arXiv subjects

Yuwei Li

Publications and source records attributed to Yuwei Li.

At least 37 records · Page 2Linked to original sources

arXiv2022

Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to Mobile Edge Computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this paper, we introduce a vehicular fog-edge computing paradigm and formulate it as a multi-stage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, road-side unit (RSU), MEC server and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit. In the second and third stages, based on Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks.

cs.DC↗

arXiv2021

Rate Splitting Multiple Access for Joint Communication and Sensing Systems with Unmanned Aerial Vehicles

This paper investigates the problem of resource allocation for joint communication and radar sensing system on rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) system. UAV simultaneously communicates with multiple users and probes signals to targets of interest to exploit cooperative sensing ability and achieve substantial gains in size, cost and power consumption. By virtue of using linearly precoded rate splitting at the transmitter and successive interference cancellation at the receivers, RSMA is introduced as a promising paradigm to manage interference as well as enhance spectrum and energy efficiency. To maximize the energy efficiency of UAV networks, the deployment location and the beamforming matrix are jointly optimized under the constraints of power budget, transmission rate and approximation error. To solve the formulated non-convex problem efficiently, we decompose it into the UAV deployment subproblem and the beamforming optimization subproblem. Then, we invoke the successive convex approximation and difference-of-convex programming as well as Dinkelbach methods to transform the intractable subproblems into convex ones at each iteration. Next, an alternating algorithm is designed to solve the non-linear and non-convex problem in an efficient manner, while the corresponding complexity is analyzed as well. Finally, simulation results reveal that proposed algorithm with RSMA is superior to orthogonal multiple access and power-domain non-orthogonal multiple access in terms of power consumption and energy efficiency.

cs.IT↗

arXiv2021

IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation

For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction. In this work, we suppose the desired HR image as an implicit continuous function of the 3D image spatial coordinate and the thick-slice LR images as several sparse discrete samplings of this function. Then the super-resolution (SR) task is to learn the continuous volumetric function from a limited observations using an fully-connected neural network combined with Fourier feature positional encoding. By simply minimizing the error between the network prediction and the acquired LR image intensity across each imaging plane, IREM is trained to represent a continuous model of the observed tissue anatomy. Experimental results indicate that IREM succeeds in representing high frequency image feature, and in real scene data collection, IREM reduces scan time and achieves high-quality high-resolution MR imaging in terms of SNR and local image detail.

eess.IV↗

arXiv2021

PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging

Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available.

cs.CV↗

arXiv2020

UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers

A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still challenging to compare fuzzers due to the inconsistency of the benchmarks, performance metrics, and/or environments for evaluation, which buries the useful insights and thus impedes the discovery of promising fuzzing primitives. In this paper, we design and develop UNIFUZZ, an open-source and metrics-driven platform for assessing fuzzers in a comprehensive and quantitative manner. Specifically, UNIFUZZ to date has incorporated 35 usable fuzzers, a benchmark of 20 real-world programs, and six categories of performance metrics. We first systematically study the usability of existing fuzzers, find and fix a number of flaws, and integrate them into UNIFUZZ. Based on the study, we propose a collection of pragmatic performance metrics to evaluate fuzzers from six complementary perspectives. Using UNIFUZZ, we conduct in-depth evaluations of several prominent fuzzers including AFL [1], AFLFast [2], Angora [3], Honggfuzz [4], MOPT [5], QSYM [6], T-Fuzz [7] and VUzzer64 [8]. We find that none of them outperforms the others across all the target programs, and that using a single metric to assess the performance of a fuzzer may lead to unilateral conclusions, which demonstrates the significance of comprehensive metrics. Moreover, we identify and investigate previously overlooked factors that may significantly affect a fuzzer's performance, including instrumentation methods and crash analysis tools. Our empirical results show that they are critical to the evaluation of a fuzzer. We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.

cs.CR↗

arXiv2020

Green Resource Allocation and Energy Management in Heterogeneous Small Cell Networks Powered by Hybrid Energy

In heterogeneous networks (HetNets), how to improve spectrum efficiency is a crucial issue. Meanwhile increased energy consumption inspires network operators to deploy renewable energy sources as assistance to traditional electricity. Based on above aspects, we allow base stations (BSs) to share their licensed spectrum resource with each other and adjust transmission power to adapt to the renewable energy level. Considering the sharing fairness among BSs, we formulate a multi-person bargaining problem as a stochastic optimization problem. We divide the optimization problem into three parts: data rate control, resource allocation and energy management. An online dynamic control algorithm is proposed to control admission rate and resource allocation to maximize the transmission and sharing profits with the least grid energy consumption. Simulation results investigate the time-varying data control and energy management of BSs and demonstrate the effectiveness of the proposed scheme.

