Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale constitutive framework of 1D blood flow modeling: Asymptotic limits and numerical methods

In this paper, a multiscale constitutive framework for one-dimensional blood flow modeling is presented and discussed. By analyzing the asymptotic limits of the proposed model, it is shown that different types of blood propagation phenomena in arteries and veins can be described through an appropriate choice of scaling parameters, which are related to distinct characterizations of the fluid-structure interaction mechanism (whether elastic or viscoelastic) that exist between vessel walls and blood flow. In these asymptotic limits, well-known blood flow models from the literature are recovered. Additionally, by analyzing the perturbation of the local elastic equilibrium of the system, a new viscoelastic blood flow model is derived. The proposed approach is highly flexible and suitable for studying the human cardiovascular system, which is composed of vessels with high morphological and mechanical variability. The resulting multiscale hyperbolic model of blood flow is solved using an asymptotic-preserving Implicit-Explicit Runge-Kutta Finite Volume method, which ensures the consistency of the numerical scheme with the different asymptotic limits of the mathematical model without affecting the choice of the time step by restrictions related to the smallness of the scaling parameters. Several numerical tests confirm the validity of the proposed methodology, including a case study investigating the hemodynamics of a thoracic aorta in the presence of a stent.
Adoption and implication of the Biased-Annotator Competence Estimation (BACE) model into COVID-19 vaccine Twitter data: Human annotation, valence prediction, and persuasiveness (preprint)

Traditional quantitative content analysis approach (human coding method) has arbitrary weaknesses, such as assuming all human coders are equally accurate once the intercoder reliability for training reaches a threshold score. We adopt a new Bayesian statistical model in Political Science, the Biased-Annotator Competence Estimation (BACE) model (Tyler, 2021), into the Communication discipline, that draws on the Bayes rule. An important contribution of this model is it takes each coder's potential biases and reliability into consideration and assigns each coder a competence parameter for predicting the more accurate latent labels. In this extended abstract, we first criticize the weaknesses of conventional human coding; and then adopt the BACE model with COVID-19 vaccine Twitter data and compare BACE with other statistical models; finally, we look forward to adopting crowdsourcing as the next step to better understand the persuasiveness of COVID-19 vaccine social media content.
Generalization capabilities of conditional GAN for turbulent flow under changes of geometry

Turbulent flow consists of structures with a wide range of spatial and temporal scales which are hard to resolve numerically. Classical numerical methods as the Large Eddy Simulation (LES) are able to capture fine details of turbulent structures but come at high computational cost. Applying generative adversarial networks (GAN) for the synthetic modeling of turbulence is a mathematically well-founded approach to overcome this issue. In this work, we investigate the generalization capabilites of GAN-based synthetic turbulence generators when geometrical changes occur in the flow configuration (e.g. aerodynamic geometric optimization of structures such as airfoils). As training data, we use the flow around a low-pressure turbine (LPT) stator with periodic wake impact obtained from highly resolved LES. To simulate the flow around a LPT stator, we use the conditional deep convolutional GAN framework pix2pixHD conditioned on the position of a rotating wake in front of the stator. For the generalization experiments we exclude images of wake positions located at certain regions from the training data and use the unseen data for testing. We show the abilities and limits of generalization for the conditional GAN by extending the regions of the extracted wake positions successively. Finally, we evaluate the statistical properties of the synthesized flow field by comparison with the corresponding LES results.
Microfoundations of Expected Utility and Response Times

This paper builds a rule for decisionmaking from the physical behavior of single neurons, the well established neural circuitry of mutual inhibition, and the evolutionary principle of natural selection. No axioms are used in the derivation of this rule. The paper provides a microfoundation to both Economics Choice Theory and Cognitive Psychologys Response Times Theory. The paper finds how classical expected utility should be modified to account for much neuroscientific evidence, and how neuroscientific correlates of choice should be linked to utility. In particular, the model implies the concept of utility is a network property and cannot be calculated as a function of frequencies in one layer of neurons alone; it requires understanding how different layers work together. The resulting rule is simple enough to model markets and games as is customary in the social sciences. Utility maximization and inaction are endogenous to the model, cardinality and independence of irrelevant alternatives, properties present in classical and random utility theories, are only met in limiting situations.
NOMA-Assisted Symbiotic Backscatter: Novel Beamforming Designs Under Imperfect SIC

