clear trend with age is evident. Conclusions. OCs are more extended than previously expected, regardless of their age. The decrease in the proportion of stars populating the clusters halos highlights the di ﬀ erent cluster evaporation processes and the short timescales they need to a ﬀ ect the clusters. Reported parameters such as cluster sizes or mass segregation levels all depend on cluster ages, but cannot be described as single functions of time.
Pattern Matching on Grammar-Compressed Strings in Linear Time

The most fundamental problem considered in algorithms for text processing is pattern matching: given a pattern $p$ of length $m$ and a text $t$ of length $n$, does $p$ occur in $t$? Multiple versions of this basic question have been considered, and by now we know algorithms that are fast both in practice and in theory. However, the rapid increase in the amount of generated and stored data brings the need of designing algorithms that operate directly on compressed representations of data. In the compressed pattern matching problem we are given a compressed representation of the text, with $n$ being the length of the compressed representation and $N$ being the length of the text, and an uncompressed pattern of length $m$. The most challenging (and yet relevant when working with highly repetitive data, say biological information) scenario is when the chosen compression method is capable of describing a string of exponential length (in the size of its representation). An elegant formalism for such a compression method is that of straight-line programs, which are simply context-free grammars describing exactly one string. While it has been known that compressed pattern matching problem can be solved in $O(m+n\log N)$ time for this compression method, designing a linear-time algorithm remained open. We resolve this open question by presenting an $O(n+m)$ time algorithm that, given a context-free grammar of size $n$ that produces a single string $t$ and a pattern $p$ of length $m$, decides whether $p$ occurs in $t$ as a substring. To this end, we devise improved solutions for the weighted ancestor problem and the substring concatenation problem.
Unsupervised Spiking Instance Segmentation on Event Data Using STDP Features

Spiking Neural Networks (SNN) and the field of Neuromorphic Engineering has brought about a paradigm shift in how to approach Machine Learning (ML) and Computer Vision (CV) problem. This paradigm shift comes from the adaption of event-based sensing and processing. An event-based vision sensor allows for sparse and asynchronous events to be produced that are dynamically related to the scene. Allowing not only the spatial information but a high-fidelity of temporal information to be captured. Meanwhile avoiding the extra overhead and redundancy of conventional high frame rate approaches. However, with this change in paradigm, many techniques from traditional CV and ML are not applicable to these event-based spatial-temporal visual streams. As such a limited number of recognition, detection and segmentation approaches exist. In this paper, we present a novel approach that can perform instance segmentation using just the weights of a Spike Time Dependent Plasticity trained Spiking Convolutional Neural Network that was trained for object recognition. This exploits the spatial and temporal aspects of the SpikeSEG network's internal feature representations adding this new discriminative capability. We highlight the new capability by successfully transforming a single class unsupervised network for face detection into a multi-person face recognition and instance segmentation network.
Tactical vs. Strategic Behavior: General Aviation Piloting in Convective Weather Scenarios

We commonly describe environments and behavioral responses to environmental conditions as “tactical” and “strategic. ” However theoretical research defining relevant environmental characteristics is rare, as are empirical investigations that would inform such theory. This paper discusses General Aviation (GA) pilots' descriptions of tactical/strategic conditions with respect to weather flying, and evaluates their ratings along a tactical/strategic scale in response to real convective weather scenarios experienced during a flight experiment with different weather information cues. Perceived risk was significantly associated with ratings for all experimental conditions. In addition, environmental characteristics were found to be predictive of ratings for Traditional IMC (instrument meteorological conditions), i.e., aural weather information only, and Traditional VMC (visual meteorological conditions), i.e., aural information and an external view. The paper also presents subjects' comments regarding use of Graphical Weather Information Systems (GWISs) to support tactical and strategic weather flying decisions and concludes with implications for the design and use of GWISs.
Synthesizing Collective Communication Algorithms for Heterogeneous Networks with TACCL

