arXiv subjects

Dan Wang

Publications and source records attributed to Dan Wang.

At least 109 records · Page 6Linked to original sources

arXiv2023

Privacy Protectability: An Information-theoretical Approach

Recently, inference privacy has attracted increasing attention. The inference privacy concern arises most notably in the widely deployed edge-cloud video analytics systems, where the cloud needs the videos captured from the edge. The video data can contain sensitive information and subject to attack when they are transmitted to the cloud for inference. Many privacy protection schemes have been proposed. Yet, the performance of a scheme needs to be determined by experiments or inferred by analyzing the specific case. In this paper, we propose a new metric, \textit{privacy protectability}, to characterize to what degree a video stream can be protected given a certain video analytics task. Such a metric has strong operational meaning. For example, low protectability means that it may be necessary to set up an overall secure environment. We can also evaluate a privacy protection scheme, e.g., assume it obfuscates the video data, what level of protection this scheme has achieved after obfuscation. Our definition of privacy protectability is rooted in information theory and we develop efficient algorithms to estimate the metric. We use experiments on real data to validate that our metric is consistent with empirical measurements on how well a video stream can be protected for a video analytics task.

cs.CR↗

arXiv2023

Exchange-Driven Intermixing of Bulk and Topological Surface State by Chiral Excitons in Bi2Se3

Topological surface states (TSS) in the prototypical topological insulator (TI) Bi2Se3 are frequently characterized using optical probes, but electron-hole interactions and their effect on surface localization and optical response of the TSS remain unexplored. Here, we use ab initio calculations to understand excitonic effects in the bulk and surface of Bi2Se3. We identify multiple series of chiral excitons that exhibit both bulk and TSS character, due to exchange-driven mixing. Our results address fundamental questions about the degree to which electron-hole interactions can relax the topological protection of surface states and dipole selection rules for circularly polarized light in TIs by elucidating the complex intermixture of bulk and surface states excited in optical measurements and their coupling to light.

cond-mat.mtrl-sci↗

arXiv2023

Virtual-Diagnostic-Based Time Stamping for Ultrafast Electron Diffraction

In this work, non-destructive virtual diagnostics are applied to retrieve the electron beam time of arrival and energy in a relativistic ultrafast electron diffraction (UED) beamline using independently-measured machine parameters. This technique has the potential to improve temporal resolution of pump and probe UED scans. Fluctuations in time of arrival have multiple components, including a shot-to-shot jitter and a long-term drift which can be separately addressed by closed loop feedback systems. A linear-regression-based model is used to fit the beam energy and time of arrival and is shown to be able to predict accurately behavior for both on long and short time scales. More advanced time-series analysis based on machine learning techniques can be applied to improve this prediction further.

physics.acc-ph↗

arXiv2023

Geometric quantizations of mixed polarizations on Kähler manifolds with T-symmetry

Let $M$ be a compact Kähler manifold equipped with a pre-quantum line bundle $L$. In [9], using $T$-symmetry, we constructed a polarization $\mathcal{P}_{\mathrm{mix}}$ on $M$, which generalizes real polarizations on toric manifolds. In this paper, we obtain the following results for the quantum space $\mathcal{H}_{\mathrm{mix}}$ associated to $\mathcal{P}_{\mathrm{mix}}$. First, $\mathcal{H}_{\mathrm{mix}}$ consists of distributional sections of $L$ with supports inside $μ^{-1}(\mathfrak{t}^{*}_{\mathbb{Z}})$. This gives $\mathcal{H}_{\mathrm{mix}}=\bigoplus_{λ\in \mathfrak{t}^{*}_{\mathbb{Z}} } \mathcal{H}_{\mathrm{mix}, λ}$. Second, the above decomposition of $\mathcal{H}_{\mathrm{mix}}$ coincides with the weight decomposition for the $T$-symmetry. Third, an isomorphism $\mathcal{H}_{\mathrm{mix}, λ} \cong H^{0}( M//_λT, L//_λT)$, for regular $λ$. Namely, geometric quantization commutes with symplectic reduction.

