arXiv subjects

Jingbo Wang

Publications and source records attributed to Jingbo Wang.

At least 199 records · Page 11Linked to original sources

arXiv2019

Measurement of Beam-Correlated Background Neutrons from the Fermilab Booster Neutrino Beam in ANNIE Phase-I

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) aims to make a unique measurement of neutron yield from neutrino-nucleus interactions and to perform R&D for the next generation of water-based neutrino detectors. In this paper, we characterize beam-induced neutron backgrounds in the experimental hall at Fermi National Accelerator Laboratory. It is shown that the background levels are sufficiently low to allow the next stage of the experiment to proceed. These measurements are relevant to other Booster Neutrino Beam (BNB) experiments located adjacent to ANNIE Hall, where dirt neutrons and sky-shine could present similar backgrounds.

physics.ins-det↗

arXiv2019

Probing the emission states of PSR J1107-5907

The emission from PSR J1107-5907 is erratic. Sometimes the radio pulse is undetectable, at other times the pulsed emission is weak, and for short durations the emission can be very bright. In order to improve our understanding of these state changes, we have identified archival data sets from the Parkes radio telescope in which the bright emission is present, and find that the emission never switches from the bright state to the weak state, but instead always transitions to the off state. Previous work had suggested the identification of the off state as an extreme manifestation of the weak state. However, the connection between the off and bright emission reported here suggests that the emission can be interpreted as undergoing only two emission states: a bursting state consisting of both bright pulses and nulls as well as the weak-emission state.

astro-ph.HE↗

arXiv2019

Wide Bandwidth Observations of Pulsars C, D and J in 47 Tucanae

We report the first wideband observations of pulsars C, D and J in the globular cluster 47Tucanae (NGC 104) using the Ultra-Wideband Low (UWL) receiver system recently installed on the Parkes 64 m radio telescope. The wide frequency range of the UWL receiver (704-4032 MHz), along with the well-calibrated system, allowed us to obtain flux density measurements and polarization pulse profiles. The mean pulse profiles have significant linear and circular polarization, allowing for determination of the Faraday rotation measure for each pulsar. Precise measurements of the dispersion measures show a significant deviation in the value for pulsar D compared to earlier results. Searches for new pulsars in the cluster are on-going and we have determined optimal bands for such searches using the Parkes UWL receiver system.

astro-ph.HE↗

arXiv2019

Shield Synthesis for Real: Enforcing Safety in Cyber-Physical Systems

Cyber-physical systems are often safety-critical in that violations of safety properties may lead to catastrophes. We propose a method to enforce the safety of systems with real-valued signals by synthesizing a runtime enforcer called the shield. Whenever the system violates a property, the shield, composed with the system, makes correction instantaneously to ensure that no erroneous output is generated by the combined system. While techniques for synthesizing Boolean shields are well understood, they do not handle real-valued signals ubiquitous in cyber-physical systems, meaning corrections may be either unrealizable or inefficient to compute in the real domain. We solve the realizability and efficiency problems by statically analyzing the compatibility of predicates defined over real-valued signals, and using the analysis result to constrain a two-player safety game used to synthesize the shield. We have implemented the method and demonstrated its effectiveness and efficiency on a variety of applications, including an automotive powertrain control system.

cs.LO↗

arXiv2019

An End-to-End Network for Panoptic Segmentation

Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing approaches utilize two independent models without sharing features, which makes the pipeline inefficient to implement. In addition, a heuristic method is usually employed to merge the results. However, the overlapping relationship between object instances is difficult to determine without sufficient context information during the merging process. To address the problems, we propose a novel end-to-end network for panoptic segmentation, which can efficiently and effectively predict both the instance and stuff segmentation in a single network. Moreover, we introduce a novel spatial ranking module to deal with the occlusion problem between the predicted instances. Extensive experiments have been done to validate the performance of our proposed method and promising results have been achieved on the COCO Panoptic benchmark.

cs.CV↗

arXiv2019

Mitigating Power Side Channels during Compilation

The code generation modules inside modern compilers such as GCC and LLVM, which use a limited number of CPU registers to store a large number of program variables, may introduce side-channel leaks even in software equipped with state-of-the-art countermeasures. We propose a program analysis and transformation based method to eliminate this side channel. Our method has a type-based technique for detecting leaks, which leverages Datalog-based declarative analysis and domain-specific optimizations to achieve high efficiency and accuracy. It also has a mitigation technique for the compiler's backend, more specifically the register allocation modules, to ensure that potentially leaky intermediate computation results are always stored in different CPU registers or spilled to memory with isolation. We have implemented and evaluated our method in LLVM for the x86 instruction set architecture. Our experiments on cryptographic software show that the method is effective in removing the side channel while being efficient, i.e., our mitigated code is more compact and runs faster than code mitigated using state-of-the-art techniques.

