Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Cardiac Troponin Release and Fluid Resuscitation on Outcomes of Patients with Sepsis

Background. Septic patients are predisposed to myocardial injury manifested as cardiac troponin release (TnR). Prognostic significance and management implications of TnR and its relationship to fluid resuscitation and outcomes in the intensive care unit (ICU) setting has not been fully elucidated. Methods. A total of 24,778 patients with sepsis from eICU-CRD (n=11,992), MIMIC-III (n=6,638) and MIMIC-IV (n=6,148) databases were included in this retrospective study. In-hospital mortality and survival at 1 year were examined using multivariable logistic, Cox regression analysis, or Kaplan-Meier survival analysis with overlap weighting adjustment, as well as generalized additive models for fluid resuscitation. Results. TnR on admission was associated with higher in-hospital mortality [adjusted odds ratios (OR)=1.33; 95% confidence interval (CI)=1.23-1.43; p<0.001 in unweighted analysis and adjusted OR=1.39; 95% CI=1.29-1.50; p< 0.001 with overlap weighting]. One-year survival was lower in patients with admission TnR (Kaplan-Meier survival analysis; p=0.002). A trend was noted for association between admission TnR and 1-year mortality [adjusted OR=1.16; 95% CI=0.99-1.37; p=0.067 in unweighted analysis] while the association was statistically significant after overlap weighting (adjusted OR=1.25; 95% CI=1.06-1.47; p=0.008). We also found that patients with admission TnR were less likely to benefit from more liberal fluid resuscitation. Adequate fluid resuscitation within a range of 70 to 90 ml/kg in the first 24 hours of ICU stay was associated with lower in-hospital mortality in septic patients without TnR but not in those with admission TnR. Conclusions. Admission TnR was significantly associated with higher in-hospital mortality and worse 1-year survival among septic patients. Adequate fluid resuscitation improved in-hospital mortality in septic patients without but not with admission TnR.
A primal-dual method for linear programming problems with fuzzy variables

Linear programming problems with fuzzy variables have been investigated by many researchers in the recent literature. Some methods to solve these problems, such as the primal simplex method and the dual simplex method, are based on the concept of comparison of fuzzy numbers by using ranking functions. In this paper, we give a new primal-dual algorithm for solving linear programming problems with fuzzy variables by using duality results, which was proposed by Mahdavi-Amiri and Nasseri (2007). This algorithm will be useful for sensitivity analysis when the activity vectors change for basic columns. [Received 29 September 2008; Revised 16 March 2009; Accepted 17 April 2009]
A new method for reduced-reference image quality assessment

Based on the characteristic that image distortion results in the change of visual sensitivity coefficients of image,a new reduced-reference(RR) image quality assessment metric is proposed.Contourlet is applied to extract the visual sensitivity coefficients of the image. Summarizing the difference of visual sensitivity coefficients between the distorted image and the reference image,we can get the measurement of the distorted image.Experimental results illustrate that the proposed method has a good consistency with the subjective assessment of human beings,and can reflect the visual perception of the image effectively.
Comparison and Analysis of Vortex Induced Vibration for Twin-Box Bridge Sections Based on Experiments in Different Reynolds Numbers

In order to compare the performance of the vortex induced vibrations(VIV) of twin-box bridge sections in different Reynolds numbers,two groups of experiments with 40 cases were carried out in TJ-1 and TJ-3 wind tunnel.The results show that VIV occurs in a more widely range of damping ratios with rather larger amplitudes in the low Reynolds number experiments.While in the high Reynolds number experiments,VIV only occurs in a much closer range of damping ratios with the amplitude being smaller.As for the guide vanes,they do decrease the amplitude of VIV in the attack angle of zero no matter the Reynolds number is high or low.However,when the attack angle exists,they only can decrease the amplitudes in the high Reynolds number experiments while the amplitudes of VIV will be increased a lot in the low Reynolds number experiments.Comparisons are also done with the experiments of the Great Belt East Bridge and the Stonecutters Bridge.The mechanisms of the experiments are detailed hereafter together with the reasons of comparisons' divergence and agreement.
Electro-epitaxial buffer layers for REBCO tape architectures

The preparation of substrates for the deposition of highly aligned superconducting thin films is an area of active research for the fabrication of second generation HTS conductors, and it is especially important that the substrates are cheap and can easily and reliably be prepared in long lengths. We have developed a new technique for the preparation of epitaxial buffer layers by simple electrodeposition onto mechanically textured substrates. Ag/Ni, Ag/NiFe and Ag/Pd/NiFe samples have been fabricated with very high degrees of texture control, and highly aligned NdBa/sub 2/Cu/sub 3/O/sub 7/ and YBa/sub 2/Cu/sub 3/O/sub 7/ films have been deposited onto these substrates. We believe that this process offers substantial advantages in scalability, speed and cost-effectiveness over all other techniques currently being investigated for the preparation of technical substrates for second generation HTS conductors.
Balance dynamics in rotating stratified turbulence

