Miracle-dz/UniFAR-FTUs huggingface dataset:
Multicode benchmark on simulated Ti K-edge x-ray absorption spectra of Ti-O compounds

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret experimental spectra and draw physical insights. Recently, there has also been growing interest in building computational XAS databases to enable data analytics and machine learning applications. However, there are non-trivial differences among commonly used XAS simulation codes, both in underlying theoretical formalism and in technical implementation. Reliable and reproducible computational XAS databases require systematic benchmark studies. In this work, we benchmarked Ti K-edge XAS simulations of ten representative Ti-O binary compounds, which we refer to as the Ti-O-10 dataset, using three state-of-the-art codes: xspectra, ocean and exciting. We systematically studied the convergence behavior with respect to the input parameters and developed a workflow to automate and standardize the calculations to ensure converged spectra. Our benchmark comparison shows: (1) the two Bethe-Salpeter equation (BSE) codes (ocean and exciting) have excellent agreement in the energy range studied (up to 35 eV above the onset) with an average Spearman's rank correlation score of 0.998; (2) good agreement is obtained between the core-hole potential code (xspectra) and BSE codes (ocean and exciting) with an average Spearman's rank correlation score of 0.990. Our benchmark study provides important standards for first-principles XAS simulations with broad impact in data-driven XAS analysis.
Fast inference of latent space dynamics in huge relational event networks

Relational events are a type of social interactions, that sometimes are referred to as dynamic networks. Its dynamics typically depends on emerging patterns, so-called endogenous variables, or external forces, referred to as exogenous variables. Comprehensive information on the actors in the network, especially for huge networks, is rare, however. A latent space approach in network analysis has been a popular way to account for unmeasured covariates that are driving network configurations. Bayesian and EM-type algorithms have been proposed for inferring the latent space, but both the sheer size many social network applications as well as the dynamic nature of the process, and therefore the latent space, make computations prohibitively expensive. In this work we propose a likelihood-based algorithm that can deal with huge relational event networks. We propose a hierarchical strategy for inferring network community dynamics embedded into an interpretable latent space. Node dynamics are described by smooth spline processes. To make the framework feasible for large networks we borrow from machine learning optimization methodology. Model-based clustering is carried out via a convex clustering penalization, encouraging shared trajectories for ease of interpretation. We propose a model-based approach for separating macro-microstructures and perform a hierarchical analysis within successive hierarchies. The method can fit millions of nodes on a public Colab GPU in a few minutes. The code and a tutorial are available in a Github repository.
Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization

This paper presents a novel methodology that uses surrogate models in the form of neural networks to reduce the computation time of simulation-based optimization of a reference trajectory. Simulation-based optimization is necessary when there is no analytical form of the system accessible, only input-output data that can be used to create a surrogate model of the simulation. Like many high-fidelity simulations, this trajectory planning simulation is very nonlinear and computationally expensive, making it challenging to optimize iteratively. Through gradient descent optimization, our approach finds the optimal reference trajectory for landing a hypersonic vehicle. In contrast to the large datasets used to create the surrogate models in prior literature, our methodology is specifically designed to minimize the number of simulation executions required by the gradient descent optimizer. We demonstrated this methodology to be more efficient than the standard practice of hand-tuning the inputs through trial-and-error or randomly sampling the input parameter space. Due to the intelligently selected input values to the simulation, our approach yields better simulation outcomes that are achieved more rapidly and to a higher degree of accuracy. Optimizing the hypersonic vehicle's reference trajectory is very challenging due to the simulation's extreme nonlinearity, but even so, this novel approach found a 74% better-performing reference trajectory compared to nominal, and the numerical results clearly show a substantial reduction in computation time for designing future trajectories.
On the Analysis of Computational Delays in Reinforcement Learning-based Rate Adaptation Algorithms

Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm may be detrimental to its performance, which in turn may decrease network performance. This aspect has been overlooked in the state of the art. In this paper, we present an analysis of common computational delays in RL-based RA algorithms, and propose a methodology that may be applied to reduce these computational delays and increase the efficiency of this type of algorithms. We apply the proposed methodology to an existing RL-based RA algorithm. The obtained experimental results indicate a reduction of one order of magnitude in the execution time of the algorithm, improving its responsiveness to link quality changes.
Fairness-Aware Data Valuation for Supervised Learning

Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairness, previous work in data valuation does not consider how training instances may influence both performance and fairness of ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data valuation framework that can be used to incorporate fairness concerns into a series of ML-related tasks (e.g., data pre-processing, exploratory data analysis, active learning). We propose an entropy-based data valuation metric suited to address our two-pronged goal of maximizing both performance and fairness, which is more computationally efficient than existing metrics. We then show how FADO can be applied as the basis for unfairness mitigation pre-processing techniques. Our methods achieve promising results -- up to a 40 p.p. improvement in fairness at a less than 1 p.p. loss in performance compared to a baseline -- and promote fairness in a data-centric way, where a deeper understanding of data quality takes center stage.
SynthVSR: Scaling Up Visual Speech RecognitionWith Synthetic Supervision

Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available tran-scribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our method, termed Synth VSR, sub-stantially improves the performance of VSR systems with synthetic lip movements. The key idea behind Synth VSR is to leverage a speech-driven lip animation model that gen-erates lip movements conditioned on the input speech. The speech-driven lip animation model is trained on an unla-beled audio-visual dataset and could be further optimized towards a pre-trained VSR model when labeled videos are available. As plenty of transcribed acoustic data and face images are available, we are able to generate large-scale synthetic data using the proposed lip animation model for semi-supervised VSR training. We evaluate the performance of our approach on the largest public VSR bench-mark - Lip Reading Sentences 3 (LRS3). Synth VSR achieves a WER of 43.3% with only 30 hours of real labeled data, outperforming off-the-shelf approaches using thousands of hours of video. The WER is further reduced to 27.9% when using all 438 hours of labeled data from LRS3, which is on par with the state-of-the-art self-supervised AV-HuBERT method. Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16.9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90,000 hours). Finally, we perform extensive ablation studies to understand the effect of each component in our proposed method.
Stabilizer Approximation III: Maximum Cut

We apply the stabilizer formalism to the Maximum Cut problem, and obtain a new greedy construction heuristic. It turns out to be an elegant synthesis of the edge-contraction and differencing edge-contraction approaches. Utilizing the relation between the Maximum Cut problem and the Ising model, the approximation ratio of the heuristic is easily found to be at least $1/2$. Moreover, numerical results show that the heuristic has very nice performance for graphs with about 100 vertices.
Event-based Agile Object Catching with a Quadrupedal Robot

Quadrupedal robots are conquering various applications in indoor and outdoor environments due to their capability to navigate challenging uneven terrains. Exteroceptive information greatly enhances this capability since perceiving their surroundings allows them to adapt their controller and thus achieve higher levels of robustness. However, sensors such as LiDARs and RGB cameras do not provide sufficient information to quickly and precisely react in a highly dynamic environment since they suffer from a bandwidth-latency trade-off. They require significant bandwidth at high frame rates while featuring significant perceptual latency at lower frame rates, thereby limiting their versatility on resource constrained platforms. In this work, we tackle this problem by equipping our quadruped with an event camera, which does not suffer from this tradeoff due to its asynchronous and sparse operation. In leveraging the low latency of the events, we push the limits of quadruped agility and demonstrate high-speed ball catching for the first time. We show that our quadruped equipped with an event-camera can catch objects with speeds up to 15 m/s from 4 meters, with a success rate of 83%. Using a VGA event camera, our method runs at 100 Hz on an NVIDIA Jetson Orin.
Small-\(x\) Quark and Gluon Helicity Contributions to the Proton Spin Puzzle

A small-$x$ helicity evolution has been derived in 2016-18 and received an important modification in 2022. This article discusses its general framework and summarizes the recent theoretical developments, including the asymptotic behaviors of helicity PDFs and $g_1$ structure function at small $x$. The latest fits to various polarized scattering data are also discussed. The results from this research program will provide important theoretical inputs for the future polarized small-$x$ measurements at the electron-ion collider (EIC).
Tests of Classical Gravity with Radio Pulsars

Tests of gravity are important to the development of our understanding of gravitation and spacetime. Binary pulsars provide a superb playground for testing gravity theories. In this chapter we pedagogically review the basics behind pulsar observations and pulsar timing. We illustrate various recent strong-field tests of the general relativity (GR) from the Hulse-Taylor pulsar PSR B1913+16, the double pulsar PSR J0737$-$3039, and the triple pulsar PSR J0337+1715. We also overview the inner structure of neutron stars (NSs) that may influence some gravity tests, and have used the scalar-tensor gravity and massive gravity theories as examples to demonstrate the usefulness of pulsar timing in constraining specific modified gravity theories. Outlooks to new radio telescopes for pulsar timing and synergies with other strong-field gravity tests are also presented.
Training Feedforward Neural Networks with Bayesian Hyper-Heuristics