cs.NI↗

arXiv2020

AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph

This paper presents a fully automatic framework for extracting editable 3D objects directly from a single photograph. Unlike previous methods which recover either depth maps, point clouds, or mesh surfaces, we aim to recover 3D objects with semantic parts and can be directly edited. We base our work on the assumption that most human-made objects are constituted by parts and these parts can be well represented by generalized primitives. Our work makes an attempt towards recovering two types of primitive-shaped objects, namely, generalized cuboids and generalized cylinders. To this end, we build a novel instance-aware segmentation network for accurate part separation. Our GeoNet outputs a set of smooth part-level masks labeled as profiles and bodies. Then in a key stage, we simultaneously identify profile-body relations and recover 3D parts by sweeping the recognized profile along their body contour and jointly optimize the geometry to align with the recovered masks. Qualitative and quantitative experiments show that our algorithm can recover high quality 3D models and outperforms existing methods in both instance segmentation and 3D reconstruction. The dataset and code of AutoSweep are available at https://chenxin.tech/AutoSweep.html.

cs.CV↗

arXiv2019

PARN: Position-Aware Relation Networks for Few-Shot Learning

Few-shot learning presents a challenge that a classifier must quickly adapt to new classes that do not appear in the training set, given only a few labeled examples of each new class. This paper proposes a position-aware relation network (PARN) to learn a more flexible and robust metric ability for few-shot learning. Relation networks (RNs), a kind of architectures for relational reasoning, can acquire a deep metric ability for images by just being designed as a simple convolutional neural network (CNN) [23]. However, due to the inherent local connectivity of CNN, the CNN-based relation network (RN) can be sensitive to the spatial position relationship of semantic objects in two compared images. To address this problem, we introduce a deformable feature extractor (DFE) to extract more efficient features, and design a dual correlation attention mechanism (DCA) to deal with its inherent local connectivity. Successfully, our proposed approach extents the potential of RN to be position-aware of semantic objects by introducing only a small number of parameters. We evaluate our approach on two major benchmark datasets, i.e., Omniglot and Mini-Imagenet, and on both of the datasets our approach achieves state-of-the-art performance with the setting of using a shallow feature extraction network. It's worth noting that our 5-way 1-shot result on Omniglot even outperforms the previous 5-way 5-shot results.

cs.CV↗

arXiv2019

SmartSeed: Smart Seed Generation for Efficient Fuzzing

Fuzzing is an automated application vulnerability detection method. For genetic algorithm-based fuzzing, it can mutate the seed files provided by users to obtain a number of inputs, which are then used to test the objective application in order to trigger potential crashes. As shown in existing literature, the seed file selection is crucial for the efficiency of fuzzing. However, current seed selection strategies do not seem to be better than randomly picking seed files. Therefore, in this paper, we propose a novel and generic system, named SmartSeed, to generate seed files towards efficient fuzzing. Specifically, SmartSeed is designed based on a machine learning model to learn and generate high-value binary seeds. We evaluate SmartSeed along with American Fuzzy Lop (AFL) on 12 open-source applications with the input formats of mp3, bmp or flv. We also combine SmartSeed with different fuzzing tools to examine its compatibility. From extensive experiments, we find that SmartSeed has the following advantages: First, it only requires tens of seconds to generate sufficient high-value seeds. Second, it can generate seeds with multiple kinds of input formats and significantly improves the fuzzing performance for most applications with the same input format. Third, SmartSeed is compatible to different fuzzing tools. In total, our system discovers more than twice unique crashes and 5,040 extra unique paths than the existing best seed selection strategy for the evaluated 12 applications. From the crashes found by SmartSeed, we discover 16 new vulnerabilities and have received their CVE IDs.