If classical quasigeostrophic (QG) flow breaks down at smaller scales, it gives rise to questions of whether higher-order nonlinear balance can be maintained, to what scale and for how long. These are naturally followed by asking how this is affected by stratification and rotation. To address these questions, we perform non-hydrostatic Boussinesq simulations where the initial data is balanced using the Baer–Tribbia nonlinear normal mode initialization scheme (NNMI), which is accurate to second order in the Rossby number, as the next-order improvement to first-order QG theory. The NNMI procedure yields an ageostrophic contribution to the energy spectrum that has a very steep slope. However, as time passes, a shallow range emerges in the ageostrophic spectrum when the Rossby number is large enough for a given Reynolds number. It is argued that this shallow range is the unbalanced part of the motion that develops spontaneously in time and eventually dominates the energy at small scales. If the initial flow is not nonlinearly balanced, the shallow range emerges at even lower Rossby number and it appears at larger scales. Through numerous simulations at different rotation and stratification, this study gives a clear picture of how energy is cascaded in different initially balanced regimes of rotating stratified flow. We find that at low Rossby number the flow mainly consists of a geostrophic part and a balanced ageostrophic part with a steep spectrum. As the Rossby number increases, the unbalanced part of the ageostrophic energy increases at a rate faster than the balanced part. Hence, the total energy spectrum displays a shallow range above a transition wavenumber. This wavenumber evolves to smaller values as rotation weakens.
Treatment Allocation with Strategic Agents

There is increasing interest in allocating treatments based on observed individual characteristics: examples include targeted marketing, individualized credit offers, and heterogeneous pricing. Treatment personalization introduces incentives for individuals to modify their behavior to obtain a better treatment. Strategic behavior shifts the joint distribution of covariates and potential outcomes. The optimal rule without strategic behavior allocates treatments only to those with a positive conditional average treatment effect. With strategic behavior, we show that the optimal rule can involve randomization, allocating treatments with less than 100% probability even to those who respond positively on average to the treatment. We propose a sequential experiment based on Bayesian optimization that converges to the optimal treatment rule without parametric assumptions on individual strategic behavior. This paper was accepted by Vivek Farias, data science. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022. 01629 .
Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers Engagement

Albeit existing evidence about the impact of AI-based adaptive learning platforms, their scaled adoption in schools is slow at best. In addition, AI tools adopted in schools may not always be the considered and studied products of the research community. Therefore, there have been increasing concerns about identifying factors influencing adoption, and studying the extent to which these factors can be used to predict teachers engagement with adaptive learning platforms. To address this, we developed a reliable instrument to measure more holistic factors influencing teachers adoption of adaptive learning platforms in schools. In addition, we present the results of its implementation with school teachers (n=792) sampled from a large country-level population and use this data to predict teachers real-world engagement with the adaptive learning platform in schools. Our results show that although teachers knowledge, confidence and product quality are all important factors, they are not necessarily the only, may not even be the most important factors influencing the teachers engagement with AI platforms in schools. Not generating any additional workload, in-creasing teacher ownership and trust, generating support mechanisms for help, and assuring that ethical issues are minimised, are also essential for the adoption of AI in schools and may predict teachers engagement with the platform better. We conclude the paper with a discussion on the value of factors identified to increase the real-world adoption and effectiveness of adaptive learning platforms by increasing the dimensions of variability in prediction models and decreasing the implementation variability in practice.
Convex Optimization-Based Policy Adaptation to Compensate for Distributional Shifts

In many real-world cyber-physical systems, control designers often model the dynamics of the physical components using stochastic dynamical equations, and the design optimal control policies for the model. At any given time, a stochastic difference equation essentially models the distribution on next states conditioned on the state and controller action at that time. Due to shifts in this distribution, modeling assumptions on the stochastic dynamics made during initial control design may no longer be valid when the system is deployed in the real-world. In safety-critical systems, this can be particularly problematic; even if the system follows the designed control trajectory that was deemed safe and optimal, it may reach unsafe states due to the distribution shift. In this paper, we address the following problem: suppose we obtain an optimal control trajectory in the training environment, how do we ensure that in the real system this optimal trajectory is tracked with minimal error? In other words, we wish to adapt an optimal trained policy to distribution shifts in the environment. We show that this problem can be cast as a nonlinear optimization problem solvable using heuristic optimization methods. However, a convex relaxation of this problem allows us to learn policies that track the optimal trajectory with much better error performance and faster computation times. We demonstrate the efficacy of our approach on two different case studies: optimal path tracking using a Dubin's car model, and collision avoidance using both a linear and nonlinear model for adaptive cruise control
Detailed β-transition rates for URCA nuclear pairs in 8-10 solar-mass stars

