Estimating the value of containment strategies in delaying the arrival time of an influenza pandemic: A case study of travel restriction and patient isolation

With a simple phenomenological metapopulation model, which characterizes the invasion process of an influenza pandemic from a source to a subpopulation at risk, we compare the efficiency of inter- and intra-population interventions in delaying the arrival of an influenza pandemic. We take travel restriction and patient isolation as examples, since in reality they are typical control measures implemented at the inter- and intra-population levels, respectively. We find that the intra-population interventions, e.g., patient isolation, perform better than the inter-population strategies such as travel restriction if the response time is small. However, intra-population strategies are sensitive to the increase of the response time, which might be inevitable due to socioeconomic reasons in practice and will largely discount the efficiency.Comment: 5 pages,3 figure

Three-Dimensional Reconstruction and Analysis of All-Solid Li-Ion Battery Electrode Using Synchrotron Transmission X-ray Microscopy Tomography

A synchrotron transmission X-ray microscopy tomography system with a spatial resolution of 58.2 nm at the Advanced Photon Source was employed to obtain three-dimensional morphological data of all-solid Li-ion battery electrodes. The three-phase electrode was fabricated from a 47:47:6 (wt %) mixture of Li(Ni1/3Mn1/3Co1/3)O2 as active material, Li1.3Ti1.7Al0.3(PO4)3 as Li-ion conductor, and Super-P carbon as electron conductor. The geometric analysis show that particle-based all-solid Li-ion battery has serious contact interface problem which significantly impact the Li-ion transport and intercalation reaction in the electrode, leading to low capacity, poor rate capability and cycle life

Geometric and Electrochemical Characteristics of LiNi1/3Mn1/3Co1/3O2 Electrode with Different Calendering Conditions

The impact of calendering process on the geometric characteristics and electrochemical performance of LiNi1/3Mn1/3Co1/3O2 (NMC) electrode was investigated in this study. The geometric properties of NMC electrodes with different calendering conditions, such as porosity, pore size distribution, particle size distribution, specific surface area and tortuosity were calculated from the computed tomography data of the electrodes. A synchrotron transmission X-ray microscopy tomography system at the Advanced Photon Source of the Argonne National Laboratory was employed to obtain the tomography data. The geometric and electrochemical analysis show that calendering can increase the electrochemically active area, which improves rate capability. However, more calendering will result in crushing of NMC particles, which can reduce the electrode capacity at relatively high C rates. This study shows that the optimum electrochemical performance of NMC electrode at 94:3:3 weight ratio of NMC:binder:carbon black can be achieved by calendering to 3.0 g/cm3 NMC density

CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis

Contrast-enhanced magnetic resonance imaging (MRI) is pivotal in the pipeline of brain tumor segmentation and analysis. Gadolinium-based contrast agents, as the most commonly used contrast agents, are expensive and may have potential side effects, and it is desired to obtain contrast-enhanced brain tumor MRI scans without the actual use of contrast agents. Deep learning methods have been applied to synthesize virtual contrast-enhanced MRI scans from non-contrast images. However, as this synthesis problem is inherently ill-posed, these methods fall short in producing high-quality results. In this work, we propose Conditional Autoregressive Vision Model (CAVM) for improving the synthesis of contrast-enhanced brain tumor MRI. As the enhancement of image intensity grows with a higher dose of contrast agents, we assume that it is less challenging to synthesize a virtual image with a lower dose, where the difference between the contrast-enhanced and non-contrast images is smaller. Thus, CAVM gradually increases the contrast agent dosage and produces higher-dose images based on previous lower-dose ones until the final desired dose is achieved. Inspired by the resemblance between the gradual dose increase and the Chain-of-Thought approach in natural language processing, CAVM uses an autoregressive strategy with a decomposition tokenizer and a decoder. Specifically, the tokenizer is applied to obtain a more compact image representation for computational efficiency, and it decomposes the image into dose-variant and dose-invariant tokens. Then, a masked self-attention mechanism is developed for autoregression that gradually increases the dose of the virtual image based on the dose-variant tokens. Finally, the updated dose-variant tokens corresponding to the desired dose are decoded together with dose-invariant tokens to produce the final contrast-enhanced MRI.Comment: The work has been accepted by MICCAI 202

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

Hybrid Model: An Efficient Symmetric Multiprocessor Reference Model

Functional verification has become one of the main bottlenecks in the cost-effective design of embedded systems, particularly for symmetric multiprocessors. It is estimated that verification in its entirety accounts for up to 60% of design resources, including duration, computer resources, and total personnel. Simulation-based verification is a long-standing approach used to locate design errors in the symmetric multiprocessor verification. The greatest challenge of simulation-based verification is the creation of the reference model of the symmetric multiprocessor. In this paper, we propose an efficient symmetric multiprocessor reference model, Hybrid Model, written with SystemC. SystemC can provide a high-level simulation environment and is faster than the traditional hardware description languages. Hybrid Model has been implemented in an efficient 32-bit symmetric multiprocessor verification. Experimental results show our proposed model is a fast, accurate, and efficient symmetric multiprocessor reference model and it is able to help designers to locate design errors easily and accurately

Signatures of quantum chaos of Rydberg dressed bosons in a triple-well potential

We study signatures of quantum chaos in dynamics of Rydberg dressed bosonic atoms held in a one dimensional triple-well potential. Long-range nearest-neighbor and next-nearest-neighbor interactions, induced by laser dressing atoms to strongly interacting Rydberg states, affect drastically mean field and quantum many-body dynamics. By analyzing the mean field dynamics, classical chaos regions with positive and large Lyapunov exponents are identified as a function of the potential well tilting and dressed interactions. In the quantum regime, it is found that level statistics of the eigen-energies gains a Wigner-Dyson distribution when the Lyapunov exponents are large, giving rise to signatures of strong quantum chaos. We find that both the time averaged entanglement entropy and survival probability of the initial state have distinctively large values in the quantum chaos regime. We further show that population variances could be used as an indicator of the emergence of quantum chaos. This might provide a way to directly probe quantum chaotic dynamics through analyzing population dynamics in individual potential wells

IGN : Implicit Generative Networks

In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN. We achieve this by using the GAN model's generator and discriminator function with the quantile regression to approximate the full quantile value for the state-action return distribution. We demonstrate improved performance on our baseline dataset - 57 Atari 2600 games in the ALE. Also, we use our algorithm to show the state-of-art training performance of risk-sensitive policies in Atari games with the policy optimization and evaluation