cs.CR↗

arXiv2019

Quantum algorithm for visual tracking

Visual tracking (VT) is the process of locating a moving object of interest in a video. It is a fundamental problem in computer vision, with various applications in human-computer interaction, security and surveillance, robot perception, traffic control, etc. In this paper, we address this problem for the first time in the quantum setting, and present a quantum algorithm for VT based on the framework proposed by Henriques et al. [IEEE Trans. Pattern Anal. Mach. Intell., 7, 583 (2015)]. Our algorithm comprises two phases: training and detection. In the training phase, in order to discriminate the object and background, the algorithm trains a ridge regression classifier in the quantum state form where the optimal fitting parameters of ridge regression are encoded in the amplitudes. In the detection phase, the classifier is then employed to generate a quantum state whose amplitudes encode the responses of all the candidate image patches. The algorithm is shown to be polylogarithmic in scaling, when the image data matrices have low condition numbers, and therefore may achieve exponential speedup over the best classical counterpart. However, only quadratic speedup can be achieved when the algorithm is applied to implement the ultimate task of Henriques's framework, i.e., detecting the object position. We also discuss two other important applications related to VT: (1) object disappearance detection and (2) motion behavior matching, where much more significant speedup over the classical methods can be achieved. This work demonstrates the power of quantum computing in solving computer vision problems.

quant-ph↗

arXiv2018

Quantum data compression by principal component analysis

Data compression can be achieved by reducing the dimensionality of high-dimensional but approximately low-rank datasets, which may in fact be described by the variation of a much smaller number of parameters. It often serves as a preprocessing step to surmount the curse of dimensionality and to gain efficiency, and thus it plays an important role in machine learning and data mining. In this paper, we present a quantum algorithm that compresses an exponentially large high-dimensional but approximately low-rank dataset in quantum parallel, by dimensionality reduction (DR) based on principal component analysis (PCA), the most popular classical DR algorithm. We show that the proposed algorithm achieves exponential speedup over the classical PCA algorithm when the original dataset are projected onto a polylogarithmically low-dimensional space. The compressed dataset can then be further processed to implement other tasks of interest, with significantly less quantum resources. As examples, we apply this algorithm to reduce data dimensionality for two important quantum machine learning algorithms, quantum support vector machine and quantum linear regression for prediction. This work demonstrates that quantum machine learning can be released from the curse of dimensionality to solve problems of practical importance.

quant-ph↗

arXiv2018

Classification of black holes in three dimensional spacetime by the $W_{1+\infty}$ symmetry

The BMS symmetry and related near horizon symmetry play important roles in holography in asymptotically flat spacetimes. They may also be crucial for solving the information paradox. But we still don't fully understand those infinite-dimensional symmetries. On the other hand, $W_{1+\infty}$ symmetry is the quantum version of area-preserving diffeomorphism of a plane. It is a dynamical symmetry of quantum Hall liquid and can be used to classify the quantum Hall universality classes. In this paper, we will show that the near horizon symmetry can be obtained from the $W_{1+\infty}$ symmetry. Based on this result, the black holes in three dimensional spacetime can be classified just as in the quantum Hall liquid. It also gives the result that the radius of black hole are quantized. This gives another evidence that our early claim "black hole can be considered as a kind of topological insulator" is correct.

gr-qc↗

arXiv2018

Compact Chiral Boson Fields on the Horizon of BTZ Black Hole

In the previous work, it was shown that the degrees of freedom on the horizon of BTZ black hole can be described by two chiral massless scalar fields with opposite chirality. In this paper, we continuous this research. It is found that the scalar field is actually a compact boson field on a circle. The compactness results in the quantization of the black hole radius. Then we quantize the two boson fields and get two abelian Kac-Moody algebras. From the boson field, one can construct the full $W_{1+\infty}$ algebra which was used to classify the BTZ black holes.

gr-qc↗

arXiv2018

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048x1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.