cs.CR↗

arXiv2019

V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing

Fuzzing is a technique of finding bugs by executing a software recurrently with a large number of abnormal inputs. Most of the existing fuzzers consider all parts of a software equally, and pay too much attention on how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this paper, we design and implement a vulnerability-oriented evolutionary fuzzing prototype named V-Fuzz, which aims to find bugs efficiently and quickly in a limited time. V-Fuzz consists of two main components: a neural network-based vulnerability prediction model and a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of the software are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which tend to arrive at the vulnerable locations, guided by the vulnerability prediction result. Experimental results demonstrate that V-Fuzz can find bugs more efficiently than state-of-the-art fuzzers. Moreover, V-Fuzz has discovered 10 CVEs, and 3 of them are newly discovered. We reported the new CVEs, and they have been confirmed and fixed.

cs.CR↗

arXiv2018

Bismuth and Antimony Based Oxyhalides and Chalcohalides as Potential Optoelectronic Materials

In the last decade the ns$^2$ cations (e.g., Pb$^{2+}$ and Sn$^{2+}$) based halides have emerged as one of the most exciting new classes of optoelectronic materials, as exemplified by for instance hybrid perovskite solar absorbers. These materials not only exhibit unprecedented performance in some cases, but they also appear to break new ground with their unexpected properties, such as extreme tolerance to defects. However, because of the relatively recent emergence of this class of materials, there remain many yet to be fully explored compounds. Here we assess a series of bismuth/antimony oxyhalides and chalcohalides using consistent first principles methods to ascertain their properties and obtain trends. Based on these calculations, we identify a subset consisting of three types of compounds that may be promising as solar absorbers, transparent conductors, and radiation detectors. Their electronic structure, connection to the crystal geometry, and impact on band-edge dispersion and carrier effective mass are discussed.

cond-mat.mtrl-sci↗

arXiv2018

Frustrated structural instability in superconducting quasi-one-dimensional K$_2$Cr$_3$As$_3$

We present neutron total scattering and density functional theory studies on quasi-one-dimensional superconducting K$_2$Cr$_3$As$_3$ revealing a frustrated structural instability. Our first principles calculations find a significant phonon instability which, under energy minimization, corresponds to a frustrated orthorhombic distortion. In diffraction studies we find large and temperature independent atomic displacement parameters which pair distribution analyses confirms and shows as resulting from highly localized orthorhombic distortions of the CrAs sublattice and coupled K displacements. These results suggest a far more complex phase diagram than previously assumed for this unusual superconductor with the likelihood of subtle interplays of structure, electron-phonon and magnetic interactions.

cond-mat.supr-con↗

arXiv2018

Ideal Bandgap in a 2D Ruddlesden-Popper Perovskite Chalcogenide for Single-junction Solar Cells

Transition metal perovskite chalcogenides (TMPCs) are explored as stable, environmentally friendly semiconductors for solar energy conversion. They can be viewed as the inorganic alternatives to hybrid halide perovskites, and chalcogenide counterparts of perovskite oxides with desirable optoelectronic properties in the visible and infrared part of the electromagnetic spectrum. Past theoretical studies have predicted large absorption coefficient, desirable defect characteristics, and bulk photovoltaic effect in TMPCs. Despite recent progresses in polycrystalline synthesis and measurements of their optical properties, it is necessary to grow these materials in high crystalline quality to develop a fundamental understanding of their optical properties and evaluate their suitability for photovoltaic application. Here, we report the growth of single crystals of a two-dimensional (2D) perovskite chalcogenide, Ba3Zr2S7, with a natural superlattice-like structure of alternating double-layer perovskite blocks and single-layer rock salt structure. The material demonstrated a bright photoluminescence peak at 1.28 eV with a large external luminescence efficiency of up to 0.15%. We performed time-resolved photoluminescence spectroscopy on these crystals and obtained an effective recombination time of ~65 ns. These results clearly show that 2D Ruddlesden-Popper phases of perovskite chalcogenides are promising materials to achieve single-junction solar cells.

cond-mat.mtrl-sci↗

arXiv2017

Properties of the ferroelectric visible light absorbing semiconductors: Sn$_2$P$_2$S$_6$ and Sn$_2$P$_2$Se$_6$

Ferroelectrics with suitable band gaps have recently attracted attention as candidate solar absorbing materials for photovoltaics. The inversion symmetry breaking may promote the separation of photo-excited carriers and allow voltages higher than the band gap. However, these effects are not fully understood, in part because of a lack of suitable model systems for studying these effects in detail. Here, we report properties of ferroelectric Sn$_2$P$_2$S$_6$ and Sn$_2$P$_2$Se$_6$ using first principles calculations. Results are given for the electronic structure, carrier pocket shapes, optical absorption and transport. We find indirect band gaps of 2.20 eV and 1.55 eV, respectively, and favorable band structures for carrier transport, including both holes and electrons. Strong absorption is found above the direct gaps of 2.43 eV and 1.76 eV. Thus these compounds may serve as useful model systems for understanding photovoltaic effects in ferroelectric semiconductors.