math.SG↗

arXiv2022

PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection

3D object detection is a critical task in autonomous driving. Recently multi-modal fusion-based 3D object detection methods, which combine the complementary advantages of LiDAR and camera, have shown great performance improvements over mono-modal methods. However, so far, no methods have attempted to utilize the instance-level contextual image semantics to guide the 3D object detection. In this paper, we propose a simple and effective Painting Adaptive Instance-prior for 3D object detection (PAI3D) to fuse instance-level image semantics flexibly with point cloud features. PAI3D is a multi-modal sequential instance-level fusion framework. It first extracts instance-level semantic information from images, the extracted information, including objects categorical label, point-to-object membership and object position, are then used to augment each LiDAR point in the subsequent 3D detection network to guide and improve detection performance. PAI3D outperforms the state-of-the-art with a large margin on the nuScenes dataset, achieving 71.4 in mAP and 74.2 in NDS on the test split. Our comprehensive experiments show that instance-level image semantics contribute the most to the performance gain, and PAI3D works well with any good-quality instance segmentation models and any modern point cloud 3D encoders, making it a strong candidate for deployment on autonomous vehicles.

cs.CV↗

arXiv2022

Synchronization of Diverse Agents via Phase Analysis

In this paper, the synchronization of heterogeneous agents interacting over a dynamical network is studied. The edge dynamics can model the inter-agent communications which are often heterogeneous by nature. They can also model the controllers of the agents which may be different for each agent or uniform for all the agents. Novel synchronization conditions are obtained for both cases from a phase perspective by exploiting a recently developed small phase theorem. The conditions scale well with the network and reveal the trade-off between the phases of node dynamics and edge dynamics. We also study the synchronizability problem which aims to characterize the allowable diversity of the agents for which controllers can be designed so as to achieve synchronization. The allowable diversity is captured in terms of phase conditions engaging the residue matrices of the agents at their persistent modes. Controller design algorithms are provided for the cases of agent-dependent and uniform controllers, respectively.

eess.SY↗

arXiv2022

A Phase Theory of MIMO LTI Systems

In this paper, we define the phase response for a class of multi-input multi-output (MIMO) linear time-invariant (LTI) systems whose frequency responses are (semi-)sectorial at all frequencies. The newly defined phase concept subsumes the well-known notions of positive real systems and negative imaginary systems. We formulate a small phase theorem for feedback stability, which complements the celebrated small gain theorem. The small phase theorem lays the foundation of a phase theory of MIMO systems. We also discuss time-domain interpretations of phase-bounded systems via both energy signal analysis and power signal analysis. In addition, a sectored real lemma is derived for the computation of MIMO phases, which serves as a natural counterpart of the bounded real lemma.

eess.SY↗

arXiv2022

Identifying Operation Equilibrium in Integrated Electricity, Natural Gas, and Carbon-Emission Markets

The decarbonization of the power sector plays a pivotal role in economy-wide decarbonization to set the world on track to limit warming to 1.5°C by 2050. Carbon emission markets can play a significant role in this transition by putting a price on carbon and giving electricity producers an incentive to reduce their emissions. In this paper, we study the operational equilibrium of an integrated regional/jurisdictional energy system that comprises an electricity market, a natural gas market, and a carbon emission market. We first propose the novel role of a regional carbon market operator (CMO). Different than the conventional cap-and-trade carbon trading mechanism, the proposed CMO manages carbon allowance trading in a centralized jurisdictional carbon emission market. We then develop the formulation to identify the operational equilibrium of the electricity, natural gas, and carbon emission markets based on their interactions. The proposed equilibrium can be obtained by solving the Karush-Kuhn-Tucker (KKT) conditions of all three operational models simultaneously. Simulation results demonstrate that the proposed approach is more flexible, consistent, and effective in mitigating carbon emissions compared with the cap-and-trade mechanism.

eess.SY↗

arXiv2022

Deep Decarbonization of Multi-Energy Systems: A Carbon-Oriented Framework with Cross Disciplinary Technologies

The retirement of unabated coal power plants, the plummeting cost of renewable energy technologies, along with more aggressive public policies and regulatory reforms, are occurring at an unprecedented speed to decarbonize the power and energy systems towards the 2030 and 2050 climate goals. This article aims to establish a carbon-oriented framework to examine the role carbon emission is playing within a power grid that is rapidly transitioning to an integrated multi-energy system. We divide the carbon flows in the multi-energy systems into three stages: carbon allowances initialization/allocation, exchanging/pricing, and circulation. Then, we discuss how cross-disciplinary technologies, such as game theory, optimization, and machine learning can facilitate the modeling and analysis of the proposed framework.