Optimal beamforming designs under imperfect successive interference cancellation (SIC) decoding for a symbiotic network of non-orthogonal multiple access (NOMA) primary users and a secondary ambient tag have been lacking. We address that issue here. The primary base station (BS) serves NOMA users and a passive tag simultaneously in this network. We develop two transmit beamforming designs to meet the user and tag requirements while mitigating the effect of imperfect SIC. Specifically, we design optimal BS transmit beamforming and power allocation to either maximize the weighted sum rate of NOMA users and the tag or minimize the BS transmit power under the minimum rate requirements while satisfying the tag minimum energy requirement. Because both these problems are non-convex, we propose algorithms using alternative optimization, fractional programming, and semi-definite relaxation techniques. We also analyze their computational complexity. Finally, we present extensive numerical results to validate the proposed schemes and to show significant performance gains while keeping the tag design intact. For example, the proposed digital beamforming increases the harvested power and data rate by 2.16e3 % and 314.5 % compared to random beamforming.
Safe Networked Robotics via Formal Verification

—Autonomous robots must utilize rich sensory data to make safe control decisions. Often, compute-constrained robots require assistance from remote computation (“the cloud”) if they need to invoke compute-intensive Deep Neural Network perception or control models. Likewise, a robot can be remotely teleoperated by a human during risky scenarios. However, this assistance comes at the cost of a time delay due to network latency, resulting in stale/delayed observations being used in the cloud to compute the control commands for the present robot state. Such communication delays could potentially lead to the violation of essential safety properties, such as collision avoidance. This paper develops methods to ensure the safety of teleoperated robots with stochastic latency. To do so, we use tools from formal veriﬁcation to construct a shield (i.e., run-time monitor) that provides a list of safe actions for any delayed sensory observation, given the expected and worst-case network latency. Our shield is minimally intrusive and enables networked robots to satisfy key safety constraints, expressed as temporal logic speciﬁcations, with high probability. Our approach gracefully improves a teleoperated robot’s safety vs. efﬁciency trade-off as a function of network latency, allowing us to quantify performance gains for WiFi or even future 5G networks. We demonstrate our approach on a real F1/10th autonomous vehicle that navigates in crowded indoor environments and transmits rich LiDAR sensory data over congested WiFi links.
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty

In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type---such as height for clothing, skin type for skincare products, and location for outdoor furniture---to estimate their values, which they may not know a priori. Customers with few relevant reviews may hesitate to make a purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so that buyers can confidently estimate their values. Simultaneously, sellers may use reviews to gauge the demand for items they wish to sell.
PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation

Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in real-world scenarios is usually incomplete due to sensor failures or transmission loss. Spatiotemporal imputation aims to fill the missing values according to the observed values and the underlying spatiotemporal dependence of them. The previous dominant models impute missing values autoregressively and suffer from the problem of error accumulation. As emerging powerful generative models, the diffusion probabilistic models can be adopted to impute missing values conditioned by observations and avoid inferring missing values from inaccurate historical imputation. However, the construction and utilization of conditional information are inevitable challenges when applying diffusion models to spatiotemporal imputation. To address above issues, we propose a conditional diffusion framework for spatiotemporal imputation with enhanced prior modeling, named PriSTI. Our proposed framework provides a conditional feature extraction module first to extract the coarse yet effective spatiotemporal dependencies from conditional information as the global context prior. Then, a noise estimation module transforms random noise to realistic values, with the spatiotemporal attention weights calculated by the conditional feature, as well as the consideration of geographic relationships. PriSTI outperforms existing imputation methods in various missing patterns of different real-world spatiotemporal data, and effectively handles scenarios such as high missing rates and sensor failure. The implementation code is available at https://github.com/LMZZML/PriSTI.
Quantum Dueling: An Eﬃcient Solution for Combinatorial Optimization

