arXiv subjects
Publications and source records attributed to Junwei Zhang.
Let $p$ be an odd prime, and suppose that $G$ is a $p$-solvable group and $φ\in {\rm IBr}(G)$ has vertex $Q$. In 2011, Cossey, Lewis and Navarro proved that the number of lifts of $φ$ is at most $|Q:Q'|$ whenever $Q$ is normal in $G$. In this paper, we present an explicit description of the set of lifts of $φ$ with a given vertex pair $(Q,δ)$ under a weaker condition on $Q$, and thus generalize their result.
Programmable metasurfaces have garnered significant attention due to their exceptional ability to manipulate electromagnetic (EM) waves in real time, leading to the emergence of a prominent area in wireless communication, namely reconfigurable intelligent surfaces (RISs), to control the signal propagation and coverage. However, the existing RISs usually suffer from limited operating distance and band interference, which hinder their practical applications in wireless relay and communication systems. To overcome the limitations, we propose an amplifying and filtering RIS (AF-RIS) to enhance the in-band signal energy and filter the out-of-band signal of the incident EM waves, ensuring the miniaturization of the RIS array and enabling its anti-interference ability. In addition, each AF-RIS element is equipped with a 2-bit phase control capability, further endowing the entire array with great beamforming performance. An elaborately designed 4*8 AF-RIS array is presented by integrating the power dividing and combining networks, which substantially reduces the number of amplifiers and filters, thereby reducing the hardware costs and power consumption. Experimental results showcase the powerful capabilities of AF-RIS in beam-steering, frequency selectivity, and signal amplification. Therefore, the proposed AF-RIS holds significant promise for critical applications in wireless relay systems by offering an efficient solution to improve frequency selectivity, enhance signal coverage, and reduce hardware size.
Given a character triple $(G,N,θ)$, which means that $G$ is a finite group with $N \vartriangleleft G$ and $θ\in{\rm Irr}(N)$ is $G$-invariant, we introduce the notion of a $π$-quasi extension of $θ$ to $G$ where $π$ is the set of primes dividing the order of the cohomology element $[θ]_{G/N}\in H^2(G/N,\mathbb{C}^\times)$ associated with the character triple, and then establish the uniqueness of such an extension in the normalized case. As an application, we use the $π$-quasi extension of $θ$ to construct a bijection from the set of $π$-defect zero characters of $G/N$ onto the set of relative $π$-defect zero characters of $G$ over $θ$. Our results generalize the related theorems of M. Murai and of G. Navarro.
Vulnerability analysis is crucial for software security. This work focuses on using pre-training techniques to enhance the understanding of vulnerable code and boost vulnerability analysis. The code understanding ability of a pre-trained model is highly related to its pre-training objectives. The semantic structure, e.g., control and data dependencies, of code is important for vulnerability analysis. However, existing pre-training objectives either ignore such structure or focus on learning to use it. The feasibility and benefits of learning the knowledge of analyzing semantic structure have not been investigated. To this end, this work proposes two novel pre-training objectives, namely Control Dependency Prediction (CDP) and Data Dependency Prediction (DDP), which aim to predict the statement-level control dependencies and token-level data dependencies, respectively, in a code snippet only based on its source code. During pre-training, CDP and DDP can guide the model to learn the knowledge required for analyzing fine-grained dependencies in code. After pre-training, the pre-trained model can boost the understanding of vulnerable code during fine-tuning and can directly be used to perform dependence analysis for both partial and complete functions. To demonstrate the benefits of our pre-training objectives, we pre-train a Transformer model named PDBERT with CDP and DDP, fine-tune it on three vulnerability analysis tasks, i.e., vulnerability detection, vulnerability classification, and vulnerability assessment, and also evaluate it on program dependence analysis. Experimental results show that PDBERT benefits from CDP and DDP, leading to state-of-the-art performance on the three downstream tasks. Also, PDBERT achieves F1-scores of over 99% and 94% for predicting control and data dependencies, respectively, in partial and complete functions.
Training an effective Machine learning (ML) model is an iterative process that requires effort in multiple dimensions. Vertically, a single pipeline typically includes an initial ETL (Extract, Transform, Load) of raw datasets, a model training stage, and an evaluation stage where the practitioners obtain statistics of the model performance. Horizontally, many such pipelines may be required to find the best model within a search space of model configurations. Many practitioners resort to maintaining logs manually and writing simple glue code to automate the workflow. However, carrying out this process on the cloud is not a trivial task in terms of resource provisioning, data management, and bookkeeping of job histories to make sure the results are reproducible. We propose an end-to-end cloud-based machine learning platform, Accelerated Cloud for AI (ACAI), to help improve the productivity of ML practitioners. ACAI achieves this goal by enabling cloud-based storage of indexed, labeled, and searchable data, as well as automatic resource provisioning, job scheduling, and experiment tracking. Specifically, ACAI provides practitioners (1) a data lake for storing versioned datasets and their corresponding metadata, and (2) an execution engine for executing ML jobs on the cloud with automatic resource provisioning (auto-provision), logging and provenance tracking. To evaluate ACAI, we test the efficacy of our auto-provisioner on the MNIST handwritten digit classification task, and we study the usability of our system using experiments and interviews. We show that our auto-provisioner produces a 1.7x speed-up and 39% cost reduction, and our system reduces experiment time for ML scientists by 20% on typical ML use cases.
