arXiv subjects

Bo Zhou

Publications and source records attributed to Bo Zhou.

At least 271 records · Page 15Linked to original sources

arXiv2019

Alpha Decay to Doubly Magic Core in Quartetting Wave Function Approach

We present a microscopic calculation of $α$-cluster formation in heavy nuclei $^{104}$Te ($α$+$^{100}$Sn), $^{212}$Po ($α$+$^{208}$Pb) and their neighbors $^{102}$Sn, $^{102}$Te, $^{210}$Pb and $^{210}$Po by using the quartetting wave function approach. Improving the local density approximation, the shell structure of the core nucleus is considered, and the center-of-mass (c.o.m.) effective potential for the quartet is obtained self-consistently from the shell model wavefunctions. The $α$-cluster formation and decay probabilities are obtained by solving the bound-state of the c.o.m. motion of the quartet and the scattering state of the formed $α$-cluster in the Gurvitz approach. Striking shell effects on the $α$-cluster formation probabilities are analyzed for magic numbers 50, 82 and 126. The computed $α$-decay half-lives of these special nuclei are compared with the newest experimental data.

nucl-th↗

arXiv2019

CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data will require automated tools to harvest data/label pairs from messy and real-world datasets, such as hospital PACS. This is the focus of our work, where we present a principled data curation tool to extract multi-phase CT liver studies and identify each scan's phase from a real-world and heterogenous hospital PACS dataset. Emulating a typical deployment scenario, we first obtain a set of noisy labels from our institutional partners that are text mined using simple rules from DICOM tags. We train a deep learning system, using a customized and streamlined 3D SE architecture, to identify non-contrast, arterial, venous, and delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as "contrast". Extensive experiments on a dataset of 43K scans of 7680 patient imaging studies demonstrate that our 3DSE architecture, armed with our aggregated loss, can achieve a mean F1 of 0.977 and can correctly harvest up to 92.7% of studies, which significantly outperforms the text-mined and standard-loss approach, and also outperforms other, and more complex, model architectures.

eess.IV↗

arXiv2019

Minimum status, matching and domination of graphs

The minimum status of a graph is the minimum of statuses of all vertices of this graph. We give a sharp upper bound for the minimum status of a connected graph with fixed order and matching number (domination number, respectively), and characterize the unique trees achieving the bound. We also determine the unique tree such that its minimum status is as small as possible when order and matching number (domination number, respectively) are fixed.

cs.DM↗

arXiv2019

Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things

The effective operation of time-critical Internet of things (IoT) applications requires real-time reporting of fresh status information of underlying physical processes. In this paper, a real-time IoT monitoring system is considered, in which the IoT devices sample a physical process with a sampling cost and send the status packet to a given destination with an updating cost. This joint status sampling and updating process is designed to minimize the average age of information (AoI) at the destination node under an average energy cost constraint at each device. This is formulated as an infinite horizon average cost constrained Markov decision process (CMDP) and transformed into an unconstrained MDP using a Lagrangian method. For the single IoT device case, the optimal policy for the CMDP is shown to be a randomized mixture of two deterministic policies for the unconstrained MDP, which is of threshold type. Then, a structure-aware optimal algorithm to obtain the optimal policy of the CMDP is proposed and the impact of the wireless channel dynamics is studied while demonstrating that channels having a larger mean channel gain and less scattering can achieve better AoI performance. For the case of multiple IoT devices, a low-complexity distributed suboptimal policy is proposed with the updating control at the destination and the sampling control at each device. Then, an online learning algorithm is developed to obtain this policy, which can be implemented at each IoT device and requires only the local knowledge and small signaling from the destination. The proposed learning algorithm is shown to converge almost surely to the suboptimal policy. Simulation results show the structural properties of the optimal policy for the single IoT device case; and show that the proposed policy for multiple IoT devices outperforms a zero-wait baseline policy, with average AoI reductions reaching up to 33%.