In this paper, we present a new algorithm for generic combinatorial optimization, which we term quantum dueling. Traditionally, potential solutions to the given optimization problems were encoded in a "register" of qubits. Various techniques are used to increase the probability of finding the best solution upon measurement. Quantum dueling innovates by integrating an additional qubit register, effectively creating a "dueling" scenario where two sets of solutions compete. This dual-register setup allows for a dynamic amplification process: in each iteration, one register is designated as the "opponent", against which the other register's more favorable solutions are enhanced through a controlled quantum search. This iterative process gradually steers the quantum state within both registers toward the optimal solution. With a quantitative contraction for the evolution of the state vector, classical simulation under a broad range of scenarios and hyper-parameter selection schemes shows that a quadratic speedup is achieved, which is further tested in more real-world situations. In addition, quantum dueling can be generalized to incorporate arbitrary quantum search techniques and as a quantum subroutine within a higher-level algorithm. Our work demonstrates that increasing the number of qubits allows the development of previously unthought-of algorithms, paving the way for advancement of efficient quantum algorithm design.
Light front synchronization and rest frame densities of the proton: Electromagnetic densities

We clarify the physical origin and meaning of the two-dimensional relativistic densities of the light front formalism. The densities are shown to originate entirely from the use of light front time instead of instant form time, which physically corresponds to using an alternative synchronization convention. This is shown by using tilted light front coordinates, which consist of light front time and ordinary spatial coordinates, and which are also used to show that the obtained densities describe a system at rest rather than at infinite momentum. These coordinates allow all four components of the electromagnetic current density to be given clear physical meanings. We explicate the formalism for spin-half targets, obtaining charge and current densities of the proton and neutron using empirical form factor parametrizations, as well as up and down quark densities and currents. Angular modulations in the densities of transversely-polarized states are explained as originating from redshifts and blueshifts due to quarks moving in different longitudinal directions.
Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients

Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.
Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent

—Despite recommender systems play a key role in network content platforms, mining the user’s interests is still a signiﬁcant challenge. Existing works predict the user interest by utilizing user behaviors, i.e., clicks, views, etc., but current solutions are ineffective when users perform unsettled activities. The latter ones involve new users, which have few activities of any kind, and sparse users who have low-frequency behaviors. We uniformly describe both these user-types as ”cold users”, which are very common but often neglected in network content platforms. To address this issue, we enhance the representation of the user interest by combining his social interest, e.g., friendship, following bloggers, interest groups, etc., with the activity behaviors. Thus, in this work, we present a novel algorithm entitled SocialNet, which adopts a two-stage method to progressively extract the coarse-grained and ﬁne-grained social interest. Our technique then concatenates SocialNet’s output with the original user representation to get the ﬁnal user representation that combines behavior interests and social interests. Ofﬂine experiments on Tencent video’s recommender system demonstrate the superiority over the baseline behavior-based model. The online experiment also shows a signiﬁcant performance improvement in clicks and view time in the real-world
Aritmethic lattices of $\SO(1,n)$ and units of group rings

We establish that standard arithmetic subgroups of a special orthogonal group ${\rm SO}(1,n)$ are conjugacy separable. As an application we deduce this property for unit groups of certain integer group rings. We also prove that finite quotients of group of units of any of these group rings determines the original group ring.
Nystrom Method for Accurate and Scalable Implicit Differentiation

The essential difficulty of gradient-based bilevel optimization using implicit differentiation is to estimate the inverse Hessian vector product with respect to neural network parameters. This paper proposes to tackle this problem by the Nystrom method and the Woodbury matrix identity, exploiting the low-rankness of the Hessian. Compared to existing methods using iterative approximation, such as conjugate gradient and the Neumann series approximation, the proposed method avoids numerical instability and can be efficiently computed in matrix operations without iterations. As a result, the proposed method works stably in various tasks and is faster than iterative approximations. Throughout experiments including large-scale hyperparameter optimization and meta learning, we demonstrate that the Nystrom method consistently achieves comparable or even superior performance to other approaches. The source code is available from https://github.com/moskomule/hypergrad.
KLIF: An optimized spiking neuron unit for tuning surrogate gradient slope and membrane potential