The enhancement of $J^π(T)$=3$^{+}$(0) state with isospin $T=0$ excited by the tensor force in the free $^{6}$Li nucleus has been observed, for the first time, relative to a shrinkable excitation in the $^{6}$Li cluster component inside its host nucleus. Comparatively, the excitation of $J^π(T)$=0$^{+}$(1) state with isospin $T=1$ for these two $^{6}$Li formations take on an approximately equal excitation strength. The mechanism of such tensor force effect was proposed due to the intensive nuclear medium role on isospin $T$=0 state.
The correlation of $αα$ was probed via measuring the transverse momentum $p_{T}$ and width $δp_{T}$ of one $α$, for the first time, which represents the spatial and dynamical essentialities of the initial coupling state in $^{8}$Be nucleus. The weighted interaction vertex of 3$α$ reflected by the magnitudes of their relative momentums and relative emission angles proves the isosceles triangle configuration for 3$α$ at the high excited energy analogous Hoyle states.
The experimental evidence for the $α$ Boson gas state in the $^{11}$C+$^{12}$C$\rightarrow$$^{23}$Mg$^{\ast}$ fusion evaporation reaction is presented. By measuring the $α$ emission spectrum with multiplicity 2 and 3, we provide insight into the existence of a three-body force among $α$ particles. The observed spectrum exhibited distinct tails corresponding to $α$ particles emitted in pairs and triplets consistent well with the model-calculations of AV18-UX and chiral effective field theory of NV2-3-la*, indicating the formation of $α$ clusters with three-body force in the Boson gas state.
Superconductivity is a macroscopic quantum phenomenon and it is commonly believed that the in-plane superconducting transition temperature (Tc) should be isotropic for superconductors with crystal structures of stacking layers (quasi two-dimensional). Here, we report the observation of piezostrain-induced in-plane anisotropy of Tc in FeSe0.5Te0.5 (FST) thin films grown on ferroelectrics. The as-prepared FST shows tetragonal phase with equal in-plane lattice parameters and absence of in-plane anisotropy of Tc. Upon applying electric fields, piezostrain induces difference of the in-plane lattice parameters (a-b) and in-plane anisotropy of Tc. The in-plane anisotropy of Tc correlates with a-b and becomes more remarkable for larger values of a-b. Some possible extrinsic effects were ruled out by experiments and analysis. It is suggested that electronic nematicity with one direction induced by piezostrain and inhomogeneous superconductivity in FST are involved in this unusual phenomenon. This work reveals the exotic behavior of FST and will stimulate exploration of similar behavior in other unconventional superconductors.
Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data.
To harness the intriguing properties of two-dimensional van der Waals (vdW) ferromagnets (FMs) for versatile applications, the key challenge lies in the reliable material synthesis for scalable device production. Here, we demonstrate the epitaxial growth of single-crystalline 1T-CrTe2 thin films on 2-inch sapphire substrates. Benefiting from the uniform surface energy of the dangling bond-free Al2O3(0001) surface, the layer-by-layer vdW growth mode is observed right from the initial growth stage, which warrants precise control of the sample thickness and atomically smooth surface morphology across the entire wafer. Moreover, the presence of the Coulomb interaction at the CrTe2/Al2O3 interface serves as an effective tuning parameter to tailor the anomalous Hall response, and the structural optimization of the CrTe2-based spin-orbit torque device leads to a substantial switching power reduction by 54%. Our results may lay out a general framework for the design of energy-efficient spintronics based on configurable vdW FMs.
With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Existing methods mostly adopt heuristic or attention-based preference aggregation strategies to synthesize group preferences. However, these models mainly focus on the pairwise connections of users and ignore the complex high-order interactions within and beyond groups. Besides, group recommendation suffers seriously from the problem of data sparsity due to severely sparse group-item interactions. In this paper, we propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals: (1) capturing the intra- and inter-group interactions among users; (2) alleviating the data sparsity issue with the raw data itself. Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups. For (2), we design a double-scale node dropout strategy to create self-supervision signals that can regularize user representations with different granularities against the sparsity issue. The experimental analysis on multiple benchmark datasets demonstrates the superiority of the proposed model and also elucidates the rationality of the hypergraph modeling and the double-scale self-supervision.