cs.IT↗

arXiv2019

Soft Subdivision Motion Planning for Complex Planar Robots

The design and implementation of theoretically-sound robot motion planning algorithms is challenging. Within the framework of resolution-exact algorithms, it is possible to exploit soft predicates for collision detection. The design of soft predicates is a balancing act between easily implementable predicates and their accuracy/effectivity. In this paper, we focus on the class of planar polygonal rigid robots with arbitrarily complex geometry. We exploit the remarkable decomposability property of soft collision-detection predicates of such robots. We introduce a general technique to produce such a decomposition. If the robot is an m-gon, the complexity of this approach scales linearly in m. This contrasts with the O(m^3) complexity known for exact planners. It follows that we can now routinely produce soft predicates for any rigid polygonal robot. This results in resolution-exact planners for such robots within the general Soft Subdivision Search (SSS) framework. This is a significant advancement in the theory of sound and complete planners for planar robots. We implemented such decomposed predicates in our open-source Core Library. The experiments show that our algorithms are effective, perform in real time on non-trivial environments, and can outperform many sampling-based methods.

cs.CG↗

arXiv2019

On the Reliability of Wireless Virtual Reality at Terahertz (THz) Frequencies

Guaranteeing ultra reliable low latency communications (URLLC) with high data rates for virtual reality (VR) services is a key challenge to enable a dual VR perception: visual and haptic. In this paper, a terahertz (THz) cellular network is considered to provide high-rate VR services, thus enabling a successful visual perception. For this network, guaranteeing URLLC with high rates requires overcoming the uncertainty stemming from the THz channel. To this end, the achievable reliability and latency of VR services over THz links are characterized. In particular, a novel expression for the probability distribution function of the transmission delay is derived as a function of the system parameters. Subsequently, the end-to-end (E2E) delay distribution that takes into account both processing and transmission delay is found and a tractable expression of the reliability of the system is derived as a function of the THz network parameters such as the molecular absorption loss and noise, the transmitted power, and the distance between the VR user and its respective small base station (SBS). Numerical results show the effects of various system parameters such as the bandwidth and the region of non-negligible interference on the reliability of the system. In particular, the results show that THz can deliver rates up to 16.4 Gbps and a reliability of 99.999% (with a delay threshold of 30 ms) provided that the impact of the molecular absorption on the THz links, which substantially limits the communication range of the SBS, is alleviated by densifying the network accordingly.

cs.IT↗

arXiv2019

Nonlocalized Clustering and Evolution of Cluster Structure in Nuclei

We explain various facets of the THSR (Tohsaki-Horiuchi-Schuck-Röpke) wave function. We first discuss the THSR wave function as a wave function of cluster-gas state, since the THSR wave function was originally introduced to elucidate the 3$α$-condensate-like character of the Hoyle state ($0_2^+$ state) of $^{12}$C. We briefly review the cluster-model studies of the Hoyle state in 1970's in order to explain how there emerged the idea to assign the $α$ condensate character to the Hoyle state. We then explain that the THSR wave function can describe very well also non-gaslike ordinary cluster states with spatial localization of clusters. This fact means that the dynamical motion of clusters is of nonlocalized nature just as in gas-like states of clusters and the localization of clusters is due to the inter-cluster Pauli principle which is against the close approach of two clusters. The nonlocalized cluster dynamics is formulated by the container model of cluster dynamics. The container model describes gas-like state and non-gaslike states as the solutions of the Hill-Wheeler equation with respect to the size parameter of THSR wave function which is just the size parameter of the container. When we notice that fact that the THSR wave function with the smallest value of size parameter is equivalent to the shell-model wave function, we see that the container model describes the evolution of cluster structure from the ground state with shell-model structure up to the gas-like cluster state via ordinary non-gaslike cluster states. For the description of various cluster structure, more generation of THSR wave function have been introduced and we review some typical examples with their actual applications.

nucl-th↗

arXiv2019

Nonlocalized motion in two-dimensional container of $α$ particles in $3^-$ and $4^-$ states of $^{12}$C