eess.SY↗

arXiv2022

Degeneration of Kahler polarizations to mixed polarizations on toric varieties

Let $(X, ω, J)$ be a toric variety of dimension $2n$ determined by a Delzant polytope. In this paper, we first construct the polarizations $\shP_{k}$ by the Hamiltonian $T^{k}$-action on $X$ (see Theorem 3.11). We will show that $\shP_{k}$ is a singular mixed polarization for $1\le k < n$, and $\shP_{n}$ is a singular real polarization which coincides with the real polarization studied in \cite{BFMN} on the open dense subset of $X$. Then for each $1\le k \le n$, we will find a one-parameter family of Kähler polarizations $\shP_{k,t}$ on $X$ that converges to $\shP_{k}$ (see Theorem 3.12). Finally, we will show that $\shH_{k,t}^{T}$ the space of $T^{k}$-invariant $J_{k,t}$-holomorphic sections converges to $\shH_{k}^{0}$ (see Theorem 3.18).

math.SG↗

arXiv2022

Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel \textcolor{black}{graph} transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains. The source code for reproducing the experimental results is available at https://github.com/daiquanyu/AdaGCN.

cs.LG↗

arXiv2022

Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling

In order to be applicable in real-world scenario, Boundary Attacks (BAs) were proposed and ensured one hundred percent attack success rate with only decision information. However, existing BA methods craft adversarial examples by leveraging a simple random sampling (SRS) to estimate the gradient, consuming a large number of model queries. To overcome the drawback of SRS, this paper proposes a Latin Hypercube Sampling based Boundary Attack (LHS-BA) to save query budget. Compared with SRS, LHS has better uniformity under the same limited number of random samples. Therefore, the average on these random samples is closer to the true gradient than that estimated by SRS. Various experiments are conducted on benchmark datasets including MNIST, CIFAR, and ImageNet-1K. Experimental results demonstrate the superiority of the proposed LHS-BA over the state-of-the-art BA methods in terms of query efficiency. The source codes are publicly available at https://github.com/GZHU-DVL/LHS-BA.

cs.CV↗

arXiv2022

Generalizable Neural Radiance Fields for Novel View Synthesis with Transformer

We propose a Transformer-based NeRF (TransNeRF) to learn a generic neural radiance field conditioned on observed-view images for the novel view synthesis task. By contrast, existing MLP-based NeRFs are not able to directly receive observed views with an arbitrary number and require an auxiliary pooling-based operation to fuse source-view information, resulting in the missing of complicated relationships between source views and the target rendering view. Furthermore, current approaches process each 3D point individually and ignore the local consistency of a radiance field scene representation. These limitations potentially can reduce their performance in challenging real-world applications where large differences between source views and a novel rendering view may exist. To address these challenges, our TransNeRF utilizes the attention mechanism to naturally decode deep associations of an arbitrary number of source views into a coordinate-based scene representation. Local consistency of shape and appearance are considered in the ray-cast space and the surrounding-view space within a unified Transformer network. Experiments demonstrate that our TransNeRF, trained on a wide variety of scenes, can achieve better performance in comparison to state-of-the-art image-based neural rendering methods in both scene-agnostic and per-scene finetuning scenarios especially when there is a considerable gap between source views and a rendering view.

cs.CV↗

arXiv2022

On the Phases of a Semi-Sectorial Matrix

In this paper, we extend the definition of phases of sectorial matrices to those of semi-sectorial matrices, which are possibly singular. Properties of the phases are also extended, including those of the Moore-Penrose generalized inverse, compressions and Schur complements, matrix sums and products. In particular, a majorization relation is established between the phases of the nonzero eigenvalues of $AB$ and the phases of the compressions of $A$ and $B$, which leads to a generalized matrix small phase theorem. For the matrices which are not necessarily semi-sectorial, we define their (largest and smallest) essential phases via diagonal similarity transformation. An explicit expression for the essential phases of a Laplacian matrix of a directed graph is obtained.