Spiking neural networks (SNNs) have attracted much attention due to their ability to process temporal information, low power consumption, and higher biological plausibility. However, it is still challenging to develop efficient and high-performing learning algorithms for SNNs. Methods like artificial neural network (ANN)-to-SNN conversion can transform ANNs to SNNs with slight performance loss, but it needs a long simulation to approximate the rate coding. Directly training SNN by spike-based backpropagation (BP) such as surrogate gradient approximation is more flexible. Yet now, the performance of SNNs is not competitive compared with ANNs. In this paper, we propose a novel k-based leaky Integrate-and-Fire (KLIF) neuron model to improve the learning ability of SNNs. Compared with the popular leaky integrate-and-fire (LIF) model, KLIF adds a learnable scaling factor to dynamically update the slope and width of the surrogate gradient curve during training and incorporates a ReLU activation function that selectively delivers membrane potential to spike firing and resetting. The proposed spiking unit is evaluated on both static MNIST, Fashion-MNIST, CIFAR-10 datasets, as well as neuromorphic N-MNIST, CIFAR10-DVS, and DVS128-Gesture datasets. Experiments indicate that KLIF performs much better than LIF without introducing additional computational cost and achieves state-of-the-art performance on these datasets with few time steps. Also, KLIF is believed to be more biological plausible than LIF. The good performance of KLIF can make it completely replace the role of LIF in SNN for various tasks.
An improved paradigm for modeling animal flights at moderate Reynolds numbers

We report on experimental and numerical studies aimed at developing an improved paradigm for modeling avian flights at moderate Reynolds numbers. A series of experiments were performed to characterize the behaviors of aerodynamic forces and moment associated with a quasi-steady rectangular wing over a range of incidence angles, {\alpha}. We demonstrate that, while the drag coefficient curve, CD({\alpha}), can be accurately modeled solely by a simple trigonometric function, the evolution of lift coefficient curve, CL({\alpha}), is governed by the sum of trigonometric and exponential functions, where the latter captures the linear variation in lift coefficient within the small-angle regime, as predicted by the linear inviscid theory. In addition, we establish an empirical relation between the location of the center of pressure and the incidence angle, which can be used in conjunction with the proposed aerodynamic formulas (i.e. CL and CD) to evaluate the pitching moment coefficient, CM ({\alpha}), about any arbitrary axis. These quasi-steady formulations are then utilized to simulate forward flight of a model that possesses dynamical characteristics of a pigeon across various flight speeds, and the results are compared against previously-reported experimental data on pigeons. We successfully demonstrate that the proposed formulas yield accurate predictions of the wing-beat frequency, at least over a range of Reynolds numbers spanning from approximately 70,000 to 150,000.
Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data

This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing the $k-th$ Hodge-Laplacian (HL) operator. In particular, we propose Laguerre polynomial approximations of HL spectral filters and prove that their spatial localization on graphs is related to the polynomial order. Furthermore, based on the bijection property of boundary operators on simplex graphs, we introduce a generic topological graph pooling (TGPool) method that can be used at any dimensional simplices. This study designs HL-node, HL-edge, and HL-HGCNN neural networks to learn signal representation at a graph node, edge levels, and both, respectively. Our experiments employ fMRI from the Adolescent Brain Cognitive Development (ABCD; n=7693) to predict general intelligence. Our results demonstrate the advantage of the HL-edge network over the HL-node network when functional brain connectivity is considered as features. The HL-HGCNN outperforms the state-of-the-art graph neural networks (GNNs) approaches, such as GAT, BrainGNN, dGCN, BrainNetCNN, and Hypergraph NN. The functional connectivity features learned from the HL-HGCNN are meaningful in interpreting neural circuits related to general intelligence.
Comprehensive Evaluation of RSB and Spectre Vulnerability on Modern Processors

Performance-enhancing mechanisms such as branch prediction, out-of-order execution, and return stack buffer (RSB) have been widely employed in today's modern processing units. Although successful in increasing the CPU performance, exploiting the design flaws and security bugs in these components have set the background for various types of microarchitectural attacks such as Spectre and Meltdown. While many attacks such as Meltdown and Spectre have been numerously implemented and analyzed on Intel processors, few researches have been carried out to evaluate their impact on ARM processors. Moreover, SpectreRSB vulnerability, the newer variant of spectre attack based on RSB, has been neglected in recent studies. In this work, we first evaluate the SpectreRSB vulnerability by implementing this attack on ARM processors, which, to the best of our knowledge, has not been implemented and analyzed on ARM processors. We further present a security evaluation of ARM processors by implementing different variants of Spectre-family attacks. By analyzing the results obtained from various experiments, we evaluate ARM processors security regarding their diverse microarchitectural designs. We also introduce a high throughput and noise-free covert channel, based on the RSB structure. Based on our experiments, the throughput of the covert channel is 94.19KB/s with negligible error.