A broken interfacial inversion symmetry in ultrathin ferromagnet/heavy metal (FM/HM) bilayers is generally believed to be a prerequisite for accommodating the Dzyaloshinskii-Moriya interaction (DMI) and for stabilizing chiral spin textures. In these bilayers, the strength of the DMI decays as the thickness of the FM layer increases and vanishes around a few nanometers. In the present study, through synthesizing relatively thick films of compositions CoPt or FePt, CoCu or FeCu, FeGd and FeNi, contributions to DMI from the composition gradient induced bulk magnetic asymmetry (BMA) and spin-orbit coupling (SOC) are systematically examined. Using Brillouin light scattering spectroscopy, both the sign and amplitude of DMI in films with controllable direction and strength of BMA, in the presence and absence of SOC are experimentally studied. In particular, we show that a sizable amplitude of DMI (0.15 mJ/m^2) can be realized in CoPt or FePt films with BMA and strong SOC, whereas negligible DMI strengths are observed in other thick films with BMA but without significant SOC. The pivotal roles of BMA and SOC are further examined based on the three-site Fert-Levy model and first-principles calculations. It is expected that our findings may help to further understand the origin of chiral magnetism and to design novel non-collinear spin textures.
Data flow analysis and optimization is considered for homogeneous rectangular mesh networks. We propose a flow matrix equation which allows a closed-form characterization of the nature of the minimal time solution, speedup and a simple method to determine when and how much load to distribute to processors. We also propose a rigorous mathematical proof about the flow matrix optimal solution existence and that the solution is unique. The methodology introduced here is applicable to many interconnection networks and switching protocols (as an example we examine toroidal networks and hypercube networks in this paper). An important application is improving chip area and chip scalability for networks on chips processing divisible style loads.
Magneto-ionic control of magnetic properties through ionic migration has shown promise in enabling new functionalities in energy-efficient spintronic devices. In this work, we demonstrate the effect of helium ion irradiation and oxygen implantation on magneto-ionically induced exchange bias effect in Gd/Ni$_{0.33}$Co$_{0.67}$O heterostructures. Irradiation using $He^+$ leads to an expansion of the Ni$_{0.33}$Co$_{0.67}$O lattice due to strain relaxation. At low He+ fluence ($\leq$ 2$\times$10$^{14}$ ions cm$^{-2}$), the redox-induced interfacial magnetic moment initially increases, owing to enhanced oxygen migration. At higher fluence, the exchange bias is suppressed due to reduction of pinned uncompensated interfacial Ni$_{0.33}$Co$_{0.67}$O spins. For oxygen implanted samples, an initial lattice expansion below a dose of 5$\times$10$^{15}$ cm$^{-2}$ is subsequently dominated at higher dose by a lattice contraction and phase segregation into NiO and CoO-rich phases, which in turn alters the exchange bias. These results highlight the possibility of ion irradiation and implantation as an effective means to tailor magneto-ionic effects.
Electrically induced ionic motion offers a new way to realize voltage-controlled magnetism, opening the door to a new generation of logic, sensor, and data storage technologies. Here, we demonstrate an effective approach to magneto-ionically and electrically tune exchange bias in Gd/Ni$_{1-x}$Co$_{x}$O thin films (x=0.50, 0.67), where neither of the layers alone is ferromagnetic at room temperature. The Gd capping layer deposited onto antiferromagnetic Ni$_{1-x}$Co$_{x}$O initiates a solid-state redox reaction that reduces an interfacial region of the oxide to ferromagnetic NiCo. Exchange bias is established after field cooling, which can be enhanced by up to 35% after a voltage conditioning and subsequently reset with a second field cooling. These effects are caused by the presence of an interfacial ferromagnetic NiCo layer, which further alloys with the Gd layer upon field cooling and voltage application, as confirmed by electron microscopy and polarized neutron reflectometry studies. These results highlight the viability of the solid-state magneto-ionic approach to achieve electric control of exchange bias, with potentials for energy-efficient magneto-ionic devices.
In this paper, to accurately characterize the low inter-mode coupling of the weakly-coupled few mode fibers (FMFs), we propose a modified inter-mode coupling characterization method based on swept-wavelength interferometry measurement, in which an integral calculation approach is used to eliminate significant sources of error that may lead to underestimation of the power coupling coefficient. Using the proposed characterization method, a low-crosstalk ring-core fiber (RCF) with low mode dependent loss (MDL) and with single span length up to 100 km is experimentally measured to have low power coupling coefficients between high-order orbital angular momentum (OAM) mode groups of below -30 dB/km over C band. The measured low coupling coefficients based on the proposed method are verified by the direct system power measurements, proving the feasibility and reliability of the proposed inter-mode coupling characterization method.
Tubal scalars are usual vectors, and tubal matrices are matrices with every element being a tubal scalar. Such a matrix is often recognized as a third-order tensor. The product between tubal scalars, tubal vectors, and tubal matrices can be done by the powerful t-product. In this paper, we define nonnegative/positive/strongly positive tubal scalars/vectors/matrices, and establish several properties that are analogous to their matrix counterparts. In particular, we introduce the irreducible tubal matrix, and provide two equivalent characterizations. Then, the celebrated Perron-Frobenius theorem is established on the nonnegative irreducible tubal matrices. We show that some conclusions of the PF theorem for nonnegative irreducible matrices can be generalized to the tubal matrix setting, while some are not. One reason is the defined positivity here has a different meaning to its usual sense. For those conclusions that can not be extended, weaker conclusions are proved. We also show that, if the nonnegative irreducible tubal matrix contains a strongly positive tubal scalar, then most conclusions of the matrix PF theorem hold.