Large ML models and datasets have necessitated the use of multi-GPU systems for distributed model training. To harness the power offered by multi-GPU systems, it is critical to eliminate bottlenecks in inter-GPU communication — a problem made challenging by the heterogeneous nature of interconnects. In this work, we present TACCL, a synthesizer for collective communication primitives for large-scale multi-GPU systems. TACCL encodes a proﬁled topology and input size into a synthesis problem to generate optimized communication algorithms. TACCL is built on top of the standard NVIDIA Collective Communication Library (NCCL), allowing it to be a drop-in replacement for GPU communication in frameworks like PyTorch with minimal changes. TACCL generates algorithms for communication primitives like A LLGATHER , A LLTOALL , and A LLREDUCE that are up to 3 × faster than NCCL. Using TACCL’s algorithms speeds up the end-to-end training of an internal mixture of experts model by 17% . By decomposing the optimization problem into parts and leveraging the symmetry in multi-GPU topologies, TACCL synthesizes collectives for up-to 80-GPUs in less than 3 minutes, at least two orders of magnitude faster than other synthesis-based state-of-the-art collective communication libraries.
MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers

Mass spectrometry is a key tool in the study of small molecules, playing an important role in metabolomics, drug discovery, and environmental chemistry. Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule and help with its identiﬁcation. Practitioners often rely on spectral library searches to match unknown spectra with known compounds. However, such search-based methods are limited by availability of reference experimental data. In this work we show that graph transformers can be used to accurately predict tandem mass spectra. Our model, MassFormer, outperforms competing deep learning approaches for spectrum prediction, and includes an interpretable attention mechanism to help explain predictions. We demonstrate that our model can be used to improve reference library coverage on a synthetic molecule identiﬁcation task. Through quantitative analysis and visual inspection, we verify that our model recovers prior knowledge about the eﬀect of collision energy on the generated spectrum. We evaluate our model on diﬀerent types of mass spectra from two independent MS datasets and show that its performance generalizes. Code available at github.com/Roestlab/massformer.
Investigating the Baryon Cycle in Interacting Dwarfs with the Very Large Array and Pan-STARRS

We present resolved H i synthesis maps from the Very Large Array of three interacting dwarf systems: the NGC 3664 dwarf pair, the NGC 3264 dwarf pair, and the UGC 4638 dwarf triplet. All three dwarf systems are captured at various stages of interaction and span a range of environments. We detect clear hallmarks of tidal interactions through the presence of H i bridges and diffuse H i extensions that surround the dwarfs. We overlay the H i data on Pan-STARRS r-band images and find further evidence of tidal interactions through coincident distorted H i and tidal stellar features in NGC 3264 and UGC 4638, and an unwound spiral arm pointing toward its smaller companion in NGC 3264. In UGC 4638, both the gas and diffuse stars are extended to similar radii east of the primary, which could indicate that the smaller dwarf in the system has already completed one pass through the primary. We additionally find that our three systems, and those from the Local Volume TiNy Titans survey, are not H i deficient and thus the interaction has not resulted in a loss of gas from the systems. A comparison with noninteracting dwarf galaxies shows that the interactions have a significant impact on the kinematics of the systems. Our new resolved H i kinematics, combined with detailed stellar and H i morphologies, provide crucial constraints for future dynamical modeling of hierarchical mergers and the baryon cycle at the low-mass scale.
Bounding treatment effects by pooling limited information across observations

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited"pooling"of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called"Manski bounds", while unlimited information pooling leads to standard inverse propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods. We show in Monte Carlo experiments and through an empirical application that our bounds are indeed robust and informative in practice.
Physical drivers of ocean wave attenuation in the marginal ice zone

Despite a recent resurgence of observational studies attempting to quantify the ice-induced attenuation of ocean waves in polar oceans, the physical processes governing this phenomenon are still poorly understood. Most analyses have attempted to relate the spatial rate of wave attenuation to wave frequency, but have not considered how this relationship depends on ice, wave and atmospheric conditions. An in-depth analysis of the wave-buoy data collected during the 2017 PIPERS programme in the Ross Sea is conducted. Standard techniques are used to estimate the spatial rate of wave attenuation α and the influence of a number of potential physical drivers on its dependence on wave period T is investigated. A power-law is shown to consistently describe the α(T) relationship, in line with other recent analyses. The two parameters describing this relationship are found to depend significantly on sea ice concentration, mean wave period and wind direction, however. Looking at cross-correlations between these physical drivers, three regimes of ice-induced wave attenuation are identified, which characterise different ice, wave and wind conditions, and very possibly different processes causing this observed attenuation. This analysis suggests that parametrisations of ice-induced wave decay in spectral wave models should be piecewise, so as to include their dependence on local ice, wave and wind conditions.
Psycho-linguistic differences among competing vaccination communities on social media