We calculate $\ensuremath{\beta}$-transition rates for URCA nuclear pairs using the $sd$-shell-model framework of Wildenthal [B. H. Widenthal, Prog. Part. Nucl. Phys. 11, 5 (1984)] with the modified Hamiltonian. We consider many $\ensuremath{\beta}$ transitions including excited states necessary for electron capture and $\ensuremath{\beta}$ decay with high Fermi energy of electrons at high electron density $\ensuremath{\rho}{Y}_{e}$ and temperature $T$ relevant for the massive star and super-asymptotic-giant-branch star evolution. We provide $\ensuremath{\beta}$-transition rates for URCA nuclear pairs ($A=23$, 25, and 27) in the range of density 8.0 $l{\mathrm{log}}_{10}\ensuremath{\rho}{Y}_{e}l$ 9.2 in steps of 0.02 (${Y}_{e}$ is the electron mole number and $\ensuremath{\rho}$ is the nucleon density in units of g cm${}^{\ensuremath{-}3}$) and temperature 8.0 $l{\mathrm{log}}_{10}Tl$ 9.2 in steps of 0.05 ($T$ is the temperature in units of K). This fine mesh is able to provide clearly the URCA density at ${\mathrm{log}}_{10}\ensuremath{\rho}{Y}_{e}=8.92$ for $A=23$ and at ${\mathrm{log}}_{10}\ensuremath{\rho}{Y}_{e}=8.81$ for $A=25$, while the URCA density is not clear for $A=27$. For the evolution of 8\char21{}10${M}_{\ensuremath{\bigodot}}$ stars, the use of the fine mesh is found to increase the cooling effect and affects the final fate of these stars.
Many-Exciton Quantum Dynamics in a Ruddlesden–Popper Tin Iodide

We present a study on the many-body exciton interactions in a Ruddlesden–Popper tin halide, namely, (PEA)2SnI4 (PEA = phenylethylammonium), using coherent two-dimensional electronic spectroscopy. The optical dephasing times of the third-order polarization observed in these systems are determined by exciton many-body interactions and lattice fluctuations. We investigate the excitation-induced dephasing (EID) and observe a significant reduction of the dephasing time with increasing excitation density as compared to its lead counterpart (PEA)2PbI4, which we have previously reported in a separate publication [J. Chem. Phys.2020, 153, 164706]. Surprisingly, we find that the EID interaction parameter is four orders of magnitude higher in (PEA)2SnI4 than that in (PEA)2PbI4. This increase in the EID rate may be due to exciton localization arising from a more statically disordered lattice in the tin derivative. This is supported by the observation of multiple closely spaced exciton states and the broadening of the linewidth with increasing population time (spectral diffusion), which suggests a static disordered structure relative to the highly dynamic lead-halide. Additionally, we find that the exciton nonlinear coherent lineshape shows evidence of a biexcitonic state with low binding energy (<10 meV) not observed in the lead system. We model the lineshapes based on a stochastic scattering theory that accounts for the interaction with a nonstationary population of dark background excitations. Our study provides evidence of differences in the exciton quantum dynamics between tin- and lead-based Ruddlesden–Popper metal halides (RPMHs) and links them to the exciton–exciton interaction strength and the static disorder aspect of the crystalline structure.
Deep Perceptual Similarity is Adaptable to Ambiguous Contexts

The concept of image similarity is ambiguous, and images can be similar in one context and not in another. This ambiguity motivates the creation of metrics for specific contexts. This work explores the ability of deep perceptual similarity (DPS) metrics to adapt to a given context. DPS metrics use the deep features of neural networks for comparing images. These metrics have been successful on datasets that leverage the average human perception in limited settings. But the question remains if they could be adapted to specific similarity contexts. No single metric can suit all similarity contexts, and previous rule-based metrics are labor-intensive to rewrite for new contexts. On the other hand, DPS metrics use neural networks that might be retrained for each context. However, retraining networks takes resources and might ruin performance on previous tasks. This work examines the adaptability of DPS metrics by training ImageNet pretrained CNNs to measure similarity according to given contexts. Contexts are created by randomly ranking six image distortions. Distortions later in the ranking are considered more disruptive to similarity when applied to an image for that context. This also gives insight into whether the pretrained features capture different similarity contexts. The adapted metrics are evaluated on a perceptual similarity dataset to evaluate if adapting to a ranking affects their prior performance. The findings show that DPS metrics can be adapted with high performance. While the adapted metrics have difficulties with the same contexts as baselines, performance is improved in 99% of cases. Finally, it is shown that the adaption is not significantly detrimental to prior performance on perceptual similarity. The implementation of this work is available online: https://github.com/LTU-Machine-Learning/Analysis-of-Deep-Perceptual-Loss-Networks
Competitive plasticity to reduce the energetic costs of learning