The first $3^-$ and $4^-$ states of $^{12}$C are studied in the present container model, in which the shift parameter is introduced to break the parity symmetry for projecting out the negative-parity states. Taking the limit as the shift parameter approaches zero and by variational calculations for one-deformed size parameter, the local energy minima are obtained for the $3^-$ and $4^-$ states. It is found that the obtained single THSR (Tohsaki-Horiuchi-Schuck-Röpke) wave functions for $3^-$ and $4^-$ states are 96% and 92% equivalent to the corresponding GCM wave functions, respectively. The calculated intrinsic densities further show that these negative-parity states of three clusters, different with the traditional understanding of rigid triangle structure, are found to have nonlocalized clustering structure in the two-dimensional container picture.

nucl-th↗

arXiv2019

Artificial Intelligence for Prosthetics - challenge solutions

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.

cs.LG↗

arXiv2018

Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation

Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this paper, we propose the Intervention Aided Reinforcement Learning (IARL) framework, which utilizes human intervened robot-environment interaction to improve the policy. We used the Unmanned Aerial Vehicle (UAV) as the test platform. We built neural networks as our policy to map sensor readings to control signals on the UAV. Our experiment scenarios cover both simulation and reality. We show that our approach substantially reduces the human intervention and improves the performance in autonomous navigation, at the same time it ensures safety and keeps training cost acceptable.

cs.RO↗

arXiv2018

A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions

Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic features. Manual segmentation is laborious, and impractical for large-scale use. For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST), clinicians typically outline the boundaries of a lesion on a single slice to extract diameter measurements. In this work, we have collected a large-scale database, named LesionVis, with pixel-wise manual 2D lesion delineations on the RECIST-slices. To extend the 2D segmentations to 3D, we propose a volumetric progressive lesion segmentation (PLS) algorithm to automatically segment the 3D lesion volume from 2D delineations using a scale-invariant and boundary-aware deep convolutional network (SIBA-Net). The SIBA-Net copes with the size transition of a lesion when the PLS progresses from the RECIST-slice to the edge-slices, as well as when performing longitudinal assessment of lesions whose size change over multiple time points. The proposed PLS-SiBA-Net (P-SiBA) approach is assessed on the lung lesion cases from LesionVis. Our experimental results demonstrate that the P-SiBA approach achieves mean Dice similarity coefficients (DSC) of 0.81, which significantly improves 3D segmentation accuracy compared with the approaches proposed previously (highest mean DSC at 0.78 on LesionVis). In summary, by leveraging the limited 2D delineations on the RECIST-slices, P-SiBA is an effective semi-supervised approach to produce accurate lesion segmentations in 3D.

cs.CV↗

arXiv2018

A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities

We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validated on the publicly available ChestX-ray14 dataset. Experimental results have demonstrated that our classification and localization prediction performance achieved significant improvement over the previous models on the ChestX-ray14 dataset. In summary, our network can produce accurate disease classification and localization, which can potentially support clinical decisions.

cs.CV↗

arXiv2018

On the transmission of uniform unicyclic hypergraphs

The transmission of a connected hypergraph is defined as the summation of distances between all unordered pairs of distinct vertices. We determine the unique uniform unicyclic hypergraphs of fixed size with minimum and maximum transmissions, respectively.

math.CO↗

arXiv2018

Investigation of the pairing effect in 10B nucleus compared with 10Be and 10C nuclei by using the extended THSR wave function

In order to study the nucleon-nucleon pairing effects in clustering nuclei, we formulate a superposed Tohsaki-Horiuchi-Schuck-Roepke (THSR) wave function, which includes both molecular-orbit and pairing configurations explicitly. With this new wave function, we investigate the abnormal deuteron-like pn-pairing effect in 10B with T=0 and S=1 (isoscalar) by comparing with isovector NN pairs (T=1, S=1) in 10Be and 10C. Energies are calculated for the ground states of 10Be, 10B and 10C nuclei, and the 1+ excited state of 10B. These energies are essentially improved comparing with studies using previous version of THSR wave function. Further more, overlaps between the total wave function and the pairing component indicate that the NN pairing effect is more visible in 10B than in 10Be and 10C. By analyzing the energies and the overlaps between wave function components, we observe two different mechanisms enhancing the formation of deuteron-like pairs in 10B. We also discuss the pairing effect by showing average distances between components in each nucleus and density distributions of valance nucleons.