Currently, the significance of social media in disseminating noteworthy information on topics such as health, politics, and the economy is indisputable. During the COVID-19 pandemic, anti-vaxxers use social media to distribute fake news and anxiety-provoking information about the vaccine, which may harm the public. Here, we characterize the psycho-linguistic features of anti-vaxxers on the online social network Twitter. For this, we collected COVID-19 related tweets from February 2020 to June 2021 to analyse vaccination stance, linguistic features, and social network characteristics. Our results demonstrated that, compared to pro-vaxxers, anti-vaxxers tend to have more negative emotions, narrative thinking, and worse moral tendencies. This study can advance our understanding of the online anti-vaccination movement, and become critical for social media management and policy action during and after the pandemic.
A Survey on Green Deep Learning

In recent years, larger and deeper models are springing up and continuously pushing state-of-the-art (SOTA) results across various fields like natural language processing (NLP) and computer vision (CV). However, despite promising results, it needs to be noted that the computations required by SOTA models have been increased at an exponential rate. Massive computations not only have a surprisingly large carbon footprint but also have negative effects on research inclusiveness and deployment on real-world applications. Green deep learning is an increasingly hot research field that appeals to researchers to pay attention to energy usage and carbon emission during model training and inference. The target is to yield novel results with lightweight and efficient technologies. Many technologies can be used to achieve this goal, like model compression and knowledge distillation. This paper focuses on presenting a systematic review of the development of Green deep learning technologies. We classify these approaches into four categories: (1) compact networks, (2) energy-efficient training strategies, (3) energy-efficient inference approaches, and (4) efficient data usage. For each category, we discuss the progress that has been achieved and the unresolved challenges. ∗Equal Contribution †This work is done during internship at ByteDance AI Lab. ar X iv :2 11 1. 05 19 3v 1 [ cs . L G ] 8 N ov 2 02 1
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement

A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption. To improve quality, previous works denoise a point cloud a posteriori after projecting the imperfect depth data onto 3D space. Instead, we enhance depth measurements directly on the sensed images a priori, before synthesizing a 3D point cloud. By enhancing near the physical sensing process, we tailor our optimization to our depth formation model before subsequent processing steps that obscure measurement errors. Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization. The designed model is validated (with parameters fitted) using collected empirical data from a representative depth sensor. To enhance each pixel row in a depth image, we first encode intra-view similarities between available row pixels as edge weights via feature graph learning. We next establish inter-view similarities with another rectified depth image via viewpoint mapping and sparse linear interpolation. This leads to a maximum a posteriori (MAP) graph filtering objective that is convex and differentiable. We minimize the objective efficiently using accelerated gradient descent (AGD), where the optimal step size is approximated via Gershgorin circle theorem (GCT). Experiments show that our method significantly outperformed recent point cloud denoising schemes and state-of-the-art image denoising schemes in two established point cloud quality metrics.
On Training Implicit Models

This paper focuses on training implicit models of infinite layers. Specifically, previous works employ implicit differentiation and solve the exact gradient for the backward propagation. However, is it necessary to compute such an exact but expensive gradient for training? In this work, we propose a novel gradient estimate for implicit models, named phantom gradient, that 1) forgoes the costly computation of the exact gradient; and 2) provides an update direction empirically preferable to the implicit model training. We theoretically analyze the condition under which an ascent direction of the loss landscape could be found, and provide two specific instantiations of the phantom gradient based on the damped unrolling and Neumann series. Experiments on large-scale tasks demonstrate that these lightweight phantom gradients significantly accelerate the backward passes in training implicit models by roughly 1.7 times, and even boost the performance over approaches based on the exact gradient on ImageNet.