The brain is not only constrained by energy needed to fuel computation, but it is also constrained by energy needed to form memories. Experiments have shown that learning simple conditioning tasks already carries a significant metabolic cost. Yet, learning a task like MNIST to 95% accuracy appears to require at least 108 synaptic updates. Therefore the brain has likely evolved to be able to learn using as little energy as possible. We explored the energy required for learning in feedforward neural networks. Based on a parsimonious energy model, we propose two plasticity restricting algorithms that save energy: 1) only modify synapses with large updates, and 2) restrict plasticity to subsets of synapses that form a path through the network. Combining these two methods leads to substantial energy savings while only incurring a small increase in learning time. In biology networks are often much larger than the task requires. In particular in that case, large savings can be achieved. Thus competitively restricting plasticity helps to save metabolic energy associated to synaptic plasticity. The results might lead to a better understanding of biological plasticity and a better match between artificial and biological learning. Moreover, the algorithms might also benefit hardware because in electronics memory storage is energetically costly as well.
Controlling a Vlasov-Poisson plasma by a Particle-In-Cell method based on a Monte Carlo framework

The Vlasov-Poisson system describes the time evolution of a plasma in the so-called collisionless regime. The investigation of a high-temperature plasma that is influenced by an exterior magnetic field is one of the most significant aspects of thermonuclear fusion research. In this paper, we formulate and analyze a kinetic optimal control problem for the Vlasov-Poisson system where the control is represented by an external magnetic field. The main goal of such optimal control problems is to confine the plasma in a certain region in phase space. We first investigate the optimal control problem in terms of mathematical analysis, i.e., we show the existence of at least one global minimizer and we rigorously derive a first-order necessary optimality condition for local minimizers by the adjoint approach. Then, we build a Monte Carlo framework to solve the state equations as well as the adjoint equations by means of a Particle-In-Cell method, and we apply a nonlinear conjugate gradient method to solve the optimization problem. Eventually, we present numerical experiments that successfully validate our optimization framework.
“It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers

Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy. This is likely to have profound effects in the classroom, where novices learning to code can now use free tools to automatically suggest solutions to programming exercises and assignments. However, little is currently known about how novices interact with these tools in practice. We present the first study that observes students at the introductory level using one such code auto-generating tool, Github Copilot, on a typical introductory programming (CS1) assignment. Through observations and interviews we explore student perceptions of the benefits and pitfalls of this technology for learning, present new observed interaction patterns, and discuss cognitive and metacognitive difficulties faced by students. We consider design implications of these findings, specifically in terms of how tools like Copilot can better support and scaffold the novice programming experience.
HyPFuzz: Formal-Assisted Processor Fuzzing

Recent research has shown that hardware fuzzers can effectively detect security vulnerabilities in modern processors. However, existing hardware fuzzers do not fuzz well the hard-to-reach design spaces. Consequently, these fuzzers cannot effectively fuzz security-critical control- and data-flow logic in the processors, hence missing security vulnerabilities. To tackle this challenge, we present HyPFuzz, a hybrid fuzzer that leverages formal verification tools to help fuzz the hard-to-reach part of the processors. To increase the effectiveness of HyPFuzz, we perform optimizations in time and space. First, we develop a scheduling strategy to prevent under- or over-utilization of the capabilities of formal tools and fuzzers. Second, we develop heuristic strategies to select points in the design space for the formal tool to target. We evaluate HyPFuzz on five widely-used open-source processors. HyPFuzz detected all the vulnerabilities detected by the most recent processor fuzzer and found three new vulnerabilities that were missed by previous extensive fuzzing and formal verification. This led to two new common vulnerabilities and exposures (CVE) entries. HyPFuzz also achieves 11.68$\times$ faster coverage than the most recent processor fuzzer.
TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration

We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Unlike existing works that generate dance movements using a single modality such as music, our goal is to produce richer dance movements guided by the instructive information provided by the text. However, the lack of paired motion data with both music and text modalities limits the ability to generate dance movements that integrate both. To alleviate this challenge, we propose to utilize a 3D human motion VQ-VAE to project the motions of the two datasets into a latent space consisting of quantized vectors, which effectively mix the motion tokens from the two datasets with different distributions for training. Additionally, we propose a cross-modal transformer to integrate text instructions into motion generation architecture for generating 3D dance movements without degrading the performance of music-conditioned dance generation. To better evaluate the quality of the generated motion, we introduce two novel metrics, namely Motion Prediction Distance (MPD) and Freezing Score (FS), to measure the coherence and freezing percentage of the generated motion. Extensive experiments show that our approach can generate realistic and coherent dance movements conditioned on both text and music while maintaining comparable performance with the two single modalities. Code is available at https://garfield-kh.github. io/TM2D/.