nucl-th↗

arXiv2018

On the $α$-spectral radius of uniform hypergraphs

For $0\leα<1$ and a uniform hypergraph $G$, the $α$-spectral radius of $G$ is the largest $H$-eigenvalue of $α\mathcal{D}(G) +(1-α)\mathcal{A}(G)$, where $\mathcal{D}(G)$ and $\mathcal{A}(G)$ are the diagonal tensor of degrees and the adjacency tensor of $G$, respectively. We give upper bounds for the $α$-spectral radius of a uniform hypergraph, propose some transformations that increase the $α$-spectral radius, and determine the unique hypergraphs with maximum $α$-spectral radius in some classes of uniform hypergraphs.

math.CO↗

arXiv2018

Semiparametrically Point-Optimal Hybrid Rank Tests for Unit Roots

We propose a new class of unit root tests that exploits invariance properties in the Locally Asymptotically Brownian Functional limit experiment associated to the unit root model. The invariance structures naturally suggest tests that are based on the ranks of the increments of the observations, their average, and an assumed reference density for the innovations. The tests are semiparametric in the sense that they are valid, i.e., have the correct (asymptotic) size, irrespective of the true innovation density. For a correctly specified reference density, our test is point-optimal and nearly efficient. For arbitrary reference densities, we establish a Chernoff-Savage type result, i.e., our test performs as well as commonly used tests under Gaussian innovations but has improved power under other, e.g., fat-tailed or skewed, innovation distributions. To avoid nonparametric estimation, we propose a simplified version of our test that exhibits the same asymptotic properties, except for the Chernoff-Savage result that we are only able to demonstrate by means of simulations.

econ.EM↗

arXiv2018

Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations

The convolutional neural network (CNN) has become a powerful tool for various biomedical image analysis tasks, but there is a lack of visual explanation for the machinery of CNNs. In this paper, we present a novel algorithm, Respond-weighted Class Activation Mapping (Respond-CAM), for making CNN-based models interpretable by visualizing input regions that are important for predictions, especially for biomedical 3D imaging data inputs. Our method uses the gradients of any target concept (e.g. the score of target class) that flows into a convolutional layer. The weighted feature maps are combined to produce a heatmap that highlights the important regions in the image for predicting the target concept. We prove a preferable sum-to-score property of the Respond-CAM and verify its significant improvement on 3D images from the current state-of-the-art approach. Our tests on Cellular Electron Cryo-Tomography 3D images show that Respond-CAM achieves superior performance on visualizing the CNNs with 3D biomedical images inputs, and is able to get reasonably good results on visualizing the CNNs with natural image inputs. The Respond-CAM is an efficient and reliable approach for visualizing the CNN machinery, and is applicable to a wide variety of CNN model families and image analysis tasks.

cs.CV↗

arXiv2018

On the $α$-spectral radius of graphs

For $0\le α\le 1$, Nikiforov proposed to study the spectral properties of the family of matrices $A_α(G)=αD(G)+(1-α)A(G)$ of a graph $G$, where $D(G)$ is the degree diagonal matrix and $A(G)$ is the adjacency matrix. The $α$-spectral radius of $G$ is the largest eigenvalue of $A_α(G)$. We give upper bounds for $α$-spectral radius for unicyclic graphs $G$ with maximum degree $Δ\ge 2$, connected irregular graphs with given maximum degree and and some other graph parameters, and graphs with given domination number, respectively. We determine the unique tree with second maximum $α$-spectral radius among trees, and the unique tree with maximum $α$-spectral radius among trees with given diameter. For a graph with two pendant paths at a vertex or at two adjacent vertex, we prove results concerning the behavior of the $α$-spectral radius under relocation of a pendant edge in a pendant path. We also determine the unique graphs such that the difference between the maximum degree and the $α$-spectral radius is maximum among trees, unicyclic graphs and non-bipartite graphs, respectively.

math.CO↗