cs.CV↗

arXiv2018

W-hairs of black holes in three dimensional spacetime

In the previous paper (arXiv:1804.09438) we found that the near horizon symmetry algebra of black holes is a subalgebra of the $W_{1+\infty}$ symmetry algebra of quantum Hall fluid in three dimensional spacetime. In this paper, we give a slightly different representation of the former algebra from the latter one. Similar to the horizon fluff proposal, based on the $W_{1+\infty}$ algebra, we count the number of the microstates of the BTZ black holes and obtain the Bekenstein-Hawking entropy.

gr-qc↗

arXiv2018

A quantum walk assisted approximate algorithm for bounded NP optimisation problems

This paper describes an application of the Quantum Approximate Optimisation Algorithm (QAOA) to efficiently find approximate solutions for computational problems contained in the polynomially bounded NP optimisation complexity class (NPO PB). We consider a generalisation of the QAOA state evolution to alternating quantum walks and solution-quality-dependent phase shifts, and use the quantum walks to integrate the problem constraints of NPO problems. We apply the recent concept of a hybrid quantum-classical variational scheme to attempt finding the highest expectation value, which contains a high-quality solution. The algorithm is applied to the problem of minimum vertex cover, showing promising results using only a fixed and low number of optimisation parameters.

quant-ph↗

arXiv2018

Learning a Discriminative Feature Network for Semantic Segmentation

Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.

cs.CV↗

arXiv2018

Randomness in Deconvolutional Networks for Visual Representation

Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture. For the random representations of an untrained CNN, we train the corresponding DCN to reconstruct the input images. Compared with the image inversion on pre-trained CNN, our training converges faster and the yielding network exhibits higher quality for image reconstruction. It indicates there is rich information encoded in the random features; the pre-trained CNN may discard information irrelevant for classification and encode relevant features in a way favorable for classification but harder for reconstruction. We further explore the property of the overall random CNN-DCN architecture. Surprisingly, images can be inverted with satisfactory quality. Extensive empirical evidence as well as theoretical analysis are provided.

cs.CV↗

arXiv2017

Black hole as topological insulator (II): the boundary modes

In the previous paper Ref.[1], it was claimed that the black hole can be considered as a kind of topological insulator. For BTZ black hole in three dimensional $AdS_3$ spacetime two evidences were given to support this claim: the first evidence comes from the black hole "membrane paradigm", and the second evidence comes from the fact that the horizon of BTZ black hole can support two chiral massless scalar field with opposite chirality. Those are two key properties of 2D topological insulator. For higher dimensional black hole the first evidence is still valid but the second fails. In this paper, starting from the boundary BF theory, which can be used to describe the boundary degrees of freedom of black hole in arbitrary dimension, we shown that the isolated horizon of $3+1-$D black hole can support massless scalar field and vector field. Those two fields can be used to construct a massless Dirac field through the $2+1-$dimensional bosonization, which also appears on the boundary of $3+1-$D topological insulators.

gr-qc↗

arXiv2017

Black hole as topological insulator (I): the BTZ black hole case

Black holes are extraordinary massive objects which can be described classically by general relativity, and topological insulators are new orders of matter that could be use to built a topological quantum computer. They seem to be different objects, but in this paper, we claim that the black hole can be considered as kind of topological insulator. For BTZ black hole in three dimensional $AdS_3$ spacetime we give two evidences to support this claim: the first evidence comes from the black hole "membrane paradigm", which says that the horizon of black hole behaves like an electrical conductor. On the other hand, the vacuum can be considered as an insulator. The second evidence comes from the fact that the horizon of BTZ black hole can support two chiral massless scalar field with opposite chirality. Those are two key properties of 2D topological insulator. We also consider the coupling with the electromagnetic field to show that the boundary modes can conduct the electricity. For higher dimensional black hole the first evidence is still valid. So we conjecture that the higher dimensional black hole can also be considered as higher dimensional topological insulators. This conjecture will have far-reaching influences on our understanding of quantum black hole and the nature of gravity.

gr-qc↗

arXiv2017

Generating 3 qubit quantum circuits with neural networks

A new method for compiling quantum algorithms is proposed and tested for a three qubit system. The proposed method is to decompose a a unitary matrix U, into a product of simpler U j via a neural network. These U j can then be decomposed into product of known quantum gates. Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. There is scope for more work in scaling this approach to larger quantum systems.

quant-ph↗