The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means of a high-level probabilistic-based heuristic. This research introduces a novel population-based Bayesian hyper-heuristic (BHH) that is used to train feedforward neural networks (FFNNs). The performance of the BHH is compared to that of ten popular low-level heuristics, each with different search behaviours. The chosen heuristic pool consists of classic gradient-based heuristics as well as meta-heuristics (MHs). The empirical process is executed on fourteen datasets consisting of classification and regression problems with varying characteristics. The BHH is shown to be able to train FFNNs well and provide an automated method for finding the best heuristic to train the FFNNs at various stages of the training process.
Mechanism of sphingolipid homeostasis revealed by structural analysis of Arabidopsis SPT-ORM1 complex

The serine palmitoyltransferase (SPT) complex catalyzes the first and rate-limiting step in sphingolipid biosynthesis in all eukaryotes. ORM/ORMDL proteins are negative regulators of SPT that respond to cellular sphingolipid levels. However, the molecular basis underlying ORM/ORMDL-dependent homeostatic regulation of SPT is not well understood. We determined the cryo–electron microscopy structure of Arabidopsis SPT-ORM1 complex, composed of LCB1, LCB2a, SPTssa, and ORM1, in an inhibited state. A ceramide molecule is sandwiched between ORM1 and LCB2a in the cytosolic membrane leaflet. Ceramide binding is critical for the ORM1-dependent SPT repression, and dihydroceramides and phytoceramides differentially affect this repression. A hybrid β sheet, formed by the amino termini of ORM1 and LCB2a and induced by ceramide binding, stabilizes the amino terminus of ORM1 in an inhibitory conformation. Our findings provide mechanistic insights into sphingolipid homeostatic regulation via the binding of ceramide to the SPT-ORM/ORMDL complex that may have implications for plant-specific processes such as the hypersensitive response for microbial pathogen resistance.
Age-Related Effects of COVID-19 Pandemic on Mechanical Reperfusion and 30-Day Mortality for STEMI: Results of the ISACS-STEMI COVID-19 Registry

Background: The constraints in the management of patients with ST-segment elevation myocardial infarction (STEMI) during the COVID-19 pandemic have been suggested to have severely impacted mortality levels. The aim of the current analysis is to evaluate the age-related effects of the COVID-19 pandemic on mechanical reperfusion and 30-day mortality for STEMI within the registry ISACS-STEMI COVID-19. Methods: This retrospective multicenter registry was performed in high-volume PPCI centers on four continents and included STEMI patients undergoing PPCI in March–June 2019 and 2020. Patients were divided according to age (< or ≥75 years). The main outcomes were the incidence and timing of PPCI, (ischemia time longer than 12 h and door-to-balloon longer than 30 min), and in-hospital or 30-day mortality. Results: We included 16,683 patients undergoing PPCI in 109 centers. In 2020, during the pandemic, there was a significant reduction in PPCI as compared to 2019 (IRR 0.843 (95%-CI: 0.825–0.861, p < 0.0001). We found a significant age-related reduction (7%, p = 0.015), with a larger effect on elderly than on younger patients. Furthermore, we observed significantly higher 30-day mortality during the pandemic period, especially among the elderly (13.6% vs. 17.9%, adjusted HR (95% CI) = 1.55 [1.24–1.93], p < 0.001) as compared to younger patients (4.8% vs. 5.7%; adjusted HR (95% CI) = 1.25 [1.05–1.49], p = 0.013), as a potential consequence of the significantly longer ischemia time observed during the pandemic. Conclusions: The COVID-19 pandemic had a significant impact on the treatment of patients with STEMI, with a 16% reduction in PPCI procedures, with a larger reduction and a longer delay to treatment among elderly patients, which may have contributed to increase in-hospital and 30-day mortality during the pandemic.
Evolutionary Multi-Objective Optimization for Virtual Network Function Placement ∗

: Data centers are critical to the commercial and social activities of modern society but are also major electricity consumers. To minimize their environmental impact, it is imperative to make data centers more energy eﬃcient while maintaining a high quality of service (QoS). Bearing this consideration in mind, we develop an analytical model using queueing theory for evaluating the QoS of a data center. Furthermore, based on this model, we develop a domain-speciﬁc evolutionary optimization framework featuring a tailored solution representation and a constraint-aware initialization operator for ﬁnding the optimal placement of virtual network functions in a data center that optimizes multiple conﬂicting objectives with regard to energy consumption and QoS. In particular, our framework is applicable to any existing evolutionary multi-objective optimization algorithm in a plug-in manner. Extensive experiments validate the eﬃciency and accuracy of our QoS model as well as the eﬀectiveness of our tailored algorithms for virtual network function placement problems at various scales.
Longitudinal visceral muscles in Drosophila fully dedifferentiate and fragment prior to their reestablishment during metamorphosis