cond-mat.mtrl-sci↗

arXiv2017

Electronic fitness function for screening semiconductors as thermoelectric materials

We introduce a simple but efficient electronic fitness function (EFF) that describes the electronic aspect of the thermoelectric performance. This EFF finds materials that overcome the inverse relationship between $σ$ and $S$ based on the complexity of the electronic structures regardless of specific origin (e.g., isosurface corrugation, valley degeneracy, heavy-light bands mixture, valley anisotropy or reduced dimensionality). This function is well suited for application in high throughput screening. We applied this function to 75 different thermoelectric and potential thermoelectric materials including full- and half-Heuslers, binary semiconductors and Zintl phases. We find an efficient screening using this transport function. The EFF identifies known high performance $p$- and $n$-type Zintl phases and half-Heuslers. In addition, we find some previously unstudied phases with superior EFF.

cond-mat.mtrl-sci↗

arXiv2017

Prediction of Nontrivial Band Topology and Superconductivity in Mg$_2$Pb

The interplay of BCS superconductivity and nontrivial band topology is expected to give rise to opportunities for creating topological superconductors, achieved through pairing spin-filtered boundary modes via superconducting proximity effects. The thus-engineered topological superconductivity can, for example, facilitate the search for Majorana fermion quasiparticles in condensed matter systems. Here we report a first-principles study of Mg$_2$Pb and predict that it should be a superconducting topological material. The band topology of Mg$_2$Pb is identical to that of the archetypal quantum spin Hall insulator HgTe, while isostructural and isoelectronic Mg$_2$Sn is topologically trivial; a trivial to topological transition is predicted for Mg$_2$Sn$_{1-x}$Pb$_x$ for x~0.77. We propose that Mg$_2$Pb-Mg$_2$Sn quantum wells should generate robust spin-filtered edge currents in analogy to HgTe/CdTe quantum wells. In addition, our calculations predict that Mg$_2$Pb should become superconducting upon electron doping. Therefore, Mg$_2$Pb is expected to provide a practical material platform for studying emergent phenomena arising from the interplay of superconductivity and band topology.

cond-mat.supr-con↗

arXiv2016

Sn(II)-containing phosphates as optoelectronic materials

We theoretically investigate Sn(II) phosphates as optoelectronic materials using first principles calculations. We focus on known prototype materials Sn$_n$P$_2$O$_{5+n}$ (n=2, 3, 4, 5) and a previously unreported compound, SnP$_2$O$_6$ (n=1), which we find using global optimization structure prediction. The electronic structure calculations indicate that these compounds all have large band gaps above 3.2 eV, meaning their transparency to visible light. Several of these compounds show relatively low hole effective masses ($\sim$2-3 m$_0$), comparable the electron masses. This suggests potential bipolar conductivity depending on doping. The dispersive valence band-edges underlying the low hole masses, originate from the anti-bonding hybridization between the Sn 5s orbitals and the phosphate groups. Analysis of structure-property relationships for the metastable structures generated during structure search shows considerable variation in combinations of band gap and carrier effective masses, implying chemical tunability of these properties. The unusual combinations of relatively high band gap, low carrier masses and high chemical stability suggests possible optoelectronic applications of these Sn(II) phosphates, including p-type transparent conductors. Related to this, calculations for doped material indicate low visible light absorption, combined with high plasma frequencies.

cond-mat.mtrl-sci↗

arXiv2016

Optimal programmable unambiguous discriminator between two unknown latitudinal states

Two unknown states can be unambiguously distinguished by a universal programmable discriminator, which has been widely discussed in previous works and the optimal solution has also been obtained. In this paper, we investigate the programmable unambiguous discriminator between two unknown "latitudinal" states, which lie in a subspace of the total state space. By equivalence of unknown pure states to known average mixed states, the optimal solution for this problem is systematically derived, and the analytical success probabilities for the optimal unambiguous discrimination are obtained. It is beyond one's expectation that the optimal setting for the programmable unambiguous discrimination between two unknown "latitudinal" states is the same as that for the universal ones. The results in this work can be used for the realization of the programmable discriminator in laboratory.

quant-ph↗