math.RA↗

arXiv2022

Feedback and control systems for future linear colliders: White Paper for Snowmass 2021 Topical Group AF07-RF

Particle accelerators for high energy physics will generate TeV-scale particle beams in large, multi-Km size machines colliding high brightness beams at the interaction point [1-4]. The high luminosity in such machines is achieved by producing very small asymmetric beam size at the interaction point, with short durations to minimize beam-beam effects. Tuning energy, timing and position of the beam for optimal performance will require high-precision controls of amplitude and phase of high-frequency electromagnetic fields and real-time processing of complex algorithms. The stability of the colliding beams has a large impact on the collider's effective luminosity. Therefore, the technology readiness level of diagnostic and control systems will be a major consideration in the collider design. The technical requirements of such systems depend on the specifics of beam parameters, such as transverse and longitudinal dimensions, charge/pulse and beam pulse format, which are driven by the accelerating technology of choice. While feedback systems with single bunch position monitor resolution below 50 nm and latency <300 ns have been demonstrated in beam test facilities, many advanced collider concepts make use of higher repetition rates, brighter beams and higher accelerating frequencies, and will require better performance, up to 1-2 order of magnitude, demanding aggressive R&D to be able to deliver and maintain the targeted luminosity.

physics.acc-ph↗

arXiv2021

Linear voltage recovery after a breakdown in a pulsed dc system

Breakdowns may occur in high-voltage applications even in ultrahigh vacuum conditions. Previously, we showed that it is important to pay attention to the post-breakdown voltage recovery in order to limit the appearance of secondary breakdowns associated with the primary ones. This can improve the overall efficiency of the high-voltage device. In this study, we focus on the optimization of the linear post-breakdown voltage recovery, with the principle aim of alleviating the problem of the secondary breakdowns. We investigate voltage recovery scenarios with different starting voltages and slopes of linear voltage increase by using a pulsed dc system. We find that a higher number of pulses during the voltage recovery produces fewer secondary BDs and a lower overall breakdown rate. Lowering the number of pulses led to more dramatic voltage recovery resulting in higher breakdown rates. A steeper voltage increase rate lead to a more localized occurrence of the secondary breakdowns near the end of the voltage recovery period. It was also found that the peak BD probability is regularly observed around 1 s after the end of the ramping period and that its value decreases exponentially with the amount of energy put into the system during the ramping. The value also decays exponentially with a half-life of (1.4$\pm$0.3) ms if the voltage only increased between the voltage recovery steps.

cond-mat.mtrl-sci↗

arXiv2021

Coherent Perfect Absorption in Tavis-Cummings Models

We theoretically study the conditions under which two laser fields can undergo Coherent Perfect Absorption (CPA) when shined on a single-mode bi-directional optical cavity coupled with two two- level quantum emitters (natural atoms, artificial atoms, quantum dots, qubits, etc.). In addition to being indirectly coupled through the cavity-mediated field, in our Tavis-Cummings model the two quantum emitters (QEs) are allowed to interact directly via the dipole-dipole interaction (DDI). Under the mean-field approximation and low-excitation assumption, in this work, we particularly focus on the impact of DDI on the existence of CPA in the presence of decoherence mechanisms (spontaneous emission from the QEs and the leakage of photons from the cavity walls). We also present a dressed-state analysis of the problem to discuss the underlying physics related to the allowed polariton state transitions in the Jaynes-Tavis-Cummings ladder. As a key result, we find that in the strong-coupling regime of cavity quantum electrodynamics, the strong DDI and the emitter-cavity detuning can act together to achieve the CPA at two laser frequencies tunable by the inter-atomic separation which are not possible to attain with a single QE in the presence of detuning. Our CPA results are potentially applicable in building quantum memories that are an essential component in long-distance quantum networking.

quant-ph↗

arXiv2021

A Sparsity Algorithm with Applications to Corporate Credit Rating

In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. We validate the sparsity algorithm with a synthetically generated dataset and we further apply it to quarterly financial statements from companies in financial, healthcare and IT sectors of the US market. We provide evidence that the counterfactual explanation can capture the nature of the real statement features that changed between the current quarter and the following quarter when ratings improved. The empirical results show that the higher the rating of a company the greater the "effort" required to further improve credit rating.

q-fin.RM↗