Although the Drosophila longitudinal visceral muscles have been shown to undergo major morphological changes during the transition from larval to adult gut musculature, there have been conflicting views as to whether these muscles persist as such during metamorphosis or whether they are built anew (Klapper 2000; Aghajanian et al. 2016). Here we present our independent analysis using HLH54Fb-eGFP as a cell type specific marker, which strengthens the proposition by Aghajanian et al. (2016) that the syncytial larval longitudinal gut muscles completely dedifferentiate and fragment into mononucleated myoblasts during pupariation before they fuse again and redifferentiate to form the adult longitudinal gut muscles.
The Impact of Race and Age on Response to Neoadjuvant Therapy and Long-Term Outcomes in Black and White Women with Early-Stage Breast Cancer

Purpose: There are a paucity of data and a pressing need to evaluate response to neoadjuvant chemotherapy (NACT) and determine long-term outcomes in young Black women with early-stage breast cancer (EBC). Methods: We analyzed data from 2,196 Black and White women with EBC treated at the University of Chicago over the last 2 decades. Patients were divided into groups based on race and age at diagnosis: Black women ≤ 40 years, White women ≤ 40 years, Black women ≥ 55 years, and White women ≥ 55 years. Pathological complete response rate (pCR) was analyzed using logistic regression. Overall survival (OS) and disease-free survival (DFS) were analyzed using Cox proportional hazard and piecewise Cox models. Results: Young Black women had the highest risk of recurrence, which was 22% higher than young White women (p=0.434) and 76% higher than older Black women (p=0.008). These age/racial differences in recurrence rates were not statistically significant after adjusting for subtype, stage, and grade. In terms of OS, older Black women had the worst outcome. In the 397 women receiving NACT, 47.5% of young White women achieved pCR, compared to 26.8% of young Black women (p=0.012). Conclusions: Black women with EBC had significantly worse outcomes compared to White women in our cohort study. There is an urgent need to understand the disparities in outcomes between Black and White breast cancer patients, particularly in young women where the disparity in outcome is the greatest.
mRNA translational specialization by RBPMS presets the competence for cardiac commitment in hESCs

The blueprints of developing organs are preset at the early stages of embryogenesis. Transcriptional and epigenetic mechanisms are proposed to preset developmental trajectories. However, we reveal that the competence for the future cardiac fate of human embryonic stem cells (hESCs) is preset in pluripotency by a specialized mRNA translation circuit controlled by RBPMS. RBPMS is recruited to active ribosomes in hESCs to control the translation of essential factors needed for cardiac commitment program, including Wingless/Integrated (WNT) signaling. Consequently, RBPMS loss specifically and severely impedes cardiac mesoderm specification, leading to patterning and morphogenetic defects in human cardiac organoids. Mechanistically, RBPMS specializes mRNA translation, selectively via 3′UTR binding and globally by promoting translation initiation. Accordingly, RBPMS loss causes translation initiation defects highlighted by aberrant retention of the EIF3 complex and depletion of EIF5A from mRNAs, thereby abrogating ribosome recruitment. We demonstrate how future fate trajectories are programmed during embryogenesis by specialized mRNA translation.
Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility

Abstract Dysfunction of regulatory elements through genetic variants is a central mechanism in the pathogenesis of disease. To better understand disease etiology, there is consequently a need to understand how DNA encodes regulatory activity. Deep learning methods show great promise for modeling of biomolecular data from DNA sequence but are limited to large input data for training. Here, we develop ChromTransfer, a transfer learning method that uses a pre-trained, cell-type agnostic model of open chromatin regions as a basis for fine-tuning on regulatory sequences. We demonstrate superior performances with ChromTransfer for learning cell-type specific chromatin accessibility from sequence compared to models not informed by a pre-trained model. Importantly, ChromTransfer enables fine-tuning on small input data with minimal decrease in accuracy. We show that ChromTransfer uses sequence features matching binding site sequences of key transcription factors for prediction. Together, these results demonstrate ChromTransfer as a promising tool for learning the regulatory code.
Phragmen–Lindelöf theorems and the asymptotic behaviour of solutions of quasilinear elliptic equations in slabs

The asymptotic behaviour of solutions of second-order quasilinear elliptic partial differential equations defined on unbounded domains in Rn contained in strips (when n = 2) or slabs (when n > 2) is investigated when such solutions satisfy Dirichlet boundary conditions and the Dirichlet boundary data have appropriate asymptotic behaviour at infinity. We prove Phragmèn–Lindelöf theorems for large classes of elliptic operators, including uniformly elliptic operators and operators with well-defined genre, establish exponential decay estimates for uniformly elliptic operators when the Dirichlet boundary data vanish outside a compact set, establish the uniqueness of solutions, and give examples of solutions for non-uniformly elliptic operators which decay but do not decay exponentially. Our principal theorems are proven using special barrier functions; these barriers are constructed by considering an operator associated to our original operator.